Comprehensive Analysis for Production Prediction of Hydraulic Fractured Shale Reservoirs Using Proxy Model Based on Deep Neural Network
Bibliographic record
Abstract
Abstract The proposed method is a model using data-driven modeling. As many databases are available, we propose new alternatives to production analysis using exploratory data analysis (EDA) and deep neural network (DNN) techniques. It is an economical and time-saving model and workflow that can replace and alternate the traditional physics-based modeling. In this study, field data used about 1239 wells from Montney shale formation in Alberta, Canada. Through EDA, we verified the correlation between each variable of datasets and data distribution, and 1143 wells were used as training data for DNN model through data preprocessing such as outlier analysis, scatter plot, etc. The database used for the study was collected through database of GeoLOGIC systems ltd. The data here is largely divided into three features. In the case of well information, completion and fracturing data, production data and well information, there are true vertical depth, latitude, longitude, and well direction. total proppant placed volume. The production data used as the dependent variable in the DNN model is cumulative gas production for 12-month. Comprehensive machine learning techniques were applied to further improve predictive performance and prevent overfitting problem. First, we analyzed EDA and statistical analysis with various variables related to productivity. Second, developed model were designed and data preprocessing was performed to select input variables that are highly correlated with the output variables. Third, through variable importance analysis based on random forest (RF), gradient boosting tree (GBM), extreme gradient booting (XGBoost). We found a parameter that is highly correlated with cumulative gas production. Finally, we proposed the applicability of categorical variables. Then we performed hyperparameter optimization, a difficult problem for the DNN model. This paper is Not only is this paper intended to propose a robust model, but it also provides the insight that petroleum engineers can use as proxy models to replace complexity reservoir modeling or estimate approximate productivity of shale reservoirs before drilling.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".