Extended Genetic Algorithm-Based Improved Model for Drilling Rate of Penetration (ROP) Using Mud Logging Data

DOI

10.2991/978-94-6463-884-4_57How to use a DOI?

Keywords

Mud Logging Data; Genetic Algorithm; Data-Driven Models; Wellbore Stability; Drilling Optimization

Abstract

The drilling rate of penetration (ROP) is essential in the oil and gas industry, influencing both exploration efficiency and cost-effectiveness. The ROP optimization needs careful attention to dynamic and static drilling parameters prediction using machine learning (ML) and gene expression programming (GEP) techniques for sustainable drilling operations and wellbore stability with proper hole cleaning, avoiding stuck-pipe incidents and minimizing any non-productive time. This study aims to i) construct the GEP-based predictive ROP model and ii) compare it with other empirical correlations and ML-based models. The ML techniques of support vector machine (SVM), neuro-fuzzy inference system (ANFIS), functional networks (FN), random forests (RF) and GEP-based models are built to predict ROP models using a database of 500 datasets of drilling and mud logging parameters, with 65% for training and 35% for validation datasets, respectively. The GEP model demonstrated its effectiveness through extensive analysis and investigation, showing a high precision with a correlation coefficient of 99%, and minimal relative error of 0.23 to predict ROP, compared to the other models of SVM, ANFIS, FN, and RF. In addition, the performance of the GEP-based improved ROP model is verified and compared with other correlations of Bingham, Maurer, and Bourgoyne and Yield model. This improved model is performed excellently with high precision. This study concludes that GEP offers higher accuracy in ROP estimation than traditional models and presents promising guidelines for model development in the petroleum and mining industry.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Mohammad Islam Miah
AU  - Mohammad Tareq Reza
AU  - Md. Mizanur Rahman
AU  - Md. Maruf Alam
PY  - 2025
DA  - 2025/11/18
TI  - Extended Genetic Algorithm-Based Improved Model for Drilling Rate of Penetration (ROP) Using Mud Logging Data
BT  - Proceedings of the 8th International Conference on Engineering Research, Innovation, and Education 2025 (ICERIE 2025)
PB  - Atlantis Press
SP  - 475
EP  - 483
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-884-4_57
DO  - 10.2991/978-94-6463-884-4_57
ID  - Miah2025
ER  -