drilling optimization

This paper proposes a methodology for preventing drillstring fatigue and failure in deep wells with large shallow doglegs.

In this paper, the authors propose a regression machine-learning model to predict stick/slip severity index using sequences of surface measurements.

This selection of cutting-edge articles spotlights how experimental concepts are now driving cost-saving strategies in unconventional development. It’s a reminder that innovation often comes from creative thinking, not just new tools or tech partnerships.

The proposed facility would be used to accelerate innovations in lithium extraction, carbon storage, and geothermal energy in addition to oil and gas technologies.

This paper analyzes the interaction of high-frequency torsional oscillations (HFTO) with lateral vibrations based on a model that accounts for the superimposed movement of whirl and HFTO at the bit.

This paper describes a collaborative project to analyze affected wells, identify commonalities, and optimize bit design and drilling parameters to mitigate the effects of borehole spiraling.

In this paper, bottomhole-assembly lateral behavior is analyzed using different types of computations, including static, dynamic, frequency-based, and time-based.

Technology uptake aimed at optimizing resources, delivering consistency, and augmenting what humans can do.

This paper highlights a new online system for monitoring drilling fluids, enabling intelligent control of drilling-fluid performance.

This paper investigates the use of machine-learning techniques to forecast drilling-fluid gel strength.