Application of LP and ML sparse spike inversion with probabilistic neural network to classify reservoir facies distribution - A case study from the Blackfoot field, Canada

Abstract

Sparse-Spike inversion techniques are used to estimate distribution of acoustic impedance in inter well region, the important parameters for characterizing the reservoir facies from seismic and well log data. The purpose of sparse spike impedance inversion is to obtain high-resolution impedance profile of the subsurface from the low resolution seismic data with the integration of well log data and enhance the interpretation of the prospective zone. In the present study, two types of sparse spike inversion techniques, namely, Linear Programming (LP) sparse spike inversion and Maximum Likelihood (ML) sparse spike inversion are applied to estimate acoustic impedance from seismic data of the Blackfoot region, Alberta, Canada. The principle objective of the study is assessing the relative performance of these techniques for estimation of petrophysical parameters to identification of prospective zones in the area. Initially, the inversion methods are applied to the composite trace near to well locations and inverted for acoustic impedance and compared with the actual impedance derived from the well log data. The result demonstrates that both curves are matching with each other very well. The correlation is estimated to be 0.97 and 0.93, Synthetic relative error (SRE) 0.23 and 0.34 and Root mean square (RMS) errors are 1125 m/s*g/cc and 1205 m/s*g/cc for Linear Programming sparse spike inversion (LPSSI) and Maximum Likelihood sparse spike inversion (MLSSI), respectively. The analysis for composite traces depicts the robustness and performance of the algorithm. Thereafter, the techniques are applied to seismic volume to estimate variation of acoustic impedance in inter well regions. The analysis of inverted impedance shows a low impedance anomaly in between 1060 and 1075 ms time intervals which may be due to presence of reservoir facies (sand channel). The analyses of the inverted results reiterate that both methods work satisfactory and show variation of reservoir facies in similar way. The results found by LPSSI shows slightly higher resolution compared to the MLSSI results. Thereafter, to enhance reservoir facies more clearly, porosity is predicted in inter well region by using probabilistic neural network (PNN) technique. The result shows very high porosity (> 15%) in between 1060 and 1075 ms time interval which corroborated with the low impedance zone and confirms the presence of sand channel. The qualitatively and quantitatively analysis of inversion results suggest that the LPSSI along with PNN provides better reservoir characterization than MLSSI and PNN combination for the Blackfoot field, Alberta, Canada.