Benefit of Wettability Change Near the Production Well in SAGD
Bibliographic record
Abstract
Abstract In a SAGD process, what are the benefits of changing formation wettability near and surrounding a production well from water-wet to oil-wet? Are these benefits, if any, strictly near-wellbore effects or are they reservoir scale? Based on our experimental experiences we investigated these issues numerically. The study indicated that:changing wettability near the production well appeared to have more than near-well effect;in a water-wet reservoir, an oil-wet zone near and surrounding the production well would noticeably shorten the fluid communication time;it would increase oil production rate, at least during early stage of production;water appeared to be partially blocked by the oil-wet zone andthe larger the oil-wet zone, the more apparent these effects were. Introduction The concept of enhancing oil production by changing the wettability surrounding the production well from water-wet to oil-wet in a water-wet reservoir is described in a recent US patent(1). Its applications were demonstrated in lab experiments conducted at Alberta Research Council. In order to examine whether this concept is applicable to field scale SAGD operation, we conduct this numerical study using CMG's STARS. Our studies include:History match the lab test results to validate the numerical model and to learn about possible key parameters;Study the potential impacts of altering wettability near production well on SAGD operation using field scale numerical simulations. HISTORY MATCH OF EXPERIMENTAL RESULTS The lab experiments were done in a 2D visualization cell. A schematic diagram of the rectangular model, 60 cm wide, 21 cm high and 3 cm thick, is shown in Figure 1. The model was packed with sand having a permeability of 300 Darcy and a porosity of about 35%. It was saturated with water. The water was then displaced with Cold Lake bitumen to complete the model saturation. Other parameters are listed in Table 1. The "water-wet" experiment was done with all waterwet sand. The "oil-wet" experiment was done with bottom portion of the cell filled with oil-wet sand. The thickness of the oil-wet zone was 4 cm. The oil wettability was achieved by coating asphaltenes onto the sand grains. Computer Modelling Group's STARS simulator was used for the numerical studies. A rectangular 40 × 1 × 21 grid system, as shown in Table 2, was used to represent the actual physical model cell. The upper and the lower horizontal wells are located in grid blocks (1:20,1,15) and (1:20,1,20), respectively. They are treated as two horizontally placed cylindrical holes with 3/8" diameter and 30 cm long. Therefore, porosities in these grid blocks containing the wells were recalculated according to the following formula: Equation (1) (Available in full paper) It resulted: Ki = 673632.41Darcy, Kj = 92.06Darcy, Kk = 226.11Darcy The calculations are summarized in the Appendix. Bitumen viscosity at various temperatures was measured separately, as listed in Table 3. Its extension to higher temperature by "double-log" fitting is shown in Figure 2.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 teacher head, 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".