Sfile AI with Embedded dtSearch Leads to Vastly Better Supermajor Oil Well Lift Failure Identification


“dtSearch gave us the adaptive keyword searching that we need.”
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“The dtSearch Engine works on structured data like SQL databases as well as other raw file data. And it includes advanced text retrieval methods we need such as concept search and fuzzy searching.”
Sfile specializes in machine learning to deliver actionable insights from Big Data. Sfile’s AI algorithms synthesize vast amounts of disparate data for significantly enhanced forecasting. In this example, Sfile used its machine learning algorithms to generate real-world results for one of Sfile’s “Supermajor” energy customers. (The largest 6 worldwide oil and gas companies are called “Supermajors.”)

The Well Management Team at this Supermajor sought to forecast artificial lift failures at oil wells, with the aim of standardizing reliability Key Performance Indicators (KPIs) across all business units. The source data for this study was widely dispersed. While some of the data resided in structured databases, other data was in Microsoft Office file formats and even legacy file formats.
 
The Supermajor’s Well Management Team turned to Sfle to synthesize the data and come up with results and recommendations. Sfile specializes in cognitive computer simulating human neural networks. AI agents and bots use advanced pattern recognition and natural language processing (NLP) to autonomously mine the various data sources.
 
Text search covering both structured and unstructured data sources is integral to Sfile’s data processing work. For both the data recognition / document filters portion of this enterprise, as well as the actual text search portion, Sfile relied on the dtSearch Engine. “dtSearch gave us the adaptive keyword searching that we need,” said Frank D. Perez, CEO, Sfile. “The dtSearch Engine works on structured data like SQL databases as well as other raw file data. And it includes advanced text retrieval methods we need such as concept search and fuzzy searching.”
 
The end result of Sfile’s work was a dramatic increase in KPI reliability for the Supermajor’s Well Management Team’s. Using traditional data analysis, the Well Management Team had uncovered 8,000 failure candidates. Applying Sfile’s advanced analytics, that number exploded to 200,000 failure candidates across the Supermajor’s business units.
 
Sfile’s machine learning algorithms work not only for the oil and gas industry, but across any technical data repositories. Sfile’s AI even works on eDiscovery data. For more information, please email contact@sfile.com or visit the company online at sfile.com
 
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