Managers, geologists, reservoir and completion engineers are faced with important challenges and questions when it comes to producing from and operating shale assets. Some of the important questions that need to be answered are: What should be the distance between wells (well spacing)? How many clusters need to be included in each stage? What is the optimum stage length? At what point we need to stop adding stages in our wells (what is the point of diminishing returns)? At what rate and at what pressure do we need to pump the fluid and the proppant? What is the best proppant concentration? Should our completion strategy be modified when the quality of the shale (reservoir characteristics) and the producing hydrocarbon (dry gas, vs. condensate rich, vs. oil) changes in different parts of the field? What is the impact of soak time (starting production right after the completion versus delaying it) on production?

Shale Analytics is the collection of the state of the art data driven techniques including artificial intelligence, machine learning, and data mining that addresses the above questions based on facts (field measurements) rather than human biases. Shale Analytics is the fusion of domain expertise (years of geology, reservoir, and production engineering knowledge) with data driven analytics. Shale Analytics is the application of Big Data Analytics, Pattern Recognition, Machine Learning and Artificial Intelligence to any and all Shale related issues. Lessons learned from the application of Shale Analytics to more than 3,000 wells in Marcellus, Utica, Niobrara, and Eagle Ford is presented in this paper along with a detail case study in Marcellus Shale.

The case study details the application of Shale Analytics to understand the impact of different reservoir and completion parameters on production, and the quality of predictions made by artificial intelligence technologies regarding the production of blind wells. Furthermore, generating type curves, performing "Look-Back" analysis and identifying best completion practices are presented in this paper. Using Shale Analytics for re-frac candidate selection and design was presented in a previous paper [1].

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