Determination of Equivalent Elastic Moduli for Coupled Geomechanical-Flow Simulation of SAGD
Bibliographic record
Abstract
Abstract When characterizing stress sensitive reservoirs for reservoir performance prediction, considering flow simulation alone is insufficient; geomechanical and flow responses should be assessed concurrently. Geostatistical techniques are well developed and can be used to build heterogeneous models of reservoir properties as input to subsequent transfer functions such as flow and geomechanical simulators. In the case of conventional flow simulation, each geological realization consists of a structural model, facies model and petrophysical property models (porosity, permeability, saturation, etc.), which are used to solve the appropriate fluid flow equations. Rock mechanical properties play a similar role in geomechanical simulation as petrophysical properties play in fluid flow. Impact of heterogeneity consideration for geomechanical properties on coupled geomechanical flow simulation of SAGD process has been discussed by Khajeh et al. (2011) and they showed that to assess more accurate uncertainty analysis on flow and geomechanical responses of the SAGD process, considering realizations of the rock mechanical properties are required. The well documented Mechanical Earth Model (MEM) is a comprehensive geological model which can be used for coupled geomechanical-flow simulation of the SAGD process; however, homogenous rock mechanical properties are typically considered instead of stochastic models for computational reasons. The geomechanical response of a reservoir is sensitive to the values selected for geomechanical properties and it is practical to consider an equivalent homogeneous continuum such that geomechanical responses obtained from the homogenized model matches the geomechanical response of the truth (heterogeneous) model. Different analytical homogenization techniques are developed to determine equivalent elastic moduli (EEM), but the majority of these techniques consider specific configurations of facies and do not work well for complex spatial configurations such as the sand/shale sequences typical of the McMurray formation of Alberta-Canada. Considering sand geomechanical properties to be representative of the reservoir is a common approach. Moreover, a mixing rule averaging approach could also be used for determination of EEM. In this work, the accuracy of these various EEM methodologies are compared to EEM values obtained numerically by optimizing the geomechanical response of the reservoir. By knowing the vertical displacement profile (VDP) at the top of the reservoir for the fine scale (truth) model and the VDP’s obtained from the listed techniques, the accuracy of considered EEM values is assessed. Sensitivity of change in VDP with respect to different operating conditions, type of elastic deformation and also spatial distribution of facies is examined.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".