Modeling extra-deep electromagnetic logs using a deep neural network

TL;DR

This paper introduces a deep neural network model that accurately reproduces extra-deep electromagnetic logs in geosteering, enabling real-time interpretation with high speed and limited training data.

Contribution

The study develops a DNN-based EM simulator trained on a limited dataset that respects geological rules, providing a fast and accurate alternative to traditional simulators.

Findings

DNN model achieves high accuracy in synthetic and real data cases.

Evaluation time per logging position is approximately 0.15 ms.

Model suitable for integration into statistical and Monte-Carlo inversion workflows.

Abstract

Modern geosteering is heavily dependent on real-time interpretation of deep electromagnetic (EM) measurements. We present a methodology to construct a deep neural network (DNN) model trained to reproduce a full set of extra-deep EM logs consisting of 22 measurements per logging position. The model is trained in a 1D layered environment consisting of up to seven layers with different resistivity values. A commercial simulator provided by a tool vendor is used to generate a training dataset. The dataset size is limited because the simulator provided by the vendor is optimized for sequential execution. Therefore, we design a training dataset that embraces the geological rules and geosteering specifics supported by the forward model. We use this dataset to produce an EM simulator based on a DNN without access to the proprietary information about the EM tool configuration or the original simulator source code. Despite employing a relatively small training set size, the resulting DNN forward model is quite accurate for the considered examples: a multi-layer synthetic case and a section of a published historical operation from the Goliat Field. The observed average evaluation time of 0.15 ms per logging position makes it also suitable for future use as part of evaluation-hungry statistical and/or Monte-Carlo inversion algorithms within geosteering workflows.