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

Abstract

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 paper formalizes the approach to creating a training dataset by selecting volumes with established gas saturation and filtration properties within the seismic wavefield. The training dataset thus created is used in a process stack of sequential application of data processing methods and ensemble machine learning algorithms. As a result, a cube of calibrated probabilities of belonging of the study space to gas reservoirs was obtained. The high efficiency of this approach is shown on a delayed test sample of three wells (blind wells). The final value of the gas reservoir prediction quality metric f1 score was 0.893846.