基于地震属性的致密碳酸盐岩储层裂缝分布的人工智能预测方法

鲍明阳; 董少群; 曾联波; 何娟; 孙福亭; 韩高松

doi:10.3799/dqkx.2022.290

Volume 48 Issue 7

Jul. 2023

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Article Navigation > Earth Science > 2023 > 48(7): 2462-2474

Bao Mingyang, Dong Shaoqun, Zeng Lianbo, He Juan, Sun Futing, Han Gaosong, 2023. Artificial Intelligence Prediction Method for Tight Carbonate Reservoir Fracture Distribution Based on Seismic Attributes. Earth Science, 48(7): 2462-2474. doi: 10.3799/dqkx.2022.290

Citation:

Bao Mingyang, Dong Shaoqun, Zeng Lianbo, He Juan, Sun Futing, Han Gaosong, 2023. Artificial Intelligence Prediction Method for Tight Carbonate Reservoir Fracture Distribution Based on Seismic Attributes. Earth Science, 48(7): 2462-2474. doi: 10.3799/dqkx.2022.290

Citation:

Bao Mingyang, Dong Shaoqun, Zeng Lianbo, He Juan, Sun Futing, Han Gaosong, 2023. Artificial Intelligence Prediction Method for Tight Carbonate Reservoir Fracture Distribution Based on Seismic Attributes. Earth Science, 48(7): 2462-2474. doi: 10.3799/dqkx.2022.290

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Artificial Intelligence Prediction Method for Tight Carbonate Reservoir Fracture Distribution Based on Seismic Attributes

doi: 10.3799/dqkx.2022.290

Bao Mingyang^{1, 2
,
,},
Dong Shaoqun^{1, 2},
Zeng Lianbo^{1, 2},
He Juan³,
Sun Futing³,
Han Gaosong^{1, 2}

1.
State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing 102249, China
2.
College of Geosciences, China University of Petroleum (Beijing), Beijing 102249, China
3.
CNOOC International Ltd., Beijing 100028, China

Received Date: 2021-12-19
Publish Date: 2023-07-25

Abstract

Abstract

Fractures are important seepage channels in tight carbonate reservoir which affect reservoir development. Fracture prediction is difficult due to the weak and complex geophysical response of fractures. Based on the deep mining of fracture characteristic information in seismic attributes, in this paper it establishes an artificial intelligence-based fracture distribution prediction method. This method uses the support vector machine algorithm to optimize the fracture sensitive attributes, and uses the gradient boosting decision tree (GBDT) algorithm to deeply explore the nonlinear relationship between the fracture development of a single well and the seismic attributes. Gradient boosting decision tree algorithm has strong robustness to outliers, and can better solve the problem of weak and complex fracture seismic response. This method has been applied to a tight carbonate reservoir in the Oligocene to Neogene Asmari Formation of an oilfield in Zagros Basin, Middle East. Five fracture sensitive seismic attributes including variance, curvature, dip deviation, dip angle and azimuth angle were optimized. The gradient boosting decision tree was used to integrate the fracture characteristics of different seismic attributes, and the fracture distribution prediction model was established to predict the fracture distribution of carbonate reservoir in the study area. Compared with conventional fracture prediction methods, the results of this method are more consistent with fracture interpretation of single well. The prediction results show that the fractures both in the northern part of the study area and near the structural heights are more developed, which is consistent with the understanding of production dynamics.
- fracture prediction,
- seismic attributes,
- tight carbonate reservoir,
- artificial intelligence,
- Zagros Basin

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References(88)

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Artificial Intelligence Prediction Method for Tight Carbonate Reservoir Fracture Distribution Based on Seismic Attributes

doi: 10.3799/dqkx.2022.290

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