Estimating randomness using seismic disorder

Abstract

The new seismic disorder attribute quantitatively describes the degree of randomness embedded in 3D poststack seismic data. We compute seismic disorder using a filter operation that removes simple structures including constant values, constant slopes, and steps in axial directions. We define the power of the filtered data as the seismic disorder attribute, which approximately represents data randomness. Seismic data irregularities are caused by a variety of reasons, including random reflection, diffraction, near-surface variations, and acquisition noise. Consequently, the spatial distribution of the seismic disorder attribute may help hydrocarbon exploration in several ways, including identifying geologic features such as fracture zones, gas chimneys, and terminated unconformities; indicating the signal-to-noise ratio to assess data quality; and providing a confidence index for reservoir simulation and engineering projects. We present three case studies and a comparison to other noise-estimation methods.