Efficient production estimation for a hydraulic fractured well considering fracture closure and proppant placement effects
Bibliographic record
Abstract
It is important to accurately estimate performances of a hydraulic fractured well, because it will be utilized to evaluate various completion parameters and furthermore to establish a future development plan. Shale gas reservoirs with fracture networks have high initial production rates but show drastic production decline as reservoirs are depleted by production. One of the reasons behind this phenomenon is an increased effective stress during production resulting in fracture closures. Gas mainly flows through hydraulic and natural fracture networks, so the fracture closures cause permeability reduction in the flow areas. In typical hydraulic fracturing operations, proppants are injected with fracturing fluid and placed in the fracture networks. Proppants play a crucial role to keep an induced hydraulic fracture open and retain a well productivity. However, only small portion of the fracture networks are filled with proppants (propped fracture) and the rest exist without proppants (unpropped fracture). Therefore, fracture closures of these regions are quite different. In this article, we have investigated to identify the combined effect of fracture closure and proppant placement on production estimation of a shale gas well. A numerical model has been developed to mimic well performances in Horn River Basin, BC, Canada. We have used pressure-dependent correlations based on experiments to consider fracture permeability alteration with changing reservoir pressure. Proppant placements are described using a fracture propagation model and this enables to classify a whole reservoir into sub regions such as propped, unpropped fracture, and matrix. By comparing with different cases, this article shows that reasonable results on gas production estimation are accomplished when considering fracture closure and proppant placement effects together.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".