We propose to use 3D orthogonal decomposition of the seismic cube flattened along the target layer to detect fractures and subtle faults or other latent features under strong noise conditions. The technology is based on principal component analysis (PCA) using computation of eigenvalues and eigenvectors of the 3D autocorrelation function of the original seismic cube. Each orthogonal component is also a cube, and their sum is very close to that of the original cube. Orthogonality means the correlation coefficient between any two components will be about zero. Since the noise and acquisition footprints have no correlation with fractures or faults, reflections or other latent features, they stand out as separate orthogonal components. Wellbore information is usually required to select an orthogonal component useful for fracture detection. Fault and fracture auto tracking technology such as Ant-Tracking (Pedersen at al., 2002) can be applied to the selected orthogonal cube to improve the fracture image.
展开▼
