Enhanced Modelling of Sand Production Through Improved Deformation and Stress Analysis
Bibliographic record
Abstract
Abstract This paper extends the capacity of the current sand production models by eliminating the influence of artificial conditions and numerical mesh on localization and deformation response in the sanding model. Past studies indicate strong size effects when using classical elastoplastic models. To rectify this deficiency, a fracture energy regularization method is implemented in the numerical model. The model incorporates both the geomechanical aspects (e.g. rock elastoplastic deformation and rock disaggregation) as well as the transport aspects (e.g. the role of seepage on rock deformation and solid release). The model employs a Mohr- Coulomb flow theory of elastoplasticity with friction hardening/cohesion softening. Emphasis is given on calibration procedure and validation of the enriched model through back analysis of triaxial and uniaxial compression tests. Next, the model is used to compare the numerical predictions with laboratory data on sand production. The comparison incorporates the stress and deformation as well as the sand volume. The calibration study shows that friction hardening and cohesion softening can satisfactorily reproduce numerically the weak sandstone response to various loading conditions. Further, computation results of strain softening material illustrate that fracture energy regularization strategy enables the model exhibit mesh invariance of the energy dissipation. Such invariance is of utmost importance in the sanding and wellbore stability studies where strain softening induces localization of plastic deformations in thin shear bands. Introduction In many physical situations (e.g., drilled wellbore), shear bands are observed. Shear bands form because of concentration of deformation in the medium in narrow bands in a process called localization. Development of a computational model that reproduces the observed damage evolution is necessary in the analysis of sanding and wellbore stability. Analytical and computational models formulated using a standard continuum description and a strength criterion are unable to recover the size effect (e.g. for wellbore stability the size effect is the effect of wellbore diameter on allowable field stresses), and are unable to produce results free of mesh design when the material undergoes degradation and strain softening. Localization can occur when a body composed of material softens (becomes weaker) with strain and the strain distribution within the body is slightly non-uniform. The element that carries the most strain then becomes weaker than the rest (i.e., it attempts to carry less stress). Since its neighbors carry the original stress, the boundaries of the weak element are thrown out of equilibrium (because the internal and external stresses do not balance). The weak element, therefore, starts straining more, which only causes it to soften more. This weak-link process is responsible for localization in a strain-softening material. Although localization is generally expected in a strain-softening material, the same process can also occur in non-softening (including strain hardening) material[1–3]. In numerical simulations, localization can occur due to boundary effects and/or variation in strength parameters[1]. In the absence of any variations in strength parameters, slight numerical artifacts can play a similar role as localization starter. In numerical simulations, localization of deformation in bands has been observed to be influenced by the numerical mesh[4–7].
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".