The rate of penetration (ROP) is a critical parameter in drilling which it reflects drilling time, efficiency, and costs. Several approaches have been investigated to handle complex and non-linear drilling parameters for precise prediction for this parameter. Machine Learning remains a promising tool for data analysis and for predicting drilling parameters with acceptable accuracy. However, most of ML algorithms are mainly coded using Python, MATLAB, or R, which require proficiency in programing skills to perform this task. In order to reach an easy, prompt, and precise results, this study conducted Dataiku Data Science Studio (DSS) ML software for predicting ROP through in-memory ML algorithms only, without using advanced or hybrid technologies. The case study was being conducted in an offshore directional gas field in the Nile delta, Egypt. The ML models used in this study are Extreme Gradient Boosted Machine (XGBoost), Gradient Boosted Trees (GBT), K-Nearest Neighbors (K-NN), and Support Vector Machine (SVM). The evaluation metrics used in this study are R ² score, Correlation Coefficient (CC), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). All models’ performance has been validated through learning curves and errors distribution analysis. The output results showed very strong overall performance, and demonstrated that XGBoost model performed best testing scores with R ² = 0.965, RMSE = 1.339, followed by GBT with R ² = 0.936, RMSE = 1.809, then K-NN with R ² = 0.849, RMSE = 2.78, then SVM achieved least performance with R ² = 0.801 and RMSE = 3.586. The study further validates feature importance analysis and SHAP values interpretation to identify dominant features governing ROP prediction.

The Standpipe Pressure (SPP) is one of the most important drilling parameters as it reflects to wellbore stability and loss circulation. Monitoring SPP values are still challenging due to complex and uncertain downhole changes while drilling. Machine Learning (ML) algorithms are most promising techniques for forecasting SPP to optimize monitoring its values under downhole conditions of pressure fluctuations, fluids gains/losses. Hybrid technologies are used to combine advanced ML models and physical-based models to prove their effectiveness for predicting SPP. However, these technologies require both more surface and subsurface data with advanced programming languages for handling with complex data inputs to perform this task. This study aimed to conduct prompt SPP prediction leveraging Dataiku Data Science Studio (DSS) using in-memory ML models without using complex approaches with respect to reaching precise results. Four ML models were adapted successfully in this study for SPP prediction: LightGBM, Extra Trees, XGBoost, and Random Forest. Four offshore wells were being drilled into the West Delta Deep Marine Egypt, provided total 5921 datapoints to predict SPP. Models performance were being evaluated through metrics of R ² scores and RMSE. All models achieved very strong performance for both training and testing dataset with R ² exceed 0.968. The results confirmed applicability of in-memory ML models for delivering accurate SPP predictions in a complex deepwater drilling. The developed methodology presented a promising template for similar applications, though local retraining on new data would be required for other fields. Features importance analysis and SHAP values were employed in this study for reaching deeper insights of the selected features and their numerical values for enhancing prediction reliability.