A novel workflow for seismic net pay estimation with uncertainty
  This paper presents a novel workflow for seismic net pay estimation with uncertainty. It is demonstrated on the Cassra/Iris Field. The theory for the stochastic wavelet derivation (which estimates the seismic noise level along with the wavelet, time-to-depth mapping, and their uncertainties), the stochastic sparse spike inversion, and the net pay estimation (using secant areas) along with its uncertainty; will be outlined. This includes benchmarking of this methodology on a synthetic model. A critical part of this process is the calibration of the secant areas. This is done in a two step process. First, a preliminary calibration is done with the stochastic reflection response modeling using rock physics relationships derived from the well logs. Second, a refinement is made to the calibration to account for the encountered net pay at the wells. Finally, a variogram structure is estimated from the extracted secant area map, then used to build in the lateral correlation to the ensemble of net pay maps while matching the well results to within the nugget of the variogram. These net pay maps are then integrated, over the area of full saturation gas, to give the GIIP distribution (Gaussian distributions for the porosity, gas expansion factor, and gas saturation for the sand end member are assumed and incorporated in the estimate of GIIP). The method is demonstrated on the Iris (UP5 turbidite) interval. The net pay is corrected for reduction in the amplitudes over part of the area due to shallow gas. The sensitivity of the GIIP to the independent stochastic variables is estimated (determining the value of information) so that business decisions can be made that maximize the value of the field. 
Michael E. Glinsky, Dale Baptiste, Muhlis Unaldi and Vishal Nagassar
physics.geo-ph