4D Seismic History Matching Incorporating Unsupervised Learning

TL;DR

This paper introduces a novel approach combining machine learning, sparsity regularisation, and seismic imaging techniques within an ensemble smoother framework to improve reservoir history matching using 4D seismic data.

Contribution

It proposes a new integrated scheme using unsupervised learning and sparse representations for petrophysical and seismic data in reservoir history matching.

Findings

Enhanced production data matching accuracy.

Better quantification of propagating water fronts.

Improved inversion stability with sparse representations.

Abstract

The work discussed and presented in this paper focuses on the history matching of reservoirs by integrating 4D seismic data into the inversion process using machine learning techniques. A new integrated scheme for the reconstruction of petrophysical properties with a modified Ensemble Smoother with Multiple Data Assimilation (ES-MDA) in a synthetic reservoir is proposed. The permeability field inside the reservoir is parametrised with an unsupervised learning approach, namely K-means with Singular Value Decomposition (K-SVD). This is combined with the Orthogonal Matching Pursuit (OMP) technique which is very typical for sparsity promoting regularisation schemes. Moreover, seismic attributes, in particular, acoustic impedance, are parametrised with the Discrete Cosine Transform (DCT). This novel combination of techniques from machine learning, sparsity regularisation, seismic imaging and history matching aims to address the ill-posedness of the inversion of historical production data efficiently using ES-MDA. In the numerical experiments provided, I demonstrate that these sparse representations of the petrophysical properties and the seismic attributes enables to obtain better production data matches to the true production data and to quantify the propagating waterfront better compared to more traditional methods that do not use comparable parametrisation techniques.