Evolution and Evaluation of SRV in Shale Gas Reservoirs: An Application in the Horn River Shale of Canada
Bibliographic record
Abstract
Summary A hybrid-hydraulic-fracture (HHF) model composed of (1) complex discrete fracture networks (DFNs) and (2) planar fractures is proposed for modeling the stimulated reservoir volume (SRV). Modeling the SRV is complex and requires a synergetic approach between geophysics, petrophysics, and reservoir engineering. The objective of this paper is to characterize and evaluate the SRV in nine horizontal multilaterals covering the Muskwa, Otter Park, and Evie Formations in the Horn River Shale in Canada, with a view to match their production histories and to evaluate the effectiveness and potential problems of the multistage hydraulic-fracturing jobs performed in the nine laterals. To accomplish this goal, the HHF model is run in a numerical-simulation model to evaluate the SRV performance in planar and complex fracture networks using good-quality microseismicity data collected during 75 stages of hydraulic fracturing (out of 145 stages performed in nine laterals). The fracture-network geometry for each hydraulic-fracture (HF) stage is developed on the basis of microseismicity observations and the limits obtained in the fracture-propagation modeling. Post-fracturing production is appraised with rate-transient analysis (RTA) for determining effective permeability under flowing conditions. Results are compared with the HHF simulation and the hydraulic-fracturing design. The HHF modeling of the SRV leads to a good match of the post-fracturing production history. The HHF simulation indicates interference between stages. The vertical connectivity in the reservoir is larger than the horizontal connectivity. This is interpreted to be the result of the large height achieved by HFs, and the absence of barriers between the formations. It is concluded that the HHF model is a valuable tool for evaluating hydraulic-fracturing jobs and the SRV in shales of the Horn River Basin in Canada. Because of the generality of the Horn River application, the same approach might have application in other shale gas reservoirs around the world.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".