The GeoClassifer(R) Python algorithm launched back in December 2020 (for petroleum, mining and renewables) can automatically read the 'body text' of geoscience, subsurface & wells documentation (PDF, PPT, Word, Excel etc) and: Classify by document type Classify by document category Classify by chronostratigraphy Classify by Lithostratigraphy Classify by well / borehole name Classify by prospect... Continue Reading →

NEW: OpportunityFinder® v4.2 has options to detect 30% more geographical/geobody entities within the body text of documents. These can support spatial and map based search & discovery. Coverage includes from well/boreholes, leads, prospects & plays to fields, deposits, localities, tracts, blocks & licenses to mountains, foldbelts, seamounts and basins. Using state-of-the-art natural language processing and... Continue Reading →

Looking to extract evidence for petroleum systems, metals & minerals, heat flow, fluid flow or aquifers & seals in reports or semi-structured databases? Or chronostratigraphy, lithostratigraphy, tectonics, depositional environment and lithology? The patented algorithms from Infoscience Technologies may give your organisation a fast start.. contact@infosciencetechnologies.com

Our algorithms combine over 75,000 different ways potential hydrocarbon occurrence, source rock, maturation, migration, reservoir, trap and seal clues may be mentioned in documents, reports and logs. Using traditional keyword search, explorers may miss up to 40% - 60% [1] of the relevant geoscience evidence buried in report collections. Based on years of research, our... Continue Reading →

Using machine learning to detect mentions of drilling and operational problems in text. Over 5,000 public domain sentences have been labelled to train a predictive machine learning model to detect wellbore drilling and operational ‘problems’ (including reservoir and production) in documents, reports and logs. This can support alerts & monitoring, health & safety, search &... Continue Reading →

The GeoClassifier® algorithm can detect operational problems in reports, documents, logs and other forms of unstructured text. Machine learning (neural networks) is used for prediction, complementing the existing machine learning model in GeoClassifier® which detects well / borehole names without using a prior list of names. These can be used to support oil & gas,... Continue Reading →

Find and discover geoscience knowledge not documents. An example of how organisations are exploiting the output from OpportunityFinder(R), generated by applying the algorithm to their unstructured text such as PDF, PPT, Word, Excel, XML/JSON, image files etc. on file shares and document management systems   This company has used Microsoft PowerBI over the top of... Continue Reading →

A very Merry Christmas to all our clients, followers and supporters, very much appreciated! Great to see growing interest in geoscience text analytics. The graph below shows visitors to the Infoscience Technologies website since the technology start-up was founded in 2018.

Discover new insights in geoscience documents, using patterns in unstructured text to detect petroleum, mineral, hydro, geothermal and hydrogen exploration opportunities. First-of-a-kind OpportunityFinderⓇ and GeoClassifierⓇ algorithms are now integrated. Teaching machines about geoscience.  Apply to deep archives, documents on your shared drive, or in Microsoft Sharepoint or Document Management Systems. Apply to external geoscience subscription reports and... Continue Reading →

The OpportunityFinder Python based Natural Language Processing (NLP) algorithm has been extended to detect clues for porphyry copper in text. Launched in early 2020 and used by organisations for petroleum and native hydrogen exploration, the algorithm uses hundreds of thousands of lexicons, taxonomies and labelled data for machine learning models. The novel Patented method combines... Continue Reading →