Sfile focuses on solving complex challenges with big data by combining powerful compute engines with machine learning algorithms to deliver actionable insights from unstructured data. Sfile's solutions translate fragmented information into structured data through efficient analysis of internal and external well files. Its Virtual Wellfile Database is able to search across the entire repository for all files related to a desired well or set of wells with filtering and classification capabilities. Using artificial intelligence-driven clustering engines and bots, the software extracts, normalizes, and validates information from disparate data sets. By improving the quality of geologic, drilling, and completion data, Sfile enables companies to develop more accurate digital models for future operations.

Enterprise Software, Software as a Service, and Consulting Services

Enterprise wide AI Driven data surveillance and data mining platform to accelerate data driven organizations.

• Auto-labeler uses automated bots to crawl through and then organize relevant data sets
• The above ability does not alter original files, but means that data scientists will spend less time trying to access information
• AI tool that transforms complex documents like drilling and completions reports into a series of normalized facts.
• Using unstructured text data to build a predictive analytics models that can help de-risk prospects
• Full normalization of all fluids and chemicals being pumped downhole

Mineral.ai - a modern model for meeting the current challenges of the E&P industry

Mineral.ai’s primary imperative is to advance E&P operators’ ability to meet its downsized operational capabilities while improving performance through machine learned models across the three constructs that Sfile has identified as critical to the E&P industry.

• Comprehensive well lifecycle data

• Completions design and economics optimization

• Legal contract optimization and compliance

Machine learning across multiple non-correlated operators enhances machine learning experiences and improves model performance. Mineral.ai allows for data to be propagated horizontally, targeting each construct, and vertically integrating these constructs together without violating the rights of any data owner.

Mineral.ai advantages:

• a formal approach to its machine learning systems across all features (Inference Models, Tensor-Flow, Neural Networks)

• elastic computing to scale properly, avoiding performance issue with scale

• encoding of patterns, consequently no client data is ever at risk

Mineral.ai benefits:

• a leaner organization where the human capital is focused on productivity rather than information, where machine learned solutions integrate seamlessly with data

• the evolution towards near real-time continuous improvement for operations

• improve reliability by vetting constructs independently from potential bias present in a single operator’s data