Machine Learning Enabled Fracture Network Imaging Using The Wirelessly Powered Smart Microchip Proppants Technology

• doi: 10.1109/IST50367.2021.9651346
• title: Machine Learning Enabled Fracture Network Imaging Using The Wirelessly powered Smart Microchip Proppants Technology
• publisher: IEEE
• isbn: 978-1-7281-7372-6
• issn: 1558-2809
• rank: 2971
• access_type: LOCKED
• content_type: Conferences
• abstract: Fracture mapping is a longstanding necessity in understanding the behavior of rocks before, during, and after hydraulic fracturing. High-resolution subsurface imaging (i.e borehole imaging) to interpret fracture networks enables improving the simulation model and ML-based surveillance tools to monitor the created fracture geometry. This paper seeks to expand the interpretation from borehole imaging using the geo-sensor data generated from 40MHz Wirelessly-Powered Smart Microchips Proppants or abbreviated as SMPs (using 180 nm CMOS technology). In this paper, an approach that uses the novel SMPs’ data to map fracture geometry more accurately is proposed, namely Intelligent Fracture Diagnostic Procedure (IGSFD). This workflow processes the transmissible data from the SMPs and generates the predictive fracture network image(s). I-GSFD is integrated between multi-dimensional projection, unsupervised clustering, and image processing algorithms. Affine transformation and a shallow ANN are integrated to control the quality of clustering.I-GSFD proves its efficacy in fracture mapping for the 3 in house designed 3D synthetic fracture networks with 100% consistency, between 75-100% in prediction capability, and between 75-100% in execution robustness. The low bounds of prediction capability (75%) and execution robustness (75%) scores are caused by the “joint” locations in the synthetic fracture network with the highest complexity. Affine transformation and the shallow ANN increase the quality of unsupervised clustering in I-GSFD. However, the joints” in one of the synthetic 3D fracture networks are still a challenge for the I-GSFD platform.
• article_number: 9651346
• pdf_url: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9651346
• html_url: https://ieeexplore.ieee.org/document/9651346/
• abstract_url: https://ieeexplore.ieee.org/document/9651346/
• publication_title: 2021 IEEE International Conference on Imaging Systems and Techniques (IST)
• conference_location: Kaohsiung, Taiwan
• conference_dates: 24-26 Aug. 2021
• publication_number: 9651277
• is_number: 9651324
• publication_year: 2021
• publication_date: 24-26 Aug. 2021
• start_page: 1
• end_page: 5
• citing_paper_count: 0
• citing_patent_count: 0
• download_count: 94
• insert_date: 20211227
• index_terms:
  • ieee_terms:
    • Integrated circuits
    • Semiconductor device modeling
    • Geometry
    • Three-dimensional displays
    • Surveillance
    • Imaging
    • Prediction algorithms
  • author_terms:
    • I-Geo Sensing Fracture Diagnostic
    • unsupervised clustering and learning
    • multi-dimensional projection
    • complex fracture modeling
    • Smart Microchip Proppants
  • dynamic_index_terms:
    • Fracture Network
    • Clustering Algorithm
    • Unsupervised Clustering
    • Predictive Capability
    • Affine Transformation
    • Projection Algorithm
    • Hydraulic Fracturing
    • Synthetic Networks
    • Robust Score
    • Hyperparameters
    • K-means
    • K-means Clustering
    • Evaluation Criteria
    • Evaluation Criterion
    • Optical Fiber
    • Fiber-optic
    • Optical Fibre
    • Use Of Imaging
    • Image Manipulation
    • Hyperparameter Tuning
    • Hyperparameter Optimization
    • Latent Space
    • Embedding Space
    • Imaging Tool
    • Synthetic Images
    • Energy Industry
    • Industrial Energy
    • Consistency Score
    • Absolute Distance
    • Use Of Transformation
• isbn_formats:
  • format: Print on Demand(PoD) ISBN, value: 978-1-7281-7372-6, isbnType: New-2005
  • format: Electronic ISBN, value: 978-1-7281-7371-9, isbnType: New-2005
• authors:
  • Author Name: Vuong Van Pham
    Affiliation: Chemical and Petroleum Engineering, University of Kansas, Lawrence, KS, US
    Author URL: https://ieeexplore.ieee.org/author/37089212928
    ID: 37089212928
    Order: 1
    Author Affiliations:
    • Chemical and Petroleum Engineering, University of Kansas, Lawrence, KS, US
  • Author Name: Amirmasoud Kalantari Dahaghi
    Affiliation: Chemical and Petroleum Engineering, University of Kansas, Lawrence, KS, US
    Author URL: https://ieeexplore.ieee.org/author/37088977264
    ID: 37088977264
    Order: 2
    Author Affiliations:
    • Chemical and Petroleum Engineering, University of Kansas, Lawrence, KS, US
  • Author Name: Shahin Negahban
    Affiliation: Chemical and Petroleum Engineering, University of Kansas, Lawrence, KS, US
    Author URL: https://ieeexplore.ieee.org/author/37088979591
    ID: 37088979591
    Order: 3
    Author Affiliations:
    • Chemical and Petroleum Engineering, University of Kansas, Lawrence, KS, US
  • Author Name: William Fincham
    Affiliation: The US Department of Energy, National Energy Technology Laboratory, Washington, D.C, US
    Author URL: https://ieeexplore.ieee.org/author/37088978863
    ID: 37088978863
    Order: 4
    Author Affiliations:
    • The US Department of Energy, National Energy Technology Laboratory, Washington, D.C, US
  • Author Name: Aydin Babakhani
    Affiliation: Department at UCLA, Samueli Electrical and Computer Engineering, Los Angeles, California, US
    Author URL: https://ieeexplore.ieee.org/author/37400339000
    ID: 37400339000
    Order: 5
    Author Affiliations:
    • Department at UCLA, Samueli Electrical and Computer Engineering, Los Angeles, California, US

Image Sensor

• sensor_type: CMOS
• resolution: Not specified
• dynamic_range: Not specified
• pixel_size: Not specified
• dark_current: Not specified

Optical Data

• focal_length: Not specified
• aperture: Not specified
• field_of_view: Not specified
• distortion: Not specified

Performance Metrics

Applications & Benefits

• cell_imaging: Not specified
• benefits: Not specified

Supporting Organizations

• supported_by: US Department of Energy (DOE)

Manuscript Details

• publication_date: 24-26 Aug. 2021

Relevancy Score

• score: 5
• missing_fields:
  • resolution
  • dynamic_range
  • pixel_size
  • dark_current
  • focal_length
  • aperture
  • field_of_view
  • distortion
  • frame_rate
  • signal_to_noise_ratio
  • sensitivity
  • shutter_speed
  • power_consumption
  • noise
  • cell_imaging
  • benefits

Processed JSON Filename

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