data science

Geochemical parameters such as total organic carbon (TOC) provides valuable information to understand rock organic richness and maturity and, therefore, optimize hydrocarbon exploration. This article presents a novel work flow to predict continuous high-resolution TOC profiles using machine learning.

Two wings and a few hours can replace dozens of boots and many months in site selection, planning, and management.

Whether inconsistent, incomplete, ambiguous, or just plain wrong, bad data is a big barrier to digital transformation.

University of Houston researchers develop oil recovery tools with ‘significantly higher accuracy’ than current methods.

This article presents a deep-learning approach, the long short-term memory network, for adaptive hydrocarbon production forecasting that takes historical operational and production information as input sequences to predict oil production as a function of operational plans.

This paper describes an approach implemented by the operator to solve research and development challenges by creating in-house infrastructure of both software and hardware.

Geolog and Petro.ai announced a strategic partnership to deliver data science products and services to the global energy industry.

Supervised learning has many commercial applications; however, such learning lacks the capability to generate new insights and knowledge. In contrast, unsupervised learning discovers the inherent structures in unlabeled data, thereby helping generate new insights and actionable knowledge from large volumes of data.

Schneider Electric University has been designed to help data center professionals expand their skills by offering free guidance on the latest technology, sustainability, and energy efficiency initiatives.

This paper evaluates learnings from the past 30 years of methods that aim to quantify the uncertainty in the subsurface using multiple realizations, describing major challenges and outlining potential ways to overcome them.