This paper describes the successful application of amplitude-versus-angle (AVA) inversion of prestack-seismic amplitude data to detect and delineate deepwater hydrocarbon reservoirs in the central Gulf of Mexico. Detailed AVA fluid/lithology sensitivity analysis was conducted to assess the nature of AVA effects in the study area based on well-log data. Standard techniques such as crossplot analysis, Biot-Gassmann fluid substitution, AVA reflectivity modeling, and numerical simulation of synthetic gathers were part of the AVA sensitivity analysis. Crossplot and Biot-Gassmann analyses indicate significant sensitivity of acoustic properties to fluid substitution. AVA reflectivity and angle-gather modeling indicate that the shale/sand interfaces represented by the top and base of the M-10 reservoir are associated with typical Class III AVA responses caused by relatively low-impedance gas-bearing sands. Consequently, prestack seismic inversion provided accurate and reliable quantitative information about the spatial distribution of lithology and fluid units within the turbidite reservoirs based on the interpretation of fluid/lithology-sensitive modulus attributes. From the integration of inversion results with analogous depositional models, the M-series reservoirs were interpreted as stacked terminal turbidite lobes within an overall fan complex. This interpretation is consistent with previous regional stratigraphic/depositional studies.
This paper describes a novel algorithm for the joint stochastic inversion of well logs and multiple angle stacks of migrated 3D pre-stack seismic data. The inversion algorithm is based on a Bayesian statistical search criterion implemented with fast Markov-Chain Monte Carlo updates. It implements a-priori measures of spatial correlation as well as specific geometrical properties of structural and stratigraphic embedding. Results consist of spatial distributions of elastic properties with a vertical resolution intermediate between that of seismic amplitude data and well logs. In addition, the algorithm provides quantitative estimates of non-uniqueness based on the statistical distribution of multiple spatial realizations derived from random initial models. It is also possible to estimate lithology and petrophysical properties such as porosity and water saturation by enforcing multi-dimensional statistical correlations between elastic and petrophysical properties sampled from well-log measurements. We describe results from the successful application of the inversion algorithm to the high-resolution characterization of hydrocarbon-producing units of a deepwater reservoir located in the central Gulf of Mexico. Sensitivity analyses of resolution and non-uniqueness at blind-well locations corroborate the reliable estimation of elastic and petrophysical properties. The estimated distributions of lithotypes and elastic properties are only marginally influenced by both the choice of inversion parameters and the assumed measures of spatial correlation.
We consider the inversion of synthetic and recorded seismic amplitude variation with angle AVA data to appraise the influence of several data-related factors that control the vertical resolution and accuracy of the estimated spatial distributions of elastic properties. We use measurements acquired in deepwater hydrocarbon reservoirs in the central Gulf of Mexico to generate synthetic seismic amplitude data and evaluate inversion results with both synthetic and recorded seismic amplitudes. Detailed sensitivity analysis of synthetic amplitude data indicates that, even in the most ideal scenario (perfectly migrated data, isotropic media, noise-free seismic amplitude data, sufficient far-angle coverage, and accurate estimates of angle-dependent wavelets and low-frequency components), input elastic models are not reconstructedaccurately by the inversion of synthetic seismic amplitudes. We attribute this result to the relatively low vertical resolution of the seismic amplitude data. P-wave impedance is the most accurate of the inverted properties, followed by S-impedance and bulk density. Additionally, sufficient far-angle coverage is crucial for the accurate estimation of 1D distributions of S-impedance and bulk density. We show that time alignment of partial-angle stacks for correcting residual NMO effects improves the vertical resolution of the estimated spatial distributions of elastic parameters and consistently decreases the data misfit. Finally, we found that the accuracy of the inverted distributions of elastic parameters is improved substantially by (1) increasing the preserved AVA information via multiple single-angle stacks, (2) correcting the P-wave velocity field used for calculating angles in partial-angle stacking, and (3) excluding far-angle data with low signal-to-noise ratios.
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