US 6,278,949 B1Granted

Method for multi-attribute identification of structure and stratigraphy in a volume of seismic data

OwnerM. Aftab Alaminventor

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Abstract

This invention discloses a method for rapid visual exploration of a volume of seismic data which does not require horizon picking or editing and yet displays all horizons with their stratigraphic features and lithologic variations in 3-D motion on a color graphics workstation. Seismic data is processed to generate multiple attributes at each event, location of a specified phase of the seismic trace, within the 3-D survey volume. Subsets of multiple attributes are interactively selected, thresholded and combined with one out of a suite of mathematical operators into a scalar function which is mapped onto a user selected color spectrum. Three dimensional features, where geological structure and stratigraphic compartments, including those that cannot be detected with conventional techniques, automatically emerge in the 3-D volume as the composition of attributes in the subset, threshold limits of attributes or the mathematical form of operator is interactively varied. By manipulating attribute volume on a graphical workstation, a user visually recognizes bodies of potential hydrocarbon reservoirs, isolates them, identifies their location, and builds a 3-D reservoir model from their structural, stratigraphic and lithologic variations.

Claims7

Dependent claims

A computer-aided method to explore, locate, and identify structural and stratigraphic features in the earth's subsurface, by processing digitized multi-dimensional seismic waveform data formatted as a plurality of time-scale or depth-scale traces wherein the data are gathered from a corresponding plurality of data measurement stations distributed in a grid pattern over a geographic region of survey, said method comprising the steps of:

a) identifying on each said trace a plurality of events, wherein an event is the occurrence of a specified phase of the waveform and associating each event with the location (x, y) of the trace in the grid and the time of occurrence ‘t’ of the event;
b) deriving temporal attributes for each event, wherein temporal attributes are parameters that characterize the waveform in a short time window containing the event and wherein said parameters include a subset that reconstructs an approximation to the input trace in said time window;
c) identifying in a small spatial subvolume that event on each laterally separated trace whose temporal attribute set is numerically most similar to the temporal attribute set of the event at the center of said subvolume;
d) estimating a local wavefront, wherein a local wavefront is an approximating surface that passes exactly through the time of the center event and best fits times of most similar events on laterally separated traces in said subvolume and further calculating normalized parameters of said local wavefront and normalized parameters of time misfit error statistics, for example, the mean and variance of time residuals averaged over most similar events;
e) estimating a local attribute surface for each attribute wherein said local attribute surface passes exactly through the attribute at the center event and best fits attributes of events that lie in closest proximity to said local wavefront in said subvolume and further calculating normalized parameters of said local attribute surface and normalized parameters of attribute misfit error statistics;
and
f) creating a superset of N attributes that include normalized attributes derived in steps b), d), and e), and associating said superset with the event location (x, y, t), thus mapping a 3-dimensional event into a point in N-dimensional attribute space.

4 more elements

A computer-aided method to explore, locate, and identify the shape and property variations of anomalous objects embedded in a complex opaque medium, by processing digitized multi-dimensional remotely sensed waveform data formatted as a plurality of time-scale or depth-scale traces wherein the data are gathered from a corresponding plurality of data measurement stations distributed in a grid pattern over a region of survey, said method comprising the steps of:

a) identifying on each said trace a plurality of events, wherein an event is the occurrence of a specified phase of the waveform and associating each event with the location (x, y) of the trace in the grid and the time of occurrence ‘t’ of the event;
b) deriving temporal attributes for each event, wherein temporal attributes are parameters that characterize the waveform in a short time window containing the event and wherein said parameters include a subset that reconstructs an approximation to the input trace in said time window;
c) identifying in a small spatial subvolume that event on each laterally separated trace whose temporal attribute set is numerically most similar to the temporal attribute set of the event at the center of said subvolume;
d) estimating a local wavefront, wherein a local wavefront is an approximating surface that passes exactly through the time of the center event and best fits times of most similar events on laterally separated traces in said subvolume and further calculating normalized parameters of said local wavefront and normalized parameters of time misfit error statistics, for example, the mean and variance of time residuals averaged over most similar events;
e) estimating a local attribute surface for each attribute wherein said local attribute surface passes exactly through the attribute at the center event and best fits attributes of events that lie in closest proximity to said local wavefront in said subvolume and further calculating normalized parameters of said local attribute surface and normalized parameters of attribute misfit error statistics;
and
f) creating a superset of N attributes that include normalized attributes derived in steps b), d), and e), and associating said superset with the event location (x, y, t), thus mapping a 3-dimensional event into a point in N-dimensional space.

4 more elements

Dependencies are inferred from the claim text.

Facts

Application: 09/369,464Filed 6 Aug 1999 · us provisional application
Priority: 25 Nov 1998
Granted: 21 Aug 2001
Term ends: Not estimatedThe source records don’t support an estimate.

Classification4 codes

G01V 1/34
Processing seismic data, e.g. for interpretation or for event detection: displaying seismic recordings or visualisation of seismic data or attributes
G01V 1/288
Processing seismic data, e.g. for interpretation or for event detection: event detection in seismic signals, e.g. microseismics
G01V 1/30
Processing seismic data, e.g. for interpretation or for event detection: analysis
G01V
Geophysics