Estimation of Oil Reservoir Transmissivity and Storativity Fields Using a Radial Basis Function Network Based on Inverse Problem Solving

IF 0.8 Q2 MATHEMATICS
V. P. Kosyakov, D. Yu. Legostaev
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引用次数: 0

Abstract

In the oil industry, there is a noticeable trend towards using proxy models to simulate various levels of complexity in order to make operational predictions. In particular, machine learning techniques are being actively developed in the context of the digitalization and automation of production processes. This paper proposes a method for combining a physically relevant fluid flow model with machine learning techniques to address the challenges of history-matching and prediction. The approach is demonstrated using synthetic oil reservoir models. The synthetic model has significant zonal inhomogeneities in the permeability and storativity fields. The simplified single-phase flow through a porous medium model was used for the proposed approach. This model was matched to the historical values of the development parameters by restoring the fields of the reservoir parameters. Properties fields were reconstructed using a radial basis functions network and a fully connected linear layer. Based on the reconstructed field, interwell connectivity coefficients were calculated, which corresponded qualitatively and quantitatively to the true interwell connectivity coefficients. The predictive characteristics of the proposed approach were evaluated by split the historical dataset into training and test time intervals.

Abstract Image

利用基于逆问题求解的径向基函数网络估算油藏渗透率和储量场
摘要 在石油工业中,一个明显的趋势是使用代理模型模拟不同程度的复杂性,以进行业务预测。特别是在生产流程数字化和自动化的背景下,机器学习技术正在得到积极发展。本文提出了一种将物理相关流体流动模型与机器学习技术相结合的方法,以应对历史匹配和预测方面的挑战。该方法使用合成油藏模型进行了演示。合成模型的渗透率和储量场具有明显的分区不均匀性。所提出的方法使用了简化的单相流流经多孔介质模型。通过恢复储层参数场,该模型与开发参数的历史值相匹配。使用径向基函数网络和全连接线性层重建属性场。根据重建的油气田,计算出了井间连通系数,这些系数在定性和定量上都与真实的井间连通系数一致。通过将历史数据集分成训练时间段和测试时间段,对所提方法的预测特性进行了评估。
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来源期刊
CiteScore
1.50
自引率
42.90%
发文量
127
期刊介绍: Lobachevskii Journal of Mathematics is an international peer reviewed journal published in collaboration with the Russian Academy of Sciences and Kazan Federal University. The journal covers mathematical topics associated with the name of famous Russian mathematician Nikolai Lobachevsky (Lobachevskii). The journal publishes research articles on geometry and topology, algebra, complex analysis, functional analysis, differential equations and mathematical physics, probability theory and stochastic processes, computational mathematics, mathematical modeling, numerical methods and program complexes, computer science, optimal control, and theory of algorithms as well as applied mathematics. The journal welcomes manuscripts from all countries in the English language.
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