Time-lapse data matching using a recurrent neural network approach

TL;DR

This paper introduces a recurrent neural network method for time-lapse seismic data matching, aiming to improve reservoir change detection by learning data dependencies and outperforming traditional matching filters in various synthetic and real data scenarios.

Contribution

The paper presents a novel RNN-based approach for seismic data matching that leverages temporal dependencies, offering improved accuracy over conventional filtering methods.

Findings

RNN effectively matches seismic traces and pre-stack data.

The method enhances 4D seismic signal quality in synthetic tests.

It sometimes surpasses traditional matching filters in repeatability and predictability.

Abstract

Time-lapse seismic data acquisition is an essential tool to monitor changes in a reservoir due to fluid injection, such as CO$_2$ injection. By acquiring multiple seismic surveys in the exact location, we can identify the reservoir changes by analyzing the difference in the data. However, such analysis can be skewed by the near-surface seasonal velocity variations, inaccuracy in the acquisition parameters, and other inevitable noise. The common practice (cross-equalization) to address this problem uses the part of the data where changes are not expected to design a matching filter and then apply it to the whole data, including the reservoir area. Like cross-equalization, we train a recurrent neural network on parts of the data excluding the reservoir area and then infer the reservoir-related data. The recurrent neural network can learn the time dependency of the data, unlike the matching filter that processes the data based on the local information obtained in the filter window. We demonstrate the method of matching the data in various examples and compare it with the conventional matching filter. Specifically, we start by demonstrating the ability of the approach in matching two traces and then test the method on a pre-stack 2D synthetic data. Then, we verify the enhancements of the 4D signal by providing RTM images. We measure the repeatability using normalized root mean square and predictability metrics and show that in some cases, it overcomes the matching filter approach.