Features of high-density transient electromagnetic sounding data inversion under oil and gas prospecting in the Nepa-Botuoba anteclise area

Abstract

The data of near-field transient electromagnetic sounding, which are widely used for the purposes of oil and gas exploration within the Nepa-Botuoba anteclise, are typically interpreted in terms of quasi-horizontally-layered models of media. The purpose of this study is to develop an approach to the inversion of transient electromagnetic sounding curves obtained via high-density 3D observation networks. The study was based on mathematical modeling, whose results allowed to estimate the lateral spatial parameters of a non-stationary electromagnetic field as well as to understand the differences between the results of one-dimensional inversion of the transient electromagnetic sounding data and the true parameters of the target horizon. As a result, the characteristic of the electromagnetic field described by an exponential function and used in the lateral-constraint inversion of high-density electromagnetic sounding was obtained. The proposed approach was tested on the practical data within the site under investigation located on the slope of the Nepa-Botuobа anteclise. It is shown that the use of spatial discrepancy under TEM sounding data inversion makes it possible to obtain geoelectric models characterized by lateral consistency of section geoelectric parameters. The application of the spatial stacking-based approach in the inversion process allows to increase the solution stability of the inverse problem of the near-field TEM sounding data.

TEM Strata Inversion Imaging with IP Effect Based on Enhanced GCN by Extracting Long-Dependency Features

“…Strata media inversion based on single stations is commonly utilized for extensive geological surveys, often involving substantial data processing [13,17,18]. Traditional optimization methods like Newton's gradient descent are computationally inefficient for massive TEM survey data [15,[19][20][21][22][23]. The rise of electromagnetic signal inversion using traditional multi-layer perceptron (MLP) addresses this challenge, with network models progressively deepening the extraction of electromagnetic signal features and geoelectric structural characteristics per layer [24][25][26].…”