Data-driven multidimensional static-dynamic models for the prediction of coalbed methane performance based on numerical models
Bibliographic record
Abstract
The accurate prediction of coalbed methane (CBM) is often challenged by geological conditions, engineering technologies, and data gaps. However, with an interdisciplinary field application of intelligent algorithms, data-driven models can effectively predict its productivity characteristics. Here, this work develops data-driven models to predict CBM static and dynamic productivity. Based on the regression relationships of static datasets, models are developed based on intelligent algorithms including multiple linear regression, random forest, support vector regression, and gradient boosting regression (GBR). Due to the temporal variations and nonlinearity of dynamic datasets, models based on recurrent neural network, long short-term memory (LSTM), and gated recurrent unit (GRU) have been developed. The results show that GBR demonstrates the best performance according to the evaluation metrics. In importance permutation, GBR prioritizes matrix porosity, hydraulic fracture intrinsic permeability and fracture permeability, and these three parameters account for nearly 90% of its contributions. Both LSTM and GRU demonstrate strong prediction capabilities in dynamic productivity. Moreover, data-driven models require substantially less time computing. LSTM possesses stronger prediction performance than GRU in the gas adsorption mass and reservoir pressure field evolution. In conclusion, this work provides a multidimensional prediction and evaluation system for the productivity prediction of unconventional resources.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".