Ensemble Machine Learning for Data-Driven Predictive Analytics of Drilling Rate of Penetration (ROP) Modeling: A Case Study in a Southern Iraqi Oil Field

Abstract

Abstract Modeling the drill bit Rate of Penetration (ROP) is crucial for optimizing drilling operations as maximum ROP causes fast drilling, reflecting efficient rig performance and productivity. In this paper, four Ensemble machine learning (ML) algorithms were adopted to reconstruct ROP predictive models: Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boost (XGB), and Adaptive Boosting (AdaBoost). The research was implemented on well data for the entire stratigraphy column in a giant Southern Iraqi oil field. The drilling operations in the oil field pass through 19 formations (including 4 oil-bearing reservoirs) from Dibdibba to Zubair in a total depth of approximately 3200 m. From the stratigraphic column, various lithology types exist, such as carbonate and clastic with distinct thicknesses that range from (40-440) m. The ROP predictive models were built given 14 operating parameters: Total Vertical Depth (TVD), Weight on Bit (WOB), Rotation per Minute (RPM), Torque, Total RPM, flow rate, Standpipe Pressure (SPP), effective density, bit size, D exponent, Gamma Ray (GR), density, neutron, and caliper, and the discrete lithology distribution. For ROP modeling and validation, a dataset that combines information from three development wells was collected, randomly subsampled, and then subdivided into 85% for training and 15% for validation and testing. The root means square prediction error (RMSE) and coefficient of correlation (R-sq) were used as statistical mismatch quantification tools between the measured and predicted ROP given the test subset. Except for Adaboost, all the other three ML approaches have given acceptable accurate ROP predictions with good matching between the ROP to the measured and predicted for the testing subset in addition to the prediction for each well across the entire depth. This integrated modeling workflow with cross-validation of combining three wells together has resulted in more accurate prediction than using one well as a reference for prediction. In the ROP optimization, determining the optimal set of the 14 operational parameters leads to the fastest penetration rate and most economic drilling. The presented workflow is not only predicting the proper penetration rate but also optimizing the drilling parameters and reducing the drilling cost of future wells. Additionally, the resulting ROP ML-predictive models can be implemented for the prediction of the drilling rate of penetration in other areas of this oil field and also other nearby fields of the similar stratigraphic columns.