Shale.ai will unlock reduced financial risk, increased recovery rates and leaner logistics
Houston, TX (October 17, 2018)
Maillance® announced today that it has released the shale.ai® machine learning platform for unconventional resources. Deployed using a SaaS model for its licensing and distribution, the shale.ai platform exploits the strength of artificial intelligence (AI), machine learning, and other data science techniques for the management and optimization of production from shale assets.
- Automated data ingestion and cleansing
- Seamless generation of productivity correlations and predictive models
- Interactive design and optimization of unconventional completions
- On-the-fly production profile history matching and forecasting at the well or asset level.
Most advanced tools provided to subsurface teams for reservoir characterization and simulation were optimized for conventional reservoirs and never designed for the long horizontal wells and complex fracture networks required to produce from an unconventional resource.
Developed by petroleum engineers for petroleum engineers, the shale.ai platform provides a decision canvas consisting of graphs, charts and analyses that engineers are accustomed to working with. The user experience does not burden the end user with the complexities of machine learning, allowing the engineer to focus on the task of understanding the reservoir, and less time on becoming a data scientist.
Resolving the inadequacies of existing modeling solutions, shale.ai achieves a step change in workflow time (no process runs for more than a couple of minutes), reduces human bias (measurements are linked directly to predictions), and enables accessibility for more users. Importantly, it is an open platform, allowing integration of customers’ bespoke machine learning algorithms and models.
“Our solutions are guided by an intimate understanding of first principles of geoscience and engineering, to accurately model the subsurface and surface flow of fluids. They are less exclusive on expertise and at least as accurate as conventional techniques. What’s more, our models are built in minutes and run in fractions of seconds”, said Jean-Paul Dessap, CEO of Maillance.
Founded by Jean-Paul Dessap after a rich career at the helm of some of the most ambitious innovation projects at Schlumberger, Maillance (www.maillance.com) is democratizing the use of artificial intelligence across the upstream oil and gas value chain. It leverages breakthroughs and advances in data analytics, big compute, mobility, IoT and machine learning, to deploy E&P workflows that achieve a step change in decision making and automation in hydrocarbon reservoirs.
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