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International Journal for Uncertainty Quantification
インパクトファクター: 3.259 5年インパクトファクター: 2.547 SJR: 0.417 SNIP: 0.8 CiteScore™: 1.52

ISSN 印刷: 2152-5080
ISSN オンライン: 2152-5099

Open Access

International Journal for Uncertainty Quantification

DOI: 10.1615/Int.J.UncertaintyQuantification.2019027680
pages 187-204

REDUCING FRACTURE PREDICTION UNCERTAINTY BASED ON TIME-LAPSE SEISMIC (4D) AND DETERMINISTIC INVERSION ALGORITHM

Liming Zhang
China University of Petroleum, Qingdao, Shandong 266580, China
Chenyu Cui
China University of Petroleum, Qingdao, Shandong 266580, China
Kai Zhang
China University of Petroleum, Qingdao, Shandong 266580, China
Yi Wang
Sinopec Research Institute of Petroleum Engineering, Beijing 100000, China
Zhixue Sun
China University of Petroleum, Qingdao, Shandong 266580, China
Jun Yao
School of Petroleum Engineering, China University of Petroleum (East China), No. 66 Changjiang West Road, Huangdao Zone, Qingdao City, Shandong Province, 266580 P.R. China
Qin Luo
Southwest Petroleum University, Chengdu 610000, China

要約

The uncertainty of hydraulic fracture is high due to the complex geological features of which there is limited accurate understanding, and the limitations of the fracture diagnosis method. However, hydraulic fractures are one of the main driving forces for oilfields to improve economic benefit and important reference imformation for further development and adjustment of oilfields. Therefore, reducing fracture morphology uncertainty is a key challenge for the further development of oilfields. To improve this situation, we present a novel method based on the time-lapse (4D) seismic and discrete network deterministic inversion (DNDI) algorithm for mapping the geometry of hydraulic fracture. The time-lapse (4D) seismic method can provide spatial and dynamic change of reservoir; this information is used by DNDI to optimize fracture geometry continually, where the embedded discrete fracture model (EDFM) is implied to simulate reservoir production, and objective function is constructed using Bayesian theory for reaching iterative convergence quickly. An uncertainty analysis of results based on the posterior probability is also presented in this paper. Finally, this method has been validated in different scale study cases.

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