Abstract

Today, the major challenge in reservoir characterization is integrating data coming from different sources in varying scales, in order to obtain an accurate and high-resolution reservoir model. The role of seismic data in this integration is often limited to providing a structural model for the reservoir. Its relatively low resolution usually limits its further use. However, its areal coverage and availability suggest that it has the potential of providing valuable data for more detailed reservoir characterization studies through the process of seismic inversion. In this paper, a novel intelligent seismic inversion methodology is presented to achieve a desirable correlation between relatively low-frequency seismic signals, and the much higher frequency wireline-log data. Vertical seismic profile (VSP) is used as an intermediate step between the well logs and the surface seismic. A synthetic seismic model is developed by using real data and seismic interpretation. In the example presented here, the model represents the Atoka and Morrow formations, and the overlying Pennsylvanian sequence of the Buffalo Valley Field in New Mexico. Generalized regression neural network (GRNN) is used to build two independent correlation models between; 1) Surface seismic and VSP, 2) VSP and well logs. After generating virtual VSP's from the surface seismic, well logs are predicted by using the correlation between VSP and well logs. The values of the density log, which is a surrogate for reservoir porosity, are predicted for each seismic trace through the seismic line with a classification approach having a correlation coefficient of 0.81. The same methodology is then applied to real data taken from the Buffalo Valley Field, to predict interwell gamma ray and neutron porosity logs through the seismic line of interest. The same procedure can be applied to a complete 3D seismic block to obtain 3D distributions of reservoir properties with less uncertainty than the geostatistical estimation methods. The intelligent seismic inversion method should help to increase the success of drilling new wells during field development.

Introduction

Reservoir characterization requires building a spatial model of the reservoir by using appropriate data gathered from previous studies. This spatial model is then used in flow simulators, which can predict reservoir performance. An accurate and reliable reservoir characterization study is indispensable in reservoir management. The major challenge in today's reservoir characterization is to integrate all different kinds of data to obtain an accurate and high-resolution reservoir model.

The concept of data analysis forms the basis of reservoir characterization. Uncertainty, unreliability, and large variety of scales due to the different origins of the data must be taken into consideration. Together with the immense size of the data sets that must be dealt with, these issues bring complex problems, which are hard to address with conventional tools. That's why unconventional computation tools have gained much interest in data analysis in recent years. Among those modern tools; intelligent systems, which mimic the mechanism of the human mind, are a way of dealing with imprecision and partial truth[1]. It should not surprise us that using intelligent systems in reservoir characterization studies has become a widely-used method in the petroleum engineering literature. Some previous intelligent reservoir characterization applications include, but are not limited to, synthetic log generation[2,3,4], permeability estimation from logs[5,6], and predicting bulk volume of oil[7].

Let us consider different types of data used in reservoir characterization: core samples provide very high resolution information about the reservoir (fraction of inches), while seismic data have a resolution in tens of feet, and well logs have in one of inches. Because of its low resolution, seismic data is routinely used only to attain a structural view of the reservoir. On the other hand, unlike core samples or well logs, which are only available at isolated localities of a reservoir, seismic data frequently provides 3D coverage over a large area. Because of this areal coverage, researchers have always aimed to use seismic data in reservoir description. Inverse modeling of reservoir properties from the seismic data is known as seismic inversion in the literature. The process presented in this paper includes modeling of the well logs from seismic data, which is also an inverse modeling process (Figure 1). This approach attracts a lot of interest and is very important because of the necessary shift from exploration to development of existing fields[8].

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