AI/machine learning
As digital technologies become commonplace, industry leaders say core engineering knowledge remains essential for making informed decisions and avoiding costly mistakes.
The deals, with a potential value exceeding $3.7 billion, are aimed at strengthening Saudi Aramco’s supply chains and expanding its use of industrial artificial intelligence.
This work applies fuzzy logic, a well-established artificial-intelligence technique, to quantitatively assess connectivity between injectors and producers in a giant presalt field in the Santos Basin of Brazil.
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Privacy concerns about AI systems are growing. So researchers are testing whether they can remove sensitive data without retraining the system from scratch. -
This paper describes an artificial intelligence deep Q network for field-development plan optimization.
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In this paper, the authors describe a model that uses augmented artificial intelligence to optimize well spacing by use of data sculpting, domain and feature engineering, and machine learning.
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SponsoredRod lift failure frequency in horizontal wells drives significant operating expenses. Understanding why these failures occur leads to a solution — production optimization with automated setpoint changes, which can extend the life of this equipment and reduce downtime.
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Cube drilling was an exciting idea several years ago. Since then, the luster seems to have faded. Now, production software company Novi Labs says machine learning may bring life back to the concept.
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Four CEOs describe what goes into turning a world of data into a data-driven world.
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The authors write that even simple deep-learning architectures can identify a leak using pressure data.
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The authors demonstrate how artificial intelligence and machine learning can help build a purely data-driven reservoir simulation model that successfully history matches dynamic variables for wells in a complex offshore field and that can be used for production forecasting.
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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 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…
