Fast-Track Completion Decision Through Ensemble-Based Machine Learning
- Han Xue (Schlumberger) | Raj Malpani (Schlumberger) | Shivam Agrawal (University of Texas Austin) | Tomislav Bukovac (Schlumberger) | Arathi L. Mahesh (Schlumberger) | Tobias Judd (Schlumberger)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Characterisation and Simulation Conference and Exhibition, 17-19 September, Abu Dhabi, UAE
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- complex fracture model, machine learning, Numerical simulation, surrogate model, completion design
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- 60 since 2007
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With the advent of high-resolution methods to predict hydraulic fracture geometry and subsequent production forecasting, characterization of productive shale volume and evaluating completion design economics through science-based forward modeling becomes possible. However, operationalizing a simulation-based workflow to optimize design to keep up with the field operation schedule remains the biggest challenge owing to the slow model-to-design turnaround cycle. The objective of this project is to apply the ensemble learning-based model concept to this issue and, for the purpose of completion design, we summarize the numerical-model-centric unconventional workflow as a process that ultimately models production from a well pad (of multiple horizontal laterals) as a function of completion design parameters. After the development and validation and analysis of the surrogate model is completed, the model can be used in the predictive mode to respond to the "what if" questions that are raised by the reservoir/completion management team.
|File Size||1 MB||Number of Pages||13|
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