Improvised approach using SQp-SQs attributes for hydrocarbon prediction: A field case study in Malay Basin

Lew, Chean Lin; Hermana, Maman; Ghosh, Deva Prasad

doi:10.1190/segam2018-2986106.1

Cited by 2 publications

(3 citation statements)

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“…Sci. 2020, 10, x FOR PEER REVIEW 8 of 17 gamma-ray log and SQs attribute resembled the resistivity for facies and hydrocarbon identification [12]. SQp and SQs also could be applied in pre-stack stack seismic inversion.…”

Section: Resultsmentioning

confidence: 99%

“…In the well log scale, SQp attribute response was equivalent to the SQp and SQs methods in well log scale have been successfully patented which was derived from elastic properties parameters [9]. In the well log scale, SQp attribute response was equivalent to the gamma-ray log and SQs attribute resembled the resistivity for facies and hydrocarbon identification [12]. SQp and SQs also could be applied in pre-stack stack seismic inversion.…”

Section: Methodsmentioning

confidence: 99%

“…The scale of quality factor of P-wave (SQp) and scale of quality factor of S-wave (SQs) methods have been successfully applied for fluid and hydrocarbon prediction in well log scale and pre-stack inversion [9][10][11][12]. In the well log, the SQp attribute has the highest correlation with gamma-ray, while SQs attribute has the highest coefficient correlation with resistivity [11].…”

Section: Introductionmentioning

confidence: 99%

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New AVO Attributes and Their Applications for Facies and Hydrocarbon Prediction: A Case Study from the Northern Malay Basin

et al. 2020

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Amplitude versus offset (AVO) analysis integration to well log analysis is considered one of the advanced techniques to improve the understanding of facies and fluid analysis. Generating AVO attributes are one solution to give an accurate result in facies and fluid characterization. This study is focused on a field of Northern Malay basin, which is associated with a fluvial-deltaic environment, where this system has high heterogeneity, whether it is vertically or horizontally. This research is aimed to demonstrate an application of the scale of quality factor of P-wave (SQp) and the scale of quality factor of S-wave (SQs) AVO attributes for facies and fluid types separation in field scale. These methods are supposed to be more sensitive to predict the hydrocarbons and give less ambiguity. SQp and SQs are the new AVO attributes, which derived from AVO analysis and created according to the intercept product (A) and gradient (B). These new attributes have also been compared to the common method, which is the Scaled Poisson’s Ratio attribute. By comparing with the Scaled Poisson’s Ratio attribute, SQp and SQs attributes are more accurate in determining facies and hydrocarbon. SQp and SQs AVO attributes are integrated with well log data and considered as the best technique to determine facies and fluid distribution. They are interpreted by using angle-stack seismic data based on amplitude contrast on interfaces. Well log data, e.g., density and sonic logs, are used to generate synthetic seismogram and well tie requirements. The volume of shale, volume of coal, porosity, and water saturation logs are used to identify facies and fluid in well log scale. This analysis includes AVO gradient analysis and AVO cross plot to identify the fluid class. Gassmann’s fluid substitution modeling is also generated in the well logs and AVO synthetics for in situ, pure brine, and pure gas cases. The application of the SQp and SQs attributes successfully interpreted facies and fluids distributions in the Northern Malay Basin.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%