Production Optimization in a Water-Alternating-Gas (WAG) Process Using Smart Proxy Modeling
Bibliographic record
Abstract
Summary This study focuses on production optimization in a water-altering-gas (WAG) process for the Norne E-segment reservoir through smart proxy modeling (SPM). SPM is an innovative approximation of numerical reservoir models that implements pattern recognition, feature engineering, and machine learning techniques to achieve computational efficiency. There are two types of SPMs constructed in this study. One is the grid-based which can predict the parameters at the grid level, such as fluid saturations in the individual grids. The other type is the well-based SPM which predicts the cumulative oil production of the individual wells. Grid- and well-based SPMs can be used to monitor saturation variations and optimize oil production in the WAG process, respectively. Moreover, this work introduces novel techniques, including sequential sampling, feature engineering, average feature ranking, and the implementation of convolutional neural network. The integration of well-based SPM with particle swarm optimization can effectively lead to optimizing the WAG design parameters, providing valuable insights. Notably, time comparisons underscore the substantial time savings achieved by SPM in optimization tasks compared to numerical simulators. This study highlights the significant potential of SPM in reservoir engineering, offering an efficient and accurate approach to predictions and optimization processes.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".