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<p>Drilling through high-pressure, salt-bearing sequences poses severe operational challenges due to rapid pore-pressure fluctuations, borehole instability, and the complex, discontinuous lithologies typical of evaporites. This study develops and rigorously validates a single, unified machine-learning (ML) framework that simultaneously predicts lithology, formation members, and synthetic gamma-ray (GR) and sonic travel-time (DT) logs directly from surface drilling parameters, providing a practical alternative when wireline logging is risky, delayed, or impractical. A depth-indexed dataset of 30,598 records from four wells in the Buzurgan oilfield was compiled, including rate of penetration (ROP), weight on bit (WOB), revolutions per minute (RPM), torque, flow rate (FR), and standpipe pressure (SPP). Five supervised algorithms were benchmarked, namely Random Forest (RF), Extreme Gradient Boosting (XGBoost), Linear Regression (LR), Support Vector Machine (SVM), and TabNet. Among them, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), tuned with Optuna and evaluated with held-out tests and blind-well validation, delivered the best performance. The optimized ensembles achieved &gt; 97% accuracy for lithology classification and &gt; 99% for formation-member identification, while the regressors showed strong agreement with wireline measurements, with coefficient of determination (R²) values of ≥ 0.93 for GR and ≥ 0.91 for DT. Feature-importance analyses indicated torque and WOB as the most influential predictors, consistent with their direct coupling to bit-rock interaction and formation strength; FR, SPP, and RPM contributed secondarily. Operationally, the framework supports real-time casing-point selection, proactive adjustments to drilling parameters, and mud-property optimization—capabilities that are especially valuable across critical salt-anhydrite intervals to reduce open-hole exposure, non-productive time (NPT), and well-control risk. Limitations include potential site-specific bias (four-well training within a single field), dependence on data quality and sensor calibration, and the need for prospective cross-field validation and concept drift monitoring as operating practices change. Nonetheless, the results demonstrate that a field-deployable, ensemble-based workflow can reliably replace or complement traditional formation-evaluation methods in high-pressure, salt-bearing environments, enabling faster, evidence-based decisions at the rig site within a transparent, interpretable ML framework.</p>

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Real-time lithology and log prediction from drilling parameters using machine learning for high-pressure salt-bearing formation, Missan oilfields, Iraq

  • 期刊论文

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  • 发表日期：

    2026年7月20日

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  • Hayder Yousif,
  • Xuri Huang,
  • Osama G. Al-Salih,
  • Hayder Shaban

摘要

Drilling through high-pressure, salt-bearing sequences poses severe operational challenges due to rapid pore-pressure fluctuations, borehole instability, and the complex, discontinuous lithologies typical of evaporites. This study develops and rigorously validates a single, unified machine-learning (ML) framework that simultaneously predicts lithology, formation members, and synthetic gamma-ray (GR) and sonic travel-time (DT) logs directly from surface drilling parameters, providing a practical alternative when wireline logging is risky, delayed, or impractical. A depth-indexed dataset of 30,598 records from four wells in the Buzurgan oilfield was compiled, including rate of penetration (ROP), weight on bit (WOB), revolutions per minute (RPM), torque, flow rate (FR), and standpipe pressure (SPP). Five supervised algorithms were benchmarked, namely Random Forest (RF), Extreme Gradient Boosting (XGBoost), Linear Regression (LR), Support Vector Machine (SVM), and TabNet. Among them, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), tuned with Optuna and evaluated with held-out tests and blind-well validation, delivered the best performance. The optimized ensembles achieved > 97% accuracy for lithology classification and > 99% for formation-member identification, while the regressors showed strong agreement with wireline measurements, with coefficient of determination (R²) values of ≥ 0.93 for GR and ≥ 0.91 for DT. Feature-importance analyses indicated torque and WOB as the most influential predictors, consistent with their direct coupling to bit-rock interaction and formation strength; FR, SPP, and RPM contributed secondarily. Operationally, the framework supports real-time casing-point selection, proactive adjustments to drilling parameters, and mud-property optimization—capabilities that are especially valuable across critical salt-anhydrite intervals to reduce open-hole exposure, non-productive time (NPT), and well-control risk. Limitations include potential site-specific bias (four-well training within a single field), dependence on data quality and sensor calibration, and the need for prospective cross-field validation and concept drift monitoring as operating practices change. Nonetheless, the results demonstrate that a field-deployable, ensemble-based workflow can reliably replace or complement traditional formation-evaluation methods in high-pressure, salt-bearing environments, enabling faster, evidence-based decisions at the rig site within a transparent, interpretable ML framework.