The middle Eocene Kalol Formation in the north Cambay Basin of India is producing hydrocarbons in commercial quantity from a series of thin clastic reservoirs. These reservoirs are sandwiched between coal and shale layers, and are discrete in nature. The Kalol Formation has been divided into eleven units (K‐I to K‐XI) from top to bottom. Multipay sands of the K‐IX unit 2–8 m thick are the main hydrocarbon producers in the study area. Apart from their discrete nature, these sands exhibit lithological variation, which affects the porosity distribution. Low‐porosity zones are found devoid of hydrocarbons. In the available 3D seismic data, these sands are not resolved and generate a composite detectable seismic response, making reservoir characterization through seismic attributes impossible. After proper well‐to‐seismic tie, the major stratigraphic markers were tracked in the 3D seismic data volume for structural mapping and carrying out attribute analysis. The 3D seismic volume was inverted to obtain an acoustic impedance volume using a model‐based inversion algorithm, improving the vertical resolution and resolving the K‐IX pay sands. For better reservoir characterization, effective porosity distribution was estimated through different available techniques taking the K‐IX upper sand as an example. Various sample‐based seismic attributes, the impedance volume, and effective porosity logs were used as inputs for this purpose. These techniques are map‐based geostatistical methods using the acoustic impedance volume, stepwise multilinear regression, probabilistic neural networks (PNN) using multiattribute transforms, and a new technique that incorporates both geostatistics and multiattribute transforms (either linear or nonlinear). This paper is an attempt to compare different available techniques for porosity estimation. On comparison, it is found that the PNN‐based approach using ten sample‐based attributes showed highest crosscorrelation (0.9508) between actual and predicted effective porosity logs at eight wells in the study area. After validation, the predicted effective porosity maps for the K‐IX upper sand are generated using different techniques, and a comparison among them is made. The predicted effective porosity map obtained from PNN‐based model provides more meaningful information about the K‐IX upper sand reservoir. In order to give priority to the actual effective porosity values at wells, the predicted effective porosity map obtained from PNN‐based model for the K‐IX upper sand was combined with actual effective porosity values using co‐kriging geostatistical technique. This final map provides geologically more realistic predicted effective porosity distribution and helps in understanding the subsurface image. The implication of this work in exploration and development of hydrocarbons in the study area is discussed.
Effectiveness of AVO techniques has always been a concern for practicing geoscientists in accessing and modelling reservoirs due to wavelet instability in seismic data. In spite of best efforts made during processing, most often, end product is mixed phase seismic data. This paper is an attempt to study the effect of phase in pre-stack seismic data for quantitative AVO analysis to characterise reservoirs for detection, delineation and development. AVO attributes corresponding to known gaseous reservoirs, computed from zero phase seismic show better AVO anomalies. Prior to AVO analysis, to understand AVO classification, petrophysical analysis was carried out for establishing the relation between elastic parameters and also with seismic signatures. AVO modelling has been used to demonstrate the superiority of zero phase seismic data at known gaseous reservoir sands. In absence of source signature, application of well log data for estimation of wavelet present in seismic was made and by dephasing, the mixed phase data was converted to optimum zero phase. In wildcat exploratory areas, explorationists try to establish AVO anomaly using available mixed phase seismic data. It is found that, at times, AVO attributes are not well defined to differentiate AVO anomalies with reference to background trend extracted from this mixed phase data set. In absence of well data, phase rotation of pre-stack gathers at fixed intervals and their analysis is the best possible solution. This has been discussed in detail by applying on a data set at pre-drill stage and confirming the AVO anomaly which has been proved by subsequent drilling. It is also demonstrated that phase rotation is a robust technique and does not generate spurious AVO response, by applying it at known dry well. This approach of analysing well, synthetic and seismic data together for AVO analysis has increased the level of confidence of interpreters for E & P activities related to the gaseous reservoirs.
Seismic amplitude has played a critical role in the exploration and exploitation of hydrocarbon in West Africa. Class 3 and 2 amplitude variation with offset (AVO) was extensively used as a direct hydrocarbon indicator and reservoir prediction tool in Neogene assets. As exploration advanced to deeper targets with class 1 AVO seismic character, the usage of seismic amplitude for reservoir presence and quality prediction became challenged. To overcome this obstacle, (1) we used seismic geomorphology to infer reservoir presence and precisely target geophysical analysis on reservoir prone intervals, (2) we applied rigorous prestack data preparation to ensure the accuracy and precision of AVO simultaneous inversion for reservoir quality prediction, and (3) we used lateral statistic method to sum up AVO behavior in regions of contrasts to infer reservoir quality changes. We have evaluated a case study in which the use of the above three techniques resulted in confident prediction of reservoir presence and quality. Our results reduced the uncertainty around the biggest risk element in reservoir among the source, charge, and trap mechanism in the prospecting area. This work ultimately made a significant contribution toward a confident resource booking.
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