Finite Difference Method‐Based Wellbore Stability Evaluation of Actual Horizontal Trajectories in Interbedded Coal
Bibliographic record
Abstract
ABSTRACT Coal‐bed methane (CBM), as a key component of unconventional natural gas production, often encounters wellbore stability challenges during development. The inherent fragmentation and weak cementation of coal contribute to wellbore instability, while horizontal drilling challenges mainly arise from thin interlayers and fractured zones. However, many existing studies rely on simplifying assumptions, making it increasingly important to accurately model the stress state around wellbores with complex trajectories. In this paper, a hybrid‐grid geological model incorporating stress‐fluid coupling is developed using the finite difference method (FDM) to more realistically represent subsurface conditions, especially at lithologic interfaces. This study investigated stress distributions around a horizontal section under varying drilling fluid densities using the established model that accounts for lithological variations, and analyzed mechanical behavior under various dogleg severity, drilling fluid soaking times, and trajectory extension conditions. The results indicate that a wellbore located near a fault or interface exhibit more uneven stress distributions and face a higher risk of instability. Increasing the density of the drilling fluid enhances wellbore support, significantly reducing damage. Once a critical density (1.70 g/cm 3 ) is reached, the effect stabilizes, ensuring wellbore stability. Moreover, larger dogleg severity expands the damage zone. As soaking time increases, the influence of shear stress on wellbore instability grows progressively stronger. Overall, the geological model provides a new approach for designing well trajectories during the pre‐drilling phase and for making real‐time adjustments during drilling.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".