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.

该研究旨在开发并应用一种自主方法，预测研究区域油气藏扩散的概率。该方法基于 3D 地震勘探数据和地质信息，实现了对地质地球物理数据的专家独立概括，并利用该概括通过假设检验和生成地质模型。

使用机器学习方法对复杂油藏进行早期地质勘探中的海陆数据进行专家独立的解释

Expert-Independent Generalization of Well and Seismic Data Using Machine  Learning Methods for Complex Reservoirs Predicting During Early-Stage  Geological Exploration