Integration

Integration

Smart Data Solutions' thorough understanding of E&P assets has enabled it to be at the forefront of data integration – the delivery of a unified view of trusted data from disparate sources hosted in a variety of formats using proven technologies.

Integration is beneficial to the rapid, comprehensive review of E&P data helping to support the decision making process. An increasingly valuable tool ideally suited to the current volume of m&a activity in the energy sector, enabling organizations to quickly grasp the value of data sets across multiple organizations.

Smart Data Solutions' digitalization strategy leverages its significant experience and unique taxonomy of classification developed on 50+ years’ company experience in geoscience data generation and analysis. This taxonomy is utilized to construct learning models used to train machine learning tools, which enable rapid, accurate classification results to be achieved from random unstructured data.

Although text analytics are a useful tool for certain document types, invaluable geoscience data, often found in the form of logs, charts, diagrams, graphs and maps, are typically not suitable for such techniques. Sophisticated machine learning technology, utilizing learning models developed from our vast collections of previously classified documents offers a solution. This technology greatly reduces the time required to accurately classify large document sets and the extraction of additional learned meta data.

Once a document, report or item is classified the technology can then be tasked to locate and extract target groups of data and information. The extracted data are run through a probability algorithm and assigned a statistical percentage of accuracy, this is subsequently reviewed by an experienced E&P technical resource and the learning machine “taught” to analyze and learn from the data. Once the learning process has defined the required data accurately, the data is ready to be loaded to an accessible integrated dataset, a final QC step is conducted to assure standards and nomenclature are applied to ensure consistency and validity for future similar analytics.

Across the diverse range of geoscience data, we use a combination of different machine learning and artificial intelligence tools and techniques. An ETL process brings the data together into a data science ready consistent geoscience data model.

The improvement of confidence in the data of organizations that is gained via integration is helping to drive reductions in cost and risk – essential to the well-being of the E&P sector.

Machine learning systems open up access to la...

Kerry Blinston | Henri Blondelle
©2017 SEG
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