Understanding Reservoir Architectures and Steam-Chamber Growth at Christina Lake, Alberta, by Using 4D Seismic and Crosswell Seismic Imaging
Bibliographic record
Abstract
Summary EnCana Corporation's Christina Lake Thermal Pilot Project located 170 km south of Fort McMurray, Alberta, Canada, uses steam-assisted gravity drainage (SAGD) technology to recover bitumen from the Lower Cretaceous McMurray formation. This paper presents an analysis of time-lapse and crosswell seismic data, as part of an overall study integrating different disciplines and technologies, to understand the effects of geology on SAGD-process performance in the pilot area. A 3D baseline survey was conducted at the start of the pilot in 2001, and two follow up surveys were conducted in 2004 and 2005. In addition, six crosswell seismic profiles were acquired by placing both sources and receivers in the vertical wellbores. The goal of the seismic surveys was to better understand steam-chamber growth and reservoir architecture by detecting lithology changes, including the occurrence and distribution of mudstone stringers. Data from the surveys, especially from the crosswell profiles, indicated significant reservoir heterogeneity, and helped to characterize reservoir architecture in the pilot area more accurately. Analysis of seismic data (both 4D and crosswell) showed steam-chamber growth and oil recovery to be influenced strongly by reservoir geology. Steam-chamber growth is especially affected by the presence of low-permeability facies in the vicinity of the SAGD well pairs. Our analysis indicates that these reservoir heterogeneities have contributed to the creation of areas within the reservoir that have been unaffected by steaming operations to date. These findings are in agreement with flow-simulation results and collectively contribute significantly to the planning of future developments.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".