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To analyze a hydraulic jump in a rectangular channel with a flow speed of 6 meters per second, follow these steps:Calculate Effective Upstream Velocity:When the downstream gate closes, a hydraulic jump forms, traveling upstream at 2 meters per second. This wave speed combines with the initial channel flow velocity, creating an effective upstream velocity.Identify Flow Velocities Before and After the Hydraulic Jump:Upstream of the hydraulic jump, the effective flow velocity includes both the...
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A hydraulic jump is a sudden rise in fluid depth in open channels, occurring when high-velocity (supercritical) flow transitions to low-velocity (subcritical) flow. This phenomenon requires an upstream Froude number greater than 1, as flows with Fr1<1 remain subcritical, making a hydraulic jump impossible due to the need for negative head loss, which violates thermodynamic principles.The characteristics of a hydraulic jump depend on the upstream Froude number and are classified as...
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An Efficient Neural-Network-Based Microseismic Monitoring Platform for Hydraulic Fracture on an Edge Computing

Xiaopu Zhang1,2, Jun Lin3,4, Zubin Chen5,6

  • 1College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130061, China. xpzhang16@mails.jlu.edu.cn.

Sensors (Basel, Switzerland)
|June 8, 2018
PubMed
Summary

This study introduces an edge-computing platform for microseismic monitoring in oil and gas production. The Edge-to-Center LearnReduce platform enhances event detection accuracy and reduces data transmission by 90%.

Keywords:
edge computingevent detectionmicroseismic monitoringneural networksprobabilistic inference

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Area of Science:

  • Geophysics and petroleum engineering
  • Artificial intelligence in resource management
  • Data science for energy production

Background:

  • Microseismic monitoring is crucial for hydraulic fracturing in oil and gas.
  • Key challenges include poor signal-to-noise ratio (SNR) affecting accuracy and real-time data transmission limitations.
  • Existing methods struggle to balance detection accuracy and efficient data handling.

Purpose of the Study:

  • To present an edge-computing platform, Edge-to-Center LearnReduce, for enhanced microseismic monitoring.
  • To address the challenges of low SNR and real-time data transmission.
  • To improve both the accuracy and efficiency of event detection in hydraulic fracturing.

Main Methods:

  • Developed an edge-computing platform with a data center and edge components.
  • Trained a hybrid neural network (convolutional neural network and long short-term memory) at the data center.
  • Deployed the trained model to edge components with added probabilistic inference for real-time event detection and data reduction.

Main Results:

  • Achieved over 96% detection accuracy in microseismic monitoring.
  • Reduced transmitted data volume by approximately 90%.
  • Demonstrated simultaneous improvement in accuracy and efficiency.

Conclusions:

  • The Edge-to-Center LearnReduce platform effectively overcomes SNR and data transmission challenges in microseismic monitoring.
  • The proposed approach significantly enhances the performance of hydraulic fracturing operations.
  • Edge computing integrated with advanced AI models offers a viable solution for real-time resource management.