data analytics

Whether buying or selling, quality, non-production data can help operators achieve improved margins and less downtime, unlocking the true potential of their wells.

This paper discusses how methane detection from oil and gas infrastructure using remote sensing technologies enables operators to quantify and minimize the emissions while gaining insight and an understanding of their operations through data analytics.

The company revealed programs for managing production and industrial asset performance. It also announced a collaboration aimed at enhancing rig visualization.

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.

Vision analytics is being used to extract insight information from video, with data inferred from existing cameras used to create a monitoring dashboard where supervisors can receive alerts at the worksite level or drill down to specific events.

The University of Texas at Austin will be home to a multidisciplinary research and education initiative, the Energy Emissions Modeling and Data Lab, which aims to address the growing need for accurate, timely, and clear accounting of greenhouse-gas emissions across global oil and natural gas supply chains.

The authors of this paper describe a software technology that balances calculated well-integrity risk with the associated financial impact for an entire integrity-surveillance program.

Industry leaders are harnessing the power of data to improve efficiencies, eliminate nonproductive time, and reduce Capex.

The data-management company has raised its investment in the technology company IWS by $7.9 million, with an agreement to invest an additional $25 million depending on the performance of IWS.

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…