Zhining Liu

Multi-waveform classification for seismic facies analysis

Seismic facies analysis provides an effective way to delineate the heterogeneity and compartments within a reservoir. Traditional method is using the single waveform to classify the seismic facies, which does not consider the stratigraphy continuity, and the final facies map may affect by noise. Therefore, by defining waveforms in a 3D window as multi-waveform, we developed a new seismic facies analysis algorithm represented as multi-waveform classification (MWFC) that combines the multilinear subspace learning with self-organizing map (SOM) clustering techniques. In addition, we utilize multi-window dip search algorithm to extract multi-waveform, which reduce the uncertainty of facies maps in the boundaries. Testing the proposed method on synthetic data with different S/N, we confirm that our MWFC approach is more robust to noise than the conventional waveform classification (WFC) method. The real seismic data application on F3 block in Netherlands proves our approach is an effective tool for seismic facies analysis. Classifying the multi-waveform in a 3D window can suppress the effect of data noise.Multi-window dip search algorithm is used to extract multi-waveform.Multilinear subspace learning is used to reduce dimension of multi-waveform.

Pre-stack-texture-based reservoir characteristics and seismic facies analysis

Seismic texture attributes are closely related to seismic facies and reservoir characteristics and are thus widely used in seismic data interpretation. However, information is mislaid in the stacking process when traditional texture attributes are extracted from post-stack data, which is detrimental to complex reservoir description. In this study, pre-stack texture attributes are introduced, these attributes can not only capable of precisely depicting the lateral continuity of waveforms between different reflection points but also reflect amplitude versus offset, anisotropy, and heterogeneity in the medium. Due to its strong ability to represent stratigraphics, a pre-stack-data-based seismic facies analysis method is proposed using the self-organizing map algorithm. This method is tested on wide azimuth seismic data from China, and the advantages of pre-stack texture attributes in the description of stratum lateral changes are verified, in addition to the method’s ability to reveal anisotropy and heterogeneity characteristics. The pre-stack texture classification results effectively distinguish different seismic reflection patterns, thereby providing reliable evidence for use in seismic facies analysis.

Enhanced coherence using principal component analysis

AbstractCoherence is a measure of similarity between seismic waveforms. It gives a quantitative description of lateral reflection changes and highlights variations of the geologic features within a seismic image. However, subtle changes in waveforms are often difficult to capture using traditional coherence measures because of the high similarity among the remaining parts in the vertical analysis window. We have developed an attribute called enhanced coherence based on principal component analysis (PCA) with the goal of reducing redundancy within the vertical analysis window, which is often composed of the parts with a high similarity between neighboring traces, and highlighting subtle lateral changes. In computing such a coherence image, we first extract seismic data within a specified time window along a picked horizon. Then, we calculate the enhanced coherence from reduced data obtained using a dimension-reduction technique. Because seismic data typically consist of large volumes, PCA is chosen for dim...

Adaptive phase k-means algorithm for waveform classification

Waveform classification is a powerful technique for seismic facies analysis that describes the heterogeneity and compartments within a reservoir. Horizon interpretation is a critical step in waveform classification. However, the horizon often produces inconsistent waveform phase, and thus results in an unsatisfied classification. To alleviate this problem, an adaptive phase waveform classification method called the adaptive phase k-means is introduced in this paper. Our method improves the traditional k-means algorithm using an adaptive phase distance for waveform similarity measure. The proposed distance is a measure with variable phases as it moves from sample to sample along the traces. Model traces are also updated with the best phase interference in the iterative process. Therefore, our method is robust to phase variations caused by the interpretation horizon. We tested the effectiveness of our algorithm by applying it to synthetic and real data. The satisfactory results reveal that the proposed method tolerates certain waveform phase variation and is a good tool for seismic facies analysis. To alleviate the effect of phase in waveform classification, the adaptive phase k-means is introduced for unsupervised seismic facies analysis. This method improves the traditional k-means algorithm by using an adaptive phase distance for waveform similarity measure, and is thus robust to phase variations caused by horizon interpretation.

A method of reconstructing complex stratigraphic surfaces with multitype fault constraints

The construction of complex stratigraphic surfaces is widely employed in many fields, such as petroleum exploration, geological modeling, and geological structure analysis. It also serves as an important foundation for data visualization and visual analysis in these fields. The existing surface construction methods have several deficiencies and face various difficulties, such as the presence of multitype faults and roughness of resulting surfaces. In this paper, a surface modeling method that uses geometric partial differential equations (PDEs) is introduced for the construction of stratigraphic surfaces. It effectively solves the problem of surface roughness caused by the irregularity of stratigraphic data distribution. To cope with the presence of multitype complex faults, a two-way projection algorithm between threedimensional space and a two-dimensional plane is proposed. Using this algorithm, a unified method based on geometric PDEs is developed for dealing with multitype faults. Moreover, the corresponding geometric PDE is derived, and an algorithm based on an evolutionary solution is developed. The algorithm proposed for constructing spatial surfaces with real data verifies its computational efficiency and its ability to handle irregular data distribution. In particular, it can reconstruct faulty surfaces, especially those with overthrust faults.

3D modeling of geological anomalies based on segmentation of multiattribute fusion

Abstract3D modeling of geological bodies based on 3D seismic data is used to define the shape and volume of the bodies, which then can be directly applied to reservoir prediction, reserve estimation, and exploration. However, multiattributes are not effectively used in 3D modeling. To solve this problem, we propose a novel method for building of 3D model of geological anomalies based on the segmentation of multiattribute fusion. First, we divide the seismic attributes into edge- and region-based seismic attributes. Then, the segmentation model incorporating the edge- and region-based models is constructed within the levelset-based framework. Finally, the marching cubes algorithm is adopted to extract the zero level set based on the segmentation results and build the 3D model of the geological anomaly. Combining the edge-and region-based attributes to build the segmentation model, we satisfy the independence requirement and avoid the problem of insufficient data of single seismic attribute in capturing the boundaries of geological anomalies. We apply the proposed method to seismic data from the Sichuan Basin in southwestern China and obtain 3D models of caves and channels. Compared with 3D models obtained based on single seismic attributes, the results are better agreement with reality.