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.


OpportunityFinder®: A codebreaker for geoscience unstructured text


Bletchley Park Bombe (replica of the original Bombe) Antoine Taveneaux CC BY-SA 3.0

Over the past few years, geoscience and data science knowledge was used to label over one million diverse geoscience sentences from public domain Internet sources (papers, reports, presentations etc.).

The purpose was to identify clues for source rock, maturation, migration, hydrocarbon occurrence, reservoir, trap and seal as mentioned in unstructured text; in such a way they could be used for automated inference. This included both obvious explicit terms and phrases, along with more subtle non-obvious textual clues.

These data are used with Natural Language Processing, Machine Learning and a First-of-a-Kind novel ‘DNA inspired’ method to create a predictive classifier. The algorithm (OpportunityFinder®) can surface non-obvious patterns of interest that may be useful to an Exploration Geoscientist.

These may be contained within any repository of reports, documents or text, too large for a person to ever read. This may include old hardcopy reports now scanned/digitized, those in different languages, external and internal to an organization. The resulting patterns, which are surfaced from trillions of permutations, can be displayed in time and space to assist the Geoscientist with discovery and ideation.

The image below in Fig 1 is a simulation of data extracted from a large body of reports and geo-referenced using OpportunityFinder®. The pie-charts represent the differing elements that have been discovered in text (e.g. potential trap/seal clues in green).


Fig 1 – Thematic play elements from text (public domain WMS Basins data) 

These allow the Geoscientist to drill down in more detail. These raw ‘DNA’ are used by the data driven pattern algorithm in OpportunityFinder® to surface potential plays, leads and opportunities that may not be obvious. These may be browsed by the geoscientist stimulating lines of thought that may not have necessarily occurred had it not been for the algorithm.

This may provide a ‘fast start’ to organizations and aid companies with geoscience exploration. The algorithm (Python) can plug-in to existing search & discovery approaches used by organizations, who can also fork their own version of OpportunityFinder® should that be required.

There are also opportunities to target a variety of geological themes not currently addressed should that be of interest.