Dynamic Real-Time Production Forecasting Model for Complex Subsurface Flow Systems with Variable Length Input Sequences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary Production time-series forecasting for newly drilled wells or those with limited flow and pressure historical data poses a significant challenge, and this problem is exacerbated by the complexities and uncertainties encountered in fractured subsurface systems. While many existing models rely on static features for prediction, the production data progressively offer more informative insights as production unfolds. Leveraging ongoing production data can enhance forecasting accuracy over time. However, effectively integrating the production stream data presents significant model training and updating complexities. We propose two innovative methods to address this challenge: masked recurrent alignment (MRA) and masked encoding decoding (MED). These methods enable the model to continually update its predictions based on historical data. In addition, by incorporating sequence padding and masking, our model can handle inputs of varying lengths without trimming, thereby avoiding the potential loss of valuable training samples. We implement these models with gated recurrent unit (GRU) and evaluate their performance in a case study involving 6,154 shale gas wells in the Central Montney Region. The data set encompasses 39 production-related features, including reservoir properties, completion, and wellhead information. Performance evaluation is based on root mean square error (RMSE) to predict 36-month production from 200 wells during testing. Empirical findings highlight the efficacy of the proposed models in handling challenges associated with variable-length input sequences, showcasing their superior performance. Our research emphasizes the value of including shorter time-series segments, often overlooked, to improve predictive accuracy, especially in scenarios with limited training samples.
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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 | 0.001 | 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