Integration of Seismic and Resistivity Methods to Map the Distribution of Oil Sand Bodies and Water Sand Channels in the McMurray Fm: Mineable and SAGD Case Studies
Bibliographic record
Abstract
Abstract High-resolution geophysical methods capable of imaging the subsurface geology in three-dimensions are proving to be important tools for the delineation of bitumen-rich zones and water saturated channels in the McMurray Formation of Northeast Alberta. At an operating oil sands mine the feasibility of water disposal in the basal water sands of the McMurray Formation was investigated. Seismic reflection data, collected originally for oil sands delineation, was supplemented with drilling, seismic, resistivity, and borehole logging surveys to map the sand channels. Isopach maps of the basal water sand and structural contour maps of the Devonian limestone surface delineate a channel that is the target for the horizontal wells. At in-situ steaming operations (Steam Assisted Gravity Drainage) the horizontal well pairs must be located in the zones that can exploit the maximum connectivity between bitumen saturated sections of the McMurray Formation. Characterizing the geological complexity of the McMurray Formation (channel geometry, shale lenses, Devonian collapse features) is commonly done using a dense pattern of exploration wells. Geophysical methods offer a more cost-effective way of targeting the exploration wells to maximize the information derived and minimize the surface disturbance. High-resolution seismic data (2D and 3D) can be used to effectively map the structure of the McMurray Formation and underlying Devonian units. Non-seismic methods such as resistivity and electromagnetic imaging can be used to derive physical properties that relate to porosity, bitumen saturation, and pore water salinity. For both steaming (100 - 300 m) and mineable (< 50 m) depths, a combination of vertical exploration wells, seismic reflection, and 2D electrical imaging is currently being used to reduce exploration risk. Future developments include imaging between vertical and horizontal boreholes and time-lapse imaging of steam fronts. Introduction The Athabasca oil sands in NE Alberta (Canada) are one of the largest deposits of bitumen in the world (Figure 1). The bitumen resides in unconsolidated Cretaceous sands at depths of between 10 m and 400 m below ground surface. The mineable deposits (< 50 m depth) comprise only 10% of the total volume of oil sands. The remaining 90% (over 189 billion m3 of oil), require the application of in-situ recovery processes such as Steam Assisted Gravity Drainage (SAGD) to yield economical reserves. Over 65% of the deposits have an average pay thickness greater than 15 m. The McMurrayFm is a predominantly continental sequence of uncemented sands and shales unconformably overlying Devonian marl and limestone with highly variable relief. The overlying Clearwater Fm and Grand Rapids Fm are interbedded silts and clays. Geophysical methods including borehole logging (Figure 2) and seismic reflection surveying (Siewert et al., 1998) have been the mainstay of exploration programs for both the shallow and the deeper deposits. In the last 10 years, geophysical methods such as transient electromagnetics (TEM), airborne magnetics, and DC-resistivity have been tested on the oil sand leases. CASE STUDIES In two recent programs, approximately 100 line-km of 2D electrical resistivity tomography (ERT), and 2D highresolution seismic data were collected to map oil sand and water sand channels.
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 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".