7. Application of SQp and SQs on fractured basement reservoir case

Previous tests were conducted on conventional reservoirs (clastic reservoir), with the objective being to test the sensitivity of SQp and SQs as indicators of lithology and fluid effect. To test the feasibility of SQp and SQs application on unconventional reservoir characterization, a well log data from fractured basement reservoir was taken from a field in the Malaysian Basin. In this well, the fracture is indicated by the image log. Image log is commonly practiced to detect the fracture of the borehole wall. However, this instrument is sensitive with diameter of borehole size. If there is a bad borehole condition of certain formation, the pad contact of the instrument with the borehole walls is not coupled properly. Hence, the fractures will not be effectively imaged. It is good to have another log as an alternative log that can be associated with the fractures which also can be derived from elastic properties. To fulfill the gap, the SQp and SQs attributes were tested by comparing it with the conventional brittleness average, fracture density log, and neutron porosity-density log to test the effectiveness of new attributes in terms of fracture density and hydrocarbon bearing identification in unconventional reservoir environment.

In this test, sample data was taken from depth 3125 to 3165 m of the fractured basement reservoir formation. The fracture density is compared to the brittleness average logs and SQP and SQS logs (Figure 8).

For this formation, the fracture density logs indicate the number of fracture on the formation. Brittleness average logs calculated from Poisson's ratio and Young's modulus was compared with fracture density logs. The results show that the brittleness average is consistent with the number of fracture density as shown in the fracture density logs. In the other side, the SQp log is also similar to the fracture density and brittleness average logs. High value of SQp is related with high fracture density and high brittleness average value. It shows that the SQp attribute also has

Figure 8.

Comparison between fracture density log, brittleness average log, SQP logs, SQS logs, and density-NPHI logs. Brittleness average looks consistent with fracture density log; SQp attribute is consistent with fracture density and SQs consistent with density-NPHI log.

potential to be used as a fracture density indicator or brittleness indicator in unconventional reservoir.

The example as shown in Figure 8 was taken from an oil field-fractured basement reservoir. Conventional interpretation to identified oil column was conducted by interpreting the neutron porosity-density log together. Oil column will be identified as a crossover between neutron porosity and density log. This crossover is called also as "butterfly effect." The crossover of neutron porosity-density logs indicates the oil column. It is clear from Figure 8 that the "butterfly effect" on neutron porosity-density log is associated with high value of SQs log. As mentioned in the previous test, the high value of SQs is indicating the hydrocarbon location. In this well, the SQs log is consistent with neutron porosity-density logs. It means that if the "butterfly effect" of neutron porosity-density log can be used to indicate hydrocarbon column in the fractured basement reservoir, the SQs attribute also can be used as indicator of hydrocarbon column for this unconventional reservoir environment.

From the test on this fractured basement reservoir well, both brittleness average, which is derived from Poison's ratio and Young's modulus and SQp attribute, have the same chance to be used as fracture density indicators, while the SQs attribute has the same potential as neutron porosity-density log in determining the location of hydrocarbon bearing. The difference is the SQs attribute can be extracted from seismic data, while the neutron porosity-density log can be analyzed on well log only. Hence, the use of SQs attributes can give us advantages to get the hydrocarbon distribution three-dimensionally.

The workflow to obtain the brittleness average and SQp and SQs attributes from seismic data is shown in Figure 9. A simultaneous inversion on pre-stack/partialstack seismic data is needed to obtain P-wave (Vp), S-Wave (Vs), and density, which will be used to calculate the brittleness average and SQp and SQs attributes using Eqs. (5) and (10), respectively. An alternative method of obtaining the SQp and SQs attributes in a reflection domain can be approached using Eq. (14).

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

Figure 9. Workflow for brittleness average and SQp and SQs attribute derivations from seismic data.
