Analytical Work Flows Enable Continuous Waterflooding Optimization for a Mature Field

Abstract

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 198586, “A New Continuous Waterflood Operations Optimization for a Mature Oil Field by Use of Analytical Work Flows That Improve Reservoir Characterization,” by Atul Yadav and Anton Malkov, SPE, Wintershall, and Essam Omara, Suez Oil Company, et al., prepared for the 2019 SPE Gas and Oil Technology Showcase and Conference, Dubai, 21–23 October. The paper has not been peer reviewed. In the complete paper, the authors present a novel approach that uses data-mining techniques on operations data of a complex mature oil field in the Gulf of Suez that is currently being waterflooded. Evidence is presented about how salinity data can be used to further justify the linkages between different wells obtained from cross-correlation analysis. The results presented in this research can be adapted to any waterflooded field to optimize recovery at frequent intervals where injection and production data are available continuously. Introduction Mature oil fields typically present challenges of increased water production and water handling. Considering the geological complexity and associated field-performance behavior, reservoir characterization to optimize water flooding is a major challenge. An integrated reservoir study was con ducted to minimize reservoir uncertainties and increase understanding of the field’s performance behavior. The acceptable history-matched model was used to estimate remaining oil potential, maintain and increase current production levels, and optimize the water-injection rate. Generally, history-matched models need to be updated throughout the life of producing fields as new subsurface data are acquired. Such integrated reservoir modeling studies, however, can be time-consuming and do not necessarily enable quicker decision-making around operational activities. The continuous recording of production and injection data presents new opportunities to apply novel analytical techniques to understand interwell connectivity in the reservoir. The current ability to store and analyze data, coupled with advances in the ability to interpret big data sets, has helped create an independent toolkit that provides analysis without the geological model. In addition, geological information such as pre-existing faults and the commingled or disconnected nature of production between different layers can be integrated to obtain and improve analyses from the analytical models. The authors analyze the results using Pearson’s cross-correlation analysis measure to obtain a qualitative analysis of the field. They also apply Spearman’s rank correlation analysis for the discussed field (henceforth named GOS for purposes of this paper) that helps compare injection and production data. The objective is to present a comparison between the analytical and the stream-lined approach to show consistency in reservoir characterization. The effective injector/producer pairs identified form an important component of the field development.