Automated Workflow for Optimizing CO2 Storage Well Placement by Coupling Static Properties and Dynamic Simulation

Carbon Capture and Storage (CCS) is a vital technology for reducing greenhouse gas emissions and mitigating climate change. Saline aquifers have emerged as a promising solution for large-scale CO2 sequestration, offering significant storage potential. However, the complex interplay of geological factors, including reservoir heterogeneity, spill point depth, and reservoir pressure, poses a significant challenge in optimizing well placement. To overcome this challenge, we have developed a cutting-edge automation workflow that combines static model properties and dynamic simulations to determine optimal well locations and completion strategies. By harnessing the power of this integrated simulation approach, our workflow seeks to maximize storage efficiency, minimize well injectivity issues and enhance overall CCS effectiveness. This methodology automates the optimization of CO2 injector placement and completion by integrating reservoir static and dynamic variables to identify the most suitable areas. The workflow involves a series of steps, including screening for high-permeability zones, connected volumes, favorable reservoir pressure, as well as spill point depth, to generate optimal "sweet spots". Wells are then strategically placed and completed at these identified sweet spots, followed by dynamic simulations to compute storage efficiency, which is compared to a base case, and the results are refined through an iterative optimization loop to maximize injectivity and storage efficiency. Our study highlights the critical necessity of automating the optimization of CO2 injector placement, as suboptimal placement can have far-reaching consequences, including compromised CO2 containment, reduced storage efficiency, and ultimately, jeopardized project viability. To mitigate these risks, the proposed workflow was rigorously tested using a dynamic reservoir simulator on a synthetic saline aquifer CO2 storage complex model. The results unequivocally demonstrate the workflow's efficacy in optimizing the number and strategic placement of wells within the reservoir, predicting significant improvements in storage efficiency. Notably, when applied to the synthetic saline aquifer model, the automated workflow predicted a significant increase in storage efficiency compared to the reference case, highlighting its substantial potential to enhance CO2 storage outcomes and contributing meaningfully to more effective and sustainable carbon sequestration strategies. This innovative workflow has the potential to revolutionize the CCS industry by providing a data-driven, optimized approach to well placement and completion. By leveraging advanced simulation techniques, we can unlock the full potential of saline aquifers for CO2 sequestration, supporting a more sustainable and environmentally conscious clean energy future.