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Seismic deconvolution plays an important role in the seismic characterization of thin-layer structures and seismic resolution enhancement. However, the trace-by-trace processing strategy is applied and ignores the spatial connection along seismic traces, which gives the deconvolved result strong ambiguity and poor spatial continuity. To alleviate this issue, we developed a structurally constrained deconvolution algorithm. The proposed method extracts the reflection structure characterization from the raw seismic data and introduces it to the multichannel deconvolution algorithm as a spatial reflection regularization. Benefiting from the introduction of the reflection regularization, the proposed method enhances the stability and spatial continuity of conventional deconvolution methods. Synthetic and field data examples confirm the correctness and feasibility of the proposed method.

The Triassic reservoir in the Jinan area of Tarim Oilfield consists largely of interbedded sand and shale. Because of the large overlap between sandstone and shale impedance, it is difficult to distinguish sandstone from shale by acoustic impedance alone. Compared to acoustic impedance, elastic impedance contains more lithologic and physical information of the reservoir. Based on meticulous well-tie calibration, elastic impedance data volumes for 10°, 20°, and 30° emergence angles are obtained using pre-stack elastic impedance inversion. A non-linear statistical relationship between elastic impedance and shale content is set up by a PNN neural network. The non-linear mapping relationship is used to predict the reservoir shale content from elastic impedance, which will depict and predict the reservoir oil-bearing sands.