- Home
- Conferences
- Conference Proceedings
- Conferences
Petroleum Geostatistics 2019
- Conference date: September 2-6, 2019
- Location: Florence, Italy
- Published: 02 September 2019
1 - 20 of 108 results
-
-
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.
-
-
-
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
-
-
-
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.
-
-
-
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.
-
-
-
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.
-
-
-
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.
-
-
-
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.
-
-
-
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.
-
-
-
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.
-
-
-
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.
-
-
-
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.
-
-
-
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.
-
-
-
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.
-
-
-
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.
-
-
-
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.
-
-
-
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.
-
-
-
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.
-
-
-
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.
-
-
-
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.
-
-
-
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.
-