Abstract
We carried out time-lapse analysis in a producing Niger Delta X-field, by first investigating the response and sensitivity of rock properties/attributes to lithology and pore fill in 3-D cross plot domain and by Gassmann’s fluid substitution modeling. Furthermore, 4-D seismic data were inverted into acoustic impedance volumes through model based inversion scheme. This served as input into a multi-attribute neural network algorithm for the extraction of rock attribute volumes based on the results of the petrophysical log analysis. Subsequently, horizon slices of rock properties/ attributes were extracted from the inverted seismic data and analyzed. In this way, we mapped hydrocarbon depleted wells in the field, and identified probable by-passed hydrocarbon zones. Thus, the integration of well and time lapse seismic (4-D) data in reservoir studies has remarkably improved information on the reservoir economic potential, and enhanced hydrocarbon recovery factor.
Highlights
Reservoir characterization is aimed at identifying hydrocarbon bearing reservoirs, delineating them and subsequently, determining the distribution of relevant physical properties such as lithology, porosity, permeability, water saturation and pore pressure, which will make for an easy determination of the reservoir’s economic potential [1]
Common lithologic units and fluid types tend to form distinct clusters in cross plot space, and this helps in making a straight forward interpretation of probable lithology and pore fill saturant
The petrophysical well log analysis in cross plot domain and fluid sensitivity modeling using Gassmann’s fluid substitution, revealed that low ρ, λρ and Ip, and high R are associated with gas and oil sands, while medium to high ρ, λρ and Ip, and low R values are associated with brine sands and shale, respectively
Summary
Reservoir characterization is aimed at identifying hydrocarbon bearing reservoirs, delineating them and subsequently, determining the distribution of relevant physical properties such as lithology, porosity, permeability, water saturation and pore pressure, which will make for an easy determination of the reservoir’s economic potential [1]. Prospect definition requires more than just mapping of geologic structures that can bear hydrocarbon It entails a more quantitative evaluation of both the static and dynamic properties of the specific reservoir. The use of inversion algorithms based on the approximations of Zoeppritz’s equations has been studied by various researchers [2] [3], for the inversion of prestack seismic data into acoustic and shear impedance volumes. Their results demonstrated the use of seismically derived attributes such as acoustic impedance, lambda-rho, Poisson impedance and Murho as effective tools for lithology and fluid prediction in a hydrocarbon reservoir
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