A scientific research paper was published as part of a research collaboration between the College of Engineering at the University of Basrah, Rumaila Operating Organization (ROO), Schlumberger (SLB), and TIBCO at an International Petroleum Technology Conference (IPTC) in February 2025 in Malaysia, indexed in the Scopus database.
Graduates from the Petroleum Engineering Department at the University of Basrah participated in the International Petroleum Technology Conference (IPTC) in February 2025 in Malaysia, sponsored by the Society of Petroleum Engineers (SPE), with their research titled:
"Maximizing Drilling Efficiency in Deviated Wells Using AI-Based ROP Optimization."
This research is derived from their graduation project and represents the outcomes of the research collaboration between the University of Basrah – College of Engineering, ROO, SLB, and TIBCO.
The study presents a machine-learning approach to optimizing the Rate of Penetration (ROP) in S-shaped wells using drilling data from southern Iraq. Dynamic Time Warping (DTW) was employed to prioritize relevant data while allowing integration of new field data and expert insights. Extra Trees Regressor and XGBoost were used to predict ROP based on 17 input variables, with Extra Trees Regressor demonstrating higher accuracy, making it the preferred model for further optimization.
PSO, GA, and FWA were applied to fine-tune key drilling parameters such as WOB, RPM, SPP, and flow rate, yielding improvements in ROP by 30.66% with GA, 22.33% with PSO, and 14.5% with FWA.
More details on the research paper can be found at the link below:
https://doi.org/10.2523/IPTC-24964-MS
