Co-optimization of Carbon Capture, Utilization, and Storage (CCUS) Project Using Iterative Latin Hypercube Sampling (ILHS)

Economic optimization of Carbon Capture, Utilization, and Storage (CCUS) projects, which simultaneously enhance oil recovery through CO₂-EOR while permanently storing CO₂, is critical to ensuring project viability amidst energy market volatility and operational uncertainties. This study develops and applies an Iterative Latin Hypercube Sampling (ILHS) algorithm, an adaptive, stratified sampling technique that accelerates convergence by iteratively re-weighting high-probability sub-regions, to determine the optimal CO₂ injection rate, using Net Present Value (NPV) as the unified economic criterion. The algorithm is coupled, via a FORTRAN driver, to the CMG-GEM compositional simulator and applied to the PUNQ-S3 field case; the economic model explicitly includes the CO₂ purchase price (US$60 t⁻¹), carbon credits (US$40 t⁻¹) and capital expenditure (CAPEX = US$40 million + US$12 000 × Qᵢ) to capture key financial drivers. Three economic scenarios combining oil prices of US$70 bbl⁻¹ and US$30 bbl⁻¹ with discount rates of 0 % and 10 % are evaluated to quantify NPV sensitivity. ILHS converged in ≤130 simulation runs (≈3 h CPU time), identifying scenario-specific optimum injection rates of 8.1–8.6 × 10³ m³ day⁻¹ that deliver NPVs ranging from US$1.9 billion to US$4.6 billion. By bridging the gap between technically oriented and financially oriented optimization, the proposed framework offers a scalable, computationally efficient approach for co-designing oil recovery and CO₂ storage under dynamic market conditions, thereby advancing field-scale CCUS decision making.