Simulation and Optimization of CO2 Huff-and-Puff Processes in Tight Oil Reservoirs
Bibliographic record
Abstract
Abstract As one of the unconventional resources, tight oil has become one of the most important contributor of oil reserves and production growth. The successful commercial production of tight oil is mainly reliant on the advancement in horizontal drilling and multistage hydraulic fracturing technique. Development of tight oil reservoirs remains in an early stage. Primary oil recovery factor in these reservoirs is very low, leaving substantial volume of oil trapped underground due to the low porosity, low permeability characteristic of tight oil reservoirs. Thus, investigation of enhanced oil recovery methods is more than imperative in tight oil reservoirs. CO2 Huff-and-Puff technology has been effectively applied in conventional reservoirs and can be tailored to adapt for the characteristics of tight oil reservoirs. In this study, the performance of water flooding in tight oil reservoir is studied and compared with that of the CO2 Huff-and-Puff process. Sensitivity analysis demonstrates that the performance of CO2 Huff-and-Puff is more sensitive to the length of gas injection and production step in each cycle, compared to the soaking time. The CO2 Huff-and-Puff process is optimized and an adaptive CO2 Huff-and-Puff process is conducted for tight oil reservoirs after primary production. Simulation results show that the adaptive cycle length CO2 Huff-and-Puff process can improve the incremental oil recovery by 11.1% over a fixed cycle length process. Finally, the inter-well interference during CO2 Huff-and-Puff is studied, and it is found that a multi-well asynchronous CO2 Huff-and-Puff pattern can improve the incremental oil recovery by 31.6% over that of a synchronous pattern.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".