OpportunityFinder® v4.2: State-of-the-art geotagging for subsurface, geoscience and Earth science documents

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 machine learning, documents (and domain evidence in documents) can be precisely geo-located on a map automatically. The algorithm works anywhere in the world without using prior lists of names. Recent findings suggest it can detect significantly more geotags than traditional approaches, inductively uncovering new data points.

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#oilandgas #mining #renewables #geothermal #hydrogeology

Automatically detecting geo-resource evidence in reports

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..

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Need help searching for petroleum system elements for exploration?

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 algorithms combine the best from Knowledge Engineering, Natural Language Processing (NLP) and Machine Learning.

Our algorithms can plug into any existing search engine, fast tracking digital transformation initiatives. Bringing state of the art intelligence to assist geoscientists oil & gas data mining and search.

#digital #geoscience #subsurface #search #documents

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[1] https://asistdl.onlinelibrary.wiley.com/doi/abs/10.1002/asi.23595

Using machine learning to detect mentions of drilling and operational problems in text.

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 & discovery as well as analogues & learning by quickly extracting where problems have or are being encountered, in volumes of unstructured text which are too vast for a person to ever realistically read through.

The model generalises, capable of surfacing types of problems that were not even present in the original training set. For example in the image shown, ‘swelling formation’ is detected in the first sentence as a potential problem. Whilst ‘swelling clays’ was labelled in the training set, the phrase ‘swelling formation’ was not. The model has inferred this based on statistical word context.

The techniques are also useful for scanned content with OCR errors, note that ‘stuck pipe’ and ‘swelling clays’ are detected even though they have spelling errors introduced by the OCR process – not uncommon!

This model adds to the existing CNN derived ML models in GeoClassifier(R) for detecting well names and subsurface topics. These models are fully integrated with the OpportunityFinder(R) algorithm for the energy transition, mining and geohealth sectors.

More at: http://www.infosciencetechnologies.com

#digitaltransformation #machinelearning #naturallanguageprocessing #georesources

Using machine learning to detect drilling, reservoir and production problems in unstructured text

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, mining, renewables, carbon capture & storage (CCS), geothermal and hydrogeology sectors.

Discover Subsurface and Geoscience Knowledge not Documents.

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 the CSV created by OpportunityFinder. In just a couple of weeks configuring an application to allow geoscientists to discover petroleum, chemical elements, mineral system and geo-resource associations in time and space as well as casting a wide net for analogue discovery.

This allows the geoscientists to uncover knowledge that typically is buried so far down traditional document search results lists – they never normally read it. The algorithm takes this a step further, using a lexicon of 75,000 terms, machine learning and Patented methods to ‘join the dots’ to suggest patterns for potential missed leads, ideas and exploration. OpportunityFinder(R) is used internationally by organisations in the oil and gas, metals and mining, geothermal and hydrogen sectors to support the energy transition.

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#geoscience #digitaltransformation #naturallanguageprocessing #Python #datascience #oilandgas #mining

Text mining for Geo-resources

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 literature. 
Visualise outputs quickly (knowledge graph like no other) in existing applications or build new ones. Combine with structured data. A digital assistant for geoscientists.
Used to identify hidden geo-resources by some of the world’s largest companies.
Supercharge your existing digital transformation initiatives with Patented geoscience technology
#machinelearning
#naturallanguageprocessing
#textanalytics
#geosciences
#geology
#artificialintelligence
#unstructuredinformation
#python
#datamanagement
#digitaltransformation
#digitalization

Text Mining: OpportunityFinder® algorithm extends into Porphyry Copper

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 these, placing a geological lens over unstructured information – turning it into structured information which can be visualised.

This can assist the Geoscientist ‘read’ hundreds of thousands/millions of notes, papers, reports, presentations, logs, maps and sketches for clues to potentially new hidden opportunities. Some ideas and opportunities may only be apparent by combining clues from many documents.

Why now? New approaches may be needed to make a contribution to the change that is likely needed. Limiting global warming to 2 degrees means more electronics, renewable energy such as wind turbines, solar panels and electric vehicles. Wood Mackenzie estimate this means an 85% increase of copper is needed by 2030. Source https://www.woodmac.com/press-releases/next-commodities-supercycle-will-be-driven-by-global-energy-transition/

More: contact@infosciencetechnologies.com

GeoClassifier® machine learning prediction: now trained with quarter of a million labelled geoscience sentences.

GeoClassifier® can automatically classify sentences, paragraphs and documents to geoscience categories and detect well/borehole names in text. GeoClassifier® uses over 250,000 labelled public geoscience sentences to train deep learning models to achieve this. When an organisation licenses the algorithm they also receive the actual training data, so can build and train their own ML models for prediction if they wish.

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