Coupling Spatial and Frequency Uncertainty Analyses in Reservoir Modeling
Bibliographic record
Abstract
Abstract Judy Creek is a large carbonate reservoir in the giant Swan Hills oil field, located in Alberta, Canada. After nearly 50 yr of production, an updated reservoir model was required for planning further development. In this chapter, we discuss reservoir modeling and uncertainty evaluation of Judy Creek. A critical basis for field development planning is the estimate of hydrocarbon pore volume initially in place, wherein porosity is a key parameter. How a three-dimensional porosity model is populated using well-log data can have a significant impact on the volumetric estimate. We developed a workflow to accurately model subsurface pore space and volumetric uncertainty. The new model built using this workflow honors the depositional characteristics of the reef complex and, thus, more realistically represents subsurface heterogeneities. Previous models underestimated the pore space because of an inference bias from the well-log data to the three-dimensional model. The new model honors the frequency statistics from the well-log data and, thus, improves the estimation of the pore volume. This study included geologic and petrophysical uncertainty analyses to evaluate volumetric uncertainty, resulting in the new model that has more pore volume than the previous models, which has implications for the field development planning of the Judy Creek reservoir. Judy Creek A pool (or Judy Creek) is a large, isolated, carbonate reef complex in the giant Swan Hills oil field ( Hemphill et al., 1970), located in west-central Alberta, Canada. Liquid hydrocarbon is stratigraphically trapped in the Late Devonian Swan Hills Formation, which has reservoir interval thickness of about 70 m (230 ft). It is areally about 14 km (9 mi) long in the north–northeast direction, nearly 13 km (8 mi) wide in the north, and approximately 6 km (4 mi) wide in the south (Figure 1A). This is a mature field with about 350 wells drilled and nearly 6000 m (19,685 ft) of cores. The reservoir contains eight third- to fourth-order transgressive-regressive (T-R) depositional sequences, including R1, R2, R3, R4, a sealing layer, R5A, R5B, and R5C ( Wendte and Muir 1995; Wendte and Uyeno, 2005). The buildup complex shows a distinct backstepping architecture, where each succeeding depositional stage is areally smaller than the preceding one (Figure 1A, B). Other detailed discussions on rock properties can be found in Jenik and Lerberkmo (1968)and Imperial Oil (1963, unpublished report).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".