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Maximum likelihood sparse spike inversion (MLSSI) method is commonly used in the seismic industry to estimate petrophysical parameters in inter-well region. In present study, maximum likelihood sparse spike inversion technique is applied to the processed 3D post-stack seismic data from the F-3 block, the Netherlands, for estimation of acoustic impedance in the region between the wells. The analysis shows that the impedance varies from 2500 to 6200 m/s/*g/cc in the region which is relatively low and indicates the presence of loose formation in the area. The correlation between synthetic seismic trace and original seismic trace is found to be 0.93 and the synthetic relative error as 0.369, which indicate good performance of the algorithm. The analysis also shows low-impedance anomaly in between 600 and 700 ms time interval which may be due to the presence of sand formation. Thereafter, the probabilistic neural network analysis is performed to predict porosity along with multi-attribute transform analysis to estimate P-wave velocity and porosity in inter-well region. These parameters strengthen the seismic data interpretation which is very crucial step of any exploration and production project. The method is first applied to the composite traces near to well locations, and results are compared with well log data. After getting reasonable results, the whole seismic section is inverted for the P-wave velocity and porosity volume. The analysis shows anomaly in between 600 and 700 ms time interval which corroborates well with the low-impedance zone which may correspond to the reservoir. This is preliminarily interpretation; however to confirm a reservoir, there is need for more petrophysical parameters to be studied. Keywords Maximum likelihood sparse spike inversion (MLSSI) • Multi-attribute linear regression • Probabilistic neural network (PNN) Abbreviations MLSSI Maximum likelihood sparse spike inversion PNN Probabilistic neural network HRS Hampson Russell software MLD Maximum likelihood deconvolution CC Correlation coefficient RMS Root mean square MLFN Multilayer feed forward neural network BLI Band-limited inversion CI Colored inversion LPSSI Linear programming sparse spike inversion MBI Model-based inversion RL Reinforcement learning

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.