Leveraging US Unconventional Data Analytics Learnings in Vaca Muerta Shale Formation.
- Alejandro Primera (Primera Resources) | Hector Klie (DeepCast.ai) | Arturo Klie (DeepCast.ai) | Maria Quesada (Primera Resources)
- Document ID
- Society of Petroleum Engineers
- SPE Europec featured at 81st EAGE Conference and Exhibition, 3-6 June, London, England, UK
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- Data Analytics, Unconventional, Artificial Intelligence, Vaca Muerta, Machine Learning
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- 183 since 2007
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Data Analytics is progressively gaining traction as a viable resource to improve forecasts and reserve estimations in most prospective US shale plays. Part of those learnings has been tested for the reserves and resources estimation of the next worldwide top-class shale play, Vaca Muerta formation in Argentina. In this work, we rely on advanced artificial intelligence methods to automate workflows for production forecasting and reserve estimation in the Vaca Muerta formation. To achieve this goal, we develop a computational platform capable of integrating several sequential operations into a single automated workflow: (1) data gathering; (2) data preparation; (3) model fitting and forecasting and, (4) EUR estimation. As new data becomes available, each of these steps is performed automatically. The proposed platform also integrates with advanced business intelligence tools that aid at facilitating graphical interpretation and communication among specialists and decision makers. Hence, the suggested workflow can deliver production forecasts several magnitudes faster than traditional workflows while maintaining accurate and engineering sound results. Having fast and reliable forecast turnarounds allow for timely tracking key differences and commonalities among multiple shale plays to facilitate informed decision strategies in unconventional field evaluation and development.
|File Size||2 MB||Number of Pages||16|
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