Development of scaling criteria and numerical simulation study of steam flooding process
Bibliographic record
Abstract
Canada contains reserves of oil sand and heavy oil resources considered to be the largest amount of unconventional hydrocarbons deposited in unfavorable conditions. It needs more efforts and technological advancement to recover oil from such reserves. Steam flooding enhanced oil recovery technique is applied for more than 70% of heavy oil reservoirs to extract the oil. Threedimensional (3D) displacement model can represent an appropriate approach and model for the steam flooding process. However, their physical limitations make it impossible to duplicate the real behavior of a reservoir in larger scale. So, it is important to develop scaling criteria for depicting the actual fluid behavior for unconventional reservoirs. Scaled physical models have the unique advantage of capturing all physical phenomena occurring in a particular process by transforming the parameters into dimensionless numbers. This concept is applicable to fluid flow through porous media, where continuous alteration of rock and fluid properties can be characterized by various dimensionless numbers. In this study a set of dimensionless groups were developed using both inspectional and dimensional analyses. The new groups of dimensionless numbers can be used to characterize the reservoir rock and fluid properties for better explanation of complex rock/fluid phenomena for the steam flooding process. It should be noted that the complete set of scaling criteria is very difficult to satisfy. Therefore, some of the similarity groups must be relaxed in order to satisfy the most important parameters of the specific reservoir activities. The choice of which requirements to relax depends on the particular process being modeled. Scaling of the phenomena considered to be least important to a particular process might be relaxed without significantly affecting the major features of the process. The choice of an approach depends on the importance of the phenomena that are not scaled by that approach. Major scaling groups were found by applying different elimination techniques. The effect of those dominant dimensionless groups on recovery was evaluated through the study of process controlling parameters. A new group which is called Dykstra-Parsons coefficient is introduced to incorporate the reservoir heterogeneity. A combined dimensionless group was proposed to characterize and evaluate the performance and found to have the largest effect on oil recovery. Sensitivity analysis of scaling numbers is performed to find out the relative effect of each dimensionless numbers on oil recovery. Finally, a numerical simulation study is performed to quantify the effect of steam quality and permeability variations for different reservoirs. This research work leads to the development of a procedure that can be applied to design a steam flooding EOR process. This process allows the assessment of different parameters to aid in the selection of optimum additive concentration to account for the uncertainties due to reservoir heterogeneity. The process is flexible; it can be applied to wide range of reservoir types as there exists a physical commonality between laboratory and field scale.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".