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We present a seismic inversion method driven by a petroelastic model, providing fine-scale geological models, in depth, fully compatible with pre-stack seismic measurements.

A permanent reservoir monitoring system has been installed for Shell at the end of 2010, on a medium heavy-oil onshore field situated in the north-east of The Netherlands, in the context of re-development of oil production by Gravity Assisted Steam Flood. The first challenge was to continuously monitor, with seismic reflection, the lateral and vertical expansion of the steam chest injected in the reservoir during production over more than a year. As the 4D seismic attributes obtained from monitoring fit the measurements made at observation, production and injector wells, the 2D monitoring system was extended to 3D in April 2012. The second challenge was quantify seismic amplitude variations in terms of petro-acoustic parameters. For that purpose 4D inversion was performed on continuous 2D and 3D seismic monitoring data in order to quantify the lateral and vertical expansion of the steam chest on a daily basis. The 4D inversion results not only point out that the inversion enables to quantify the 4D effects in terms of P-impedance variations, but also greatly improves the vertical location of these events. Moreover, the percentage of maximum impedance variations and the thickness where these variations are observed are in good agreement with the petro-elastic model.

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Structural information in subsurface seismic images is critical for reservoir delineation, reserve estimation, and well planning. However, by its very nature, it is also uncertain. One cause of the image uncertainty is the migration velocity model that directly affects the position of migrated events, both laterally and vertically. (The term "velocity" is meant in the broad sense; i.e., it also includes the anisotropy parameters.) We present a method that accounts for uncertainties in a velocity model estimated by tomography and translates them into the migrated domain. Standard-deviation attributes on target horizon positions or layer thicknesses are extracted. The method includes quality controls for validating the estimated uncertainty attributes before integration with other downstream or interpretative information. The method is demonstrated on a North Sea area covered by data from multiple seismic surveys. We observe that uncertainties increase with model complexity or depth and decrease as the illumination diversity increases. The computed uncertainty maps constitute a valuable source of information for hierarchizing (both qualitatively and quantitatively) different areas in the survey. For the purpose of reservoir risk analysis, we combine our technique with other information (e.g., interpretation uncertainties) to map how uncertainties in the depth of the structural spill point impact the gross rock volume (GRV) estimation of a reservoir.

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We present an overview of new and recent applications of multivariate geostatistical filtering techniques applied to 3D and 4D processing.

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.