modeling

This paper tests several commercial large language models for information-retrieval tasks for drilling data using zero-shot, in-context learning.

In this paper, a dynamic multiphase-flow simulator is used to evaluate the effectiveness and suitability of using a subsea capping stack to respond to a CO₂ well blowout.

In this study, artificial-intelligence techniques are used to estimate and predict well status in offshore areas using a combination of surface and subsurface parameters.

Virtual reality and related visualization technologies are helping reshape how the industry views 3D data, makes decisions, and trains personnel.

The authors of this paper describe how deployment of dual-casing cement-bond-logging technology has provided critical insights in real time for decision-making on remedial jobs.

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.

At SPE’s Permian Basin Energy Conference, operators shared behind-the-scenes details on innovations such as drilling horseshoe wells and trimulfrac completions along with in-basin challenges such as handling produced water.

This paper presents a specialized workflow that aims to quantify the severity of condensate banking and subsequently optimize reservoir development strategies for a deep formation in the Permian Basin.