Prediction of Shale-Gas Production at Duvernay Formation Using Deep-Learning Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
Summary 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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it