RESERVOIR PERFORMANCE SIMULATION AND PROCESS-TO-SIMULATOR QUALITY CONTROL TO SUPPORT EOR METHOD SELECTION IN CARBONATE-CONTAINING RESERVOIRS
Abstract
Enhanced oil recovery (EOR) selection requires reservoir performance simulation that reproduces not only production trends but also the physical mechanisms controlling pressure, fluid behavior, multiphase flow, gas mobility, well constraints, and long-term reservoir response. This study presents a reservoir performance simulation and process-to-simulator quality-control workflow for CO₂-based EOR evaluation in anonymized carbonatecontaining Field X, Kazakhstan. The field-scale simulation uses a three-dimensional ECLIPSE E300 compositional model with 93 × 88 × 78 grid cells, 194,413 active cells, and a 40-year forecast period. The framework integrates compositional pressure-volume-temperature/equation-of-state (PVT/EOS) behavior, minimum miscibility pressure (MMP), three-phase relative permeability, hysteresis, vertical connectivity and gravity override, injection scheduling, bottomhole-pressure (BHP) control, wellbore thermal response, CO₂ tracking, and mass-balance verification. Each mechanism is linked to a simulator functional block, diagnostic output, sensitivity test, and acceptance criterion. A representative ECLIPSE E300 process-to-code sequence – RUNSPEC, GRID, PROPS, SOLUTION, SCHEDULE, SUMMARY, followed by MATLAB post-processing – provides traceability from reservoir physics to forecast interpretation. Sensitivity analysis identifies vertical connectivity, gas relative permeability, MMP uncertainty, fault transmissibility, hysteresis, and BHP constraints as the principal controls on simulated reservoir performance. Continuous CO₂ injection increases cumulative oil production from 17.96 to 25.37 Mt, yielding 7.41 Mt of incremental oil (+41.3%); at year 40, the CO₂ case maintains 238.6 t/d while the Base case declines to zero. The results demonstrate that reservoir performance simulation provides a rigorous engineering basis for forecasting reservoir behavior, quantifying model sensitivity, validating physical consistency, and supporting defensible EOR method selection.