A Data-Driven Model for History Matching and Prediction

Abstract

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 173213, “INSIM: A Data-Driven Model for History Matching and Prediction for Waterflooding Monitoring and Management With a Field Application,” by Hui Zhao, SPE, Yangtze University; Zhijiang Kang, China Petroleum and Chemical Corporation; Xiansong Zhang, China National Offshore Oil Corporation; Haitao Sun and Lin Cao, Yangtze University; and Albert C. Reynolds, The University of Tulsa, prepared for the 2015 SPE Reservoir Simulation Symposium, The Woodlands, Texas, USA, 23–25 February. The paper has not been peer reviewed. In this paper, the authors derive and implement an interwell numerical simulation model (INSIM) that can be used as a calculation tool to approximate the performance of a reservoir under waterflooding. In INSIM, the reservoir is characterized as a coarse model consisting of a number of interwell control units, and each unit has two specific parameters: transmissibility and control pore volume. INSIM is applied to perform history matching for parameter estimation and to infer interwell connectivity and geological characteristics. Introduction History matching with a reservoir simulator is the most common way to condition rock-property fields to production data. However, production data are never sufficient to resolve the reservoir properties (e.g., gridblock permeabilities), and few assisted-history- matching tools exist in commercial reservoir simulators. Consequently, when a reservoir simulator is used as the forward model when history matching, the number of reservoir parameters is often reduced to a small number on the basis of computational experiments and physical insight. Although the INSIM methodology introduced here does limit the number of history- matching parameters, the primary objective of INSIM is to provide a fast, simplified simulation model to calculate flow and transport sufficiently well that, when INSIM is used for history matching, the resulting history-matched model can be entered into INSIM to provide reasonable future predictions and provide information on the flow dynamics of the reservoir. It is hoped that the model and methodology presented here will prove useful for monitoring and understanding waterflooding operations conducted on a black-oil reservoir and that INSIM will ultimately be useful for waterflooding optimization. Models based on the statistical correlation or the connectivity between injectors and producers estimated from flow-rate data have been used previously to characterize reservoirs for the purpose of waterflooding management. Unlike previous correlation-based models, INSIM is able to effectively predict the water cut and oil-production rate and hence can be used as the forward model for assisted history matching of these data. Specifically, the model can be used in automatic history matching. Moreover, the model is derived directly from the correct two-phase-flow mass- balance equations, and thus the transmissibilities derived from history matching reflect an average transmissibility between wells. In addition, because INSIM is based on simulation flow equations, it can incorporate large changes in flow rates, flow directions, and injector allocation factors—the interaction between pairs of producers and the conversion of producers to injectors. At the same time, INSIM retains the computational efficiency of previous correlation-based models that incur far less computational cost than a traditional numerical reservoir simulator.