Digital Oil Field
The Seismos full-scale research center opens to industry, universities, and national laboratories, advancing acoustic sensing across upstream energy, pipelines, critical infrastructure, and national security.
This research focuses on combining physics-based expert rules with machine learning to improve the detection of failure-related events in electrical submersible pumps.
A combination of physics principles and machine-learning techniques is used in this work to develop a virtual flowmeter for oil-production optimization in electrical-submersible-pump-lifted oil wells, resulting in reliable generalization.
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Companies are bringing satellite monitoring to the unconventional oilfield—namely the Permian Basin—where they are training machine learning models to track and predict drilling and completions work. -
Discussions of big data and its management are increasing across our industry and disciplines. This selection of technical papers takes a look at data mining, the ethical issues associated with it, and the status of data-driven methods.
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Remote condition monitoring of offshore platform equipment tracks performance data, watching for deviations from baseline benchmarks. Unexpected variances can be investigated and serviced by technicians dispatched to target the root causes—an approach called condition-based maintenance.
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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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Predicting the trajectory of a satellite, or a well, requires sophisticated analysis to reduce the huge uncertainties. That adds to the many things drillers should be thinking about, which can be overwhelming.
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These days, it is all about digital. Click to find out what industry leaders from Encana, Google, Schlumberger, and Shell have to say about the ongoing transformation towards data-driven profitability.
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The data collected via monitoring and metering applications are increasingly viewed as central to assessing production performance and in decision making to optimize field development and operations.
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Having blockchain on an oil rig means everyone on the job shares a view of a project from start to finish. This could facilitate innovations that cut costs, but organizational change is required.
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The objective of this paper is to illustrate a methodology for identifying the value of information (VOI) in reservoir management—in particular, for deriving the conditional probabilities of success when new and imperfect data are acquired.
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Initial development of inflow tracers was designed to provide qualitative information about the location of water breakthrough in production wells. The proof of concept and application for water detection initiated the development of oil tracers for oil-inflow monitoring.
