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Petroleum Geostatistics 2019
- Conference date: September 2-6, 2019
- Location: Florence, Italy
- Published: 02 September 2019
1 - 100 of 108 results
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Using Deep Directional Resistivity for Model Selection and Uncertainty Reduction in the Edvard Grieg Depth Conversion
Authors O. Kolbjornsen, P. Dahle, M.D. Bjerke, B.A. Bakke and K.R. StraithSummaryWe consider the problem of depth conversion at the Edvard Grieg field. Measurements of deep directional resistivity suggest that the top surface on Edvard Grieg is much smoother than what is indicated by the interpretation of seismic reflectors. We investigate this problem by tools of standard depth conversion, by integrating measurements from deep directional resistivity into the standard kriging equations. We propose a statistical model which is able to reveal whether we should introduce a smoothing term for the time interpretations to improve the mapping of the top surface.
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Inference of PluriGaussian Model Parameters in SPDE Framework
Authors D. Renard, N. Desassis and X. FreulonSummaryNew methodology for infering proportions and lithotype rule used for PluriGaussian Model
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Parametric Covariance Estimation in Ensemble-based Data Assimilation
Authors J. Skauvold and J. EidsvikSummaryEnsemble-based data assimilation methods like the ensemble Kalman filter must estimate covariances between state variables and observed variables to update ensemble members. In high-dimensional, geostatistical estimation settings where the system state consists of spatial random fields, spurious entries in estimated covariance matrices can degrade the predictive performance of posterior ensembles. We propose to avoid spurious correlations by specifying a parametric form for the state covariance, and fitting this model to the forecast ensemble. The idea is demonstrated on a partially synthetic North Sea test case involving forward stratigraphic modeling.
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To Drill Or Not To Drill? Mature North Sea Field Case Study
Authors L. Adamson, A. Kidd, T. Frenz and M. SmithSummaryThis paper shows the importance of uncertainty quantification for the mature small pool offshore field development. Mature small pool offshore field development is associated with high risks and high costs. Often projects in such fields are economically marginal therefore, the uncertainty quantification is very important to understand the full range of outcomes before making the ultimate decision on a given project. In this paper, we suggest an integrated workflow to account for a vast number of both static and dynamic uncertainties. To include the uncertainties into the project, we create an Uncertainty Matrix to group a huge number of uncertainties into a much manageable number of variables. In this work, we also address the challenges of capturing geological realism through facies modelling and propagating it whilst performing Uncertainty Studies. We demonstrate the application of the suggested workflow on a mature North Sea Brent Field with a limited data set. The subsequent results directly influence an infill well drilling decision on this field, which currently has two production wells and one injection well to date.
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PluriGaussian Simulations with the Stochastic Partial Differential Equation (SPDE) Approach
Authors N. Desassis, D. Renard, M. Pereira and X. FreulonSummaryIn this work, the Stochastic Partial Differential Equation approach is used to model the underlying Gaussian random fields in the PluriGaussian models. This approach allows to perform conditional simulations with computational complexity nearly independent of the size of the data sets. Furthermore, by using non-homogeneous operators, this framework allows to handle varying anisotropies and model complex geological structures. The model is presented and the proposed simulation algorithm is described. The methodology is illustrated through two synthetic data sets.
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Geostatistics: Necessary, but Far from Sufficient
Authors A.A. Curtis, E. Eslinger and S. NookalaSummaryAn explanation is given of both where and why there are several major steps in the reservoir characterisation and modelling process in which geostatistics are of little avail and for which other technologies must be used before geostatistics can then be invoked. A workflow is presented which overcomes one of the more intractable problems in reservoir characterisation: that of moving petrophysical properties, including saturation-dependent properties, from a fine scale to a coarser scale in the absence of suitable grids. Without a rigorous solution to this problem, the subsequent use of geostatistical algorithms to distribute what may be poor quality properties data is questionable. The solution, termed the CUSP workflow, uses a unique parametrisation based on Characteristic Length Variables (CLVs) which honour the principles of hydraulic similitude. A Bayesian-based Probabilistic Multivariate Clustering Analysis is used to carry out the Classification and Propagation of petrophysical properties based on the CLVs. The CUSP workflow is scale independent and has been implemented in readily available software. An example of the application of the workflow to move petrophysical properties from the core-plug scale to the wireline log scale is presented and an example for moving from the log scale to the geocell scale is provided.
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Challenges and Solutions of Geostatistical Inversion for Reservoir Characterization of the Supergiant Lula Field
Authors E. Kneller, L. Teixeira, B. Hak, N. Martinho Cruz, T. Oliveira, J. Marcelo Cruz and R. Santos CunhaSummaryThe creation of reservoir model properties has become an art of bringing together hard and soft data, gathering ideas of geologists and geophysicists, constraining them with measured values in- and outside wells. Through lifecycle of the oil field the information coverage is growing - new wells are being drilled, new seismic acquisitions are performed, and new geological concepts are developed. The Brazilian pre-salt fields are no exception. However, these fields experience additional challenges, where the carbonates show significant lateral and vertical variability and the salt layer limits illumination and penetration of the seismic signal. In this paper, we investigate performance of three techniques on the Lula field: simulation, which "propagates" properties between wells; deterministic inversion, which transforms seismic amplitudes into elastic properties; and geostatistical inversion, which combines simulation and seismic-driven inversion. We demonstrate that geostatistical inversion brings together the best of both techniques and helps address the challenges of characterization of pre-salt carbonates.
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Geostatistical Interpolation of Non-stationary Seismic Data
Authors F. Turco and L. AzevedoSummaryThe problem of sparsely collected seismic data is one of the main issues in reflection seismology, because most advanced data processing techniques require a dense and regular seismic data grid. We present a geostatistical seismic data interpolation technique based on sequential stochastic simulations with local structural anisotropies. This technique, contrary to conventional existing data-driven seismic interpolation approaches based on sparsity, prediction filters, or rank-reduction, predicts the value of seismic amplitudes at non-sampled locations by exploiting the statistics of the recorded amplitudes, which are used as experimental data for the geostatistical interpolation in the original data domain. Local mean and variance are computed on-the-fly to define intervals of the global conditional distribution function, from where amplitude values are stochastically simulated. The parameters to define subsets of experimental data from which mean and variance are calculated are given by local variogram models, which in turn are obtained from a local dip and azimuth estimation in the t-x-y domain. The geostatistical seismic data interpolation technique is applied to synthetic and real 2D and 3D datasets in both postand pre-stack domains. Besides being computationally cheaper than other methods, because the interpolation is carried out directly in the original data domain, the proposed technique provides a local quantitative analysis of the reliability of the interpolated seismic samples, which can be exploited in following processing steps.
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Expecting the Unexpected: The Influence of Elastic Parameter Variance on Bayesian Facies Inversion
Authors C. Sanchis, R. Hauge and H. KjonsbergSummaryBayesian inversion is used for the prediction of lithology and fluids from AVO seismic data. We assume a multidimensional Gaussian rock physics prior model for the elastic parameters. In this study, we look at the role of the elastic parameters variance in the prior model and how it can impact facies predictions. When the facies classes contained in the prior model have different variance, this difference influences the inversion beyond just adding uncertainty to the seismic reflections. We examine the balance between the influence of this variance and the match with expected seismic data. Our results show that although the variance influence may lead to unexpected results in synthetic scenarios, it also helps to predict the facies configuration when the seismic data follows the prior distribution and forward model.
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Geological Heterogeneous Effect on Fluid Flow and Solute Transport during Low Salinity Water Flooding
Authors H. Al-Ibadi, K. Stephen and E. MackaySummaryThis paper examines the impact of heterogeneity on Low Salinity Water flooding (LSWF) for a realistic field scale models. We examine various scenarios of permeability variations to cover a wide range of heterogeneity possibilities. Since heterogeneity is known to induce fingering and crossflow effects at the fine scale during conventional water flooding. We analyse these effects where the LSWF process is related to a change in wettability, to determine what should be captured, in terms of solute dispersion, in typical coarse scale simulation models.
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Assisted History Matching of 4D Seismic Data - A Comparative Study
Authors K. Fossum and R.J. LorentzenSummaryIn this work we present two unique workflows for assisted history matching of seismic and production data, and demonstrate the methods on a real field case. Both workflows use an iterative ensemble smoother for the data assimilation, but differ in data representation and localization method. Further, publicly available seismic data are inverted for acoustic impedance using two different approaches. In addition, correlated data noise is estimated for the 4D attributes using different techniques. History matching results are presented for selected production and seismic data, and estimated parameters are shown for one layer in the model. Both workflows demonstrate that ensemble based iterative smoothers can successfully assimilate large amounts of correlated data. Despite methodological differences in the workflows, both methods are able to make significant improvements to the data match. The work demonstrates promising advances towards assisted assimilation of big data-sets for real field cases.
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Stochastic Realizations of Gaussian Random Fields: Analysis and Comparison of Modeling Methods
Authors R. Gazizov, A. Bezrukov and B. FeoktistovSummaryHere a mathematical approach which can be used for comparison of three methods for modeling Gaussian random fields is developed. Namely, the known methods of Sequential Gaussian Simulation and Spectral Modeling as well as new method based of Fourier transform and spectral modeling random fields of Fourier coefficients are considered. We show that these methods give equivalent result when specific Gaussian fields are modeled. Also we discuss advantages and limitations of these methods, their applicability in practice problems, computational complexity and ways for their effective realizations.
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A Statistical Workflow for Mud Weight Prediction and Improved Drilling Decisions
Authors J. Paglia and J. EidsvikSummaryWe study a drilling situation based on real data, where the high-level problem concerns mud weight prediction and a decision about casing in a section of a well plan. Sensitivity analysis is done to select the most relevant input parameters for the mud weight window. In doing so, we study how the uncertainties in the inputs affects uncertainties in the mud weight window. Our approach for this is based on distance-based generalized sensitivity analysis, and we discover that the pore pressure and unconfined compressive strength are the most important input parameters. Building on this insight, a statistical model is fitted for the mud weight window and the two main input parameters, keeping in mind their geostatistical trends and dependencies. Finally we use the fitted model to the decision situation concerning casing, in a trade-off between drilling risks and costs. We conduct value of information analysis to determine the optimal data gathering scheme at a given depth, for making better decisions about casing or not. In spite of being case specific, we aim to develop a workflow that could be applied in other drilling contexts.
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Uncertainty Evaluation to Improve Geological Understanding for More Reliable Hydrocarbon Reserves Assessment: Case Study
Authors E. Kharyba, S. Frolov, M. Blagojevic, J. Kukavic, R. Yagfarov and A. BelanozhkaSummaryThe purpose of this project was to calculate oil reserves in order to make a decision on further workover activities. The main uncertainties faced were: permeability and porosity due to the lack of core from the target interval, PVT parameters due to the absence of downhole oil sample, 2D seismic profiles instead of 3D. In addition, there were risks connected to the proximity of OWC and faults possibly intersecting the wellbore.
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Automatic Scenarios Extraction from Depth Uncertainty Evaluation
Authors P. Correia, J. Chautru, Y. Meric, F. Geffroy, H. Binet, P. Ruffo and L. BazzanaSummaryThe structurally lowest point in a hydrocarbon trap that can retain hydrocarbons is called a Spill Point and characterizing these locations over a depth horizon is a common approach in trap analysis. However, a horizon is an uncertain object typically produced through a time to depth conversion procedure which might involve several different variables like time, velocity, and fault position. Each of those variables brings its own uncertainty. By using geostatistical simulations, we produce different realizations of the depth horizons and further process them individually to determine the probability of presence of reservoirs and spill points associated to highly probable reservoirs. This paper presents a methodology to achieve such results including our analysis algorithm for trap and spill point characterization. By using a case-study we demonstrate that only proper characterization of all relevant realizations in the uncertainty space show us the possible scenarios, and their impact on traps volume.
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Feedback Between Gravity and Viscous Forces in Two-phase Buckley-Leverett Flow in Randomly Heterogeneous Permeability Fields
Authors P. Alikhani, A. Guadagnini and F. InzoliSummaryData on hydrocarbon reservoir attributes (e.g., permeability, porosity) are only available at a set of sparse locations, thus resulting (at best) in an incomplete knowledge of spatial heterogeneity of the system. This lack of information propagates to uncertainty in our evaluations of reservoir performance and of the resulting oil recovery. We consider a two-phase flow setting taking place in a randomly heterogeneous (correlated) permeability field to assess the feedback between viscous and gravity forces in a numerical Monte Carlo context and finally characterize oil recovery estimates under uncertainty for a water flooding scenario. Our work leads to the following major conclusions:
- Uncertainty in the spatial distribution of permeability propagates to final oil recovery in a way that depends on the feedback between gravity and viscous forces driving the system.
- Uncertainty of final oil recovered (as rendered in terms of variance) is smallest for vertical flows, consistent with the observation that the gravity effect is largest in such scenarios and is dominant in controlling the flow dynamics.
- Uncertainty of final oil recovered tends to be higher when there is competition between the effects of gravity and viscous forces, the latter being influenced by the strength of the spatial variability of permeability.
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An Integrated Approach to Uncertainty Management on the Example of Alexander Zhagrin Field
Authors A. Sidubaev, A. Melnikova, K. Grigoryev and S. TarasovSummaryThe formation of the exploration program is a complex process that requires an integrated approach. A successful exploration program is based on a multi-level analysis of all possible geological uncertainties, probabilistic assessment of reserves and resources, analysis of tornado charts, maps of variation coefficients, and calculation of the value of information. This work considers the consistent formation and execution of exploration works on the example of Alexander Zhagrin field, which allowed to start production in two years after discovery of oil field in autonomous conditions. The research region is located in the Khanty-Mansiisk autonomous districtof the Tyumen region. The main potentially productive formation is the river-dominated delta sediments of the Cretaceous complex represented by the stratum AS-9.
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A Bayesian Approach to Uncertainty Quantification in Geophysical Basin Modeling
Authors A. Pradhan and T. MukerjiSummaryGeophysical basin modeling helps constrain the non-uniqueness of seismic velocity inversion methods by employing basin modeling to incorporate geo-history constraints into inversion. Traditionally, basin modeling is performed in a deterministic manner and thus does not facilitate uncertainty quantification. We present a Bayesian approach for propagation of basin modeling uncertainties into velocity models. Our methodology constitutes defining prior probability distributions on uncertain basin modeling parameters and likelihood models on basin modeling calibration data. Posterior realizations of basin models are generated by sampling the prior, performing Monte-Carlo basin simulations and evaluating the corresponding likelihood values. These posterior models are finally linked to velocity models by rock physics modeling. We demonstrate the applicability of our proposed workflow using a 2D real case study from Gulf of Mexico.
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Advantage of Stochastic Facies Distribution Modeling for History Matching of Multi-stacked Highly-heterogeneous Field of Dnieper-Donetsk Basin
Authors A. Romi, O. Burachok, M.L. Nistor, C. Spyrou, Y. Seilov, O. Djuraev, S. Matkivskyi, D. Grytsai, O. Goryacheva and R. SoymaSummaryMost of the fields in the Basin of the current study are represented by multi-stacked thin reservoirs with total thickness up to 2 thousand meters containing oil, gas-condensate and dry gas with high lateral and vertical heterogeneity. The asset in this study is a mature gas field with more than 50 years of production history, that consists from 15 gas-bearing sands of variable gas composition, that are in commingled production through the slotted liner completions while some of the sands are not yet under development and therefore, shouldn't be considered in history matching and excluded from material balance P10 reserves calculation but rather in P50 and P90 resources.
This paper shows how the application of stochastic approach for facies modeling followed by petrophysical porosity, in the presence of non-resolutive 3D seismic could help to guide the property distribution and evaluate geological uncertainties. The next very important step in the applied workflow was flow-based ranking and selection of representative case based on connected (drained) volumes that helps to achieve history match for selected base case in the presence of additional high uncertainty in contact levels and quality of production data.
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Incorporating Discrete Microfacies Sequences to Improve Permeability Estimation in Sandstone Reservoirs
More LessSummarySeveral cases have been conducted to address the permeability modeling and estimation, but all were not accurate because of the heteroscedasticity between data. Therefore, integrating the microfacies sequences into permeability modeling became a crucial to obtain accurate prediction and then improve the overall reservoir characterization. The discrete microfacies distribution leads to distinct regression lines given each microfacies type. Therefore, the Random Forest (RF) algorithm was considered in this paper for microfacies classification and Smooth Generalized Additive Modeling (sGAM) was considered for permeability modeling as a function of well logging data and the predicted discrete microfacies distribution. The well logging records that were incorporated into the microfacies classification and permeability modeling: SP, ILD and density porosity logs. These two approaches were adopted in a well in a sandstone reservoir, located in East Texas basin. The effectiveness of using RF and sGAM approaches was investigated by their performance to handle wide ranges of data given the five microfacies types. More specifically, the Random Forest Modeling was super accurate to predict the microfacies distribution at the missing intervals for the same well and other wells. Moreover, the sGAM resulted to obtain accurate modeling and prediction of permeability in high and low permeable intervals.
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Extraction of Petrophysical Information and Formation Heterogeneity Estimation from Core Photographs by Clustering Algorithms
By D. EgorovSummaryCore data is the most reliable and representative source of information, however it is very expensive. It is the only thing that can be used for precise description of geological situation in a target formation including its depositional environment, petrophysical properties and oil saturation distribution, reservoir heterogeneity and compartmentalization degree induced by secondary processes. Despite the fact that core retrieving operation can increase well cost in few times only a little amount of core information is utilized during field development projects as a huge part of it is represented by qualitative description which is hard to be implemented into digital quantitative geological modeling process.
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Amplitude Supported Prospects, Analysis and Predictive Models for Reducing Risk of Geological Success
Authors I. Tishchenko and I. MallinsonSummaryDirect Hydrocarbon Indicators (DHI) are commonly used for exploration prospects. Amplitudes as an independent source of information could be used as conditional probability within Bayes Theorem to assess risk of geological success. Following research is aiming to construct predictive model for estimating probability of hydrocarbons observing DHI, P(dhi|hc). In order to build such model, we used Rose & Associates DHI Interpretation and Risk Analysis Consortium database, which contains extensive descriptions of 336 drilled prospects, with known results, across various categories: Geology, Data Quality, Amplitude Characteristics and Pitfalls. Multiple Logistic Regression was used for predicting probability P(dhi|hc). Three methods were considered within the study: two data-driven models - stepwise regression and lasso shrinkage method plus the third one, a combination of data-and expertise- driven approach - stepwise regression plus manual addition of predictors to the model. All three models with key predictors are described and give similar accuracy of prediction − 77%. Performed data analysis and calculated models reveal several insights into R&A DHI Consortium database and amplitude prospects characterisation. The best method to create such models is probably a combination of data and expertise driven approaches, while selection of most appropriate model is a question of company's strategy.
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Application of Long Short-term Memory Algorithm for Prediction of Shale Gas Production in Alberta
More LessSummaryShale resources are developed with high-density drilling and differ to predict future production rate with conventional methods such as decline curve analysis (DCA). We developed a prediction model for shale production using machine learning (ML) algorithm. Long short-term memory algorithm, which is suitable for training time-series data, is applied to more than 300 shale gas wells at Duvernay formation in Alberta. To increase performance of the prediction model, an additional feature, shut-in (SI) period, is introduced for input layer. SI information is easily extracted from production history and it is highly correlated with production rate. The trained model can give reliable and stable prediction of shale gas production rates. Especially, the two-feature model (production and SI period) shows better performance than the one-feature model (production only). Therefore, a domain knowledge for petroleum industry has an important role to build reliable ML model. The developed model can dramatically reduce analysis cost comparing with DCA, which requires expert's judgement for each shale well. Also, this concept can be extended to other conventional and unconventional production wells.
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Machine Learning-based Approach for Automated Identification of Produced Water Types from Conventional and Unconventional Reservoirs
Authors P. Birkle, M. Zouch, M. Alzaqebah and M. AlwohaibiSummaryProduced water represents the most commonly recovered fluids in oil and gas operations. They are composed of natural fluids from deep groundwater systems (formation water) or artificial fluids from operations, or a mixture between both fluid components.
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Application of a Serial Denoising Autoencoder for Geological Plausibility of a Channelized Reservoir in History Matching
More LessSummaryDenoising autoencoder (DAE) is utilized to preserve and improve geological reality and plausibility in a channelized reservoir model during history matching by ensemble smoother with multiple data assimilation (ES-MDA). As one of history matching methods, ES-MDA calibrates reservoir properties such as rock facies corresponding to production history. While ES-MDA modifies reservoir parameters, it recognizes them only as figures not honoring to geological features. Thus, conservation of geological characteristics during calibration of reservoir parameters is challenging in ES-MDA. DAE is trained to restore lost connectivity and pattern of an original geological concept and it is applied to posterior reservoir models after an assimilation by ES-MDA. ES-MDA combined with DAE shows not only geologically enhanced channel models but also well-matched production prediction.
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Automatic Recognition of Lithological Units in Gas-bearing Shale Complex with Neural Networks (the Baltic Basin, Poland)
By K. BobekSummaryThe study was performed to check the possibility of automatic recognition of lithological units of shale complex and testing their heterogeneity in different parts of the Baltic Basin. For this purpose, two separate neural network models have been trained based on geophysical logs datasets: one for perspective units recognition, and second for tuffite interbeds detection. Obtained results have compared with units distinguished by sedimentologists in continuous core profiles. The performance of trained networks has been checked for two boreholes B-7 and B-1 not included in the training process. In case of the borehole B-7, the units predicted by the model match well to those distinguished manually in borehole core. In contrary, the score of model performance for the B-1 borehole was unsatisfactory. Significant differences in the lithological profile of this borehole were also recognized by sedimentologists. Due to the location of borehole B-1 in the marginal part of the basin, most of the properties of the studied units differ relevantly from recognized in other investigated boreholes. The performance of the network trained for tuffite interbeds detection is considered satisfactory. The results imply that the trained models might be applied in the boreholes where the core profiles are modest or non-existent.
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Geomodeling Using Generative Adversarial Networks and a Database of Satellite Imagery of Modern River Deltas
Authors E. Nesvold and T. MukerjiSummarySeveral studies on deep generative models for use in geomodeling show encouraging results with binary training data. An important question is what type of training data to use, since realistic 3D geology with natural variability is difficult to create. The advent of multiple types of remote sensing data of subaerial and subaqueous sedimentary patterns provides new possibilities in this context. Here, we train a Wasserstein GAN using 20,000 multispectral satellite images of subsections of 40 modern river deltas. The generated output has three facies and a background facies, and all quantitative evaluation methods of the unconditional output show a close overlap between the model and training data distributions. Standard MCMC sampling conditional on soft and hard data works well as long as the likelihood model is balanced against the prior model. Transfer learning, i.e. fine-training a small subset of the network parameters on smaller dataset of interest, such as highly non-stationary images of river deltas with similar characteristics, also shows promising results.
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Ensemble-based Kernel Learning to Handle Rock-physics-model Imperfection in Seismic History Matching: A Real Field Case Study
Authors X. Luo, R. Lorentzen and T. BhaktaSummaryModel imperfection is ubiquitous in geophysical data assimilation problems. A common approach to accounting for model errors is to treat them as random variables following presumed distributions. While such a treatment renders certain algorithmic convenience, its underpinning assumptions may often be invalid in practice. In this study, we adopt an alternative approach, and treat the characterization of model errors as a functional approximation problem, which can be solved using a generic machine learning method, such as kernel-based learning adopted here. To enable a seamless integration of kernel-based learning into ensemble data assimilation, we also develop an ensemble-based kernel learning approach. We show that an existing iterative ensemble smoother can be naturally employed as the learning algorithm, which thus inherits all the practical advantages of ensemble data assimilation algorithms, such as derivative-free, fast implementation, and allowing uncertainty quantification and applications to large scale problems. To demonstrate the efficacy of ensemble-based kernel learning, we apply it to handle model errors in a rock physics model used in history matching real 4D seismic data from the full Norne field. Our experiment results indicate that incorporating kernel-based model error correction into 4D seismic history matching helps improve the qualities of estimated reservoir models, and leads to better forecasts of production data.
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3D Geological Image Synthesis from 2D Examples Using Generative Adversarial Networks
Authors G. Coiffier and P. RenardSummaryRecently, Generative Adversarial Networks (GAN) have been proposed as a potential alternative to Multipoint Statistics (MPS) to generate stochastic fields from a large set of training images. A difficulty for all the training image based techniques (including GAN and MPS) is to generate 3D fields when only 2D training data sets are available. In this paper, we introduce a novel approach called Dimension Augmenter GAN (DiAGAN) enabling GANs to generate 3D fields from 2D examples. The method is simple to implement and is based on the introduction of a random cut sampling step between the generator and the discriminator of a standard GAN. Numerical experiments show that the proposed approach provides an efficient solution to this long lasting problem.
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Deep Stochastic Inversion
Authors L. Mosser, O. Dubrule and M.J. BluntSummaryNumerous geophysical tasks require the solution of ill-posed inverse problems where we seek to find a distribution of earth models that match observed data such as reflected acoustic waveforms or produced hydrocarbon volumes. We present a framework to create stochastic samples of posterior property distributions for ill-posed inverse problems using a gradient-based approach. The spatial distribution of petrophysical properties is created by a deep generative model and controlled by a set of latent variables. A generative adversarial network (GAN) is used to represent a prior distribution of geological models based on a training set of object-based models. We minimize the mismatch between observed ground-truth data and numerical forward-models of the generator output by first computing gradients of the objective function with respect to grid-block properties and using neural network backpropagation to obtain gradients with respect to the latent variables. Synthetic test cases of acoustic waveform inversion and reservoir history matching are presented. In seismic inversion, we use a Metropolis adjusted Langevin algorithm (MALA) to obtain posterior samples. For both synthetic cases, we show that deep generative models such as GANs can be combined in an end-to-end framework to obtain stochastic solutions to geophysical inverse problems.
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Integrated Geo-modelling and Ensemble History Matching of Highly Faulted Turbiditic Reservoir Model
Authors V. Zaccardi, A. Abadpour, N. Haller, P. Berthet, D. Rappin and J. Grange-PraderasSummaryData assimilation methods and multi-realisation geomodelling workflow have been applied to history match a heavily faulted turbidic reservoir. With this approach it was possible to take into account a large number of uncertainties related to seismic data interpretation, geological modelling and dynamic data.
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Ensemble History Matching Enhanced with Data Analytics - A Brown Field Study
Authors E. Tolstukhin, E. Barrela, A. Khrulenko, J. Halotel and V. DemyanovSummaryThis paper presents a methodology used in a subsurface uncertainty study for redevelopment of an oil field in the North Sea. A fractured chalk reservoir was depleted for more than 30 years with limited water injection. The uncertainty study aims to find an ensemble of geologically consistent scenarios that would honor production history. The scenarios then serve as input for the redevelopment concept selection, well placement and economic evaluation. The challenge in this study was that the field has long production history that must be respected. In addition, the uncertainty that may not be resolved by HM must be preserved in the scenarios in order to estimate all the risks and capture all the potential associated with the remaining oil pockets and future well targets. For the brown field, it is difficult to analyze all the information and utilize its full potential. In this work we use data analytics can improve efficiency of ensemble history matching by analyzing links between the static and dynamic model ensemble update: screening of the initial ensemble, model localization based on spatial analysis dynamic observations to the parameter update and identification of conflicts between groups of production observations that prevent balanced model update.
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Updating MPS Facies Realizations Using the Ensemble Smoother with Multiple Data Assimilation
Authors A. Thenon, A. Abadpour and T. ChugunovaSummaryThis paper is presenting the results of a study focused on the update of facies models generated by multipoint statistics (MPS) within an ensemble history matching workflow. The tested parameterization consists in updating the uniform random numbers used by MPS to generate the facies realizations with an ensemble method as proposed in the work of Hu et al. (2012). The novelty of this study lies in use of the ensemble smoother with multiple data assimilation (ES-MDA) to update the random number realizations. Tests on synthetics cases show that increasing the iteration number within ES-MDA can significantly improve the quality of the history-match achieved by this approach. The workflow is briefly detailed in the first part of the paper. Then, we present and discuss the history-matching results for two synthetic cases: an inverted five-spot 2D model with channels and a more complex 3D model with dunes.
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Improving Local History Match Using Machine Learning Generated Regions from Production Response and Geological Parameter Correlations
Authors T. Buckle, R. Hutton, V. Demyanov, D. Arnold, A. Antropov, E. Kharyba, M. Pilipenko and L. StulovSummaryWe present a data driven workflow to improve local history match quality by identifying model regions from correlation between production response and geological modelling parameters for use in an assisted history matching framework. This paper outlines the implementation and results from a large mature field case study. Regions are identified by calculating the partial correlation between individual well production misfits and uncertain geological modelling parameters across 500 models. Wells are then categorised into three groups based on their correlations: positive, negative and insignificant. A probabilistic neural network (PNN) is trained on the location of each well and its group. A map of regions can then be calculated using the PNN. The parameters used to define the region map are then varied separately in each region in an assisted history matching loop. In the full field case study, an 8.8% improvement in oil rate misfit within the positively correlated well group was achieved by regional modification of the net-to-gross multiplier, with no detrimental effect on the other groups match quality. This case study demonstrates the effective identification and utilisation of geologically and dynamically inferred regions which improve the local history match
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Fluid Flow Consistent Geostatistical History Matching of an Onshore Reservoir
Authors E. Barrela, L. Azevedo, A. Soares and L. GuerreiroSummaryThis paper shows the application to a real field case of an iterative geostatistical history matching technique, integrating geological and engineering consistency. Current trends reflect a growing interest on developing workflows that simultaneously integrate petrophysical modeling with dynamic calibration of reservoir models to historical production data. Contrary to manual history matching techniques, where model perturbation often disregards geological or physical realism leading to poor production forecast, this example introduces geological consistency through geostatistical simulation and physical realism by using streamline regionalization, while holding the predictive capability of resulting petrophysical models. This is achieved by iteratively updating the reservoir static properties using stochastic sequential simulation and co-simulation, constrained to production data, while using streamline information for electing preponderant flow production regions of the model, focusing property perturbation. In order to capture the complex subsurface heterogeneities of the reservoir, petrophysical property realizations are obtained using the direct sequential simulation and co-simulation with multi-local distribution functions. The location and proportion of reservoir facies is also automatically updated throughout the iterative procedure, using Bayesian Classification. The technique was successfully applied to a real case study, located in North-East onshore Brazil, resulting in multiple history matched models that better reproduce historic data.
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Revising the Method of Ensemble Randomized Maximum Likelihood
Authors P.N. Raanes, G. Evensen and A.S. StordalSummaryA popular (iterative ensemble smoother) method of history matching is simplified. An exact relationship between ensemble linearizations (linear regression) and adjoints (analytic derivatives) is established.
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Theoretical and Practical Aspects of Stein Variational Gradient Descent with Applications to Data Assimilation
Authors A. Storksen Stordal, R.J. Moraes, P. Nima Raanes and G. EvensenSummaryWe have introduced and applied the Stein variational gradient descent (SVGD) algorithm to the reservoir history matching problem. The method has been extended with a Monte Carlo approximation of the gradient of the measurement function using the reproducible property of the reproducing kernel Hilbert space in order to avoid adjoint implementation. Furthermore we have implemented SVGD with p-kernels in order to extend the applicability to higher dimension than the standard Gaussian kernels used in the literature. The gradient approximation was validated both theoretically and on a toy model. Implementation on larger problems will be addressed in the future. We also showed using a reservoir model that the SVGD with p-kernels provided better estimates of the uncertainty than the standard Gaussian kernels. Further comparison with ensemble-based methods and other gradient-based methods is left for future research.
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Well Logs Inversion Into Lithology Classes: Comparing Bayesian Inversion and Machine Learning
More LessSummaryLithology classification is a crucial challenge in geological research. Lithologies at thelocations without cores need to be predicted by using indirectly geophysics measurementssuch as well logs and seismic data. In this study, we use the spatial dependency of sedimentsand well logs data for inversion into lithologies by a kernel-based hidden Markov model(HMM) and a gated recurrent unit (GRU) model. We estimate model parameters from twotraining wells and predict lithologies from well log observations for a blind test well. Thepredictions are also compared to results from a deep neural network (DNN) model, whichassumes spatial independence. Results indicate that the lithology predictions supplied by theHMM and GRU models are more reliable than the ones from DNN in term of classificationaccuracy and make more senses in geological interpretation. Moreover, the HMM providesquantifications of the classification uncertainty.
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Well Log Data Standardization, Imputation and Anomaly Detection Using Hidden Markov Models
SummaryThe main goal of this work is to develop a systematic approach to work with raw well log data. Toaccomplish this goal, we propose to fit a simple unsupervised generative model to the input data and au-tomate the preprocessing step using the generative model. This approach allows to detect the anomaliesin the data as the regions that the model struggles to explain (i.e., samples with extremely low like-lihood), infer approximations to the missing features using the Bayes rule and incorporate additional expert knowledge in the design of the model.
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Seismic Estimation of Sub-resolution Reservoir Properties with Bayesian Evidential Learning: Application to an Offshore Delta Case
Authors A. Pradhan and T. MukerjiSummaryWe present a Bayesian framework that facilitates estimation of and uncertainty quantification of sub-resolution reservoir properties from seismic data. Our workflow exploits learning the direct relation between seismic data and reservoir properties by the evidential learning approach to efficiently estimate sub-resolution properties. The major focus of this paper is on a real case study of seismic characterization of reservoir layers significantly below the seismic resolution. We employ deep neural networks as summary statistics within Approximate Bayesian Computation to estimate posterior uncertainty of reservoir net-to-gross from 3D pre-stack seismic data without an explicit inversion.
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Monte Carlo-based Framework for Quantification and Updating of Geological Model Uncertainty with Borehole Data
More LessSummaryUncertainty quantification is of importance for reservoir appraisals. In this work, we provide an automated method for uncertainty quantification of geological model using well borehole data for the reservoir appraisal. In our method, when new wells are drilled, multiple components of the geological model are updated jointly and automatically by means of a sequential decomposition following geological rules. During updating, we extend the direct forecasting method to perform such joint model uncertainty reduction. Our approach also enables updating geological model uncertainty without conventional model rebuilding, which significantly reduces the time-consumption. The application to a gas reservoir shows that, this proposed framework can efficiently update the geological model and reduce the prediction uncertainty of the gas storage volume jointly with all model variables.
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Boolean Spectral Analysis in Categorical Reservoir Modelling
Authors N. Ismagilov, V. Borovitskiy, M. Lifshits and M. PlatonovaSummaryThe work introduces a new method for simulation of facies distribution for two categories based on Fourier analysis of Boolean functions. According this method, two categories of facies distributed along vertical wells are encoded as Boolean functions taking two values. The subsequent simulation process is divided into three consecutive steps. First, Boolean functions of well data are decomposed into a binary version of Fourier series. Then, decomposition coefficients are simulated over 2-dimensional area as stationary random fields. Finally, synthetic data in the interwell space is reconstructed as Fourier sum from simulated coefficients. The new method was implemented in an experimental software and tested on a case of real oil field.
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Stochastic Seismic Inversion Based on a Fuzzy Model
Authors E. Kovalevskiy and M. VolkovaSummaryThe cause of the low efficiency of geostatistical seismic inversion based on sequential Gaussian simulation (SGS) is explained as follows. In spite of the non-stationary type of initial borehole impedance sections, SGS generates stationary realizations of the same vertical sections. This results in the stationary cubes of predicted impedance values in which all deterministic features are erased. This article proposes using impedance section realizations obtained from a fuzzy model rather than from SGS. The first accurately represent the local statistics of borehole impedance sections, which allows the resulting impedance cubes to clearly show the deterministic features of a geological object. The method is illustrated with an example of the inversion for a real seismic cube.
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Integrated Res Characterization Tool to Construct High Resolution Geological Model in MR FM of DF Field
Authors A. AlShamali, N. Verma, R. Quttainah, A. Tiwary, G. Alawi and M. Al RaisiSummaryThis paper establishes the approach of finding a relationship between reservoir rock typing (RRT)-derived from core and well log data to generate full field continuous RRT models that can be used to predict RRT in undrilled locations and predicting blind wells In the present study, a total of 16 wells were analyzed uses a unique technology that integrates all data using artificial intelligence where a neural network is trained and tested using existing data. Multiple realizations are created, analyzed and validated through blind well testing.The RRT prediction was carried out using 'Ipsom' module in TECHLOG and the module is based on supervised neural network technology.This module using the core RRT as desired log and well logs as an input curves for the neural network training, RRT in the cored intervals were used as training set to obtain the neural network engine which will be used to predict the un-cored intervals and wells.Once the RRT were defined, the attempt was made to generate permeability and saturation height function for each of these RRTs. For permeability computation, FZI (Flow Zone Indicator) and Simple Geometric Regression Type Equations were tested. Having compared both methods of regression, it was observed that there was a minimal difference between both methods using FZI or using a simple regression type in permeability prediction. However, FZI gave a slightly better result when compared with the simple regression method within the range of data in the field.
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Raising the Bar: Electrofacies as a Framework for Improving the Practice of Geomodeling
By D. GarnerSummaryA key impact on reservoir studies is a rigorous strategy around facies for modeling. The industry practices across small to large companies are highly variable regarding generating facies logs. Geomodeling workflows and geostatistical algorithms treat the facies log variable as hard conditioning information. Facies logs in practice have errors and carry petrophysical inconsistencies, real quality issues, which are not head-on addressed by the time they are used in a geomodeling workflow. Establishing electrofacies modeling best practices in the petroleum industry can help improve the preparation of facies logs for modeling and improve the fidelity of many geomodeling processes. This material presents basic theory, practical considerations, and example results from up to four different fields, depending on poster size. Further discussion is intended to further illustrate benefits of the use of electrofacies and help mature the understanding of the workflows which are not widely used.
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Statistical Characterisation of Fluvial Sand Bodies: Implications for Complex Reservoir Models
Authors M. Franzel, S. Jones, I.H. Jermyn, M. Allen and K. McCaffreySummaryThe three-dimensional geometry of fluvial channel sand bodies has received considerably less attention than their internal sedimentology, despite the importance of sandstone body geometry for subsurface reservoir modelling. The aspect ratio (width/thickness, W:T) of fluvial channels is widely used to characterize their geometry. However, this does not provide a full characterization of fluvial sand body shape, since one W:T ratio can correspond to many different channel geometries. The resultant over- or underestimation of the cross-sectional area of a sand body can have significant implications for reservoir models and hydrocarbon volume predictions. There is thus a clear need for the generation of versatile, quantitative, and statistically robust models for sand body shape. The main aim of this research is to develop a new statistically-based approach that will provide quantitative data, derived from outcrop analogues, to fully constrain stochastic fluvial reservoir models. Here, we describe the construction of a new shape database and conduct a preliminary qualitative analysis in order to understand measurement and other uncertainties, and to explore the catalogue of shape configurations. A future quantitative analysis will develop a predictive model to enable forecasting of reservoir channel sand body geometries and shapes that can be built into existing reservoir models.
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Integrated Reservoir Characterization and Multiscale Heterogeneity Modeling of Stacked Meander-belt Deposits, Lower Cretaceous McMurray Formation, Alberta
Authors S. Nejadi, J.A. Curkan, P.R. Durkin, S.M. Hubbard and I.D. GatesSummaryThe McMurray Formation is composed of large-scale fluvial meander-belt deposits that are highly heterogeneous. Repeated cut and fill events within the formation have led to a complex amalgam of stacked stratigraphic architectural elements. Lithological properties vary both laterally and vertically over short distances in the McMurray Formation. The youngest deposits of the reservoir studied at the Surmont site are well imaged using 3D seismic data; calibration with well-data enables construction of a particularly detailed reservoir model. The underlying deposits are characterized using wire line logs, core data, and stratigraphic dip analysis. For modeling purposes, internal stratigraphic architecture of both reservoir levels is mapped and distinct fluvial meander-belt architectural elements, including point bars, counter point bars, side bars and abandoned channel fills, are characterized as distinct zones. Each zone is characterized by distinct morphology, facies associations, petrophysical properties, and thus, reservoir potential. Deterministic geobody interpretations are implemented to guide geostatistical simulations; spatial distribution of facies are constrained to the mapped architectural elements. Constraining parameter estimations to deterministically interpret meander-belt architectural elements improves the predictive capability of the reservoir model. This modeling workflow preserves geological realism in models, allows spatial uncertainty to be captured adequately, and improves the ability to optimize development.
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Statistical Characteristics of a Fractured Model from Seismic Data via Topological Analysis of Diffraction Images
Authors M. Protasov, T. Kchachkova, D. Kolukhin and Y. BazaikinSummaryA workflow for recovering fracture network characteristics from seismic data is considered. First, the presented discrete fracture modeling technique properly describes fracture models on the seismic scale. The key procedure of the workflow is 3D diffraction imaging based on the spectral decomposition of different combination s of selective images. Selective images are obtained by the prestack asymmetric migration procedure, while spectral decomposition occurs in the Fourier domain with respect to the spatial dip and the azimuth angles. At the final stage, we propose a topological analysis based on the construction of a merge tree from the obtained diffraction images. The results of the topological algorithm are modeling parameters for the discrete fractures. To analyze the effectiveness of the proposed workflow, a statistical comparison of the recovered parameters and true model parameters are provided. We use the Kolmogorov -Smirnov test for a statistical analysis of the fracture lengths, while the behavior of the Morisita index shows the statistical distribution of the modeled fracture corridors. Numerical examples with synthetic realistic models demonstrate a detailed, reliable reconstruction of the statistical characteristics of the fracture corridors.
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The Effect of Fracture Clustering on Confined Fractured Zones: Numerical Modeling and Analyses
Authors A. Alali, K. Marfurt and N. NakataSummarySeismic reflection amplitude variation with offset and azimuth (AVOaz) provides a traditional technique to detect fracture in the subsurface and deduce their properties. AVOaz relies on the Effective Medium Theory (EMT) which treats the fractured formation as anisotropic medium. The underlying assumption of EMT is that the fractures are uniformly disturbed and sufficiently close such that only specular reflections from the boundaries of the fractures are observed. In contrast, for randomly spaced fractures clustering takes place and individual scattering occurs, and the assumptions of effective medium theory are violated.
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Using Seismic Images for Scaling of Statistical Model of Discrete Fracture Networks
Authors D. Kolyukhin and M. ProtasovSummaryThe presented paper addresses the modeling and seismic imaging of fractured reservoirs. A three-dimensional statistical model of a discrete fracture network is developed. A flexible and efficient method to generate the random realizations of the statistical model for an arbitrary computational grid is suggested. The problem of scaling the developed fracture model using the analysis of seismic images for different grid steps is studied. Particular attention is paid to the models with a multifractal distribution of fractures.
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Simulation of Near-fault Damage Zones
Authors V. Lisitsa, V. Tcheverda, D. Kolyukhin and V. VolianskaiaSummaryWe present a workflow for geostatistical modelling of the faults and damage zones. The approach is based on the combination of the numerical simulation of geological faults formation using meshless Discrete Element Method with further estimation of the statistics of strains distribution in the damage zones. After that, this information is used to incorporate the damage zones to a structural surfaces representing faults (as results of conventional seismic interpretation). Further on the mechanical properties of the rocks in the damage zones are updated to construct the grid-based geological model for either seismic of hydrodynamic simulations.
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Tease out More - Advanced Porosity Analysis in Fractured Reservoirs Combining Statistical Method with Outcrop Data
Authors J. Püttmann, U. Eickelberg and J. HoheneggerSummaryStatistical analysis are presented for the description of a porosity-permeability system in order to transfer tectonic facies classification to log data and to improve flow unit determination. Two working hypothesis are investigated: a) Porosities at each measured section point represent an accumulation of distinct porosity classes and b) Significant periods can be identified in oscillating porosities. The four major workflow steps of the statistical analysis are described. Decomposition, non-linear regression, and periodograms delivered encouraging results to understand the porosity composition of the multi-fractured dolomite. Five porosity components of high statistical significance are identified and related to tectonic influence factors. Furthermore, results of sinusoidal regression show significant trends, which might be related to deformation history and complexes. Decomposition of oscillating functions resulted in classes of significant periods, where sinusoidal oscillations with specific period lengths are represented. Finally, statistical analysis reveal different porosity distributions depending on the logging tool generation, which can have a considerable impact on the reserve estimation. Statistical analysis of log data -if applicable - are a fast and cost-effective approach to support reservoir characterisation. The study show that the use of statistical analysis of log data can provide significant information to develop or validate static and dynamic reservoir models
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Stochastic Modeling from Ponta Grossa Formation: Integrating Outcropping and Subsurface Data
SummaryBuild a geological 3D framework based on the interplay between subsurface-outcrop integration, and data scarcity represents a tough task for geoscientists. In a basin-scale, it is paramount to reduce the quandary related to either limited areas with clusterization or extensive areas with voids by using a pragmatic methodology. This work aims (i) to present an efficient methodology for explorational scale, which correctly represents the geology even with lack of entry-data; (ii) to test the method, by using as a case of study the sediments from Ponta Grossa Fm., Parana Basin; (iii) to validate the method, by using QA, and (iv) to compare with the preconceived analogical interpretation made by several authors. Two stochastic models were generated comparing SIS technique without using a variogram (pure Monte Carlo) with the SIS using the cell size variogram. The simulations had distributed the processed lithofacies, demonstrating the general trend of sand bodies observed in the field. The P50 represented the expected stacking pattern for this sort of high-energy environment. The proposed model had represented the overall stratigraphy. This work represents a partial model that should be compared with forward stratigraphic modeling that utilizes Navier-Stokes set of equations.
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Forward Model Applied to Channelized Turbidite Systems: A Case Study of the Benin Major Valley Fill
Authors M. Lemay, F. Ors, J. Grimaud, J. Rivoirard and I. CojanSummaryChannelized turbidite systems are associated with extensive hydrocarbon reservoirs. Yet building realistic turbidite reservoir models is still a challenge. The process-based model Flumy was initially developed to simulate the long-term evolution of aggrading fluvial meandering systems in order to build three dimension reservoir facies blocks. We take advantage of some similarities between the two environments to transpose the model to channelized turbidite systems by simulating the main processes at play in the submarine realm: channel lateral migration, avulsion, aggradation, overflowing, flow stripping, and sediment transport. A flow compatible with the input channel geometry parameters is first built. This flow controls the channel evolution through time and thus the stratigraphic architecture of deposits, as well as their grainsize. In this study, we present the application of Flumy to the case study of the Benin major valley. The simulation successfully reproduces the morphology of the valley, most of observed geomorphic features, and the various styles of filling architectures. It also results in a complex grainsize arrangement which controls reservoir connectivity. This study shows that the model reproduces realistic stratigraphic architecture and can be used to simulate channelized turbidite reservoirs.
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3D Geological Model: A Geostatistical Approach of Turbidite Deposits, Los Molles Fm, Neuquen Basin, Argentina
Authors A.S. Da Silveira, M.R. De Vargas, V. Engelke, P.S.G. Paim, M. Morris and J.E. FaccionSummaryA recurrent challenge of geological modeling is bridging the gap between data with different resolution, such as the outcrop with the exploration resolution. By only integrating outcrop data from Arroyo La Jardineira, Neuquén Basin (AR), we integrated the object-based stochastic simulation for four depositional sequences that register a turbidite succession deposited in a deep-marine setting. This study aims (i) to determine a concise geological model derived from a plethora of simulations; (ii) to validate the uses of object-modeling as a constraint to facies distribution, and (iii) to evaluate the uncertainties when the data is scarce. The 3D numerical model allows the quantification of geological parameters, by testing contrasting geological scenarios. A quantitative sedimentological model was build integrating and using data derived from outcrops. The methodology utilized in this work enhanced the outcropping analysis, being a predictive tool to estimate faciological heterogeneities in subsurface explorational models.
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Combining Stratigraphic Forward Modelling with Multiple-point Statistics - A Case Study from Seismic to Tracer Response
Authors J. Peisker, A. Miller and M.J. EbnerSummaryStochastic reservoir modeling is an integral part of quantifying subsurface uncertainties. Classical geostatistical methods like Gaussian random function and multi-point geostatistics (MPS) are robust and cheap in computing time. However, these methods are based on mathematical/statistical concepts and therefore lack geological plausibility. Physical modeling with stratigraphic forward modeling (SFM), on the other hand, is capable of generating detailed 3D simulations of the geological realm. Conditioning SFM to e.g. well log data is expensive and not always successful. A hybrid approach of SFM with MPS can support the conditioning. This approach generates concept driven models that match the well data while also keeping geological continuity. Experiments were done on the mature 7th Tortonian oil reservoir in, Austria. Classical geostatistical approaches failed to generate enough dynamically diverse prior models to envelop the production data. First one geological process (SFM) model was generated and conditioned to well data. The result was then used as a training image (TI) for MPS. These results better match the wells while still preserving the geological information from SFM. All simulation models have been initialized and dynamically simulated. In comparison with the common geostatistical approach, they are dynamically more diverse while being more constrained by geological concepts.
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3D Multiple-points Statistics Simulations of the Roussillon Continental Pliocene Reservoir Using DeeSse
Authors V. Dall'Alba, P. Renard, J. Straubhaar, B. Issautier, C. Duvail and Y. CaballeroSummaryThis study presents a novel workflow that was developed to model the internal heterogeneity of a complex 3D reservoir using the Multiple-point Statistics (MPS) algorithm DeeSse. We propose to demonstrate the applicability of multivariate MPS simulation on a complex study site in the south of France. The modelled reservoir is the Continental Pliocene layer (PC) that is part of the Roussillon reservoir in the Perpignan's region. For this purpose, we use the direct sampling algorithm DeeSse and demonstrate its applicability on a large study site. New procedures are proposed to account for known geological constraints during simulations. In order to represent the complex sedimentary history of the plain, we create a non-stationnary training image (TI) that is used coupled to auxiliary variables maps during the simulation.
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Simplified Direct Sampling Method for Geostatistical Multiple-point Simulations
Authors P. Juda, J. Straubhaar and P. RenardSummaryThe Direct Sampling (DS) algorithm is a statistical multiple-point simulation technique based on training images. It allows modeling spatial fields that contain a wide range of complex structures and has applications in reservoir characterization (in hydrology and petroleum engineering), mining (ore reserve estimation), or climate modeling. The DS simulation quality depends in a complex manner on the choice of three main parameters (threshold, number of neighboring nodes and scan fraction), whose selection can be tedious and computationally expensive. To reduce the parameter space, we propose a modified version of the DS algorithm without the distance threshold parameter. While this version of the algorithm produces simulations of comparable quality, it has only two main parameters, and thus it is easier to tune and understand for users. It also requires comparable CPU time and can be applied to the same class of problems as the original Direct Sampling algorithm.
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Multiple Point Statistics with Pyramids Application on the Multi-scale Multi-structure Training Images
Authors T. Chugunova, J. Straubhaar and P. RenardSummaryMultiple Point Statistics (MPS) is now a well-known geostatistical method. In practice, one of the first operational need is to reproduce the heterogeneity with its small and large scales features, which are often present in natural phenomena. To respond to this need, multiple grid or flexible size template can be used. But unfortunately, even using these options, the large scale structure is not correctly reproduced or not reproduced at all. The human eye may make an abstraction from small scale texture and capture the large scale feature. Could the MPS approach be inspired by this idea to "see" large scale organization? A possible solution is to use a pyramidal representation of the Training Image similar to a Google Earth satellite image storage. This idea was implemented on the basis of the Multiple Point Direct Sampling algorithm (MPDS-pyramid) and this work presents its application to one synthetic and one real cases.
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A Geology Driven Workflow Combining Process-like, Surface-based and Unstructured Meshing for Reservoir Modelling
By D. LedezSummaryReservoir modelling is playing a fundamental role in developing and producing hydrocarbon reserves, as the integration tool for static and/or dynamic data and concepts. Standard workflows are primarily built in a linear way: fault framework modelling, stratigraphic modelling, gridding, facies and petrophysical modelling, upscaling, flow simulation and history matching. In order to increase complexity in reservoir models to capture accurately the geological heterogeneity driving the flow and, consequently, to have a better predictability of our models, unstructured meshes have been considered. But geological data have their own specificities, making direct use of CAD algorithms often irrelevant: internal boundaries, strong vertical anisotropy, small angles… T hen, meshing algorithms tailor-made for Geosciences need to be devised. Particularly, this implies that especially designed property modelling algorithms are needed to cope with such unstructured meshes, if no mapping / upscaling is desired. Pushing forward our willing to reset geology as the integrative process might finally involve to invert the usual reservoir modelling workflow, by meshing after simulating sedimentary bodies. Therefore, we propose a new workflow by finding a synergy around genetic-like modelling, surface-based modelling and unstructured meshing.
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Characterizing Connectivity in Heterogeneous Porous Media Using Graph Laplacians
Authors E. Nesvold and T. MukerjiSummaryIt is desirable to be able to generate and compare grid-free representations of geological structure at multiple scales without having to create a detailed earth model. Graphs are a natural framework both as spatially explicit models of structure and for characterization of connectivity properties. Here, we use the fast marching method and spectral clustering to map point clouds over permeability cubes and discrete geology bodies as graphs at the desired scale. We also show how the spectral properties of these graphs, i.e. the eigenvalue distribution of the graph Laplacian, can be used to characterize connectivity structure and to compute distributions for different types of geology. Possible applications are sensitivity analysis of geomodeling input parameters, Bayesian graph representations of geology and flow simulation over the resulting graphs.
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Direct Geostatistical Simulation on Unstructured Grids I: Recent Improvements for Additive Variables
More LessSummaryThis paper presents an upgraded workflow to address direct geostatistical simulations on unstructured grids. Comparing to previous approaches, this algorithm is based on a recently proposed spectral decomposition applicable to a wide class of variograms and allowing for non-stationarity. The method is encapsulated in a workflow dedicated to unstructured grids including facies modeling and hydrocarbon in place computations. The proposed methodology is able to treat any kind of grid; it takes into account the support effect and it decreases drastically the computation time compared to previous approaches based on Sequential Gaussian Simulations. The method is illustrated on a synthetic example and some results on a true case study are also provided.
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Direct Geostatistical Simulation on Unstructured Grids II: A Proposal for Non-additive Variables
Authors P. Mourlanette, P. Biver, P. Renard and B. NoetingerSummaryFor non-additive variables such as permeability, no simple solution is available for direct geostatistical simulation on unstructured grids. The standard approach uses a regular grid whose cell size is constant and should correspond to the measurement scale. The permeability is simulated on that grid with any standard geostatistical simulation technique. In a second step, the permeability is upscaled on the coarser unstructured grid. To minimize the loss of time and memory involved in this method, we propose a new workflow to directly simulate permeability on unstructured grids. The method is based on the use of the power averaging law and a local estimation of its exponent for each cell of the unstructured grid. A surface of response for the exponent is built using experimental design and a set of numerical upscaling experiments. It allows estimating rapidly the local exponents as a function of the cell dimensions and geostatistical parameters. In each cell, permeability is simulated on random points using spectral turning bands and the values are averaged using power law and the local exponents. We present the results of our method on a few synthetic cases and discuss the different benchmark available.
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Topologically Constrained Boolean Discrete Fracture Network Models
By T. ManzocchiSummaryRandom Boolean Discrete Fracture Network Models cannot reproduce the topological or clustering characteristics of natural fracture systems. Line placement rules have been developed for 2D Boolean fracture models allowing creation of models with widely varying topology, connectivity and clustering for a given fracture intensity and length distribution. Topology is defined by the relative proportions of I-, Y- and X-nodes present. Connectivity is characterised by the proximity of the system to its percolation threshold. Clustering is defined by the coefficient of variation of spacing between fractures measured on a scan-line. A set of numerical experiments has been run to determine the critical connectivity of 2D isotropic fracture systems as a function of fracture length distribution and topology. Differences in fracture clustering emerge from the models. Results indicate that determination of fracture intensity, topology and clustering may be sufficient to determine macroscopic fracture system connectivity irrespective of the fracture length distribution.
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Quantifying Uncertainty of Fracture Intensity in Reservoirs
More LessSummaryModelling fracture intensity at the reservoir scale is challenging due to the scarcity of spatially exhaustive data and the sampling bias caused by the small support size of wellbore image logs. Multiple attempts have aimed at accounting for this sampling bias, but most have treated the fracture intensity measured from well logs as hard data that needs to be honoured during geostatistical modelling. In this paper, we demonstrate that there may be large uncertainties associated with upscaling well log derived fracture intensities to the reservoir scale, and then provide a mechanism for quantifying this uncertainty and provide a workflow for propagating it through the reservoir modelling process. Specifically, we use Bayesian inference from data collected empirically from a set of fit-for-purpose prior fracture networks. We then develop a workflow to model the reservoir fracture intensity uncertainty away from the wells, while integrating non-linear multivariate secondary data. 3D models of probability density function of reservoir fracture intensity are thus obtained for the entire reservoir, which can then be used to generate different scenarios of discrete fracture networks. Models created with this approach are compared between different simulation methods, demonstrating the value of accounting for non-linearity in secondary data.
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Correlation Analysis of Fracture Intensity Descriptors with Different Dimensionality in a Geomechanics-constrained 3D Fracture Network
Authors W. Zhu, B. Yalcin, S. Khirevich and T. PatzekSummary3D intensity parameters of fracture networks cannot be measured directly and are usually correlated with the lower dimensionality intensity parameters, such as P21, P10. A comprehensive correlation analysis between lower dimensionality measures, P10, P20, P21, I2D (total number of intersections per unit area) and higher dimensionality ones, P30, P32, I3D (total number of intersections per unit volume) are investigated. We also correlate small cube samples and underlying fracture networks that represent cores or tunnels. The fracture networks are constrained by geomechanics principles and outcrop data to make them geologically meaningful. We show that orientation of fracture samples impacts correlations between the 2D and 3D parameters and samples parallel to the principal stresses yield better correlations. 3D intensity parameters, P30, I3D, and P32 can be predicted from 2D or small cube samples. However, 1D intensity P10 doesn’t have a strong correlation with 3D intensity parameters. The size of cube samples should be larger than 10 percent of the original size to capture main structural information. Furthermore, the minimum number of samples to reach a good correlation from 2D and cube samples are 20 and 60 respectively.
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Upscaling of Plastic Geomechanical Properties to Reproduce Anisotropic Failure in Heterogeneous Continua
Authors B. Zhang, R. Chalaturnyk and J. BoisvertSummaryThe importance of geomechanical simulation is well documented for projects associated with significant pressure and temperature changes. Deformations and failure zones in reservoirs will impact fluid flow, caprock integrity, and well integrity. However, geological modelling cell sizes are typically at the centimetre scale in order to incorporate geological features resulting in models with millions of cells which are computationally expensive for current reservoir-geomechanical simulations. One option to overcome these computational challenges is to properly upscale geomechanical properties and simulate at a larger scale with fewer gridblocks. While many current upscaling techniques normally assume failure criteria for each upscaled cell, anisotropic failure response caused by sub-grid heterogeneity are significant complicating factors for heterogeneous continua. A local numerical upscaling technique is proposed to obtain the anisotropic failure criteria for heterogeneous continua. The implemetation of it in a highly heterogeneous IHS system shows a large difference in M-C failure envelopes in different directions caused by different failure modes. With the optimum loading rate selected for the local triaxial tests based on a sensitivity analysis, the proposed techinique can be efficiently applied in large-scale models and determine anisotropic strength parameters which can reproduce the change of shear strength at different stress state.
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A Flexible Markov Mesh Prior for Lithology/fluid Class Prediction
Authors H. Tjelmeland, X. Luo and T. FjeldstadSummaryWe consider the problem of predicting the spatial distribution of lithology/fluid classes from observed seismic data. We formulate the problem in a Bayesian setting and argue that the best choice of prior for this problem is a Markov mesh model. To obtain a flexible prior we formulate a general class of Markov mesh models and a corresponding hyper-prior for the model parameters of the Markov mesh model. We discuss three different strategies for how to combine the hierarchical Markov mesh prior, a training image and a likelihood model for the observed seismic data, to obtain predictions of the lithology/fluid classes. We present results from a case study for a seismic section from a North Sea reservoir. In particular the results show larger connectivity in the lithology/fluid classes when using our flexible Markov mesh prior, compared to what one gets with a simpler, manually specified Markov random field prior.
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A Workflow for Generating Hierarchical Reservoir Geomodels Conditioned to Well Data with Realistic Sand Connectivity
Authors D.A. Walsh and T. ManzocchiSummaryConventional geostatistical modelling methods are unable to reproduce the low connectivity typical of deep marine turbidite reservoirs at high net:gross ratios, because the connectivity of these geomodels is inevitably controlled by their net:gross ratio. Previous studies have developed modelling methods that can honour independently both the low connectivity and high net:gross ratios of these systems at different hierarchical scales, however they are unable to honour available well data. We present a new workflow for building reservoir geomodels conditioned to well data, with realistic levels of sand connectivity and hierarchical stacking.
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K-fold Cross-validation of Multiple-point Statistical Simulations
Authors P. Juda, P. Renard and J. StraubhaarSummaryIn reservoir models, the choice of spatial interpolation or stochastic simulation methods for subsurface properties is crucial when dealing with heterogeneous media. Multiple-point statistics (MPS) algorithms allow to simulate complex structures but they are controlled by hyper-parameters whose identification can be tedious. Furthermore, many different geostatistical methods and models are available. In this work, we present an application of K-fold cross-validation for the selection of a spatial simulation method. The proposed technique allows to rank models based on their predictive accuracy and is completely generic: it can handle categorical and continuous variables, as well as compare MPS algorithms to variogram-based, or object based models. It can be used for the selection of any type of parameters, including the choice of the training image. We demonstrate the performance of the method on a synthetic test case used previously for benchmarking training image selection techniques and on a real field application including non-stationarity.
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Multiple-point Statistics Based on Gaussian Pyramids of the Training Image
Authors J. Straubhaar, P. Renard and T. ChugunovaSummaryIn this work, we present a new multiple-point statistics (MPS) method combining the direct sampling algorithm and the use of multiple-resolution representations of the training image (TI) through Gaussian pyramids. First, the pyramid is built by applying convolution with a Gaussian-like kernel, which provides versions of the TI at lower resolutions. Then, successive MPS simulations are performed within a pyramid: 1) a simulation is done in the lowest resolution level, 2) the result is used to condition a simulation in the next (finer) level, and 3) this last step is repeated until the initial resolution is simulated. This technique allows to guide the MPS simualtions and to obtain results that better reproduce the spatial statistics of the TI, compared to the results of MPS without pyramids.
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Successful Reconstruction of the Fluvial Conceptual Model on Gullfaks Sør with Object Modelling
Authors M.L. Vevle, I. Aarnes, K. Solheimsnes, C.G. Knudsen, R. Hauge and A. SkorstadSummaryWe show that object models are able to handle real world data complexity by applying a recently published object model to a North Sea reservoir. The reservoir used is the Statfjord formation of Gullfaks Sør, which has rich alluvial-fluvial sandstone deposits. For this reservoir, object modelling of channel objects and crevasse splays is preferred as it provides better geometric control of the channels and crevasses than indicator/data-driven models. However, earlier object models have had problems with conditioning to the amount of well data here. With this new approach, we can condition perfectly on well data, while also reducing the run time compared to previous models. The article addresses the improvements of the well conditioning which is central as it enhances the possibilities of doing automatic modelling of multiple realizations without any subjective modifications by the field geologists around wells. The improvements implicate that the necessary manual time for the geologists to create a good model can be reduced, which again implicates both cost-saving and a more robust automatable model. Our results demonstrate that object models have a vital role to play even in the current data-driven market of our industry.
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Using Geological Process Modeling to Enhance Lithofacies Distribution in a 3-D Model: An Example
Authors D. Otoo and D. HodgettsSummaryA major challenge in reservoir modeling is the accurate representation of lithofacies in a defined framework to honor geologic knowledge and available subsurface data. Considering the impact of lithofacies distribution on reservoir petrophysics, a two-stage methodology was applied to enhance lithofacies characterization in the Hugin formation, Volve field. The approach applies the Truncated Gaussian Simulation method that relies on sediment patterns and variograms, derived from geological process simulations. The methodology involves: (1) application of the geological process modeling (Petrel-GPMTM) software to reproduce stratigraphic models of the shallow-marine to marginal-marine Hugin formation (2) define lithofacies distribution in GPM outputs by using the property calculator tool in PetrelTM. Resultant lithofacies trends and variograms are applied to constrain facies modeling. Data includes: seismic data and 24 complete suites of well logs. The Hugin formation consists of a complex mix of wave and riverine sediment deposits within a period of transgression of the Viking Graben. Twenty depositional models were reproduced using different geological process scenarios. GPM-based facies models show an improvement in lithofacies representation, evident in the geologically realistic distribution of lithofacies in inter-well volumes, leading to the conclusion that a robust stratigraphic model provides an important stratigraphic framework for modeling facies heterogeneities.
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Random Walk for Simulation of Geobodies: A New Process-like Methodology for Reservoir Modelling
By G. MassonnatSummaryA new approach for modelling geobodies in clastic reservoirs has been developed using a random walk approach. It allows generating very realistic images of geology in the 3D space, with the preservation of sediment continuity along the sedimentation profile. The method is based on the simulation of sediment transportation path through the computation of trajectories. These ones are then dressed with parametric surfaces for generating the geobodies in the gridded reservoir model. The workflow includes: 1) the computation of the transportation flow for generating stream lines; 2) the simulation of sediment trajectories guided by the stream lines; 3) the dressing of the trajectories according to the location in the 3D space. The method enables an easy conditioning to well static hard data, but also an unusual conditioning on various dynamic and seismic information and data.
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Improving Geological Robustness into Iterative Geostatistical Seismic Inversion
Authors P. Pereira, I. Calçôa, L. Azevedo, R. Nunes and A. SoaresSummaryIn geostatistical seismic inversion methods the model perturbation and update is performed by stochastic sequential simulation and co-simulation algorithms in a regular Cartesian grid and using a global variogram model to describe the spatial continuity pattern of the subsurface petro-elastic property. These approaches do not capture heterogeneous small-scale features being hard to be reproduced when dealing to highly non-stationary geological environments. This work integrates local anisotropy steering volumes to describe local anisotropies within iterative geostatistical seismic inversion methods. The incorporation of local structural and spatial information allow to obtain more consistent spatial distribution of rock properties while avoids any transformation of inversion grid during simulation process by traditional geostatistical simulation techniques. The proposed methodology of this work was successfully applied to synthetic and real application examples.
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The Data Integration Approach for Prospecting Missed Intervals. An Example Based on Gazprom Neft Assets
Authors E. Akhmetvaliev, A. Belanozhka, M. Pilipenko and K. KyzymaSummaryBesides the attempts to find solutions how to obtain “good” new data, there are many attempts to find solutions how to re-interpret old data. In Gazprom Neft, there are many activities on creating new log data interpretation methods, on implementation of the neural network and machine learning methods. It is crucial for fields with thousands of wells. Yet these modern methods have their own limitations related to data variability and specifics. Thus, there are many cases when we need to focus on integration of all old and sometimes “bad” data for solving the relevant production tasks especially when the well number is not too big.
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Automated Facies Classification for Seismic Inversion
Authors R. Beloborodov, J. Gunning, M. Pervukhina, I. Emelyanova, M.B. Clennell and J. HauserSummaryWe introduce an algorithm for simultaneous facies classification and fitting of rock physics models from multivariate well log data. Special features of the methodology are designed to render it resilient to data outliers. The algorithm is a robustified and globalized variety of the expectation-maximization algorithm, using reweighted robust nonlinear regression steps for the maximisation step, and heavy-tailed distributional models for the expectation step. Facies classifications are natural byproducts of the expectation step, and optimised rock physics models are produced by the maximisation step. The practical advantages of the approach are illustrated using data from the Satyr-5 well, located in the Northern Carnarvon Basin, North West Shelf of Australia. Outputs of the algorithm include facies labels and free parameters in the corresponding rock-physics models, which can be easily interpreted and directly used in downstream workflows such as facies-based seismic inversion.
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Integration of Production Optimization Strategy in Reservoir Petrophysical Models
Authors K. Al-Mala Khudhur, O. Fabusuyi, L. Azevedo and A. SoaresSummaryGeostatistical methods for reservoir characterization aims at obtaining petrophysical models conditioned to different direct and indirect data. For example geophysical data, such as seismic and electromagnetic data, through geostatistical inversion algorithms, well log data using stochastic simulations and production data by geostatistical history matching processes. The objective of the proposed methodology of this study, is to generate numerical models of a reservoir petrophysical properties, conditioned to a production strategy obtained with a closed loop optimization technique. In a first step of the proposed methodology a best production strategy, L0, is obtained by closed loop optimization using Particle Swarm Optimization. In a second step, one intends characterizing the spatial dispersion of parameters Z(x), conditioned to L0, by using an iterative procedure based on stochastic simulations of Z(x). In this way one succeed to obtain a geological consistent solution of petrophysical properties Z(x), which are conditioned to the chosen production strategy L0, while optimizing the spatial patterns characteristics of Z(x) like connectivity of sand bodies.
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Optimization of the Development of the Yurubcheno-Tokhomsky Field Based on the Conceptual Geological Model
By N. KutukovaSummaryThe article describes an approach of forecasting areas of high quality or poro-perm properties of carbonate reservoirs based on the integration of multi-scale geological and geophysical information from core to special seismic data processing. Conceptual geological model is the result of an integrated approach of studying core, seismic data and analysis of well productivity. The presented integrated approach is shown on the example of a unique field of Yurubcheno-Tokhomskoe, located in Eastern Siberia. A conceptual model of the Riphean natural reservoir based on the results of a comprehensive core study, seismic data and analysis of well productivity is presented. The integration of multi-scale studies allowed to create and to develop the basic principles for constructing a model of the Riphean natural oil-reservoir, to create forecast maps. Currently production drilling planning is based on the presented conceptual model.
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Conditioning the Probability Field of Facies to Facies Observations Using a Regularized Element-free Galerkin (EFG) Method
Authors B. Sebacher, R. Hanea and S. MarzavanSummaryIn this paper, we present a methodology to condition the prior probability field of the facies to the facies observations collected at the well locations. The prior probability field of the facies usually comes from seismic inversion and the facies observations are the result of the examination of the cores extracted at the well locations. Consequently, the prior probability field is not directly conditioned to facies observations. The presented methodology relies on a regularized form of the element-free Galerkin (EFG) method. The regularization has been introduced in order to account for the prior, whereas the EFG is an interpolation technique with a moving least squares criterion. The methodology presented here consistently updates the prior probability field of facies with the facies data collected at some locations in the reservoir domain. We present two case studies: one in which hard facies data are considered and a second where hard and soft facies observations are involved in the conditioning.
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A Simulation Analysis of CO2 Capture and Underground Storage Monitoring in Smeaheia
Authors S. Anyosa, S. Bunting, J. Eidsvik and A. RomdhaneSummaryThe emissions of CO2 are an environmental problem and one possible solution is its capture and conduct underground storage (CSS). However, there is potential risk of leakage, and to aid in this challenge we propose to use statistical modeling for efficient monitoring and classification of sealing and leakage scenarios in the Smeaheia aquifer, in Norway. In this work, the approach is based on geostatistical simulations of the CO2 plume in the aquifer and on generating synthetic seismic data, for both leak and seal scenarios. The knowledge of estimated leakage probabilities allows better monitoring schemes and early leakage detection over time, which can be designed to support the decision making process on CO2 CSS projects.
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Statistical Description of Weak Interface Properties and their Impact on Hydraulic Fracture Height
Authors C. Hammerquist, Y. Aimene, J. Nairn and A. OuenesSummaryInterfaces are recognized to a be major in-situ layered reservoir realities that are challenging the oil and gas industry. Their impact on the hydraulic fracture vertical growth is well established and yet their properties are difficult to assess with the current limited data. The purpose of this analysis is to estimate the hydraulic fracture vertical growth in layered rock while focusing on the heterogeneous interfaces properties. The model combines deterministic tools based on the Material Point Method (MPM) geomechanical model which includes interface modeling tools with a stochastic description of the interfaces in the geomechanical model. Combining this modeling capability with the Monte Carlo simulations, makes it possible to propagate the uncertainty of the interface properties to resulting fracture heights. The model shows a large difference of fracture heights in the presence of interfaces. The analysis demonstrated the ability of the model to capture the range of uncertainties in hydraulic fracture heights that can be input in probabilistic frac design and analysis software.
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Application of Deep Learning in Reservoir Simulation
Authors S. Ghassemzadeh, M. Gonzalez Perdomo and M. HaghighSummaryReservoir simulation plays a vital role as oil and gas companies rely on them in the development of new fields. Therefore, a reliable and fast reservoir simulation is a crucial instrument to explore more scenarios and optimize the production. In each simulation, the reservoir is divided into millions of cells, and rock and fluid attributes are assigned to these cells. Then, based on these attributes, flow equations are solved with time-consuming numerical methods. Given the recent progress in machine learning, the possibility of using deep learning in reservoir simulation has been investigated in this paper. In the new approach, fluid flow equations are solved using a deep learning-based simulator instead of time-consuming mathematical approaches. In this paper, we studied 1D Oil Reservoir and 2D Gas Reservoir. Data sets generated using the numerical models were used to create the developed simulators. We used two metrics to evaluate our models: Mean Absolute Percentage Error (MAPE) and correlation coefficient (R2). Given the low value of these matrics (MAPE < 15.1%, R2 >0.84 for 1D and MAPE < 0.84%, R2 ≈ 1 for 2D), the results confirmed that the deep learning approach is reasonably accurate and trustworthy when compared with mathematically derived models.
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Inverse Modeling with Deep Neural Network and K-medoids Clustering Under Uncertain Geological Scenarios
More LessSummaryThis paper presents an inverse modeling based on deep-neural-network, of which scheme integrates data-encoding with stacked autoencoder and k-medoids clustering to select the adequate geo-models for the supervised-training dataset in the presence of uncertain geological scenarios. The reliable geological scenarios are essential at the successful history matching as well as the accurate forecasting but the limited data obstruct the consideration of well-production-performances. The developed method reduces the errors of matching and forecasting profiles as workflow stages, and results out the reliable plausible geo-models satisfying different well-oil-rates. K-medoids clustering screens error-prone geo-models implementing flow-response-based distances. The results show that deep-neural-network can be applicable as a robust history-matching tool under multiple geological interpretations.
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Geostatistical Seismic Shale Rock Physics AVA Inversion
Authors M. Cyz and L. AzevedoSummaryThe main goal of reservoir characterization is the description of the subsurface rock properties (i.e. porosity, volume of minerals and fluid saturations). This is commonly done in a sequential, two-step, approach: elastic properties are inferred from seismic inversion, which are then used to compute rock properties by applying calibrated rock physics models. However, this sequential procedure may lead to biased predictions as the uncertainties may not be propagated through the entire process. To overcome these limitations, here we propose the inference of shale rock properties directly from seismic data using a geostatistical direct shale rock physics AVA inversion. The purpose of the proposed geostatistical direct shale rock physics AVA inversion is to extract the properties included in the composition of a shale volume, such as brittleness, TOC and porosity from the seismic reflection data. The proposed method is applied to a real dataset from a Lower Paleozoic shale reservoir in Northern Poland.
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Features of Factor Models in Seismic
More LessSummarySome seismic models that are close to classical models of analysis of variance are considered. They allow you to analyze and identify statistically significant variations of factors, which are important in data processing, as well as for solving inverse seismic problems. The focus is on the properties of these models, which distinguish them from the classical models. These properties are determined by the structure of observations characteristic of real seismic data, and the kinds of directions, allocated in the forming of factor models. As a result, new particularities appear in the model parameter estimation problem. In particular, the ambiguity increases, and to eliminate it, methods using truncation of observations and the formation of some additional conditions based on an analysis of the internal interaction between factors are proposed.
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Statistical Properties of Multiplicative Factor Models
More LessSummaryThe results of the study, based on the study of the probabilistic characteristics of a random variable which appear after logarithmic of spectra of the path intervals, are presented. They make it possible to understand the properties of both the methods of factor decomposition and the estimates of the target parameters obtained or derived from them. In particular, such an analysis allows conclusions to be drawn about the quality of the primary observations and the degree of approximation of the highlighted signal component, as some regular element present in the observed wave field. Thus, the issue of the possibility of applying a particular model in processing the available experimental data can be resolved. As a rule, the distinguished regular element carries the basic information used later on at the level of interpretational models.
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Iterative Approach of Gravity and Magnetic Inversion through Geostatistics
Authors A. Volkova and V. MerkulovSummaryIn this work was demonstrated iterative approach of potential fields (gravity and magnetics) inverse problem solution with the aim of correct accounting of sedimentary section. The approach consist of simple model creation and further potential fields forward solution and comparison with the base case and after that model modification. Different iterations of simple model construction were tested on the “ground truth” detailed model with the main features of the West Siberia Palaeozoic deposits. The objective of the research is to minimize effect of sedimentary layers on the gravity and magnetic fields by modeling with geostatistical approach (stochastic simulation with trends) and explain how to use this approach according to real data.
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Study on the Fine Prediction of Ediacaran Fractured-vuggy Karst Reservoir
More LessSummaryFractured-vuggy carbonate reservoir has strong anisotropy and complex fracture-vuggy distribution. The quantitative description of dissolved pore and fracture is the key to reservoir prediction. The elastic parameter pairs of P-wave impedance and the P-S wave velocity ratio can be utilized to better remove the siliceous layers with the low P-wave impedance and low P-S wave velocity ratio, to identify the low P-wave impedance and the comparatively lower P-S wave velocity ratio, in order to reduce the ambiguities of the reservoir prediction. The curvature and texture attribute profiles have significant differences in response to different reservoir types, and their characteristics are mainly manifested as the texture attributes with a good connectivity and a large scale dissolution hole response and as the volume curvature attributes with the responses to faults and micro-cracks. Threshold fusion method is used to realize the attribute fusion, which can realize the spatial distribution of fracture and cave carving.
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Seismic Attenuation in Two-Scale Porous Fractured Media — A Numerical Study
Authors V. Lisitsa, M. Novikov, Y. Bazaikin and D. KolyukhinSummaryWe present a numerical study of seismic wave propagation in fluid-filled fractured-porous media. We consider models of fractured media with two typical scales. The first one is the scale of a single fracture, the second one is the scale of the percolating fracture clusters. We generated the models with different percolation length, suggested and approach to characterize the geometrical properties of the clusters and then performed simulation of seismic wave propagation. According to the results of the simulation, we observe strong dependence of seismic attenuation on the length of the percolating clusters due to both the fracture-to-background wave-induced fluid flows and due to the scattering. Whereas fracture-to-fracture flows are not distinguishable at acoustic frequency band.
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Seismic Tools to Mitigate the Challenges of Thin Tight Carbonate Reservoir: A Case Study
Authors S.K. Bhukta, E. Al-Shehri, S.K. Singh, P.K. Nath, A.S. Al-Ajmi, B. Khan and A. NajemSummaryTo identify a thin tight carbonate reservoir facies is one of the most challenging exploration task due to its spatial variation in terms of depostional settings, tectonics and diagenesis. The gross depositional environment plays a crucial role for insitu carbonate reservoir facies. However, the reservoir facies preservation depends on subsequent carbonate diagenesis. Though, the degree of diagenesis sometimes enhances the porosity but occasionally it ceases the porosity. However, the usage of the conventional seismic data analysis as well as state of the art tools like quantitative seismic inversion based reservoir characterization, geostatistical approach of waveform classification and the advent of the new machine learning tools like probabilistic fault likelihood, thin likelihood abetted to encompass the spatial variation, to identify the presence and hetrogeneity of the reservoir facies. Here, we have utilised these seismic tools through an integrated approach with other geological, geophysical and drilling data for futher hydrocarbon exploration and delineation.
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Geostatistical Analysis of Seismic Data for Regional Modeling of the Broom Creek Formation, North Dakota, USA
Authors A. Livers-Douglas, M. Burton-Kelly, B. Oster and W. PeckSummaryThe Energy & Environmental Research Center is investigating the feasibility of safely and permanently storing at least 50 million tonnes of CO2 in North Dakota, United States. A regional geologic model of the injection target was created: the eolian sandstones of the Permian Broom Creek Formation. This study demonstrates how seismic data, covering a subset of the overall model region, were integrated using both multiple-point statistics (MPS) and variogram analysis. Seismic geobody interpretation enabled MPS training image development to define a lithofacies distribution, which was then used to constrain petrophysical property distributions. Alternatively, a seismic porosity inversion volume was used to calculate variograms, which were then applied in property distributions throughout the greater region. The mean and standard deviation of the porosity distributions were nearly identical in both, but porosity in the MPS case was bimodal (attributed to the facies model) versus a unimodal distribution in the variogram analysis case. These results do not indicate one approach is altogether better than the other, but geologic characteristics and control point density may make one approach more suitable. Relative agreement between the methods indicates the biggest overall benefit to a project occurs simply in having seismic data to inform model construction.
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Modeling Study of the Unconventional Bakken Formation for Gas Injection EOR
Authors L. Jin, T. Jiang, N. Dotzenrod, S. Patil, R. Klenner, J. Sorensen and N. BosshartSummaryThe application of horizontal well and hydraulic fracturing technologies makes it profitable to produce a significant quantity of oil and gas from the extremely tight Bakken Formation. Typically, these fractured horizontal wells produce 5%-15% of original oil in place in the primary depletion stage, leaving a significant volume of oil in the reservoir. However, enhanced oil recovery (EOR) techniques have shown promise and are critical to the future development of the Bakken Formation. A series of modeling and simulation activities have been conducted in this study aimed at effectively modeling and simulating the production/EOR processes in complex fractured unconventional reservoirs. A geologic model and three simulation models with different scales were developed to investigate the flow behavior in the tight reservoirs and predict the gas injection EOR performance. Multiple interacting continua (MINC) and embedded discrete fracture model (EDFM) approaches were used to construct the fracture-matrix grid blocks in the models. The MINC method captured the early rapid transient flow but encountered numerical challenges in the gas injection simulation. The EDFM approach provided an effective way to improve simulation efficiency by reducing numerical failures in the computational process, which is critical for unconventional reservoir simulation efforts.
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New Insights Into the Spatial Distribution of Complex Carbonate Channels Using Geostatistical Approach: A Case Study
Authors A. Al-Ali, K. Stephen and A. ShamsSummaryRecently seismic inversion method and geostatistical tools has been widely used in reservoir modelling workflows due to its excellent ability to capture the complex geobodies. In this study, the objective of this work is to characterize the spatial distribution of the Mishrif carbonate in the West Qurna Oil Field using seismic inversion results, well log data, rock physics model. Identification of the spatial distribution of channel fairway and lithology are keys for constructing Mishrif reservoir model, which have a great impact on the development of the most prolific reservoir in the field Mishrif reservoir.
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Geostatistical Filtering of Noisy Seismic Data Using Stochastic Partial Differential Equations (SPDE)
Authors M. Pereira, C. Magneron and N. DesassisSummaryAn innovative geostatistical filtering approach is presented in this paper. It is based on Stochastic Partial Differential Equations (SPDE) and the idea is to solve kriging equations with the finite element method which requires the subdivision of a whole domain into simpler parts. This approach enables to deal with local variographic parameters while using a unique neighborhood even on large datasets. It opens the door to the operational processing of the most complex noise issues on seismic data. Post-stack and pre-stack. The methodology is described in details and two case studies are presented.
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Joint Facies/Elastic Inference in Waveform Inversion
By J. GunningSummarySeismic AVO inversion for elastic parameters jointly with litho-fluid categories from migrated seismic data is now an established technique. Compared to conventional techniques based on adding smooth background models to impedance inversions, it has several advantages, including the ability to constrain elastic parameters to welllog-data distributions, and ability to directly predict fluids. These methods rely on the migrated amplitudes being inverted being faithfully scaled to reflectivity, and are vulnerable to the presence of non--primary seismic energy which is not modelled by conventional Born-style imaging, such as mode conversions or multiples. In any deconvolutional style inversion such wave energy adds to the noise rather than the signal. We show that it is possible to perform joint elastic/facies inversion on raw shot records, using a full-wave modelling operation in the likelihood of a hierarchical Bayesian inversion, with optimisation performed using the expectation-maximisation algorithm. Such full wave techniques can in principle model all wave modes, and should theoretically have higher S/N ratios than their AVO equivalents. Illustrative examples in high--contrast lithologies using joint acoustic/facies full wave inversion show that cleaner inversions are produced in this regime compared to convolutional methods based on traditional imaging.
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Adaptive Ensemble-based Petrophysical Inversion for Seismically Constrained Static Model Building
Authors R. Moyen, R. Porjesz, P. Roy, R. Sablit, R. Alamer and F. AbdulazizSummarySeismic inversion produces a limited number of elastic variables (up to 3) however, the subsurface model is often described using a much larger number of variables such as porosity, clay content, fluids content, pressure etc. Through the use of a Petro-Elastic Model (PEM), it is possible to link the petrophysical properties to the elastic ones, but this forward model is not easily reversible as a given combination of elastic attributes (P -Impedance, Vp/Vs ratio...) can result from many possible combinations of petrophysical properties. Our adaptive ensemble optimization approach addresses this issue by sampling the solution space of this non-linear non-convex quadratic inverse problem through an ensemble-based model. A prior ensemble constructed from a prior model of petrophysical properties is used to sample the uncertainty of the parameters before entering the inversion process. Each petrophysical sample of the ensemble is then updated to reduce the mismatch between the elastic response given by the PEM and the elastic attributes. This update is given by a Gauss-Newton like approach where the first derivative matrix is adaptively estimated from sub-ensembles of petrophysical parameters and their corresponding forward model responses. The final ensemble provides an estimation of the uncertainty on the petrophysical parameters after the inversion process. We apply this technique on an on-shore clastic gas field in Pakistan, as part of an integrated multi-disciplinary workflow to obtain a robust, high-resolution static model integrating geology, sedimentology, petrophysics and seismic data. Stochastic modelling techniques are used to create three scenarios of varying levels of seismic influence, for a more rigorous uncertainty analysis.
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Seismic Waveform Inversion of Elastic Properties Using an Iterative Ensemble Kalman Smoother
Authors M. Gineste and J. EidsvikSummaryProbabilistic inversion of subsurface elastic properties using seismic reflection data is considered. The methodology makes use of data partitioning as a divide-and-conquer strategy, while the conditioning to data makes use of an iterative ensemble Kalman smoother. Augmenting the ensemble Kalman framework with an variational approach is found suitable when conditioning on larger sets of seismic waveform data. The methodology is exemplified using a synthetic case for the inversion of acoustic- and shear velocity and density.
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Self-updating Local Distributions in Geostatistical Seismic Inversion
Authors L. Azevedo, J. Narciso, R. Nunes and A. SoaresSummaryNumerical three-dimensional elastic models are a central piece of information in the geo-modelling workflow as they are often used to predict the spatial distribution of the reservoir properties such as porosity, volume of minerals and fluid saturations. Geostatistical seismic inversion methods have increasing its importance within this context due to their ability to infer high-resolution models while assessing uncertainties related to the spatial distribution of the inverted properties. Iterative geostatistical seismic inversion methods use stochastic sequential simulation and co-simulation as the perturbation technique of the model parameter space and a global optimizer based on cross-over genetic algorithms to ensure the convergence of the method. We propose a new alternative approach for model perturbation based on the concept of self-updating the local distributions of the elastic property. Iteratively, local probability distribution functions of the elastic property of interest are built and updated at seismic samples within the inversion grid based on the local mismatch between observed and synthetic seismic. This method avoids local fast convergence at early steps of the iterative procedure and allows assessment of local uncertainties at the seismic sample scale. The method was implemented in geostatistical acoustic inversion and applied to a non-stationary synthetic and a real case example with a blind well test.
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Bayesian Rock Physics Inversion for CO2 Storage Monitoring
Authors B. Dupuy, P. Nordmann, A. Romdhane and P. EliassonSummaryWe present a two-step inversion workflow for quantitative CO2 monitoring. In the first inversion step, we carry out CSEM inversion and seismic FWI with uncertainty assessment. The uncertainty is propagated in the second step (rock physics inversion) using a Bayesian formulation. We show sensitivity tests and case study at Sleipner to highlight the importance of uncertainty estimation in the full workflow.
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