machine learning

For organizations that do it well, data management provides a competitive edge in an increasingly digital oil field. But teams all too often are so busy managing all the moving parts of data management that they take their eye off of “the prize”—the payoff after you have put everything into place to sustain successful data management.

Texas A&M University-Qatar in collaboration with the SPE Qatar Section conducted a 2-day virtual workshop on flow assurance, carbon reduction, and digitalization. Participants included more than 200 professionals associated with academia, research institutes, and industry from 23 countries.

SPE Data Science and Engineering Analytics Technical Director Silviu Livescu and SPE Reservoir Technical Director Rodolfo Camacho address some of the challenges in the application of data analytics, artificial intelligence, and machine learning to several reservoir engineering problems.

GlobalData’s report "Robotics in Oil and Gas" notes that, while robotics has been a part of the oil and gas industry for several decades, growing digitalization and integration with artificial intelligence, cloud computing, and the Internet of Things have helped diversify robot use cases within the industry.

The service company said it plans to use DataRobot’s artificial intelligence capabilities in its production-optimization and well-construction digital platforms.

Synthetic data generation is a solution that allows citizen data scientists and auto ML users to quickly and safely create and use business-critical data assets. Benefits go beyond democratizing data access, and even those with privileged data access are building synthetic data generators into their work flows.

This paper describes a novel method based on machine learning to maintain an evergreen competency database. The tool reduces discrepancies between organizational requirements and the actual talent deployment by using unstructured corporate data.

This paper highlights the potential of machine learning to be used as a tool in assisting the drilling engineer in bit selection through data insights previously overlooked.

Large geological models are needed for modeling the subsurface processes in geothermal, carbon-storage, and hydrocarbon reservoirs. The size of these models contributes to the computational cost of history matching, engineering optimization, and forecasting. To reduce this cost, low-dimensional representations need to be extracted. Deep-learning tools, such as autoenc…

This paper discusses a waterflood optimization system that provides monitoring and surveillance dashboards with artificial-intelligence and machine-learning components to generate and assess insights into waterflood operational efficiency.