A Composite Model for Multi-Stage Fractured Horizontal Wells in Heterogeneous Reservoirs
Bibliographic record
Abstract
Abstract Analytical multi-linear models are popular in the well testing analysis of multi-stage fractured horizontal wells (MFHWs). But such models are challenged now. In high density network of hydraulic fractures, the fluid flow around fracture tips no longer behaves like linear flow. Moreover, the production behaviors of hydraulic fractures are different at different stages in heterogeneous reservoirs. To overcome the two challenges, this study develops a semi-analytical composite model for MFHWs in heterogeneous reservoirs. The stimulated reservoir is regarded as a combination of several closed sub-systems with hydraulic fractures centered or uncentered. Each sub-system is further divided into several regions based on reservoir heterogeneity and flow characteristics. Reservoir and hydraulic fracture properties can be different in those regions. The production from each region is modeled by the solutions of linear flow equations or radial flow equations or source/sink functions. The production from a sub-system is modeled by coupling the solutions of regions in the sub-system. The production from the whole reservoir is then modeled by coupling the solutions of all sub-systems. The composite model was validated by analytical solutions in homogeneous reservoirs and numerical solutions in heterogeneous reservoirs. Calculations of production pressure and rates with this composite model generate type curves for the pressure and rate transient analysis of MFHWs. Two field cases are further analyzed with the type curves. One novelty of this model is to consider the non-linear flow around hydraulic fracture tips. Therefore, our model is better prepared for incorporating pressure-dependent heterogeneous reservoir properties in the future. The other novelty of this study is the ability to model the different production behavior at different fracture stages in complex heterogeneous reservoirs.
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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 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".