Identifying the Types of Loading Mode for Rock Fracture via Convolutional Neural Networks

Zhang, Zhenguo; Zhang, Gongheng; Huang, Jie; Wu, Mingyang

doi:10.1029/2021jb022532

Cited by 16 publications

(4 citation statements)

References 43 publications

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“…By focusing the attention of the neural networks on specific features, fracture loading mode (Z. Song et al., 2022) and fracture saturation (Nolte & Pyrak‐Nolte, 2022) are successfully inferred from the laboratory earthquakes. Similarly, although the deterministic prediction of time‐to‐failure in natural environments remains elusive, a couple of studies (Jasperson et al., 2021; Shreedharan et al., 2021) show that ML has the ability to predict time‐to‐failure and the stress state from the laboratory earthquake data.…”

Section: Highlightsmentioning

confidence: 99%

Machine Learning Developments and Applications in Solid‐Earth Geosciences: Fad or Future?

O’Malley

Beroza

et al. 2023

JGR Solid Earth

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After decades of low but continuing activity, applications of machine learning (ML) in solid Earth geoscience have exploded in popularity. This special collection provides a snapshot of those applications, which range from data processing to inversion and interpretation, for which ML appears particularly well suited. Inevitably, there are variations in the degree to which these methods have been developed. We hope that the progress seen in some areas will inspire efforts in others. Challenges remain, including the formidable task of how geoscience can keep pace with developments in ML while ensuring the scientific rigor that our field depends on, but with improvements in sensor technology and accelerating rates of data accumulation, the methods of ML seem poised to play an important role for the foreseeable future.

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Section: Highlightsmentioning

confidence: 99%

Machine Learning Developments and Applications in Solid‐Earth Geosciences: Fad or Future?

O’Malley

Beroza

et al. 2023

JGR Solid Earth

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“…At present, fracture network volume fracturing is the primary technology for efficient development of unconventional oil and gas [3][4][5]. In the process of fracturing, natural fractures expand continuously, and brittle rocks produce shear slip, forming a fracture network with staggered natural fractures and artificial fractures to increase the reconstruction volume and improve the initial production and final recovery.…”

Section: Introductionmentioning

confidence: 99%

Study on Local Brittleness of Rock Based on Multiple Linear Regression Method: Case Study of Shahejie Formation

et al. 2023

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Rock brittleness index is an important parameter that affects the volume fracturing effect of an unconventional reservoir. The higher the brittleness index, the larger the communication area of fracture network formed under volume fracturing. However, rock brittleness has strong heterogeneity, which seriously affects the propagation of volume fracture network. Most existing rock brittleness characterization methods start from macro brittleness and cannot analyze the influence of heterogeneity on reservoir brittleness. The mineral composition analysis method can characterize the heterogeneity of a reservoir by analyzing the influence of mineral composition on brittleness, but this is limited to qualitative analysis on different minerals, resulting in large errors. In this study, the relationship between macro mechanical properties and micro mineral composition of rocks was analyzed using multiple linear regression analysis and a large number of experimental core parameters on the Shahejie Formation to understand the quantitative characterization of rock brittleness distribution in a heterogeneous reservoir. The results showed that quartz, plagioclase, analcite, siderite, and pyrite were high brittle minerals while clay minerals were low brittle minerals. This method provides a new focus for the analysis of brittle distribution of heterogeneous reservoirs and an additional method to study rock fracture law.

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“…More complex than parameterization of AE signals is the use of complete waveforms. Representative for many applications in the last years only three recent publications are quoted [8][9][10]. In the first publication, fully convolutional neural networks (FCNN) are used for source localization of microseismic events in civil engineering.…”

Section: Introductionmentioning

confidence: 99%

“…In the second publication, deep learning methods were used to classify rock fractures under different loading conditions in the laboratory. This involved converting AE waveforms into timefrequency images, and then using multiple convolutional neural networks (CNNs) to determine the loading modes of rock fractures [9]. The third paper reported the application of convolutional neural networks to electrocardiograms time series forming structures analogous to seismic waveforms in medical technology for predictive detection of myocardial scars [10].…”

Section: Introductionmentioning

confidence: 99%

Classification of Located Acoustic Emission Events Using Neural Network

Manthei

Guckert

2022

J Nondestruct Eval

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Location of acoustic emission (AE) events is one of the main evaluation tools in AE analysis. Reliable location of AE sources enables accurate investigation of the mechanisms that led to a crack in the material. It is known that the location errors are influenced by several factors, including the accuracy of the elastic wave arrival time reading, the geometric distribution of the AE sensors, and most importantly, by the physical properties of the propagation medium. The aim of this study is the application of a neural network to classify clustered AE events, which were detected during six hydraulic fracturing tests in massive salt rock. A fully connected feed forward network was used for pattern recognition and classification of the input events according to target classes. For input data the signal arrival time profiles of the longitudinal (L) and transversal (T) elastic waves were used to train, to test, and to validate the neural network. In total 765 AE events were classified in various target classes. Receiver operating characteristic analysis (ROC) was applied for analyzing the result of the neural network approach. The neural network classified clustered events correctly, while few spatially scattered events outside the region of a cluster could not be matched to any cluster. Bootstrap analysis showed that the results are robust and demonstrates the high potential of Deep Learning (DL) methods in the location of AE events.

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Identifying the Types of Loading Mode for Rock Fracture via Convolutional Neural Networks

Cited by 16 publications

References 43 publications

Machine Learning Developments and Applications in Solid‐Earth Geosciences: Fad or Future?

Machine Learning Developments and Applications in Solid‐Earth Geosciences: Fad or Future?

Study on Local Brittleness of Rock Based on Multiple Linear Regression Method: Case Study of Shahejie Formation

Classification of Located Acoustic Emission Events Using Neural Network

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