Potential challenges in reservoir characterization guided

Unfortunately, the quest to use seismic attributes in reservoir characterization has not always been a straightforward mission due to a number of key challenging points. The number of assumptions employed, the scale difference between seismic and well data, and the physical meaning of seismic attributes are among the most important challenges. The following discussion sheds light on these challenges that tend collectively to obscure the process of integration between seismic and well data.

“The rock-fluid system is so complicated that virtually all the theories for such a system have to make major assumptions to simplify the mathematics” (Wang, 2000). The various types of assumptions having major impacts on the results of seismic guided reservoir study can be classified into two main levels.

The first level of assumptions is related to the fundamental mathematics describing the interaction between seismic waves and rock-fluid response. As an example, Dewar (2001) explained six different fundamental assumptions used to constrain the equation of Gassmann’s. A full discussion regarding these assumptions can be found in Wang (2001). Other assumptions used in petrophysics, such as those used in Archie’s equation (Archie, 1942) to estimate water saturation, fall under this category of assumptions.

The second level of assumptions is more statistical. They are set to ensure the validity of the general statistical argument that is used to build a representative model by integrating well and seismic data.

In reservoir characterization studies, it is not always a plausible option to collect additional data. Therefore, it is generally assumed that the size and the quality of the collected data are sufficient to define a representative model.

In addition to a high signal-to-noise ratio, which is seen as a major prerequisite, Chambers and Yares (2002) stated that success in such studies depends on many factors related to the quality of seismic data, such as proper zero-phase seismic processing, true- amplitude recovery of seismic data, and sufficient frequency content at a reservoir level. On the other hand, it is also essential to assume that the available well data is considered as a representative subset of a larger reservoir population.

Scale related heterogeneities are the second main challenges faced in integrated- reservoir characterization study. The integrated data represent a collection of different sourced information that mainly includes seismic data, its attributes, well log data and other core-measured reservoir properties. Consequently, these data are collected at various scales. Figure 6 outlines the projected amount of variation in scale or frequency between the core derived measurements, well log measurements and seismic derived attributes.

The resolution of well logs and cores are less than 0.3 meters, while seismic resolution is often no better than 15 meters (Dewar, 2001). In terms of the frequency ranges, seismic data or seismic derived attributes are measured as functions of continuous frequency of (10–200 Hz), log data of (~ 10 kHz), and laboratory-measured properties of (100 kHz–2 MHz) frequency bands (Wang 2001).

The band-limited nature of the seismic data frequencies (typically of 10-80Hz) has a major impact on the sensitivity of seismic attributes. The low frequencies are usually missing from the seismic data. These frequencies are extremely important if a quantitative interpretation is claimed (Latimer et al., 2000). Finally, averaging a reservoir property and a selected seismic attribute over a delimited window adds a new

component of complexity to establish good correlation between seismic attributes and well data measurements.

Figure 6. Schematic wavelet of typical wavelength for a) laboratory measurement, b) sonic logging tool, and c) seismic data (after Yang and Stewart, 1997).

The third type of challenges facing a researcher in using seismic attributes is strictly related to seismic attributes and their physical meaning. Unfortunately, most of the seismic attributes, used in reservoir seismology, are not conclusive from geological or petrophysical point of views. Failing to establish solid relationships has led to more confusion in understanding these attributes and their use (Chen and Sidney, 1997). Among many reasons, this inherited ambiguity may be related to the fact that seismic attributes are not independent pieces of information but, they represent various ways to portray a limited amount of basic information or a subset of the total information (Barnes, 2001). The amount of information contained in a seismic attribute is not clear, and how this information are linked to or contained in other attributes is ambiguous. The

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confusion is simply attributed to the fact that a given seismic attribute can describe seismic data by quantifying specific data characteristics.

Recent estimates indicate current technology allows calculating several hundred seismic attributes (Chambers and Yares, 2002), which is seen as a major drawback. From an interpretational point of view, it can be said that the more seismic attributes available, the more difficult it becomes to select representative or appropriate attributes.

Furthermore, the vast number of available attributes has caused many of the seismic attributes to be duplicates of each other. Many different names describe the same information (Barnes, 2001). As an example, seismic amplitude can be computed in terms of its mean, rms, minimum, maximum, etc. A cross-plot of the mean and the rms amplitudes will not provide additional information. Additionally, extracting seismic attributes across a limited window may lead to the same effect. The minimum amplitude and the maximum negative amplitude attribute maps will be identical if we set a window across the central trough portion of the seismic signal.

The difficulty in establishing a physical linkage between seismic attributes and well measured reservoir properties, providing that they are statistically correlated, is another major hurdle to make sound generalizations. Few direct relations can truly be established between most of the available attributes and the physical or the geological properties (Chambers and Yares, 2002).

Acoustic impedance, the product of density and velocity across a given interface, is one of the important seismic attributes. It can be conclusively and meaningfully related to many reservoir properties like lithology, porosity and pore fill (Latimer et al., 2000). This unique criteria possessed by acoustic impedance is mainly attributed to the fact that acoustic impedance is considered as a layer or rock property, which makes this attribute very close in nature to all of the log measured reservoir properties (Latimer et al., 2000). On the other hand, seismic derived attributes are described as interface properties. Most of the seismic attributes are portrayed as interface attributes. As a result, the information contained in a seismic attribute reading across an interface is very condensed and delimited to the region defined by a definite sample interval.

In summary, recent knowledge gained from reservoir characterization studies proved the ability of seismic attributes to provide insight into the data. Despite their inherent ambiguities, experience showed that it is possible to associate certain seismic attributes with certain physical reservoir properties. While no precise relationships have been established between the majority of known attributes and the physical or geological characteristics of the earth, Taner (2001) explained that the power of seismic attributes is contained in their ability to be used as effective discriminators.