Gas trap prediction from 3D seismic and well test data using machine learning
This is an incremental improvement for the oil and gas industry, specifically aiding in gas reservoir prediction.
This work tackled the problem of predicting gas traps from 3D seismic and well test data by developing a methodological approach that uses ensemble machine learning on a curated training dataset, achieving an f1 score of 0.893846 on a blind test sample of three wells.
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.