Use of machine learning technology to model the distribution of lithotypes in the Permo-Carboniferous oil deposit of the Usinskoye field

IF 2.4 Q2 MINING & MINERAL PROCESSING Journal of Mining Institute Pub Date : 2023-03-02 DOI:10.31897/pmi.2022.101
D. Potekhin, S. Galkin
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Abstract

Permo-Carboniferous oil deposit of the Usinskoye field is characterized by an extremely complex type of the void space with intense cross-sectional distribution of cavernous and fractured rock. In this study, for this production site, the process of 3D geological modeling has been implemented. At the first stage, it provided for automated identification of reservoir volumes by comparing the data of core and well logging surveys; at the second stage, identification of rock lithotypes according to Dunham classification is performed on the basis of comparison of thin sections examination and well logging data. A large array of factual information enables the use of machine learning technology on the basis of Levenberg – Marquardt neural network apparatus toward achievement of our research goals. The prediction algorithms of reservoir and rock lithotype identification using well logging methods obtained on the basis of the training samples are applied to the wells without core sampling. The implemented approach enabled complementing the 3D geological model with information about rock permeability and porosity, taking into account the structural features of the identified lithotypes. For the Permo-Carboniferous oil deposit of the Usinskoye field, the volumetric zoning of the distribution of different rock lithotypes has been established. Taking into account the lithotypes identified based on machine learning algorithms, density and openness of fractures were determined, and fracture permeability in the deposit volume was calculated. In general, during the implementation, the machine learning errors remained within 3-5 %, which suggests reliability of the obtained predictive solutions. The results of the research are incorporated in the existing 3D digital geological and process model of the deposit under study.
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利用机器学习技术对Usinskoye油田二叠系-石炭系油藏的岩性分布进行建模
Usinskoye油田二叠石炭系油藏具有极其复杂的孔隙类型和强烈的洞状裂隙岩石截面分布特征。本研究针对该生产现场,实施了三维地质建模过程。第一阶段,通过对比岩心和测井数据,实现储层体积的自动识别;第二阶段,在薄片检查与测井资料对比的基础上,根据Dunham分类进行岩型识别。大量的事实信息使得在Levenberg - Marquardt神经网络设备的基础上使用机器学习技术来实现我们的研究目标。将利用训练样本得到的测井方法进行储层预测和岩性识别的算法应用于无岩心取样井。考虑到已识别岩性的结构特征,该方法能够将岩石渗透率和孔隙度信息与3D地质模型相补充。针对Usinskoye油田二叠-石炭系油藏,建立了不同岩性分布的体积分带。结合机器学习算法识别的岩性,确定裂缝密度和开度,计算裂缝渗透率。一般来说,在实施过程中,机器学习误差保持在3- 5%,这表明获得的预测解决方案的可靠性。研究结果被纳入研究矿床的现有三维数字地质和过程模型。
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来源期刊
Journal of Mining Institute
Journal of Mining Institute MINING & MINERAL PROCESSING-
CiteScore
7.50
自引率
25.00%
发文量
62
审稿时长
8 weeks
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