Simulation of a Multistage Fractured Horizontal Well with Finite Conductivity in Composite Shale Gas Reservoir through Finite-Element Method
Bibliographic record
Abstract
Different from oil properties, gas properties (gas formation factor, viscosity, and Z -factor, etc.) have nonlinear behaviors with pressure changes. However, many scholars use the average pressure or pseudopressure concept to simplify the phenomenon for easier solutions. Gas flow in shales is believed to be a complex process with multiple flow mechanisms including continuum flow, slip flow, diffusion, ad-desorption, and the stress sensitivity of fractures (natural or induced) permeability in multiscaled systems of nano- to macroporosity. Multistage hydraulic fracturing not only creates the stimulated rock volume (SRV) to improve production but also makes the flow in shales more complex. In this work, a rectangular composite model for a multistage fractured horizontal well (MFHW) with finite conductivity in shale gas considering the multiple flow mechanisms and multi-nonlinearities is developed. Comparing with the existing models for MFHW in shale, the model presented here takes strong nonlinearity of gas properties, hydraulic fracture asymmetry, fracturing efficiency, and SRV region into account, which is more in line with field practice. Numerical simulation of fully implicit control volume finite element (CVFE) based on unstructured 3D tetrahedral mesh is proposed to obtain the production performance of MFHW. Sensitivity analysis focuses on the effects of nonlinearity, Langmuir volume, stress sensitivity, finite conductivity, and SRV type on the production performance. The research and the numerical results obtained in this work can provide theoretical guidance to efficient and scale development for shale gas reservoir.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".