Robust ensemble learning frameworks for predicting minimum miscibility pressure in pure nitrogen and gas mixtures containing nitrogen–crude oil systems: Insights from explainable artificial intelligence
Bibliographic record
Abstract
Abstract Miscible gas injection techniques, such as nitrogen injection, are among the attractive enhanced oil recovery (EOR) techniques for improving oil recovery factors in oil reservoirs. A key challenge in implementing these techniques is accurately determining the minimum miscibility pressure (MMP). While laboratory experiments offer reliable results, they are costly and time‐consuming, and existing empirical correlations often have moderate accuracy, which limits their practical use. In this study, robust ensemble methods, namely light gradient boosting machine (LightGBM), extra trees (ET), and categorical boosting (CatBoost), were implemented for modelling MMP in pure nitrogen and gas mixtures containing nitrogen–crude oil systems. An extensive experimental database involving 164 data points was used to elaborate on the predictive models. The findings revealed that the proposed ensemble methods achieved outstanding accuracy in training and test datasets, with ET consistently outperforming the other models. The ET model provided the most consistent MMP predictions with a total root mean square error (RMSE) of only 0.3197 MPa and a determination coefficient of 0.9976. Additionally, the ET model exhibited very small RMSE values across a broad range of operational conditions. Furthermore, the Shapley additive explanations (SHAP) method further validated the interpretability of the ET model, allowing for clear insights into the impact of input features. This study underlines the significant potential of machine learning to enhance MMP prediction in pure nitrogen and gas mixtures containing nitrogen–crude oil systems, thereby aiding in the appropriate design of this kind of EOR process and supporting better decision‐making in reservoir management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".