Lithology and hydrocarbon mapping from multicomponent seismic data

Elastic rock properties can be estimated from prestack seismic data using amplitude variation with offset analysis. P‐wave, S‐wave and density ‘reflectivities’, or contrasts, can be inverted from angle‐band stacks. The ‘reflectivities’ are then inverted to absolute acoustic impedance, shear impedance and density. These rock properties can be used to map reservoir parameters through all stages of field development and production. When P‐wave contrast is small, or gas clouds obscure reservoir zones, multicomponent ocean‐bottom recording of converted‐waves (P to S or Ps) data provides reliable mapping of reservoir boundaries. Angle‐band stacks of multicomponent P‐wave (Pz) and Ps data can also be inverted jointly. In this paper Aki‐Richards equations are used without simplifications to invert angle‐band stacks to ‘reflectivities’. This enables the use of reflection seismic data beyond 30° of incident angles compared to the conventional amplitude variation with offset analysis. It, in turn, provides better shear impedance and density estimates. An important input to amplitude variation with offset analysis is the V_s/V_p ratio. Conventional methods use a constant or a time‐varying V_s/V_p model. Here, a time‐ and space‐varying model is used during the computation of the ‘reflectivities’. The V_s/V_p model is generated using well log data and picked horizons. For multicomponent data applications, the latter model can also be generated from processing V_s/V_p models and available well data. Reservoir rock properties such as λρ, μρ, Poisson's ratio and bulk modulus can be computed from acoustic impedance, shear impedance and density for pore fill and lithology identification. λ and μ are the Lamé constants and ρ is density. These estimations can also be used for a more efficient log property mapping. V_p/V_s ratio or Poisson's ratio, λρ and weighted stacks, such as the one computed from λρ and λ/μ, are good gas/oil and oil/water contact indicators, i.e., pore fill indicators, while μρ mainly indicates lithology. μρ is also affected by pressure changes. Results from a multicomponent data set are used to illustrate mapping of gas, oil and water saturation and lithology in a Tertiary sand/shale setting. Whilst initial log crossplot analysis suggested that pore fill discrimination may be possible, the inversion was not successful in revealing fluid effects. However, rock properties computed from acoustic impedance, shear impedance and density estimates provided good lithology indicators; pore fill identification was less successful. Neural network analysis using computed rock properties provided good indication of sand/shale distribution away from the existing wells and complemented the results depicted from individual rock property inversions.