Machine Learning Application in Enhancing Drilling Performance

Nautiyal, Aditi; Mishra, Amit Kumar

doi:10.1016/j.procs.2023.01.068

Cited by 8 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Oil and Gas are the two primary non-renewable natural resources of energy on which the infrastructure of the modernized world stood upon [1]. Drilling is a complex activity whose key performance indicator is the Rate of Penetration (ROP), higher ROP signifies higher drilling efficiency [2]. ROP is the rate at which the hole is drilled or the formation is removed.…”

Section: Introductionmentioning

confidence: 99%

Drill Bit Selection and Drilling Parameter Optimization using Machine Learning

Nautiyal,

Mishra

2023

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

Machine Learning (ML) Algorithms have demonstrated their tremendous application in optimizing and enhancing the performance of various complex operations in the field of science and technology. In this research work, ML is applied to address two of the most critical factors affecting the drilling performance in the Oil and Gas Industry, which are drilling bit selection and drilling parameters optimization. Rate of Penetration is a key performance indicator of drilling efficiency, higher ROP signifies higher drilling efficiency. In this research work, a hyperparameter tuned Random Forest Regressor algorithm with an accuracy of 0.73 based on the coefficient of determination i.e., R2 Score, is used to develop ROP prediction model and subsequently drill bit selection and drilling parameters optimization is performed using Particle Swarm Optimization. The developed model has practical applicability in the selection of drill bit and optimization of drilling parameters in the Oil and Gas field. Higher ROP results in less drilling time, which correspondingly results in less capital expenditure on the project.

show abstract

Section: Introductionmentioning

confidence: 99%

Drill Bit Selection and Drilling Parameter Optimization using Machine Learning

Nautiyal,

Mishra

2023

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

show abstract

Rock fracture identification with measurement while drilling data in down-the-hole drills

Tian,

Hou,

Ding

et al. 2024

Bull Eng Geol Environ

View full text Add to dashboard Cite

A new approach for real-time prediction of stick–slip vibrations enhancement using model agnostic and supervised machine learning: a case study of Norwegian continental shelf

Elahifar,

Hosseini

2023

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

Efficient and safe drilling operations require real-time identification and mitigation of downhole vibrations like stick-slip, which can significantly diminish performance, reliability, and efficiency. This pioneering research introduces a robust machine learning approach combining model-agnostic regression techniques with Bayesian Optimized Extra Trees (BO_ET) to accurately predict stick-slip events in real-time using downhole sensor data. The model is rigorously tested and validated on a substantial offshore dataset comprising over 78,000 data points from a Norwegian continental shelf (NCS) oil field. The key input features encompassing real-time downhole and surface drilling parameters are carefully selected, including critical variables such as collar rotational speed, shock risks, annular pressure, torque, mud flow rate, drill string vibration severity, and other relevant measurements. These parameters offer significant insights into the occurrence of harmful stick-slip vibrations. Among several sophisticated machine learning models, the Extra Trees (ET) algorithm demonstrates superior performance with the lowest errors of 5.5056 revolutions per minute (r/min) Mean Absolute Error (MAE) and 9.9672 r/min Root Mean Square Error (RMSE) on out-of-sample test data. Further hyperparameter tuning of the ET algorithm via Bayesian Optimization dramatically reduces errors down to 0.002156 MAE and 0.024495 RMSE, underscoring the significant innovation and advantages of the proposed approach. By seamlessly incorporating real-time downhole sensor data and drill string mechanics, the model enables reliable identification of stick-slip events as they occur downhole. This grants opportunities to optimize critical drilling parameters including revolutions per minute (RPM), weight-on-bit (WOB), mud flow rates, and more to effectively mitigate stick-slip severity and improve the rate of penetration (ROP). Integrating the approach into automatic driller systems on offshore rigs offers immense benefits for drilling operations through substantially increased efficiency, fewer premature failures, lower costs, and significantly improved productivity and safety. Overall, this research strongly emphasizes the immense transformative potential of advanced data analytics and machine learning in enabling more efficient, economical, and sustainable drilling practices. The proposed model demonstrates clear superiority over existing methods and establishes a robust and reliable platform for real-time stick-slip prediction and mitigation, maximizing drilling performance. Graphical abstract

show abstract

Machine Learning Application in Enhancing Drilling Performance

Cited by 8 publications

References 8 publications

Drill Bit Selection and Drilling Parameter Optimization using Machine Learning

Drill Bit Selection and Drilling Parameter Optimization using Machine Learning

Rock fracture identification with measurement while drilling data in down-the-hole drills

A new approach for real-time prediction of stick–slip vibrations enhancement using model agnostic and supervised machine learning: a case study of Norwegian continental shelf

Contact Info

Product

Resources

About