Reservoir classification based on petrophysical properties of Devonian siliciclastic sediments, Russian Platform, Republic of Bashkortostan

Nigmatzyanova, Albina; Gubaydullina, A.A.; Burikova, T. V.; Dushin, A. S.; Privalova, O. R.; Chervyakova, A. N.; Akhmetzyanov, R. V.

doi:10.24887/0028-2448-2018-4-22-25

Cited by 2 publications

(2 citation statements)

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“…Объектом исследования является залежь тиманского горизонта терригенного девона. Литологически данные отложения представлены породами, разделенными между собой пачками алевролитов, аргилиллитов и песчаников (пласты D3tm) [4]. Коллекторские свойства ограничены по площади распространения (коэффициент распространения коллектора на исследуемом участке равен 0,6) и замещаются глинистыми разностями.…”

Section: Petroleum Engineeringunclassified

“…The object of the study is the deposit of the Timan horizon of the terrigenous Devon. Lithologically, these deposits are represented by rocks separated by bundles of siltstones, argillites and sandstones (D3tm) [4]. Collector properties are limited in area of distribution (the distribution coefficient of the collector in the study area is 0.6) and are replaced by clay differences.…”

Section: Reservoir Characteristicsmentioning

confidence: 99%

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Case Study Approach to the Uncertainty of Deposit Geometry Determination in Geological and Hydrodynamic Modeling

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Файзова

Кавиева

et al. 2020

NGD

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Авторами в данной статье описан процесс корректировки геологической модели единичной залежи тиманского горизонта (пласта D3tm.2) Тузлукушевского месторождения на основе анализа разработки и адаптации гидродинамической модели (ГДМ).

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Section: Petroleum Engineeringunclassified

Section: Reservoir Characteristicsmentioning

confidence: 99%

Case Study Approach to the Uncertainty of Deposit Geometry Determination in Geological and Hydrodynamic Modeling

Дзюба

Файзова

Кавиева

et al. 2020

NGD

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Physics Driven AI Coreflooding Simulator for SCAL Data Analysis

Mathew

Tembely

Al-Shalabi

et al. 2020

Day 1 Mon, November 09, 2020

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Two of the most critical properties for multiphase flow in a reservoir are relative permeability (Kr) and capillary pressure (Pc). To determine these parameters, a meticulous interpretation of coreflooding and centrifuge experiments is necessary. In this work, a machine learning technique was incorporated to assist in the determination of these parameters quickly and synchronously. A state of the art framework was developed where a large database of Kr and Pc curves were generated based on existing mathematical models. This database was used to perform thousands of coreflood simulation runs representing drainage steady state experiments. The results obtained from the corefloods including pressure drop and water saturation profile along with other conventional core analysis data were fed as features into the machine learning model. The entire data set was split into 70% for training, 15% for testing, and the remaining 15% for the model validation. The 70% of the training data teaches the model to capture fluid flow behavior inside the core. K-fold cross validation technique was also utilized to increase the accuracy of the model. The trained/tested model was thereby employed to estimate Kr and Pc curves based on available experimental results. The values of the coefficient of determination (R2) was used to assess the accuracy and efficiency of the developed model. The respective cross plots indicate that the model is capable of making accurate predictions with error percentage less than 2% on history matching experimental data. Furthermore, the latter implies that the AI-based model is capable of determining Kr and Pc curves with less effort and better reliability as opposed to the conventional way of creating an entire simulation model. Additionally, the results include multiple options for Kr and Pc curves from which the best solution can be determined using engineering judgement. This is unlike solutions from existing commercial software, which usually provides only a single solution. The model currently focusses on the prediction of Kr and Pc curves for drainage steady state experiments; however, the work can be extended to capture the imbibition cycle as well.

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Reservoir classification based on petrophysical properties of Devonian siliciclastic sediments, Russian Platform, Republic of Bashkortostan

Cited by 2 publications

References 0 publications

Case Study Approach to the Uncertainty of Deposit Geometry Determination in Geological and Hydrodynamic Modeling

Case Study Approach to the Uncertainty of Deposit Geometry Determination in Geological and Hydrodynamic Modeling

Physics Driven AI Coreflooding Simulator for SCAL Data Analysis

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