elastic impedance inversion in practice

Elastic Impedance Inversion in Practice*Milos Savic, ARCO British Ltd., Bruce VerWest, ARCO British Ltd., Ron Masters, ARCO Exploration,Arcangelo Sena, ARCO Exploration, and Dean Gingrich, ARCO Alaska Inc

Reservoir characterization should integrate allavailable seismic, petrophysical and geologicalinformation into the volumetric distribution ofreservoir properties like porosity, permeability andsaturation. Each contributing piece of evidence isincomplete alone. Wells can measure many reservoirproperties at high vertical resolution, but offer onlysparse sampling laterally, often at considerableexpense. Seismic data provides nearly continuouslateral sampling at relatively low cost, but with muchless vertical resolution. It also measures keyreservoir properties less directly. Inversion forelastic impedance is the latest improvement in acontinuing process of integrating seismic and welldata for reservoir characterization.

Seismic inversion for acoustic impedance has beenwidely practiced in the industry. By "inversion", wemean converting seismic reflection amplitudes intoimpedance profiles. This involves removing thebandpass filter ("wavelet") imposed by seismicacquisition and processing. Well control is used tocalibrate the scaling of the wavelet from seismic towell log units, and also to restore the low frequencycomponent of the profile. Thus we recover anabsolute measurement of impedance throughout theseismic data set, usually a 3D volume. The benefitsare numerous:• The broader bandwidth of the impedance data

maximizes vertical resolution and minimizestuning effects.

• Interpreting volumes rather than surfaces is moregeologically intuitive. It simplifies lithologicand stratigraphic identification, and supportsstatic reservoir models of any complexity.

• Since the data is no longer zero-mean, thedynamic range in any given color display scale ismore than doubled, increasing confidence inrelatively subtle features.

• Calibrated seismic impedance predictscorrelative petrophysical properties like porosity,clay content, and net/gross, throughout theseismic data volume.

Before the advent of elastic impedance, this lastbenefit was diluted by a caveat. Seismic reflectionamplitudes are dominantly, but not exclusively,generated by contrasts in acoustic impedance. The

standard practice of calibrating stacked seismic data to anacoustic impedance log ignores AvO effects, riskingfailure and missing an opportunity. Normal incidencereflection of compressional plane waves depends only oncontrasts in acoustic impedance--density times velocity.Conventional multi-channel seismic data also recordsoblique reflections, which have been influenced byconcurrent mode conversion from compressional to shearwaves. Thus the amplitude variation with offset (AvO)exhibited by seismic reflection events depends on contrastsin shear velocity (Vs) as well as compressional velocity(Vp) and density. AvO is widely exploited as a directhydrocarbon indicator and/or a reservoir prediction tool,because reservoirs often have anomalous Vp/Vs ratios, andconsequently anomalous AvO behavior. Failure to accountfor these effects can result in significant miss-ties betweenseismic and log data, and inaccurate impedance estimation,especially in reservoirs. Extracting both an acousticimpedance volume from normal incidence data and ananalogous elastic impedance volume from wide-angle dataoffers an opportunity for an additional measurement,which can improve prediction of porosity and lithology.

Elastic impedance is intuitively obvious, but elusive inpractice. Just as contrasts in an acoustic impedance profile,convolved with a wavelet, generate normal incidenceseismic data, there should be an "elastic impedance" profilewhose contrasts would generate wide-angle reflections. Theproblem is that there is no simple closed form expression forthis quantity. There are different ways to approximate it.One is to integrate Aki and Richards' (1980) approximationfor the Zoeppritz equation (Connolly, 1998 and Sena, 1997).The result is useable but unsettling, with angle-dependent(or even depth-dependent) fractional units. We use anapproximation derived from a different series expansion ofthe Zoeppritz equations (VerWest 1998, VerWest et al, 2000this issue), which always has the same units as acousticimpedance.

Figure 1 shows a cross-plot of elastic impedance (verticalaxis) vs. acoustic impedance (horizontal axis) for well A1.The color bar gives Gamma Ray values where blue andgreen colors represent shales while red and yellowrepresent sands (yellow - the cleanest sand). One canobserve that the cleanest reservoir sands have the sameacoustic impedance (AI) range as some shales (around22000 on horizontal axis). However, the same sands onelastic impedance (EI) axes have consistently lower value

than the corresponding shales. This means that onecannot distinguish shales from sands using onlyacoustic impedance (or equivalently the near

reflectivity) but if one uses both AI and EI simultaneouslyone can design a cut-off trend line that can help distinguishsands from shales.

Figure 1 Cross-Plot of Acoustic Impedance versus Elastic Impedance for well A1. AcousticImpedance is on horizontal axis (acimp) and Elastic Impedance is on vertical axis (Impedance). Thelog display on the right features Gamma Ray log in blue, AI log in dark grey, and EI log in red.

Once the elastic well logs are available for well tiesand wavelet estimation, seismic inversion of far angledata can be carried out with any standard commercialtool available.

Figure 2 shows result of Acoustic Impedance inversionaround one of the wells from Alpine field. In this casenear seismic data is tied to the acoustic impedance logand resulting wavelet used for inversion.

Figure 3 shows result of elastic impedance inversion. In thiscase far seismic data is tied to elastic impedance at 30degrees.

Figure 4 shows the Net Pay map estimated from elasticimpedance data. More about use of elastic impedance forNet Pay prediction can be found in Gingrich et al, 2000 (thisissue).

Figure 2 Detail of Acoustic Impedance volume.

Figure 3 Detail of Elastic Impedance volume at the same location.

AcousticImpedance Gamma Ray

ElasticImpedance Gamma Ray

Figure 4 Net pay map estimated using laterally varying velocity field. The trajectories of all originallyplanned horizontal wells are superimposed. Black lines are rejected horizontal completions. However,by analyzing the red horizontal trajectories one can observe additional room for improvement(optimization of existing trajectories including additional rejections).

Conclusions

• Inversion of near-angle seismic datato Acoustic Impedance andinversion of far-angle data to ElasticImpedance was successfully carriedout.

• Inverted Acoustic Impedance aswell as Elastic Impedance givesvery good tie to the well log derivedAcoustic Impedance and ElasticImpedance.

• The net-pay map based on ElasticImpedance ties most of the wells.The net-pay prediction deterioratesin the vicinity of faults.

• This new integrated seismicreservoir study will help reduceproduction drilling costs by anestimated $30,000,000 at Alpine.

ReferencesAki, K., and Richards, P.G., 1980, QuantitativeSeismology: Theory and Methods, W.H. Freeman andCo.Connolly, P., 1999, Elastic Impedance, The LeadingEdge, April Issue, 438-452.Sena, A., 1997, Far Angle Stack Seismic Inversion: AFeasibility Study, AEPT Research Memorandum 97-0013 .VerWest, B., 1998, Elastic Impedance for theInversion of Far Offset Seismic Sections, ARCO U.S.Patent Notes.Gingrich, D., Hughes, D., Kerr, D., Savic, M.,Hannon, R., Knock, D., and Sena, A., 2000, AlpineOil Field: Geophysics from wildcat to development,SEG E. Abstr., Calgary, Canada.VerWest, B., Masters, R., and Sena, A., 2000, ElasticImpedance Redefined, SEG E. Abst., Calgary,Canada

AcknowledgementAuthors would like to express their gratitude to ARCOAlaska Inc. and Anadarko Petroleum Corporation forpermission to publish this project.