Xuan Feng

A shale rock physics model for analysis of brittleness index, mineralogy and porosity in the Barnett Shale

We construct a rock physics workflow to link the elastic properties of shales to complex constituents and specific microstructure attributes. The key feature in our rock physics model is the degrees of preferred orientation of clay and kerogen particles defined by the proportions of such particles in their total content. The self-consistent approximation method and Backus averaging method are used to consider the isotropic distribution and preferred orientation of compositions and pores in shales. Using the core and well log data from the Barnett Shale, we demonstrate the application of the constructed templates for the evaluation of porosity, lithology and brittleness index. Then, we investigate the brittleness index defined in terms of mineralogy and geomechanical properties. The results show that as clay content increases, Poissons ratio tends to increase and Youngs modulus tends to decrease. Moreover, we find that Poissons ratio is more sensitive to the variation in the texture of shales resulting from the preferred orientation of clay particles. Finally, based on the constructed rock physics model, we calculate AVO responses from the top and bottom of the Barnett Shale, and the results indicate predictable trends for the variations in porosity, lithology and brittleness index in shales.

Constructing the convex quadratic function for the evaluation of crack density of HTI media using P- and converted waves

In future years, cracked reservoirs will be an important target for oil and gas exploration. Cracks play an essential role in the evaluation and prediction of cracked reservoirs. The amplitude variation with azimuth (AVA) method of studying crack properties is becoming an important means of evaluating cracks and now the P-wave AVA method can be used in the inversion of crack density. In order to improve the precision of inversion, we constructed a convex objective function on the basis of the combination of P- and converted-wave data. We then used conjugate gradient methods for the inversion of crack density. Numerical calculations in both fluid-filled and dry crack models and stability analysis of this method proved its viability and stability.

Incorporating attenuation effects into frequency-domain full waveform inversion from zero-offset VSP data from the Stokes equation

With the development of seismic exploration technology, the wave equations used to model the characteristics of wave propagation are becoming increasingly complex. Generally, more than one physical parameter is included in these wave equations, and merely inverting one parameter from these equations cannot help to accurately understand the subsurface structure. To gain further insight into the structure or oil- and gas-reservoir characteristics of the subsurface, multi-parameter inversion is essential. In this study, density, velocity, and viscosity coefficient are jointly reconstructed from the Stokes equation by frequency-domain full waveform inversion based on zero-offset VSP data. The inverse problem is transformed to find the optimal solution to an optimization problem, and the steepest-descent method is used. The sensitivities of the parameters to the misfit function are analysed. Two inversion strategies, namely, the one-by-one parameter inversion and joint inversion strategies, are tried and compared. The joint inversion strategy is proved to be better and thus used in subsequent analyses. In the numerical tests, we compare different frequency-selection strategies and determine the influences of initial models and number of frequencies to the rebuilt models. Numerical tests show that multi-parameter inversion by frequency-domain FWI is feasible and reliable.

Iterative Seismic Data Interpolation beyond Aliasing Using Seislet Transform

The regular and fine sampling along the time axis is common, whereas good spatial sampling is often more expensive or prohibitive and therefore is the main bottleneck for seismic resolution. We generalize missing data interpolation, a special case of data regularization, as a basis pursuit problem and use a Bregman iteration with seislet transform for recovering missing data. The seislet transform employs antialiasing dip pattern to handle aliasing information, which utilizes the scale-invariance property of prediction-error filters (PEFs). Bregman iteration provides the practical interpolation characteristics such as fast iteration convergence and reasonable interpolating result. Benchmark synthetic and field data tests confirm the effectiveness of the proposed iterative algorithm.

Complex seismic wavefield interpolation based on the Bregman iteration method in the sparse transform domain

In seismic prospecting, field conditions and other factors hamper the recording of the complete seismic wavefield; thus, data interpolation is critical in seismic data processing. Especially, in complex conditions, prestack missing data affect the subsequent highprecision data processing workflow. Compressive sensing is an effective strategy for seismic data interpolation by optimally representing the complex seismic wavefield and using fast and accurate iterative algorithms. The seislet transform is a sparse multiscale transform well suited for representing the seismic wavefield, as it can effectively compress seismic events. Furthermore, the Bregman iterative algorithm is an efficient algorithm for sparse representation in compressive sensing. Seismic data interpolation methods can be developed by combining seismic dynamic prediction, image transform, and compressive sensing. In this study, we link seismic data interpolation and constrained optimization. We selected the OC-seislet sparse transform to represent complex wavefields and used the Bregman iteration method to solve the hybrid norm inverse problem under the compressed sensing framework. In addition, we used an H-curve method to choose the threshold parameter in the Bregman iteration method. Thus, we achieved fast and accurate reconstruction of the seismic wavefield. Model and field data tests demonstrate that the Bregman iteration method based on the H-curve norm in the sparse transform domain can effectively reconstruct missing complex wavefield data.

3D Fault Detection Using Structure Prediction and Nonstationary Similarity

Automatic detection of geological discontinuities, eg., faults, is a crucial problem in the interpretation of 3D seismic data. We present a new fault detection technique based on the nonstationary similarity attribute. We use local dip pattern to form a structural prediction of seismic traces from neighboring traces. Local similarity between original data and predicted data enhances the coherency-type analysis with identification. In comparison with coherency attribute using slide-window correlation, nonstationary similarity indicates local measurement of fault. Numerical tests using field data in 2D and 3D confirm the effectiveness of the proposed technique for 3D fault detection.

Data-driven Weighted Median Filter and Its Application to Structure-enhancing Filtering

Random noise attenuation is a persistent problem in seismic interpretation, most edge detection and coherence algorithms are often sensitive to random noise. We present a new structure-enhancing method, which aims at reducing random noise while protecting structural information. We use local dip pattern to form a structural prediction of seismic traces from neighboring traces. The local similarity between prediction traces and original traces can be calculated to design a data-driven weighted median filter. Numerical test using 2-D field data and comparison with structurally similarity-mean filter confirm the effectiveness of the proposed structure-enhancing filter.