The current state-of-the-art in reservoir characterization is to input properly conditioned gathers into a prestack simultaneous impedance inversion workflow. Simultaneous inversion is defined as the simultaneous solution of all unknown quantities in a reflectivity equation. If two-term inversion is run, then the outputs are P-impedance (AI) and S-impedance (SI). These two quantities are then crossplotted and pay zones delineated using polygons created from a similar crossplot of upscaled AI and SI well logs.This workflow is stable as long as: (1) the well logs have been properly conditioned, (2) the crossplot of rock properties from upscaled logs indicates AI and SI are capable of delineating pay, and (3) the seismic gathers have been properly processed and conditioned and are in good enough shape to reveal AVO character.If any of these three conditions are not met, simultaneous inversion alone is unlikely to produce acceptable results for reservoir characterization. These restrictions on data quality often spur the search for other types of data (in addition to impedance) in order to reduce the prospect risk profile. An example of an alternate data type is attenuation, which measures frequency anomalies rather than reflectivity changes with offset.The objective of this paper is to show how attenuation estimation can be used in combination with simultaneous impedance inversion for prospect evaluation.Method. This paper builds on research that has been published in the past several years on the measurement of attenuation in logs, synthetic seismic, and surface seismic data. The simple workflow shown in Figure 1 summarizes the steps necessary to properly integrate and calibrate attenuation measurements in the reservoir characterization process.These steps are: (1) correction of acoustic and density logs and calibration to a rock physics model, (2) calculation of attenuation logs; (3) calculation of full waveform synthetic gathers incorporating attenuation; (4) attenuation estimation of the stacked synthetic gathers, (5) attenuation estimation of the stacked seismic data, (6) calibration of both attenuation responses, and finally, (7) analysis of the seismic attribute volumes.Discussion of well log processing and the gather conditioning required for impedance inversion are beyond the scope of this paper and will not be addressed. Furthermore, the steps involved in prestack impedance inversion will only be addressed inasmuch as is required to establish the validity of the inversion product.After the logs are conditioned, the first step is the calculation of attenuation logs from wireline data. For this we use the Dvorkin heterogeneous Q model (Dvorkin and Uden, 2004; Dvorkin and Mavko, 2006). This model states that attenuation can be related to moduli using the KramersKronig causality principle and the standard linear solid model (Mavko et al., 1998) as:( 1) where Q -1 Max is the maximum inverse quality factor, M H is the compressional modulus at very high frequency, and M L is the compressional modulus at very lo...
This paper is the third part in a reservoir characterization series. Its objective is to demonstrate the necessity of understanding the rock property responses of a reservoir so that the project results can correctly interpreted. The first step is to check and correct acoustic and density well log curves. For the current study a combination of Raymer for density and Greenburg-Castagna for Vs were applied in the shallow zone above the reservoir. Within the turbidite reservoir section a laminated sand fluid substitution was used to understand its behavior as fluid content varies, and a matrix substitution to understand its behavior as sand content varies. Synthetic gathers were calculated for all models using both ray traced and full waveform algorithms. These exercises showed that AVO analysis could be used to detect fluid changes in the seismic data but not for detecting sand content changes. Rock physics crossplots, however, could make this distinction. The seismic inversion was calibrated to acoustic impedance (AI), shear impedance (SI), and Poisson's Ratio (PR) well log curves and clearly revealed that acoustic anomalies seen in this prospect were the result of sand content changes and not the result of fluid saturation changes.
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