ANN based interwell connectivity analysis in cyber-physical petroleum systems

Haibo Cheng, Xiaoning Han, Peng Zeng, Haibin Yu, Evgeny Osipov, Valeriy Vyatkin

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

2 Citations (Scopus)


In cyber-physical petroleum systems (CPPS), accurate estimation of interwell connectivity is an important process to know reservoir properties comprehensively, determine water injection rate scientifically, and enhance oil recovery effectively for oil and gas (OG) field. In this study, an artificial neural network (ANN) based analysis method is proposed to estimate interwell connectivity. The generated neural network is used to define the mapping function between production wells and surrounding injection wells based on the historical water injection and liquid production data. Finally, the proposed method is applied to a synthetic reservoir model. Experimental results show that ANN based approach is an efficient method for analyzing interwell connectivity.

Original languageEnglish
Title of host publicationProceedings of the 17th IEEE International Conference on Industrial Informatics, INDIN 2019
Number of pages7
ISBN (Electronic)9781728129273
Publication statusPublished - 1 Jul 2019
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Industrial Informatics - Aalto University, Helsinki-Espoo, Finland
Duration: 22 Jul 201925 Jul 2019
Conference number: 17

Publication series

NameIEEE International Conference on Industrial Informatics
ISSN (Print)1935-4576
ISSN (Electronic)2378-363X


ConferenceIEEE International Conference on Industrial Informatics
Abbreviated titleINDIN
Internet address


  • Artificial neural network (ANN)
  • Cyber-physical petroleum systems(CPPS)
  • Interwell connectivity
  • Long short-term memory (LSTM)
  • Waterflooded reservoir


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