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Record W4401480367 · doi:10.56952/arma-2024-1022

Modelling the Hydraulic Injection Fracture Response in Layered Media for Megablock Experiments

2024· article· en· W4401480367 on OpenAlexaffabout
Earl Magsipoc, Giovanni Grasselli

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFracture (geology)Hydraulic fracturingComputer scienceMaterials scienceGeologyGeotechnical engineering

Abstract

fetched live from OpenAlex

ABSTRACT: Finite-discrete element method (FDEM) models were employed to explore the fracturing response of Montney Formation outcrop samples to hydraulic fracturing. Variability envelopes were employed to characterize its behaviour under different compressive loading orientations with respect to the layering in the samples. These envelopes were used to conduct calibration using Bayesian optimization. Four calibration schemes were then used as the material parameter set for simulating the "megablock" experiments under gravity loading. In these experiments, the models demonstrated a complex pattern of fracturing around the borehole, including planar fractures that extended both vertically and horizontally. Interestingly, fractures that intersected with layering interfaces could lead to horizontal fractures, despite the influence of gravity-induced stress. An isotropic parameter set used to simulate a mortar megablock sample with embedded weak DFNs also showed fracturing preference along the DFNs and horizontally against gravity due to the availability of fluid storage in the DFN cavities and ductility. 1. INTRODUCTION Hydraulic fracturing (HF) is often performed in layered sedimentary formations. These embedded layers with varying mineralogy may impact the strength and elastic characteristics of the overall material when subjected to failure (Guo et al., 2018). While it is difficult to investigate these impacts at reservoir depths, numerical modelling, supplemented with laboratory testing and experimentation, can provide insights into these mechanisms (Yushi et al., 2016). Megablock hydraulic fracturing experiments are proposed to provide laboratory access to the fracture formation. Three megablocks (0.7 × 0.7 × 0.7 m3) are currently being prepared for laboratory hydraulic fracturing tests to observe surface deformation, acoustic emission, and fracture response to layering anisotropy. Two of these specimens are Montney Formation (MF) outcrop acquired from Kamenka Quarry in Canmore, AB, Canada. The last megablock is a concrete mortar with 30 cm layers made to simulate an anisotropic material and to provide pilot test material before proceeding with the rock specimens. While the pilot mortar specimen will provide learning lessons in implementing data acquisition and monitoring systems, numerical modelling can also provide insights into the expected fracture mechanisms and help make decisions in implementing the megablock testing program such as optimal sensor placement, expected weak areas, and monitoring areas for deformation.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.018
GPT teacher head0.253
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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