Research on the intelligent automatic correlation method of oil-bearing strata based on pattern constraints: An example of accretionary stratigraphy of the Shishen 100 block in Shinan Oilfield of the Bohai Bay Basin

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  • 1. College of Artificial Intelligence, China University of Petroleum-Beijing, Beijing 102249, China;
    2. College of Geosciences, China University of Petroleum-Beijing, Beijing 102249, China;
    3. Oil and Gas Development Management Center of Shengli Oilfield, Dongying 257000, China

Received date: 2023-08-09

  Revised date: 2023-12-27

  Online published: 2023-12-28

Abstract

The existing automatic correlation methods are mainly data-driven methods, which are difficult to adapt to the automatic correlation of oil-bearing strata with large changes in lateral sedimentary facies and strata thickness. We propose to introduce knowledge-driven in automatic correlation of oil-bearing strata, constraining the correlation process by stratigraphic development patterns and improving the similarity measuring machine and conditional constraint dynamic time warping algorithm to automate correlation of marker layers and the interfaces of each strata, forming an intelligent automatic correlation method of oil-bearing strata based on pattern constraints. The application in Bohai Bay Basin Shishen 100 block shows that the coincidence rate of the marker layers identified by this method is over 95.00%, and the average coincidence rate of identified oil-bearing strata reaches 90.02%, which is about 17 percentage points higher than that of the existing automatic correlation methods. The accuracy of the automatic correlation of oil-bearing strata has been effectively improved.

Cite this article

WU Degang, WU Shenghe, LIU Lei, SUN Yide . Research on the intelligent automatic correlation method of oil-bearing strata based on pattern constraints: An example of accretionary stratigraphy of the Shishen 100 block in Shinan Oilfield of the Bohai Bay Basin[J]. Petroleum Exploration and Development, 0 : 20240219 -20240219 . DOI: 10.11698/PED.20230427

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