A Simultaneous Matrix-Depletion Model for Charaterizing Fractured Reservoirs
Bibliographic record
Abstract
Abstract Existing transient triple-porosity models for fractured horizontal wells do not converge to linear dual-porosity models (DPM) in the absence of micro-fractures (MF). The reason is the assumption of sequential-depletion from matrix to MF, and from MF to hydraulic-fractures (HF). This can result in unreasonable estimates of MF and/or HF parameters. Hence, a quadrilinear flow model (QFM) is proposed which relaxes the sequential-depletion assumption. To allow simultaneous matrix-MF and matrix-HF depletion, the matrix volume is conceptually divided into two sub-domains; one feeds HF and the other feeds MF. This breaks a single 2-D problem into two 1-D problems. Using Laplace transforms, the flow equations are solved under constant-rate and constant-pressure well constraints. Type-curves are generated by numerically inverting the resulting Laplace-space solutions to time-space using Gaver-Stefhest algorithm. QFM converges to the linear sequential triple-porosity model (STPM) in the absence of matrix-HF communication; and converges to the DPM in the absence of MF. Flow-regimes observed comprise linear, bilinear, and boundary dominated. The number of flow-regimes depends on the matrix-MF, matrix-HF andMF-HF communication coefficient values. QFM matches production history of two fractured horizontal wells completed in Bakken and Cardium Formations. Reservoir parameters like HF half-length, HF and MF permeabilities and MF spacing are estimated from the history match. These reservoir parameters are estimated as ranges of values instead of single values to reflect the non-uniqueness of the type-curve match. A QFM comparative study reveals that STPM underestimates MF spacing while DPM overestimates HF half-length.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".