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Facies classification at the well location is generally based on sedimentological models, however, the extension of the classification of an individual well into the entire reservoir model is contingent on the calibration of a rock physics model that links rock and fluid properties with geophysical measurements such as seismic velocities and/or electromagnetic-derived resistivities. The goal of this work is to present a workflow to define a geologically consistent facies classification at the well location, to accurately reconstruct this classification using elastic and electrical properties, and to extend the classification to the 3D reservoir model. The initial classification at the well location is obtained using traditional statistical methods applied to computed rock properties such as mineralogical volumes, porosity, density and permeability. The facies reconstruction based on elastic/electrical properties is obtained using a Bayesian approach that combines rock physics with statistical models. The workflow is illustrated through the application to the Rock Springs Uplift field, Wyoming, which hosts several potential CO2 storage reservoirs.
Facies classification aims to assign a rock type or class to each location of a 3D reservoir model, based on the available rock and fluid properties. At the well location, the classification can be based on measured well log data, formation-evaluation computed curves, and core samples. However, far away from the well, most of these properties are not available and the classification must be derived from geophysical properties estimated from surface measurements, such as seismic and electromagnetic properties. Several methods for facies classification have been presented in literature (Doyen, 2007; Avseth et al., 2005; MacGregor, 2012). These methods generally differ from the mathematical approach (deterministic or statistical methods) and for the input data (core samples, well logs, or geophysical inverted attributes). The scale of core samples allows petrophysicists to generate a very detailed facies description; however, the extension of this classification to well log and to 3D reservoir models is difficult to achieve due to the lower resolution and data noise of well logs and surface geophysical measurements respectively. The aim of this work is to show that the integration of a rock physics model in the classification and the use of statistical methods for uncertainty quantification can overcome this limitation.
Estimation of reservoir properties and monitoring of fluid flow is an important modeling component in hydrocarbon exploration and production. Geophysical data (elastic and electrical) and reservoir rock properties can be linked via rock physics models. In this application, we develop a joint inversion approach of P-wave velocity and resistivity well logs for total porosity and total volume of fluid. We applied our inversion workflow to wireline data measured at the well location of the Rock Springs Uplift, Wyoming, USA, a potential CO2 storage reservoir. The inversion scheme is applied to the actual well dataset and to the same set of well logs filtered at lower resolutions. Results show that the rock and fluid volumes, or at least their trends in the case of filtered data, were predicted by the inversion scheme. Although the method was applied only at the well location, the inversion workflow can be extended to the entire reservoir model to predict the rock and fluid properties of the reservoir away from the well location.
One of the emerging technologies in geophysics is the stochastic inversion of geophysical data for the prediction of rock and fluid properties. The probability distribution of the geophysical properties of interest can be computed using a probabilistic inverse method. The integration of stochastic inverse methods and geophysical modeling allows generating multiple reservoir models of rock and fluid properties that honor the geophysical measurements. Stochastic approaches allow sampling multiple solutions from the posterior distribution of the model parameters and quantifying the uncertainty in the model parameter predictions. Stochastic inversion algorithms can be applied to seismic inversion problems as well as petrophysical inversion problems. In this work, we discuss analytical and numerical approaches, as well as their advantages and disadvantages.
Presentation Date: Monday, September 25, 2017
Start Time: 1:50 PM
Presentation Type: ORAL
Traditionally static reservoir models are obtained as a solution of an inverse problem where reservoir properties, such as porosity and lithology, are estimated from seismic data. With the emergence of time-lapse reservoir models, we can integrate static and dynamic reservoir properties in the seismic reservoir characterization workflow. Here, we propose a methodology to jointly estimate rock properties, such as porosity, and dynamic property changes, such as pressure and saturation changes, from time-lapse seismic data. This methodology is based on a full Bayesian approach to seismic inversion and can be divided into two steps. First we estimate the conditional probability of elastic properties and their relative changes, then we estimate the posterior probability of static rock properties and dynamic property changes. We applied the proposed methodology to a synthetic reservoir study where we created a synthetic seismic survey for a real dynamic reservoir model including pre-production and production scenarios. The final result is then a set of point-wise probability distributions that allow us to predict the most probable reservoir models at each time step and to evaluate the associated uncertainty.