Deep Learning Geoscience Named Entity Recognition

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.

OpportunityFinder® and Renewables Geothermal Projects

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 of decarbonisation and resource management.

#geothermal #renewables #naturallanguageprocessing #artificialintelligence #geoscience #energy #bgs


Announcing V2.0 released. Discover new oil & gas exploration ideas, leads, plays and opportunities in your unstructured text. Also exploit your unstructured text for analogues.

OpportunityFinder® – “first of its kind” pattern based geoscience search.


Example showing autoclassification output from GeoClassifier® from a selection of public domain geoscience documents. The proportion of topics are clustered in a Pearson dendrogram heatmap. Those above the mean are in red, below the mean in dark blue relative to the corpus/collection. Easy to see clusters of documents predominantly about certain topics and to spot ‘anomalies’ – which can be interesting to see and read further.

A Gift to the Geoscience Community: GEOCLASSIFIER® – A Predictive Geological Text Classifier


To welcome in 2021 we are gifting GEOCLASSIFIER® – a geological machine learnt text classifier to not-for-profit organisations.

This assists information searching, filtering and discovery of geoscience topics in text. Even documents predominantly about one topic, often reference other geoscience topics buried within their pages. Automatically surfacing these topics could lead to insights that may otherwise go unnoticed.

Over 125 Million words from public geological texts were used to build the models.

The models in GEOCLASSIFIER® enable the automatic classification of text by industry sector; Metals and Mining, Engineering, Environmental, Geothermal, Hydrogeology, Petroleum and Planetary Geology.

They also classify by topic including; Mineralogy, Petrology, Sedimentology, Igneous, Metamorphic, Lithology, Volcanology, Commercial, Palaeontology, Geophysics, Tectonics, Geochemistry, Diagenesis, Hydrothermal, Glaciology, Geomorphology and Stratigraphy.

#artificialintelligence #machinelearning #textmining #geology #cognitiveassistant

Complex Geoscience Knowledge Graphs from Unstructured Text

The OpportunityFinder® algorithm automatically produces complex geoscience Knowledge Graph networks from unstructured text.

The algorithm uses ‘DNA profiling inspired’ techniques to populate the graph.

This enables interesting patterns and new knowledge to be surfaced that are beyond the reach of other approaches.

To find out more information and arrange a presentation or pilot contact:

OpportunityFinder update – digitally transforming Geoscience Opportunity Generation workflows using Natural Language Processing (NLP)

OpportunityFinder Milestone: the geoscience Python algorithm is now trained on 25,000 terms & phrases for specifically identifying clues for source rock, maturation, migration, reservoir, hydrocarbon occurrence, trap and seal in unstructured text (reports, presentations and papers).

This is used by its one-of-a-kind pattern based discovery method to assist the Geoscientist and surface possible leads, opportunities, analogues and plays that may have been overlooked.