Apparatus, system and method for the efficient storage and retrieval of 3-dimensionally organized data in cloud-based computing architectures

• US 9,182,913 B2
• Filed: 10/17/2012
• Issued: 11/10/2015
• Est. Priority Date: 10/18/2011
• Status: Active Grant

• Current Assignee: Ubiterra Corporation
• Original Assignee: Ubiterra Corporation
• Inventors: Flanagan, Peter W.
• Primary Examiner(s): Wong, Leslie
• Application Number:
  US13/654,316
• Publication Number:
  US 20130097170A1
• Time in Patent Office: 1,119 Days
• US Class Current: 1/1
• CPC Class Codes: G01V 1/302 in 3D data cubes
• CPC Class Codes: G01V 1/345 Visualisation of seismic data or attributes, e.g. in 3D cubes
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/355 Class or cluster creation or modification
• CPC Class Codes: G06F 3/0611 in relation to response time
• CPC Class Codes: G06F 3/064 Management of blocks
• CPC Class Codes: G06F 3/067 Distributed or networked storage systems, e.g. storage area networks [SAN], network attached storage [NAS]
• CPC Class Codes: H04L 67/568 Storing data temporarily at an intermediate stage, e.g. caching

Abstract

A cloud based storage system and methods for uploading and accessing 3-D data partitioned across distributed storage nodes of the system. The data cube is processed to identify discrete partitions thereof, which partitions may be organized according to the x (e.g., inline), y (e.g., crossline) and/ or z (e.g., time) aspects of the cube. The partitions are stored in unique storage nodes associated with unique keys. Sub-keys may also be used as indexes to specific data values or collections of values (e.g., traces) within a partition. Upon receiving a request, the proper partitions and values within the partitions are accessed, and the response may be passed to a renderer that converts the values into an image displayable at a client device. The request may also facilitate data or image access at a local cache, a remote cache, or the storage partitions using location, data, retrieval, and/or rendering parameters.

Claims

1. A method of efficient data set management comprising:

partitioning a three dimensional data set into a plurality of related data sets;

generating a unique partition key for each of the plurality of related data sets, the unique partition key associated with one of a plurality of respective storage nodes to which a particular data set of the plurality of related data sets will be stored, the unique partition key based on a combination of integer division operations including inline value/denominator one and crossline value/denominator two, wherein the denominator one comprises a range of inline values and the denominator two comprises a range of crossline values;

generating a unique sub partition key for each data element within each of the plurality of related data sets, the unique sub-partition key based on a combination of integer division operations including inline value/denominator one and crossline value/denominator two, wherein the denominator one comprises a range of inline values and the denominator two comprises a range of crossline values; and

storing each of the plurality of related data sets into one of the plurality of respective storage nodes within a storage cloud.

2. The method of claim 1 wherein:

the plurality of related data sets comprises a plurality of groups of data sets with each group comprising a range of x-axis identifiers, a range of y-axis identifiers, and a plurality of values along a third axis, each group of data sets being stored into the plurality of respective storage nodes based on an association with one of the unique partition keys.

3. The method of claim 2 wherein each group of data sets comprises a set of seismic trace data from the three dimensional data set with the range of x-axis identifiers comprising a range of inline values of the seismic trace data, the y-axis identifiers comprising a range of crossline values of the seismic trace data, and the plurality of values along the third axis comprising reflection data at intervals wherein the intervals are time intervals or depth intervals.

4. The method of claim 2 wherein each of the unique partition keys is based upon a combination of the integer values of:

x-axis identifier/the range of x-axis identifiers and y-axis identifier/the range of y-axis identifiers.

5. The method of claim 4 wherein the x-axis identifier is an inline value and the range of x-axis identifiers is 32 and wherein the y-axis identifier is a crossline value and the range of y-axis identifiers is 32.

6. The method of claim 2 wherein the unique sub-partition key is based upon a combination of x % (the range of x-axis identifiers) and y % (the range of y-axis identifiers).

7. The method of claim 6 wherein x is an inline value, the range of x-axis identifiers is 32, y is a crossline value, and the range of y-axis identifiers is 32.

8. The method of claim 4 wherein each of the unique partition keys is based further on the combination with the integer values of z-axis identifier/the plurality of values along the third axis, and further comprising generating the unique sub-partition key using z % (the plurality of values along the third axis).

9. The method of claim 1 wherein each of the plurality of respective storage nodes is a virtual storage node.

10. A computing system comprising:

at least one server computing device configured to execute a plurality of executable instructions to perform the operations of;

partitioning a seismic data set into a plurality of data partitions, each of the plurality of data partitions comprising a group of related seismic trace data values;

generating a plurality of unique storage keys for the plurality of data partitions, each of the plurality of unique storage keys associated with one of a respective plurality of unique storage partitions, the plurality of unique storage keys based on a combination of integer division operations including inline value/denominator one and crossline value/denominator two, wherein the denominator one comprises a range of inline values and the denominator two comprises a range of crossline values; and

storing each group of related seismic trace data values in one of the respective plurality of unique storage partitions according to one of the plurality of unique storage keys.

11. The computing system of claim 10 wherein the plurality of executable instructions are further configured to provide the operations of:

receiving a request for a seismic data slice; and

obtaining seismic trace data values associated with the seismic data slice from one or more of the respective plurality of unique storage partitions using one or more of the plurality of unique storage keys.

12. The computing system of claim 11 wherein the seismic data slice is an inline slice, a crossline slice, a time slice, an arbitrary slice, or a complex surface.

13. The computing system of claim 11 wherein the plurality of executable instructions are further configured to provide the operation of:

passing the seismic trace data values associated with the seismic data slice to a renderer for rendering into an image.

14. The computing system of claim 13 wherein the seismic trace data values associated with the seismic data slice are rendered into the image based on one or more rendering parameters received in the request.

15. The computing system of claim 10 wherein each of the respective plurality of unique storage partitions are virtual storage partitions.

16. A method of efficient data set management comprising:

obtaining a data cube comprising a set of values three-dimensionally organized along a first axis, a second axis, and a third axis;

dividing the data cube into a plurality of discrete partitions, each of the discrete partitions comprising a plurality of values;

mapping each of the discrete partitions to a one-dimensional space to form a one-dimensional partition representation;

generatinq a plurality of unique storage keys for the plurality of discrete partitions, each of the plurality of unique storage keys associated with one of a respective plurality of discrete storage partitions, the plurality of unique storage keys based on a combination of integer division operations including inline value/denominator one and crossline value/denominator two, wherein the denominator one comprises a range of inline values and the denominator two comprises a range of crossline values; and

storing the plurality of discrete partitions in one of the respective plurality of discrete storage partitions based on an association of the one-dimensional partition representation with a plurality of storage nodes in a cloud storage.

17. The method of claim 16, wherein each of the discrete partitions is identified with a unique partition key, the plurality of discrete partitions being mapped to the one-dimensional space based on the unique partition keys.

18. The method of claim 17, wherein each of the unique partition keys is a concatenation of a first axis identifier and a second axis identifier, the first axis identifier comprising a range of the plurality of values along the first axis and the second axis identifier comprising a range of the plurality of values along the second axis.

19. The method of claim 18, wherein the range of the plurality of values along the first axis are a range of inline values and the range of the plurality of values along the second axis are a range of crossline values.

20. The method of claim 16, wherein the plurality of values for each of the discrete partitions are identified with a unique sub-partition key and stored within a corresponding storage node of the cloud storage based on the sub-partition keys.

21. The method of claim 16, wherein the data cube is divided into the plurality of discrete partitions based on a combination of integer division and modulus operations.

22. The method of claim 21, wherein the combination of integer division and modulus operations include a first axis value/denominator one and a first axis value % denominator one and a second axis value/denominator two and a second axis value % denominator two, the denominator one comprising a range of the plurality of values along the first axis and the denominator two comprising a range of the plurality of values along the second axis.

23. The method of claim 22, wherein the combination of integer division and modulus operations further include a third axis value/denominator three and a third axis value % denominator three, the denominator three comprising a range of the plurality of values along the third axis.

24. The method of claim 16, wherein the data cube is a seismic stack, the first axis is an inline axis, the second axis is a crossline axis, and the third axis is a time axis.

25. The method of claim 24, wherein the plurality of discrete partitions are columns and the plurality of values are seismic trace values.