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Collaboration agreement lays foundation for advancing tech and know-how for harsh environment operations.
A Houston-based geothermal startup just got "liked" by the parent company of Facebook.
The industry is balancing brains and bots as it squeezes out barrels of oil production.
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Energy efficiency is crucial for the oil and gas industry, where operational costs and environmental impact are under constant scrutiny. Predicting and managing electrical consumption and peak demand accurately, especially with the variability of weather conditions, is a significant challenge. This work presents a neural network model trained on historical weather and…
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Cost concerns temper public appetite for clean energy while companies struggle to find investors for projects.
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A best practice for nominating SPE members for awards is to form a nomination posse—a committee of 4 to 5 people that works to match candidates with the awards that best suits them. Providing respect and recognition for your colleagues is a wonderful way to find purpose in your own career.
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This paper presents a clear and consistent method for determining dead and live crude EACNs using a single reliable method, highlighting a graphical way to determine the optimal salinity and its uncertainties using real data.
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Chevron looks into using remotely operated vehicles to scrub marine growth from subsea structures.
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This paper describes a data-driven approach for liquid-loading detection and prediction that harnesses high-frequency gas-rate and tubinghead-pressure measurements to identify the onset of liquid loading and correct critical rates computed by empirical methods.
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This paper outlines the importance of numerical rate transient analysis for dry gas wells, describing a simple, fully penetrating planar fracture model.
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This paper describes an approach that combines rock typing and machine-learning neural-network techniques to predict the permeability of heterogeneous carbonate formations accurately.
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This study describes the performance of machine-learning models generated by the self-organizing-map technique to predict electrical rock properties in the Saman field in northern Colombia.
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Geo Oceans has achieved class sign off for the world’s first entirely robotic FPSO cargo oil tank special survey.
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The authors of this paper review the advantages of machine learning in complex compositional reservoir simulations to determine fluid properties such as critical temperature and saturation pressure.
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This study compares seven imputation techniques for predicting missing core-measured horizontal and vertical permeability and porosity data in two wells drilled in the North Rumaila oil field in southern Iraq.
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It’s hard to create corporate or master data strategies when every user group you talk to has a different idea of what “good” should look like from their perspective.
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A seismic prediction model is developed and presented in a case study to simulate the magnitude and timing of triggered seismic events with the intent to manage and mitigate environmental impacts resulting from induced seismicity during subsurface development activities.