Abstract: A method for predicting pore pressure is described which initially involves obtaining, for one point in a volume of Earth, a value of pore pressure and one or more seismic attributes. A relationship is determined between the value of pore pressure and the seismic attribute, for example by use of a neural network. Seismic data is then obtained for the volume of earth and the same attributes as selected above when determining relationship are extracted from the seismic data for another point in the volume. The extracted seismic attributes are then used as inputs to the previously determined relationship to produce as an output, a prediction of pore pressure at the other point. The seismic attributes are frequency related seismic attributes and include for example instantaneous frequency, weighted mean frequency, instantaneous pseudo-quality factor, instantaneous dominant frequency, instantaneous bandwidth, instantaneous phase, effective bandwidth, peak frequency, envelope and energy half time.

Abstract: A method for predicting pore pressure is described which initially involves obtaining, for one point in a volume of Earth, a value of pore pressure and one or more seismic attributes. A relationship is determined between the value of pore pressure and the seismic attribute, for example by use of a neural network. Seismic data is then obtained for the volume of earth and the same attributes as selected above when determining relationship are extracted from the seismic data for another point in the volume. The extracted seismic attributes are then used as inputs to the previously determined relationship to produce as an output, a prediction of pore pressure at the other point. The seismic attributes are frequency related seismic attributes and include for example instantaneous frequency, weighted mean frequency, instantaneous pseudo-quality factor, instantaneous dominant frequency, instantaneous bandwidth, instantaneous phase, effective bandwidth, peak frequency, envelope and energy half time.