Abstract: Rock physics guided migration is disclosed to enhance subsurface three-dimensional geologic formation evaluation. In one embodiment, a geologic interpretation is generated based on a seismic data volume. Sets of compaction and acoustic formation factor curves are generated, and these are combined into a set of velocity-relationship curves. A pore pressure is derived and used to establish a pore pressure state. A rock physics template is then generated utilizing the derived information. This rock physics template can be used to refine geologic formation evaluation with any suitable form of migration technique.

Abstract: A profile is produced based on measured survey data, where the profile contains indications corresponding to refraction events at different depths in a subterranean structure. Based on the profile and a critical angle model that correlates different concentrations of a given material to respective critical angles, a quantity of the given material in a subterranean structure at a particular depth is determined.

Abstract: A method to predict tars and tar sands in petroleum exploration and production in a subsurface volume by building a viscoelastic property model of the volume using rock behavior including tars together with seismic inversion to derive a lithology volume identifying tars.

Abstract: Rock physics guided migration is disclosed to enhance subsurface three-dimensional geologic formation evaluation. In one embodiment, a geologic interpretation is generated based on a seismic data volume. Sets of compaction and acoustic formation factor curves are generated, and these are combined into a set of velocity-relationship curves. A pore pressure is derived and used to establish a pore pressure state. A rock physics template is then generated utilizing the derived information. This rock physics template can be used to refine geologic formation evaluation with any suitable form of migration technique.

Abstract: An appropriate rock physics model is chosen from well-log data and/or experience. The rock physics model is then used to generate a plurality of attribute values, including compression and shear velocities, at different porosities and gas hydrate saturations. Gas hydrates are classified into different ranges of porosity and hydrate saturation based on the population of multiple attributes, and probability density functions for each individual gas hydrate class are created. Probability density distribution functions for individual attributes and a joint conditional probability density functions are created using a Bayesian function. The conditional probability of the occurrences of gas hydrate classes given a set of values of the chosen attributes derived from seismic inversion and or well measurements is inverted. Finally, a maximum a-posteriori (MAP) rule is employed to obtain the optimal porosity and hydrate saturation estimation.

Abstract: A profile is produced based on measured survey data, where the profile contains indications corresponding to refraction events at different depths in a subterranean structure. Based on the profile and a critical angle model that correlates different concentrations of a given material to respective critical angles, a quantity of the given material in a subterranean structure at a particular depth is determined.

Abstract: A method to predict tars and tar sands in petroleum exploration and production in a subsurface volume by building a viscoelastic property model of the volume using rock behavior including tars together with seismic inversion to derive a lithology volume identifying tars.

Abstract: A method for generating an average gamma factor for recorded PS data and recorded PP data. The method includes mapping one or more arrival times of the recorded PS data onto one or more expected arrival times of the recorded PP data using an estimated average gamma factor, identifying one or more corresponding pairs of events in the recorded PS data and the recorded PP data using forward modeling, applying geophysical reasoning on the corresponding pairs of events and generating the average gamma factor.

Abstract: An appropriate rock physics model is chosen from well-log data and/or experience. The rock physics model is then used to generate a plurality of attribute values, including compression and shear velocities, at different porosities and gas hydrate saturations. Gas hydrates are classified into different ranges of porosity and hydrate saturation based on the population of multiple attributes, and probability density functions for each individual gas hydrate class are created. Probability density distribution functions for individual attributes and a joint conditional probability density functions are created using a Bayesian function. The conditional probability of the occurrences of gas hydrate classes given a set of values of the chosen attributes derived from seismic inversion and or well measurements is inverted. Finally, a maximum a-posteriori (MAP) rule is employed to obtain the optimal porosity and hydrate saturation estimation.

Abstract: A method for generating an average gamma factor for recorded PS data and recorded PP data. The method includes mapping one or more arrival times of the recorded PS data onto one or more expected arrival times of the recorded PP data using an estimated average gamma factor, identifying one or more corresponding pairs of events in the recorded PS data and the recorded PP data using forward modeling, applying geophysical reasoning on the corresponding pairs of events and generating the average gamma factor.