A comprehensive review of computer vision for reservoir modelling and data assimilation

The integration of computer vision (CV) techniques into reservoir modeling and data assimilation is reshaping petroleum engineering, driven by advances in machine learning and the growing availability of high-resolution imaging data. Traditional reservoir characterization is challenged by uncertaint...

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Bibliographic Details
Published in:Discover applied sciences Vol. 7; no. 12; pp. 1399 - 24
Main Author: Kazemi, Alireza
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.12.2025
Springer Nature B.V
Springer
Subjects:
Accuracy
Applied and Technical Physics
Artificial intelligence
Chemistry/Food Science
Computed tomography
Computer vision
Data assimilation
Data augmentation
Data collection
Decision making
Deep learning
Diffusion rate
Digital rock physics
Earth science
Earth Sciences
Engineering
Environment
Fault detection
Fluid flow
Geology
Image resolution
Image segmentation
Kalman filters
Machine learning
Management systems
Materials Science
Medical imaging
Permeability
Petroleum engineering
Physics
Porosity
Pressure distribution
Real time
Regression analysis
Regression models
Reservoir management
Reservoir modelling
Reservoirs
Review
Seismic interpretation
Simulation
Subjectivity
Surrogate modelling
Synthesis
Uncertainty
Well logs
Computer vision
Seismic interpretation
Digital rock physics
Reservoir modelling
Surrogate modelling
Data assimilation
ISSN:3004-9261, 2523-3963, 3004-9261, 2523-3971
Online Access:Get full text
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Summary:The integration of computer vision (CV) techniques into reservoir modeling and data assimilation is reshaping petroleum engineering, driven by advances in machine learning and the growing availability of high-resolution imaging data. Traditional reservoir characterization is challenged by uncertainty arising from sparse, heterogeneous data sources, such as well logs, core samples, and seismic volumes, as well as subjective interpretations and limited resolution. While CV techniques do not eliminate these sources of uncertainty, they provide automated, data-driven methods that can reduce interpreter bias, improve consistency, and enhance feature extraction across various scales. In digital rock physics, the combination of micro-CT and SEM imaging with segmentation and regression models enables improved estimation of petrophysical properties such as porosity and permeability. In seismic interpretation, deep learning-based CV models automate fault detection, horizon picking, and facies classification, offering faster and more consistent outputs. Generative models, including GANs and diffusion networks, further contribute by synthesizing realistic geological images for data augmentation and training. CV techniques are also emerging in dynamic reservoir management, where image-based features support history matching and CNN-based surrogate models offer fast approximations of fluid flow behavior, supporting real-time decision-making. Despite these advances, challenges remain, including the scarcity of labeled datasets, poor generalizability of models across geological settings, and limited integration with physics-based simulations. This review synthesizes recent progress in applying computer vision to reservoir modeling and data assimilation. It highlights current capabilities, outlines persistent sources of uncertainty, and discusses future directions for building scalable, reliable, and data-informed reservoir management systems. Article highlights Computer vision tools help geoscientists interpret subsurface images more accurately and consistently. New AI models speed up reservoir simulations, saving time and boosting decision-making efficiency. Synthetic geological images created by AI reduce the need for expensive, hard-to-get field data.
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ISSN:3004-9261
2523-3963
3004-9261
2523-3971
DOI:10.1007/s42452-025-07743-2