Exebenus and Ops Excellence sign partnership agreement to promote machine learning and automation solutions in Oman

Exebenus AS and Ops Excellence LLC have signed a partnership agreement to promote machine learning and automation solutions to oil and gas operators in Oman, with particular focus on the Exebenus Current ML™ Real-Time ROP Optimization and Stuck Pipe machine learning agents. “The partnership with Exebenus gives us the opportunity to support our clients in making a step change in their digital transformation,” said Michael Schonewald, Ops Excellence Managing Director.

Machine learning agent significantly reduces drilling time on ultra-deepwater exploration well

Location Gabon, West Africa Ultra deepwater Challenge Show that real-time rate of penetration (ROP) optimization can signifi cantly contribute to improving ROP, decreasing drilling time and reducing costs, even when an auto driller is used. Solution The agent was used by RTO engineers to provide the rig crew with real-time drilling parameter recommendations for optimizing…

ROP optimization agent reduces drilling time in offshore development side-track well

Location Offshore, deepwater, Malaysia Challenge Improve formation insight and increase drilling performance in a clay and sandstone environment known to cause slow rate of penetration (ROP). Solution Field trial real-time ROP optimization agent in a side-track well section. Run stuck pipe hole cleaning agent to understand risk of increased cuttings in suspension and cave-ins. Results…

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.