AVO Definition and Processing Objectivesprometheus-products.com/newavo.pdfin the amplitude, Ross and...
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AVO Definition and Processing Objectives AVO classification for Clastic reservoirs (after Rutherford and Williams). AVO Overview Amplitude variations with offset (AVO) are a prestack inter- pretation technique to indicate rock lithology and fluid content. To qualify the presence of oil and gas, AVO trends can be measured on CMP data and compared to model or known field examples. The use of AVO attributes in con- junction with high resolution mi- grated stacked data has resulted in numerous successful explora- tion and development wells worldwide. Anomalous AVO responses indicative of hydrocarbons are caused by the elastic and physical properties of hydrocarbon satu- rated rocks. Density and Poissons ratio differences caused by the pres- ence of oil and gas in the rock ma- trix is what creates the anomaly. These hydrocarbon-related changes can result in amplitude trends that can be detected and exploited on prestack seismic data. Processing the data for AVO analysis involves careful param- eterization of seismic processing algorithms that preserve relative amplitude on the CMP data. Typi- cally after preprocessing, perform- ing a velocity moveout correction (NMO/LNMO/ALNMO), dip moveout (DMO) and migration, the data is qualified for AVO at- tribute generation. In general, an AVO attribute for a CMP record is a time series that shows a change of amplitude with offset. The simplest are low angle and high angle stacks. More informa- tion can be obtained from AVO linear (two term) regression curves. Amplitude trends related to increasing average incidence re- flection angle of a CMP record is calculated over all time samples. The time series of gradient values and extrapolated zero-offset val- ues are basic AVO attributes. The results of these measurements us- ing Shuey (1985) nomenclature are the normal incidence trace (A) and the gradient trace (B) for each CMP. Different combinations of A and B provide AVO attributes that can be focused for specific types of anomalies. For long off- set and anisotropic reflection data, in addition to special moveout considerations, an extension of the regression analysis from two terms to three terms is required. Rutherford and Williams (1989) proposed three classes of AVO for clastic gas-charged reser- voirs based upon acoustic imped- ance constrasts and Poissons ra- tio contrasts. Class 3 (Bright Spots) The best known, Class 3, is a bright spot related response usu- ally associated with a gas charged reservoir encased in shale. For this class, a large acoustic impedance contrast produces large negative zero-offset amplitude, and a con- trast in Poissons ratio results in more negative amplitudes with in- creasing offset. On a stacked seis- mic section this response would appear stronger than the zero-off- set response because of the AVO contribution. Since the A and B terms for Class-3 reservoirs are quite large and have the same sign, the product (A*B) and other like attribute combinations are fre- quently used to indicate AVO anomalies of this type. Class 2 (Dim Spots) For this class, the acoustic impedance contrast is small, how- ever contrasts in Poissons ratio re- sults in large negative amplitudes with increasing offset. Because the zero offset reflection amplitude is small (either positive or negative) the multiplcative attributes used with Class-3 reservoirs are not ap- propriate for this AVO type. In these instances a better attribute to denote hydrocarbons is a scaled sum of the A and B.
Transcript of AVO Definition and Processing Objectivesprometheus-products.com/newavo.pdfin the amplitude, Ross and...
AVO Definition andProcessing Objectives
AVO classification for Clastic reservoirs (after Rutherford and Williams).
AVO OverviewAmplitude variations with
offset (AVO) are a prestack inter-pretation technique to indicaterock lithology and fluid content.To qualify the presence of oil andgas, AVO trends can be measuredon CMP data and compared tomodel or known field examples.The use of AVO attributes in con-junction with high resolution mi-grated stacked data has resultedin numerous successful explora-tion and development wellsworldwide.
Anomalous AVO responsesindicative of hydrocarbons arecaused by the elastic and physicalproperties of hydrocarbon satu-rated rocks. Density and Poisson�sratio differences caused by the pres-ence of oil and gas in the rock ma-trix is what creates the anomaly.These hydrocarbon-related changescan result in amplitude trends thatcan be detected and exploited onprestack seismic data.
Processing the data for AVOanalysis involves careful param-
eterization of seismic processingalgorithms that preserve relativeamplitude on the CMP data. Typi-cally after preprocessing, perform-ing a velocity moveout correction(NMO/LNMO/ALNMO), dipmoveout (DMO) and migration,the data is qualified for AVO at-tribute generation. In general, anAVO attribute for a CMP recordis a time series that shows achange of amplitude with offset.The simplest are low angle andhigh angle stacks. More informa-tion can be obtained from AVOlinear (two term) regressioncurves. Amplitude trends relatedto increasing average incidence re-flection angle of a CMP record iscalculated over all time samples.The time series of gradient valuesand extrapolated zero-offset val-ues are basic AVO attributes. Theresults of these measurements us-ing Shuey (1985) nomenclature arethe normal incidence trace (A) andthe gradient trace (B) for eachCMP. Different combinations of
A and B provide AVO attributesthat can be focused for specifictypes of anomalies. For long off-set and anisotropic reflection data,in addition to special moveoutconsiderations, an extension of theregression analysis from twoterms to three terms is required.
Rutherford and Williams(1989) proposed three classes ofAVO for clastic gas-charged reser-voirs based upon acoustic imped-ance constrasts and Poisson�s ra-tio contrasts.
Class 3 (Bright Spots)The best known, Class 3, is a
bright spot related response usu-ally associated with a gas chargedreservoir encased in shale. For thisclass, a large acoustic impedancecontrast produces large negativezero-offset amplitude, and a con-trast in Poisson�s ratio results inmore negative amplitudes with in-creasing offset. On a stacked seis-mic section this response wouldappear stronger than the zero-off-set response because of the AVOcontribution. Since the A and Bterms for Class-3 reservoirs arequite large and have the samesign, the product (A*B) and otherlike attribute combinations are fre-quently used to indicate AVOanomalies of this type.
Class 2 (Dim Spots) For this class, the acoustic
impedance contrast is small, how-ever contrasts in Poisson�s ratio re-sults in large negative amplitudeswith increasing offset. Because thezero offset reflection amplitude issmall (either positive or negative)the multiplcative attributes usedwith Class-3 reservoirs are not ap-propriate for this AVO type. Inthese instances a better attributeto denote hydrocarbons is a scaledsum of the A and B.
Class-3 AVO anomaly delineated by the product A * B. The reservoir is highlighted by the bright spots shown in green.
Class-2 AVO anomaly delineated by optimally summing scaled A and B. Sincethe amplitudes of the anomalous events are small, the events usually cannot beseen on a stacked section. The reservoir is highlighted above by blue and red.
Class 1 (High Impedance)For this class, the large
acoustic impedance contrast andcontrasts in Poisson�s ratio resultsin large positive amplitudes thatdecrease with offset.
2-D versus 3-D AVO3-D AVO refers to using 3-D
volumes of unstacked seismic datawith 3-D velocity cubes for AVOcomputations. Using a full 3-Dsurvey instead of one or two 2-Dlines better portrays the AVO ex-tent and character of the reservoir.Not only do you have a true-am-plitude processed volume, but aspatially correct AVO response,which contains pore fluid andstratigraphic information. 3-DAVO may be used to identify AVOanomalies to focus in on areasneeding more detailed modelinganalysis. Also, in areas that haveClass-2 AVO response character-istics, because the reflections inthe migrated volume are weak, a3-D AVO attribute volume helpsilluminate the reservoir.
AVO at PGS TensorPGS Tensor offers a full com-
pliment of AVO measurements for2-D and 3-D surveys. Whetheryou are looking for bright spots(Class 3), dim spots (Class 2), orhigh impedance reservoirs (Class1), PGS Tensor�s AVOATT pro-
Incorrect moveout using NMO. This data is a Class-2 anomaly thatrequires proper moveout to be detected.
Correct moveout using LNMO -- long offset normal moveout. The faroffset stack from these records show the Class-2 anomaly.
Class-3 AVO anomaly delineated by Ds -- change on Poisson's ratio. The reservoir is highlighted by events with largePoisson's ratio changes in blue and red.
gram computes a sample-by-sample AVO attribute or a con-strained AVO attribute, which willgenerate a superior AVO measure.Attributes generated by AVOATTinclude:
A Normal incidencestack or zero- offsetstack
B Gradient or slopestack
DsDsDsDsDs (delta-sigma)Change in Poisson�sratio stack
F Far Range stackM Mid Range stackN Near Range stackRs Shear reflectivity
estimateBeam Angular swath stackGF Goodness of fit
And combined attributes:
A*B AVO Product orSlope-Interceptproduct
A*DsDsDsDsDs AVO product ofnormal incidenceand delta-sigma
Sign (A)*B Restricted gradientScaled A+B Scaled sum of
normal incidenceand gradient
CMP 1497 CMP 1498 CMP 1499 CMP 1500
CMP 1497 CMP 1498 CMP 1499 CMP 1500
Copyright 1997 PGS Tensor, Inc.
PGS Tensor�s ANGSTK pro-gram provides an ensemble ofangle stacks (unlimited number)for each CMP input record. A dif-ference of a low angle stack tracefrom a high angle stack trace is anAVO attribute that is an indicatorof a Class-2p anomaly (a subclassof Class 2 that has a sign changein the amplitude, Ross andKinman 1995).
PGS Tensor has special AVOprocessing support programs suchas LNMO and ALNMO to prop-erly moveout long offset data andanisotropic data. Also, there arequality control programs,AVOPREP and AVOQC, to helpensure correct parameter selec-tion.
Suggested Reading1. R.J. Schuey, 1985, a sim-
plification to Zoepritz equations,Geophysics, 50, 609-614
2. Rutherford and Williams,1989, Amplitude vs. offset varia-tions in gas sounds, Geophysics 54,680-689.
3. G.C. Smith and M.Gidlow, 1987, Weighted stackingfor rock property estimation anddetection of gas, Geophysical Pros-pecting, 39
4. J.P. Castagna and H.W.Swan, 1997, Principles of AVOcrossplotting, The Leading Edge 16,no.4, 337-342.
5. Christopher Ross, 1995, 3DAVO in mature fields: improved
mature field development with3D/AVO technology, First Break,Volume 13, No. 4, April, 139-145.
6. C.P. Ross and D.L.Kinman, 1995, Non bright-spotAVO: two examples, Geophysics 60,1398-1408.
7. C.P. Ross, J.G. Starr, D.H.Carlson, 1996, Comparison of AVOusing ocean bottom seimic andmarine streamer, EAGE abstracts.
8. C.P. Ross, 1997, AVO andnonhyperbolic moveout: a practi-cal example, First Break, volume 15,No. 2, February, 43-48.
9. David H. Carlson, 1997, 3Dlong offset nonhyperbolic velocityanalysis, SEG abstracts.
