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A robust and detailed reservoir model is an essential requirement when a fast track approach drives the development of a green field. Such a tool can only be developed through the orchestration of Geological and Geophysical (G&G) and Reservoir Engineering disciplines. This integration effort is, first of all, aimed at identifying the key characteristics of the reservoir most impacting its dynamic behavior at different scale and, eventually, at capturing them with the proper modelling approach.
This paper decribes such approach to the case of a complex deep-water reservoir belonging to slope-toe of slope environment. A 3D integrated static model was built by incorporating core and log data, their petrophysical interpretation, a description of the depositional and architectural elements, a quantitative seismic reservoir characterization and the few dynamic information available at this early development stage.
The implemented geomodeling workflow focused on heterogenetiy that could affect reservoir performance such as structural-stratigraphic discontinuities that could act as hydraulic barriers. Facies in the interwell space were distributed by applying seismic-derived 3D trends. Facies distribution eventually provided the framework within which petrophysical properties modelling was performed. During the implementation of this integrated G&G and Reservoir workflow, continuous crosschecks of consistency and robustness of the model led to elaborate the final product.
The resulting reservoir model captured critical uncertainties (e.g. degree of reservoir heterogeneity including stratigraphic discontinuities) leading to an optimized development scheme, that allowed to minimize risks, despite the few data available.
This paper describes a new integrated workflow that successfully bridges the gap between different measurement scales to characterise a clastic reservoir. This workflow combines thin sections, well logs and elastic curves (Vp, Vs and Rho or any combination of these) to generate a petro-elastic log facies classification that is not only calibrated to core data, but at the same time constitutes a shared key input for seismic inversion classification and reservoir modelling.
The application of this workflow is deemed crucial whenever seismic attributes are required to be used as a driver for properties distribution within a geological model. Given the inherent constraint of the seismic resolution, the recommended logfacies model has to honour a robust petrophysical reservoir characterisation (porosity, permeability) while assuring the maximum discrimination within the elastic inversion space (e.g. P-Impedance vs. Vp/Vs ratio) with a minimum number of classes.
In this paper, we outline a framework for building an integrated log facies model that includes the following steps: first, a facies model is defined at the scale of thin sections by means of a clustering technique. This represents the reference for a core-supported facies classification that in turn is linked to the sedimentological facies. A porosity-permeability relationship is analysed as it represents a common petrophysical domain for both facies models: thin sections and core data. The "poro-perm" relations are preserved when moving to log scale, where a petro-elastic log facies model is generated with a small number of classes. Secondly, Formation Evaluation (FE) is carried out to provide a simple, general and robust petrophysical model paramount not only for reservoir characterisation but also to be used as input for a dedicated Rock Physics Model (RPM), which links petrophysics and seismic velocities. FE and RPM are tuned one to the other until optimised. Thereafter, in order to generate classes that are not affected by hydrocarbon effects, fluid replacement modelling is performed to produce synthetic elastic curves in brine condition, that are input - together with porosity and volumes of minerals - to the facies classification. The procedure iterates until a few log facies are simultaneously discriminated in the petrophysical (e.g. Porosity vs. Permeability), petro-elastic (e.g. Porosity vs. Vp/Vs ratio) and in the elastic space of seismic inversion (e.g. P-Impedance vs. Vp/Vs ratio). Finally, once the log facies classes are defined at the well log scale, we use a dedicated technique to scale-up the petro-elastic log facies to the seismic domain, together with the petrophysical and the elastic curves. While the log facies are the hard data for geological modelling and support for petrophysical properties distribution, the corresponding upscaled log facies curves are used to populate the template for seismic elastic attributes classification to facies probabilities.
The successful application to a green field in ultra-deep water environment, characterised by few development wells, proves the robustness of integrating all available data from thin sections to seismic scale in reservoir characterisation.