Compressive sensing (CS) provides a new basis for sampling that can increase sampling efficiency for seismic data acquisition by an order of magnitude. A major challenge for this new technology is to show that theoretical increases in sampling efficiency can be translated to real efficiency gains in the field. Along with efficiency gains, data quality must be preserved in order to gain acceptance of a new acquisition technology. CS designs require solution of large optimization problems that are consistent with compressive sampling theory. We refer to our optimization framework for CS-based acquisition design and processing as compressive seismic imaging (CSI). We illustrate our CSI framework on example projects for ocean-bottom node, narrow-azimuth marine streamer, and land vibroseis acquisition. The ocean-bottom-node project was conducted in the UK North Sea during the difficult winter season. A CSI dual-source design was used to significantly reduce shooting time for this project. The project was completed on time, under budget, and with data quality that exceeded the quality of an overlapping uniformly sampled survey. The narrow-azimuth marine CSI survey project was acquired in offshore Australia for field development purposes. Nonuniform CSI sampling was used to increase sampling efficiency for both sources and cables, resulting in significant improvements in data quality and lateral resolution. The land vibroseis project was conducted on the North Slope of Alaska. In this case, the goal was to acquire a development survey of sufficient size within a short time window. Nonuniform CSI sampling was used to support the use of 10 or more vibrators shooting simultaneously, along with improving sampling efficiency for both sources and receivers. Compared to conventional designs, the CSI survey achieved an order of magnitude improvement in field acquisition efficiency and step-function improvements in data quality. These examples show that theoretical improvements in sampling efficiency from CS can make real and significant impacts on seismic data acquisition and processing.