Feasibility of Combining Gas-Assisted Gravity Drainage GAGD with Water Injection in a Thick Heterogeneous Carbonate Reservoir Using 3-D Experimental Physical Model and Simulation
Bibliographic record
Abstract
Abstract Enhancing oil recovery from thick heterogeneous carbonate reservoirs poses great challenges, be it through waterflooding or gasflooding. In this study, a three-layer 3D physical model was established based on artificial core technology and scaling criteria, taking into account the mineral composition, petrophysical properties, pore structure, wettability, heterogeneity, dynamic, bottom aquifer, interlayers, and well pattern. Experiments were carried out under circumstance of high temperature and high pressure. A numerical simulation model incorporated with local geological characteristics was built for subject well-area unit. The development process and ultimate oil recovery of conventional water injection, gas-assisted gravity drainage (GAGD), edge-bottom water injection (EBWI), and a method of combination of GAGD and EBWI proposed in this paper, named CGE were studied. For this kind of reservoirs, the experiments showed that, oil recovery of GAGD and EBWI were 40.01% and 37.11%, respectively, higher than conventional waterflooding process, while CGE had the most potential with oil recovery 43.85%. The numerical simulation showed that, oil recovery of CGE was 49.87% and the water cut was extremely low in the first 11 years. GAGD significantly increased Bond Number and had strong water control ability, but it relied on the pressure accumulation, thus there was a non-effective period. EBWI had no obvious non-effective periods, and thus enhancing oil recovery in the early stage through forced gravity displacement. CGE is a feasible and efficient combination development method, although there is still a problem of water control in the later stage. In addition to the research of EOR methods, this paper also helps broaden the means of researching carbonate reservoirs and design more schemes, based on the successful implement of the experiments.
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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 | 0.001 | 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".