Abstract: According to one embodiment, a near-field data is used to determine a taper length that can isolate the source signature at the top of near-field data with minimum interaction with the Green's function. In some embodiments, a range of taper lengths is selected and for each length after tapering the near-filed data, converting each filtered near-field data to its minimum-phase equivalents. Summing pairwise cross-correlation of all of the minimum-phase equivalent wavelets at the zero-lag provides an attribute that shows how much the tapered portions of the near-field data look alike. An acceptable taper size will be the one that has the highest summation value. Finally, the average of the minimum-phase equivalents of tapered near-field data with the selected taper size is the estimated source signature.

Abstract: A method is herein presented to statistically combine multiple seismic attributes for generating a map of the spatial density of fractures. According to an embodiment a first step involves interpreting the formation of interest in 3D seismic volume first to create its time structure map. The second step is creating depth structure of the formation of interest from its time structure map. In this application geostatistical methods have been used for depth conversional, although other methods could be used instead. The third step is extraction of a number of attributes, such as phase, frequency and amplitudes, from the time structure map. The next step is to project the fracture density onto the top of the target formation. The final step is to combine these attributes using a statistical method known as Multi-variant non-linear regression to predict fracture density.

Abstract: A method is herein presented to statistically combine multiple seismic attributes for generating a map of the spatial density of fractures. According to an embodiment a first step involves interpreting the formation of interest in 3D seismic volume first to create its time structure map. The second step is creating depth structure of the formation of interest from its time structure map. In this application geostatistical methods have been used for depth conversional, although other methods could be used instead. The third step is extraction of a number of attributes, such as phase, frequency and amplitudes, from the time structure map. The next step is to project the fracture density onto the top of the target formation. The final step is to combine these attributes using a statistical method known as Multi-variant non-linear regression to predict fracture density.

Abstract: In various embodiments, the present disclosure describes methods for processing seismic data to concurrently produce a velocity model and a depth image. Various embodiments of the methods include: a) acquiring seismic data; b) generating a shallow velocity model from the seismic data; c) generating a stacking velocity model using the shallow velocity model as a guide; d) generating an initial interval velocity model from the stacking velocity model; and e) generating an initial depth image using the initial interval velocity model. The methods also include iterative improvement of the initial depth image and the initial interval velocity model to produce improved depth images and improved interval velocity models. Improvement of the depth images and the interval velocity models is evaluated by using a congruency test.

Abstract: In various embodiments, the present disclosure describes methods for processing seismic data to concurrently produce a velocity model and a depth image. Various embodiments of the methods include: a) acquiring seismic data; b) generating a shallow velocity model from the seismic data; c) generating a stacking velocity model using the shallow velocity model as a guide; d) generating an initial interval velocity model from the stacking velocity model; and e) generating an initial depth image using the initial interval velocity model. The methods also include iterative improvement of the initial depth image and the initial interval velocity model to produce improved depth images and improved interval velocity models. Improvement of the depth images and the interval velocity models is evaluated by using a congruency test.