Petrophysical Seismic Inversion to Determine More Accurate and Precise Reservoir Properties

Abstract

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.

Consolidating rock-physics classics: A practical take on granular effective medium models

“…proves to be a tough challenge for deterministic petrophysical inversions, such as the one proposed by Bornard et al (2005), which only provide a single estimate of rock properties that best match the recorded seismic data without quantification of the associated uncertainties. If statistical rock physics, through the simulation of a large number of possible scenarios, can help quantify these uncertainties, only the introduction of additional data (such as V S , velocity attenuation, or directional velocities in the case of an anisotropic medium) can effectively lead to a more accurate estimation of the rock properties.…”

Maximum likelihood classification for facies inference from reservoir attributes

“…When dealing with both P-and S-impedance values, a generalization accounting for several random functions is necessary: This is the aim of the algorithm presented in Section 3.2. Very often, inverted impedance maps are further interpreted in terms of porosity maps [3,7]. In this case, the observed random function U is the porosity field.…”

Preselection of Reservoir Models From a Geostatistics-Based Petrophysical Seismic Inversion

“…The reservoir model is then iteratively perturbed until the objective function is small enough. Seismic-matching techniques can refer either to inverted seismic data as acoustic impedances or to raw seismic data (Bornard et al 2005;Bosch et al 2009), thus skipping the first seismic-inversion step introduced previously.…”

“…The minimization of the objective function is driven by PSO instead of gradient techniques (Bosch et al 2009) or simulated annealing (Bornard et al 2005): We end up with a group of reservoir models instead of one (or a few single ones, when the optimization process is repeated from different starting points). In addition, the GDM is applied to vary the spatial distributions of the target petrophysical properties so that their two-order statistics (mean and covariance) are unchanged.…”