1. Reservoir Modeling
Seismic data are routinely and effectively used to estimate the structure of reservoir bodies, but often play no role in the essential task of estimating the spatial distribution of reservoir properties. Reservoir property mapping is usually based solely on wellbore data, even when high resolution 3D seismic data are available. "Wellbore data" includes information typically obtained from a wireline log or core sample from an oil well and is typically the desired fine grain information for characterizing a "reservoir property."
Porosity, permeability, fluid and gas saturation, and other reservoir properties are measured at high accuracy near oil wells (e.g. by wireline logs), but these data do not assure reliable estimates of reservoir properties away from the wells. Seismic waves are not limited to wells, and seismic data may contain useful information about reservoir properties between the wells.
Processing of seismic data produces "seismic attributes" which may be effectively used to delineate the structure. See e.g., M. T. Taner, F. Koehler, and R. E. Sheriff, 1979, Complex Trace Analysis, Geophysics 44,1041-1063 and L. Sonneland, O. Barkved, and O. Hagness, 1990, Construction and Interpretation of Seismic Classifier Maps, EAEG meeting in Copenhagen. Seismic attributes are mathematical transformations on the data, computed either poststack (e.g., reflection intensity, instantaneous frequency, acoustic impedance, dip, azimuth) or prestack (e.g., AVO or moveout parameters). Well data are not used in their computation: attributes are purely properties of the seismic data alone. Otherwise, any analysis of the significance of seismic attributes to reservoir properties will be frustrated. Other seismic attributes include: acoustic impedance and velocity; reflection heterogeneity and instantaneous frequency; depth; dip and azimuth.
Specific seismic attributes may be related to specific reservoir properties. For example, acoustic impedance estimated from reflectivity by inversion of seismic data is an important seismic attribute. FIG. 13, shows cross sections of seismic data--reflectivity and acoustic impedance, together with wellbore data--porosity and water saturation. From FIG. 13, porosity does not appear directly related to the reflectivity, but it seems related to acoustic impedance--high impedance seems to imply low porosity. However, it is unclear how to actually use acoustic impedance to estimate porosity.
One problem in using seismic attributes is that their relation to rock properties is not obvious. For example, it is unclear how to use AVO to estimate gas saturation. Even if estimates are made, the confidence level in such estimates are unknown. There are unknown local factors that may affect the data in unexpected ways, and it is risky to predict functional relationships among seismic attributes and reservoir properties based on a simplified theoretical analysis with no familiarity of what "works" in a certain region. Region familiarity is built by comparing seismic and wellbore data. There is a need for interpretation methods and tools to build region familiarity, quantify its reliability, and subsequently use it to estimate properties. There is a need for a method to identify statistically-significant associations of seismic attributes and reservoir properties in any area, to determine the functional relationships implicit in these associations, to use them to predict the distribution of reservoir properties, and to quantify the reliability of the estimates.
2. Artificial Neural Networks
A great deal of recent research has been published relating to the application of artificial neural networks in a variety of contexts. See, for example, U.S. Pat. Nos. 5,134,685, 5,129,040, 5,113,483, and 5,107,442 (incorporated by reference). Artificial neural networks are computational models inspired by the architecture of the human brain. As a result three constraints are usually imposed on these models. The computations must be performed in parallel, the representation must be distributed, and the adjustment of network parameters (i.e., learning) must be adaptive. From an engineering perspective ANNs are adaptive, model-free estimators that estimate numerical functions using example data. While many different types of artificial neural networks exist, two common types are radial basis function (RBF) and back propagation artificial neural networks.