Compositional Simulation, Artificial Intelligence Optimize Water Injection

Abstract

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 198995, “Low-Salinity Water-Injection Optimization in the Namorado Field Using Compositional Simulation and Artificial Intelligence,” by Diana Mercado Sierra, SPE, Argenis Alvarez Rojas, and Victor Salazar Araque, Computer Modelling Group, prepared for the 2020 SPE Latin American and Caribbean Petroleum Engineering Conference, 27-31 July, Virtual. This paper has not been peer reviewed. The complete paper discusses optimization of a development plan involving low-salinity water injection (LSWI). This methodology combines compositional simulation and a mathematical optimization tool that uses artificial intelligence to maximize the net present value (NPV) of the project under evaluation. The adapted methodology allowed an optimal development plan, considering the uncertainty associated in the reservoir, by use of multiple geostatistical realizations and simultaneous history-matched models. LSWI LSWI is an enhanced-oil-recovery technique in which the salinity of the injected water is controlled with the objective of increasing the recovery factor. Of the mechanisms proposed for LSWI, wettability alteration is the most widely accepted for describing the increase of the recovery factor. The wettability change in reservoirs with the presence of sand-stones mainly is attributed to the multionic exchange between the injected fluid and the clay surface, as is the double layer expansion. From a technical point of view, the LSWI success is related strongly to reservoir lithology. An integrated analysis and optimization study was performed using numerical simulation to evaluate LSWI as an alternative for increasing the recovery factor in the Namorado field in the Campos Basin of Brazil.