Machine learning and nanoparticles for enhancing condensate recovery in gas condensate reservoirs
Bibliographic record
Abstract
Abstract Condensate buildup near the wellbore during gas production leads to reduced gas flow and decreased reservoir productivity. This study presents a hybrid approach that integrates experimental wettability alteration using nanoparticles (SiO 2 , CaCO 3 , Al 2 O 3 ) with advanced machine learning (ML) models to enhance condensate recovery in gas condensate reservoirs. Laboratory core flooding experiments were conducted under reservoir‐representative conditions, and the resulting datasets were analyzed using six ML algorithms: support vector machine (SVM), random forest (RF), artificial neural network (ANN), linear regression (LR), least squares boosting (LSBoost), and Bayesian methods. Among all models, SVM demonstrated the highest predictive performance across all nanoparticle types. For CaCO 3 , the model achieved R 2 = 0.998, RMSE = 0.473, and MAPE = 0.491% under residual condensate saturation (Re), and R 2 = 0.987, RMSE = 0.258, MAPE = 2.458% under residual oil saturation (Sor) conditions. For SiO 2 , SVM yielded R 2 = 0.997, RMSE = 0.569, MAPE = 0.551% (Re) and R 2 = 0.991, RMSE = 0.569, MAPE = 0.551% (Sor). For Al 2 O 3 , the model obtained R 2 = 0.978, RMSE = 1.776, MAPE = 1.682% (Re) and R 2 = 0.966, RMSE = 0.725, MAPE = 4.762% (Sor). Overall, CaCO 3 nanoparticles provided the highest recovery enhancement, improving condensate recovery by up to 18%. The integration of nanoparticle‐assisted EOR with ML‐based prediction offers a powerful strategy for optimizing recovery in complex gas condensate reservoirs.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 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 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".