Drilling
More than 900 drilling and well professionals from 30+ countries gathered at APDT 2026 in Bali to explore how AI, automation, and advanced well technologies are driving safer, smarter, and more efficient drilling across Asia Pacific.
This paper presents laboratory-testing methods and evaluation criteria designed to improve understanding of both the origin and extent of formation damage associated with CO2 injection.
This work introduces an analytical model for nonisothermal CO2 injection that accounts for both Joule-Thomson cooling and interformation heat exchange.
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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. -
The integration of the latest state-of-the-art technology in modeling and measurement of drilling dynamics and borehole quality has enhanced the understanding of bit, bottomhole assembly, and drillstring design. This can be seen in recent work on analysis of cutter-formation interaction, borehole-quality improvement, and modeling improvements.
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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.
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In this paper, bottomhole-assembly lateral behavior is analyzed using different types of computations, including static, dynamic, frequency-based, and time-based.
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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.
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Digitalization and automation of the drilling process drive the need for an interoperability platform in a drilling operation, where a shared definition and method of calculation of the drilling process state is a fundamental element of an infrastructure to enable interoperability at the rigsite.
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This paper highlights a new online system for monitoring drilling fluids, enabling intelligent control of drilling-fluid performance.
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This paper investigates the use of machine-learning techniques to forecast drilling-fluid gel strength.
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This paper aims to close some of the many knowledge gaps that exist in the field of drilling CO₂ storage wells, a task that is expected to involve handling an influx of CO₂ into the drilling fluid.
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This year’s primary selections for the Drilling and Completion Fluids Technology Focus reflect now-well-established industrywide emphases on machine learning, automation, and the achievement of successful drilling of CO₂ storage wells.
