Petrophysical Reservoir Property Prediction from Seismic Data. Bridging the Geophysical Properties to Geological Information. Case Study from Marib Basin Yemen, Alif Formation

Abstract

Abstract Objectives The objective of this study is to understand the reservoir properties distribution of the Alif formation from seismic data and to Enhance the seismic interpretation model for well planning Support. This is to minimize the drilling risk associated with rapid spatial heterogeneity and compartmentalization of the current reservoir rocks. Seismic inversion and Rock physics are powerful tools that delivers information about intra-wells rocks elastic attributes and reservoir properties such as porosity, saturation, and rock lithology classification. The paper provides detailed and comprehensive technical details of the Surface Seismic and Rockphysics Inversion study that has been carried out on the current project to characterize the reservoir properties of the Alif formation from the seismic Challenges There are several challenges related to the characterization of the current reservoir rocks. The reservoir rocks are affected by a series of complex fault systems. In addition, the rocks are characterized by a high horizontal and vertical heterogeneity of their reservoir properties distribution (Porosity, Saturation, and Rock Facies), which is tough to resolve using seismic imaging techniques alone. It was crucial to go beyond seismic imaging techniques to better characterize the reservoir rocks in terms of reservoir properties such as Effective Porosity, Water Saturation, and Rock, Classification. Geophysical logs including sonic and density logs, measure rock properties near the borehole. The reliability of these logs is affected by borehole diameter and shape, as well as drilling fluid invasion. Acoustic well logs need to be conditioned before being used in geophysical analysis as any error propagate through the subsequent processes. Surface seismic data quality is impacted by residual noise, multiples contamination, and improper amplitude balancing. To optimize processing for inversion studies. Processes include curvelet domain noise attenuation, high-resolution Radon anti-multiple, and extended interbed multiple Prediction. Offset dependent amplitude and spectral balancing are applied to maintain seismic amplitude fidelity.