A semianalytical model of fractured horizontal well with hydraulic fracture network in shale gas reservoir for pressure transient analysis
Bibliographic record
Abstract
Accurate construction of a seepage model for a multifractured horizontal well in a shale gas reservoir is essential to realizing the forecast of gas well production, the pressure transient analysis, and the inversion of the postfracturing parameters. This study introduces a method for determining the fracture control region to characterize the flow area of the matrix within the hydraulic fracture network, distinguishing the differences in the flow range of the matrix system between the internal and external regions caused by the hydraulic fracture network structure. The corresponding derivation and solution methods of the semi-analytical seepage model for fractured shale gas well are provided, followed by the application of case studies, model validation, and sensitivity analysis of parameters. The results indicate that the proposed model yields computational results that closely align with numerical simulations. It is observed that disregarding the differentiation of matrix flow area between the internal and external regions of the fracture network led to an overestimation of the estimated ultimate recovery, and the boundary-controlled flow period in typical well testing curves will appear earlier. Because hydraulic fracture conductivity can be influenced by multiple factors simultaneously, conducting a sensitivity analysis using combined parameters could lead to inaccurate results in the inversion of fracture parameters. Cited as: Cui, Q., Zhao, Y., Zhang, L., Chen, M., Gao, S., Chen, Z. A semianalytical model of fractured horizontal well with hydraulic fracture network in shale gas reservoir for pressure transient analysis. Advances in Geo-Energy Research, 2023, 8(3): 193-205. https://doi.org/10.46690/ager.2023.06.06
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".