Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Earth science
Reexamination Certificate
2000-06-02
2002-06-04
McElheny, Jr., Donald E. (Department: 2862)
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Earth science
Reexamination Certificate
active
06401042
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention relates to the general subject of reducing noise in seismic and seismic derived rock property data; deriving various new data from input seismic and seismic derived rock property data which highlight spatial changes in subsurface structure, stratigraphy, lithology and rock fluids; and to the analysis and interpretation of such data.
2. Description of the Prior Art
Seismic data is acquired to provide information about the subsurface structure, stratigraphy, lithography and fluids contained in the rocks. Acquired seismic data records are the response of a seismic energy source after passing through and being reflected by rocks in the subsurface. Seismic data can be acquired at or close to the earth's surface or can be acquired along boreholes. After acquisition, seismic data is typically processed to a set of seismic traces, where each trace represents the seismic response at a certain surface x,y location. The trace itself consists of a series of samples of the seismic response, usually ordered to correspond to increasing seismic travel time or, after depth conversion, increasing depth. Dependent on the acquisition geometry, the seismic traces are usually processed and organized to form lines with regularly spaced traces along the surface. The seismic data along such lines can be viewed as sections through the earth. Seismic data is referred to as 2D seismic data when the lines are in different directions or are far apart relative to the spacing of the traces. Seismic data is referred to as 3D seismic data when the acquisition is such that the processing results in a set of seismic lines that are organized sequentially and where the x,y trace locations form a regular grid and such that the spacing of the seismic lines generally is within the same order of magnitude as the spacing of the traces within the lines. In practice, the lines along which the data is acquired are called inlines and lines orthogonal to the inlines are referred to as crosslines.
FIG. 1
shows a seismic section with a number of seismic data traces taken from the 3D seismic data cube of which the x,y grid is shown in
FIG. 2.
2D and 3D seismic data sets are subsequently analyzed and interpreted, generally on computer workstations with specialized software, to reveal the subsurface structure, stratigraphy, lithography and fluids, and to so predict the location, structure, stratigraphy, lithology and fluid distribution of hydrocarbon reservoirs, associated aquifers and other subsurface features of interest.
FIG. 3
shows a structural interpretation of the seismic data of FIG.
1
. This interpretation delineates the overall reservoir zone, within which high seismic amplitudes correlate to oil sands. The interpretation also shows structural and stratigraphic relationships. Structural relationships typically relate to faulting, for example in
FIG. 3
the interpretation shows how the layers defined by the horizons are broken up by the faults. Stratigraphic relationships typically relate to deposition and erosion. For example an interpretation may show how an erosional surface truncates lower lying layers.
The amplitudes of the seismic data are primarily determined by the strength of the reflection of seismic waves at layer boundaries. The reflection strength in turn is determined by changes in certain physical parameters of the rocks when going from one layer to the next. These physical parameters are determined by the physical properties of the rock matrix, i.e. the rock with empty rock pores, and fluids contained in the pores, jointly referred to as ‘rock property data’. Changes in the rock matrix can be caused by changes in the lithology (rock mineral composition and build-up). Changes in fluids arise from changes in fluid type: water, oil and gas; or changes in properties of the fluid types. Using modern computer algorithms, rock property data can be estimated from the amplitudes of the seismic data. Rock property data which may be directly estimated from seismic data includes acoustic impedance, elastic impedance, pressure wave velocity, shear wave velocity and density. Further rock property data can also be drived directly or indirectly using functional, statistical or other relationships between the different rock properties. Seismic derived rock property data can be directly used to analyze changes in lithology and fluids in layers. Also, information about structure and stratigraphy is maintained and often even enhanced relative to seismic data. Use of seismic derived rock property data in subsurface analysis and interpretation is therefore often preferred over the use of seismic reflection data, or is done in conjunction with seismic data subsurface analysis and interpretation. For the same reason the subject method is preferably applied to seismic derived rock property data.
FIG. 4
shows the same section as
FIG. 1
, but now shows a section through an acoustic impedance rock property cube derived from the seismic reflection data. Changes in acoustic impedance result in changes of seismic reflection coefficients. In other words, acoustic impedance is a layer property whereas the seismic reflection coefficients relate to the layer interface. Analysis of the difference between the seismic data and the acoustic impedance data reveals that oil bearing sands and their boundaries can be more accurately interpreted from the acoustic impedance data than from the seismic data itself.
To characterize an interpreted horizon or fault plane the dip and azimuth may be calculated at each horizon point. As illustrated in
FIG. 5
, the dip at an horizon point is the angle from the vertical to the gradient vector of a plane tangent to the horizon surface at the horizon point. The azimuth is the angle of the projection of the gradient vector on a horizontal plane calculated clockwise generally relative to North.
One key aspect of seismic and seismic derived rock property data is that generally this data does not contain sufficient information to at each sample determine all the required data about the structure, stratigraphy, lithology and fluid at that sample. Additional information is provided by analyzing and interpreting spatial variations in the seismic and seismic derived rock property data. For example, from the character of a spatial change it can be determined if the change is due to a change in structure, e.g. a fault, or due to a change in lithology or in fluids. The problem is that the information about the spatial variations is often not easily discerned or readily quantified from the seismic or seismic derived rock property data. This motivates the need for methods which generate data which highlight spatial changes in subsurface structure, stratigraphy, lithology and fluids, and for methods to analyze and interpret such data.
Methods have been described which focus on calculating certain measures of spatial discontinuity using only seismic data. These methods do not utilize the information captured in an interpretation of the seismic data. The proposed method departs from existing methods by employing a subsurface model, based on an available interpretation, to drive the calculation of new types of measures of spatial changes in subsurface structure, stratigraphy, lithology and fluids. These measures are derived from changes in the amplitudes of seismic data or seismic derived rock property data along horizons. One such measure is the gradient of the amplitudes, for distinction with the gradient of a geometric surface, referred to as the property gradient. This property gradient is determined at each horizon point by fitting a surface through amplitudes at the horizon point and surrounding horizon points, and calculating the gradient of this surface. Large gradients correspond to rapid lateral changes. An alternative method to characterize the amplitude changes is by smoothing the amplitude data along the horizon by filtering, and then taking the difference of the filtered and input data as measure of the rate of change of the am
Tijink Paul Johannes Albertus
Van Riel Paul
Jason Geosystems B.V.
McElheny Jr. Donald E.
Webb Ziesenheim & Logsdon Orkin & Hanson, P.C.
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