Evolution and Evaluation of SRV in Shale Gas Reservoirs: An Application in the Horn River Shale of Canada
Bibliographic record
Abstract
Abstract A hybrid hydraulic fracture (HHF) model composed of (1) complex discrete fracture networks (DFN) 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 considering the initial hydraulic fracturing efficiency, fracture network complexity, mechanics, and microseismicity distribution along 145 stimulated stages in a multilateral horizontal well on the Muskwa, Otter Park and Evie Formations in the Horn River Shale in Canada. Hydraulic fracturing jobs in shale reservoirs are designed with a view to achieve economic production by exploiting fracture network complexity. The task involves significant challenges in modeling and forecasting, which complicates the examination of operations to enhance their performance, including refracturing or infill drilling. In this study, an HHF is run in a numerical simulation model to evaluate the SRV performance in planar and complex fracture networks using microseismicity data collected during 75 stages of hydraulic fracturing in the Horn River shale. Post-fracturing production is appraised with Rate Transient Analysis (RTA) for determining effective permeability under flowing conditions, compare to the numerical simulation and the hydraulic fracturing design. Fracturing stages with larger fracture patch sizes, associated with the microseismic events in a fixed stress drop, correspond to higher stimulated areas, fracture conductivity, and gas production. Several microseismic events are observed in the heel of the laterals that are aligned to the far field NE stresses, indicated a loss of efficiency along the wellbore lateral during hydraulic fracturing. The hydraulic propagation modeling revealed increment of the leak-off coefficient, related to the natural fractures and communication with other stages. The production performance is evaluated in the numerical model, to measure interference between stages. The SRV, modeled with HHF networks, is able to match the post-fracturing production history. Fracture mechanics is important in order to understand the flowing performance of the reservoir. The inclusion of propagating models and RTA allowed to characterize possible fracture geometries in the reservoir and to observe limitations inherent to large dispersion and uncertainty of the microseismicity cloud. Also, to observe areas where the stimulation may have propped natural fractures totally or partially, which will benefit the production of gas. This study presents a better understanding and characterization of the SRV in shale gas reservoirs, especially in those cases where microseismicity dispersion is problematic and where the SRV is not easily delimited.
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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 | 0.000 | 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".