Digital oilfield

Polar coordinate heat maps representing the Von Mises probability densityfor a series of slides, with black lines denoting estimated α. Each ring corresponds to aslide. The left shows individual slide estimates, and the right shows the filtered estimates

Drilling automation

Integration of Physics Models and ML Algorithms Enhances Automated Sliding Performance

This paper presents a multifaceted approach leveraging precise rig control, physics models, and machine-learning techniques to deliver consistently high performance in a scalable manner for sliding.

Fig. 1—Autonomous drilling platform. RT = real time. ROC/RDC = remote operation center/remote drilling center.

Drilling automation

Autonomous Directional Drilling and Geosteering Enhances Real-Time Decision-Making

This paper proposes a novel approach toward drilling maximum-reservoir-contact wells by integrating automated drilling and geosteering software to control the downhole bottomhole assembly, thereby minimizing the need for human intervention.

Fig. 1—Improvement in field efficiency factor as a result of the implementation of the MPFM autodiagnostic system. KPI = key performance indicator.

Unconventional/complex reservoirs

Multiphase-Flowmeter Systems Use Autodiagnostic Solution

This paper offers an exploration into the field applications of multiphase flowmeters (MPFMs) across global contexts and the lessons learned from implementation in a smart oil field that uses several types of MPFM.