Automatic Driller Optimization Application Using Machine Learning and Artificial Intelligence Drives Consistent Performance in an Operator’s West Texas Drilling Program
Authors: Abbas Raafat , Simon Aaron , Dunbar Doug , Serrano Rodrigo , Creegan Andrew , Prochaska Ernie 
Published: October 2023

A large operator and drilling rig contractor worked collaboratively with a service company using a machine learning and artificial intelligence application to adjust setpoints on the auto driller to optimize performance in a West Texas drilling program. This application, which employed a novel usage of mud pulse telemetry, contributed to consistent performance in intermediate and lateral sections on multiple pads.

The trial was conducted in the Midland Basin using Advisory Mode (manual) settings in the intermediate and lateral sections. The trial was considered successful with a decision made to move to the next level with Control Mode (automated) settings in the Delaware Basin. A unique aspect of this trial was the creation of an execution process that combined all the participating parties including the operator, drilling rig contractor, directional drilling company and the service company. This process involved documentation, training, testing, and communication with onsite, field and office personnel. Learnings were an integral part of this process and used to improve the performance on each section/well/pad. These lessons were used to update and optimize the engine’s logic as needed.

The drilling rig contractor and service company worked together to integrate the rig control system and the auto driller application, performing lab testing and implementation at the rig site. After several development upgrades the operator wanted to test the system out on a second drilling rig in another area in the Delaware Basin.

A drilling rig with a different control system was designated for testing, the same process was used to integrate the rig control system with the auto driller application, along with lab testing and implementation at the rig site.

One of the objectives and focus was to match machine learning to human responses optimizing drilling parameters. During the testing, an accelerated development process was used adding functionality to the auto driller application that included active responses to downhole shocks and vibrations. Once the participants, operator, drilling rig contractor, directional drilling company, performance engineers, programmers and service support were satisfied with the reliability and repeatability with the application, six more drilling rigs were added to the program.

There were initial measurable performance gains optimizing the drilling parameters, bottom hole assemblies and auto driller. The longer-term achievement of this application provided consistent enhanced performance using the auto driller. Automating this process aids the Driller by providing a tool that levels experience. The ongoing results of this program prove that long term sustainable performance is provided using this application on multiple control system drilling rigs. As a result of these efforts, a savings of approximately half a day when compared to manual operations was realized when drilling two mile lateral sections.