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Pattern-Based History Matching Maintains Consistency for Complex-Facies Reservoirs

A challenging problem of automated history-matching work flows is ensuring that, after applying updates to previous models, the resulting history-matched models remain consistent geologically. This is particularly challenging in formations with complex connectivity patterns. In this work, the authors introduce a novel machine-learning approach with the aim of preserving the main connectivity patterns of previous reservoir models during history matching of complex geologic formations.

Introduction

The authors introduce a machine-learning algorithm to incorporate discrete connectivity patterns in history matching of complex-geologic-facies models. This is achieved by splitting the introduced history-matching optimization problem into two iterative subproblems: a continuous approximation of the solution that is obtained by solving a regularized least-squares inversion (while maintaining the expected connectivity of the patterns) followed by a machine-learning-based mapping of the continuous solution to the discrete feasible set defined by previous models. The second step involves a machine-learning approach that uses offline training to implement the mapping. The offline learning process uses the k-nearest neighbor (k‑NN) algorithm to construct local pattern (feature) vectors and compare them with the feature vectors in the training data set. For each spatial template, the feature vectors with the smallest distance in the learning data set are selected and their corresponding label vectors (i.e., multivariate discrete patterns) are identified and stored. Once all local patterns are scanned and processed using a defined template size, an aggregation step is applied on the overlapping templates to incorporate the multipoint statistics patterns collectively in assigning discrete labels to each gridblock.

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 190128, “Pattern-Based History Matching for Reservoirs With Complex Geologic Facies,” by A. Golmohammadi, M.R. Khaninezhad, and B. Jafarpour, SPE, University of Southern California, prepared for the 2018 SPE Western Regional Meeting, Garden Grove, California, USA, 22–27 April. The paper has not been peer reviewed.
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Pattern-Based History Matching Maintains Consistency for Complex-Facies Reservoirs

01 April 2019

Volume: 71 | Issue: 4

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