Case Study: History Matching Using Enhanced Permeability Modifications (EPMs) for Court Bakken Heavy Oil Reservoir
Bibliographic record
Abstract
Abstract Computer modeling of heavy oil reservoirs has become progressively more challenging in order to represent geological reality and its impact on fluid flow. A method has been developed in which completion, production, and injection information, coupled with the static well data, are used to model enhanced permeability channels that have been created around the wells as a result of production and/or injection. Court heavy oil field located in West Central Saskatchewan, Canada is a Bakken heavy oil reservoir (17°API, in-situ viscosity of ~155 c.p). The field, which has been under an active waterflood depletion strategy for the last 20 years, has achieved a recovery factor to date of ~ 24%. The geological complexities found in the Bakken Formation at Court introduce many challenges for history matching. It was discovered that successful history matching required the introduction of enhanced permeability modifications (EPMs). The EPM regions were superimposed over the geological model. Flow simulations using the EPMs region concept resulted in a significant improvement in the overall quality of the history match at both the group and field levels. Sensitivity studies were also undertaken to determine the minimum EPM requirement to gain the history match. This paper describes how to use completion, production and injection data in the flow model to predict the high permeability zones in the vicinity of a wellbore. As it will be discussed in more detail, this technique allows for the achievement of a reasonable history match using a Black Oil reservoir simulator. The model was history matched to liquid rate, water cut, and pressure. The simulation model has been used to evaluate the current waterflood and predict the possible future enhanced oil recovery (EOR) opportunities for this field.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".