AI/machine learning
The downtime of manufacturing machinery, engines, or industrial equipment can cause an immediate loss of revenue. Reliable prediction of such failures using multivariate sensor data can prevent or minimize the downtime. With the availability of real-time sensor data, machine-learning and deep-learning algorithms can learn the normal behavior of the sensor systems, dis…
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. -
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…
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Being able to deploy machine-learning applications at the edge is the key to unlocking a multibillion-dollar market. TinyML is the art and science of producing machine-learning models frugal enough to work at the edge, and it's seeing rapid growth.
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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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