Analysis of Production History for Unconventional Gas Reservoirs With Statistical Methods
- Srimoyee Bhattacharya (University of Houston) | Michael Nikolaou (University of Houston)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- June 2013
- Document Type
- Journal Paper
- 878 - 896
- 2013. Society of Petroleum Engineers
- 5.5.8 History Matching, 7.6.6 Artificial Intelligence
- 19 in the last 30 days
- 912 since 2007
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Unconventional gas resources have dramatically changed the future energylandscape. Developing these resources involves substantial risk. Such risk canbe mitigated if information gathered at initial stages of the development of afield is used efficiently and effectively to guide future development. Avariety of tools--such as decline-curve analysis (DCA), type-curve analysis,simulator history matching, and artificial intelligence (AI)--is used to thateffect. These tools accomplish partially overlapping but different tasks.Additional tools that could not only facilitate the analysis of historical databut also guide future development would be of value. In this work, we proposean efficient methodology that can use historical production data from existingwells to answer questions such as the following: Which wells will behavesimilarly? Which wells will behave differently from each other or from standardexpectations? Which factors will contribute to these differences? How can datafrom existing wells be used to anticipate the performance of new wells? Theproposed methodology relies on standard principal component analysis (PCA) andprincipal-component regression (PCR). The application of PCA to historicalproduction data from twelve wells in the Holly Branch field quickly identifiedwells with distinct behavior. The subsequent investigation of pressure and thecompletion data for these wells revealed reasons for such distinct behavior.Finally, a simple linear model was built with PCR, with good ability to predictproduction from new wells, as assessed through cross-validation. The value ofthe efficiency offered by the proposed methodology would be much higher forlarger data sets, for which manual analysis of production data is morecumbersome.
|File Size||2 MB||Number of Pages||19|
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