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Presentation materials
In the last few decades, deep learning (DL) has afforded solutions to macroscopic problems in petroleum engineering, but mechanistic problems at the microscale have not benefited from it. Mechanism studies have been the strong demands for the emerging projects, such as the gas storage and hydrate production, and for some problems encountered in the storage process, which are common found as...
- OBJECTIVE/SCOPE
Geologic CO2 sequestration (GCS) has been considered as a promising engineering measure to reduce global greenhouse emission. Real-time monitoring of CO2 leakage is an essential aspect of large-scale GCS deployment. This work introduces a deep-learning-based algorithm using a hybrid neural network for detecting CO2 leakage based on bottom-hole pressure measurements....
A workflow has been proposed to directly predict the upscaled absolute permeability of a rock core from CT images whose resolution is insufficient to directly calculate the pore-scale permeability. The workflow employed the deep learning technique with the raw CT image data of rocks and their corresponding permeability values, which were obtained through high-resolution flow simulation on...
Characterisation of the internal 3-dimensional (3D) structure of complex porous materials has been revolutionised with deep-learned image processing and segmentation, promising second-scale scan times with hour-scale quality, and beyond-human multi-label segmentation accuracy at a fraction of the time. However, these claims are currently only true for single-sample, single-domain cases using...
Although the global energy sector is shifting from the fossil-based energy systems to the renewable energy resources, the conventional energy development techniques has received increasing attentions with the mature development and the recharge by AI. With the help of AI techniques, drawing lessons from thousands of years of traditional energy development in the technology transition into the...
Reservoir parameter inversion is an important technique in oil and gas exploration and development that can estimate the reservoir physical properties, such as skin factor and permeability, using observed data, such as well test data and production data. In this paper, we propose a physical accelerated neural network with multiple residual blocks (PRNN-Acc) for multiple parameter inversion of...
The estimation of pore-scale multiphase flow fields in complex geometries using deep learning has proven challenging. This is partly because researchers have historically focused on model architecture and data quality, while the volume and variety of data may have been inadequate to capture the intricacies of multiphase flow. In this work, we introduce a novel deep learning methodology to...
The exploration of CO2 capture and storage has become a crucial element in strategies aimed at mitigating climate change, where deep saline aquifers are of particular interest due to their extensive storage capacity and widespread availability. The complexities involved in effectively monitoring and simulating CO2 behavior within these geological formations present significant challenges. To...
Direct 3D imaging of natural or synthetic porosity below ~1μm in diameter often requires the application of Focused Ion Beam Scanning Electron Microscopy (FIB-SEM). This technique has several limitations: high cost and time demands, instrument availability, complex sample preparation and low field-of-view (FoV), restricting its suitability for operational industrial studies. SliceGAN, a...
Currently, there is rapid development in the approaches for constructing and utilizing digital cores. Digital Rock Physics (DRP) methods allow for quick and non-destructive acquisition of rock properties. The process of digital rock physics involves two primary stages: model construction and simulation of physical processes on the created models.
For heterogeneous reservoir rocks, the usage...
Generative Adversarial Networks (GANs) have been a typical example of how machine learning has been successfully applied, using three-dimensional images as training datasets, to generate realizations of the pore space, as well as to produce super-resolution images. We further this work with a new generative model: diffusion models (DMs), to generate images of both the pore space and two fluid...
In the context of climate change mitigation, underground/subsurface hydrogen storage (UHS) is regarded as a solution that could help tackle the imbalance in renewable energy supply. Excess energy can be stored as molecular hydrogen (H2) and re-used when it is needed. To enable large-scale storage in underground geologic formations, reservoir simulation of cyclic loading scenarios will be used...
High-quality digital rocks are essential for high-precision pore-scale modeling. However, limited by the imaging hardware, meeting the requirements of high resolution (HR) and a wide field of view (FOV) simultaneously is challenging. In this study, we propose a novel Efficient Attention Super-Resolution Transformer (EAST) to boost digital rock quality, which reconstructs HR details from low...
Over the past ten years, diverse machine learning techniques have been extensively employed in forecasting output for non-traditional reservoirs. Nevertheless, these techniques primarily utilized discrete point data obtained from field databases, such as well drilling, completion, monitoring, experiments, and production data of horizontal wells. However, this data fails to capture the spatial...
Digital rock analysis has shown promise in visualizing geological microstructures and elucidating transport mechanisms in subsurface rocks, particularly in unconventional reservoirs such as tight sandstone and shale. Accurate image reconstruction techniques, which provide valuable insights into the pore network, grain distribution and connectivity, are essential to capture the intricate...
Underground hydrogen storage (UHS) presents a viable solution for storing excess energy in suitable geological sites, ensuring a stable and scalable energy supply [1]. While extensive experience exists in underground natural gas storage [2], the significant differences in the properties of hydrogen pose unique challenges [3]. To deepen insights into the hydrogen recovery in UHS projects,...
Field tests and laboratory experiments indicate that the spatial distribution of hydrate saturation in hydrate reservoirs is non-uniform. This non-uniform distribution significantly impacts the reservoir’s temperature changes, and gas and water production rates during reservoir development. Currently, the primary methods for determining hydrate saturation distribution in porous media are...
The objective of this research is to establish a consistent relationship between nonlinear numerical simulations and the obtained results for use in inverse analysis. We simulate the shape of breakouts, taking into account inelastic deformation of high-porosity limestone, using developed finite element methods under various in-situ conditions. Subsequently, the dataset is employed to train...
Due to the complex composition of oil and gas resources, reservoir engineers usually switch between different mathematical models when describing the properties of petroleum reservoirs. In addition to the commonly used black oil model, various compositional models have been proposed. Some EOR techniques, such as polymer flooding, must be simulated based on the framework of compositional...
Machine learning (ML) has revolutionized various aspects of underground seepage, geological modeling, reservoir numerical simulation, production optimization, and big data analysis in the oil and gas industry. In particular, when it comes to reservoir development, ML methods, e.g., deep learning (DL) and intelligent computing, have proven to be superior to traditional methods in terms of...
Objectives
This study aims to optimize the characterization and prediction of permeability and relative permeability in porous media through a multi-faceted approach. The primary objectives include achieving accurate 3D reconstruction of rock core images, implementing advanced deep learning models for segmentation, and addressing computational challenges associated with the Lattice...
The production forecasting of shale gas wells is an important research topic in the natural gas industry. The underground pore structure is extremely complicated after hydraulic fracturing. Conventionally, researchers try to construct forecasting models via theoretical or experimental approaches. However, both theoretical and experimental approaches are faced with difficulties. Firstly, many...
The anticipation of fluid transport behavior within porous media holds significant importance in a diverse array of applications, encompassing subsurface hydrology (Hu and Pfingsten 2023); petroleum industry (Moslemipour and Sadeghnejad 2021), geothermal energy utilization (Meller et al. 2017), and secure subsurface storage of hydrogen or CO2 (Esfandi et al. 2023, Kanaani et al. 2023)....
Based on deep generative adversarial networks, we present a comprehensive framework for image multiscale fusion and multi-component auto-segmentation, which aims to create a precise digital rock − a key part of Digital Rock Physics to predict the petrophysical properties of porous media. Compared to low-resolution images with a large field of view (FoV), high-resolution (HR) rock images are...
Contaminated soils with hazardous persistent organic pollutants, such as Polycyclic Aromatic Hydrocarbons (PAHs), present significant challenges for efficient remediation, attributable to their low mobility and bioavailability. The technique of Propylene Glycol (PG)-mixed steam enhanced extraction has emerged as a promising remediation technology, markedly increasing the solubilization and...
Machine/deep learning (ML/DL) have emerged as powerful tools for driving science innovation. These methods tend to be data hungry, with large volumes that are not likely to be collocated. Further, industry or professional companies typically have a vested interest in keeping their data private. Recent work has shown the viability of federated learning (FL), a ML /DL framework where multiple...
Physics-informed neural network (PINN) is an innovative universal function approximator which adds physical constraints to neural network to make the fitting results satisfy the physical laws better. In this paper, a physics-informed residual network (PIResNet) is proposed to solve the single-phase seepage equation without labeled data. The loss function is constructed by summarizing the...
ABSTRACT
In subsurface flow settings, deep-learning-based surrogate modeling is shown to be an effective approach to deal with cases that require a substantial amount of model simulations. However, a large number of high-fidelity training simulations are usually required to construct these deep-learning-based surrogate models. For large-scale models, it can be computationally prohibitive to...
X-ray imaging has become an indispensable tool in the study of porous media, significantly enhancing our understanding of multiphase flow within these pore structures. High-resolution X-ray images enable researchers to accurately measure or calculate critical rock properties such as porosity, interfacial surface area, curvature, and contact angle distributions. These images are also pivotal in...
Numerical simulation is a vital tool for analyzing and predicting fluid flow in porous media. Physics-Informed Neural Networks (PINNs) can work out systems of partial differential equations (PDEs) by leveraging the universal approximation ability of Neural Network (NN), offering a novel approach for numerical model solving. However, current PINN-based methods are rarely used to simulate...
Brine-gas interfacial tension (γ) is an essential parameter to determine fluid dynamics, trapping and distributions at pore-scale, thus influencing gas storage capacities and securities at reservoir-scale. However, γ is a complex function of pressure, temperature, ionic strength and gas composition, thus very time-consuming and costly to cover all these influencing factors by experiment....
Reactive flows in porous media play an important role in our life and are crucial for many industrial, environmental and biomedical applications. Very often the concentration of the species at the inlet is known, and the so-called breakthrough curves, namely the time dependent concentration at the outlet, are the quantities which could be measured or computed numerically. The measurements and...
The carbonate reservoir in the first member of Maokou Formation (Maokou-1 Member) of Middle Permian in Sichuan Basin have the characteristics of self-generating and, self-storage. Maokou-1 Member is expected to become a new field of unconventional gas reservoir exploration in carbonate rocks. The organic matter and pore development of Maokou-1 Member carbonate rocks are closely related to...
