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We introduce a stratigraphic inversion method that simultaneously integrates pre-stack seismic data with petrophysical and geological data. We use simulated annealing to invert directly for reservoir properties such as porosity, lithology and fluid content in a 3D geocellular model. Well and seismic data are integrated in their respective domains along with physical constraints at different vertical scales to produce an optimal solution. Application of user-defined Petro-Elastic Models (PEM) is a key element of the proposed methodology. In addition to connecting the inverted properties to the seismic response, the PEMs are used to maintain consistency between the time, depth and derived velocities throughout the inversion process. The proposed methodology overcomes the limitations faced by many existing techniques with regards to vertical resolution, time-to-depth conversion and the link between seismic response and reservoir properties. The result of our petrophysical seismic inversion is a fine-scale shared earth model in depth that is consistent with both log and seismic data and can be used for reservoir performance prediction. After demonstrating the robustness of the method on synthetic data, we present a result from a real dataset. The proposed methodology has been successfully applied to porosity inversion on one of the largest undeveloped oil fields in the North Sea. A fine-scale reservoir model has been obtained which reveals previously undetected geological structures and leads to a better understanding of the reservoir zone. Introduction Challenges of seismic reservoir characterization. Detailed 3D reservoir models are increasingly relied upon for prediction of reservoir performance, in particular through flow simulation. These models are commonly required to contain petrophysical information about lithology, rock properties (such as porosity, permeability, grain density, dry frame modulus, shear modulus, etc) and fluid properties (such as saturations, densities and compressibilities) on a very fine vertical scale, with a typical resolution of one meter. It is widely acknowledged that a better integration of all available measurements is the key to improving the reliability of the reservoir model, and therefore the reliability of the decisions based upon it. The reservoir model must be coherent as far as possible with the seismic volumes, wireline logs, core plug analyses and well production data, which are the response of the same subsurface to different experiments. In this paper we will focus more specifically on the integration of static information. Seismic data in particular are an invaluable source of information as they provide an extensive coverage with dense and regular lateral sampling, especially when compared to the sparse well locations. However, the integration of seismic data into the reservoir characterization process poses a number of challenges. Although the subsurface physically exists in depth, seismic traces portray it in two-way travel time, which is related to the depth domain via the wave propagation velocity. Similarly, seismic amplitudes are a highly indirect measurement of reservoir property variations. Seismic reacts to changes in the elastic properties of the subsurface, which are themselves related to petrophysical characteristics but also affected in a complex way by many factors. Finally, the vertical resolution that is recoverable from seismic data is low compared to the target geologic resolution: whereas wireline logging tools in wells can resolve details to within a few centimeters to a few meters, ten meters is a typical order of magnitude for seismic.

Acquiring four-component seismic data with wideazimuth geometry provides an opportunity to build a very complete seismic picture for reservoir description. The recording of the full vector wavefield allows creation of both PS-wave data as well as P-wave images which contain different but complementary information. It also provides full-azimuth illumination of the subsurface. Azimuthal images improve definition of structural features, such as faults, that may only be illuminated within certain preferential shot-receiver azimuths. Differences in azimuthal images can also be very sensitive fault indicators in the case of small vertical displacements. Furthermore, and of crucial importance at the reservoir scale, wide-azimuth P-wave and PS-wave data lend themselves to the evaluation of azimuthal anisotropy. These attributes provide valuable spatial constraints in the characterization of heterogeneously distributed subseismic scale fractures.In 2003-04, Occidental Petroleum of Qatar, in partnership with Qatar Petroleum, acquired one of the first wideazimuth 4-C surveys in the Middle East, offshore Qatar which covered the Idd el Shargi "north dome" and "south dome" fields ( Figure 1). The reservoirs are within gentle anticlinal structures and have highly permeable fault zones and fracture corridors with small near-vertical displacements, which are often below the resolution of conventional narrow-azimuth streamer seismic. The reservoir now is undergoing secondary recovery with an extensive water-flooding program (Shifflet, 2003). For effective reservoir management, it is therefore of great importance to accurately detect the high-permeability zones for well placement and to increase the efficiency of injection programs.The ocean-bottom cable (OBC) data were acquired by Multiwave Geophysical using a patch geometry, with orthogonal receiver cable and shotline directions, providing full azimuthal coverage to offsets of 3000 m, equivalent to an offset/depth ratio of 2 for the main target zone. The nominal fold was 240 in the natural bin size of 9.4 ǂ 25 m. This gave sufficiently high signal/noise ratios, when used in conjunction with a more optimal bin size for imaging, to create eight azimuthlimited data sets for both the P and PS data. In total, one billion traces were acquired, making it one of the largest OBC surveys shot and processed to date. The main focus of the project was to apply an azimuth-friendly processing workflow (Gomez et al., 2004) to provide the optimum data preconditioning for a robust azimuthal anisotropy analysis following the methodology introduced by Angerer et al. (2003).In this paper, we present the main aspects of an integrated workflow comprising an adequate acquisition, specialized seismic processing, and robust attribute extraction for reservoir characterization. P-wave processing. The main objective of the study was the preservation of the azimuthal signal for subsequent anisotropy analysis. This had to be weighed against other objectives such as applying aggressive noise attenuation, or pu...

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