Reliable Connectivity Evaluation in Conventional and Heavy Oil Reservoirs: A Case Study From Senlac Heavy Oil Pool, Western Saskatchewan
Bibliographic record
Abstract
Abstract Evaluating interwell connectivities can provide important information for reservoir management by identifying flow conduits, barriers, and injection imbalances. The multiwell productivity index (MPI)-based method is a recently-developed tool to infer interwell connectivity based on injection/production data. Previously, the MPI method worked well when tested on several synthetic cases under ideal conditions. In this paper, we show the application of the method on a field case, the heavy-oil Senlac field in Saskatchewan. Nonideal but common conditions, such as the unavailability of injector and producer BHP's and short term and frequent producer shut-ins, may have a large affect on the results of the MPI method. By using the similarities of the MPI method and another connectivity evaluation procedure, the capacitance model (CM), we define a new connectivity parameter that is less sensitive to nonideal conditions. Dramatic changes of the mobility ratio in heavy oil fields still affect the performance of the model but, by applying a dynamic multiwell productivity index, we reduce this problem. Temporary shut-in of the producers within the sampling interval also leads to less accurate estimation of connectivity parameters and production rates. By applying an equivalent skin model and using the average rate formula, we can overcome this problem. Compared to connectivity parameters defined in previous studies, the one defined here is more robust and less sensitive to the specific circumstances that are common in field cases. The dynamic model suggested in this paper helps us to model cases with variable mobility ratios more accurately. Applying the modifications suggested here improves the fit between predicted and actual production. Using the new connectivity parameters in Senlac, we observed good agreement between the connectivity map and the geological features of the reservoir. The procedures and modifications described in this paper enable us to use the MPI method more effectively in field cases with common nonideal conditions, including heavy oil waterfloods. Insensitivity of the model to changing well conditions provides a more versatile tool to analyze field data. Furthermore, if we choose to use the CM instead of the MPI, we find that using information from the MPI can benefit the application of the CM. Applying these approaches, we can have a more reliable understanding of the reservoir heterogeneity and quick prediction of reservoir performance to optimize the waterflood.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".