Analysis, Inversion, Geostatistical Simulation, and Visualization of Porosity in a carbonate Gas Reservoir

Abstract

Abstract Integration of well and seismic data is key to predicting reservoir rock properties from seismic data. This presentation will cover the analysis and integration of well and seismic data for a gas reservoir. This involves several steps which include:post-stack wavelet processing to increase the frequency content of the seismic data,tying the wells to the seismic data,determining a common wavelet for the entire survey,inversion of the seismic data to acoustic impedance, andconditional simulation of porosity using acoustic impedance as a guide. One hundred 3D conditional simulations of porosity were created to quantify uncertainty in porosity estimation. The conditional simulations were processed to produce an average phi-h map for the field in addition to their use in calculating a cumulative probability distribution of reservoir pore volume. The average 3D porosity volume derived from the conditional simulations was loaded into a 3D visualization package for visual analysis of the maximum porosity zones within the field. Introduction The goal of this study was to quantify the uncertainty in the total reservoir pore volume due to porosity. Uncertainty in theinterval velocities within the reservoir was also examined. However, we did not evaluate the reservoir pore volume uncertainty due to depth conversion, water saturation, or other parameter. The field has seven wells and is covered by a 3D seismicsurvey (Fig. 1). Two of the wells lack either a sonic or density log or both logs above the reservoir interval. Because of the large volume of seismic data and the small number of wells, we felt that it would be useful if the range of reservoir properties between the wells could be constrained using the seismic data. Acoustic impedance (AI) is a logical choice for estimating porosity (0). Acoustic impedance should be related to porosity. Frequently, porosity values are calculated from both the sonic and density logs. The Wyllie travel time equation (Wyllie et al, 1956) relates travel time to fractional porosity in the following equation: (available in full paper) Procedures And Observations The study was implemented as follows:The well logs were interpreted and related to rock type. Lithofacies were identified from the well logs.The neutron porosity, sonic, and density logs were analyzed and calibrated with core data to interpret porosities within the reservoir.The post-stack seismic data was processed to increasethe frequency content and to zero the minimum phase component of the wavelet.The well logs were calibrated to seismic data.Wavelets were extracted from the seismic data near the well locations and averaged to determine a common wavelet for the field.The average wavelet was used to invert the seismic data to acoustic impedance throughout the reservoir interval.The inverted acoustic impedance was crossplotted against average gross porosity at a 4 ms sample interval. (Assuming a velocity of 10,000 feet/sec, a 4 ms interval (two-way travel time) represents 20 feet.)The porosities and interval velocities were geostatistically simulated at a 4 ms sample interval over the field.Volumetrics were calculated on the final geostatistical simulations.