Seismic surveys are often constrained by the time needed to activate all the required sources so the source signals do not interfere with each other. Simultaneous source seismic acquisition, also referred to as blended acquisition, is an effective method for reducing the cost and improving the quality of seismic surveys by eliminating the requirement that the sources do not interfere with each other. Independent simultaneous shooting is a unique form of blended acquisition in which sources operate independently of each other and the receiver recording is continuous. This acquisition method is particularly efficient and robust in obtaining high-density source grids for land and marine surveys. Processing the simultaneous source data depends on the randomness of the shot times to create a situation where the signal is coherent, and the interference is random in common-receiver gathers. Although the older and simpler method of separating interfering shots with random noise attenuation works well when the residual interference noise left by the random noise attenuation is acceptable, higher separation quality is possible using a shot separation process based on sparse inversion and compressive sensing methodology. We found that the resulting simultaneous source surveys produced images that were equivalent to or better than conventional seismic surveys, while requiring less acquisition effort, thus reducing costs.

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We present an iterative approach for quasi-continuous time-lapse seismic reservoir monitoring. This approach involves recording sparse data sets frequently, rather than complete data sets infrequently. In other words, it involves acquiring a completely sampled baseline data set followed by sparse monitor data sets at short calendar-time intervals. We use the term "sparse" to describe a data set that is a small fraction of what would normally be recorded in the field to reconstruct a high-spatial-resolution image of the subsurface. Each monitor data set could be as little as 2% of a single, complete conventional data set. The series of recorded time-lapse data sets is then used to estimate missing, unrecorded data in the sparse data sets. The approach was tested on synthetic and field crosswell traveltime data sets. Results show that this approach can be effective for quasi-continuous reservoir monitoring. Also, the accuracy of the estimated data increases as more sparse data sets are added to the time-lapse data series. Finally, a moving estimation window can be used to reduce computational effort for estimating data.

An approach for quasi-continuous, geophysical time-lapse monitoring with sparse seismic data is proposed. This approach takes advantage of the small changes in the seismic property of a geological reservoir that are expected to occur in a small time interval. The goal of this approach is to obtain high temporal and spatial resolution in reconstructed, time-lapse geophysical images using comparable resources that would have provided high spatial but low temporal resolution images with conventional approaches. This is done by acquiring spatially sparse data at small time intervals. In this case, a spatially sparse dataset refers to that dataset which is a small fraction (as little as 5%) of what would be acquired to reconstruct a high spatial resolution tomographic image of the subsurface. The high spatial resolution obtained by the proposed approach occurs because unrecorded data are predicted from future and past data. With high temporal and spatial resolution, early detection of important reservoir changes is more likely to occur.

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