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 papers selected for this issue focus on key factors in achieving a successful well test. They also apply reservoir fundamentals as well as sound engineering judgment, with examples from conventional and unconventional assets. -
A survey of oil and gas decision-makers by Ernst & Young sheds light on how companies are formulating their digital strategies.
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Blockchain technology may have gotten its start by keeping cryptocurrency traders honest, but its usefulness is expanding. And the oil and gas industry is taking advantage.
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The term digital oil field has become a buzzword in the oil and gas industry these days, with the mention of it bringing up pictures of computers, flashy screens, and programming to mind. In reality, the concept goes beyond these.
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Two new centers in Bergen, Norway will lean on emerging digital technology to oversee much of the Norwegian operator’s offshore operations.
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Companies are bringing satellite monitoring to the unconventional oilfield—namely the Permian Basin—where they are training machine learning models to track and predict drilling and completions work.
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Discussions of big data and its management are increasing across our industry and disciplines. This selection of technical papers takes a look at data mining, the ethical issues associated with it, and the status of data-driven methods.
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Remote condition monitoring of offshore platform equipment tracks performance data, watching for deviations from baseline benchmarks. Unexpected variances can be investigated and serviced by technicians dispatched to target the root causes—an approach called condition-based maintenance.
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“Greedy pursuit” in the realm of algorithms is a good thing. Saudi Aramco studied such algorithms to produce images simulating the flow inside a pipe’s cross section, possibly reducing the need for separator-based multiphase flowmeters.
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Predicting the trajectory of a satellite, or a well, requires sophisticated analysis to reduce the huge uncertainties. That adds to the many things drillers should be thinking about, which can be overwhelming.
