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

• URL: http://arxiv.org/abs/2401.12717v1
• Date: Tue, 23 Jan 2024 12:39:15 GMT
• Title: Gas trap prediction from 3D seismic and well test data using machine learning
• Authors: Dmitry Ivlev
• Abstract summary: 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. As a result, a cube of calibrated probabilities of belonging to the study space to gas reservoirs was obtained.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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