Stochastic (or geostatistical) impedance inversion techniques have great potential in addressing key questions in reservoir characterization. They work at the vertical scale of reservoir models and, therefore, at higher resolution than the seismic data. They produce multiple equiprobable results which provide an assessment of, the uncertainties, and they are ideally suited for integrating non-seismic information in the inversion process. However, two issues have slowed the acceptance of stochastic impedance inversion techniques. First, there is suspicion of 'unconstrained random noise generators', which appear to offer extra information for free, and, secondly, managing and extracting value from multiple realizations is difficult. For these reasons, faster deterministic inversion approaches, resulting in a single lower-resolution impedance volume, with less quantified uncertainty, are more commonly used when building reservoir models.To address the first issue, we have developed ways of integrating 3D constraints from sedimentary modelling with the geostatistical impedance inversion method, since these two approaches bring complementary information on reservoir properties. The resulting high-resolution multiple realizations of impedance are combined with uncertainties from petrophysical regression analysis to produce multiple realizations of reservoir properties (e.g. porosity), and from each an estimate of total pore volume. We illustrate the benefits of this multiple realization work flow applied to data from a shallow marine siliciclastic reservoir. A comparison of the seismic/ sedimentologically constrained reservoir models with those constrained by well data only has demonstrated more accuracy and better control on the spatial variability of reservoir properties. In this example, however, adding more constraints results in a broader range of possible reservoir models and a more meaningful uncertainty assessment. We conclude that our models constrained by well data only were derived with unrealistic simulation parameters and an over-optimistic assessment of a priori uncertainty.

The Jenein Sud area is located in the south of Tunisia about 350 km from the Mediterranean Sea. Five successful wells have been drilled in the area and proved that structures containing various fluids are present and can be produced at economic rates. In this paper, the main challenges encountered in the area and how they were tackled is described. Pethrophysical parameters: Owing to diagenetic processes such as chloritisation, siderite cementation and quartz overgrowth, the porosity to permeability relationship is diffuse and cannot be consider for permeability estimation. Therefore, all available petrophysical datasets were integrated and clustered by using an appropriate rock typing concept, reflecting the influence of pore geometries on flow and storage. Sand distribution: Sand layers could not be correlated laterally due to the prograding environment encountered and the large distance between the drilled wells. To capture the uncertainties, a multitude of geological models was created, integrating available seismic data, outcrop studies and trends apparent from regional geology. Fluids: The individual structures consist of a large number of stacked sands. The fluids in the sands vary from dry gas to volatile oil. All the sands have individual hydrocarbon/water contacts. To determine the fluid composition of the layers and overall performance, MDT samples were taken from the sands and several production tests were performed. An overall Equation Of State model was used to describe the comingling of individual sand layers and to optimise the surface facility design. Small scale structures: To appraise the area, wells have been drilled in individual structures rather than appraisal wells into selected structures. The wells are used to prove sufficient hydrocarbons and the composition of the hydrocarbons. This information was used to select an appropriate surface facility design capable of handling the fluids and to optimise the production strategy of the area.

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Seismic technology advances and well established reservoir workflow industry practices have led to a more accurate description of subsurface through reservoir models. Seismic driven reservoir characterization is a dynamic process in which iterative calibration and interpretation of information contributes to geologically sound and quantitatively calibrated model. Whether the main goal of the reservoir characterization is structural or stratigraphic, or even a quantitative reservoir property calculation and distribution, it is essential to focus efforts that contribute to: (1) the quality of the input data; (2) the best acquisition/processing methods and, (3) the calibration and correction of seismic-well log data that contribute in lowering uncertainties.This presentation deals with the cons and pros in using seismic as main driver while building reservoir models. Three cases are presented Case 1: Seismic reservoir architecture of prograding events in a falling seal level stage.

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