Prediction of high-quality reservoirs using the reservoir fluid mobility attribute computed from seismic data

• Published in: Journal of petroleum science & engineering Vol. 190; p. 107007
• Main Authors: Zhang, Yijiang, Wen, Xiaotao, Jiang, Lian, Liu, Jie, Yang, Jixin, Liu, Songming
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.07.2020
• Subjects: High resolution; Reservoir fluid mobility; Reservoir prediction; Synchrosqueezing generalized S-transform and the Lucy-Richardson algorithm; Time-frequency analysis; Synchrosqueezing generalized S-transform and the Lucy-Richardson algorithm; Time-frequency analysis; High resolution; Reservoir fluid mobility; Reservoir prediction
• ISSN: 0920-4105; 1873-4715
• DOI: 10.1016/j.petrol.2020.107007

The reservoir fluid mobility, which can reflect the pore connectivity, is an important seismic attribute in reservoir development. The current method to obtain reservoir fluid mobility is based on rock physical experiments. However, the current method can only obtain the reservoir fluid mobility at the well location, rather than for a large range within the exploration area. To accurately extract the reservoir fluid mobility of a large portion of an exploration area, we propose a new method of calculating the reservoir fluid mobility using seismic data. First, the algorithm obtains the approximate reservoir fluid mobility by substituting the reflection coefficient of the dominant frequency content at the low-frequency end of the seismic data for its instantaneous spectrum. Then, the synchrosqueezing generalized S-transform and the Lucy-Richardson algorithm (SSGST-LR) are used to calculate the instantaneous spectrum of the reservoir fluid mobility. Rearrangement of the generalized S-transform's results can improve the temporal and spatial resolutions of the time-frequency transform. Moreover, the interference between the different frequency signals can be eliminated more effectively by the Lucy-Richardson algorithm. Finally, by performing fluid replacement in the well, we demonstrate the influence of fluid type on fluid mobility. We used synthetic data and real data to verify the superiority of our method. This example shows that our proposed method can accurately extract the reservoir fluid mobility and predict the distribution of high-quality reservoirs in an exploration area. •The synchrosqueezing generalized S-transform is proposed to improve the resolution of time-frequency transform.•The synchrosqueezing generalized S-transform is introduced into the calculation of the reservoir fluid mobility.•The fluid types in the well were replaced, and the influence of the fluid types on fluid mobility is discussed.