Investigation of Cyclic Gas Injection in the Gas Condensate Window of Unconventional Reservoirs
Bibliographic record
Abstract
Abstract Reservoirs such as Duvernay, Montney and Eagle Ford are segmented in different areas, ranging from predominantly dry gas regions, to wet gas and oil regions. Extensive research has focused on the application of enhanced recovery methods in the oil window of such reservoirs. In this paper, we discuss the application of enhanced recovery in the gas condensate window, with the objective to investigate the impact of diffusion on liquid dropout and vaporization on a matrix level. The Maxwell-Stefan equations were used to account for diffusion phenomena in the medium, and a phase behavior routine was implemented including nano-confinement effects. Numerical experiments were performed to evaluate the range of variability of recovery factors in a cyclic gas injection scenario. Methane was used as injection gas, and 1, 2 and 4 cyclic injection stages were modelled at the scale of a matrix block. Sensitivity was performed using a leaner and a richer gas composition, as well as two levels of permeability (50 and 100 nD). This allowed detailed investigation of time and location of occurrence of liquid dropout through saturation profile maps. Due to molecular partitioning, the phase envelope shifts as production proceeds, generating an accumulation of heavier hydrocarbons in the medium. Since injectivity is reduced in lower permeability media, injection pressure ramp up needs to be controlled to prevent condensate blockage. As a result, longer cycles are needed in the lower permeability case to achieve equivalent recovery. Liquid dropout is recurrent during production after each injection cycle, however, increasing the number of stages resulted in overall lower liquid saturation during subsequent production. Additionally, saturation profile maps indicate that the locus of condensate banks varies between each stage. As more injection stages are performed, a leaner gas remains in the vicinity of the fracture boundary and the condensate bank is formed further into the matrix block. Although more cycles improved recovery of heavier hydrocarbons, faster cycles resulted in lesser penetration of the injection gas into the porous medium. This behavior is more accentuated in the lower permeability cases. Nevertheless, recovery of heavier fractions is still higher compared to the primary production base case. Sensitivity studies will dictate the optimum number of stages for a fixed timeframe. In this work we use a combination of physics involved in flow in tight reservoirs to demonstrate how saturation profile maps can be used as a tool to improve enhanced recovery strategy.
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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) | 0.000 | 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 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".