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Application of Artificial Intelligence and Big Data in Oil and Gas Exploration and Exploitation October 19-21

Published
American Association of Petroleum Geologists (AAPG)

With the rapid development of science and technology, artificial intelligence, big data and other analytics methods have applied in various fields of social life, industrial development and scientific research. More people are applying analytics to explore reservoirs and manage upstream operations. To strength communication and academic exchange in upstream oil and gas industry, especially in artificial intelligence and big data research results and application experience, AAPG and State Key Laboratory of Oil & Gas Reservoir Geology and Exploitation will hold a conference named "Application of Artificial Intelligence and Big Data in Oil and Gas Exploration and Exploitation" in October 2019. Warmly welcome experts and colleagues to attend the symposium.

Sponsors
  • State Key Laboratory of Oil & Gas Reservoir Geology and Exploitation
  • American Association of Petroleum Geologists (AAPG)
  • Geological Society of China
  • Chinese Petroleum Society
Organizers
  • State Key Laboratory of Oil & Gas Reservoir Geology and Exploitation
  • American Association of Petroleum Geologists (AAPG)
  • Chengdu University of Technology
  • Southwest Petroleum University
Co-sponsors
  • Oil and Gas Survey Center, China Geological Survey Bureau
  • Research Institute of Petroleum Exploration & Development
  • Sinopec Petroleum Exploration and Production Research Institute
  • China United Coalbed Methane Co., Ltd
  • Guangzhou Institute of Geochemistry, Chinese Academy of Sciences
  • Sinopec Southwest Oil & Gas Company
  • Sinopec Exploration Branch Company
  • Petrochina Changqing Oilfield Company
  • Peking University
  • Nanjing University
  • Tongji University
  • Zhejiang University
  • Chongqing University
  • Northwest University
  • Jilin University
  • China University of Geosciences (Beijing)
  • China University of Geosciences (Wuhan)
  • China University of Petroleum (Beijing)
  • China University of Petroleum (East China)
  • Xi'an Shiyou University
  • Northeast Petroleum University
  • Yangtze University
  • Chongqing University of Science & Technology
  • Central South University
  • Hainan University
Academic Committee

Chairs:  Ma Yongsheng,  Susan Nash

Co-Chairs:  Jia Chengzao,  Yuan Shiyi

Members

  • Kang Yuzhu
  • Luo Pingya
  • Zhou Shouwei
  • Wang Chengshan
  • Jin Zhijun
  • Zhao Wenzhi
  • Sun Longde
  • Li Yang
  • Zou Caineng
  • Sun Jinsheng
  • Yang Chunhe
  • Xiao Xianming
  • Liu Zhifei
  • Wang Yunpeng
  • Liu Qingyou
  • Zhao Jinzhou
  • Liu Shugen
  • Yitian Xiao
  • David Leary
  • Michael Abrams
  • Tongwei Zhang
  • Qinhong Hu
  • John Yilin Wang
  • Hairuo Qing
  • Jiashun Yu
Organizing Committee

Chairs:  Zhou Shouwei,  Susan Nash

Co-Chairs:  Liu Qingyou,  Zhao Jinzhou,  Yitian Xiao,  Liu Shugen

Members:

  • Cao Junxing
  • Chen Hongde
  • Chen Shipou
  • Chen Xuehua
  • Deng Bin
  • Deng Hucheng
  • Guo Jianchun
  • Guo Xiao
  • Hou Mingcai
  • Li Yong
  • Li Xiaoping
  • Li Zhongquan
  • Li Xiangjun
  • Lu Zhengyuan
  • Ran Bo
  • Wang Xingzhi
  • Zhou Wen
  • Tongwei Zhang
  • Qinhong Hu
  • Hairuo Qing

General Secretary:  Zhou Wen,  David Leary

Under General Secretary  Guo Xiao,  Lu Zhengyuan

Topics
1. Big Data, Machine Learning, Deep Learning in Exploration and Production

Use of analytics in upstream of oil and gas industry, such as seismic acquisition and processing, petrological analysis, core information, geochemistry, Geomechanics and Converting unstructured into structured data.

2. Analytics for Finding and Producing Hydrocarbons

Including: seismic imaging breakthroughs, reservoir characterization techniques, Migration pathways and preferential enrichment (sweet spots), Summary of new applications, programs, cloud-based services

3. Predicting Fluid Behaviors in Reservoirs

Including: Predicting in fluid behaviors in the reservoir, drilling, completion and production

4. Analytics in Enhanced Oil Recovery in Mature Fields

Including: machine learning for enhanced recovery, harnessing sensors and the Industrial Internet of Things (IIOT), deep learning and pattern recognition for optimizing water flooding and waste discharge, dynamic modeling for production, using hybrid solutions (solar, wind, etc.)

5. Other researches and applications of artificial intelligence and big data analysis related to oil and gas industry
Date and Venue
  • Date  October 19-21, 2019
  • Venue  International Conference Center @ Teaching Building No.9, Chengdu University of Technology, China.
Preliminary Schedule
  • Oct. 25, 2019: Onsite registration
  • Oct. 26, 2019: Opening ceremony & Keynote speeches
  • Oct. 27, 2019: Keynote speeches & Closing ceremony
Fees
  • Author Registration: USD $300 (CNY ¥2000)
  • Student or Accompanying Person Registration: USD $150 (CNY ¥1000)
Communication Form

Chinese and English, on-site simultaneous interpretation and exchange form translation will be provided.

The conference sets up different types of academic exchanges and reports such as plenary speeches and poster presentations. (Sub-sessions presentations will be set up according to the number of participants)

Presentation certifications will be provided to the presenters after each presentation in sub-sessions.

Abstract Requirements
  1. The submission only requires abstracts, using Chinese or English (English: 600-1000 words; Chinese: 500-800 words).
  2. Please submit your abstract by E-mail to and .

The submission deadline is August 31, 2019.

Conference Contacts
  • Wang Xingjian
    Tel: 138 8201 9827; 028-84079071
    E-mail: ;
  • Meng Wanbin
    Tel: 136 8838 8100
    E-mail:
    Correspondence address: No. 1, Erxianqiao East 3td Road, Chenghua District, Chengdu Sichuan, 610059.
  • Hao Xu
    Tel: 18782961672
    E-mail:
    Correspondence address: No. 1, Erxianqiao East 3td Road, Chenghua District, Chengdu Sichuan, 610059.
Others

Conference notification website: Chengdu University of Technology home page (http://www.cdut.edu.cn); AAPG home page (https://www.aapg.org).

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