The aim of this study is to develop and apply an autonomous approach for predicting the probability of hydrocarbon reservoirs spreading in the studied area. Autonomy means that after preparing and inputting geological-geophysical information, the influence of an expert on the algorithms is minimized. The study was made based on the 3D seismic survey data and well information on the early exploration stage of the studied field. As a result, a forecast of the probability of spatial distribution of reservoirs was made for two sets of input data: the base set and the set after reverse-calibration, and three-dimensional cubes of calibrated probabilities of belonging of the studied space to the identified classes were obtained. The approach presented in the paper allows for expert-independent generalization of geological and geophysical data, and to use this generalization for hypothesis testing and creating geological models based on a probabilistic representation of the reservoir. The quality of the probabilistic representation depends on the quality and quantity of the input data. Depending on the input data, the approach can be a useful tool for exploration and prospecting of geological objects, identifying potential resources, optimizing and designing field development.

翻译：本研究旨在开发并应用一种自主化方法，用于预测研究区域油气储层分布的概率。自主化意味着在准备并输入地质-地球物理信息后，专家对算法的影响被降至最低。研究基于三维地震勘探数据及研究区早期勘探阶段的井资料开展。最终，针对两组输入数据——基础数据集与反标定后数据集——完成了储层空间分布概率预测，并获得了研究空间所属识别类别的标定概率三维体。本文提出的方法能够实现地质与地球物理数据的专家无关泛化，并可利用该泛化结果进行假设检验，以及基于储层概率表征创建地质模型。概率表征的质量取决于输入数据的质量与数量。根据输入数据的特性，该方法可作为地质目标勘探与预探、潜在资源识别、油田开发优化及方案设计的有效工具。