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SPE technical papers synopsized in each monthly issue of JPT are available for download for SPE members for 2 months. These January and February papers are available now. -
The authors make the case that data science captures value in well construction when data-analysis methods, such as machine learning, are underpinned by first principles derived from physics and engineering and supported by deep domain expertise.
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The authors write that, by wireline formation testing of a sandstone formation adjacent to a sand/shale laminated reservoir in the Weizhou shale-oil region of the Beibu Gulf, key reservoir information can be directly obtained.
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Everywhere you look these days, there is talk of how advances in big data, artificial intelligence, and machine learning will revolutionize virtually every aspect of our lives. This month, we will look at how researchers in the drilling domain are using this potential to improve well construction.
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These papers provided insights and advances into field-operations automation, machine-learning-assisted petrophysical characterization, and fluid-distribution analysis in unconventional assets.
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This study introduces a cleanup- and flowback-testing approach incorporating advanced solids-separation technology, a portable solution, equipment automation, improved metallurgy, and enhanced safety standards.
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In this paper, the authors propose a regression machine-learning model to predict stick/slip severity index using sequences of surface measurements.
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A Shell partnership with YPF marks a significant milestone for the Argentina LNG export facility, raising new questions about the nation’s potential to unlock the economic power of its vast shale reserves.
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Updates about global exploration and production activities and developments.
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