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Wednesday, March 2 • 4:30pm - 4:50pm
Data Analytics Approaches & Tools: SweetSpot Identification Using Machine Learning for Unconventionals

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Reducing cost in well drilling and completion while improving the productivity of unconventional (UNC) reservoirs is vitally important. The physics model based simulation technologies that achieved great success in exploring conventional reservoirs have not been as effective when applied to UNC plays. The best methodology to determine where to drill and how to complete remains elusive. It is challenging to accurately and rapidly characterize the high EUR regions of a UNC play with early exploration data to provide guidance on where to drill new wells.

Machine learning (ML) techniques are data driven and can incorporate pertinent information from input data sources and learn the underlying complex and hidden inter-relationships and patterns. ML provides a promising means to tackle the complex exploration and production problems arising from UNC plays, where the underlying physics is not well known, or where the physical models are highly uncertain.

This contribution describes ML methodologies to tackle the particular challenge: Based on available exploration and production data (often scarce) from a play, can we accurately predict the emerging top productive areas, the so called sweetspots? The workflow has two stages (i) data integration and preprocessing, which generate a set of feature variables (or predictors) from original data; (ii) Predictive modeling, where a predictive model is built based on the predictors and production data using machine learning algorithms. The workflow has been applied to unconventional datasets for sweetspot identification. The results show that the methodology provides promising potentials.

avatar for Mingqi Wu

Mingqi Wu

Statistical Consultant, Shell
Statistical Consultant / Data Scientist

Wednesday March 2, 2016 4:30pm - 4:50pm
BioScience Research Collaborative Building (BRC), Room 103

Attendees (10)