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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.

Stochastic inversion of 3-D seismic data is used increasingly for reservoir characterization. It provides information on the reservoir at a much finer scale than deterministic inversion and delivers multiple scenarios for uncertainty analysis. In this work, a stochastic seismic inversion workflow has been developed to characterize porosity variations in the Thamama reservoir of the giant Field, onshore Abu Dhabi. In contrast to traditional band-limited inversion, this stochastic inversion workflow first generates high frequency models of acoustic impedance which can be used directly for geomodelling without downscaling. Next, a collocated co-kriging sequential Gaussian simulation technique has been applied to generate fine scale 3-D porosity realizations constrained by the impedance stochastic models and by log porosity data. In the example, post-stack stochastic inversion is combined with stochastic porosity modeling to characterize the uncertainty in the spatial distribution of thin, low porosity / permeability intra-Thamama layers, which adversely affect the field water flood performance. These thin layers have been mapped using seismic-constrained stochastic workflow. P10, P50 and P90 porosity realizations have been generated which represent more or less pessimistic scenarios of lateral extent of the tight zone along the flank of the field. A number of blind wells demonstrate that the seismic-based workflow provides more accurate porosity predictions than a purely well-based reservoir model. Specific technical contributions of the work include:Demonstrate the value of seismic information for characterizing mature carbonate reservoirsImplement field-specific stochastic workflow to characterize uncertainty in spatial extent of thin reservoir flow barriers from 3-D seismic dataPerform seismic inversion directly in fine-scale stratigraphic grid to facilitate integration with the field geomodel

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