What is it really and what are its limitations?

Machine Learning: magical or mathematical statistics? De-mystifying AI and setting it in a geo-exploration context.

Using 3D animation software and surface models for new hillshade mapping techniques to produce hyper-realistic 3D-like maps.

There are millions of drill cuttings samples from thousands of oil and gas wells sitting unloved and under-utilised in warehouses and core repositories around the world, but there is now real momentum behind the concept of digitalising entire national archives.

This seismic fault interpretation workflow integrates geological knowledge, machine learning (ML) and deterministic techniques aimed at mapping and characterizing a fault framework with geological consistency.

Leveraging artificial intelligence to improve efficiency, predictability and performance in seismic acquisition and processing.

Will automation in ocean bottom seismic revolutionise operations and cut costs?

Tiny seismic nodes making high density land seismic accessible to all industries.

Many may argue about the best use for Artificial Intelligence: is it in the exploration or the development of oil and gas resources? This article illustrates with case studies that AI sits comfortably in both, and forms a bridge between the two, allowing interpretation continuity.

The first outing of the world’s most sophisticated airborne gravity gradiometer has already yielded fascinating insights into the unexplored Egyptian subsurface; improving geological understanding and reducing hydrocarbon exploration risk.

The idea of the ‘digital twin’ is catching on in the oil and gas industry. But what exactly is it, and how can it help?

In 2006, a ‘moonshot’ R&D project was launched to develop interactive software for visualising and processing pre-stack seismic data and is now being used to monitor oil and gas production in producing fields.

An image-based approach for identifying pores and particles in unconventional reservoirs for planning carbon dioxide-based enhanced oil recovery and associated storage (CCS).

The experiences, lessons learned, and best practices of Chinese geoscientists working on difficult hydrocarbon reservoirs can be of benefit to others.

Machine learning in oil and gas exploration and production has successfully begun to scale up from pilots to specific and bespoke industrial solutions – but must remain grounded in the real world.