Evolution and Application of Geoscience Technology to Integrated Reservoir Characterisation for Enhanced Heavy Oil Recovery - Cold Lake Field, Alberta, Canada
Bibliographic record
Abstract
Abstract The Cold Lake heavy oil field in Western Canada was discovered in the late 1950's and contains approximately 5.3x109 m3 (33 billion barrels) of heavy crude bitumen. Commercial production utilizing Cyclic Steam Stimulation (CSS) commenced in 1985. By the end of 2010 over 159x106 m3 (1 billion barrels) of bitumen had been produced. The Cold Lake Heavy Oil field currently produces approximately 24,000 m3/d (150,000 barrels per day). The geological models and concepts that supported early production were based on well log and core data only. Early use of 2D seismic data showed limited utility for imaging the high net-to-gross reservoir. However, the advent of high-resolution 3D and 4D seismic in the early 1990's provided renewed interest in seismic data for imaging thermal processes. These early "4D" surveys were small in area but provided data critical for imaging the steam heated regions (thermal conformance) in the reservoir and managing follow-up drilling and completion strategies. Recent advances in 3D and 4D seismic processing and analysis now allow quantifiable estimations of thermal reservoir conformance to guide enhanced recovery methods and depletion planning. A novel use of microseismic technology was developed in the mid 1990's to assist in the early detection of casing failures and fluid releases into the overlying shales and aquifers to mitigate environmental, safety and economic consequences. Since 1998 microseismic monitoring has been integrated into commercial operations and with our interpretation of depositional geometries to further our understanding of steam migration. Today, Cold Lake has more than 100 dedicated microseismic monitoring wells making it one of the largest microseismic monitoring networks in the oil and gas industry. In the mid 2000's larger, high-frequency 3D seismic surveys were acquired for reservoir characterization. 3D seismic data are integrated field-wide with other data to build predictive geologic models and to assess recoverable bitumen volumes for multiple extraction technologies. These models enable maximum efficiency of resource development while reducing reliance on relatively expensive core data. Several enhancements to the recovery process are in the testing stage today to improve existing development or facilitate future developments in more challenging resource areas. Continued geoscience data collection and integration will drive efficient development of these new opportunities.
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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 | 0.000 | 0.001 |
| 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".