%0 Journal Article %A Zhang, Liming %A Cui, Chenyu %A Zhang, Kai %A Wang, Yi %A Sun, Zhixue %A Yao, Jun %A Luo, Qin %D 2019 %I Begell House %K fracture prediction uncertainty, Bayesian theory, time-lapse seismic (4D), deterministic inversion, reservoir fracturing %N 2 %P 187-204 %R 10.1615/Int.J.UncertaintyQuantification.2019027680 %T REDUCING FRACTURE PREDICTION UNCERTAINTY BASED ON TIME-LAPSE SEISMIC (4D) AND DETERMINISTIC INVERSION ALGORITHM %U https://www.dl.begellhouse.com/journals/52034eb04b657aea,3f762e845665d4bd,738f47ca64d7c860.html %V 9 %X 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. %8 2019-04-23