Elastic-Based Brittleness Estimation from Seismic Inversion DOI: http://dx.doi.org/10.5772/intechopen.82047

to denote fluid changes. The application of this concept on real well log data can be used to justify this assertion. To do so, another test is carried out to investigate the performance of these attributes in identifying the fluid type and lithological effect. Visual comparison and crossplotting of these attributes on well log data and comparisons with other lithology and fluid indicator (gamma ray and water saturation) logs are presented in Figure 2.

Figure 6a shows three different reservoir targets, two reservoirs are saturated by gas, and another reservoir is wet (water saturated). All three different reservoirs are indicated as low SQp values. In SQp attribute there is no different responses between gas sand and wet sand; all sand formations are shown as low SQp value. This example shows that SQp attribute is not sensitive with fluid type, only sensitive to the lithology changes. The formation of shale and sand is distinguished clearly as well as in the gamma ray log, while in terms of SQs attribute, both gas and

### Figure 6.

(a) SQp and SQs responses compared to lithology log (gamma ray) and water saturation log and its coefficient correlations are obtained from the crossplot (a, right). (b) SQp and SQs test on different well. The SQp attribute is also similar to gamma ray log, and SQs is similar to resistivity logs.

sand reservoirs have higher value than wet sand. It shows that this attribute is more sensitive to the fluid type than lithology changes. The confirmation of the fluid content is shown by water saturation log, which is also similar to the SQs log.

An example from another field in offshore Malaysia (Figure 6b) shows that SQp response is also similar to the gamma ray logs, which supports the hypothesis that this attribute can be used to identify lithology changes in the same way as the gamma ray. Meanwhile, the SQs attribute, which was compared to the resistivity logs, shows that this attribute has high similarity to the resistivity logs. Resistivity log is commonly used to identify the fluid type of the formation; this log is sensitive with the changes of fluid type. Thus, SQs attribute also has potential to be used as fluid indicator. In Figure 6b, hydrocarbon formation is indicated by high resistivity value which also is shown in the SQs log. Hydrocarbon formation is indicated as a high SQs value, while brine/water sand will have a lower value.

The separation between lithology and fluid effect is identified easily in the crossplot. Optimum separation between lithological and fluid effects should be orthogonal to each other. To test the effectiveness of SQp and SQs attributes in discriminating the lithology and fluid effect, the crossplot of SQp-SQs has been compared with other elastic properties: lambda-rho vs. mu-rho crossplot as shown in Figure 7. The first and second Lame constants (see Section 3) multiplied with density are defined as lambda-rho and mu-rho, respectively. These attributes are a pair of elastic properties that are commonly used to discriminate the lithology and fluid. Figure 7 shows two different crossplots of mu-rho versus lambda-rho and SQp versus SQs crossplots color-coded by lithology log. All attributes, mu-rho, lambda-rho, SQp, and SQs, are calculated from the same sonic, shear, and density logs. The end members of lithology are classified into four types of lithology: shale sand, wet sand, shaly sand/siltstone sand, and pay sand. The types of lithology are defined by taking the cutoff on the volume of clay, gamma ray, porosity, and water saturation logs. The cutoff for shale was Vclay >0.4, gamma ray >80; shaly siltstone is Vclay <0.4, gamma ray <80, and porosity <0.05; wet sand is Vclay <0.4, porosity >0.05, water saturation >0.85; and pay sand is Vclay <0.4, porosity >0.05, and water saturation <0.85. The lithology log was used to identify the performance or sensitivity of attribute or elastic properties in predicting the lithology and hydrocarbon.

In the mu-rho versus lambda-rho crossplot (Figure 7a), gas sand still can be separated from wet sand and shale. However, in this crossplot, it is still difficult to define the separation between lithological and fluid effects. Conversely, in SQp-SQs crossplot (Figure 7b), it is not only gas sand and wet sand that are separated, but also the effect of lithology and fluid are separated optimally. In the SQp axis, different lithologies, shale and sand, are distinguished, while in SQs axis that lithology is not separated. The SQs can distinguish gas sand (net pay), wet sand, and shaly sand stone clearly. This SQs axis shows the effect of fluid. Therefore, SQp versus SQs shows an optimum separation between lithological and fluid effect. Lithological effect is distributed along the vertical axis (SQp), while different fluid is distributed along the horizontal axis (SQs). Both lithological and fluid effects are separated orthogonally.

The crossplotting of SQp versus SQs can separate the lithology and pore fluid effects in 90 degrees. It shows that these attributes purely represent either the lithology effect or fluid effect and not both. This orthogonal separation between lithology and pore fluid is the same as what other methods such as the extended elastic impedance (EEI) method would have achieved. In the EEI method, the maximum separation is carried out by projecting the data in the fluid and lithology projection line by calculating the proper chi angle for the projection [10].

Elastic-Based Brittleness Estimation from Seismic Inversion DOI: http://dx.doi.org/10.5772/intechopen.82047

Figure 7.

Lithological and fluid effect separation using crossplot method. (a) Mu-rho versus lambda-rho, (b) SQp versus SQs attribute. The lithology members consist of pay sand (gas sand), wet sand, shaly siltstone, and shale. Data taken from east Malaysian offshore.

Fortunately, in the SQp and SQp crossplotting, the projection line of lithology and fluid is constructed automatically as the orthogonal axis. This is one advantage when performing the interpretation using this attribute.
