3,644 research outputs found
Bayesian Learning of Gas Transport in Three-Dimensional Fracture Networks
Modeling gas flow through fractures of subsurface rock is a particularly
challenging problem because of the heterogeneous nature of the material.
High-fidelity simulations using discrete fracture network (DFN) models are one
methodology for predicting gas particle breakthrough times at the surface, but
are computationally demanding. We propose a Bayesian machine learning method
that serves as an efficient surrogate model, or emulator, for these
three-dimensional DFN simulations. Our model trains on a small quantity of
simulation data and, using a graph/path-based decomposition of the fracture
network, rapidly predicts quantiles of the breakthrough time distribution. The
approach, based on Gaussian Process Regression (GPR), outputs predictions that
are within 20-30% of high-fidelity DFN simulation results. Unlike previously
proposed methods, it also provides uncertainty quantification, outputting
confidence intervals that are essential given the uncertainty inherent in
subsurface modeling. Our trained model runs within a fraction of a second,
which is considerably faster than other methods with comparable accuracy and
multiple orders of magnitude faster than high-fidelity simulations
Graph-Informed Neural Networks for Regressions on Graph-Structured Data
In this work, we extend the formulation of the spatial-based graph convolutional networks with a new architecture, called the graph-informed neural network (GINN). This new architecture is specifically designed for regression tasks on graph-structured data that are not suitable for the well-known graph neural networks, such as the regression of functions with the domain and codomain defined on two sets of values for the vertices of a graph. In particular, we formulate a new graph-informed (GI) layer that exploits the adjacent matrix of a given graph to define the unit connections in the neural network architecture, describing a new convolution operation for inputs associated with the vertices of the graph. We study the new GINN models with respect to two maximum-flow test problems of stochastic flow networks. GINNs show very good regression abilities and interesting potentialities. Moreover, we conclude by describing a real-world application of the GINNs to a flux regression problem in underground networks of fractures
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Big Data in the Oil and Gas Industry: A Promising Courtship
The energy industry remains one of the highest money-producing and investment industries in the world. The United States’ own economic stability depends greatly on the stability of oil and gas prices. Various factors affect the amount of money that will continue to be invested in producing oil. A main disadvantage to the oil and gas industry is its lack of technological adaptation. This weakens the industry because the surest measures are not currently being taken to produce oil in optimally efficient, safe, and cost-effective ways. Big data has gained global recognition as an opportunity to gather large volumes of information in real-time and translate data sets into actionable insights. In a low commodity price environment, saving time, reducing costs, and improving safety are crucial outcomes that can be realized using machine learning in oil and gas operations. Big data provides the opportunity to use unsupervised learning. For example, with this approach, engineers can predict oil wells’ optimal barrels of production given the completion data in a specific area. However, a caveat to utilizing big data in the oil and gas industry is that there simply is neither enough physical data nor data velocity in the industry to be properly referred to as “big data.” Big data, as it develops, will nonetheless significantly change the energy business in the future, as it already has in various other industries.Petroleum and Geosystems Engineerin
Modeling and design of heterogeneous hierarchical bioinspired spider web structures using generative deep learning and additive manufacturing
Spider webs are incredible biological structures, comprising thin but strong
silk filament and arranged into complex hierarchical architectures with
striking mechanical properties (e.g., lightweight but high strength, achieving
diverse mechanical responses). While simple 2D orb webs can easily be mimicked,
the modeling and synthesis of 3D-based web structures remain challenging,
partly due to the rich set of design features. Here we provide a detailed
analysis of the heterogenous graph structures of spider webs, and use deep
learning as a way to model and then synthesize artificial, bio-inspired 3D web
structures. The generative AI models are conditioned based on key geometric
parameters (including average edge length, number of nodes, average node
degree, and others). To identify graph construction principles, we use
inductive representation sampling of large experimentally determined spider web
graphs, to yield a dataset that is used to train three conditional generative
models: 1) An analog diffusion model inspired by nonequilibrium thermodynamics,
with sparse neighbor representation, 2) a discrete diffusion model with full
neighbor representation, and 3) an autoregressive transformer architecture with
full neighbor representation. All three models are scalable, produce complex,
de novo bio-inspired spider web mimics, and successfully construct graphs that
meet the design objectives. We further propose algorithm that assembles web
samples produced by the generative models into larger-scale structures based on
a series of geometric design targets, including helical and parametric shapes,
mimicking, and extending natural design principles towards integration with
diverging engineering objectives. Several webs are manufactured using 3D
printing and tested to assess mechanical properties
Performance Analysis of Multi-Task Deep Learning Models for Flux Regression in Discrete Fracture Networks
In this work, we investigate the sensitivity of a family of multi-task Deep Neural Networks (DNN) trained to predict fluxes through given Discrete Fracture Networks (DFNs), stochastically varying the fracture transmissivities. In particular, detailed performance and reliability analyses of more than two hundred Neural Networks (NN) are performed, training the models on sets of an increasing number of numerical simulations made on several DFNs with two fixed geometries (158 fractures and 385 fractures) and different transmissibility configurations. A quantitative evaluation of the trained NN predictions is proposed, and rules fitting the observed behavior are provided to predict the number of training simulations that are required for a given accuracy with respect to the variability in the stochastic distribution of the fracture transmissivities. A rule for estimating the cardinality of the training dataset for different configurations is proposed. From the analysis performed, an interesting regularity of the NN behaviors is observed, despite the stochasticity that imbues the whole training process. The proposed approach can be relevant for the use of deep learning models as model reduction methods in the framework of uncertainty quantification analysis for fracture networks and can be extended to similar geological problems (for example, to the more complex discrete fracture matrix models). The results of this study have the potential to grant concrete advantages to real underground flow characterization problems, making computational costs less expensive through the use of NNs
Designing Volumetric Truss Structures
We present the first algorithm for designing volumetric Michell Trusses. Our
method uses a parametrization approach to generate trusses made of structural
elements aligned with the primary direction of an object's stress field. Such
trusses exhibit high strength-to-weight ratios. We demonstrate the structural
robustness of our designs via a posteriori physical simulation. We believe our
algorithm serves as an important complement to existing structural optimization
tools and as a novel standalone design tool itself
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