Novel Approach to Determining Unconsolidated Reservoir Properties: Fabric and Flow in Oil Sands
Bibliographic record
Abstract
URTeC 1581573 Unconsolidated reservoirs are reservoirs that differ from the traditional rock reservoir model and are inherently complex. It is well known that hydrocarbon recovery is strongly influenced, not only by the fabric and arrangement which exist in the producing reservoirs but is also governed by the fluid interfacial properties. A large study on fabric and reservoir characteristics including properties which influence flow was carried out on oil sands from the estuarine environment of the McMurray Formation from the Athabasca region in Canada. The approach of this study was to view oil sands material as unconsolidated material and in keeping with the initial fabric analysis findings, reservoir material was studied according to fabric components such as coarse material (quartz grains), fine material (silts and clays), bitumen and voids. Methods used for the overall study included: field analysis, thin section analysis, point counting analysis, image analysis, sedimentation studies, SEM, microCT analysis, NMR and core analysis. From the initial 2D fabric analysis, the coarse components of the host material were determined to be fine to medium sized quartz grains embedded in the micromass and in some locations clay bridges were observed between grains. Clays can obstruct the water envelope around the grains and these features differ from the established water wet model. Grain size variations and differences in porosity occur in the host and bioturbated material. Fabric analyses provide key details regarding reservoir characteristics and provide insight into the quality of payzones. Analysis of fabric components from cores samples representing three different estuarine depositional environments and initial findings are presented and discussed. Morphological characteristics of coarse components (quartz grains) along with mechanical features, such as etching on the grain surfaces, observed in SEM suggested the samples were from a transitional environment such as estuarine, agreeing with details logged from the core. The core samples were subjected to microCT scanning at a resolution of 9 microns and 3D pore network models were created from which pore morphology along with key engineering properties related to modeling flow were obtained. Reservoirs fabric characteristics along with reservoir engineering properties are easily obtained from this new approach and methods, which enhance the understanding of reservoir quality.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".