Xuekai Sun

Q estimation using modified S transform based on pre-stack gathers and its applications on carbonate reservoir

Pre-stack seismic data is acknowledged to be more favorable in estimating Q values since it carries much more valuable information in traveltime and amplitude than post-stack data. However, the spectrum of reflectors can be strongly altered by nearby reflector or side lobes of the wavelet, which thereby degrades the accuracy of Q estimation based on the pre-stack spectral ratio method. To solve this problem, we propose a method based on the modified S-transform (MST) for estimating Q values from pre-stack gathers, in which Q values can be obtained with regression analysis based on the relationship between spectral ratio slope and the square of offset. Through tests on a numerical model, we first prove advantages of this pre-stack spectral ratio method compared to the traditional post-stack method. Besides, it is also shown that application of MST would lead to a much more focused intercept, which is the kernel for the pre-stack method. Therefore, the accuracy of Q estimation using MST is further improved when compared with that of conventional S-transform (ST). Based on this Q estimation method, we apply relevant processing methods (e.g. inverse Q filtering and dynamic Q migration) in practice, in order to improve imaging resolution and gathering quality with better amplitude and phase relationships. Applications on a carbonate reservoir witness remarkable enhancements of the imaging result, in which features of faults and deep strata are more clearly revealed. Moreover, pre-stack common-reflection-point (CRP) gathers obtained by dynamic Q migration well compensate the amplitude loss and correct the phase. Its ultimate pre-stack elastic inversion result better characterizes the geologic rules of complex carbonate reservoir predominated by secondary-storage-space.

Application of Probabilistic Neural Network on Complex Carbonate Reservoir Prediction

Carbonate reservoir in Tarim basin is more complex for its strong heterogeneity and anisotropy. Different from the usual, this paper mainly focused on how to employ PNN as a comprehensive method in detecting the dissolved caves for carbonate reservoir, in which advantages of inversion and attributes are both used. During the entire workflow of PNN, we emphasize the importance of seismic attributes searching and cross validation. And the P-velocity and S-velocity of a test area selected from ZG8 area are predicted by utilizing PNN technique, and the interpretation of Formation Yingshan is conducted on the established indicator of Vp/Vs. Results show that the resolution is greatly boosted and the dissolved caves are distinctively and vividly characterized, which is seldom achieved by pre-stack inversion.

Improved Gabor Deconvolution and Its Extended Applications

In log time-frequency spectra, the nonstationary convolution model is a linear equation and thus we improved the Gabor deconvolution by employing a log hyperbolic smoothing scheme which can be implemented as an iteration process. Numerical tests and practical applications demonstrate that improved Gabor deconvolution can further broaden frequency bandwidth with less computational expenses than the ordinary method. Moreover, we attempt to enlarge this method’s application value by addressing nonstationary and evaluating Q values. In fact, energy relationship of each hyperbolic bin (i.e., attenuation curve) can be taken as a quantitative indicator in balancing nonstationarity and conditioning seismic traces to the assumption of unchanging wavelet, which resultantly reveals more useful information for constrained reflectivity inversion. Meanwhile, a statistical method on Q-value estimation is also proposed by utilizing this linear model’s gradient. In practice, not only estimations well agree with geologic settings, but also applications on Q-compensation migration are favorable in characterizing deep geologic structures, such as the pinch-out boundary and water channel.

Foamy Oil Formation by Gas Injection and the Effect on Heavy Oil Recovery

In this study, produced natural gas reinjection process is proposed to form renewed foamy oil for heavy oil recovery. A new experimental procedure was developed to prove that an in situ foamy oil could be formed during the gas injection process. A field scale numerical study was performed to further analyze foamy oil behavior and its influential mechanisms. The study showed that natural gas huff-n-puff process could increase heavy oil recovery by inducing foamy oil. The findings of this study could be useful to solve the problem of disappearance of foamy oil mechanism.