artificial intelligence
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The SPE Research Portal uses artificial intelligence technology, fortified by industry knowledge, to address the long-term challenges of finding and analyzing information in unstructured data. -
One of the major characteristics of petroleum data analytics is its incorporation of explainable artificial intelligence (XAI). Predictive models of petroleum data analytics are not represented through unexplainable black-box behavior. Predictive models of petroleum data analytics are reasonably explainable. This second part of a two-part series presents the use of XA…
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One of the major characteristics of petroleum data analytics is its incorporation of explainable artificial intelligence (XAI). Predictive models of petroleum data analytics are not represented through unexplainable black-box behavior. Predictive models of petroleum data analytics are reasonably explainable. This first part of a two-part series presents the history of… -
Earlier this year, 19 teams competed in a machine-learning contest held by the Data Analytics Study Group of SPE’s Gulf Coast Section. The was the first competition of its kind for SPE. Here, the organizers of the contest present some of the techniques used and lessons learned from the Machine Learning Challenge 2021.
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The Magnus oil field in the North Sea catalyzed a major leap for subsea control systems. The original development of the multiplexed electrohydraulic control system has developed into a multimillion-dollar subsea controls industry.
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Petrolern has received a $1.15-million grant from the US Department of Energy to develop and commercialize its technology that models in-situ stresses by using available data.
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A plunger lift optimization software has been developed that enables set-point optimization at scale.
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A novel PCP configuration was developed from phased design trials and experience in cold heavy-oil production with sand (CHOPS) wells. This configuration uses a modified rotor to create alternating sections of contact and noncontact within a conventional stator.
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The cultural perception of AI is often suspect because of the challenges in knowing why a deep neural network makes its predictions. So researchers try to crack open this black box after a network is trained to correlate results with inputs. But what if the goal of explainability could be designed into the network's architecture, before the model is trained and withou…
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Shell, C3 AI, Baker Hughes, and Microsoft have teamed up to launch the Open AI Energy Initiative platform that is designed to present AI-based reliability applications to improve operational efficiency.
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ODDS—organization, due diligence, data, and scrub. These four important steps can make sure you are ready to implement artificial intelligence in a way that leads to a successful project.
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The AI partnership combines Wood’s optimization platforms with Cognite’s Data Fusion product to develop solutions for the energy transition and free up data from fragmented and legacy systems.
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