Deep Learning Reservoir Parameter Prediction Based on Seismic Attribute Reduction: Take Ledong Area of Yinggehai Basin as an Example

LIU Shiyou; QU Fuliang; ZHOU Fan; DENG Lifeng

doi:10.15953/j.ctta.2021.048

Volume 31 Issue 5

Sep. 2022

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CT Theory and Applications > 2022 > 31(5): 577-586. > DOI: 10.15953/j.ctta.2021.048

LIU S Y, QU F L, ZHOU F, et al. Deep learning reservoir parameter prediction based on seismic attribute reduction: take ledong area of yinggehai basin as an example[J]. CT Theory and Applications, 2022, 31(5): 577-586. DOI: 10.15953/j.ctta.2021.048. (in Chinese).

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Deep Learning Reservoir Parameter Prediction Based on Seismic Attribute Reduction: Take Ledong Area of Yinggehai Basin as an Example

1.
Haikou Branch, CNOOC China Limited, Haikou 570000, China
2.
School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China

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Received Date: November 22, 2021
Accepted Date: February 25, 2022
Available Online: March 09, 2022
Published Date: September 30, 2022

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Abstract

Abstract

As an important indicator to describe reservoir characteristics, reservoir modeling and fluid model, the accurate estimation of reservoir physical parameters can provide a powerful reference for reservoir prediction, but the traditional inversion method of reservoir physical parameters can not give consideration to inversion accuracy and spatial continuity. To solve the above problems, this paper introduced seismic attributes as input of deep learning algorithm. Aiming at the information redundancy among seismic attributes, random forest-recursive elimination method was used to reduce the seismic attributes, thus a prediction method of reservoir physical property parameters based on seismic attribute reduction was finally established. The actual data test results showed that the prediction results of reservoir physical parameters by deep learning based on seismic attribute reduction presented good accuracy and lateral resolution, which confirmed the effectiveness of the proposed method.
- deep learning,
- seismic attribute reduction,
- random forest algorithm,
- reservoir physical parameters

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Deep Learning Reservoir Parameter Prediction Based on Seismic Attribute Reduction: Take Ledong Area of Yinggehai Basin as an Example

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