Text mining algorithms were used to discover hidden geo-resource (metals, elements, minerals) associations in reports, maps, sketches and logs from the archives of the Geological Survey of Queensland in Australia. The Geological Survey of Queensland have made a number of excellent improvements recently increasing the accessibility of these data. A subset of report packages over... Continue Reading →

Cairn Energy deploy Infoscience Technologies’ OpportunityFinder® to detect evidence for hydrocarbon plays within unstructured text.Cairn Energy is an independent, UK based energy company focused on oil and gas exploration, development and production. www.cairnenergy.comInfoscience Technologies is an Artificial Intelligence tech start-up, extracting geoscience knowledge from unstructured text. www.infosciencetechnologies.com

Using Artificial Intelligence to search for native hydrogen in the subsurface. Australian exploration company Buru Energy have chosen Infoscience Technologies OpportunityFinder®algorithm to detect evidence for potential native hydrogen in historical documentation. Native hydrogen occurrences have been accidentally discovered historically, but little to no efforts have been made to target it industrially. Where appropriate, the use... Continue Reading →

The OpportunityFinder® algorithm has now exceeded 50,000 terms in its lexicon for detecting petroleum systems automatically in text. This is combined with hundreds of thousands of labelled data for machine learning. These can support laser like tasks, improve search & discovery, insights, knowledge mining and also support the tuning of very large language models. http://www.infosciencetechnologies.com

Some research I conducted recently comparing the counts of potential oil and gas source rock "mentions" by geological age in unstructured text, to some (rather old) actual data published in the literature on the age of hydrocarbons generated from source rocks in producing oil & gas fields. Over 48,000 terms from a lexicon were applied to... Continue Reading →

Figure 1 - Mineral Associations to HC Source Rock (x-axis) and HC Occurrence (y-axis) Natural Language Processing (NLP) and Machine Learning was applied to 16 million geoscience sentences using over 48,000 lexicon terms. Their frequency of association (in the same sentence) is shown in Fig 1. Some patterns confirm known associations, such as the association... Continue Reading →

Figure 1 - Association (Text) Frequency between chemical elements (by group) and hydrocarbon source rock (x-axis) and hydrocarbon occurrence (y-axis). Deriving Hydrocarbon to Metal / Mineral associations found in unstructured text for use as potential ore analogues and exploratory data analysis: Applying Machine Learning and Natural Language Processing (NLP) to 16 Million Geoscience Sentences. Hydrocarbon... Continue Reading →

Fascinated by this Merycoidodon (Oreodont) fossil skull.  An extinct herbivore from the Oligocene (~25 Million Years Ago) with no known close living relatives today. Related to the camel but 'sheep-like', it would have likely resembled a pig in appearance, but with a longer body, at about 1.5 metres with short limbs and probably moved in herds... Continue Reading →

We are using Deep Learning to leverage the unique 45,000 petroleum system related textual clues in OpportunityFinder®. Designed for automation, the clues combined with auto-annotation of millions of sentences allow a deep learning model to generalise (learn). This enables the detection of valid clues in geoscience text (reports, presentations, papers) not present in the original... Continue Reading →

OpportunityFinder® is being tested within a renewables geothermal project in collaboration with the British Geological Survey. BGS are investigating mine water in underground abandoned coal mines as a low carbon sustainable heat source for housing and manufacturing, and have several other potential use cases for knowledge extraction from their data archives to meet the challenges... Continue Reading →