On the Role of Molecular Diffusion in Modelling Enhanced Recovery in Unconventional Condensate Reservoirs
Bibliographic record
Abstract
Abstract Molecular diffusion is a transport mechanism often ignored in conventional, non-fractured multi-component petroleum upstream simulations due to the predominance of convection. In unconventional fractured reservoirs, diffusion plays a vital role in hydrocarbon production. A "shale" reservoir is characterized by thin, ultra-tight matrix blocks surrounded by natural or induced fractures. This creates conditions in which diffusion fluxes could be significant. In ultra-tight formations, convection is a slow process, and the presence of thin blocks surrounded by fractures increases the contact area, both of which favors diffusion. In this paper, we discuss the application of cyclic gas injection to enhance recovery in tight reservoirs in the gas condensate window. A fully implicit model is implemented with the objective to investigate the impact of diffusion on liquid dropout and vaporization on a matrix level. Diffusion fluxes are implemented considering a gradient in total chemical potential as driving force. Additionally, since capillary forces are significant in ultra-tight formations, phase equilibria calculations are modified to account for nano-confinement effects. Sensitivity is performed on matrix block size and injection gas composition (pure C1, a mixture of C1 and CO2, and a mixture of C1, C2 and C3), and the role of diffusion is evaluated for each scenario. As gas is injected, the composition of heavier hydrocarbon fractions in the gas phase significantly increases due to vaporization of condensate. Molecular diffusion helps to spread composition banks. As a result, liquid dropout is delayed during the subsequent production stages. Heavier fractions remain in the gas phase for longer periods, which ultimately enhances its recovery. In addition to that, retention of heavier fractions due to condensate dropout is intensified as the size of the matrix block increases. Longer matrix blocks result in lower swept length for the same number of cycles. As a result, liquid dropout occurs earlier because feed of gas at in-situ composition diffuses from the center of the matrix block towards the fracture boundary. We demonstrate that heavier components recovery is more affected by molecular diffusion than lighter components. Furthermore, it is observed that molecular diffusion strongly influences time and location of occurrence of liquid dropout in tight gas condensate reservoirs. Implementation of a rigorous model that includes convection, diffusion, adsorption and phase change allowed to investigate the commingling effects of different physics involved in enhanced recovery in unconventional reservoirs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".