Nuclear magnetic resonance T_2 spectrum:multifractal characteristics and pore structure evaluation

Pore structure characteristics are important to oil and gas exploration in complex low-permeability reservoirs. Using multifractal theory and nuclear magnetic resonance （NMR）, we studied the pore structure of low-permeability sandstone rocks from the 4th Member （Es4） of the Shahejie Formation in the south slope of the Dongying Sag. We used the existing pore structure data from petrophysics, core slices, and mercury injection tests to classify the pore structure into three categories and five subcategories. Then, the T2 spectra of samples with different pore structures were interpolated, and the one- and three-dimensional fractal dimensions and the multifractal spectrum were obtained. Parameters a （intensity of singularity） andf（a） （density of distribution） were extracted from the multifractal spectra. The differences in the three fractal dimensions suggest that the pore structure types correlate with a andf（a）. The results calculated based on the multifractal spectrum is consistent with that of the core slices and mercury injection. Finally, the proposed method was applied to an actual logging profile to evaluate the pore structure of low-permeability sandstone reservoirs.

A Bregman adaptive sparse-spike deconvolution method in the frequency domain

To improve the anti-noise performance of the time-domain Bregman iterative algorithm,an adaptive frequency-domain Bregman sparse-spike deconvolution algorithm is proposed.By solving the Bregman algorithm in the frequency domain,the influence of Gaussian as well as outlier noise on the convergence of the algorithm is effectively avoided.In other words,the proposed algorithm avoids data noise effects by implementing the calculations in the frequency domain.Moreover,the computational efficiency is greatly improved compared with the conventional method.Generalized cross validation is introduced in the solving process to optimize the regularization parameter and thus the algorithm is equipped with strong self-adaptation.Different theoretical models are built and solved using the algorithms in both time and frequency domains.Finally,the proposed and the conventional methods are both used to process actual seismic data.The comparison of the results confirms the superiority of the proposed algorithm due to its noise resistance and self-adaptation capability.

Improvement and application of preprocessing technique for multitrace seismic impedance inversion

The conventional poststack inversion uses standard recursion formulas to obtain impedance in a single trace.It cannot allow for lateral regularization.In this paper,ID edge-preserving smoothing(EPS)fi lter is extended to 2D/3D for setting precondition of impedance model in impedance inversion.The EPS filter incorporates a priori knowledge into the seismic inversion.The a priori knowledge incorporated from EPS filter preconditioning relates to the blocky features of the impedance model,which makes the formation interfaces and geological edges precise and keeps the inversion procedure robust.Then,the proposed method is performed on two 2D models to show its feasibility and stability.Last,the proposed method is performed on a real 3D seismic work area from Southwest China to predict reef reservoirs in practice.

Marchenko imaging based on self-adaptive traveltime updating

Marchenko imaging obtains the subsurface reflectors using one-way Green’s functions,which are retrieved by solving the Marchenko equation.This method generates an image that is free of spurious artifacts due to internal multiples.The Marchenko imaging method is a target-oriented technique;thus,it can image a user specified area.In the traditional Marchenko method,an accurate velocity model is critical for estimating direct waves from imaging points to the surface.An error in the velocity model results in the inaccurate estimation of direct waves.In turn,this leads to errors in computation of one-way Green’s functions,which then affects the final Marchenko images.To solve this problem,in this paper,we propose a self-adaptive traveltime updating technique based on the principle of equal traveltime to improve the Marchenko imaging method.The proposed method calculates the time shift of direct waves caused by the error in the velocity model,and corrects the wrong direct wave according to the time shift and reconstructs the correct Green’s functions.The proposed method improves the results of imaging using an inaccurate velocity model.By comparing the results from traditional Marchenko and the new method using synthetic data experiments,we demonstrated that the adaptive traveltime updating Marchenko imaging method could restore the image of geological structures to their true positions.

基于HHT与PCA及K-maxmin聚类的地层划分方法及其应用

划分不同级次的层序界面、识别地层内部的沉积旋回类型是层序地层分析的重要基础.利用主成分分析、希尔伯特黄等方法,有效地提取了隐藏在测井信号深度域和频率域中的丰富的地质信息,最终利用提取的信息进行地层层序的划分.结果表明:经过K-maxim聚类方法、主成分分析后的深度域信号能更加准确的识别薄层,而经过Hilbert-Huang变换后的频率域信号能更加准确识别突变点的位置,适用于确定层序界面.通过机器学习的思想以及信息融合技术,将2种方法结合.以测井数据的深度域信息为参考,以测井数据在频率域的突变点在沉积界面处反映灵敏为依据,将玛湖凹陷区NX1井处的三叠系百口泉组划分成1个长周期基准面旋回、1个短周期基准面旋回.利用该方法划分的结果不仅与已有的划分成果具有一致性而且其中的百口泉组的地层划分更加精细,为利用测井资料进行高分辨率层序地层划分提供了新思路.