Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2502.11523

AI-Assisted Thin Section Image Processing for Pore-Throat Characterization in Tight Clastic Rocks

Muhammad Risha
View original source ↗Read source document ↗Export metadata ↓

Abstract

The characterization of pore-throat structures in tight sandstones is crucial for understanding fluid flow in hydrocarbon reservoirs and groundwater systems. Both thin-section and Mercury Intrusion Capillary Pressure (MICP) offer insights rock petrophysical parameters. However, thin-section analysis is limited by its 2D nature and subjective interpretation, while MICP provides 3D pore-throat distributions, it lacks direct visualization of pore morphology. This study evaluates AI-assisted thin-section image analysis for pore-throat characterization by comparing its results to MICP-derived measurements. A machine learning-based workflow was developed using color thresholding, K-Means clustering, and medial axis transformation to segment pore structures in thin-section images. Throat width, porosity, and permeability were quantitatively assessed against MICP to determine the accuracy and reliability of the technique. The analysis of 26 sandstone samples outlined differences between the two methods. Thin-section analysis showed porosity values from 1.37% to 53.37%, with average pore-throat sizes between 5.63 micron and 30.09 micron, while permeability estimates ranged from 0.01 mD to 344.35 mD. Correlation analysis showed moderate agreement for throat size (r=0.62) and permeability (r=0.61), but weaker for porosity (r=0.32), highlighting the differences in how each method captures pore connectivity. Results demonstrate that the AI-assisted segmentation provides a scalable and reproducible approach but is constrained by thin-section imaging resolution. While MICP remains reliable for permeability evaluation, its comparison with AI-driven image analysis helps assess the reliability of the method. Future research should refine segmentation algorithms, incorporate pretrained data to validate AI-derived pore-throat attributes for improved reservoir quality assessment and predictive modeling.

Explore related subjects

physics.geo-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Muhammad Risha. 2025-02-18. AI-Assisted Thin Section Image Processing for Pore-Throat Characterization in Tight Clastic Rocks. https://arxiv.org/abs/2502.11523

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Parameter-Efficient Adaptation of Pre-Trained Vision Foundation Models for Active and Passive Seismic Data Denoising

The demand for high-resolution subsurface imaging and continuous Earth monitoring has driven rapid growth in active and passive seismic data from dense geophone deployments, distributed acoustic sensing (DAS) arrays, and large-scale 2D and 3D surveys. This expansion makes complex noise suppression increasingly challenging, especially when signal fidelity must be preserved. Conventional supervised deep learning methods are often task-specific, require large paired datasets, and can suffer from domain shift under new acquisition conditions. Foundation models offer a promising alternative, but pre-training seismic foundation models from scratch requires massive domain-specific data and substantial computation. We propose an efficient framework that repurposes general-purpose Vision Foundation Models (VFMs) for geophysical tasks through Parameter-Efficient Fine-Tuning. The architecture uses a pre-trained VFM, a DINOv3 encoder, adapted with Low-Rank Adaptation (LoRA) to enable effective feature adaptation with few additional parameters. To improve robustness under unseen field conditions without ground truth, we introduce a kurtosis-guided unsupervised test-time adaptation module that updates only LoRA parameters during inference. This module self-calibrates the model to site-specific noise by identifying information-rich regions via kurtosis and performing self-training without labeled data. Experiments on public exploration seismic images and DAS vertical seismic profiling data from the Utah FORGE site show that the framework matches or outperforms domain-specific models. Tests on unseen cross-site data from a land survey in China and the Groß Schönebeck geothermal site in Germany further demonstrate strong generalization and effective signal-noise separation. These results highlight the potential of adapting pre-trained VFMs to data-intensive problems in exploration seismology.

physics.geo-ph↗
arXiv2026

Shallow-to-deep velocity model building via diffusion models-Part I: Method and Proof of concept

Seismic velocity model building (VMB) is fundamental for understanding subsurface structures. Traditional methods demand high-quality starting models and, also, remain limited in resolution in coverage and computationally intensive. Recent generative diffusion model-based approaches capture statistical priors to support traditional inversion methods, but these approaches do not account for the top to bottom progression of information (layer stripping) involved in surface recorded data, where deep velocity information depends on the shallow. To address this issue, we propose a depth-progressive diffusion framework that constructs velocity models incrementally from shallow to deep by propagating prior information. Our method trains on paired shallow-deep velocity patches with variable overlap and explicit depth encoding, integrating multiple geophysical constraints including well logs and seismic images (representing structural information). During inference, we synthesize overlapping depth slices using a progressive algorithm and merge them with Gaussian-weighted blending to eliminate boundary artifacts. This approach leverages both learned geological distributions and observed shallow priors while providing uncertainty quantification. Extensive numerical experiments on in-distribution tests and an out-of-distribution test demonstrate excellent VMB accuracy with a strong correlation between predicted uncertainty and actual errors. As a proof of concept, this part I employs idealized structural constraints derived from vertical reflectivity to validate the methodological framework. The companion paper (Part II) extends the approach to realistic structural constraints relying on migrated images with field data applications.

physics.geo-ph↗
arXiv2026

Shallow-to-deep velocity model building via diffusion models-Part II: Realistic scenarios

Full-waveform inversion (FWI) requires accurate initial velocity models to avoid cycle-skipping, but constructing such models remains challenging in practice. Building on the depth-progressive diffusion framework introduced in Part~I, which relied on idealized reflectivity constraints, this work adapts the methodology to realistic exploration scenarios. We replace perfect structural information with migration-derived attributes extracted from seismic images, and introduce smooth background velocity models from tomography as additional conditioning inputs. The framework jointly leverages background/migration velocity, migrated structural information, and sparse well measurements to synthesize high-resolution velocity models through depth-progressive generation. Validation on synthetic examples demonstrates superior accuracy compared to conventional interpolation and alternative deep learning methods, with generated models successfully initializing FWI and mitigating cycle-skipping even in complex geological structures. Field data confirms practical applicability: despite training on synthetic data, the method generalizes effectively to field conditions, producing velocity models with synthetic data response that nearly match observed seismic data. As a result, this framework establishes a practical pathway to deploy generative diffusion models for velocity model building under realistic constraints.

physics.geo-ph↗