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Petroleum Geostatistics 2019
- Conference date: September 2-6, 2019
- Location: Florence, Italy
- Published: 02 September 2019
41 - 60 of 108 results
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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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