Predicting the breakdown pressure in hard rock material subjected to hydraulic fracturing and quantifying fluid flow within a fracture network
Bibliographic record
Abstract
This research is part of the Natural Heat Exchange Engineering Technology for Mines (NHEET) project conducted by Mirarco Mining Innovation. The NHEET project consists of developing a system using natural means to provide economically significant thermal regeneration capacity through a volume of rock fragments for ventilating mine workings. This system can provide heating (during winter) and cooling (during summer) of air on seasonal basis, without using artificial refrigeration. Optimizing the system requires creation of a specific volume of rock fragments having, among other criteria, a pre-determined porosity and fragment size distribution to meet the thermal storage and ventilation requirements of the mine site. This research is part of the NHEET project’s scope of work and investigates an alternative system, which consists of a fractured rock mass with sufficient fracture density and connectivity to admit enough airflow for the NHEET system requirements. This alternative system has the potential of reducing the footprint at surface. Firstly, the hydraulic fracturing (HF) method is investigated for preconditioning the rockmass with the objective of strategically creating additional fractures. Increasing the volumetric fracture intensity and fracture network connectivity within the rock mass can optimize airflow within the fracture network. A numerical predictive model for the breakdown pressure in hard rock subjected to hydraulic fracturing is developed using the lattice spring modeling method for HF simulation. The developed numerical model is calibrated based on the results obtained from a HF field experiment conducted in a northern Ontario mine. Secondly, a laboratory experiment is conducted to quantify fluid flow through a fracture network. In this context, a 3D physical model representing a fractured rock mass is generated using 3D printing technology. The 3D printed model is fixed into an experimental setup for fluid flow measurements. This experiment allowed for establishing the behaviour of the changing pressure to fluid transfer through fracture openings. The flow-pressure measurements are compared to a simple model for the volumetric flow rate in a block of naturally fractured rock with a number of fractures. The numerical model developed, and laboratory results obtained in this thesis provide valuable information for the construction of a NHEET system. The numerical predictive model for the breakdown pressure in hard rock subjected to HF is a tool to evaluate the amount of fluid pressure needed to create additional fractures in the rock mass and facilitate the planning of HF operations. The pressure-flow rate laboratory measurements are key data that can be used to calibrate a subsequent numerical simulation at a larger scale, representative of the NHEET system. Additionally, direct fluid flow measurements in fracture networks are useful to assess the influence of various fracture properties (e.g. intensity, connectivity, aperture) on fluid flow.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".