Gas Pressure Cycling (GPC) and Solvent-Assisted Gas Pressure Cycling (SA-GPC) Enhanced Oil Recovery Processes in a Thin Heavy Oil Reservoir
Bibliographic record
Abstract
In this paper, gas pressure cycling (GPC) and solvent-assisted gas pressure cycling (SA-GPC) were developed as two new and effective enhanced oil recovery (EOR) processes. Eight coreflood tests were conducted by using a 2-D rectangular sandpacked physical model with a one or two-well configuration. More specifically, two cyclic solvent injection (CSI), three GPC, and three SA-GPC tests were conducted after the primary production, whose pressure was declined in steps from Pi = 3.0 MPa to Pf = 0.2 MPa. It was found that the CSI tests had poor performances because of the known CSI technical shortcomings and an additional technical issue of solvent trapping found in this study. Quick heavy oil viscosity regainment resulted in the solvent-trapping zone. In contrast, C3H8-GPC test at a pressure depletion step size of ∆PEOR = 0.5 MPa and C3H8-SA-CO2-GPC test at ∆PEOR = 1.0 MPa had the highest total heavy oil recovery factors (RFs) of 41.9% and 36.6% of the original oil-in-place (OOIP) among the two respective series of GPC and SA-GPC tests. The better performances of these two tests than C3H8- or CO2-CSI test were attributed to the effective displacement of the foamy oil toward the producer in the two-well configuration. Thus the back-and-forth movements of the foamy oil in CSI test in the one-well configuration were eliminated in these GPC and SA-GPC tests. Furthermore, C3H8-GPC test outperformed C3H8-SA-CO2-GPC test in terms of the heavy oil RF and cumulative gas-oil ratio (cGOR) because of the formation of stronger foamy-oil flow and the absence of CO2, which reduced the solubility of C3H8 in the heavy oil in the latter test. In summary, different solvent-based EOR processes were ranked based on the heavy oil RFs as follows: C3H8-GPC > C3H8-SA-CO2-GPC > CO2-GPC > C3H8-CSI > CO2-CSI.
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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 | 0.000 |
| 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) | 0.000 | 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".