Shear-wave velocity estimation using multiple logs and multicomponent seismic AVO interpretation: Gulf of Thailand

Shear-wave velocity (V_s) is very important to seismic interpretation. Shear-wave logs, however, are often missing. Existing V_s estimation methods either require detailed rock volumetric and fluid prediction first or give large errors in the study area: a gas field in the Gulf of Thailand where sediments consist of sand-shale mixtures and coals. Two petrophysical characteristics of the sand-shale mixtures are worth noting. First, when the clay content increases, their compressional-wave velocity (V_p) and V_s increase when the clay content is low and decrease when the clay content is high. This velocity behavior separates the sand-shale mixtures into two groups: the porous sand with low clay content and the low porosity sand-shale with high clay content. Second, the porous sands usually have higher velocities but lower density than the low porosity sand-shale. Based on these and other petrophysical characteristics, two methods of V_s estimation are given for the three groups of sediments: the porous sand, the low porosity sand-shale, and the coal.; P-wave AVO technique, as a powerful hydrocarbon prospecting too], is applied almost routinely in practice now. However, current AVO analyses assume a generalized Gardner's equation for the density-V_p relationship across the interface, which is not true in some areas including the study gas field. Therefore, I derived an AVO analysis technique without a predefined density-V_p relationship.; Multicomponent seismic data are becoming popular. Information extracted from converted-wave AVO should significantly enhance that obtained from P-wave AVO. Unfortunately, there is not much literature available. Therefore, I analyzed the characteristics of converted-wave AVO under different shear-wave acoustic-impedance contrasts and V_p/ V_s ratios, and derived a method for converted-wave AVO crossplotting to detect hydrocarbon and lithologic variations.; Finally, it has always been difficult to discriminate low and high gas saturations theoretically and practically prior to drilling. Consequently, drilling into non-commercial gas reservoirs has been called a “technical success.” However, I found very good partial gas indicators (PGIs) using multicomponent seismic AVO data. Theoretical results show that the proposed PGIs can separate regions of high gas saturation from low saturation areas and distinguish water saturation changes from porosity or clay content changes.