Coupling Geomechanics and Transport in Naturally Fractured Reservoirs
Bibliographic record
Abstract
Large amounts of hydrocarbon reserves are trapped in naturally fractured reservoirs which arechallenging in terms of accurate recovery prediction because of their joint fabric complexity andlithological heterogeneity. Canada, for example, has over 400 billion barrels of crude oil in fracturedcarbonates in Alberta, most of this being bitumen of viscosity greater than 106 cP in the GrosmontFormation, which has an average porosity of about 13-15%. Thermal methods are the most commonexploitation approaches in such viscous oil reservoirs which, in the case of steam injection, are associatedwith up to 275-300°C temperature changes, leading to considerable thermoelastic expansion. Thistemperature change, combined with pore pressure changes from injection and production processes, leadsto massive effective stress variations in the reservoir and surrounding rocks. The thermally-induced(thermoelastic) stress changes can easily be an order of magnitude greater than the pore pressure effectsbecause of the high intrinsic stiffness of the low porosity limestone and bounding strata. Study of thesestress-pressure-temperature effects requires a thermo-hydro-mechanical (THM) coupling approach whichconsiders the simultaneous variation of effective stress, pore pressure, and temperature and theirinteractions. For example, thermal expansion can lead to significant joint dilation, increasing themacroscopic, joint-dominated transmissivity by an order of magnitude in front of and normal to thethermal front, while reducing it in the direction tangential to the heating front. This leads to stronginduced anisotropy of transport processes, which in turn affects the spatial distribution of the heatingarising from advective heat transfer.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".