Changes

MR=〖(V_s*Rho)〗^2=〖SI〗^2

MR=〖(V_s*Rho)〗^2=〖SI〗^2

as MR is strongly controlled by the shear impedance (SI) and shear modulus (μ), the magnitude of this property does not really affected by pore fluids, but rather by the lithology.  

as MR is strongly controlled by the shear impedance (SI) and shear modulus (μ), the magnitude of this property does not really affected by pore fluids, but rather by the lithology.  

[[File:GeoWikiWriteOff2021-Muamamr-Figure1.png|thumbnail|Figure 1. Well log section showing the difference of rock physics properties between clean sandstone (Sand A), clayey sandstone (Sand B), and claystone. ]]

Figure 1. Well log section showing the difference of rock physics properties between clean sandstone (Sand A), clayey sandstone (Sand B), and claystone.  

 

As the available dataset is composed of gas saturated sands, one can model the expected elastic properties under wet condition by utilizing Gassmann’s Fluid Substitution[2], [3]). The result of fluid substitution on Sand A and Sand B are shown on Figure 2. It can be observed that wet sands are acoustically harder (higher Vp and Rho, but with minor change in Vs) compared to gas sands, some of these wet sands are acoustically harder than the claystones.  

As the available dataset is composed of gas saturated sands, one can model the expected elastic properties under wet condition by utilizing Gassmann’s Fluid Substitution[2], [3]). The result of fluid substitution on Sand A and Sand B are shown on Figure 2. It can be observed that wet sands are acoustically harder (higher Vp and Rho, but with minor change in Vs) compared to gas sands, some of these wet sands are acoustically harder than the claystones.  

[[File:GeoWikiWriteOff2021-Muamamr-Figure2.png|thumbnail|Figure 2. Fluid substitution result of Sand A and Sand B.]]

Figure 2. Fluid substitution result of Sand A and Sand B.  

 

These property differences can be easily highlighted on AI-Vp/Vs and LR-MR crossplots. On AI-Vp/Vs crossplot (Figure 3), it can be observed that claystones have higher AI and Vp/Vs compared with Sand A and Sand B (Sand A has the lowest AI and Vp/Vs). Some parts of Sand B have overlapping AI with claystone and therefore may exhibit uncertainties in discriminating the reservoir and non-reservoir lithology by utilizing this property. Vp/Vs on other hand, successfully separated the reservoir and non-reservoir lithology. In the case of wet sands, it can be observed that these sands have higher AI but smaller Vp/Vs. It can be concluded that AI may have some uncertainties to discriminate the reservoir and non-reservoir lithology (with the exception of clean gas sand), whereas Vp/Vs has a better performance result in separating them.  

These property differences can be easily highlighted on AI-Vp/Vs and LR-MR crossplots. On AI-Vp/Vs crossplot (Figure 3), it can be observed that claystones have higher AI and Vp/Vs compared with Sand A and Sand B (Sand A has the lowest AI and Vp/Vs). Some parts of Sand B have overlapping AI with claystone and therefore may exhibit uncertainties in discriminating the reservoir and non-reservoir lithology by utilizing this property. Vp/Vs on other hand, successfully separated the reservoir and non-reservoir lithology. In the case of wet sands, it can be observed that these sands have higher AI but smaller Vp/Vs. It can be concluded that AI may have some uncertainties to discriminate the reservoir and non-reservoir lithology (with the exception of clean gas sand), whereas Vp/Vs has a better performance result in separating them.  

[[File:GeoWikiWriteOff2021-Muamamr-Figure3.png|thumbnail|Figure 3. AI-Vp/Vs crossplot of the utilized dataset.]]

Figure 3. AI-Vp/Vs crossplot of the utilized dataset.  

 

On LR-MR crossplot (Figure 4), it can be observed that claystones have the higher LR but lower MR compared to Sand A and Sand B, whereas Sand A has the lowest LR (attributed to the presence of gas that is easily compressible) but similar MR compared to Sand B as this property is more sensitive towards the change in lithology rather than fluid content. In the case of wet sands, the LR is similar to that of the claystones but with higher MR, similar to the gas sands.  

On LR-MR crossplot (Figure 4), it can be observed that claystones have the higher LR but lower MR compared to Sand A and Sand B, whereas Sand A has the lowest LR (attributed to the presence of gas that is easily compressible) but similar MR compared to Sand B as this property is more sensitive towards the change in lithology rather than fluid content. In the case of wet sands, the LR is similar to that of the claystones but with higher MR, similar to the gas sands.  

 

Figure 4. LR-MR crossplot of the utilized dataset.  

[[File:GeoWikiWriteOff2021-Muamamr-Figure4.png|thumbnail|Figure 4. LR-MR crossplot of the utilized dataset.]] 

These results suggest the validity of rock physics analysis to identify the presence of reservoir, where it successfully discriminate between Sand A, Sand B, wet sands and claystones. It should be noted that each property has their own functions and limitations. These crossplots can be utilized to determine the rock physics cut-off parameter for each lithology and later applied as a quality control for more advance geophysical methods such as seismic inversion[1], [4], [5].  

These results suggest the validity of rock physics analysis to identify the presence of reservoir, where it successfully discriminate between Sand A, Sand B, wet sands and claystones. It should be noted that each property has their own functions and limitations. These crossplots can be utilized to determine the rock physics cut-off parameter for each lithology and later applied as a quality control for more advance geophysical methods such as seismic inversion[1], [4], [5].  

R=((〖AI〗_2-〖AI〗_1))/((〖AI〗_2+〖AI〗_1))=((V_p2*〖Rho〗_2)-(V_p1*〖Rho〗_1))/((V_p2*〖Rho〗_2)+(V_p1*〖Rho〗_1))

R=((〖AI〗_2-〖AI〗_1))/((〖AI〗_2+〖AI〗_1))=((V_p2*〖Rho〗_2)-(V_p1*〖Rho〗_1))/((V_p2*〖Rho〗_2)+(V_p1*〖Rho〗_1))

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.

 

Figure 5. Schematic diagram to create synthetic seismogram.  

[[File:GeoWikiWriteOff2021-Muamamr-Figure5.png|thumbnail|Figure 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[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.  

   

   

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.]] 

   

   

Synthetic seismic view of Sand A on an asymmetrical anticline structure is shown on Figure 7. Gas-water contact was modeled which properties were derived from fluid substitution. It can be observed that below the modeled GWC, amplitude polarity reversal can be identified due to the change from acoustically softer gas saturated sand to acoustically harder water saturated sand. This method is a common workflow to guide the seismic interpretation.  

Synthetic seismic view of Sand A on an asymmetrical anticline structure is shown on Figure 7. Gas-water contact was modeled which properties were derived from fluid substitution. It can be observed that below the modeled GWC, amplitude polarity reversal can be identified due to the change from acoustically softer gas saturated sand to acoustically harder water saturated sand. This method is a common workflow to guide the seismic interpretation.  

==Rock Physics Diagnostic==

==Rock Physics Diagnostic==

This method was first introduced[9] to assess the change of velocity-porosity relations under different geological conditions, including pore shape, mineralogy, and post-depositional processes such as compaction and cementation (e.g. cemented sands are harder than uncemented rocks and compacted rocks at deeper depths are harder than the less compacted rocks at shallower depths due to the difference in effective stress)[10]. Each of these geological conditions follows different velocity-porosity relations (rock physics model) and therefore, in absence of geological information (e.g. petrography), we can predict the cause of such velocity-porosity relations. Brief discussion these rock physics models are summarized below[10]:  

This method was first introduced[9] to assess the change of velocity-porosity relations under different geological conditions, including pore shape, mineralogy, and post-depositional processes such as compaction and cementation (e.g. cemented sands are harder than uncemented rocks and compacted rocks at deeper depths are harder than the less compacted rocks at shallower depths due to the difference in effective stress)[10]. Each of these geological conditions follows different velocity-porosity relations (rock physics model) and therefore, in absence of geological information (e.g. petrography), we can predict the cause of such velocity-porosity relations. Brief discussion these rock physics models are summarized below[10]:  

To conduct rock physics diagnostic, it is essential to eliminate as much variations as possible, such as saturation[9] because velocity depends on saturation. It is suggested to utilize the velocity log under wet condition to eliminate such variation. Figure 8 shows the total porosity-Vp crossplot of Sand A and Sand B where it can be observed that both sands follow the Friable Sand rock physics model indicating that the change in velocity and porosity of the sands are attributed to the difference in sorting[9]. Sand B has lower porosity but slightly higher velocity compared to Sand A due sorting deterioration caused by the clays.  

To conduct rock physics diagnostic, it is essential to eliminate as much variations as possible, such as saturation[9] because velocity depends on saturation. It is suggested to utilize the velocity log under wet condition to eliminate such variation. Figure 8 shows the total porosity-Vp crossplot of Sand A and Sand B where it can be observed that both sands follow the Friable Sand rock physics model indicating that the change in velocity and porosity of the sands are attributed to the difference in sorting[9]. Sand B has lower porosity but slightly higher velocity compared to Sand A due sorting deterioration caused by the clays.  

As this approach may help interpreting the cause of such change in velocity-porosity relations as a function of subsurface geology in an area, this workflow can be utilized to predict the elastic properties of the rocks away from well control (e.g. to expect what seismic amplitude that corresponds to sand reservoir). Other examples of the application of this method have been reported by several authors[13], [14], [15].  

As this approach may help interpreting the cause of such change in velocity-porosity relations as a function of subsurface geology in an area, this workflow can be utilized to predict the elastic properties of the rocks away from well control (e.g. to expect what seismic amplitude that corresponds to sand reservoir). Other examples of the application of this method have been reported by several authors[13], [14], [15].  

 

Figure 8. Rock physics diagnostic (velocity-porosity relation) of the utilized dataset.  

[[File:GeoWikiWriteOff2021-Muamamr-Figure8.png|thumbnail|Figure 8. Rock physics diagnostic (velocity-porosity relation) of the utilized dataset.]] 

==References==

==References==