Infoscience Technologies was delighted to guest author an article on Natural Language Processing (NLP) in the Geosciences for Halliburton’s September issue of Subsurface Insights magazine. This month’s issue is a Minerals special.
Detecting entities such as well names in unstructured text can be useful for many aspects of information discovery.
Lookup lists from corporate databases and regular expression pattern rules can be useful. They do have limitations though, it can be difficult to predict sometimes what may lie within thousands of old reports and documents.
Having a machine learning model tuned and trained on thousands of public domain examples may help and support existing digitalisation activities. This new capability was added to the GeoClassifier® algorithm recently.
The screenshot shows some examples in oil & gas, geothermal, hydrogeology, mining and carbon capture sectors.
Tested on several hundred UK License Reports gave 96% accuracy detecting 718 well names.
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.
Oxford, UK 18th June 2021: Infoscience Technologies Ltd, the pioneer in extracting geoscience and subsurface knowledge from text, is delighted to announce the United States Patent and Trademark Office has granted a patent for the OpportunityFinder® technology.
The patent award is for a Natural Language Processing System which suggests Geo-Resource Ideas & Plays from Unstructured Text. The invention uses a novel method to move beyond documents, sentences and entities – to detect interesting patterns in text relevant to the geo-resource sector.
The new patent reinforces that Infoscience Technologies is on the leading edge of digital transformation in the geoscience and subsurface sectors.
The OpportunityFinder® algorithm is currently used by organisations in the Petroleum Exploration, Geothermal, Mining and Hydrogen sectors.
The Python OpportunityFinder® algorithm 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 names are used in text can differ from reference lists of names. This can support industry and academia needs.
Mining Palaeontological data from large amounts of text has led to new scientific discoveries Peters et al (2014) .
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 the past 40 years manually tagged to hydrocarbons were analysed using the OpportunityFinder® algorithm equating to over 2 Million sentences. Using Natural Language Processing (NLP), Knowledge Engineering and Machine Learning, locational information, Chronostratigraphy and Lithostratigraphy were automatically extracted along with co-occurrence data.
Indicator mineral evidence (as well as direct evidence) for critical resources were detected (such as Rare Earth Elements (REE), Gold, Silver, Copper and Nickel), despite not being mentioned in the petroleum report package metadata. These were ranked by several factors including speculation.
The Geological Survey of Queensland (GSQ) is the state’s custodian of geoscience knowledge and data. GSQ collects and provides geoscience data, information and advice about Queensland’s mineral and energy resources and resource potential. https://geoscience.data.qld.gov.au/
Infoscience Technologies Limited is an Artificial Intelligence tech start-up, extracting geoscience knowledge from unstructured text. www.infosciencetechnologies.com
The heatmap chart below shows co-occurrences between minerals driven from the text, clustered automatically using Pearson’s. This may produce interesting associations that may warrant further investigation.
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.
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 lexicon.
Whilst an algorithm will never read text like a Geoscientist, we can teach it some elements. The advantage is reading differently to us, and processing larger volumes than any person can read.
This allows us to detect evidence, join the dots and stimulate business ideas we may not have had without the assistance of the algorithm.