Learning based prediction of cuttings concentration for enhancing hole cleaning efficiency in eccentric and deviated...

Directional drilling often encounters challenges such as eccentric annulus conditions caused by the weight of the drill string and oscillations, compounded by gravity-induced cuttings accumulation that obstructs flow and impedes drilling processes due to inefficient hole cleaning. This study focuses on addressing these issues by developing machine learning (ML) models to predict cuttings concentration (CA) in eccentric deviated wells, aiming to enhance predictive accuracy and optimize hole-cleaning operations. The research employs multiple ML algorithms including back propagation neural network (BPNN), radial basis function network (RBFN), and support vector machine (SVM). Models are trained using comprehensive field data from six deviated wells in the Gulf of Suez, Egypt, with inputs comprising rheological properties, drilling operation parameters, cutting transport velocity ratio (V_TR), and carrying capacity index (CCI). The models undergo rigorous validation to ensure robustness and accuracy, employing both internal validation techniques to avoid overfitting and extensive testing across varying degrees of eccentricity. The developed RBFN model demonstrated superior performance compared to existing empirical and fuzzy logic models, achieving a relation coefficient (R) of 0.993 and an average absolute error (AAE) of 1.18 at an eccentricity degree (ε) of 0.5. In further validation within neighboring test wells, the RBFN model accurately predicted CA across different eccentricities, showing high reliability with R-values of 0.984, 0.978 and 0.971 and AAE-values of 1.1, 1.4 and 1.7 for = 0, 0.4 and 0.8, respectively. Sensitivity analyses confirmed the critical influence of V_TR and CCI, with their impact most pronounced at the highest eccentricity tested. This study presents a significant advancement in drilling technology by integrating advanced ML methodologies to improve the monitoring and optimization of hole-cleaning efficiency in deviated wells. The novel application of these sophisticated models offers a promising solution to real-time challenges in drilling operations, enhancing efficiency and reducing operational risks associated with eccentric deviated wells. Incorporating ML models into routine drilling operations can potentially transform standard practices, making this approach a valuable asset in the field of petroleum engineering.