Fast and Seamless Merging of Post-Stack Seismic Volumes

During exploration, it is common to have access to multiple 3D post-stack seismic volumes, with different acquisition parameters, geometries and data quality that means conducting integrated interpretation and analyses of data between and across surveys can be challenging. Merging a 3D seismic survey would ideally require full re-processing using pre-stack data. However, given time, budgetary, data availability, or compute constraints, this is not always a practical solution. An alternative solution is to merge post-stack seismic volumes, which can provide a suitable substitute for many regional structural interpretation workflows. However, achieving a seamless post-stack seismic merge is challenging and often matching amplitudes, phase, and time shifts consistently cannot be done using bulk corrections across an entire survey due to lateral changes in geology. Seams at the boundaries between seismic surveys or changes in waveform characteristics will create challenges for interpretation, especially for the predictive tools being adopted for automated interpretation workflows. For example, a seam between two datasets is a large discontinuity that will lead to a potential false positive fault prediction. The seam may also prevent horizon interpretation tools from traversing between the two volumes. To reduce these challenges, we present an automated workflow for seamlessly merging two or more post-stack 3D seismic volumes that will enable easier interpretation workflows across regions where interpretations need to span multiple volumes. Traditionally, survey merging is achieved by applying seismic data interpolation/regularization, phase rotation, amplitude matches, and the optimal bulk time shift obtained to several seismic surveys to condition the different vintages and produce a seamless merged data set. This has been mainly done by designing a post-migration match filter which estimates wavelets in each time zone after isolating trace segments in the defined window and derived by least-squares time-domain with a smoothness constraint. However, after applying those estimated time, phase and amplitude adjustments, the surveys especially their spectra are matched in a loose sense. In case where the low frequency legacy data and high-resolution modern data are required to merge, the final spectrum is closer to low frequency spectrum of legacy data. The varying levels of resolution, acquisition patterns, and data quality in seismic surveys make their integration complex task. To overcome these challenges a mixture of selected deep-learning, optimization and classical machine-learning algorithms will be utilized to analyze and process data from multiple surveys. These algorithms will identify overlaps in the data and employ advanced techniques to match the spectra and phase of the seismic data, resulting in a seamless transition between different seismic surveys. Ultimately a complete merged 3D regional seismic survey will be generated. Various machine learning algorithms, including different types of neural networks will be utilized to analyze the data, detect gaps, and fill them with accurate data values.