--> --> Avo Analysis for Pre-Messinian Exploration Targets; A New Approach to Identify Gas-Bearing Sandstone Reservoirs

AAPG Africa Region, The Eastern Mediterranean Mega-Basin: New Data, New Ideas and New Opportunities

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Avo Analysis for Pre-Messinian Exploration Targets; A New Approach to Identify Gas-Bearing Sandstone Reservoirs

Abstract

During the last few decades, the Nile Delta basin proved its potential as a world class gas provinces. The last few years witnessed a number of multi-trillion cubic feet (TCF) gas discoveries in the deepwater. In the West Delta Deep Marine (WDDM) concession, most of the discoveries were made in Pliocene deep-marine turbidite reservoirs. Only two dry wells were drilled deeper into pre-Messinian targets (Oligocene–Miocene). Although the pre-Messinian play had higher gas volumes, the risks were higher and was therefore considered less attractive than shallower plays. Recent discoveries in the Oligo–Miocene sequences, coupled with the advanced use of 3D seismic attributes, AVO analysis, and prestack inversion, encouraged us to reconsider the prospectivity of the pre- Messinian section. To mature the current leads and/or define new ones, many seismic attributes can be used to detect the reservoirs successfully. However, the differentiation between gas sand and brine sand is still the main challenge. In terms of AVO response, both gas and brine sands follow class I, and in extreme cases, gas sand will follow class IIp. The ability of Intercept-Gradient to distinguish gas sands, brine sands, and background rock, is dependent on a number of interacting factors such as effective porosity, fluid fill, Vsh, and cap rock elastic properties. Thus, the AVO classification is not the optimum solution to delineate the deep gas sand reservoirs. In this paper, we’ve invented a new approach; starting from stochastically modeling the different possibilities of the gas and brine sands in the Intercept/Gradient domain. Then, the Intercept and Gradient were rotated and projected in an Extended Elastic Reflectivity (EER) form at a certain angle to represent a fluid stack volume. This fluid projection provides the best separation between the two partially-overlapped clouds; gas sands and brine sands. To maximize the benefit of this volume and to consider the uncertainty of the input data, cumulative distribution functions have been designed for the gas and brine sand probabilities. The probability volumes were tested at a dry well location and tie perfectly the well results. The gas sand probability volume was then used for deep targets detection and showed promising new leads.