Performance of CO2 Foam Huff and Puff in Tight Oil Reservoirs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 CO 2 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 CO 2 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 CO 2 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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it