Simulations of In-Situ Upgrading Process: Interpretation of Laboratory Experiments and Study of Field-Scale Test
Bibliographic record
Abstract
Summary In–situ upgrading (IU) is a promising method of improved viscous– and heavy–oil recovery. The IU process implies a reservoir heating up and exposure to a temperature higher than 300°C for a time period long enough to promote a series of chemical reactions. The pyrolysis reactions produce lighter oleic and gaseous components, while a solid residue remains underground. In this work, we developed a numerical model of IU using laboratory experience (kinetics measurements and core experiments) and validated the results by applying our model to an IU field–scale test published in the literature. Finally, we studied different operational conditions in a search for energy–efficient configurations. In this work, two types of IU experimental data are used from two vertical–tube experiments with Canadian bitumen cores (0.15 and 0.69 m). A general IU numerical model for the different experimental setups has been developed and compared with experimental data, using a commercial reservoir–simulator framework. This model is capable of representing the phase distribution of pseudocomponents, the thermal decomposition reactions of bitumen fractions, and the generation of gases and residue (solid) under thermal cracking conditions. Simulation results for the cores exposed to a temperature of 380°C and production pressure of 15 bar have shown that oil production (per pseudocomponent) and oil–sample quality were well–predicted by the model. Some differences in gas production and total solid residue were observed with respect to laboratory measurements. Computer–assisted history matching was performed using an uncertainty–analysis tool with the most–important model parameters. To better understand IU field–scale test results, the Shell Viking pilot (Peace River) was modeled and analyzed with the proposed IU model. The appropriate gridblock size was determined and the calculation time was reduced using the adaptive mesh–refinement (AMR) technique. The quality of products, the recovery efficiency, and the energy expenses obtained with our model were in good agreement with the field test results. In addition, the conversion results (upgraded oil, gas, and solid residue) from the experiments were compared with those obtained in the field test. Additional analysis was performed to identify energy–efficient configurations and to understand the role of some key variables (e.g., heating period and rate and the production pressure) in the global IU upgrading performance. We discuss these results, which illustrate and quantify the interplay between energy efficiency and productivity indicators.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".