A machine learning model for predicting multi-stage horizontal well production
Bibliographic record
Abstract
In this study, a hybrid convolutional-recurrent neural network (c-RNN) is evaluated for making predictions of the five-year cumulative production profiles in multistage hydraulically fractured wells. The model was trained by using a combinations of completion parameters, rock mechanical properties, and well spacing and completion order for each stage of 74 wells in the Montney Formation in Alberta. The prediction accuracy of the various combinations was measured by using the mean average percent error and mean absolute error generated through the leave-one-out method. The best combination of inputs was found to be the rock mechanical properties surrounding each perforation cluster, the proppant amount used for every stage, and the spacing and completion order of neighboring wells. The novelty of this study is that the input variables used are at the stage level rather than the average of the entire well. The accuracy of the model was found to increase exponentially as the production of multiple wells was aggregated. The approach yields insights for planning new well drills in fields with existing development since it provides the ability to run multiple field development scenarios without having to spend capital.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".