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A high quality broadband impedance solution by inverting seismic amplitudes is one of the most common industrial standards to integrate geophysical inputs into geological models during field development studies and planning. The use of accurately quantified petrophysical volumes derived from seismic data during geological model building can help improve the understanding of the reservoir and in maximizing the value of seismic data. During this case study for two sour gas carbonate reservoirs- reservoir units C & D, in an onshore Abu Dhabi field, the acoustic impedance volume from seismic inversion and the seismic data itself were further converted into a porosity volume by a probabilistic neural network (PNN) approach. This non-linear transform approach establishes the link between seismically-derived impedance and porosity through an optimized training correlation and error method approach at each well location. A combination of the following factors pertaining to training dataset, established a successful neural network based porosity prediction workflow for the reservoirs: Amplitude preservation of seismic data (target-reservoir specific re-processing)Many calibration wells with good quality calculated log porosityHigh quality inverted seismic impedance. A high correlation of predicted porosity to measured well porosity was achieved at all the well locations. In addition, reservoir unit C porosity grades from a very high but thinner porosity at the top to medium-high thicker porosity at the base, which was well resolved within the available seismic bandwidth and the PNN method of porosity prediction matched this variation of the log porosity. High cross-validation scores achieved during PNN training for porosity prediction, in effect provided the confidence in prediction away from the wells. Furthermore, a close match of predicted and measured porosity at blind test wells drilled in the field extended the confidence in the porosity prediction results. The porosity prediction successfully delineated regionally known non-porous lineaments and faults (encountered while drilling wells) on mean porosity maps of the two reservoirs. The main objective of reservoir-focused reprocessing was to minimize the amplitude damaging effects of the near-surface, mainly due to presence of large sand dunes, and adequately prepare the data for seismic inversion. Successfully estimating seismically derived porosity was an important tool for well planning, geosteering long reach horizontals and also a key input for reservoir characterization to improve our understanding and distribution of reservoir properties in our geologic model.

The first 4D seismic survey in Congo was acquired over the Moho-Bilondo Field in January 2011, two years and a half after first oil. 4D was expected to help in tracking the movements of injected water inside complex turbiditic bodies and to follow the rise of the oil-water contact. 4D anomalies can be highlighted using seismic attributes such as envelope amplitude and RMS amplitude differences but these standard 4D attributes have a limited vertical resolution and difficulties were encountered to distinguish depletion from water injection. A simultaneous 4D inversion workflow was therefore applied to extend the seismic bandwidth and estimate quantitatively the changes in reservoir acoustic impedance. Spatially variant rock physics constraints were introduced to improve the imaging of the water pathway around key injector wells. Preliminary analysis of the inverted 4D attributes shows an improved image of the fluid movements inside the reservoir compared to conventional 4D attributes.

The need to understand field-scale reservoir heterogeneity using seismic data requires implementing advanced solutions such as stochastic seismic inversion to go beyond the resolution of seismic data. Conventional seismic inversion techniques provide relatively low-resolution reservoir properties but do not provide quantitative estimates of the subsurface uncertainties. The objective of this study was to carry out a facies dependent geostatistical seismic inversion to generate multi-realization reservoir properties to improve the geological understanding of the two adjacent offshore fields in Abu Dhabi. An integrated approach of rock physics modelling and geostatistical inversion followed by porosity co-simulation was undertaken to characterize the spatially varying lithofacies and porosity of the complex carbonate reservoirs. Necessary checks to ensure highest quality data input included: 1) Rock physics modelling and shear sonic prediction 2) Invasion correction and production effect correction of elastic logs 3) Seismic feasibility analysis to define seismic facies and 4) Six angle stacks optimally defined to preserve AVO/AVA signature followed by AVO/AVA compliant post-stack processing. Subsequently, the joint facies driven geostatistical inversion was conducted to invert for multiple realizations high-resolution lithofacies and elastic rock properties. Finally, porosity was co-simulated and later ranked to map important geological variations. Based on the rock physics analysis, a 4 facies classification scheme (Porous Calcite, Porous Dolomite, Tight Calcite-Dolomite and Anhydrite) was adopted and used as input in the joint facies-elastic inversion. Before the geostatistical inversion, a deterministic inversion was performed that helped in refining the horizon interpretation of the surfaces used as a framework for the inversion. In geostatistical inversion, results are guided by variograms, facies, prior probability density functions, wells, inversion grid and seismic data quality. At start of the joint inversion, the parameters for inversion are defined in an unconstrained fashion aiming to obtain unbiased parameters which are blind to well control. Finally, using elastic properties constrained at the well locations, the joint geostatistical inversion was run to obtain multiple realizations of P-impedance, S-impedance, density and lithofacies. The cross-correlation between seismic and inverted synthetics was high across the whole area for all the partial angle stacks, with the lowest cross-correlation observed in the far angle stack. Lithofacies and elastic properties were used to co-simulate for porosity. The porosity results were then ranked to provide the P10, P50 and P90 models to be used for reservoir property model building. This study is an example of stochastically generating geologically consistent reservoir properties through high-resolution seismically constrained inversion results at 1ms vertical sampling. Lithofacies and elastic properties were jointly inverted, and co-simulated porosity results provided insights into high-resolution reservoir heterogeneity analysis through the ranking of equiprobable multiple realizations.

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