Tensor-based method for representation, analysis, and reconstruction of seismic data

• US 9,176,247 B2
• Filed: 08/17/2012
• Issued: 11/03/2015
• Est. Priority Date: 10/06/2011
• Status: Active Grant

• Current Assignee: Exxonmobil Upstream Research Company (Exxon Mobil Corporation)
• Original Assignee: Exxonmobil Upstream Research Company (Exxon Mobil Corporation)
• Inventors: Imhof, Matthias, Dimitrov, Pavel, Kaufhold, John P.
• Primary Examiner(s): Breene, John
• Assistant Examiner(s): Islam, Mohammad K
• Application Number:
  US13/588,780
• Publication Number:
  US 20130090857A1
• Time in Patent Office: 1,173 Days
• Field of Search: 702/2, 702/14, 702/17
• US Class Current: 1/1
• CPC Class Codes: G01V 1/30 Analysis G01V1/50 takes precedence
• CPC Class Codes: G01V 1/301 for determining seismic cross-sections or geostructures
• CPC Class Codes: G01V 1/345 Visualisation of seismic data or attributes, e.g. in 3D cubes
• CPC Class Codes: G01V 2210/63 Seismic attributes, e.g. amplitude, polarity, instant phase

Abstract

Method for representing seismic data as a spatially varying, second-order tensor field (23). The spatial relationships expressed in these tensors are exploited to link, classify, or separate neighborhoods (22); or to infer global or relational properties among them, thereby suggesting geobodies despite a noisy background. The tensors may be decomposed into their fundamentals that may either be used directly as derivative datasets or attributes, or may be used to facilitate linkage, classification, or separation of neighborhoods or analysis of linkage patterns. Decomposition may be by eigenvalues, with the eigenvalues used to define attributes called ballness, plateness and stickness (24). Alternatively, connections may be made between points in the data where the tensor has been computed, called tokens (21), based on tensor voting and polarity. Or, the connections may be based on a distance measure between tokens.

Claims

1. A computer-implemented method for analyzing a volume of seismic or seismic attribute data, either being referred to as “

the seismic data volume,”

to discover spatial location and shape of geologic features of a subsurface region, comprising;

making a tensor representation of the seismic data volume, and then grouping the data by a tensor voting method, wherein components of a tensor are computed, using a computer, to represent each of a plurality of selected spatial points, called tokens, in the seismic data volume;

making, with a computer, connections between tokens based on the tensor voting but also on a binary polarity attribute;

generating, with a computer, a visualization of the connections between the tokens in order to discover spatial location and shape of geologic features of the subsurface region; and

using the visualization of the connections between the tokens to locate hydrocarbons within the subsurface region.

2. The method of claim 1, wherein the binary polarity attribute is formed by giving each token a + or −

sign according to whether it corresponds to a peak or trough of a seismic data trace, and wherein the binary polarity attribute is used during or after the tensor voting process to support or cast doubt upon a proposed linkage between tokens, or before the tensor voting process to group the tensors before voting.

3. The method of claim 2, wherein a linkage between tokens of the same sign resulting from the tensor voting process is interpreted as indicating that their corresponding spatial locations belong to a common subsurface geological layer, and a linkage between tokens of opposite sign from the tensor voting process is interpreted as either signifying termination of a layer or a fault interrupting a layer.

4. The method of claim 1, wherein the tensor for each token is computed from seismic data associated with a selected spatial neighborhood of that token.