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Since the early 1980s, when Ostrander demonstrated in apractical setting how amplitudes could change with offseton gathers due to the presence of gas, the industry hasstrived to understand, define, redefine, and improve thetechnology we call amplitude variation with offset (AVO). Wehave known for decades that AVO is controlled by the var-

ious rock physics parameters of the earth as we transmit andreceive acoustic signals. However, it has only been in thelast few yearsdue to improved acquisition, processing, andinterpretation techniquesthat we have been able to deriveand better understand some of these rock physics parame-ters.

Rutherford and Williams in 1989 defined three classesof AVO (Castagna and Swan adding another class a few yearslater) and the industry now routinely discusses AVO interms of these classes 1, 2, 3, and 4. AVO interpretation hasevolved from just comparing modeled gathers to real gath-ers, to include approaches such as AVO inversion, elasticimpedance, AVO attribute crossplots, lambda-mu-rho analy-sis, and fluid factor. Terms such as intercept, gradient, andZoeppritz approximations are commonly discussed in theinterpretation of prospects today.

In the effort to better understand the lithologies andpore fluids of the earth in exploring and developing oil andgas, the papers in this special section use many of the termsand approaches described above with some additional ana-lytical techniques proposed. The first two papers are AVOcase studies. Eissa and Castagna employ AVO techniquesto detect high-impedance gas sand reservoirs in stratigraphictraps in the northern Arkoma Basin. These fluvial-deltaicAtokan sandstones display class 1 characteristics and werefound to display a positive intercept, negative gradient, andpolarity change at far offsets. Chopra and Pruden performedan AVO analysis over a Cretaceous gas field in southernAlberta. Derived Lam parameters (lambda-rho and mu-rho)

were successfully integrated with several other seismicattribute volumes by means of a probabilistic neural net-work. The results included 3D volumes containing loggamma ray and bulk density which led to two successfulgas wells.

In an effort to better understand rock physics trends,especially in deeply buried and deepwater environments,Avseth et al. incorporate information on depositional andcompaction trends in trying to predict the presence of hydro-carbons. They employ a depth dependent probabilistic AVOapproach that enables the prediction of the most likely lithol-ogy and pore fluid from seismic data. Gonzalez et al. use aPS converted wave elastic impedance (PSEI) formulation tohelp discriminate areas of high and low gas saturation. Inthis study the use of PSEI compared to P-wave data

improved by 20% the success of distinguishing commercialgas from fizz water. Egreteau and Thierry discuss the impor-tance of postprocessing analysis on AVA (amplitude varia-tion with angle) data. They demonstrate the importance ofthis analysis by testing the stability of the AVAinversion withrespect to the signal-to-noise ratio, the flatness of the events

within the common image gathers and the corridor mute.Their results are demonstrated on a data set in the NorthSea that improved analysis of the Brent reservoir.

In an analysis of 592 Tertiary and Cretaceous sandstonesamples from offshore Brazil, Dillon et al. evaluate the per-formance of eight different elastic parameter fluid indica-tors. In younger, poorly consolidated reservoirs essentiallyall the approaches were sufficient, but in the older rocks thefluid indicator Ip2 - CIs2 [Ip=P impedance, Is=S impedance,C=(Vp/Vs)2] provided the best results. However, on nor-mal seismic data due to typical poor signal-to-noise, IpIsappeared to work best.

Young and LoPiccolo propose a new classificationscheme that partly redefines and expands the Rutherfordand Williams, and Castagna and Swan AVO classifications.This scheme divides the intercept-versus-gradient crossplotinto 10 parts, with the northeastern half representing non-conforming sands and the southwestern half denoting con-formable sands. The results of this approach are shown withthree examples where the pay zones and lithologies areknown. In the final paper, Van Koughnet and Lindsaydescribe the density cube, which is derived from a nonlin-ear three-parameter AVO inversion process. They indicatethe density cube is sensitive to hydrocarbon signatures notresolved by conventional amplitude and AVO methods.

The papers presented in this special section are excel-lent examples of where AVO interpretation to derive rockphysics parameters is headed. With the industry more rou-tinely employing prestack time- and depth-migrated seis-

mic data (improving gathers and offset stacks), developingnew interpretation techniques, and applying AVOapproaches in regions not previously attempted, we areperhaps at the threshold of a rock physics revolution.However, to advance this revolution we must keep in mindwhat Richard Cooper stated in Upstream G&G technol-ogySo where does all this stuff come from? (TLE, August2003) in relation to 3D data and rock physics derived prop-erties: We have been standing at this threshold for severalyears, if not a decade or two. Therefore, the industry must

be diligent and dedicated to advancing AVO technology aswe try to unravel the physics of the earth. TLE

ROCKYRODEN AND REBECCA LATIMER

An introductionRock geophysics/AVO

OCTOBER 2003 T HE LEADING EDGE 98