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Fluid Prediction from 3-D Seismic Data in Deep-Water Sandstone Reservoirs, Veracruz Basin, Southeastern Mexico

By

FOUAD, KHALED, and JENNETTE, DAVID C.

Bureau of Economic Geology, John A. and Katherine G. Jackson School of Geosciences, The University of Texas at Austin, Austin, TX,

SOTO-CUERVO, ARTURO

PEMEX Exploración y Producción, Veracruz, Mexico

 

In a regional study of the Veracruz Basin, depositional systems and reservoir styles were defined for the upper Tertiary section. Interpretations were based mainly on a 9,000-km, 2-D seismic grid and two 3-D surveys. The 240-km2 Cocuite survey, shot over the producing Cocuite gas field, provided a unique opportunity to investigate fluid and rock-property relationships in deep-water reservoirs. The producing reservoirs consist of sinuous channels formed at the toe of prograding clinoforms, turbidite lobes, and turbidite channel complexes. Reservoir geometries were successfully imaged using conventional interval and windowed attribute-extraction techniques. Numerous tested and candidate DHI’s were delineated and mapped from several stratigraphic units.

Inversion and neural-network training models were developed to improve the tie between amplitude and gas occurrence. Gas sands on well logs are associated with an abrupt decrease in impedance. Seismic inversion was then attempted to differentiate between gas-filled and water-filled sandstones. Synthetic models were generated at the well locations using different fluid saturations to determine the effect of varying fluid type on the seismic signal. A supervised neural network, based on the synthetic models, was trained to look for a nonlinear relationship between impedance log and seismic traces. Neural network validation was achieved by systematically hiding data from the network, running another training session, and then observing predicted versus known relationships. The results of the inversion lead to quantitative calibration of the seismic amplitudes and capture the range of gas and water occurrences at the reservoir intervals.