Data & Analytics
Switching from continuous circulation to cyclic huff-‘n’-puff operation in enhanced geothermal systems can significantly delay thermal breakthrough, sustain higher production temperatures, and improve long-term economic performance.
The two companies say they plan to work together to use agentic AI to increase the capabilities of technical experts.
This article is the first in a Q&A series from the SPE Methane Emissions Management Technical Section (MEMTS) on methane intelligence and how oil and gas teams translate emissions data into credible decisions and measurable reductions.
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Concern has been growing in the oil and gas industry about the high frequency of mooring line failures. While physical tension sensors can be difficult and costly to maintain, machine learning has shown to be a more-accurate and less-costly method for structural integrity assessment. -
Digital is replacing manual at Sanchez Oil & Gas. Since its implementation in 2017, the company’s Production Maintenance Tracker application has been transforming its operations management by bringing together all the assets, departments, and routes into one hub.
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The era of Big Data is coming to an end as the focus shifts from how we collect data to processing that data in real-time. Big Data is now a business asset supporting the next eras of multicloud support, machine learning, and real-time analytics.
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Photogrammetry—stitching together images to create photorealistic 3D models—can be part of a larger industrial digitalization strategy that aims to liberate data from its silos, connect it to other relevant information, and make it available to the workers who need it.
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Shell is continuing its exploration of blockchain with yet another investment in the technology, this time investing in LO3, a startup using a modified version of the Ethereum blockchain to make it easier for individuals to buy and sell locally produced energy.
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Digital transformation: It’s a phrase that seems to be on the lips of everyone in the oil and gas industry, and that was certainly true at the inaugural Energy in Data conference held in Austin. The conference, however, showed that the transformation is more than on its way. It’s here.
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Firms such as Netflix have had a solid digital transformation strategy. Now, industrial companies, including in the oil and gas sector, are on a similar, if less dramatic, trajectory.
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First developed as a proprietary system by a large Permian Basin operator, this hydraulic fracturing schedule exchange will be run by a data company and opened up to the entire North American shale sector.
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Usually, field engineers manually pick events such as start and end times out of hydraulic-fracturing pumping data. This manual process is time-consuming and prone to error. Now, a Denver-based company is using machine learning to identify these events more accurately and consistently.
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Unlike structured data, unstructured data is information that either does not have predefined labels or is not organized in a predefined template, and inefficient management of this data is holding back the industry.
