Mechanisms for Huff-n-Puff Cyclic Gas Injection into Unconventional Reservoirs
Bibliographic record
Abstract
Abstract Unconventional oil reservoirs such as the Eagle Ford have had tremendous success over the last decade, but challenges remain as flow rates drop quickly and recovery factors are low; thus, enhanced oil recovery methods are needed to increase recovery. Interest in cyclic gas injection has risen as a number of successful pilots have been reported; however, little information is available on recovery mechanisms for the process. This paper evaluates oil swelling caused by diffusion and advection processes for gas injection in unconventional reservoirs. To accurately evaluate gas penetration into the matrix, the surface area of the hydraulic fractures needs to be known, and in this work, three different methods are used to estimate the area: volumetrics, well flow rates and linear fluid flow equations. Fick's law is used to determine the gas penetration depth caused by diffusion, and the linear form of Darcy's law is used to find the amount from advection. Then, with the use of swelling test information from lab tests, we are able to approximate the amount of oil recovery expected from cyclic gas injection operations. During the gas injection phase, gas from the fractures can enter the matrix by both advection (Darcy driven flow) and diffusion. We estimate that over 200 million scf of gas can enter the matrix during a 100 day injection/soak period. Using typical reservoir and fluid parameters, it appears that 40% is due to diffusion and 60% is due to advection. Sensitivity analysis shows that these numbers vary considerable based on the parameters used. Analytical models also show that during a 100 day production timeframe, over 14,000 stock tank barrels (STB) of oil can be produced due to huff-n-puff gas injection. Both gas injection and oil recovery amounts are compared to recent Eagle Ford gas injection pilot data, and the model results are consistent with the field pilot data. By determining the relative importance of the different recovery mechanisms, this paper provides a better understanding of what is happening in unconventional reservoirs during cyclic gas injection. This will allow more efficient injection schemes to be designed in the future.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 teacher head, 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".