Reservoir Characterization Using Seismic Inversion Based on Sparse Layer Reflectivity and Hybrid Genetic Algorithms: A Comparative Case Study of Blackfoot, Canada

Abstract

This research paper introduces a comparative case study on reservoir characterization through seismic inversion techniques. The study specifically explores sparse layer reflectivity and a hybrid approach involving genetic algorithms and pattern search. The research assesses the effectiveness of these methodologies in delineating subsurface properties, with a particular focus on acoustic impedance. Through meticulous analysis, the paper aims to identify the strengths and limitations of each method, considering factors such as parameter estimation precision, computational efficiency, and adaptability to complex geological structures. The findings contribute valuable insights for selecting optimal seismic inversion techniques in reservoir characterization, advancing our understanding of how the integration of sparse layer reflectivity and hybrid genetic algorithms can enhance subsurface imaging accuracy and reliability. The results obtained from our inversion process significantly enhance the interpretation of seismic data by providing detailed insights into the subsurface. Both the sparse layer reflectivity (SLR) and hybrid genetic algorithm (HGA) algorithms have exhibited outstanding performance when applied to real datasets. The inverted impedance section reveals notable low acoustic impedance ranging from 8000 to 8500 m/s g/cc. This distinct zone, identified as a reservoir (sand channel), is located within the time interval of 1040–1065 ms. Our observations indicate that HGA demonstrates superior correlation results not only in the vicinity of well locations but also over a broader spatial range, suggesting its potential to provide higher-resolution outcomes compared to SLR.