Experimental and Numerical Study of Strategies for Improvement of Cyclic Solvent Injection in Thin Heavy-Oil Reservoirs
Bibliographic record
Abstract
Summary To overcome the problems of slow mixing in the vapor-extraction (VAPEX) process and regaining high oil viscosity in the cyclic solvent process (CSP), we introduce a new process for thin heavy-oil reservoirs known as the enhanced cyclic solvent process (ECSP). In ECSP, two types of hydrocarbon solvents are cyclically injected in two separate slugs: one slug is more volatile (methane) and the other is more soluble (propane or ethane) in heavy oil. In this study, experiments of primary depletion, CSP, cyclic gas-alternating-water (GAW) or inverse water-alternating-gas (WAG) injection, ECSP, and surfactant-enhanced CSP at relatively low-to-intermediate pressures in a visual rectangular sandpack (with a thickness/length ratio of 1:32) filled with crude oil, gas, and brine (replicating the actual field conditions pertaining to a thin reservoir after the primary depletion) are presented. The effect of operational pressure, initial production pressure, more soluble solvent type (propane/ethane), propane-slug size, and initial oil saturation on the ECSP performance are evaluated. Moreover, the effects of well location, initial production pressure, production end pressure, system repressurization using water injection, and adding an oil-soluble surfactant before methane injection on the CSP are investigated. The performance of CSP is compared with that of ECSP, cyclic inverse WAG, surfactant-enhanced CSP, and extended waterflood (EWF). The experimental results indicate that the performance of cyclic solvent injection decreases in this order: ECSP using methane/propane, surfactant-enhanced CSP, cyclic inverse WAG using water/methane, ECSP using methane/ethane, and CSP using methane. ECSP using methane/propane outperforms surfactant-enhanced CSP and cyclic inverse WAG only if a relatively larger propane-slug size (20 to 35%) is injected. The cyclic inverse WAG (using an offset to the CSP well to repressurize the sandpack by water before conducting methane CSP) significantly improves the recovery and its rate, and it reduces the gas requirement. Also, injecting an oil-soluble foaming surfactant before methane enhances the CSP recovery factor (RF) and rate by one order of magnitude, making them comparable with those of ECSP using methane and a large slug of propane. In discussing these results, the significance of and the interplay between various recovery mechanisms in CSP, ECSP, and surfactant-enhanced CSP is highlighted in the order in which they occur during the injection cycle (viscous fingering, phase change and dissolution of solvent, diffusion and convective dispersion, and capillary mixing), soaking (solvent diffusion and mixing, oil swelling, and viscosity reduction), and the production cycle (foamy-oil flow, solution gas drive, and wellbore inflow). The results of this mechanistic analysis of CSP, ECSP, and surfactant-enhanced CSP during the injection, soaking, and production cycles render an improved paradigm for a holistic performance evaluation and understanding of cyclic solvent-injection processes. The interplay between various observed recovery mechanisms reveals various advantages of ECSP, surfactant-enhanced CSP, and cyclic inverse WAG over traditional CSP.
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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".