machine learning

Geophysicist Markos Sourial discusses advances in seismic imaging, the challenges of modern data processing, and what they mean for the next wave of subsurface professionals.

This paper explains that the discovery of specific pressure trends, combined with an unconventional approach for analyzing gas compositional data, enables the detection and prediction of paraffin deposition at pad level and in the gathering system.

This paper discusses a comprehensive hybrid approach that combines machine learning with a physics-based risk-prediction model to detect and prevent the formation of hydrates in flowlines and separators.

Adaptability, collaboration, and digital technologies are all pages in Aramco’s oilfield R&D playbook.

This paper proposes how the strengths of cloud computing can become key enablers for oil and gas organizations in helping them enhance their overall security posture and manage risks within operational-technology environments.

This paper presents a physics-informed machine learning method that enhances the accuracy of pressure transient analysis, predicting reservoir properties to enhance waste slurry injection and waste disposal.

Oil and gas experts encourage human/AI partnerships that can “supercharge” capabilities to create competitive advantages.

This paper presents a novel workflow with multiobjective optimization techniques to assess the integration of pressure-management methodologies for permanent geological carbon dioxide storage in saline aquifers.

Deploying artificial intelligence across an enterprise requires thinking beyond the pilot.

This paper presents the development of a robust, physics-based, and data-driven workflow for modeling mud loss in fractured formations and predicting terminal mud loss volume and time, as well as equivalent hydraulic fracture aperture.