Technology
This article examines how domain experts can use no-code ML platforms to explore decision-relevant problems, validate hypotheses, quickly build prototypes, and engage more effectively with data science teams when solutions transition toward production.
Over the past decade, oilfield service companies have transformed logging-while-drilling (LWD) development into a faster, collaborative, system-level process that delivers improved reliability from the first run and makes development philosophy as important as the technology itself.
Saipem's center includes a full-scale simulator designed to replicate real operational scenarios to deliver an immersive training experience.
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The oil and gas industry is one of the few examples on a global scale of how the need for further production improvement pushes professionals to customize existing models. Physical models are often reworked in order to expand their use in various industries. This article examines ten equations that help drive the petroleum industry. -
Lithium-rush miners are flocking to Arkansas more than 100 years after an oil discovery in the Smackover Field.
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In the final part of this three-part series, we extend our learning of Part 2 to the multivariate model and train a single model to predict three outcomes: oil, gas, and water.
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This article focuses on the introduction of one of the flow-network-based models called GPSNet that has growing popularity in the literature and shows promising results during our proof-of-concept applications.
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In Part 2 of this three-part series, we dive into a practical example using the production data of Equinor’s Volve field data set.
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In Part 1 of this three-part series, we use long short-term memory (LSTM), a machine learning technique, to predict oil, gas, and water production using real field data.
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Explore the history of Howard Hughes Sr., inventor of the two-cone roller bit, in Part 1 of this two-part series highlighting the Hughes family legacy in the oil and gas industry.
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Register today for the SPE AI Hackathon taking place 7–9 May in Dubai.
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This article provides an overview of risk-based measurement, monitoring, and verification (MMV) for efficient, long-term CO2 storage.
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This article provides a mirror between the past and the present of well intervention technologies, including R&D to advance to downhole robots and autonomous intervention methods.
