Fully Coupled Numerical Modelling of Ground Surface Uplift in Steam Injection
Bibliographic record
Abstract
Abstract Steam injection for EOR involves high temperatures, usually high pressures, large induced stresses, and associated volume changes, including the effects of shear dilation. Conventional reservoir simulation fails to predict associated transient ground surface movements because it does not consider coupled geomechanics effects. We present a fully coupled, thermal half-space model using a hybrid DDFEM method, in which a simultaneous FEM (Finite Element Method) solution is adopted for the reservoir and the surrounding thermally affected zone, and a DD (displacement discontinuity) method used for the elastic, non-thermal zone. This approach provides transient ground surface movements in a natural manner. Introduction Enhanced oil recovery (EOR) methods involving high pressures and steam injection (steamflooding, steam line-drive, cyclic steam stimulation, steam-assisted gravity drainage) are accompanied by large volume changes in the reservoir horizon. Ground movements in excess of 300 mm heave or subsidence are registered during injection and production cycles. Reservoir simulation cannot address this phenomena without due consideration of geomechanics effects. The first attempts to account for coupled pore fluid behavior and soil deformation led to Terzaghi's one-dimensional consolidation theory, still used in soil mechanics. Since Biot's theory of consolidation[3] was introduced to petroleum engineering by Geertsma[18], with the coined term "poroelasticity", coupled analysis of petroleum geomechanics effects has been widely advocated[13,16,29]. Coupled reservoir simulation can be carried out in a loosely coupled fashion[17, 28] or with a tightly coupled scheme[20, 21, 32], and comparisons of different coupling techniques can be found[9, 27]. In coupled reservoir simulation, computing challenges persist for large-scale 3D applications to real cases where there are a huge number of equations to solve iteratively; e.g. thermoporo- elasto-plastic analyses involving several simultaneous diffusion processes (Darcy, Fourier, Fick). In regions of large pressure, temperature, concentration, or stress gradients, accurate solution requires small-scale discretization. If the problem has strong non-linearity, such as changes in permeability, compressibility, or other properties arising from changes in pressure and effective stress, the computational effort increases by several orders of magnitude because multiple iteration loops are needed. These issues mean that accurate analysis of realistic complex 3D problems is challenging, and will so remain as we seek to solve larger and larger coupled problems involving non-linear responses. Also, more accurate coupled reservoir modeling requires that a sufficiently large domain be analyzed because mixed stress-displacement boundary conditions are difficult to incorporate. In an analytical solution developed by Rothenburg et al.[25] for stress-coupled transient radial flow of a compressible fluid into a fully penetrating well, the stiffness of the overburden is shown to be an essential coupling element which must be taken into account. Settari[30] and Osorio et al.[22] also suggest that the analysis domain should include overburden, sideburdens and underburden for better accommodation of the coupling effects of stress changes and flow. Hettema et al. [19] demonstrate that accurate depletion-induced subsidence modeling requires understanding of the reservoir and surrounding rock mechanical response to the depletion. surrounding rock mechanical response to the depletion. To partly address this di
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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) | 0.001 | 0.001 |
| 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".