As well as detecting Geo-resources in unstructured text reports, papers and logs, OpportunityFinder can detect and disambiguate all kinds of geological concepts. High level lithology groupings in the Williston Basin are shown above in the Beeswarm chart. contact@infosciencetechnologies.com

Sentiment analysis of the last ten years of Guardian news articles mentioning geoscience. Geoscience is often mentioned in context to natural geohazards. However, there might be a trend towards increasing negative sentiment over time that may warrant further exploration. #geosciences #naturallanguageprocessing #oilandgas #mining #hydrogeology #geohazards

Topics mentioned with articles containing the word ‘geoscience’, past 10 years from The Guardian newspaper. Words are clustered automatically by their similarity (cosine) shown by the colour groupings. #geosciences #informationscience

The Python OpportunityFinder® algorithm from Infoscience Technologies can now automatically detect fossil names and their associated Lithostratigraphic Units and Geological Ages without a prior list of names. This can be useful because it is not always possible to predefine all the names and variations you are likely to come across in text. Furthermore, the way... Continue Reading →

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 →

In April 2021 I worked on the world’s first large scale text analytics project to detect natural hydrogen. Working with a natural resources exploration company, this was to detect both explicit and implicit evidence for natural hydrogen in legacy oil & gas documentation. This contributed to the development of a new hydrogen play. It was... 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 →