Non-intrusive Subdomain POD-TPWL Algorithm for Reservoir History Matching

TL;DR

This paper introduces a non-intrusive, domain-decomposed POD-TPWL algorithm for reservoir history matching, enabling efficient model reduction and parameter estimation without modifying legacy code.

Contribution

It develops a novel subdomain POD-TPWL method combining domain decomposition, RBF interpolation, and trajectory linearization for efficient reservoir data assimilation.

Findings

Achieves comparable results to finite-difference history matching.

Requires only 2-3 full-model simulations per uncertain parameter.

Efficiently generates calibrated model ensembles without extra full simulations.

Abstract

This paper presents a non-intrusive subdomain POD-TPWL (SD POD-TPWL) algorithm for reservoir data assimilation through integrating domain decomposition (DD), radial basis function (RBF) interpolation and the trajectory piecewise linearization (TPWL). It is an efficient approach for model reduction and linearization of general non-linear time-dependent dynamical systems without intruding the legacy source code. In the subdomain POD-TPWL algorithm, firstly, a sequence of snapshots over the entire computational domain are saved and then partitioned into subdomains. From the local sequence of snapshots over each subdomain, a number of local basis vectors is formed using POD, and then the RBF interpolation is used to estimate the derivative matrices for each subdomain. Finally, those derivative matrices are substituted into a POD-TPWL algorithm to form a reduced-order linear model in each subdomain. This reduced-order linear model makes the implementation of the adjoint easy and resulting in an efficient adjoint-based parameter estimation procedure. The performance of the new adjoint-based parameter estimation algorithm has been assessed through several synthetic cases. Comparisons with the classic finite-difference based history matching show that our proposed subdomain POD-TPWL approach is obtaining comparable results. The number of full-order model simulations required is roughly 2-3 times the number of uncertain parameters. Using different background parameter realizations, our approach efficiently generates an ensemble of calibrated models without additional full-order model simulations.