Changes

where R is the reflectivity coefficient, AI1, Vp1, and Rho1 are the acoustic impedance, P-wave velocity, and density of the upper layer, whereas AI2, Vp2, and Rho2 are the acoustic impedance, P-wave velocity, and density of the lower layer. On Figure 5, (1) denotes the “bright” amplitude due to the sharp change in AI, whereas (2) denotes the “dim” amplitude due to the smaller change in AI.

where R is the reflectivity coefficient, AI1, Vp1, and Rho1 are the acoustic impedance, P-wave velocity, and density of the upper layer, whereas AI2, Vp2, and Rho2 are the acoustic impedance, P-wave velocity, and density of the lower layer. On Figure 5, (1) denotes the “bright” amplitude due to the sharp change in AI, whereas (2) denotes the “dim” amplitude due to the smaller change in AI.

[[File:GeoWikiWriteOff2021-Muamamr-Figure5.png|thumbnail|Figure 5. Schematic diagram to create synthetic seismogram.]]   

[[File:GeoWikiWriteOff2021-Muamamr-Figure5.png|framed|center|{{Figure number|5}}Schematic diagram to create synthetic seismogram.]]   

To predict the seismic amplitude variation with offset (AVO) or angle (AVA), several approximations can be used[6], [7], [8]. To predict this AVO response however, Vs data is required. Figure 6 shows the expected seismic response between Sand A or Sand B (under gas saturated and wet condition) with their respective overlying and underlying claystones. It can be observed that under gas saturated condition, both sands have Class III AVO (increase in amplitude with increasing angle), whereas under wet condition, both sands have class II AVO (dim amplitude at Near and Far angle). It should be noted that different lithology (e.g. cemented sand and unconsolidated sand) may have different amplitude response. The occurrence of such sands can be interpreted by carrying out petrography analysis or rock physics diagnostic.  

To predict the seismic amplitude variation with offset (AVO) or angle (AVA), several approximations can be used.<ref name=6AkiRichards>Aki, K. and P. G. Richards, 1980, Quantitative seismology: Theory and methods, San Francisco, W. H. Freeman and Co., 557 p.</ref><ref name=7Shuey>Shuey, R. T., 1985, A simplification of the Zoeppritz equations: Geophysics, v. 50, no. 4, p. 609-614.</ref><ref name=8Fattietal>Fatti, J. L., G. C. Smith, P. J. Vail, P. J. Strauss, and P. R. Levitt, 1994, Detection of gas in sandstone reservoirs using AVO analysis: A 3-D seismic case history using the geostack technique: Geophysics, v. 59, no. 9, p. 1362-1376.</ref> To predict this AVO response however, Vs data is required. Figure 6 shows the expected seismic response between Sand A or Sand B (under gas saturated and wet condition) with their respective overlying and underlying claystones. It can be observed that under gas saturated condition, both sands have Class III AVO (increase in amplitude with increasing angle), whereas under wet condition, both sands have class II AVO (dim amplitude at Near and Far angle). It should be noted that different lithology (e.g. cemented sand and unconsolidated sand) may have different amplitude response. The occurrence of such sands can be interpreted by carrying out petrography analysis or rock physics diagnostic.  

   

   

[[File:GeoWikiWriteOff2021-Muamamr-Figure6.png|thumbnail|Figure 6. Expected AVO response of (A) Sand A under gas case, (B) Sand A under wet condition, (C) Sand B under gas case, and (D) Sand B under wet condition.]]   

[[File:GeoWikiWriteOff2021-Muamamr-Figure6.png|thumbnail|Figure 6. Expected AVO response of (A) Sand A under gas case, (B) Sand A under wet condition, (C) Sand B under gas case, and (D) Sand B under wet condition.]]   

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6 Aki, K. and Richards, P. G., 1980, Quantitative Seismology: Theory and Methods, W. H. Freeman and Co.  

9 Dvorkin, J. and Nur, A., 1996, Elasticity of High-Porosity Sandstones: Theory for Two North Sea Data Sets, Geophysics, 61(5), pp. 1363-1370.  

7 Shuey, R. T., 1985, A Simplification of the Zoeppritz Equations, Geophysics, 50(4), pp. 609-614.  

10 Avseth, P., Mukerji, T, Mavko, G. and Dvorkin, J., 2010, Rock-Physics Diagnostics of Depositional Texture, Diagenetic Alterations, and Reservoir Heterogeneity in High-Porosity Siliciclastic Sediments and Rocks – A Review of Selected Models and Suggested Work Flows, Geophysics, 75(5), pp. 75A31-75A47.  

8 Fatti, J. L., Smith, G. C., Vail, P. J., Strauss, P. J. and Levitt, P. R., 1994, Detection of Gas in Sandstone Reservoirs Using AVO Analysis: A 3-D Seismic Case History Using the Geostack Technique, Geophysics, 59(9), pp. 1362-1376.

11 Dvorkin, J., Nur, A. and Yin, H., 1994, Effective Properties of Cemented Granular Material, Mechanics of Materials, 18, pp. 351-366.  

Dvorkin, J. and Nur, A., 1996, Elasticity of High-Porosity Sandstones: Theory for Two North Sea Data Sets, Geophysics, 61(5), pp. 1363-1370.  

9 Avseth, P., Mukerji, T, Mavko, G. and Dvorkin, J., 2010, Rock-Physics Diagnostics of Depositional Texture, Diagenetic Alterations, and Reservoir Heterogeneity in High-Porosity Siliciclastic Sediments and Rocks – A Review of Selected Models and Suggested Work Flows, Geophysics, 75(5), pp. 75A31-75A47.  

12 Avseth, P., Dvorkin, J., Mavko, G. and Rykkje, J., 2000, Rock Physics Diagnostic of North Sea Sands: Link Between Microstructure and Seismic Properties, Geophysical Research Letters, 27, pp. 2761-2764.  

10 Dvorkin, J., Nur, A. and Yin, H., 1994, Effective Properties of Cemented Granular Material, Mechanics of Materials, 18, pp. 351-366.  

13 Avseth, P., 2000, Combining Rock Physics and Sedimentology for Seismic Reservoir Characterization of North Sea Turbidite Systems, Ph.D Dissertation, Stanford University.  

11 Avseth, P., Dvorkin, J., Mavko, G. and Rykkje, J., 2000, Rock Physics Diagnostic of North Sea Sands: Link Between Microstructure and Seismic Properties, Geophysical Research Letters, 27, pp. 2761-2764.  

14 Hossain, Z. and MacGregor, L., 2014, Advanced Rock-Physics Diagnostic Analysis: A New Method for Cement Quantification, The Leading Edge, 33(3), pp. 310-316.  

12 Avseth, P., 2000, Combining Rock Physics and Sedimentology for Seismic Reservoir Characterization of North Sea Turbidite Systems, Ph.D Dissertation, Stanford University.

15 Antariksa, G., Muammar R. and Lee, J., 2021, Performance Evaluation of Machine Learning-based Classification with Rock-Physics Analysis of Geological Lithofacies in Tarakan Basin, Indonesia, Journal of Petroleum Science and Engineering.

 

 

14 Antariksa, G., Muammar R. and Lee, J., 2021, Performance Evaluation of Machine Learning-based Classification with Rock-Physics Analysis of Geological Lithofacies in Tarakan Basin, Indonesia, Journal of Petroleum Science and Engineering.