Digital Oil Field
This paper explores how artificial intelligence (AI), cognitive models, and integrated digital tools can transform readiness from periodic drills into a continuous, measurable capability.
This paper presents a competency-centered, data-driven approach implemented to strengthen control-room emergency-response capability through a cloud-hosted, scenario-based virtual plant simulator.
This paper presents an autonomous, data-driven solution designed specifically for intermittent well optimization.
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The authors of this paper describe a procedure that enables fast reconstruction of the entire production data set with multiple missing sections in different variables. -
This paper presents a physics-assisted deep-learning model to facilitate transfer learning in unconventional reservoirs by integrating the complementary strengths of physics-based and data-driven predictive models.
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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.
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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 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 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 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.
