Generation of Micro-Fracture Clouds in Rocks: Mechanisms and Applications
Bibliographic record
Abstract
ABSTRACT: Volume increasing or decreasing processes in rocks can lead to inter or intragranular fractures in subsurface formations. The volume change can be caused by chemical reactions involving fluid, reactive transport, and coupling with the mechanical deformation of the grains. Depending on stress, material properties and coupled thermo-hydro-mechanical-chemical (THMC) conditions, grain scale fractures may propagate and coalesce, forming a cloud of well-connected fracture networks. The mechanisms responsible for propagation, coalescence and inhibition of cloud fractures are not well known. In this study, we review coupled micro-fracture network generation mechanisms as published in the literature and observed in field, experiments, and models. We then introduce simple (decoupled or weakly coupled) models within the framework of analytical and hybrid finite-discrete element models (FDEM). We discuss key parameters that promote (or limit) the generation of micro-fracture clouds under in-situ conditions. Based on the review of literature and preliminary model results, we discuss the implications of micro-fracture generation mechanisms on subsurface storage, hydrogen exploration/production and super-hot supercritical geothermal technologies. 1. INTRODUCTION Rock deformation and fracturing characteristics under high temperature and pressure (HTHP) conditions are of fundamental significance to emerging subsurface engineering technologies such as super-hot supercritical thermal resources, hydrogen and CO2 storage, and geologic hydrogen production. To unlock the full potential of these applications, it is important to fundamentally understand the fracture initiation, propagation, coalescence and resulting permeability alteration processes and be able to model them at different scales (grain to wellbore to reservoir scale) within the framework of Thermo-Hydro-Mechanical-Chemical (THMC) coupled HTHP systems. For example, serpentinization is a THMC coupled process that occurs when mafic and ultra-mafic rocks come in to contact with water at specific pressure-temperature conditions to produce hydrogen. This reaction can lead to a significant volume increase, swelling pressures and associated micro-fracture clouds: which in turn can generate additional permeability pathways for flow. Continuous flow of water and mineralization can lead to favorable conditions for a self-driven micro-fracture network, as this process can repeat in a loop: creating a progressive change in host rock permeability.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".