Research Note: Sparsity‐Aware Multiple Microseismic Event Localization Blind to the Source Time‐Function

Jamali-Rad, Hadi; Tang, Zijian; Campman, Xander; Droujinine, Alexander; Leus, Geert

doi:10.1111/1365-2478.12166

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…Microseismic fracture monitoring (MFM) provides important information about volumetric stress/strain and failure mechanisms in reservoirs and, thus, helps analyse and affect the productivity level of wells using hydraulic fracturing (LeCampion, Jeffrey, and Detournay 2004;Jamali-Rad et al 2015). The hydraulic fracturing process is shown in Fig.…”

Section: Microseismic Fracture Monitoringmentioning

confidence: 99%

Internet of Things‐based wireless networking for seismic applications

Jamali-Rad

Campman

2018

Geophysical Prospecting

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There is growing pressure from regulators on operators to adhere to increasingly stricter regulations related to the environment and safety. Hence, operators are required to predict and contain risks related to hydrocarbon production and their infrastructure in order to maintain their licence to operate. A deeper understanding of production optimisation and production‐related risk requires strengthened knowledge of reservoir behaviour and overburden dynamics. To accomplish this, sufficient temporal and spatial resolution is required as well as an integration of various sources of measurements. At the same time, tremendous developments are taking place in sensors, networks, and data analysis technologies. Sensors and accompanying channels are getting smaller and cheaper, and yet they offer high fidelity. New ecosystems of ubiquitous wireless communications including Internet of Things nowadays allow anyone to affordably connect to the Internet at any time and anywhere. Recent advances in cloud storage and computing combined with data analytics allow fast and efficient solutions to handle considerable amounts of data. This paper is an effort to pave the way for exploiting these three fundamental advances to create Internet of Things‐based wireless networks of seismic sensors. To this aim, we propose to employ a recently developed Internet of Things‐based wireless technology, so‐called low‐power wide‐area networks, to exploit their long range, low power, and inherent compatibility to cloud storage and computing. We create a remotely operated minimum‐maintenance wireless solution for four major seismic applications of interest. By proposing appropriate network architecture and data coordination (aggregation and transmission) designs, we show that neither the low data rate nor the low duty cycle of low‐power wide‐area networks imposes fundamental issues in handling a considerable amount of data created by complex seismic scenarios as long as the application is delay tolerant. In order to confirm this claim, we cast our ideas into a practical large‐scale networking design for simultaneous seismic monitoring and interferometry and carry out an analysis on the data generation and transmission rates. Finally, we present some results from a small‐scale field test in which we have employed our Internet of Things‐based wireless nodes for real‐time seismic quality control over clouds.

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Section: Microseismic Fracture Monitoringmentioning

confidence: 99%

Internet of Things‐based wireless networking for seismic applications

Jamali-Rad

Campman

2018

Geophysical Prospecting

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“…Microseismic fracture monitoring (MFM) provides important information about volumetric stress/strain and failure mechanisms in reservoirs and thus helps to analyze and affect the productivity level of wells using hydraulic fracturing (Jamali-Rad et al (2015); LeCampion et al (2004)). The hydraulic fracturing process is shown in Fig.…”

Section: Microseismic Fracture Monitoring (Mfm)mentioning

confidence: 99%

IoT-based Wireless Networking for Geoscience Applications

Jamali-Rad¹,

Campman²

2017

79th EAGE Conference and Exhibition 2017 - Workshops

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There is growing pressure from regulators on operators to adhere to increasingly stricter regulations related to the environment and safety. Hence operators are required to predict and contain risks related to hydrocarbon production and their infrastructure in order to maintain their license to operate. A deeper understanding of production optimization and production-related risk requires strengthened knowledge of reservoir behavior and overburden dynamics. To accomplish this, sufficient temporal and spatial resolution is required as well as an integration of various sources of measurements. At the same time, tremendous developments are taking place in sensors, networks, and data analysis technologies. Sensors and accompanying channels are getting smaller and cheaper and yet they offer high fidelity. New ecosystems of ubiquitous wireless communications including Internet of Things (IoT) nowadays allow anyone to affordably connect to the Internet at any time and anywhere. Recent advances in cloud storage and computing combined with data analytics allow fast and efficient solutions to handle considerable amounts of data. This paper is an effort to pave the way for exploiting these three fundamental advances to create IoT-based wireless networks of seismic sensors.To this aim, we propose to employ a recently developed IoT-based wireless technology, so called lowpower wide-area networks (LPWANs), to exploit their long range, low power, and inherent compatibility to cloud storage and computing. We create a remotely-operated minimum-maintenance wireless solution for four major seismic applications of interest. By proposing appropriate network architecture and data coordination (aggregation and transmission) designs we show that neither the low data-rate nor the low duty-cycle of LPWANs impose fundamental issues in handling a considerable amount of data created by complex seismic scenarios as long as the application is delay-tolerant. In order to confirm this claim, we cast our ideas into a practical large-scale networking design for simultaneous seismic monitoring and interferometry and carry out an analysis on the data generation and transmission rates. Finally, we

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“…Besides the source location and function, the source mechanism inversion is also critical for passive seismic data. A frequency domain method is proposed to localize micro-seismic events blind to the source time function [22]. However, they confine their search to the hypo-centers of the sources and the normalized amplitudes of the moment tensors.…”

Section: Introductionmentioning

confidence: 99%

Elastic Micro-Seismic Full Waveform Inversion with an Unknown Source

Wang¹,

Alkhalifah²

2018

Proceedings

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In micro-seismic event measurements, pinpointing the passive source's exact spatial and temporal location is paramount. This research advocates for the combined use of both P-and S-wave data, captured by geophone monitoring systems, to improve source inversion accuracy. Drawing inspiration from the secondary source concept in Elastic Reflection Full Waveform Inversion (ERFWI), we introduce an equivalent source term. This term combines source functions and source images. Our optimization strategy iteratively refines the spatial locations of the source, its temporal functions, and associated velocities using a full waveform inversion framework. Under the premise of an isotropic medium with consistent density, the source is defined by two spatial and three temporal components. This offers a nuanced source representation in contrast to the conventional seismic moment tensor. To address gradient computation, we employ the adjoint-state method. However, we encountered pronounced non-linearity in waveform inversion of micro-seismic events, primarily due to the unknown source origin time, resulting in cycle skipping challenges. To counteract this, we devised an objective function that is decoupled from the source origin time. This function is formulated by convolving reference traces with both observed and predicted data. Through the concurrent inversion of the source image, source time function, and velocity model, our method offers precise estimations of these parameters, as validated by a synthetic 2D example based on a modified Marmousi model. This nested inversion approach promises enhanced accuracy in determining the source image, time function, and velocity model.Preprint. Under review.

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Research Note: Sparsity‐Aware Multiple Microseismic Event Localization Blind to the Source Time‐Function

Cited by 5 publications

References 18 publications

Internet of Things‐based wireless networking for seismic applications

Internet of Things‐based wireless networking for seismic applications

IoT-based Wireless Networking for Geoscience Applications

Elastic Micro-Seismic Full Waveform Inversion with an Unknown Source

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