Qualitative and quantitative comparison of the genetic and hybrid genetic algorithm to estimate acoustic impedance from post-stack seismic data of Blackfoot field, Canada. Issue 2 (16th December 2022)

• Record Type: Journal Article
• Authors: Maurya, S P; Singh, R; Mahadasu, P; Singh, U P; Singh, K H; Singh, R; Kumar, R; Kushwaha, P K
• Is Part Of: Geophysical journal international. Volume 233:Issue 2(2023)
• Page Start: 932
• Page End: 949
• Publication Date: 2022-12-16
• Subjects: Permeability and porosity -- Inverse theory -- Joint inversion -- Neural network, fuzzy logic -- Hybrid optimization -- Seismic inversion; Geophysics -- Periodicals; 550
• DOI: 10.1093/gji/ggac495
• Languages: English
• ISSNs: 0956-540X

Abstract

SUMMARY: In this study, seismic inversion is carried out using a genetic algorithm (GA) as well as a hybrid genetic algorithm (HGA) approach to optimize the objective function designed for the inversion. An HGA is a two steps coupled process, where a local optimization algorithm is applied to the best model obtained from each generation of the GA. The study aims to compare the qualitative as well as the quantitative performance of both methods to delineate the reservoir zone from the non-reservoir zone. Initially, the developed algorithm is tested on synthetic data followed by its application to real data. It is found that the HGA for synthetic data is providing more accurate and high-resolution subsurface information as compared with the conventional GA although the time taken later is less as compared with the former methods. The application to real data also shows very high-resolution subsurface acoustic impedance information. The interpretation of the impedance section shows a low impedance anomaly zone at (1055–1070) ms time interval with impedance ranging from (7500 to 9500) m s −1 *g cc −1 . The correlation between seismic and well data shows that the low impedance zone is characterized as a clastic glauconitic sand channel (reservoir zone). In seismic inversion using an HGA, one can delineate the areal extent of the reservoir zone from the non-reservoir zone more specifically as compared to the GA-derived impedance. The convergence time of HGA is 4.4 per cent more than GA and can be even more for larger seismic reflection data sets. Further, for a more detailed analysis of the reservoir zone and to cross-validate inverted results, an artificial neural network (ANN) is applied to data, and porosity volume is predicted. The analysis shows that the low impedance zone interpreted in inversion results are correlating with the high porosity zone found in ANN methods and confirm the presence of the glauconitic sand channel. This study is important in the aspect of qualitative as well as quantitative comparison of the performance of the GA and HGA to delineate sand channels.