Generalization with reverse-calibration of well and seismic data using machine learning methods for complex reservoirs predicting during early-stage geological exploration oil field

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.

Materials and methods. The prediction was made based on the 3D seismic survey data and well information on the early exploration stage of the studied field. The results of the lithological interpretation of logging from nine wells were used, four of which penetrated the object vertically or subvertically, while the remaining five were drilled horizontally through different stratigraphic parts of the Achimov sedimentary complex, which is the object of this study. The paper presents an approach based on a technological stack of machine learning algorithms with the task of binary classification and modification of the geological-geophysical dataset. The study includes the following sequence of actions: creation of data sets for training, selection of features, reverse-calibration of data, creation of a population of classification models, evaluation of classification quality, evaluation of the contribution of features in the prediction, ensembling the population of models by stacking method.

As a result, a prediction was made — a three-dimensional cube of calibrated probabilities of belonging of the studied space to the class of reservoir and its derivative in the form of the map of effective thicknesses of the Achimov complex of deposits was obtained. Assessment of changes in the quality of the forecast depending on the use of different data sets was carried out.

Conclusion. The reverse-calibration method proposed in this work uses the uncertainty of geophysical data as a hyperparameter of the global tuning of the technological stack, within the given limits of the a priori error of these data. It is shown that the method improves the quality of the forecast. The technological stack of machine learning algorithms used in this work allows expert-independent generalization of geological and geophysical data, and use this generalization to test hypotheses and create geological models based on a probabilistic view of the reservoir. The approach, formalizes the generalization of data about the target, using only factual information such as lithology along the wellbore and seismic data. Depending on the input data, the approach can be a useful tool for finding and exploring geologic targets, identifying potential resources, and optimizing and designing reservoir development systems.

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