data science

Energy efficiency is crucial for the oil and gas industry, where operational costs and environmental impact are under constant scrutiny. Predicting and managing electrical consumption and peak demand accurately, especially with the variability of weather conditions, is a significant challenge. This work presents a neural network model trained on historical weather and…

It’s hard to create corporate or master data strategies when every user group you talk to has a different idea of what “good” should look like from their perspective.

The two companies announced a 10-year partnership to work on digital solutions to challenges including carbon capture, storage, and use.

This paper investigates the use of machine learning to rapidly predict the solutions of a high-fidelity, complex physics model using a simpler physics model.

Developing alternative power supplies with wide-scale reliability, dependability, and minimization or elimination of GHG emissions within feasible capex/opex scenarios is the brass ring of sustainability and energy security—and data are helping us get there.

From the first supercomputer to generative AI, JPT has followed the advancement of digital technology in the petroleum industry. As the steady march of innovation continues, four experts give their views on the state and future of data science in the industry.

The increasing demand for critical minerals—the building blocks of an electrified future—is creating opportunities for the oil and gas industry to apply its extensive knowledge, tools, and data to help meet the demand.

In this paper, the authors present data analyses to comprehensively evaluate the performance of a steady-state multiphase-flow point model in predicting high-pressure, near-horizontal data from independent experiments.

The authors of this paper describe a procedure that enables fast reconstruction of the entire production data set with multiple missing sections in different variables.

This paper presents a physics-assisted deep-learning model to facilitate transfer learning in unconventional reservoirs by integrating the complementary strengths of physics-based and data-driven predictive models.