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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.

This paper introduces a new permeability estimation method using the equivalent rock element model which consists of two pore components, one parallel and the other perpendicular to the potential gradient. Both formation resistivity factor and permeability are related to porosity and pore structure, but permeability also depends on pore scale and internal specific pore area. In water wet rocks, irreducible water saturation is a parameter closely related to pore size or scale and internal specific area. Irreducible water occupies space in both parallel and perpendicular pore components and hinders fluid passage. The relative volume occupied in each component varies with many factors such as pore scale and interconnection, fluid properties, mineralogy and wetting characteristics of the mineral/fluid system. This non-uniform occupancy is approximated by a simple power function. An efficient flow porosity is defined by modifying efficient electrical porosity using irreducible water saturation to account for scale and specific pore area. The correlation between efficient flow porosity and permeability is much greater than those in many of the commonly used methods. This method has been applied to several sets of cores from different parts of the world. Better correlations between measured and estimated permeability have been obtained in all datasets compared with other published approaches, such as porosity versus logarithm of permeability, Kozeny-Carman, Wyllie-Rose, and Timur methods. Introduction Permeability is one of the most critical reservoir properties and is probably one of the most difficult to accurately obtain for reservoir description. Permeability can be directly measured in core analysis or computed from well tests. Core analysis measures properties on a scale of centimeters. Well tests cover an area orders of magnitude larger, but its vertical resolution is much lower than desired. A possible linkage between core and well test analyses is to estimate permeability from properties measurable by well logs. For decades, many efforts have been made to estimate permeability using indirect measurements. Some follow statistical approaches1,2 such as neural networks to find correlations between permeability and various measurements. Others attempt to relate permeability with different properties through a physical model.3 These two approaches are sometimes integrated.4,5 Kozeny6 and Carman7 related permeability with porosity and surface area of grains exposed to fluid flow. They proposed a simple relationship that states permeability is directly proportional to the cube of porosity and inversely proportional to the square of pore surface area per unit volume of rock. For randomly packed spheres, permeability is estimated as:Equation 1 where k is permeability; f is porosity; and Ag is surface area of grains exposed to fluid flow per unit volume of solid material. The specific surface area is difficult to measure directly by conventional methods and is often determined from core sample analysis. Mavko and Nur8 suggested that Kozeny-Carman equation needs to be modified for porosity below percolation threshold. Wyllie and Rose9 proposed a modification to the Carman-Kozeny equation and substituted irreducible water saturation for specific surface area. They conjectured that grain surface area is approximately related to irreducible water saturation, Swir. Their work could be expressed as:Equation 2 where B is a coefficient related to hydrocarbon type and gravity; B' is a correction factor for data fitting. A more generalized Wyllie-Rose relationship is sometimes written as:Equation 3 where P, Q, and R are tuning parameters to be calibrated from the fit to core measurements.

simple physical model that reflects the two main components in a pore structure and accounts for the first order transfer effect. This ensures compliance with physical boundary conditions and increases the predictiveness.(2) The innovative inclusion of critical water saturation avoids the underestimation of water saturation at low water saturations. It links electrical measurements with capillary pressure measurements and allows the two observations to cross-validate and complement each other. (3) The proposed method is demonstrated to match core measurements from different rock and pore types with a single model. P p Pf PfEquivalent Rock Element Model in 2D view

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.