Resource Library

By D. Stoddart, G. Valeras, A. Papapostolou, G. Xenogiannopoulos, D. Oikonomou, B. Alaei, D. Austin, E. Larsen, I. Martin, and E. Zabihi Naeini

This paper demonstrates a methodology and workflow for the rapid identification of missed pay zones throughout many thousands of wells and crucially provides actual examples of missed pay in wells from the North Sea. 

By M. Powney and J.Masi, Geoex MCG; D. Austin, T. Citraningtyas, M. Dyrendahl, B. Alaei and A. Jacobsen, Earth Science Analytics; S. Cornelius, F.Dias and P. Emmet, BVGS

A clear candidate for CCUS is the US Gulf of Mexico (GoM). Exploration has been ongoing in the region since the late 1930’s meaning there is a plethora of information about the offshore from many operators.

By Sindre Jansen, Adriana Citlali Ramirez, David Went and Bezhad Alaei (TGS, ESA)

The first example of artificial intelligence (AI) geological interpretation on a large scale, densely sampled ocean bottom node (OBN) exploration dataset, Utsira OBN, is presented here. 

By Eirik Larsen, Earth Science Analytics

A collaborative cloud environment brings new insights to old concepts, captures expertise, and increases agility of data interpretation. 

By S. Jansen, A.C. Ramirez, D. Went, & B. Alaei 

The first example of artificial intelligence (AI) geological interpretation on a large scale, densely sampled ocean bottom node (OBN) exploration dataset, Utsira OBN, is presented here. 

By Ehsan Z. Naeini, Eirik Larsen, Dimitris Oikonomou, and Behzad Alaei, Earth Science Analytics

Demonstration of two cases of digital transformation facilitating integration of disciplines, data and expertise.

By Eirik Larsen, Earth Science Analytics

The digitalization of samples of oil and gas cuttings will enable the geoscience community to get a broader picture of the subsurface.

By Eirik Larsen, Earth Science Analytics

In this two-part series, experts talk about the changing role of data analytics, its challenges and opportunities for the oil and gas sector..

By Adriana Citlali Ramirez and Sindre Jansen, TGS; and Eirik Larsen, Earth Science Analytics

Earth Science Analytics and TGS have combined the power of machine learning and historical data with the modern Utsira OBN survey.

By the Norwegian Petroleum Directorate (NPD)

Exploring for oil and gas on the Norwegian shelf is still important – and old wells can help us discover more.

By Jennifer Pallanich, Hart Energy

Machine learning and related technologies are speeding up the time-consuming task of well analysis, allowing speedy processing of vast volumes of data in the Norwegian North Sea.

By Ronny Setså, Geo365

Et pågående internasjonalt prosjekt undersøker hvorvidt digitalisering og analyse av eksisterende data kan hjelpe leteselskaper å finne oversette forekomster i Nordsjøen. Foreløpige resultater indikerer stort potensial.

By Earth Science Analytics

Earth Science Analytics delivered the first ever cross border machine learning project awarded by the Norwegian Petroleum Directorate and Oil and Gas Technology Centre. 

By Earth Science Analytics

Finding suitable sites for CO2 storage requires a robust and reliable 3D mapping of subsurface reservoir properties.

By Earth Science Analytics

Geological interpretation on a large-scale, densely sampled OBN exploration dataset covering over 1,500 square kilometres.

By Earth Science Analytics

Digitisation and analysis of all cuttings samples from all released wells on the NCS