On the evaluation of crude oil oxidation during thermogravimetry by generalised regression neural network and gene expression programming: application to thermal enhanced oil recovery
Bibliographic record
Abstract
Enhancing oil recovery using in-situ combustion (ISC) is an attractive alternative, especially for heavy crudes. During ISC, part of the hydrocarbon is pyrolysed/oxidised, which generates heat and deposits fuel in the combustion front. In this study, crude reactions during ISC are modelled after their thermogravimetry thermo-oxidative profiles using advanced machine learning systems. The model inputs include the weight per cent of asphaltenes, resins, and °API gravity of the oil as well as the heating rate and the temperature. Four types of artificial neural networks (ANNs); namely multilayer perceptron (MLP), generalised regression neural network (GRNN), cascade-forward neural network (CFNN), and radial basis function (RBF) neural network, were employed to develop models for accurate prediction of the weight per cent of residual crude oil based on 2289 experimental data points. Moreover, three optimisation algorithms; including Bayesian Regularisation (BR), Levenberg–Marquardt (LM), and Scaled Conjugate Gradient (SCG) were applied in the training step of MLP and CFNN to improve the prediction ability. GRNN provided the most accurate prediction with ∼2.3% overall average absolute per cent relative error and coefficient of determination of 0.9983. GRNN model is reliable for crude oils with °API gravity of 5–35 and up to 820°C. Lastly, a mathematical correlation was developed to estimate the residual crude oil from thermogravimetry analysis using gene expression programming (GEP). GEP also predicted the thermo-oxidative profile with high accuracy. On the basis of sensitivity analysis, residue formation during crude oil oxidation was impacted the most by the temperature, oil °API gravity, and asphaltenes content, respectively. The Leverage approach identified 2.9% of the data points as doubtful.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".