Building Trust in AI/ML Solutions: Key Factors for Successful Adoption in Drilling Optimization and Hazard Prevention

Abstract

The use of AI/ML technologies has provided breakthrough performance in automated predictive data analytics. With the increasing amount of data available during drilling operations, data driven AI/ML solutions lay out the future of current technologies for drilling optimization and hazard prevention. Fast adoption and appeal of these technologies to the industry could be explained by a few reasons: AI/ML enables digital transformation by using only real-time data without extensive requirements for contextual data so that engineering and data input processes can be fully automated;AI/ML solutions predict outputs based on the data trends allowing to solve problems where conventional models are hard to implement or are not sensitive enough to identify subtle anomalies;Targeted solutions address specific problems and become more applicable in the modern digital ecosystem,Due to previous reasons, such technologies are easier to implement and to scale up in the operational environment. Successful adoption of AI/ML technologies lies in its validation and trust in the operational environment. Based on the project experience from various parts of the world, prerequisites for building trust have proven to be: high performance AL/ML technology;matured IT infrastructure with relevant support services to enable digital transformation;monitoring specialists in an established RTOC or rigsite team to validate solution decisions;good communication protocol and established responsibilities of the RTOC and rig team to validate the impact of the predictions and to apply for operations. The success factors are consequently related to technology, infrastructure and "soft" aspects like work processes, team interactions and defined roles and responsibilities. Each of these areas will be addressed individually.