Spatiotemporal Clustering-Based Formulation Aids Multiscale Modeling

Abstract

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 195329, “A General Spatiotemporal Clustering-Based Nonlocal Formulation for Multiscale Modeling of Compartmentalized Reservoirs,” by Soheil Esmaeilzadeh, Stanford University, and Amir Salehi and Gill Hetz, Quantum Reservoir Impact, et al., prepared for the 2019 SPE Western Regional Meeting, San Jose, California, 23-26 April. The paper has not been peer reviewed. In the complete paper, a novel hybrid approach is presented in which a physics-based nonlocal modeling framework is coupled with data-driven clustering techniques to provide a fast and accurate multiscale modeling of compartmentalized reservoirs. The research adds to the literature by presenting a comprehensive work on spatiotemporal clustering for reservoir-studies applications that considers the clustering complexities, the intrinsic sparse and noisy nature of the data, and the interpretability of the outcome. Introduction History matching is the most time-consuming phase in any reservoir-simulation study. As a means of accelerating reservoir simulations, a 2018 study proposed an approach in which a reservoir is treated as a combination of multiple interconnected compartments that, under a range of uncertainty, can capture the reservoir’s response during a recovery process. In this work, the authors extend that approach to represent a reservoir in a multiscale form consisting of multiple interconnected segments. To identify segments of the reservoir, spatial, temporal, and spatiotemporal unsupervised data-mining clustering techniques are used. Then, a novel nonlocal formulation for flow in porous media is presented in which the reservoir is represented by an adjacency matrix describing the neighbor and non-neighbor connections of comprising compartments. The reservoir is divided automatically into distinct compartments in which direction-dependent multiphase-flow communication is a function of nonlocal phase potential differences. With the segmented reservoir and the uncertain parameters, a robust history-matching technique is used to reproduce the reservoir’s historical response. Finally, for the segmented and history-matched reservoir, forecasting is performed. The complete paper first describes the spatiotemporal clustering framework, followed by an overview of the compartmentalized reservoir simulation framework, in which multitank history material balance and multi tank predictive material balance are explained. Next, the history-matching approach is described. These sections are not included in the current synopsis. The complete paper concludes with results of clustering, multitank material balance, and history matching for a real-world reservoir.