DSDE: Features

This research developed a clear framework for assessing and selecting fit-for-purpose software. The study focuses on the role of a data-driven approach in the decision process, with application to operational software systems in the oil and gas industry.

Speakers from various disciplines and professions focus on industry trends, challenges, technology applications and advances. Scheduled DL events are in-person meetings only.

Foundation models are rapidly emerging as a transformative force across industries. While their effect on natural language processing and computer vision is well-documented, their potential in specialized engineering domains, particularly within the critical oil, gas, and broader energy sectors, is vast and increasingly recognized. This article explores how these powe…

Buoy-based camera footage, analyzed by artificial intelligence, can help reduce the risk of birds colliding with offshore wind farm turbines.

New strides in computer vision, well controls indicators, and BOP alignment were showcased at the recent Offshore Technology Conference.

Gautam Swami, manager of corporate R&D at NOV and SPE member, shares his experiences in building a career in oil and gas R&D, discusses how innovation is shaping the industry, and offers guidance to young professionals.

This article presents a comparative study evaluating four machine-learning approaches, including three deep-learning methods, for forecasting gas and condensate production over a 5-year horizon.

This article is the second in a Q&A series from the SPE Research and Development Technical Section focusing on emerging energy technologies. In this piece, Madhava Syamlal, CEO and founder of QubitSolve, discusses the present and future of quantum computing.

The full potential of data can only be realized when it is viewed not in isolation but as part of the dynamic triad of hydrocarbons, the data, and the people who interpret it and act on it.

Over decades of exploration and production, the oil and gas sector has accumulated vast amounts of legacy data in various formats. Artificial intelligence and machine learning present an opportunity to transform how this unstructured data is processed and used, enabling significant improvements in operational efficiency and decision-making.