Characterizing Tight Oil Reservoirs With Dual- and Triple-Porosity Models
Bibliographic record
Abstract
The response of existing transient triple-porosity models for fractured horizontal wells do not converge to that of linear dual-porosity model (DPM) in the absence of natural/microfractures (MFs). The main reason is the assumption of sequential-depletion from matrix to MF, and from MF to hydraulic-fractures (HFs). This can result in unreasonable estimates of MF and/or HF parameters. Hence, the authors proposed a quadrilinear flow model (QFM) in a previous paper which relaxes this sequential-depletion assumption to allow simultaneous matrix–MF and matrix–HF depletion. Also, it is proved that QFM simplifies to both DPM and linear sequential triple-porosity model (STPM). This work considers the implications of applying QFM, STPM, and DPM type-curves and analysis equations on production data of two fractured horizontal wells completed in the Bakken and Cardium Formations. A comparative study of the reservoir parameters estimated from the application of these models to the same production data reveals two key results. First, the application of DPM on the production data from reservoirs with active MF could result in overestimation of HF half-length. This happens to compensate for the extra fluid depletion pathways provided by MF. Second, the application of STPM on the production data from the reservoirs with active matrix–HF communication could result in overestimation of the MF intensity. Results from this study are significant when selecting the appropriate model for interpreting production data from fractured horizontal wells completed in formations with or without active MF. The DPM is appropriate if analog studies (e.g., outcrop, microseismic and image log analyses) reveal high fracture spacing aspect ratio (negligible MF) in the reservoir. Fracture spacing aspect ratio is MF spacing divided by the HF spacing. The STPM is appropriate if analog studies reveal low spacing aspect ratio (e.g., matrix–HF face damage or high MF intensity within a given HF spacing). QFM is appropriate for all fracture spacing aspect ratios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".