Solution-Gas Drive in Heavy Oil: Field Prediction and Sensitivity Studies Using Low Gas Relative Permeability
Bibliographic record
Abstract
Abstract The favourable behaviour of heavy oil reservoirs under solution- gas drive has intrigued the oil industry for a long time. Many mechanistic models have been proposed to explain this behaviour. This paper investigates one of the theories that attributes the low producing GOR and high recovery of heavy oil to low gas mobility. A simulation study was carried out on a commercial black oil simulator to match the "typical" production data of heavy oil fields in Lindbergh and Frog Lake. Sensitivity studies were carried out to investigate the effect of gas relative permeability on oil recovery. The effect of sand production, which is an integral part of heavy oil production in Western Canada, was also investigated. A parameter was used in the simulator to account for the increased permeability due to sand production. The results indicate that the field performance can be matched by using low gas mobility and incorporating improved permeability due to sand production. Neither of them alone was sufficient for matching the field performance. The use of a low gas mobility was successful to explain high pressure-gradients in the field, by acting as a pressure maintenance mechanism, and leading to a high recovery under solution-gas drive. Intruduction PanCanadian has been one of the major operators in the Lindbergh and Frog Lake heavy oil fields of northeast Alberta(1). The production in these fields is from the Upper Mannville group. The producing formations contain oil of 12 – 14 ° API with in situ viscosities of 3,000 – 10,000 cp. The fields have shown recovery much in excess of what can be predicted by applying conventional flow equations. Many heavy oil wells under primary production have produced about 15% of the original oil in place (OOIP) in 10 years(1). The primary oil production in these fields is accompanied by sand production and is called "Cold Production." A typical well in the Lindbergh and Frog Lake fields under depletion has produced an average of 9,200 m3 of oil and about 230 m3 of sand in 1,000 days(1). Several tests have been carried out to determine the mechanism responsible for the favourable behaviour of these reservoirs. Metwally and Solanki(1) presented a simulation model to match the field production behaviour. The authors postulated that reservoir porosity and permeability are increased due to sand production. Additionally, the authors showed that a pressure support mechanism needs to be incorporated to match recovery data; an external aquifer was therefore incorporated into the model. Similar to Metwally and Solanki(1), we use a commercial simulator to match the production data. We show that the use of low values for gas relative permeability results in pressure maintenance and substantial oil production. Accounting for the low gas mobility along with the improved permeability resulted in a match of the field data. Sensitivity studies are reported, which show that both the effects of low gas mobility and improved permeability are required for a match.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".