Gas trap prediction from 3D seismic and well test data using machine learning

The aim of this work is to create and apply a methodological approach for predicting gas traps from 3D seismic data and gas well testing. The proposed approach is based on binary classification algorithms with training on well data. The study includes the following sequence of operations: interpretation of gas well test results to determine the radius of radial gas filtration (IARF); correlation of the top and bottom of productive horizons based on seismic data in the near-wellbore space; creating volumes of space for positive and negative class; dividing the sample into training and validation; creation of a separate test sample for the metamodel; creation of a feature space - extraction of seismic wavefield attributes; creation of data sampling - assignment of a vector of seismic attributes to each point of space within the volumes of classes; selection of features; basic model training; generalized assessment of trait contribution; creation of an ensemble of classification models using a metamodel - logistic regression; prediction of the probability of space belonging to gas reservoirs; evaluation of forecast quality on a test sample. The paper formalizes an approach to creating a training dataset by selecting volumes with established gas saturation and filtration properties within a seismic wavefield. The volumes divide the studied space into positive and negative classes. Positive class is a volume of gas-saturated sands, identified by the results of detailed correlation of the gas sandstone top and bottom, within which there is a region with a boundary along the radius of continuous radial gas filtration.