Geology Driven History Matching

Matveev, Ivan; Shishaev, G.; Eremyan, G.; Demyanov, Vasily; Popova, Oksana; Kaygorodov, Sergey; Belozerov, Boris; Uzhegova, Iuliia; Konoshonkin, D.; Коровин, Михаил Олегович

doi:10.2118/196881-ms

Cited by 3 publications

References 1 publication

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Multiphase Flow in Highly Fractured Shale Gas Reservoirs: Review of Fundamental Concepts for Numerical Simulation

Seales

2020

Journal of Energy Resources Technology

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Conventional hydrocarbon reservoirs, from an engineering and economic standpoint, are the easiest and most cost-efficient deposits to develop and produce. However, as economic deposits of conventional oil/gas become scarce, hydrocarbon recovered from tight sands and shale deposits will likely fill the void created by diminished conventional oil and gas sources. The purpose of this paper is to review the numerical methods available for simulating multiphase flow in highly fractured reservoirs and present a concise method to implement a fully implicit, two-phase numerical model for simulating multiphase flow, and predicting fluid recovery in highly fractured tight gas and shale gas reservoirs. The paper covers the five primary numerical modeling categories. It addresses the physical and theoretical concepts that support the development of numerical reservoir models and sequentially presents the stages of model development starting with mass balance fundamentals, Darcy’s law and the continuity equations. The paper shows how to develop and reduce the fluid transport equations. It also addresses equation discretization and linearization, model validation and typical model outputs. More advanced topics such as compositional models, reactive transport models, and artificial neural network models are also briefly discussed. The paper concludes with a discussion of field-scale model implementation challenges and constraints. The paper focuses on concisely and clearly presenting fundamental methods available to the novice petroleum engineer with the goal of improving their understanding of the inner workings of commercially available black box reservoir simulators. The paper assumes the reader has a working understanding of flow a porous media, Darcy’s law, and reservoir rock and fluid properties such as porosity, permeability, saturation, formation volume factor, viscosity, and capillary pressure. The paper does not explain these physical concepts neither are the laboratory tests needed to quantify these physical phenomena addressed. However, the paper briefly addresses these concepts in the context of sampling, uncertainty, upscaling, field-scale distribution, and the impact they have on field-scale numerical models.

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Multiphase Flow in Highly Fractured Shale Gas Reservoirs: Review of Fundamental Concepts for Numerical Simulation

Seales

2020

Journal of Energy Resources Technology

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Machine Learning Clustering of Reservoir Heterogeneity with Petrophysical and Production Data

Konoshonkin

Shishaev

Matveev

et al. 2020

Day 1 Tue, December 01, 2020

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Reservoir development decisions strongly depend on our understanding on reservoir heterogeneity, which is often subject to sparse and conflicting data, interpretational bias and constraints imposed by the modelling assumptions. The work tackles a challenging task of accurately and quickly identifying and describing uncertainty in the spatial distribution of reservoir heterogeneity derived from geological well data and with respect to a geological concept. We propose a metric based machine-learning approach to identify and describe spatial trends in reservoir heterogeneity/facies property distribution using wireline and production data. We demonstrate how the proposed method can help to partition reservoir heterogeneity and discover and verify spatial trends for a real mature producing field in the Western Siberia. The obtained clustering of reservoir facies based on the wireline logs (alpha-SP) demonstrated a good agreement with the reservoir zonation based on manual log interpretation and the geological concept. Clustering based on individual well production profiles has confirmed the reservoir partitioning and matched some of the reservoir features aligned with the prevailing geological concept. The outcome of the proposed method helps to improve the facies distribution model by integrating the discovered spatial trends into a geostatistical model and account for uncertainty in the depositional scenario that is difficult to quantify based on manual interpretation.

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Factoring in Scale Effect of Core Permeability at Reservoir Simulation Modeling

et al. 2022

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Summary This paper presents approaches for qualitative and quantitative estimation of the scale effect for the plug (30 mm diameter) and full-diameter (100 mm diameter) core samples based on statistical methods and data analysis including student and Pearson’s tests, stepwise regression, correlations, and other statistical approaches. Different methods were used to analyze carbonate cores from Elets reservoirs D3(el1) and D3(el3) located at Lambeyshorskoye field. The analysis has identified the core data scale effect. In the course of the study, a matrix of correction scale factors was constructed for the plug samples to reduce the scale effect. The work results open up the potential for using plug core samples on a par with full-diameter ones for addressing the problems of permeability array refinement for geologic (cells size 100×100 m) and dynamic models (cells size 50×50 or 25×25 m) and upscaling.

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Geology Driven History Matching

Cited by 3 publications

References 1 publication

Multiphase Flow in Highly Fractured Shale Gas Reservoirs: Review of Fundamental Concepts for Numerical Simulation

Multiphase Flow in Highly Fractured Shale Gas Reservoirs: Review of Fundamental Concepts for Numerical Simulation

Machine Learning Clustering of Reservoir Heterogeneity with Petrophysical and Production Data

Factoring in Scale Effect of Core Permeability at Reservoir Simulation Modeling

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