Discrete Modeling of Natural and Hydraulic Fractures in Shale-Gas Reservoirs
Bibliographic record
Abstract
Abstract Interconnections of hydraulic fractures and pre-existing natural fractures provide key channels for shale gas to flow at economic rates. Micro-seismic mapping has proved that the resulting fracture system is much more complex compared to most conventional reservoirs due to the massive multistage, multi-cluster hydraulic fracturing stimulations. It becomes crucially important to develop advanced approaches to model such a complex system to better understand the recovery mechanisms and to optimize stimulation and development plans of shale gas reservoirs. Recent advances in geological interpretations and micro-seismic mapping enable realistic modeling of hydraulic and pre-existing fracture network. Consequently, it is possible, to certain extent, to represent actual large-scale fracture distribution in reservoir modeling and simulation of shale gas development. In this paper, we apply the Discrete Fracture Modeling (DFM) approach to represent large-scale fractures individually and explicitly. This entails highly constrained unstructured gridding and construction of a connection (transmissibility) list of all neighboring cells. The geo-mechanical impact on micro-fracture system is modeled by "effective media", in which the rock permeability is sensitive to the stress change induced by hydraulic fracturing and pressure drawdown. Simulations have been performed based on the detailed modeling of an actual shale gas reservoir with considering various mechanisms including adsorption/desorption, matrix-fracture transfer, and non-Darcy effects. Sensitivity studies by varying the production rate, pressure and hydraulic fracture parameters could be onducted to provide guidance on optimizing stimulation and production designs. We have proposed and implemented an innovative simulation workflow that effectively captures multi-scale and multi-physics flow behavior caused by complex fracture system and geo-mechanical impact. A field-scale case study demonstrates that the proposed workflow certainly improves predictability in development of shale gas reservoirs.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".