Seismic Inversion Techniques Choice And Benefits Fb May2011


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This is a paper developed to show the benefits of seismic inversion in a Deterministic and Geestatistical view.

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Seismic Inversion Techniques Choice And Benefits Fb May2011

  1. 1. first break volume 29, May 2011 special topic Unconventional Resources and the Role of TechnologySeismic inversion techniques: choice and benefitsK. Filippova (Fugro-Jason),* A. Kozhenkov (Fugro-Jason) and A. Alabushin (LUKOIL-Komi)provide an overview of the general principles of deterministic and geostatistical inversionsof seismic data. They demonstrate with a case study from the Timano-Pechora province thatreservoir properties prediction methodology using geostatistical partial stacks inversion canfacilitate high resolution 3D distribution of reservoir properties used to plan production wellspacing, further verified by drilling.A dvances in computer technologies in recent years have led to the rapid growth of seismic amplitude interpretation techniques, notably seismic inversion which has spread widely throughout modern work-flows. Seismic data these days includes not only full stack data ties based on the relation between petrophysical and elastic rock properties obtained from the interpretation of measured well log data. At present, several independent methodologies can be distinguished within the spectrum of the 2D and 3D seismicbut also data stacked in various offset and angle of incidence interpretation approaches: multi-dimensional attribute analy-ranges. Such approaches to seismic amplitude interpretation sis, neural networks, AVO analysis, inversion, etc. (Avsethenable accurate estimation of elastic properties in target res- set al. 2005). While each of these approaches has its ownervoir intervals. advantages and disadvantages, the applicability of any par- At the current stage of seismic exploration technology, 2D ticular technique depends first of all on the available data andand 3D seismic surveys have demonstrated their efficiency geological tasks.and reliability. In particular, 2D seismic surveys are used to Seismic inversion technologies can be classified as follows:build structural frameworks of the subsurface with a certain n By the type of seismic data used for inversion (inversion ofdegree of reliability (Omar et al. 2006). However, in complex full stack seismic data or inversion of partial stack data).structural settings this technique has proven to be insufficient With full stack inversion only one elastic property (acousticand thus should mainly be used at the initial stage of field impedance) can be estimated. With partial stack inversion,exploration to reveal promising exploration targets and to which uses amplitude variation with offset, multiple elasticprovide an initial estimate of their potential as hydrocarbon- properties such as P-Impedance, S-Impedance, Vp/Vs ratio,bearing reservoirs. and density can be estimated. To overcome the limitations of 2D seismic data, the use n By the mathematical approach to the solution of the inverseof 3D seismic data technologies increased and led to the problem. Here we distinguish deterministic and geostatisti-rapid adoption of attribute analysis. Attribute analysis is cal approaches that result in a different level of detail ofessentially based on estimating correlations between one or estimated reservoir properties. Deterministic algorithmsmore seismic attributes and reservoir properties of interest (Jarvis et al., 2004; Pendrel et al., 2003) can only provide a(Ampilov, 2008) solution within the seismic bandwidth, while geostatistical As attribute analysis developed, the methods of studying algorithms can include fine-scale details beyond the seismicseismic amplitudes evolved to use not only full stack seismic bandwidth (Francis, 2006).data, but also partial stacks and seismic gathers, particularlyto analyze how reflected wave amplitude varies with offset – a Although a variety of different techniques for seismic datamethod known as AVO analysis (Foster et al., 2010). As the inversion are available in modern software packages, thenext step in evolution of seismic interpretation techniques, choice of the appropriate technique should be determinedAVO analysis is widely used for exploration of gas reservoirs by the complexity of the geological conditions and the rangein young clastic formations, as well as for detection of of problems to be solved. Our experience accumulated tohydrocarbon saturated reservoirs in oil fields already under date allows us to state that reservoir model construction bydevelopment. means of 3D seismic inversion techniques is controlled by In recent years, a number of seismic inversion techniques three key points:have been developed, finally making it possible to pass from 1. Seismic data qualitythe analysis of reflection coefficients at acoustic interfaces 2. Elastic vs. petrophysical properties relationships from wellto the analysis of elastic properties of formations (Avseth et logs (Lithology, porosity, Sw, Vp, Vs, density);al.,2005). This makes it possible to estimate reservoir proper- 3. Applicable inversion technique.* Corresponding author, E-mail:© 2011 EAGE 103
  2. 2. special topic first break volume 29, May 2011Unconventional Resources and the Role of TechnologyFigure 1 Comparison of acquired (left panel) and modelled (right panel) gather data in the target interval (cross-correlation shown in colour).Seismic data quality The second area is mainly focused on preparing data forAcquisition of seismic data suitable for reliable reservoir seismic inversion and includes a rock physics approach. Itsmodelling starts by designing a proper seismic survey for a primary objectives are:particular geological feature of interest. Seismic modelling n Continuous reconstruction of formation properties alongcan be used as an aid to establish acquisition parameters the wellbore, both for reservoirs and non-reservoirsand enable adjustment of the spread layout to achieve an n Characterization of rocks in terms of their elastic proper-optimum fold and offset distribution at the target interval, tiesthus assuring the best signal-to-noise ratio within the entire n Establishment of causal relationships between elastic andrange of offsets (Singleton, 2009). petrophysical properties such as porosity, clay content and The key element determining reservoir model qual- fluid saturationity is seismic data processing, which should be focused onpreserving seismic amplitudes over the full and partial stack A petrophysical bulk model of the target formations is thevolumes. In the processing special attention should be paid to main prerequisite for reliable discrimination of formationseffectiveness of noise suppression, good static correction, and in the elastic properties domain and for the selection of thethorough estimation of stacking velocities. A good method of type of seismic inversion. The model is based on the resultschecking whether the behaviour of amplitudes is preserved at of core descriptions and drill cuttings, as well as on theall processing steps is by modelling the seismic gathers at well interpretation of well logs. To model elastic properties bylocations and comparing them to processed data (Figure 1). rock physics modelling, it is important to maintain a unified approach with the interpretation of log data (petrophysicalWell logs of elastic and petrophysical properties interpretation) in all wells of a field.Well log data is the next source of information without Equations and parameters assumed in log interpretationwhich it is impossible to reliably predict meaningful reservoir are subsequently used in elastic properties modelling, whichproperties. In present-day analysis and interpretation of well is performed given the volume fractions and properties oflog data, two distinct areas can be distinguished. minerals composing the solid phase of a rock (clay minerals, The first area is traditional, ‘classic’ interpretation for quartz, limestone, dolomite, etc.) and fluid saturation.which the primary objectives are: A bulk model should be built in order to estimaten Detection of reservoirs in target pay formations and deter- the material composition of the entire rock-fluid matrix. mining their properties; Important information for rock physics modelling is pro-n Estimation of reserves vided by the results of the elastic properties determination104 © 2011 EAGE
  3. 3. first break volume 29, May 2011 special topic Unconventional Resources and the Role of Technologyfrom the core data under in-situ conditions (Mavko et al., Let us consider what geological tasks can be solved using2009). Additional information for rock physics modelling different inversion techniques using an example of a complexof the mineral composition, bulk density, and mineralogical oilfield located in the Timan-Pechora Basin in northeastdensity comes from analyses performed on core samples. European part of Russian Federation. It was already notedA comprehensive suite of well log data and core studies is previously that specific information is required to perform anecessary to do this. successful inversion. In this oilfield a wide range of measure- Thus, the quality and completeness of the input suite ments were available: 2of well log data and core studies determine the reliability n 3D seismic survey was acquired (250 km )of a petrophysical model and the reliability of the target n Eight wells were drilledformation discrimination in the domain of elastic properties. -  P-sonic and density logs existed in each of the eight wellsThis bulk model should be sufficient for the evaluation of -  S-sonic logs existed in seven of the eight wellsthe composition of the formation as well as its structure and -  VSP surveys were conducted for four of the eight wellsproperties, not only in the target pay intervals but also in the -  Core data was available for three of the eight wellssurrounding strata. The target formations are Carboniferous shallow waterInversion technique shelf carbonates. Stratigraphically they are confined to theAs mentioned above, we can now consider four major inver- Moscovian (C2m), Bashkirian (C2b), and Upper and Lowersion techniques: Serpukhovian (C1s2 -C1s1b) stages at depths ranging fromn Deterministic inversion of full stack seismic data 2800–3600 m. The seismic and P-impedance sections of then Simultaneous deterministic partial stack inversion study intervals are presented in Figure 2.n Geostatistical inversion of full stack seismic data In accordance with the available core data, the Carbon-n Simultaneous geostatistical partial stack inversion iferous formations consist of carbonate rocks (limestones, dolomites) and are characterized by significant heterogene-In order to come up with an appropriate inversion and ity in their petrophysical properties. The thickness of anreservoir characterization method that shows a reliable individual reservoir layer varies from 1-12 m. The mainseparation of the chosen lithofacies, it is necessary to analyze geological objective was a detailed reservoir characterizationthe geological tasks to define the range of reservoir thickness to optimize the production drilling pattern. To do this, it wasin the target interval and to perform a feasibility study on necessary to build 3D models of the reservoir and its porositywell log data. distribution in the target intervals.Figure 2 Comparison of seismic data and inverted P-Impedance for the target interval.© 2011 EAGE 105
  4. 4. special topic first break volume 29, May 2011Unconventional Resources and the Role of Technology Figure 3 P-Impedance histograms for the target interval in geostatistical scale (limestone reservoir in cyan, dolomite reservoir in green, non-reservoir in dark blue). Let us analyze different inversion techniques and their colour, limestone reservoir in blue, dolomite reservoir in green,potential and limitations for solving the posed geological and non-reservoir is transparent. We can see that the lithotypetasks. In doing so, we will adhere to the following sequence: distribution obtained from seismic inversion is too blurred andn Cross-plot analysis of the relationship between elastic a lower vertical resolution is observed compared to the wells, properties and petrophysical parameters in the target inter- which is why many reservoir intervals remain ‘undetected’ and vals to determine the type of inversion are classified as non-reservoirs.n Decide what seismic data is necessary for the chosen inver- sion technique Simultaneous deterministic partial stack inversionn Comparison of the inversion results to the well log data When performing this simultaneous deterministic inversion, and analysis of potential of the applied inversion technique partially stacked (3D / 2D) seismic data are simultaneously inverted into elastic property volumes or sections. The elasticDeterministic full stack inversion properties are typically acoustic impedance, shear impedance,In the inversion process full stack (3D/2D) seismic data are Vp/Vs, and density. Simultaneous analysis of these propertiesinverted into volumes or sections of acoustic impedance with provides more possibilities to estimate lithology distribution,vertical resolution of about 1/8 of wavelength (Chopra et al., porosity, and hydrocarbon saturation than is possible from2006). Measured P-sonic and density logs data at least in one acoustic impedance only. However, this type of inversionwell are required to apply this inversion technique. requires a more thorough data preparation: The deterministic inversion workflow starts from elastic n The data should be processed to preserve amplitudes withinproperties cross-plot analysis aimed to determine the best the seismic gathers for the entire range of offsetslithology types separation with respect to different elastic n During seismic data processing pre-stack migration must beproperties. Figure 3 shows P-Impedance histogram for differ- performed and partial stacks should be obtainedent lithology types in the target interval. It can be seen that n In addition to P-sonic and density well log data, S-sonic logssuch lithology types as dolomite reservoir, limestone reservoir, are required at least in some wellsand non-reservoir cannot be differentiated reliably usingthis elastic parameter. When this kind of inversion is used, The first step in this case study was the extraction of six partialonly the high-porosity limestone reservoir can be detected, stacks from the seismic gather data in the offset range fromcorresponding to the lower values of acoustic impedance. 176–3851 m. This is equivalent to angles from 5–430 in theFigure 4 shows an example of the spatial reservoir distribution target interval of the Carboniferous formations. After that aobtained from a deterministic full stack inversion. The blue feasibility study was performed. Figure 5 shows the cross-plotbodies correspond to limestone reservoir, the wells are plotted of well log data where the colours denote lithology types. In thein overlay for comparison. In the wells the lithology types Vp versus Vs elastic parameter domain, conditions exist allow-from petrophysical interpretation are shown: anhydrite in red ing optimal discrimination between limestone reservoir, dolo-106 © 2011 EAGE
  5. 5. first break volume 29, May 2011 special topic Unconventional Resources and the Role of Technology Figure 4 Reservoir distribution obtained from deter- ministic full stack inversion (reservoir is shown in cyan, the rest is non-reservoir).mite reservoir, and non-reservoir. As such, the parameterization properties. Limitations of the second approach are: low verticalin the Vp, Vs, and density domain was chosen for inversion. resolution since the properties are accurate only within seismic As a result of the simultaneous deterministic partial stack bandwidth, the inverted properties generally do not coincideinversion, volumes of the following elastic properties were precisely with measured well logs, and only one solution exists.derived: Vp, Vs, and density. Using the cross-plot shown in On the other hand, the advantage of such models is that theyFigure 5, a spatial distribution of lithology types was obtained provide independent estimation of reservoir properties fromfrom inverted Vp and Vs volumes. Figure 6 shows the seismic data between the wells and have a much higher laterallimestone and dolomite reservoir distribution. The lithology resolution.types from logs shown at the well locations demonstrate the Geostatistical inversion is a methodological approach thatreliability of the results. It can be seen that the inverted data combines the advantages and minimizes the disadvantages ofeffectively reflect both known trends: the lateral reservoir the two approaches of geological modelling mentioned above.distribution and an increase of dolomitization in the lower It generates stochastic realizations of lithology and elasticsection. This was verified by the core studies and the results properties of a reservoir, which not only reproduce priorof petrophysical interpretation. These results can be used to lithology probability trends, spatial variograms, and jointdelineate reservoir zones but not for quantitative evaluation probabilistic distributions of elastic properties but also repro-of, for instance, the net reservoir thickness, because these duce the measured well logs and closely match the 3D seismiclithotypes distribution volumes are quite coarse due to seismic data within the desired noise levels (Sams et al., 1999).data resolution constrains.Geostatistical inversionThe ultimate objective of the integrated interpretation ofwell log and 3D seismic data is to build a geological model.Generally speaking, two approaches to geological modellingcan be identified. The most widespread approach is to use welldata for the stochastic interpolation of properties within thestructural framework defined from seismic data (geostatisticalreservoir modelling). The advantages of a geological modelbased on well data include high vertical resolution, precise tieto well data, and multiple realizations enabling probabilisticanalysis and risk assessment. However, the accuracy of reser-voir properties generated by such approaches decreases rapidlyaway from well control since the seismic data is only used as astratigraphic guide or trend (Sams, 2001). The second approach consists of using deterministic Figure 5 Cross-plot of well log data in geostatistical scale, showing P-velocityseismic inversion results to populate a model with reservoir versus S-velocity with lithology types in colour.© 2011 EAGE 107
  6. 6. special topic first break volume 29, May 2011Unconventional Resources and the Role of Technology Figure 6 Limestone and dolomite reservoir distri- bution obtained from simultaneous deterministic partial stack inversion (limestone reservoir in cyan, dolomite reservoir in green, the rest is non-reservoir). One of the differences between deterministic and geosta- consistency in the form of 3D variograms and the range andtistical inversion is the creation of an a priori model. Strictly multivariate distributions of elastic properties for each lithol-speaking, deterministic inversion does not use any prior model ogy type and geologic zone as probability density functions. Abut instead uses low-frequency trends of elastic properties statistical model example for one of the types of reservoir istogether with other constraints to define the solution area. shown in Figure 7.In the geostatistical inversion approach, a geostatistical prior Differences also exist in the output data and in themodel is used to capture various kinds of probabilistic knowl- methods to interpret the results. In deterministic inversion,edge about the reservoir structure, such as spatial property elastic property cubes or sections are output. Using cross-plotsFigure 7 Statistical model created in the elastic property domain from well data for the reservoir (stratigraphic grid scale is 50 m x ¼ ms).108 © 2011 EAGE
  7. 7. first break volume 29, May 2011 special topic Unconventional Resources and the Role of TechnologyFigure 8 Multiple unconstrained realizations of lithology types distribution obtained from geostatictical simultaneous partial stack inversion (anhydrite in red,reservoir in green, non-reservoir is shown in dark blue colour). Vertical scale is ¼ ms.of elastic versus reservoir properties the elastic properties interval was 1/2 to 1/8 of sample rate of seismic data, thecan then be converted into volumes or sections of reservoir lithology types were reservoir and non-reservoir, and theproperties such as porosity, reservoir lithology types, and elastic properties were Vp, Vs, and density. Reservoir andsaturation. Geostatistical inversion solves simultaneously for non-reservoir probability volumes were calculated based onboth lithology types and continuous properties, providing a analysis of multiple realizations of lithology types volumes.set of equally probable property distributions which enables Just as is the case in deterministic simultaneous partialprobabilistic analysis of the results. stack inversion workflows, it is necessary to perform well Geostatistical inversion, like the deterministic inversion ties, estimate individual wavelets for each of the partialis represented by two algorithms, one of which uses a full stacks, and accurately pick the horizons that define thestack cube and the other uses partial stacks. As was already target interval top and bottom. It is important to compensateshown in Figures 3 and 5, two elastic properties, Vp versus misalignment between partial stacks because it stronglyVs, are needed in our case in order to differentiate between affects the accuracy of the elastic property estimation. Thethe desired lithology types. The target intervals consist of next step of the work is the transition from petrophysicala bunch of thin reservoir layers where the time thickness lithology type definition to the ‘lithology types’ for inversion.(1–1.5 ms in two-way time) is beyond seismic resolution. In The main criterion for such a translation is the separationorder to obtain detailed reservoir distribution geostatistical in elastic properties between different lithologies (Figure 5).partial stack inversion was applied. For the target intervals the following ‘lithology types’ were The key inputs for geostatistical inversion are partial defined:seismic stacks (in this case study six stacks were used) and n For the Moscovian (C2m), Bashkirian (C2b), and Uppercorresponding estimated wavelets. Prior statistical informa- Serpukhovian (C1s2) stages: dolomite reservoir, limestonetion about the elastic properties corresponding to the reservoir, non-reservoirlithology types is also required. This statistical information n For the Lower Serpukhovian (C1s1): anhydrite and reser-was acquired from the available well logs. The statistics with voira set of partial stacks were input to this inversion process. n For the C1s1_b (Lower Part of Lower Serpukhovian stage): Twenty realizations of high-resolution lithology types reservoir and non-reservoirdistribution with jointly inverted elastic properties wereobtained from simultaneous geostatistical partial stack For each lithology type in the target interval the statisticalinversion. The sample rate sample rate within the target model was created using the Vp, Vs, and density data from© 2011 EAGE 109
  8. 8. special topic first break volume 29, May 2011Unconventional Resources and the Role of TechnologyFigure 9 A) Reservoir probability from sum of 20 unconstrained geostatictical inversion realizations (yellow = 20 out of 20 realizations delivered reservoir lithol-ogy type, dark blue = 0 out of 20). B) Section of most probable (P50) discrete property type based on 20 unconstrained realization of geostatictical partial stackinversion. C) Section of most probable (P50) discrete property type based on 20 constrained realization of geostatictical partial stack inversion.110 © 2011 EAGE
  9. 9. first break volume 29, May 2011 special topic Unconventional Resources and the Role of TechnologyFigure 10 Section of predicted lithology types delivered from geostatictical inversion with lithology log in new drilled well overlaid (limestone reservoir in blue,dolomite reservoir in green, the rest is non-reservoir). A) Well 1; B) Well 2; C) Well 3.© 2011 EAGE 111
  10. 10. special topic first break volume 29, May 2011 Unconventional Resources and the Role of Technologythe petrophysical interpretation and the rock physics model- n Signal-to-noise ratio for each partial seismic stack. Thisling, (Figure 7). was estimated from the results of deterministic inversion Geostatistical inversion runs on a stratigraphic grid, so, n Prior probability of lithology types in target interval. Thisin order to generate a framework for it, it is necessary to was defined basically from the lithology proportions esti-have detailed correlations of stratigraphic boundaries in the mated from the well log data and to a lesser degree thosetarget intervals and to define the vertical size of a cell. In this estimated from the deterministic inversion modelcase study, the vertical cell size was set to ¼ ms, as this cor- n 3D variograms for modelling of lithology types andresponded to the expected size of the thin features of interest elastic parameters. This was defined from the integratedwithin the reservoir layers. geological and geophysical analysis of the target forma- After all input data were prepared, the following inver- tions and from interpretation of deterministic inversionsion parameters were optimized: results Reservoir properties Reservoir properties Absolute error Relative error (%) from drilling from geostatictical inversion well 1 Net Thickness, m 19,2 18,7 0,5 2,6 NPV, m 2,4 2,3 0,1 4,6 Net Pay, m 7,5 6,9 0,6 8,0 HCPV, m 0,9 0,91 0,01 1,1 well 2 Net Thickness, m 30,8 29,1 1,7 5,5 NPV, m 3,5 3,2 0,3 7,4 Net Pay, m 6,0 5,0 1,0 16,7 HCPV, m 0,9 0,8 0,1 11,5 well 3 Net Thickness, m 15,5 14,4 1,1 7,1 NPV, m 2,2 1,9 0,3 11,5 Net Pay, m 6,0 7,0 1,0 16,7 HCPV, m 0,9 1,0 0,1 11,0Table 1 Comparison of reservoir properties obtained from simultaneous geostatistical partial stack inversion with data from newly drilled wells (* NPV - net porevolume; ** HCPV - hydrocarbon pore volume).Figure 11 Comparison of reservoir distribution obtained from geostatictical partial stack inversion (left) and deterministic partial stack inversion (right) – reservoirin green, the rest is non-reservoir.112 © 2011 EAGE
  11. 11. first break volume 29, May 2011 special topic Unconventional Resources and the Role of TechnologyNext, unconstrained geostatistical inversion was performed data were used to obtain 3D distributions of petrophysicalwithout inclusion of wells, i.e., only the statistical model of properties, for example porosity, by means of co-simulation.lithology types, the seismic data, and the wavelets were used. The lithology type probability volumes (Figure 9a)The results obtained from this cross validation approach were enable us to evaluate the reliability of the modelling, asanalyzed from the following viewpoints (Figures 8, 9a, and 9b) well as making evaluation of, for example, the net thicknessn Stability of the solution: What is the variability of the within a certain percentile (P10, P50, P90). details from one realization to another? QC procedures are very important in the geostatisticaln Reliability of the tie between the well data and the elastic inversion workflow. A series of QC procedures were per- properties and lithology types estimated from inversion formed to check the quality of inversion results:n Agreement between the statistical reservoir models n Analysis of correlation coefficients between the input seis- described by well log data and those obtained from the mic data and the synthetics obtained during inversion. All results of geostatistical inversion run without being explic- partial stacks were involved in this process itly constrained to well logs n Analysis of signal-to-noise ratio maps for each partial stackn Compliance of the posterior property distributions with n Analysis of residuals, the difference between the seismic the prior geological model and synthetic data, both in the time and the amplitude- frequency domainThe final step in the workflow was the creation of con-strained realizations, where the lithology and continuous However, data from the newly drilled wells are the bestproperty distribution volumes are tied to well data (Figure quality and reliability control of 3D reservoir models cre-9c). As with the unconstrained modelling this means the ated by seismic inversion. In the interval of the Moscovian,calculation of 20–30 equally probable realizations, on the Bashkirian, and Upper Serpukhovian formations the verifica-basis of which a probabilistic evaluation of the results was tion of the constructed model was made using three newlylater made. The most probable lithology types distribution drilled the target interval and the minimum, maximum and aver- The match between the predicted lithology types dis-aged elastic property volumes were produced. Finally, these tribution and the results of petrophysical interpretationFigure 12 Comparison of dolomite and limestone reservoir distribution obtained from geostatictical partial stack inversion and deterministic partial stack inversion(limestone reservoir in blue, dolomite reservoir in green, the rest is non-reservoir).© 2011 EAGE 113
  12. 12. special topic first break volume 29, May 2011Unconventional Resources and the Role of Technology(lithology type log) at new well locations is illustrated in properties made by deterministic inversion is often sufficient,Figure 10. Table 1 presents quantitative estimates of the as in many cases it can reveal zones with better reservoir.precision of reservoir property prediction. To make this In the case when a more detailed model of a hydrocarboncomparison the following reservoir parameters were used: pool is needed, the geostatistical inversion technique showsnet thickness, net pay, and net pore volume. NPV is calcu- excellent results (Figsures 11, 12). The additional detail islated as net thickness x porosity, and hydrocarbon pore vol- critical when building geological and hydrodynamic modelsume (HCPV) is net pay x porosity. For all of the mentioned or planning a production well pattern, especially in complexparameters, absolute and relative errors of prediction were carbonate reservoirs with high lateral variability and limitedcalculated. The average relative error of the predicted net well coverage.reservoir thickness is not more than 5%. The same for netpay is 14%, for net pore volume 8%, and for hydrocarbon References:pore volume 8%. Ampilov, Y.P. [2008] From Seismic interpretation to the modeling and The results shown in this case study demonstrate that the estimation of oil and gas fields. Spektr, Moscow.estimation of reservoir properties in carbonate formations Avseth, P., Mukerji, T. and Mavko, G. [2005] Quantitative seismic interpre-based on geostatistical partial stack inversion is confirmed by tation. Cambridge University Press.newly drilled wells and can be successfully used for planning Chopra S., Castagna J. and Portniaguine, O. [2006] Seismic resolution andproduction well locations. thin bed reflectivity inversion. CSEG Recorder, 1, 19–25. Foster, D.J., Keys, R.G. and Lane F.D. [2010] Interpretation of AVOConclusion anomalies. Geophysics, 75(5), A3–75A13.At present, there are several independent techniques of seis- Francis, A. [2006] Understanding stochastic inversion: part 1. First Break,mic data interpretation, and each of them can be beneficial 24(11), 66–67.depending on the data available for a given field and the Jarvis, K., Folkers, A., Mesdag, P. [2004] Reservoir characterization ofgeological tasks to be solved. Seismic inversion proved to be the Flag Sandstone, Barrow Sub-Basin, using an integrated, multipa-the most advanced, accurate, and reliable method of reser- rameter seismic AVO inversion technique. The Leading Edge, 23(8),voir characterization. It is critical that the data involved in 798-800.the inversion workflow meet certain requirements. The first Mavko, G., Mukerji ,T. and Dvorkin, J. [2009] The rock physicsstep to success is seismic survey design, high quality acquisi- handbook: tools for seismic analysis in porous media. Cambridgetion, and state-of-the-art seismic data processing aimed at University Press.preservation of amplitudes. Also, in order to obtain meaning- Omar, V. J., Torres-Verdin, C. and Lake, L. [2006] On the value of 3Dful inversion results, well logs describing elastic properties amplitude data to reduce uncertainty in the forecast of reservoir pro-of the rocks (P-sonic, S-sonic and density log) are required. duction. Journal of Petroleum Science and Engineering, 20, 269-284.The choice of the necessary number of elastic parameters Pendrel, J., Bunn, G. and Rindschwentner, J. [2003] Simultaneous(P-impedance or Vp, Vs, and density) should be made on AVO Inversion to P-Impedance and Vp/Vs. CSEG Annual Meetingthe basis of an analysis of the ability to solve the geological Abstracts.tasks by using one or the other set of elastic parameters. Such Sams, M. [2001] Geostatistical lithology modeling. ASEG 15th Geophysi-analysis also determines the type of inversion: full stack or cal Conference and Exhibition, Extended Abstracts.partial stacks. Sams, M., Atkins, D., Said, N., Parwito, E and van Riel, P. [1999] Stochastic The choice between deterministic and geostatistical inversion for high resolution reservoir characterization in the Centralalgorithms should be made based on two key points: seismic Sumatra Basin. SPE Asia Pacific Improved Oil Recovery Conference,resolution compared to reservoir thickness and the avail- Kuala Lumpur, SPE 57260.ability of high quality petrophysical and core data. At the Singleton, S. [2009] The effects of seismic data conditioning on prestackinitial stage of field development, estimation of reservoir simultaneous impedance inversion. The Leading Edge, 28(7), 772–781. Ava ilab EAGE Vienna 2011 le n ow Visit Conference papers Check it out at EarthDoc!114 © 2011 EAGE