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The deal adds over 1,000 potential drilling locations and almost doubles oil production for Houston-based Magnolia.
Average output reached 13.6 million BOPD, extending a period of sustained growth that has kept the US at the forefront of global oil production.
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The number of high-impact wells drilled across the globe this year are expected to be on trend with the most recent 5-year average. -
This study integrates physics-based constraints into machine-learning models, thereby improving their predictive accuracy and robustness.
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This paper introduces a machine-learning approach that integrates well-logging data to enhance depth selection, thereby increasing the likelihood of obtaining accurate and valuable formation-pressure results.
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This study aims to use machine-learning techniques to predict well logs by analyzing mud-log and logging-while-drilling data.
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A compelling triptych of recent research showcases the burgeoning capacity of machine learning to unlock substantial efficiencies and enhance decision-making across the exploration and production lifecycle.
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This study presents the development of a novel modeling tool designed to predict condensate emulsions, focusing on key factors causing emulsions such as pH, solid content, asphaltene concentration, droplet size, and organic acids.
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This study explores enhancing gas production through a novel combination of prestimulation using a coiled tubing unit and high-rate matrix acidizing.
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This work introduces a fast, methodical approach to detect liquid loading using easily available field data while avoiding traditional assumptions and to determine critical gas rates directly from field data.
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Bad vibes are being addressed by contractors as operators push to go faster, deeper, and longer with unconventional wells.
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A New Texas Two-Step: Why One Eagle Ford Producer Is Using Hydrocarbons for Well Stimulation and EORBlackBrush Oil & Gas tells JPT about its use of natural gas liquids and condensate to increase oil recovery in horizontal shale wells.
