Abstract:
Blind wavelet extraction and de-convolution is performed on seismic data to enable its practical usage in seismic processing and to provide quality control of data obtained in areas where data from wells are not available. The wavelet extraction and deconvolution are realized in the time domain by iteration, producing a mixed phase wavelet with minimal prior knowledge of the actual nature of the wavelet. As a result of the processing, the de-convolved seismic reflectivity is obtained simultaneously.
Abstract:
A separate three-dimensional (3D) integration filter mask if precomputed for each of x, y , and z dimensions with a given operator length. A portion of a 3D post- stack seismic data set is received for processing and loaded into a generated 3D-sub- cube. The separate 3D integration filter masks are applied to the loaded 3D-sub-cube to generate filtered 3D-sub-cube data. The square mean of the 3D-sub-cube is calculated to generate smoothed 3D-sub-cube data.
Abstract:
The present disclosure describes methods and systems, including computer-implemented methods, computer program products, and computer systems, for processing geophysical data. One computer-implemented method includes obtaining a set of raw geophysical data, wherein the raw geophysical data include 3-Dimensional (3D) coordinates; grouping, by a data processing apparatus, the set of the raw geophysical data into a plurality of subsets; and processing, by the data processing apparatus, each subset of the raw geophysical data using a 3D norm zero objective energy function to generate a subset of smoothed geophysical data, wherein the smoothed geophysical data is used to build a subsurface model.
Abstract:
A system and method perform structure-preserving smoothing (SPS) using a data adaptive method for smoothing 3D post-stacked seismic attributes which reduces random noise while preserving the structure without prior computation of its orientation. The data is smoothe within a neighborhood sub-window along a set of predefined orientations, and the best smoothing result is then selected for output. The orientation corresponding to the best result often approximates the true structure orientation embedded in the data, so that the embedded structure is thus preserved. The SPS method can also be combined with median, alpha-trim, symmetric near neighbor, or edge-preserving filters. The SPS method is an effective way to reduce random noise and eliminate noise footprints, and to enhance coherence and curvature attributes.
Abstract:
A system and method identify and display random noise in three dimensional (3-D) seismic data utilizing a 3-D operator to reduce the effects of seismic structure on noise identification. The 3-D operator is derived using statements of required performance in 3-D. The 3-D operator is applied on a pixel-by-pixel basis to each of the pixels in the 3-D post- stacked data to display images in a 3-D display or to output an estimate of noise that is substantially independent of the image structure. The resulting display is generated in colors to indicate noise amplitude to facilitate location of noisy regions in the original display.
Abstract:
Four or more seismic attributes are integrated or merged into imaging formats and displayed for geological interpretation via extended quantization. Multi-attribute integration and classification improves the ability to identify geologic facies, and reservoir properties such as thickness, fluid type, or fracture intensity and orientation. The extended quantization groups up to eight attributes as a single attribute for geophysical data classification. Data group reduction criteria are provided to reveal common geological targets in the data, while preserving small variations or thin layers often found in hydrocarbon reservoirs. By combining multiple attributes, image quality is enhanced while providing analysts the ability to observe channels that might not be visible in any single attribute.
Abstract:
Subsurface reservoir properties are predicted despite limited availability of well log and multiple seismic attribute data. The prediction is achieved by computer modeling with least square regression based on a support vector machine methodology. The computer modeling includes supervised computerized data training, cross-validation and kernel selection and parameter optimization of the support vector machine. An attributes selection technique based on cross-correlation is adopted to select most appropriate attributes used for the computerized training and prediction in the support vector machine.