The Value of Rate, Temperature and Pressure Surveillance in Steam Assisted Gravity Drainage SAGD Process
Bibliographic record
Abstract
Abstract Steam Assisted Gravity Drainage (SAGD) is used in West Canadian Sedimentary Basin (WCSB) for heavy oil recovery and numerical simulation is quantitative tool, which can be used to evaluate the SAGD performance and test developments options. This paper starts with the assumption that a single model is unlikely to be correct, and cannot represent the range in the outcomes due to the reservoir complexity and fluid heterogeneity. An alternative is to generate a number of models through an assisted history matching (AHM) process, with the intent of providing realistic estimates of the remaining uncertainty and quantifiable options to reduce the uncertainty. For a new piece of surveillance to add value, our working hypothesis is that the new surveillance should not be correlated with existing surveillance to avoid redundancy, and that the reduction in the range of acceptable models provides a way of quantifying the value. For our SAGD study, we are evaluating if temperature and pressure surveillance in observation wells has value, and how they should be included in the objective function. A synthetic two-dimensional SAGD model was used in the simulation, with a known truth case against which we can test the efficiency of different types of surveillance to recover the true reservoir performance. Three observation wells are distributed through the model with five stations for temperature and pressure measurements. To test the value of the temperature measurements and observer well, a blind searching algorithm was use initially to avoid introducing a search bias that could distort the correlation. Rate, pressure and, temperature misfits were calculated for each experiment. The model with less than 5% rate misfit were filtered and run for several years in prediction with the same depletion plan. The improvement in the lower confidence bound for the value of the field used as the benefit of the surveillance. A second test was to use an exploitative search technique on a simple arithmetic objective function. As expected, the global minimum did not represent the truth case performance, though the truth case was in the intersection of the surveillance constraints of matching pressure, rate and temperature within noise. The correlation between temperature and pressure surveillance show some correlation, such that there is some redundancy, yet the analysis of the models resulting from both parts showed that pressure and temperature surveillance have reduced the uncertainty in the future outcomes by 84% in cumulative oil production and 82% in the NPV. The proposed workflow used here will allow quantifying the potential of adding a new surveillance to the field and/or specifying the number and locations of the required observation wells which properly capture the steam chamber development and pressure propagation throughout SAGD reservoir.
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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) | 0.000 | 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) | 0.000 | 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".