Surrogate-Based Analysis of Chemical Enhanced Oil Recovery – A Comparative Analysis of Machine Learning Model Performance
Bibliographic record
Abstract
Abstract Optimizing decision and design variables for Chemical EOR is imperative for sensitivity and uncertainty analysis. However, these processes involve multiple reservoir simulation runs which increase computational cost and time. Surrogate models are capable of overcoming this impediment as they are capable of mimicking the capabilities of full field three-dimensional reservoir simulation models in detail and complexity. Artificial Neural Networks (ANN) and regression-based Design of Experiments (DoE) are common methods for surrogate modelling. In this study, a comparative analysis of data-driven surrogate model performance on Recovery Factor (RF) for Surfactant-Polymer flooding is investigated with seven input variables including Kv/Kh ratio, polymer concentration in polymer drive, surfactant slug size, surfactant concentration in surfactant slug, polymer concentration in surfactant slug, polymer drive size and salinity of polymer drive. Eleven Machine learning models including Multiple Linear Regression (MLR), Ridge and Lasso regression; Support Vector Regression (SVR), ANN as well as Classification and Regression Tree (CART) based algorithms including Decision Trees, Random Forest, eXtreme Gradient Boosting (XGBoost), Gradient Boosting and Extremely Randomized Trees (ERT), are applied on a dataset consisting of 202 datapoints. The results obtained indicate high model performance and accuracy for SVR, ANN and CART based ensemble techniques like Extremely Randomized Trees, Gradient Boost and XGBoost regression, with high R2 values and lowest Mean Squared Error (MSE) values for the training and test dataset. Unlike other studies on Chemical EOR surrogate modelling where sensitivity was analyzed with statistical DoE, we rank the input features using Decision Tree-based algorithms while model interpretability is achieved with Shapely Values. Results from feature ranking indicate that surfactant concentration, and slug size are the most influential parameters on the RF. Other important factors, though with less influence, are the polymer concentration in surfactant slug, polymer concentration in polymer drive and polymer drive size. The salinity of the polymer drive and the Kv/Kh ratio both have a negative effect on the RF, with a corresponding least level of significance.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".