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Rock physics guided AVO: A holistic approach to sand detection

Husam M. AlMustafa

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Abstract

Rock physics plays a critical role in lithology and fluid prediction, and amplitude-versus-offset (AVO) modeling. It is a quantitative tool and a necessary step in understanding and interpreting seismic amplitudes for predicting lithofacies distribution. In this case study, I employed AVO inversion, guided by rock physics, to detect reservoir quality sands in the Unayzah Reservoir in central Saudi Arabia. The inversion scheme is a simultaneous pre-stack inversion algorithm based on Zoeppritz’s equations. The procedure uses near- and far-angle stacks and angle-dependent wavelets to determine the elastic parameters: P-wave impedance, S-wave impedance and Vp/Vs ratio. To interpret the pre-stack inversion results the data was constrained by rock physics. Initially rock physics templates (RPT) were built, which are geologically driven, basin-specific, theoretical rock physics models. Geologic constraints on RPT included lithology, mineralogy, porosity, depth (pressure) and temperature. RPT cross-plots were built for acoustic impedance (AI) versus Vp/Vs, which were overlain by trends for lithology and porosity. This allowed performing rock physics analysis, not only on well log data, but also on seismic data (prestack inversion results). Initially, RPT were validated by well-log data before applying them to seismic data. Input data and model assumptions affect the accuracy of the information obtained from the template. I used RPT to guide the classification of the seismic inversion results. The pre-stack inversion of AI and Vp/Vs within the target zone was projected onto the template to generate lithofacies map that help distinguish reservoir sands from silty shales.

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What this paper is about

Rock physics plays a critical role in lithology and fluid prediction, and amplitude-versus-offset (AVO) modeling. It is a quantitative tool and a necessary step in understanding and interpreting seismic amplitudes for predicting lithofacies distribution. In this case study, I employed AVO inversion, guided by rock physics, to detect reservoir quality sands in the Unayzah Reservoir in central Saudi Arabia. The inversion scheme is a simultaneous pre-stack inversion algorithm based on Zoeppritz’s equations. The procedure uses near- and far-angle stacks and angle-dependent wavelets to determine the elastic parameters: P-wave impedance, S-wave impedance and Vp/Vs ratio. To interpret the pre-stack inversion results the data was constrained by rock physics. Initially rock physics templates (RPT) were built, which are geologically driven, basin-specific, theoretical rock physics models. Geologic constraints on RPT included lithology, mineralogy, porosity, depth (pressure) and temperature. RPT cross-plots were built for acoustic impedance (AI) versus Vp/Vs, which were overlain by trends for lithology and porosity. This allowed performing rock physics analysis, not only on well log data, but also on seismic data (prestack inversion results). Initially, RPT were validated by well-log data before applying them to seismic data. Input data and model assumptions affect the accuracy of the information obtained from the template. I used RPT to guide the classification of the seismic inversion results. The pre-stack inversion of AI and Vp/Vs within the target zone was projected onto the template to generate lithofacies map that help distinguish reservoir sands from silty shales.

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Available abstract

Rock physics plays a critical role in lithology and fluid prediction, and amplitude-versus-offset (AVO) modeling. It is a quantitative tool and a necessary step in understanding and interpreting seismic amplitudes for predicting lithofacies distribution. In this case study, I employed AVO inversion, guided by rock physics, to detect reservoir quality sands in the Unayzah Reservoir in central Saudi Arabia. The inversion scheme is a simultaneous pre-stack inversion algorithm based on Zoeppritz’s equations. The procedure uses near- and far-angle stacks and angle-dependent wavelets to determine the elastic parameters: P-wave impedance, S-wave impedance and Vp/Vs ratio. To interpret the pre-stack inversion results the data was constrained by rock physics. Initially rock physics templates (RPT) were built, which are geologically driven, basin-specific, theoretical rock physics models. Geologic constraints on RPT included lithology, mineralogy, porosity, depth (pressure) and temperature. RPT cross-plots were built for acoustic impedance (AI) versus Vp/Vs, which were overlain by trends for lithology and porosity. This allowed performing rock physics analysis, not only on well log data, but also on seismic data (prestack inversion results). Initially, RPT were validated by well-log data before applying them to seismic data. Input data and model assumptions affect the accuracy of the information obtained from the template. I used RPT to guide the classification of the seismic inversion results. The pre-stack inversion of AI and Vp/Vs within the target zone was projected onto the template to generate lithofacies map that help distinguish reservoir sands from silty shales.

Key concepts: Inversion (geology), Seismic inversion, Lithology, Geology, Amplitude, Amplitude versus offset, Inverse transform sampling, Porosity

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