An integrated model to investigate optimized CO2 utilization and risk mitigation in offshore hydrocarbon reservoirs

Abstract

This study addresses the dual challenge of balancing increasing energy demands with climate change mitigation through optimized carbon dioxide (CO2) utilization (CO2U) for enhanced oil recovery (EOR). This work presents a composite dynamic risk model for CO2-EOR operations, synergizing deterministic and data-driven methodologies for post-CO2 injection initiation. The primary focus is to gain a further understanding of the impact of CO2 injection on reservoir functionality, stability, and potential leaks in offshore deployment. The integrated novel model involves a spatial and temporal deterministic model with a data-driven strategy, incorporating an artificial neural network (ANN) fine-tuned via particle swarm optimization (PSO) for co-optimization of oil production and CO2 storage. Furthermore, an innovative early warning index system (EWIS) is proposed by integrating a dynamic Bayesian network (DBN) model for probabilistic loss evaluation and pressure maintenance impact assessment. The proposed DBN strategy generates dynamic risk profiles, facilitating a cost-efficient and computationally simpler risk monitoring system. The findings from the study show a recovery of 17.14 thousand barrels (Mbbls) from the total original oil-in-place (OOIP) through this CO2-EOR approach in its debut year, coupled with over 74% CO2 retention. The proposed model offers a robust tool for concurrent optimization of oil production and CO2 storage, whilst ensuring meticulous dynamic risk analysis throughout CO2U processes.