Seismic velocity analysis for class II sands

Details Seismic velocity analysis for class II sands Seismic velocity analysis for class II sands

1999-12-16

2001-09-11

McElheny, Jr., Donald E. (Department: 2862)

Data processing: measuring, calibrating, or testing

Measurement system in a specific environment

Earth science

Reexamination Certificate

active

06289285

ABSTRACT:

This invention relates to seismic prospecting for oil or gas reservoirs, and more specifically it relates to data processing techniques applied in the velocity analysis of seismic signals which aid in detecting hydrocarbon bearing Class II sand formations.
BACKGROUND OF THE INVENTION
For many years seismic exploration for oil and gas reservoirs has involved the use of a source of seismic energy and its reception by an array of seismic detectors, generally referred to as geophones. When used on land, the source of seismic energy can be a high explosive charge electrically detonated in a borehole located at a selected point on a terrain, or another energy source having capacity for delivering a series of impacts or mechanical vibrations to the earth's surface. The acoustic waves generated in the earth by these sources are partially transmitted back from strata boundaries and reach the surface of the earth at varying time intervals, depending on the distance and the characteristics of the subsurface traversed. These returning waves are detected by the geophones, which function to transduce such acoustic waves into representative electrical analog signals. In use an array of geophones is generally laid out along a line to form a series of observation stations within a desired locality, the source injects acoustic signals into the earth, and the detected signals are recorded for later processing using digital computers where the analog signals are generally quantized as digital sample points, e.g., one sample every two milliseconds, such that each sample point may be operated on individually. Accordingly, seismic field records are reduced to vertical and/or horizontal cross sections which approximate subsurface features. The acoustic source and the geophone array are then moved along the line to a new position and the process repeated to provide a seismic survey. More recently, seismic surveys involve geophones and sources laid out in generally rectangular grids covering an area of interest so as to expand real coverage and enable construction of three dimensional (3D) views of reflector positions over wide areas.
After exploration of an area is completed, data relating to energy detected at a plurality of geophones will have been recorded, where the geophones are located at varying distances from the shotpoints. The data is then reorganized to collect traces from data transmitted at various shotpoints and recorded at various geophone locations, where the traces are grouped such that the reflections can be assumed to have been reflected from a particular depth point within the earth, i.e., a common midpoint (CMP). The individual traces are then corrected for the differing distance the seismic energy travels through the earth from the corresponding shotpoints, to the common midpoint, and upwardly to the various geophones. This step includes correction for the varying velocities through the rock layers of different types. The correction for the varying spacing of shotpoint/geophone pairs is referred to as “normal move out.” After this is done the group of signals from the various midpoints are summed. Because the seismic signals are of a sinusoidal nature, the summation process serves to reduce noise in the seismic record, and thus increasing its signal-to-noise ratio. This process is referred to as the “stacking” of common midpoint data, and is well known to those skilled in the art.
The seismic traces can be processed to provide various measures of the velocity at which seismic waves travel in the earth as a function of depth. The process of determining a velocity for use in the stacking of CMP gathers, or for more detailed velocity determinations, is referred to as velocity analysis. The desired stacking velocity is that velocity producing the maximum coherency in primary reflection data. Accordingly, velocity analysis is essentially a “trial and error” procedure to determine the time corrections necessary to align reflections recorded at a common midpoint with varying source-receiver distances (offsets). A velocity spectrum is a display of trial stacking velocities as a function of travel time with a contoured display of coherency for a particular location or a common midpoint location along the seismic line. The most commonly used measure of coherency is the semblance coefficient. Such semblance velocity spectra can be produced by techniques well known to those skilled in the art, and can be automatically produced using commercially available software.
The sole purpose of these data processing efforts is to facilitate the final and most critical phase of the seismic exploration method, namely, data interpretation, which is reduction of the data to a realistic model of subsurface strata illustrating both structural configurations and geologic characteristics.
Seismic interpretation involves prospecting for rocks that have both high and low porosity. Class II sands, which represents the transition between high and low porosity sandstone reservoir rocks, often have an anomalous amplitude versus offset response that includes a reversal of polarity in the field records, depending on whether shot/receiver pairs are closely spaced or far apart (i.e., near versus far offset). The problem today is that the above mentioned standard seismic velocity analysis and stacking programs using a convenient measure of coherence, e.g., semblance performed with a computer, are intentionally designed to remove any polarity reversals in the common midpoint depth gathers. Therefore, the evidence for existence of these Class II sands is often obscured or completely destroyed in standard seismic velocity analysis processing.
Accordingly, it is an object of this invention to generate seismic velocity analyses and to stack sections in such a manner as to accentuate the existence, position and quality of Class II sand polarity reversals.
It is a more exact object of this invention to distinguish hydrocarbon-bearing Class II sand formations from neighboring shales and limestones.
It is a further object to locate polarity reversals directly on vertical seismic data sections.
A still further object of this invention is to improve seismic amplitude-versus-offset (AVO) analyses by using velocities picked to accentuate a polarity reversal.
SUMMARY OF THE INVENTION
According to the present invention the foregoing and other objects and advantages are attained in a computer implemented method and apparatus for processing seismic trace data in a velocity analysis which contours measures of coherency for a seismic section having traces showing polarity reversal with offset. This velocity analysis is based on a sliding window correlation technique, and uses an extended crosscorrelation algorithm in determining a measure of multi-trace coherence.
In a preferred embodiment, the sliding window correlation technique first gathers CMP traces, applies a normal moveout correction to all traces in the gather, and then analyzes a few adjacent traces located in a subgroup of the gather for multi-trace coherency over a preselected time window. In this sliding window technique, a multi-trace coherency value is determined that is based on a crosscorrelation combined with an absolute amplitude difference value between the waveforms of the two traces being compared. This extended cross-correlation is referred to herein as an RB-algorithm. The sliding window is incrementally moved horizontally over the seismic section, and with each movement the analyzed subgroup is replaced with a new subgroup to be analyzed. The RB-algorithm for multi-trace coherency is applied at each increment. This process is continued until all of the horizontal subgroups of traces have been processed at the particular time window. The individual RB subgroup values are then summed to give a composite coherency value for that velocity and time window increment. The time window is then incremented downward to the next time step and the RB-algorithm is again applied to subgroups as described above until a desired number of time windows have been processed. Thi

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