Digital oilfield
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 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.
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.
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The authors of this paper propose an automated approach to sand prediction and control monitoring that improved operational efficiency by reducing time spent on manual analysis and the decision-making process in a Myanmar field. -
We must admit that the oil field is still in the early days of its digital journey. It’s time to give serious thought to the expectation/reality gap, the cultural differences between the way we’ve always done things and the way that digital is changing us, and the pain points that may trip us up unless we’re careful.
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This paper presents a comprehensive technical review of applications of distributed acoustic sensing.
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The heated global floating rig market has day rates for high-end units climbing over a half million dollars and toward a newbuild cycle that will (likely) never come.
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This paper presents an intelligent tube solution that combines data retrieved by the sensors with the actual resistance of each pipe in the well to allow adjustment of production parameters while ensuring installation safety.
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This paper describes an intelligent completion design installed in two deepwater wells with dual-zone stack-pack sand-control lower completions and the installation of an intermediate string to isolate the reservoir in each zone.
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This paper describes a solutions hub that integrates engineering tools to maximize value and improve decision quality using recent digital technologies.
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This paper proposes a series of work flows to simplify model deployment and set up an automatic advisory system to provide insight in justifying an engineer’s day-to-day engineering decisions.
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This paper presents an artificial intelligence algorithm called dual heuristic dynamic programming that can be used to solve petroleum optimization-control problems.
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This paper describes an approach implemented by the operator to solve research and development challenges by creating in-house infrastructure of both software and hardware.
