No abstract

The identification of salt-dome boundaries in migrated seismic data volumes is important for locating petroleum reservoirs. The presence of noise in the data makes computer-aided salt-dome interpretation even more challenging. We have developed noise-robust algorithms that could label boundaries of salt domes effectively and efficiently. Our research is twofold. First, we used a texture-based gradient to accomplish salt-dome detection. We found that by using a dissimilarity measure based on the 2D discrete Fourier transform, the algorithm was capable of efficiently detecting salt-dome boundaries with accuracy. At the same time, our analysis determined that the proposed algorithm was robust to noise. Once the detection is performed for an initial 2D seismic section, we track the initial boundaries through the data volume to accomplish an efficient labeling process by avoiding the parameter tuning that would have been necessary if detection had been performed for every seismic section. The tracking process involves a tensor-based subspace learning process, in which we built texture tensors using patches from different seismic sections. To accommodate noise components with various levels in a texture tensor, we used noise-adjusted principal component analysis, so that principal components corresponding to greater signal-to-noise-ratio values might be selected for tracking. We validated our detection and tracking algorithms through experiments using seismic data sets acquired from the Netherlands offshore F3 block in the North Sea with very encouraging results.

Salt domes, an important geological structure, are closely related to the formation of petroleum reservoirs. In many cases, no explicit strong reflector exists between a salt dome and neighboring geological structures. Therefore, interpreters commonly delineate the boundaries of salt domes by observing a change in texture content. To stimulate the visual interpretation process, we propose a novel seismic attribute, the gradient of textures, which can quantify texture variations in three-dimensional (3D) space. On the basis of the attribute volume, we apply a global threshold to highlight regions containing salt-dome boundaries. In addition, with region growing and morphological operations, we can remove noisy boundaries and detect the boundary surfaces of salt domes effectively and efficiently. Experimental results show that by utilizing the strong coherence between neighboring seismic sections, the proposed method can delineate the surfaces of saltdome boundaries more accurately than the state-of-the-art detection methods that label salt-dome boundaries only in twodimensional (2D) seismic sections.

Texture-based methods have proven to be useful in the detection of salt bodies in seismic data. In this abstract, we present three computationally inexpensive texture attributes that strongly differentiate salt bodies from other geological formations. The proposed method combines the three texture attributes along with region boundary smoothing for delineating salt boundaries. Our first proposed attribute is directionality, which differentiates between regions where texture lacks any specific direction (potentially, salt) and areas with directional texture. The second attribute is the smoothness of texture, while the third is based on edge content. Our results show that the directionality attribute effectively detects salt bodies in all the seismic images used in testing. The other two attributes correct the false positives detected by the directionality. The overall results show that the proposed method can fairly detect salt regions when compared to manual interpretation.