Numerical Investigation on Effect of Dual Casings Design on Casing Collapse Prevention, Case Study: One of Iranian Southwest Oilfields
Bibliographic record
Abstract
ABSTRACT: This study investigates the effectiveness of a dual-casing design in reducing casing collapse during CO2 injection in a southwestern Iranian oilfield, where over 100 of 450 wells have experienced severe collapse within the high-pressure Gachsaran Formation, particularly across marly and salt layers. A representative well was analyzed, featuring a dual-cased (9x7-inch) section in the upper Gachsaran interval using 9%-inch V-150 BTS and 7-inch P-110 BTS casings, and a single 7-inch P-110 BTS liner in the lower section, where multiple collapses were reported. A comprehensive 3D geomechanical model was developed from surface to underburden, including the Gachsaran and Asmari formations. One-way hydromechanical coupling was used to simulate stress, strain, and displacement changes due to reservoir pressure variations. These were applied as boundary conditions to a near-wellbore 3D finite element mesh incorporating actual casing and cement properties. Material behavior was modeled using Mohr-Coulomb for anhydrite, Cam-Clay for marl, and creep for salt.The results confirmed the integrity of dual-cased sections, where inner casing stress was about 50% lower than in the outer casing, indicating strong support. Conversely, single-cased sections showed stress beyond yield strength and large deformations. The study recommends extending dual casing (V-150 and P-110) into deeper intervals to enhance well integrity in similar geological conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".