Physics-Constrained Production Forecasting with Direct Fracture Characterization by Field Data
Bibliographic record
Abstract
Abstract Fractured reservoir simulations often rely on abstract fracture parameters—such as length, height, and width—that are difficult to validate with direct measurements, introducing biases in production forecasting. This research presents Assimilation Neural Network (AssimNet), a hybrid data-driven and simulation-proxy framework that completes the history-matching process during training. By directly integrating real-world measurements (e.g., wellhead, completion, logging data) into simulation inputs, AssimNet ensures direct characterization from field data and provides physics-constrained production forecasting. AssimNet leverages a simulation proxy to capture the underlying physics of numerical models. It encodes simulation inputs and the field measurements into the shared latent space, and a shared decoder reconstructs simulation inputs from the field-derived latent representation, ensuring physically consistent and interpretable outputs. Evaluated on 1,488 wells from the Montney Shale, AssimNet consistently outperforms traditional data-driven models in data-sparse environments. AssimNet is the first hybrid model that directly translates field measurements into physics- consistent simulation parameters optimized for the given simulator. It offers a more transparent, efficient, and interpretable approach to reservoir characterization and production forecasting, enabling faster, data-driven decisions in subsurface engineering. Unlike traditional physics-informed neural networks or neural operators, AssimNet tightly couples production data with numerical simulation, minimizing reliance on abstract assumptions and iterative tuning. By embedding simulation within a data-driven architecture, it enhances generalization, improves accuracy in data-sparse regions, and reduces uncertainty in fractured reservoir forecasting.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".