Work

Fast 2D forward modeling of electromagnetic propagation well logs using finite element method and data-driven deep learning

Year: 2025

Type: article

Abstract: We propose a novel workflow for fast forward modeling of well logs in axially symmetric 2D models of the near-wellbore environment. The approach integrates the finite element method with deep residual neural networks to achieve exceptional computational efficiency and accuracy. The workflow is demonstrated through the modeling of electromagnetic propagation resistivity logs, where the measured responses exhibit a highly nonlinear relationship with formation properties. The motivation for this research is the need for advanced modeling algorithms that are fast enough for use in modern quantitative interpretation tools, where thousands of simulations may be required in iterative inversion processes. The proposed algorithm achieves a remarkable enhancement in performance, being up to 3,000 times faster than the finite element method alone when utilizing a GPU. While still ensuring high accuracy, this makes it well-suited for practical applications when reliable payzone assessment is needed in complex environmental scenarios.

Source: Artificial Intelligence in Geosciences

DOI: 10.1016/j.aiig.2025.100112

Authors: A.M. Petrov, A.R. Leonenko, K.N. Danilovskiy, O.V. Nechaev

Institutions: Institute of Petroleum Geology and Geophysics, Siberian Branch of the Russian Academy of Sciences

Language: English

Topic: Geophysical and Geoelectrical Methods

Subfield: Geophysics

Field: Earth and Planetary Sciences

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $700

Funder: Ministry of Education and Science of the Russian Federation