Seismic attributes used for reservoir simulation: Application to a heavy oil reservoir in Canada

Abstract

Cold heavy oil production with sand has become one of the main non-thermal schemes for developing heavy oil reservoirs in Canada. One challenge in modeling the fluid flow in the reservoir simulation studies is reservoir heterogeneity. Several seismic attributes were used to estimate the porosity (ranging from 19% to 35%) at the Plover Lake oil sands reservoir in Canada. First, the top and the base of the reservoir were mapped based on several seismic attribute volumes that include the density. From petrophysical analysis we learned that density is a key physical property in differentiating between sand and shale within the oil sands. Probabilistic neural network (PNN) analysis was used to derive the relationship between density log data and external attributes (PP and PS migrated stacks, AVO attributes and inversion results). Secondly, we used geostatistics to estimate a porosity map within the reservoir. The study is based on a set of porosity logs at well locations and several seismic attribute maps. Kriging, cokriging, kriging with external drift (KED) and multiattribute analysis for maps plus KED, were tested in order to improve the results. The KED with porosity from multiattribute analysis is the most realistic, honoring the wells and the seismic.

Porosity Maps – Interactive Exploration and Visual Analysis of Porosity in Carbon Fiber Reinforced Polymers

“…The porosity topic is not limited to material science alone. Seismic engineers use porosity mapping based on geostatistical methods to find oil reservoirs [DL08]. Britz Because of the big number of pores in a CFRP specimen, a drill-down approach is essential for exploring the data.…”

Integrated study of seismic and well data for porosity estimation using multi-attribute transforms: a case study of Boonsville Field, Fort Worth Basin, Texas, USA

“…Multiple attribute analysis uses the geostatistical methods to integrate multiple attributes derived from surface seismic data for porosity estimation (Guerrero et al 2010). Mostly, multi-linear regression (Russell et al 2002;Calderon and Castagna 2007) and neural network analysis (Chopra and Marfurt 2006;Dumitrescu and Lines 2008) are used to predict the target well log properties from various seismic attributes. These methods use the well data as a training point for the statistical model in order to derive the relationship between the seismic and well data.…”