A Two-Phase Type-Curve Method with Fracture Damage Effects for Hydraulically Fractured Reservoirs
Bibliographic record
Abstract
Abstract Type-curve analysis on flowback and production data is a powerful tool in characterizing hydraulic fractures (HF) and reservoir properties. In order to evaluate HF characteristics and their dynamics for multi-fractured horizontal wells (MFHWs) in hydrocarbon reservoirs, we provide a novel type-curve method and an iterative workflow. The type curve incorporates the fracture damage effect, which is characterized by choked-fracture skin factor, into the two-phase flow in HF and matrix domains. The type-curve method can be applied to inversely estimate choked-fracture skin factor, fracture pore-volume, fracture premeability, and fracture permeability modulus through the analysis of two-phase production data. By introducing the new dimensionless parameters, the non-uniqueness problem of the proposed semianalytical method is significantly reduced by incorporating the complexity of fracture dynamics into one set of curves. The proposed type curve's accuracy is examined by numerical simulations of a shale gas and shale oil reservoir. The validation results demonstrate the good match of analytical type curves and numerical data plots and confirms the accuracy of the proposed approach in estimating the static and dynamic fracture properties. The flexibility and robustness of the proposed method are illustrated using the field example from a shale oil MFHW. The interpreted results from the flowback analysis of the field example offers a quantitative insight of fracture properties and dynamics.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".