Development And Application Of Emulsion-based Conformance Control Method For Enhanced Bitumen Recovery By Steam-assisted Gravity Drainage
Bibliographic record
Abstract
Abstract Bottom water, which is defined as a zone below the base of bitumen pay with high water saturation, has been encountered in many currently operated steam-assisted gravity drainage (SAGD) projects. It has been widely accepted that the bottom water lying under bitumen in SAGD act as a heat sink which is a result of the much higher mobility of water compared to that of the bitumen. This has a detrimental effect on the project economy because of the resulted greater steam-oil ratio (SOR) and less oil recovery. Therefore, it is essential to reduce the mobility of bottom water in SAGD operations to avoid the water coning and steam loss to the bottom water. In this study, emulsion-based conformance control treatment is introduced and investigated in oil sands reservoirs to reduce the mobility of bottom water to avoid the water coning and steam loss to the bottom water during the SAGD operations. To accurately simulate the emulsion plugging performance during the SAGD process, it is essential to optimize the emulsion flow model in water saturated porous media to better describe the flow mechanism. There are three main mathematical models applied to describe the dynamics of emulsion flow in porous media: the bulk viscosity model, the retardation model and the filtration model. In this paper, the newly proposed emulsion flow model was optimized on the basis of the conventional filtration model with considering the process of emulsion droplets trapped and released by the pore throats. Three different types of emulsion flow tests were collected and analysed from previous publications, including permeability reduction of an emulsion in a specific sandpack, an emulsion slug injection followed by water injection in a specific sandpack and conformance control performance in parallel-sandpack. The optimized emulsion flow model can successfully simulate all of the three different types of emulsion flow process and have a good agreement with the experimentally obtained results. A new method for predicting permeability reduction by emulsion plugging was firstly determined by fully incorporating with the emulsion and sandpack properties. By successfully simulating the emulsion plugging process in a specific sandpack followed by water injection, a desired conformance control performance can be predicted by injection of a prepared emulsion in a specific parallel-sandpack. This may give an instruction on optimal emulsion design in the field application. The optimized emulsion flow model shows a wide application in different types of experiments and flexible control on the parameters, showing a huge potential for the reservoir-scale simulation.
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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".