Diffusion-Dominated Proxy Model for Solvent Injection in Ultra-Tight Oil Reservoirs
Bibliographic record
Abstract
Abstract Enhanced oil recovery (EOR) by solvent injection offers significant potential to increase recovery from shale oil reservoirs, which are typically between 3 and 7% OOIP. The rather sparse literature on this topic typically models these tight reservoirs based on conventional reservoir processes and mechanisms, such as by convective transport using Darcy's law, even though there is little physical justification for this treatment. The literature also downplays the importance of the soaking period in huff'n'puff In this paper we propose for the first time a more physically-realistic recovery mechanism based solely on diffusion-dominated transport. We develop a diffusion-dominated proxy model assuming first-contact miscibility (FCM) to provide rapid estimates of oil recovery for both primary production and the solvent huff'n'soak'n'puff (HSP) process in ultra-tight oil reservoirs. Simplified proxy models are developed to represent the major features of the fracture network. The key results show that diffusion-transport only can reproduce the primary production period within the Eagle Ford shale and model the HSP process well, without the need to use Darcy's law. The mechanism for recovery is based solely on density and concentration gradients. Primary production is a self-diffusion process, while the HSP process is based on counter-diffusion. Incremental recoveries by HSP are several times greater than primary production recoveries, showing significant promise in increasing oil recoveries. We calculate ultimate recoveries for both primary production and for the HSP process, and show that methane injection is preferred over carbon dioxide injection. We also show that the proxy model, to be accurate, must match the total matrix contact area and the ratio of effective to total contact area with time. These two parameters should be maximized for best recovery.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".