Yousef Kazemzadeh

Scale formation during waterflooding presents a persistent challenge to reservoir productivity, frequently resulting in significant permeability reduction. This study introduces an innovative hybrid modeling approach combining response-surface-methodology (RSM) and machine-learning (ML) techniques to predict and optimize permeability loss in carbonate reservoirs subjected to operational variables in the presence of the scale inhibitor DTPMP. A series of 45 coreflood experiments, designed using Central-Composite-Design of RSM (CCDRSM), were performed on carbonate core samples to evaluate the effects of key variables—inhibitor dosage, temperature, sulfate concentration, pore volume, and injection rate—on the permeability ratio (Kd/Ki). The synthetic formation water was rich in calcium and chloride ions, while the injection water contained variable sulfate concentrations (1000–5000 ppm) to induce controlled calcium sulfate scaling. Experimental data were used to develop an RSM-based statistical model and six ML-based models, including Linear-Regression (LR), Support-Vector-Regression (SVR), Gaussian-Process-Regression (GPR), Regression-Tree (RT), Random-Forest (RF), and LSBoost. Among the developed CCD-RSM and CCD-ML models, the CCD-MLGPR exhibited superior accuracy (R² = 0.9991; RMSE = 0.0056), positioning it as the most robust and reliable predictive tool. Sensitivity analysis further revealed that pore volume and inhibitor dosage were the most influential variables affecting Kd/Ki, with pore volume exerting a negative impact and inhibitor dosage a positive one. Furthermore, integration of the MLGPR model with Particle Swarm Optimization (PSO) enabled the identification of optimal operational strategies that effectively minimized formation damage, maintaining Kd/Ki above 0.90 even under worst-case conditions. These findings demonstrate the efficacy of the proposed CCD-ML hybrid methodology as a predictive and optimization tool, offering practical insights for proacti