Performance of CO2 Foam Huff and Puff in Tight Oil Reservoirs
Bibliographic record
Abstract
The challenges associated with unconventional reservoirs are related to their intrinsic nature: extremely low porosity and permeability. Combinations of horizontal wells and multistage hydraulic fracturing techniques have been developed to overcome the production obstacles and unlock the vast amount of oil in place in such formations. However, oil production still exhibits a sharp decline within the first 2 years after the stimulation, leading to an oil recovery of less than 15%. Thus, enhanced oil recovery methods need to be investigated to further increase the production rates and the recovery. In this study, laboratory experiments and numerical simulations were conducted to evaluate the performance of the CO2 foam huff and puff process and its impacts on oil recovery in tight oil formations. More specifically, the foam half-life was measured as a function of surfactant concentration and followed by the foam drainage ratio and its rheological properties in the subsequent tests. Reservoir simulations were conducted using the lab data and the field data collected from Cardium formation. Sensitivity analyses were finally carried out to investigate the effects of controlling variables on the CO2 foam performance. Experimental results revealed that the optimal surfactant concentration was found to be 0.2%, which is the critical micelle concentration point. Simulation results show that CO2 foam huff and puff can increase the oil recovery by more than 11% compared to that of the primary production. Moreover, sensitivity analyses show that the production time, injection time, and soaking time are the main effecting parameters, while the injection rate and the incremental injection rate are less important.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".