3D Water Saturation Estimation Using AVO Inversion Output and Artificial Neural Network; A Case Study, Offshore Angola
Authors: Hasan Hesham , Bouchet Francois D. , Chidundo Maximiano , Nashaat Michael , Simào Joào , De Sà Mario , Ferreiras Lidia. F. , José Manuel. N 
Published: November 2020

Thanks to high-resolution seismic enabled by hardware and software technology advances, 3D seismic surveying techniques have become particularly effective in hydrocarbon prospection and development. This paper, based on two seismic datasets, well data and production history from deep-offshore Angola, Block 4, highlights how seismic data may be used for both reservoir characterization (static model and in-place) and reservoir monitoring (saturation changes). In the following we shall present in detail the applied seismic characterization method, with particular emphasis on: (i) advanced data conditioning and modeling, (ii) sequential elastic inversion for the integration of two datasets of different vintages, (iii) utilization of Bayesian techniques for reservoir characterization and probability density functions and finally (iv) Neural Network application for the assessment of oil saturation.

The high resolution seismic data contains strong AVO signature, but to exploit these fully, preserving the signal throughout the process is essential: (i) seismic inversion feasibility analysis and synthetic gathers confirmed the quality of the far offset up to 55 degrees; (ii) this allows producing reliable density inversion products, using data beyond 35 degrees angles with adequate angle bands (iii) to get best inversion results a tailored pre-stack data conditioning was applied using five partial stacks with particular attention to amplitude preserving and flattening at reservoir level; (iv) finally well and seismic results were integrated through inversion and litho-classification, which compared well with well data.

The high-fidelity inversion results allowed trying a novel approach through an additional step: saturation estimation based on Neural Network (NN) technique: (i) a labeled data set of elastic well logs, namely, P-wave Impedance, P-wave and S-wave velocities ratio and Density were used to train a Neural Network engine to estimate the water saturation from petrophysics. A low level of cross-validation error was achieved and deemed acceptable. The trained NN model seemed to be able to estimate the Water Saturation logs accurately as confirmed by blind well tests. A saturation cube was generated by applying the trained NN model on the three properties established through pre-stack inversion.

Owing to the excellent quality of the recent (2018) high resolution seismic data, and despite log quality issues (poor borehole condition at some wells), high-fidelity elastic inversion could be achieved for both datasets. This in-turn led to a significant reservoir characterization uplift (additional in-place, better segregation and distribution of reservoir facies for simulation).

Finally, the comparison between inversion results from the older dataset (prior to production) and the recent one allowed highlighting saturation variations pointing to swept and unswept portions of the field.