The identification process of different lithologies, hydrocarbons, and water-saturated zones in oil and gas industries involves petrophysical studies that are carried out by geoscientists using different software packages. This study aims to propose a method by integrating mean cluster analysis and well logs to identify dominant lithologies, pore fluids, and fluids contact. For this purpose, initially, K-mean cluster analysis is applied to density log and P-wave velocity data of three wells in order to group them into different clusters. Based on centroids of each cluster, different lithologies have been identified. The density log equation has been utilized to compute the porosity of each cluster, and the mean of each density log cluster is used as matrix density. Next, sonic log equation has been inverted to compute the fluid velocity and the mean of each P-wave velocity cluster is used as matrix velocity. For the fluid density, sonic and density log equations are jointly inverted to compute the fluid velocity of each cluster. The fluid bulk modulus and acoustic impedance are computed using fluid density and velocity. Based on the results of K-mean cluster analysis, different lithologies (shale, sandstone, and limestone) have been recognized successfully. In well-1, hydrocarbon and water-saturated zones are successfully identified and fluids contact has been established in the zone of interest. However, well-2 and well-3 did not show any indications of the presence of hydrocarbon in the respective zones.
In the past, seismic exploration technique was mainly used for gathering information about subsurface rock structures and fluids by analyzing the travel time, reflection amplitude, and phase variations. However, nowadays, many additional seismic attributes have been introduced by the seismic interpreters, which aid in the visualization of subsurface geological structures, facies, and lithologies. This research aims to identify the pore fluids in the reservoir using post-stacked seismic data without requiring well log data. Gassmann's equation, a well-known equation for fluid substitution, has been used for fluid substitution in this research. To test the proposed technique, a three-layer geological anticline model has been used. The third layer of the model represents a reservoir which is saturated with water, except its top part which is fully saturated with petroleum. Fluid identification is achieved by using fluid density, velocity changes, and acoustic impedance (AI). P-wave velocity and AI are measured from post-stacked seismic data and its inversion, from which the saturated rock density and compressional modulus (M) are calculated. Using this information, saturated rock density and compressional modulus are inverted for fluid velocity and density, respectively, to identify the pore fluid.
Continuous wavelet transformation (CWT) as a new mathematical tool has provided deep insights for the identification of localized anomalous zone in the time series data set. In this study, a three-layer geological model is investigated by CWT to locate seismic reflections temporally and spatially. This model consists of three layers, where the third layers of the anticline structure are assumed to act as a pure sandstone hydrocarbon reservoir with 10% porosity. The equation of Gassmann has been implemented for the pore fluid substitution in the reservoir. Synthetic seismic data are generated for the three-layer geological model. Due to the presence of noise, it is always difficult to interpret seismic data. But, CWT has the ability of noise reduction, improving the visualization of a data set and locating the anomalies in terms of scalogram and 3D CWT coefficients. Synthetic seismic data of the geological structure are transformed by CWT. The successful transformation of P-wave velocity, synthetic seismic data and acoustic impedance inversion provided evidence to distinguish different interfaces accurately. CWT has successfully located seismic reflections by localizing high-energy spectrum within the cone of influence. Three high-energy spectrums have been identified at 0.8 s, 1 s and 1.07 s, and it exactly matches the seismic data and three-layer geological model.
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