A Three-Level Graded Characterization Framework for the Mesoscopic Mechanical Behavior of Ultra-Deep Heterogeneous Dolomite Based on the Combined Finite-Discrete Element Method

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Authors: Aisheng Sun, Yunhu Lu, Ye Yan, Yang Xia, Hongyu Wu, Da YIN, Hongda Li
Cited by: 1

Abstract

Ultra-deep dolomite formations exhibit pronounced mechanical heterogeneity gov-erned by mineral composition, inter-particle cementation, and observation scale, com-plicating their mechanical characterization. Because intact downhole cores are diffi-cult to obtain, a mesoscopic numerical framework that reproduces and classifies such behavior is needed. A two-dimensional mesoscopic model of Cambrian ultra-deep do-lomite from the Tarim Basin was developed using the combined finite-discrete element method, incorporating mineral composition from X-ray diffraction and scanning elec-tron microscopy with energy-dispersive spectroscopy. A Weibull-distribution discreti-zation scheme was introduced to represent matrix and cementation heterogeneity separately. Sensitivity analyses examined three factor groups: mineral composition and spatial distribution, matrix and cementation shape parameters, and mesh size as the observation scale. Accordingly, a three-level graded characterization framework was proposed, in which the compositional level governs strength magnitude, the structural level governs the elastic modulus, pre-peak nonlinearity and failure mode, and the scale level governs response representativeness and convergence. Quantitative grading indicators and threshold ranges were extracted for each level. Triaxial valida-tion against laboratory experiments at confining pressures of 0 to 220 MPa confirmed strength and stress-strain response consistent with the proposed grading. The model and framework offer a transferable methodological reference for the mesoscopic me-chanical evaluation of ultra-deep heterogeneous dolomite.

Fields of Study & Subjects

Rock Mechanics and Modeling Drilling and Well Engineering Hydraulic Fracturing and Reservoir Analysis

Publication details

Journal / source
Preprints.org
Publisher
MDPI AG
Publication date
8 June 2026
Document type
Preprint