Technology Focus: EOR Operations (June 2023)

Abstract

While supply disruptions and price volatility are not new to the oil and gas industry, the dramatic drop in oil prices because of the COVID-19 pandemic severely affected global enhanced oil recovery (EOR) projects. To add fuel to the fire, the current geopolitical scenario leading to a surge in commodity prices and supply chain disruptions exacerbated this situation by negating any chances of recovery, at least in the short term. Concurrently, the rise in energy consumption and increasing demand for energy security worldwide warrants the need to increase global hydrocarbon production. However, rising environmental concerns with regard to greenhouse gas emissions, coupled with a decrease in the availability of easy-to-produce hydrocarbon resources, mandate the need for a gradual shift toward optimized recovery strategy in a sustainable and cost-efficient manner. As a result, current circumstances dictate focusing on low-cost and low-carbon EOR barrels to secure energy supply in the shorter term while continuing to develop innovative technologies and strategies for enhancing production in the longer term. Energy consumption for mature waterfloods increases sharply at higher water cuts primarily because of the recirculation of water through the existing swept pathways. Lower volumetric sweep in reservoirs leads to excessive water production and less oil recovery. To curb the menace of excess water production, conventional EOR techniques such as polymerflooding or in-depth conformance-control techniques are mostly implemented. Both methods have demonstrated cost savings because of reduced watercuts and reductions in CO2 generation by lowering reinjection of produced water. Additional costs of injectants, transport, and topside facilities, however, contribute to higher project costs. Early optimization measures for existing waterfloods using machine-learning (ML) approaches could enable optimal reservoir management and production by delineating reservoir heterogeneities more efficiently and predicting more-effective interwell connectivity. Furthermore, ML could contribute directly to the optimization and surveillance of EOR applications, which would subsequently affect the performance and the cost of such projects, thereby supporting the achievement of low-cost and low-carbon EOR barrels. Recommended additional reading at OnePetro: www.onepetro.org. SPE 214268 A Novel Approach To Combine Models To Evaluate Interwell Connectivity in a Waterflooded Reservoir With Limited Injection History by Yanfidra Djanuar, Dragon Oil, et al. SPE 210657 A Laboratory-to-Field Approach and Evaluation of Low-Salinity Waterflooding Process for High-Temperature/High-Pressure Carbonate Reservoirs by Hemanta Kumar Sarma, University of Calgary, et al. IPTC 22733 Like Cures Like Microbial Enhanced Oil Recovery in Biodegraded Crude by Thanapong Ketmalee, PTTEP, et al.