Simulations of In-Situ Upgrading Process: Interpretation of Laboratory Experiments and Study of Field-Scale Test
Bibliographic record
Abstract
Abstract In-situ upgrading (IU) is a promising method of improved viscous and heavy oil recovery. The IU process implies a reservoir heating up and exposition to temperature higher than 300°C for long enough time 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 based on lab experiences (kinetics measurements and core experiments) and validated results applying our model to an IU test published it the literature. Finally, we studied different operational conditions searching 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 m and 0.69 m). A general IU numerical model for the different experimental setups has been developed and compared to experimental data, using a commercial reservoir simulator framework. This model is capable to represent the phase distribution of pseudo-components, the thermal decomposition reactions of bitumen fractions and the generation of gases and residue (solid) under the cracking conditions. Simulation results for the cores submitted to 370°C and production pressure of 15 bar, have shown that oil production (per pseudo-component) 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 on the base of the most important model parameters. In order to better understand IU field-scale test results, the Shell's Viking pilot (Peace River) was modeled and analyzed with proposed IU model. The appropriated grid-block size was determined and calculation time was reduced using the adaptive mesh refinement 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. Also the conversion results (upgraded oil, gas and solid residue) from the experiments were compared to 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, 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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".