The Kalol Field in the Cambay Basin of India was discovered in 1961 and has been producing through more than 608 wells from Kalol reservoirs but the cumulative production up until 2008 was only 95.19 MMbbl. Reasons for low recovery have been ascribed to poor reservoir facies and limited reservoir thicknesses. In the Kalol Field, several thin clastic reservoirs are sandwiched within the main reservoir and exhibit lateral variations in lithology.

Attribute-based inversion (ABI) is effective at delineating potentially prospective areas and reveals the existence of a good reservoir facies cluster in the south, SE and NW corners of our study area, near the Kalol Field. However, this method cannot decipher the depositional setting or the finer details of facies elements. To obtain a smooth and fine-tuned facies model, geostatistical modelling is adopted taking the ABI output (seismic-facies model) as the initial model. The advantage of geostatistical modelling is that it always honours the input data and respects the positional variograms of the geology.

Adding spectral decomposition with red–green–blue (SD-RGB) colour blending reveals the existence of a meandering river in the study area. This river is interpreted to have deposited crevasse splays and channel facies along the river banks. These two facies are the main producing contributors to a well that has maintained a higher production than any other well in this field. This facies-based approach is also effective in determining the reservoir geometry and quality consistent with the interpretation of the depositional environment.

In ABI, 3D attribute volumes of petrophysical properties are calculated using a genetic algorithm inversion and artificial neural network using a non-linear correlation between seismic and log properties. The calculated 3D attribute volumes of petrophysical properties are subsequently utilized for seismic facies classification. In contrast to ABI, SD-RGB colour blending has been solely utilized for co-visualization of different band-limited amplitude volumes from spectral decomposition. Conventional seismic inversion has now been replaced by an integrated approach combining ABI, geostatistical modelling and SD-RGB colour blending in an effort to delineate the remaining potential of the field, and to improve the geological success and ultimate recovery.

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