History Matching of Frequent Seismic Surveys Using Seismic Onset Times at the Peace River Field, Canada
Bibliographic record
Abstract
Abstract We present a novel and efficient approach to integrate frequent time lapse (4D) seismic data into high resolution reservoir models based on seismic onset times, defined as the calendar time when the seismic attribute crosses a pre-specified threshold value at a given location. Our approach reduces multiple time- lapse seismic survey data into a single map of onset times, leading to substantial data reduction for history matching while capturing all relevant information regarding fluid flow in the reservoir. Hence, the proposed approach is particularly well suited when frequent seismic surveys are available using permanently embedded sensors. Our history matching workflow consists of two stages: global and local. At the global stage of history matching, large-scale features such as regional permeabilities, pore volumes, temperature and fluid saturations are adjusted to match seismic and bottomhole pressure data using a Pareto-based multiobjective history matching workflow. Rather than an artificial subdivision of the domain, the history matching regions are naturally defined based on an eigen-decomposition of the grid Laplacian and a spectral clustering of the second eigenvector (fiedler vector). The global updating is followed by local history matching whereby cell permeabilities are adjusted to further refine the history match using semi- analytic, streamline-based model parameter sensitivities. The power and efficacy of our proposed approach is illustrated using synthetic and field applications. The field example involves steam injection into a heavy oil reservoir at Pad 31 in the Peace River Field (Alberta, Canada) with daily time lapse seismic surveys recorded by a permanently buried seismic monitoring system (Lopez et al. 2015). In our specific application, we have used time lapse data (in terms of two-way travel time) from a Cyclic Steam Stimulation (CSS) cycle in the pad with a total of 175 seismic surveys. With a single onset time map derived from this data we were able to capture the propagation of pressure and saturation fronts and significantly improve the dynamic model through the estimation of permeability distribution, fluid saturation evolution and swept volume. With this methodology we correctly identified and further refined the location of stimulated zones as inferred before from reservoir engineering judgement and manual adjustments aiding better understanding of CSS behavior in the studied field. The results clearly demonstrate the effectiveness of the onset time approach for integrating large number of seismic surveys by compressing them into a single map. Also, the onset times appear to be relatively insensitive to the petro elastic model but sensitive to the steam/fluid propagation, making it a robust method for history matching of time lapse surveys.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".