Numerical Simulation of Hydraulically Induced Fracture Network Propagation in Shale Formation
Bibliographic record
Abstract
Abstract Hydraulic fracturing treatment has been proven to be one of the key technologies for shale gas development. Micro-seismic mapping data has shown that hydraulic fracturing stimulation has often resulted in complex fracture network due to the geological complexity of shale formations. Hydraulic fracturing is a coupled process of shale formation deformation and flow of engineered fluid that includes water, proppant and other chemicals. Moreover, the pre-existing natural fractures in shale formation may complicate hydraulic fracture propagation process and alter its Young's modulus. In this paper, we have developed a numerical model for modeling hydraulic fracture propagation in highly fractured shale formation. The proposed numerical model has integrated turbulent flow, rock stress response, interactions of hydraulic fracture propagation with natural fractures, and influence of natural fractures on formation Young's modulus. Mixed finite element method is employed for numerical solution of the nonlinear partial differential equations. The proposed model has been validated with bi-wing hydraulic fracture model through regression tests. The preliminary numerical results illustrate the significant differences in modeling hydraulic fracture propagation in comparison with current models that assume laminar flow in hydraulic fracture process. Two simulated cases with different initial natural fracture mapping are given. The preliminary numerical results show that length and density of natural fracture have significant impact on formation Young's modulus, and interactions between hydraulic fracture and natural fractures create the complex fracture network. This model provides an opportunity to optimize hydraulic fracturing stimulation design through numerical simulations, which is vital in shale reservoir development. Introduction Hydraulic fracturing stimulation is one of the key technologies in shale gas development and has often, as shown in micro seismic mapping, resulted in complex fracture network geometry due to the interaction between hydraulic fracture and pre-existing natural fractures (Fisher et al. 2002; Maxwell et al. 2002; Daniels et al. 2007; Le Calvez et al. 2007; X. Wang et al. 2011). Though it has been widely used in oil and gas industry for many years, it remains a great challenge to quantitatively characterize the hydraulically induced fracture network in shale gas development due to the complexity of the network and lack of the observational data. Hydraulic fracturing is a stimulation process, in which engineered fluids, a mixture of water, sand and chemicals, are pumped into shale formation at high pressure and rate to create an induced fracture network. In the stimulation process, high flow rate and fluid pressure initiate and propagate the hydraulically induced fracture. The induced fracture can open the natural fracture or cross it, or can be rested by the natural fracture once it intersects with a natural fracture (Blanton, 1982; Warpinski, N.R, 1987; Renshaw, C.E., 1995, and N. Potluri, 2005). Consequently, it is critical to model the coupling effects of the flow process and the fracture propagation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".