Data mining/analysis
Over decades of exploration and production, the oil and gas sector has accumulated vast amounts of legacy data in various formats. Artificial intelligence and machine learning present an opportunity to transform how this unstructured data is processed and used, enabling significant improvements in operational efficiency and decision-making.
A roundtable discussion during CERAWeek pointed to the necessity of a mindset shift for the oil and gas industry to tap into AI’s true potential.
Technology and partnerships play a pivotal role in how the oil industry finds and produces energy from frontier regions and brownfields, both now and in the future.
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SponsoredFor surveying, exploration, analytics, and a whole host of processes, liberated, contextualized data tailored to the environments of E&P subsurface will empower confidence, speed, reliability, agility, and most importantly, innovation. This is how Aker BP is doing it. -
If design A yields the same 90-day production at 10% lower cost in a series of wells than design B wells, is design A the better one? Using pressure-based fracture measurements, the separability of variables between two completion designs can be evaluated.
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For the upstream industry, where improvement in efficiency or production can drive significant financial results, there is no question that the size of the digital prize is huge. So are the challenges.
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As part of the deal, Pertamina is moving all of its petrotechnical applications to the iEnergy cloud service, which is run by Halliburton arm Landmark.
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The provider of subscription-based analytics services for the North American oil and gas sector continues its streak of purchasing data-focused firms.
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The data-driven maintenance program incorporates riser condition, usage, and fatigue analysis with a risk-based inspection process.
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SPE is planning a series on petroleum data analytics at its Houston Training Center. The series will kick off with Week One: Subsurface Analytics on 24–28 February and will be led by University of West Virginia Professor of Petroleum and Natural Gas Engineering Shahab Mohaghegh.
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This paper investigates the most important independent variables, including petrophysics and completion parameters, to estimate ultimate recovery with a machine-learning algorithm. A novel machine-learning model based on random forest regression is introduced to predict estimated ultimate recovery.
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The December issue of the peer-reviewed SPE Journal includes a spotlight section on data analytics, presenting paper SPE 195698, “Prediction of Shale-Gas Production at Duvernay Formation Using Deep-Learning Algorithm.”
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The November issues of SPE’s peer-reviewed journals SPE Reservoir Evaluation & Engineering and SPE Production & Operations include papers addressing data analytics and machine learning.
