Directional/complex wells
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.
This paper presents a study of trapped-torque incidents during drillpipe connection, highlighting contributing factors and root causes and proposing prevention and mitigation measures for these transient events.
In this study, a method was developed to analyze the effects of drilling through transitions on bit-cutting structures and construct an ideal drilling strategy using a detailed drilling model.
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This paper discusses novel acoustic techniques used to identify productive zones and areas of sand production in a well with a sanding event. -
ADNOC awarded a new round of contracts to international service companies, intending to apply directional drilling and LWD to boost production by getting a better assessment of its reservoirs.
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An automated detection application helped identify stringers early for an operator of a mature field in the North Sea while drilling multilateral wells through reservoir sections containing hard calcite stringers interspersed in sand formations with low unconfined compressive strength. The application guided drilling decisions that minimized invisible lost time and sh…
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The drilling of wells in shale and granite shares a common need—faster drilling is required to make it work.
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The paper describes the experience of using a machine-learning model prepared by the ensemble method to prevent stuck-pipe events during well construction in extended-reach wells.
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Artificial intelligence and machine learning, which can effectively mine deep insight from a large amount of drilling operational data and synthetic forward modeling data, are starting to shape the design and operations of extended-reach and complex wells.
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This paper describes a successful U-turn trajectory test in South Texas that has created new economic project opportunities for the company and increased optionality within challenged lease spaces across the field.
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Capitalizing on technology that originated with NASA delivers savings on directional drilling operations.
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This paper presents a screenout-classification system based on Gaussian hidden Markov models that predicts screenouts and provides early warning.
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The paper highlights two case studies to illustrate successful development of marginal prospects to unlock remaining potential.
