Real-time Estimation Of Downhole Equivalent Circulating Density (ECD) Using Machine Learning And Applications

SPE-208675-MS: Tim S. Robinson, Dalila Gomes, Exebenus;  Meor M. Hakeem Meor Hashim, M. Hazwan Yusoff, M. Faris Arriffin, and Azlan Mohamad, PETRONAS Carigali Sdn Bhd; Tengu Ezharudin, Eswadi Othman, Faazmiar Technology SDN BHD

Abstracts: Despite many drilling technology improvements during recent years, hole cleaning remains a significant challenge. The variation of equivalent circulation density (ECD) is a symptom of borehole instability. Therefore, the ability to accurately estimate ECD is a key consideration for preventing hole cleaning problems that may lead to a stuck pipe, and well pressure management more generally.

Unsupervised machine learning: A well planning tool for the future

OMEA2022-78423: Peter Batruny, PETRONAS Carigali Sdn Bhd, Tim Robinson, Exebenus

Abstracts: In recent years, the industry has sought insights from abundant data generated by drilling operations. One of the key focus areas is the rate of penetration (ROP) which impacts costs directly, and emissions indirectly. Previous work has succeeded in predicting and optimizing ROP, however was limited to specific fields and small-scale applications. This limitation stems from unobserved information between different fields or operations that often impacts model usability. This paper provides a new way of well planning by leveraging the power of unsupervised machine learning to deliver higher drilling efficiency, lower costs, and less uncertainty.

Successful Development and Deployment of a Global ROP Optimization Machine Learning Model

OTC-31680-MS: Timothy S. Robinson, Dalila Gomes, Exebenus, Peter Batruny, Meor M. Hakeem Meor Hashim, M. Hazwan Yusoff, M. Faris Arriffin, and Azlan Mohamad, PETRONAS Carigali Sdn Bhd

Abstracts: Drilling rate of penetration (ROP) is a major contributor to drilling costs. ROP is influenced by many different controllable and uncontrollable factors that are difficult to distinguish with the naked eye. Thus, machine learning (ML) models such as neural networks (NN) have gained momentum in the drilling industry. Existing models were either field-based or tool-based, which impacted the accuracy outside of the trained field. This work aims to develop one generally applicable global ROP model, reducing the effort needed to re-develop models for every application.