Integration of 4D Seismic Data in Reservoir Characterization with Facies Parameter Uncertainty
Bibliographic record
Abstract
Reservoir exploration and production are always conducted in presence of geological uncertainty that is an inevitable result of incomplete data and heterogeneity at all scales. Modeling subsurface geology based on limited data is subject to uncertainty and its accurate assessment plays a key role in resource estimation and reservoir management decision making. Canadian oil sand reservoirs are the third largest oil reserves in the world and play a key role in the economy of Canada. There are many challenges and technical details associated with the enhanced oil recovery technologies that are required to produce high-viscosity oil. This increases the importance of an accurate model of geological uncertainty as a necessary input for the exploration planning and reservoir management. An accurate assessment of geological uncertainty requires the modeling workflow to consider (1) all available sources of data to be reproduced and (2) model parameter uncertainty to be included. The geological uncertainty is then represented by multiple geostatistical realizations that can be used simultaneously for optimal reservoir management decision making. In this thesis, a practical framework is developed to improve the model of geological uncertainty. A realistic model of geological uncertainty requires parameter uncertainty associated with the input statistical parameters to be considered. Limited well data does not permit unambiguous specification of the required parameters. These parameters often have a global and widespread influence on the resources and reserves. One of the main contributions of this research is to quantify prior proportion uncertainty for categorical variables such as facies in presence of a trend. The trend model provides additional information about the subsurface geological setting. Facies modeling is of great significance for reservoir characterization as it explains a major aspect of spatial heterogeneity and geological uncertainty. Large-scale flow patterns are often controlled by the spatial arrangement and continuity of facies because, the variability of permeability in between facies is more significant compared to that within facies. Each source of data provides information about the reservoir with different scales and levels of precision. Although there are well-established geostatistical techniques for stochastic simulation of the reservoir conditioned to static data, practical integration of information obtained from dynamic data remains a major challenge. The changes in reservoir properties including fluid saturation, pressure and temperature can be monitored by dynamic data to obtain information about the large scale connectivity and quality of fluid flow within the reservoir. A novel methodology is proposed for effective integration of dynamic data into the geological modeling workflow. This methodology is based on geostatistical enforcement of anomalies identified from dynamic sources of data such as 4D seismic. All geostatistical realizations are updated to honor the information obtained from the dynamic data that become available during the reservoir life cycle.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".