Development of a data‐driven fuzzy screening model for enhanced oil recovery methods using an adaptive weighting system
Bibliographic record
Abstract
Screening of enhanced oil recovery (EOR) methods is a main prerequisite for EOR planning and design. In this study, an integrated data‐driven screening model (DDSM) is developed to improve EOR screening using the combined capabilities of the fuzzy expert approach (FEA) and support vector regression (SVR) techniques. In this study, EOR field data from the past 40 years were reviewed to generate an updated and reliable EOR criteria table as a basis to construct a fuzzy screening model. The DDSM was evaluated to determine the quantitative screening and ranking of EOR methods using seven field datasets, including the fast forecasting of the nominated EOR methods. In order to improve screening performance, a fuzzy model was integrated using 4 SVR models to predict the adaptive weights of the screening parameter for decision making. The SVR models can predict the recovery factor (RF) of EOR methods including gas, chemical, steam, and combustion to calculate the adaptive effective weight of the screening parameters. The SVR models were trained with datasets generated from simulations of the EOR process. The absolute average error (AAE) of the SVR models from the simulation varied within the range of 0.078–0.095 for the prediction of the RF. The DDSM results were compatible to the data published in other literature. In addition, the developed model can provide comparable results to common screening software. The results showed improvements due to the adaptive weighting system on the EOR methods’ screening for the studied reservoirs relative to the fuzzy engine with constant weights. The presented integrated model can guide the screening process to select the efficient EOR method in practical applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".