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 Conventional seismic inversion has now been replaced by an integrated approach combining ABI, geostatistical modelling and SD-RGB colour blending in an effort to delineate the remaining potential of the field, and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.
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
Uncertainty is not an inherent feature of a reservoir; it is the result of lack of knowledge and understanding about the reservoir. Uncertainty can be modeled, but there is no objective measure of uncertainty. All geostatistical methods used in estimation of reservoir parameters are inaccurate; hence, modeling of “estimation error” in the form of uncertainty analysis is important. Uncertainty is associated with each process involved in geologic modeling, including data acquisition, data processing, interpretation, structural modeling, facies modeling, petrophysical modeling, and volume estimation, which affects the ability to understand the reservoir behavior and make reliable production forecasts and risk-free decisions. Hence, uncertainty analysis is a prerequisite measure for initial calculation of oil in place in any field. In the estimation of stock-tank oil initially in place (STOIIP) in Kalol reservoir, Cambay Basin, India, a geologic uncertainty study was initiated to identify and quantify the input parameters of greatest impact in the reservoir model. Results showed that the structural uncertainty has maximum impact and volume expansion factor has minimum impact in the estimation of stock-tank oil initially in place, whereas lithologic and petrophysical uncertainties have equal impact. Hence, during structural, facies, and petrophysical modeling, one has to take special precaution before finalizing the reserve calculation. Therefore, uncertainty and sensitivity analysis in the estimate of stock-tank oil initially in place should be applied on a routine basis because it greatly helps in improving fluid estimate from the field and ultimately the economics of the investments.
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