System and method for delineating spatially dependent objects, such as hydrocarbon accumulations from seismic data

• US 6,411,903 B2
• Filed: 05/21/2001
• Issued: 06/25/2002
• Est. Priority Date: 09/15/1998
• Status: Expired due to Term

• Current Assignee: Bush Seismic Technologies, LLC
• Original Assignee: Ronald R. Bush
• Inventors: Bush, Ronald R.
• Primary Examiner(s): McElheny, Jr., Donald E.
• Application Number:
  US09/862,138
• Publication Number:
  US 20010037178A1
• Time in Patent Office: 400 Days
• Field of Search: 702/14, 702/16, 702/17, 702/18, 702/13, 702/2, 367/73, 706/929, 703/10
• US Class Current: 702/14
• CPC Class Codes: G01V 1/30 Analysis G01V1/50 takes precedence
• CPC Class Codes: G01V 3/38 Processing data, e.g. for analysis, for interpretation, for correction
• CPC Class Codes: Y10S 706/929 Geological, e.g. seismology

Abstract

A neural network based system, method, and process for the automated delineation of spatially dependent objects is disclosed. The method is applicable to objects such as hydrocarbon accumulations, aeromagnetic profiles, astronomical clusters, weather clusters, objects from radar, sonar, seismic and infrared returns, etc. One of the novelties in the present invention is that the method can be utilized whether or not known data is available to provide traditional training sets. The output consists of a classification of the input data into clearly delineated accumulations, clusters, objects, etc. that have various types and properties. A preferred but non-exclusive application of the present invention is the automated delineation of hydrocarbon accumulations and sub-regions within the accumulations with various properties, in an oil and gas field, prior to the commencement of drilling operations.

Claims

1. An apparatus for performing the automated prediction of locations of hydrocarbon producing areas and non-producing areas directly from seismic data gathered in an area comprising:

a computer; and

software installed on the computer, wherein the software includes a neural network developed using seismic training data relating to one or more hydrocarbon producing areas and seismic training data relating to one or more hydrocarbon non-producing areas, and wherein the neural network is used to generate predictions of locations of hydrocarbon producing areas and hydrocarbon non-producing areas by applying the neural network to the gathered seismic data.

2. The apparatus of claim 1, further comprising a second computer for providing parallel processing of the software.

3. The apparatus of claim 1, further comprising a plurality of additional computers for providing parallel processing of the software.

4. A method for the automated prediction of locations of hydrocarbon producing areas and non-producing areas directly from seismic data gathered in an area comprising the steps of:

developing a neural network using seismic training data relating to one or more hydrocarbon producing areas and seismic training data relating to one or more hydrocarbon non-producing areas;

applying the neural network to at least a portion of the seismic data generate predictions of locations of hydrocarbon producing areas and hydrocarbon non-producing areas of the area; and

determining the types of hydrocarbons present in the predicted locations.

5. The method of claim 4, further comprising the steps of:

developing the neural network to distinguish sub-regions within hydrocarbon producing areas;

applying the neural network to at least a portion of the seismic data to distinguish sub-regions within the hydrocarbon producing areas; and

determining the types of hydrocarbons present in the sub-regions.

6. A system for predicting locations of hydrocarbon producing areas and non-producing areas from seismic data gathered in an area comprising:

a computer;

a neural network developed using seismic training data relating to one or more hydrocarbon producing areas and seismic training data relating to one or more hydrocarbon non-producing areas;

a storage area for storing the gathered seismic data; and

one or more software systems used for applying the neural network to the gathered seismic data to generate predictions of locations of hydrocarbon producing areas and hydrocarbon non-producing areas.

7. The system of claim 6, further comprising a plurality of computers for processing information in parallel.

8. The system of claim 6, wherein the predictions are generated in real-time as the seismic data is gathered.

9. The system of claim 8, wherein the seismic data is gathered using Vibroseis.

10. A method for the automated prediction of locations of hydrocarbon producing and non-producing areas directly from seismic data gathered in area comprising the steps of:

developing an algorithm that iteratively presents a set of seismic data relating to one or more hydrocarbon producing areas and seismic data relating to one or more hydrocarbon non-producing areas to a portion of the algorithm that has a goal of minimizing the error over all of the data by propagating the error value back after each iteration and performing appropriate adjustments to a function that takes on characteristics or patterns in the data;

terminating the algorithm after a sufficient number of iterations for the function to have taken on sufficient characteristics or patterns in the data; and

applying the function containing the characteristics or patterns to at least a portion of the seismic data to generate predictions of locations of hydrocarbon producing areas and hydrocarbon non-producing areas of the area.

11. The method of claim 10, wherein the method is performed in real-time as seismic data is gathered.

12. The method of claim 10, wherein the seismic data is gathered using Vibroseis.