Use of maximum likelihood sparse spike inversion and probabilistic neural network for reservoir characterization: a study from F-3 block, the Netherlands

Abstract

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

Prediction of Petrophysical Properties using Post Stack Seismic Inversion and Geostatistical Techniques over F-3 Block, Netherlands - A Comparative Study

“…Previous research has already shown that reservoir porosity and density can be measured using statistical methods and intelligent systems [10,[31][32][33]. However, the majority of the time, neural network techniques rely on a linear fit between seismic features and reservoir properties [34][35][36][37]. This paper aims to use seismic attributes to estimate petrophysical parameters from well-log data by applying PNN and MLFN neural network techniques.…”