Tight Gas Production Prediction in the Southern Montney Play Using Machine Learning Approaches
Bibliographic record
Abstract
Abstract Recently, the machine learning approach has been used to forecast tight gas production from unconventional resources. However, the performance of machine learning-based predictive models has not been successful with respect to actual field production. The poor performance has been ascribed to several factors, including the relatively few field data and few input data from geological, geomechanical, and operational information. This study uses big data analytics to develop a prediction model for Southern Montney Play in the province of British Columbia, Canada. First, a complete dataset is built, including ten geological, geomechanical, and operational characteristics for 3146 horizontal wells in Southern Montney Play. Then, the relationships between the first-year production and input parameters are evaluated, and controlling factors are identified. Finally, a comparative study of prediction models with distinctive training algorithms is conducted to find the best algorithm for predicting first-year production. The results reveal that the top features that contribute most to tight gas productivity are total injection volume, porosity, and formation pressure. Features with secondary effects are net thickness, fracturing depth, and number of stages. The other features, including permeability, gas saturation, horizontal length, and cumulative proppant injection, are the least related. The Random Forest algorithm with the highest correlation coefficient (R2=0.82) and lowest mean absolute error (MSE=0.15) is picked. The Random Forest-based production prediction matches the observed field production, indicating that the northeastern portion of the study area has the highest estimated tight gas productivity. This procedure can be applied to additional scenarios involving tight gas production and used to guide the future site selection and fracturing job size, thereby achieving effective tight gas development.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".