Integrated Modeling for Assisted History Matching and Production Forecasting of Low Salinity Waterflooding

Abstract

Summary From a technical point of view, the success or failure of LSW (low salinity waterflooding) projects strongly depends on reservoir geology; however, this has not been systematically evaluated in the past and the effects of clay minerals are often neglected in conventional reservoir simulation. This paper presents one of the first studies on integrated modeling, assisted history matching (HM) and production forecasting of field-scale LSW. To handle this complex recovery process, we used a comprehensive ion-exchange model, fully coupled with geochemistry specially designed for the modeling of LSW physical phenomena in an EOS reservoir simulator. The model is capable of accounting for the critical role of the properties, quantity, and distributions of clay minerals. We developed an integrated modeling approach that involves the combination of geological software, a reservoir simulator, and a robust optimizer in a big-loop workflow for sensitivity analysis, HM, optimization, and uncertainty assessment. The numerical simulation results indicate that LSW's performance depends critically on the reservoir geological characteristics. Multiple geological realizations can be automatically generated from the big-loop approach that are needed for fast and accurate HM and optimization of LSW. In sandstone reservoirs, clay content varies across regions, resulting in differences in ion-exchange capacity and weights of the relative permeability modification. We introduce the scaled-equivalent-fraction ion exchange which is associated with the calculated Cation-Exchange-Capacity function; the wettability alteration will be shifted based on both the ion exchange and the clay content in each grid block. The key parameters for successful field-scale LSW HM include: clay distribution/quantity associated with different facies, relative permeability modification, wettability alteration thresholds, reservoir minerals, geochemical reactions, and operating conditions. Finally, LSW HM by tuning reservoir parameters only may lead to poor prediction results, while the integrated modeling approach provides much better forecasting results to the true history data. The work presented in this paper contributes to an understanding of the critical roles of reservoir geology on the field-scale LSW performance, in particular, for substantially reducing HM errors, accurately predicting the future production, maximizing oil recovery and minimizing the risks of LSW implementation.