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ORIGINAL RESEARCH article

Front. Energy Res.
Sec. Advanced Clean Fuel Technologies
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1411751

Innovative Machine Learning for Drilling Fluid Density Prediction: A Novel Central Force Search-adaptive XGBoostin HPHT Environments

Provisionally accepted
Elangovan Muniyandy Elangovan Muniyandy 1*Shanmugasundar G Shanmugasundar G 2Manjunatha R Manjunatha R 3Robert Cep Robert Cep 4Logesh K Logesh K 5Vikas Kaushik Vikas Kaushik 6Srinadh Raju S Srinadh Raju S 7
  • 1 Saveetha Medical College & Hospital, Chennai, Tamil Nadu, India
  • 2 Sri Sai Ram Institute of Technology, Chennai, Tamil Nadu, India
  • 3 Jain University, Bengaluru, Karnataka, India
  • 4 VSB-Technical University of Ostrava, Ostrava, Moravian-Silesian Region, Czechia
  • 5 Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India
  • 6 Chandigarh Engineering College, Ajitgarh, Punjab, India
  • 7 Raghu Engineering College, Visakhapatnam, Andhra Pradesh, India

The final, formatted version of the article will be published soon.

    Oil and gas industries are having a special dilemma when it comes to High Pressure, High Temperature (HPHT) drilling as the accuracy forecasting of the drilling fluid density (DFD) is a vital factor for safe and efficient operations. Complicated relationships and inconsistencies in HPHT situations are rarely mapped by current forecasting models while their buggy performance and safety risks during drilling can be underestimated. In this research, we propose a novel machine learning (ML) approach to enhance the accuracy of DFD anticipation under HPHT conditions: Central Force Search-adaptive extreme Gradient Boosting (CFS-XGB). The paper uses a dataset that has drilling variables together with the DFD for HPHT situations to examine the accuracy of the CFS-XGB model. Excluding the abnormalities of data or mistakes, the reliability of the original data is maintained by applying min-max normalization. After that, finding the important features with the help of the boosted principal component analysis (BPCA) approach to the normalized data will make the CFS-XGB methodology's prediction efficacy to ensure a major improvement. This research is experimented in Python platform and the performance of the proposed CFS-XGB method is analyzed in terms of MSE, R 2 and AAPRE metrics. The suggested approach performs better than the current methods in forecasting drilling fluid concentration in HPHT settings, according to the experimental data. This development in predictive modelling helps to increase the productivity and safety of drilling operations, which eventually helps the oil and gas sector manage the challenges posed by HPHT drilling settings.

    Keywords: drilling, Oil and gas sector, fluid density, Machine Learning (ML), high

    Received: 05 Apr 2024; Accepted: 23 Sep 2024.

    Copyright: © 2024 Muniyandy, G, R, Cep, K, Kaushik and S. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Elangovan Muniyandy, Saveetha Medical College & Hospital, Chennai, Tamil Nadu, India

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.