In-Situ Combustion Experience in Heavy Oil Carbonate
Bibliographic record
Abstract
Abstract In-situ Combustion (ISC) has the potential to be the most effective enhanced oil recovery (EOR) method for heavy oil recovery. However, it is considered a high risk failure rate process since it is hard to predict the combustion reactions between complex crude oil and heterogeneous rock matrices. If the reservoir rock is reactive, like carbonates, the risk factor of ISC increases. This research studies the ISC performance for different rock matrices found in Alberta, Canada. The reaction kinetics analyses were carried out by Thermogravimetric Analysis/Differential Scanning Calorimetry (TGA/DSC) on bulk oil, and its Saturates, Aromatics, Resins, and Asphaltenes (SARA) fractions at a constant heating rate (20°C/min). To better understand the combustion behavior of bulk oil, pseudo blends of SARA fractions were prepared and systematic TGA/DSC tests were run on these blends. Furthermore, the impact of reactive porous media on fuel formation mechanism was investigated by using clays and carbonates. The reaction kinetic analyses on bulk oil and SARA fractions suggest that asphaltenes, resins, and aromatics fractions go into endothermic reactions at elevated temperatures, still the asphaltenes fraction generates the greatest amount of heat. The two-component interaction of asphaltenes with other fractions produces less heat than the combustion of asphaltenes alone. The three- component pseudo blend prepared by mixing asphaltenes-resins-aromatics produced the highest amount of heat and we believe that in bulk oil combustion saturates by acting as an ignitor provides the necessary heat to initiate the reactions among aromatics, resins, and asphaltenes fractions. Moreover, the effect of reactive porous media on reaction kinetics of bulk oil were investigated at a constant heating rate (20°C/min). Carbonates and clays were used. Since the clays act like a catalyst, heat generation was observed lower with clays than carbonates and carbonates required more heat for successful combustion. While the heating rates during in-situ combustion are generated naturally, in TGA/DSC tests, a constant heating rate is applied. In this study, we used 20°C/min as the heating rate. If the same heating rate during ISC cannot be reached, then, the heat generation cannot be as high as reported in this manuscript. Thus, it is important to extend the systematic TGA/DSC analyses presented in this paper. Tests should be conducted at varying heating rates on different oil samples and on their SARA fractions.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".