A Multiphase, Multicomponent Reservoir-Simulation Framework for Miscible Gas and Steam Coinjection
Bibliographic record
Abstract
Summary The solvent thermal resource innovation process (STRIP), a downhole steam-generation technology, has the capacity to show improved recovery factors with a significantly reduced environmental footprint compared with traditional thermal-enhanced-oil-recovery (TEOR) methods, most notably by delivering all the combustion heat to the pay zone. In this effort, a quarter-symmetry inverse-five-spot model and a multiphase, multicomponent reservoir-simulation framework were used to simulate the STRIP technology. Commercial simulators such as STARS - Thermal and Advanced Processes Reservoir Simulator [Computer Modelling Group Ltd. (CMG), Calgary, Alberta, Canada; CMG 2015b] often use the K-value approach to simulate TEOR. However, the method cannot simulate STRIP's carbon dioxide (CO2) and steam coinjection because the K-value method does not consider miscible gas injection. On the other hand, CMG's GEM - Compositional and Unconventional Simulator (CMG 2015a) includes the effects of miscible gases but does not provide comprehensive support for steam-injection processes, which are better handled by STARS. The novel simulation framework developed here leverages and combines the individual strengths of STARS (thermal features) and GEM (compositional features). In this framework, STARS simulated steam injection (but cannot directly simulate the effects of CO2) and was the governing model that synchronized temperature, pressure, and phase saturations for two parallel iterations of the GEM models (GEM-1 and GEM-2) at each timestep. Immiscible methane (CH4) was added to GEM models to maintain gas saturations equivalent to the STARS model. GEM-1 simulated hot-water and CH4 injection, but at increased rates to yield a pressure field and gas saturations equivalent to STARS. A final run of GEM-1 injected both CO2 and hot water and demonstrated the expected increase in oil production. Calibrated injection rates from GEM-1 were specified in GEM-2 to ensure equivalence of the pressure field. Next, the GEM-2 model also simulated hot-water and CH4 injection, but matched both water and oil productions along with oil saturations from the final GEM-1 run by altering relative permeabilities. Finally, the updated relative permeabilities were fed back to STARS, and iteration proceeded. Results from this framework were verified against a STARS steam-injection simulation. Finally, when considering coinjection of CO2, STRIP's superior performance was demonstrated through increased oil recovery and a lower steam/oil ratio (SOR).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".