MY210316A - Computer-implemented method and system for predicting geohazard risk or pipeline strain in relation to a pipeline system using machine learning - Google Patents

Computer-implemented method and system for predicting geohazard risk or pipeline strain in relation to a pipeline system using machine learning

Info

Publication number
MY210316A
MY210316A MYPI2022006971A MYPI2022006971A MY210316A MY 210316 A MY210316 A MY 210316A MY PI2022006971 A MYPI2022006971 A MY PI2022006971A MY PI2022006971 A MYPI2022006971 A MY PI2022006971A MY 210316 A MY210316 A MY 210316A
Authority
MY
Malaysia
Prior art keywords
pipeline
training
target
machine learning
geohazard
Prior art date
Application number
MYPI2022006971A
Inventor
Joehan ROHANI Muhammad
Syazwan Kamil Abdullah M
Putra I WAYAN Eka
Amirian Ehsan
HIDZIR Hazwani
Sukri HASAN M
Fazren Helmi Nordin M
Original Assignee
Petroliam Nasional Berhad Petronas
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Petroliam Nasional Berhad Petronas filed Critical Petroliam Nasional Berhad Petronas
Priority to MYPI2022006971A priority Critical patent/MY210316A/en
Priority to PCT/MY2023/050096 priority patent/WO2024123170A1/en
Priority to CN202380093553.3A priority patent/CN121359141A/en
Priority to MX2025006591A priority patent/MX2025006591A/en
Publication of MY210316A publication Critical patent/MY210316A/en

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/18—Network design, e.g. design based on topological or interconnect aspects of utility systems, piping, heating ventilation air conditioning [HVAC] or cabling
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V99/00—Subject matter not provided for in other groups of this subclass
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/14—Pipes
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Software Systems (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Medical Informatics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Geometry (AREA)
  • Data Mining & Analysis (AREA)
  • Mathematical Physics (AREA)
  • Computing Systems (AREA)
  • Computer Hardware Design (AREA)
  • Computational Linguistics (AREA)
  • Molecular Biology (AREA)
  • General Health & Medical Sciences (AREA)
  • Biophysics (AREA)
  • Biomedical Technology (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Computational Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Pit Excavations, Shoring, Fill Or Stabilisation Of Slopes (AREA)

Abstract

A computer implemented method (300) for predicting geohazard risk or pipeline strain in relation to a pipeline system using machine learning is described. In an embodiment, the method (300) comprises: (i) receiving a plurality of training data sets for discrete locations of a geographical area (302), each training data set comprising training pipeline inspection data and training geophysical and geological data associated with a respective discrete location of the geographical area; (ii) processing the plurality of training data sets to generate a plurality of training score sets (304), each training score set comprising training pipeline hazard scores and training geophysical and geological hazard scores associated with each of the discrete locations; (iii) training a machine learning model using the plurality of training score sets (306) to obtain a trained machine learning model (242); (iv) receiving a target data set for a target location (308) associated with the pipeline system, the target data set comprising target pipeline inspection data and target geophysical and geological data; (v) processing the target data set to generate a target score set (310) comprising pipeline hazard scores and geophysical and geological hazard scores associated with the target location; and (vi) generating a predicted geohazard risk value or a predicted pipeline strain value for the target location using the trained machine learning model (312) in response to the target score set. A machine learning system (200) for predicting geohazard risk or pipeline strain is also described.
MYPI2022006971A 2022-12-08 2022-12-08 Computer-implemented method and system for predicting geohazard risk or pipeline strain in relation to a pipeline system using machine learning MY210316A (en)

Priority Applications (4)

Application Number Priority Date Filing Date Title
MYPI2022006971A MY210316A (en) 2022-12-08 2022-12-08 Computer-implemented method and system for predicting geohazard risk or pipeline strain in relation to a pipeline system using machine learning
PCT/MY2023/050096 WO2024123170A1 (en) 2022-12-08 2023-12-08 Computer-implemented method and system for predicting geohazard risk or pipeline strain in relation to a pipeline system using machine learning
CN202380093553.3A CN121359141A (en) 2022-12-08 2023-12-08 Computer-based methods and systems for predicting geological hazard risks or pipeline strain associated with pipeline systems using machine learning.
MX2025006591A MX2025006591A (en) 2022-12-08 2025-06-05 Computer-implemented method and system for predicting geohazard risk or pipeline strain in relation to a pipeline system using machine learning

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
MYPI2022006971A MY210316A (en) 2022-12-08 2022-12-08 Computer-implemented method and system for predicting geohazard risk or pipeline strain in relation to a pipeline system using machine learning

Publications (1)

Publication Number Publication Date
MY210316A true MY210316A (en) 2025-09-11

Family

ID=91379866

Family Applications (1)

Application Number Title Priority Date Filing Date
MYPI2022006971A MY210316A (en) 2022-12-08 2022-12-08 Computer-implemented method and system for predicting geohazard risk or pipeline strain in relation to a pipeline system using machine learning

Country Status (4)

Country Link
CN (1) CN121359141A (en)
MX (1) MX2025006591A (en)
MY (1) MY210316A (en)
WO (1) WO2024123170A1 (en)

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119339597B (en) * 2024-09-05 2025-11-21 中铁十八局集团有限公司 System and method for maintenance training and emergency treatment based on shield tunneling machine cabin
CN119067456B (en) * 2024-11-05 2025-06-13 珠江水利委员会珠江水利科学研究院 A risk prevention and control information management method and system for water conservancy projects
CN119671278A (en) * 2024-12-04 2025-03-21 南方电网科学研究院有限责任公司 Method, device and computer equipment for assessing geological hazards along power grids
CN119942019B (en) * 2025-04-09 2025-06-20 中国海洋大学 Mountain area Gu Gaocheng reconstruction method based on paleobiological data and geochemical data
CN120634324B (en) * 2025-08-13 2025-11-11 成都秦川物联网科技股份有限公司 Debris Flow Emergency Monitoring System, Methods, and Media Based on Internet of Things Big Data Model

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11454354B2 (en) * 2017-03-28 2022-09-27 Nec Corporation Pipe diagnosis apparatus, asset management apparatus, pipe diagnosis method, and computer-readable recording medium
EP4078247B1 (en) * 2019-12-18 2025-10-29 Services Pétroliers Schlumberger Methods and systems for subsurface modeling employing ensemble machine learning prediction trained with data derived from at least one external model
CN114861477B (en) * 2021-02-03 2024-12-31 中国石油天然气股份有限公司 Casing reinforcement parameter determination method, device, computer equipment and storage medium
CN113011027B (en) * 2021-03-22 2022-04-01 中国航空油料集团有限公司 Main control factor identification method for landslide hazard of oil pipeline based on SPH-FEM coupling algorithm
CN115034767B (en) * 2022-07-15 2023-06-02 广州高新工程顾问有限公司 BIM-based intelligent control method and system for deep foundation pit construction safety

Also Published As

Publication number Publication date
CN121359141A (en) 2026-01-16
MX2025006591A (en) 2025-09-02
WO2024123170A1 (en) 2024-06-13

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