Prediction of Shale-Gas Production at Duvernay Formation Using Deep-Learning Algorithm
- Kyungbook Lee (Korea Institute of Geoscience and Mineral Resources) | Jungtek Lim (SmartMind) | Daeung Yoon (Hanyang University and Chonnam National University) | Hyungsik Jung (Seoul National University)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- December 2019
- Document Type
- Journal Paper
- 2,423 - 2,437
- 2019.Society of Petroleum Engineers
- decline curve analysis, shale gas production, deep learning, long short-term memory, shut-in period
- 100 in the last 30 days
- 340 since 2007
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Decline-curve analysis (DCA) is an easy and fast empirical regression method for predicting future well production. However, applying DCA to shale-gas wells is limited by long transient flow, a unique completion design, and high-density drilling. Recently, a long short-term-memory (LSTM) algorithm has been widely applied to the prediction of time-series data. Because shale-gas-production data are time-series data, the LSTM algorithm can be applied to predict future shale-gas production. After information for 332 shale-gas wells in Alberta, Canada, is obtained from a commercial database, the data are preprocessed in seven steps, including cutoffs for well list, data cleaning, feature extraction, train and test sets split, normalization, and sorting for input into the LSTM model. The LSTM model is trained in 405 seconds by two features of production data and a shut-in (SI) period from 300 wells. The two-feature case shows a better prediction accuracy than both the one-feature case (i.e., production data only) and the hyperbolic DCA, where the three methods are tested on unseen data from 15 wells. The two-feature case can predict future production rates according to the SI period and provide a stable result for available time-series data.
|File Size||1 MB||Number of Pages||15|
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