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

2024/01/23 by Dmitry Ivlev, Ivlev, Dmitry
Earth and Planetary Sciences · Engineering · #FOS: Computer and information sciences #FOS: Physical sciences #Geophysics (physics.geo-ph) #Hydraulic Fracturing and Reservoir Analysis #Machine Learning (cs.LG) #Reservoir Engineering and Simulation Methods #Seismic Imaging and Inversion Techniques

paper · pdf · doi:10.48550/arxiv.2401.12717

openalex publication_date 2024/01/23 · openalex created_date 2024/01/25 · openalex updated_date 2026/07/28

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.

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