Data & Analytics
The emerging vision is an industry where engineers focus on judgment, risk assessments, and accountability.
This paper presents the large-scale deployment and operational integration of an advanced, automated rheometer in the Ghawar gas field, one of the world’s most prolific and technically complex hydrocarbon developments.
Case studies from Devon Energy, ConocoPhillips, and others show how clever engineering and technology are being used to improve recovery and asset value.
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The use of data-intensive decision making and smart risk-management solutions has resulted in the improvement of the ethical foundations underlying the industry. These digital tools and machine-based cognitive processes for risk-avoidance have also helped restore the public’s trust in the industry. -
Cyberattacks are often seen as an IT issue, but the intelligence gained during the development of an effective cybersecurity protocol may serve a broader role as a business driver for energy. What should companies look for in assessing threats?
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BHGE is developing an analytics and machine-learning approach that offers descriptive and predictive insights on frac hits, with the aim of eventually offering a real-time monitoring capability to be deployed during frac jobs.
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The execution of process automation projects depends on the completion of tasks that are not necessarily related to automation, hampering project development timelines. How do automation solutions, such as digital twins, help to overcome these challenges?
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The international major has been playing with intelligent programs for years, but this new deal shows that it is now ready to scale those efforts up to cover hundreds of thousands of pieces of equipment.
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The 5-year-old software startup is getting noticed by the oil and gas industry for its ability to accelerate analytics projects by taking care of all the tedious work involved with data wrangling.
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“Greedy pursuit” in the realm of algorithms is a good thing. Saudi Aramco studied such algorithms to produce images simulating the flow inside a pipe’s cross section, possibly reducing the need for separator-based multiphase flowmeters.
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Well-placement optimization is one of the more challenging problems in the oil and gas industry. Although several optimization methods have been proposed, the most-used approach remains that of manual optimization by reservoir engineers.
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This paper proposes a new method of economic prediction on the basis of expert library and oilfield databases. The method takes into account geological factors and the effect of production factors on the economic prediction.
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A new geostatistics modeling methodology that connects geostatistics and machine-learning methodologies, uses nonlinear topological mapping to reduce the original high-dimensional data space, and uses unsupervised-learning algorithms to bypass problems with supervised-learning algorithms.
