data analytics

The trial phase of the agentic program used AI agents and combined large-language-model technology with data collected from more than 15% of ADNOC’s onshore and offshore wells.

SLB said it plans to integrate INT’s technology into its digital data and artificial intelligence platforms.

The authors make the case that data science captures value in well construction when data-analysis methods, such as machine learning, are underpinned by first principles derived from physics and engineering and supported by deep domain expertise.

A recent survey conducted by Rackspace Technology reveals new attitudes about using the cloud, including a change from using the public cloud to using private, on-site clouds or a hybrid of the two.

This selection of cutting-edge articles spotlights how experimental concepts are now driving cost-saving strategies in unconventional development. It’s a reminder that innovation often comes from creative thinking, not just new tools or tech partnerships.

A comprehensive, digitized water-management application has been designed to streamline and enhance the monitoring and management of water resources used in hydraulic fracturing.

This case study uses distributed temperature sensing (DTS) technology to monitor a cemented and plugged well in the Alaska North Slope, highlighting the versatile potential of DTS in long-term monitoring and establishing a workflow that makes the most of that potential.

The SPE Europe Energy GeoHackathon aims at educating and disseminating knowledge to all the participants on how data science applications can support geothermal energy developments and drive the energy transition. Boot camps begin 21 October.

This paper proposes a holistic, automatic, and real-time characterization of cuttings/cavings, including their volume, size distribution, and shape/morphology, while integrating 3D data with high-resolution images to pursue this objective for use in the real-time assessment of hole cleaning sufficiency and wellbore stability and, consequently, for the prediction, prev…

This paper presents a novel modeling framework for predicting residual oil saturation in carbonate rocks. The proposed framework uses supervised machine learning models trained on data generated by pore-scale simulations and aims to supplement conventional coreflooding tests or serve as a tool for rapid residual oil saturation evaluation of a reservoir.