“Attribute based Inversion” a tool for reservoir characterization: a case study-Kalol Field, Cambay Basin, India

Abstract

With major oil and gas discoveries diminishing in number, industry is turning its attention to redevelop fields with reservoirs (Res) like silts which have otherwise been accorded lower priority earlier. It has always been a challenge to identify the locales with better Re facies development in un-drilled areas of a field and most often many development wells either go dry or turn out to be poor producers, significantly increasing the cost of production from a given field. Kalol Field, Cambay Basin, India is a several decade old discovery with a significant number of development wells. However, the oil recovery remained hardly around 10 %. Most often, the contributing factor for this low recovery is poor Re facies (tight silts) within the major producing sequences like Kalol IX and Kalol X. Hence identifying areas of better Re facies remained a challenging task before the geo-scientists. To overcome this challenge a workflow has been developed for Re characterization based on an "Attribute based Inversion" technique, in which 3D attribute volume of petrophysical properties are calculated through genetic inversion algorithm using a nonlinear correlation between seismic property and log property. Calculated 3D attribute volume of petrophysical properties are utilized further for Re classification and finally geostatistical modeling is performed for Re modeling. The adopted approach is operative even if the Re is very thin (beyond seismic resolution) and can provide a way to generate 3D attribute volumes of log property from seismic and well log data. This approach is also effective in determining the Re geometry and quality of Re, which may help in planning future drilling locations. The application of the workflow has been illustrated with a case study from Kalol Field, Cambay Basin. The obtained results shows that the proposed approach is effective enough in resolving 2-8 m thick Re within Kalol formation. It gives an idea about the Re quality (good or bad Re) and geometry of the Kalol Reservoir in the field. Volumetric calculation shows that there is still

Geo-Body and Geostatistical Modelling of Carbonate Reservoir Facies Architecture and Characterization

“…All the petrophysical analysis logs, PHI, and Sw logs are upscaled in the structural grid. In a similar approach, the discrete facies logs are also upscaled using 'most of' (a geostatistical algorithm) within the structural grid [16,17]. It is observed that RMS greater than 1 corresponds to reservoir facies at the well location.…”

“…The upscaled porosity and water saturation logs are populated in the reservoir facies through geostatistical analysis (Kriging Interpolation). In the non-reservoir segment of the facies model, zero porosity and 100% water saturation are assigned during property distribution [16,17]. Fig.…”