Iterative Coupling Between Geomechanical Deformation and Reservoir Flow
Bibliographic record
Abstract
This article, written by Technology Editor Dennis Denney, contains highlights of paper SPE 97879, "An Overview of Iterative Coupling Between Geomechanical Deformation and Reservoir Flow," by D. Tran, SPE, L. Nghiem, SPE, and L. Buchanan, SPE, Computer Modelling Group Ltd., prepared for the 2005 SPE International Thermal Operations and Heavy Oil Symposium, Calgary, 1–3 November. With the help of geomechanics, many physical phenomena can be explained (e.g., reservoir compaction and subsidence, casing failure, or pore collapse). Several methods for coupling geomechanics to fluid flow in the reservoir have been proposed. The iterative-coupled method has proved effective. Introduction The coupling of a reservoir simulator to a geomechanics module has wide application in petroleum production. With the aid of geomechanics, many phenomena can be explained, such as compaction, subsidence, wellbore stability, and pore collapse, as well as loss and gain in production. In a traditional reservoir simulator, subsidence can be estimated by a simple formula without knowing the geomechanical response. In some problems, such as primary production and linear materials, the subsidence computed by a reservoir simulator alone may give results that are comparable to the coupled solutions. Yet, when nonlinear materials are used, the results obtained with a conventional simulator will be much different from those obtained with a flow/geomechanics-coupled simulator. The main reason is that in a coupled simulator, the flow is affected strongly by the stress and strain through the porosity. However, in a conventional simulator, this stress dependence is ignored. Therefore, if a stress-sensitive reservoir is considered, the solution obtained from a conventional simulator cannot deliver expected results. In addition, for thermal problems, a conventional thermal simulator does not properly account for thermal stresses, the effects of which can be significant. The coupling between reservoir flow and geomechanical deformation can appear in various forms.The fully coupled approach is the tightest coupling because deformation and reservoir pressure and temperature are solved simultaneously.The iterative-coupled approach is less tight than the full-coupling method because the geomechanics calculations are performed one step after the reservoir-flow calculations.The explicit-coupled approach is considered a special case of the iterative-coupled approach. The information from a reservoir simulator is sent to a geomechanics module, but the calculations in the geomechanics module are not fed back to the reservoir simulator. Reservoir flow is not affected by the geomechanical responses calculated by the geomechanics module. The full-length paper details basic equations for reservoir flow and solid deformation to show how variables in those equations are coupled. Only the iterative-coupled approach and explicit-coupled approach are discussed. Examples related to different constitutive models such as an elastoplastic model, a plastic-cap model, and a pseudodilation/recompaction model are used to demonstrate the effects of such coupling.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".