Bin constraints for generating a histogram of microseismic data

1. A method for processing microseismic data from a subterranean region, the method comprising:

identifying, by operation of a computer system, groupings of data points, the data points based on microseismic data from a subterranean region; and

constraining the identification of the groupings such that each grouping includes at least a minimum number of the data points, and such that the data points in each grouping have at most a maximum extent of variation, wherein:

identifying the groupings comprises:

identifying a first grouping containing the minimum number of data points;

adding additional data points to the first grouping as long as the data points in the first grouping have at most the maximum extent of variation when accounting for the additional data points; and

identifying subsequent groupings by an iterative process that includes, for each subsequent grouping:

identifying the subsequent grouping containing the minimum number of data points; and

adding additional data points to the subsequent grouping as long as the data points in the subsequent grouping have at most the maximum extent of variation when accounting for the additional data points;

the identified constrained groupings of data points generate, in real-time, updated fracture planes induced by a fracturing treatment; and

the fracturing treatment is modified, in real-time, by the updated fracture planes.

2. The method of claim 1 , wherein the data points represent basic planes, each defined by a coplanar subset of microseismic events and having an orientation relative to a common axis, and the method comprises identifying the basic planes from the microseismic data.

3. The method of claim 2 , comprising:

identifying the number of basic planes in each of the groupings; and

identifying a dominant fracture plane orientation based on the number of basic planes in one or more of the groupings.

4. The method of claim 1 , comprising generating a histogram based on the groupings, wherein each grouping corresponds to a respective bin in the histogram.

5. The method of claim 1 , wherein the iterative process includes, for each subsequent grouping:

if adding additional data points causes the subsequent grouping to exceed the maximum extent of variation when accounting for the additional data points, and

if removing one or more data points from the subsequent grouping decreases the extent of variation of the subsequent grouping when accounting for the additional data points, as compared to the extent of variation of the subsequent grouping without the additional data points,

adding the additional data points to the subsequent grouping while removing the one or more data points from the subsequent grouping.

6. The method of claim 5 , wherein the iterative process includes identifying a subset of the data points that cannot be added to any of the groupings without causing the grouping to exceed the maximum extent of variation.

7. The method of claim 1 , comprising defining, independent of the data points, the maximum extent of variation and the minimum number of data points.

8. The method of claim 1 , wherein the maximum extent of variation comprises a maximum standard deviation.

9. A computing system comprising:

data processing apparatus; and

memory storing computer-readable instructions that, when executed by the data processing apparatus, cause the data processing apparatus to perform operations comprising:

identifying groupings of data points, the data points based on microseismic data from a subterranean region; and

constraining the identification of the groupings such that each grouping includes at least a minimum number of the data points, and such that the data points in each grouping have at most a maximum extent of variation, wherein:

identifying the groupings comprises:

identifying a first grouping containing the minimum number of data points;

adding additional data points to the first grouping as long as the data points in the first grouping have at most the maximum extent of variation when accounting for the additional data points; and

identifying subsequent groupings by an iterative process that includes, for each subsequent grouping:

 identifying the subsequent grouping containing the minimum number of data points; and

 adding additional data points to the subsequent grouping as long as the data points in the subsequent grouping have at most the maximum extent of variation when accounting for the additional data points;

the identified constrained groupings of data points generate, in real-time, updated fracture planes induced by a fracturing treatment; and

the fracturing treatment is modified, in real-time, by the updated fracture planes.

10. The computing system of claim 9 , wherein the data points represent basic planes, each defined by a coplanar subset of microseismic events and having an orientation relative to a common axis, and the operations comprise:

identifying the basic planes from the microseismic data;

identifying the number of basic planes in each of the groupings; and

identifying a dominant fracture orientation based on the number of basic planes in one or more of the groupings.

11. The computing system of claim 9 , wherein the operations further comprises generating a histogram based on the groupings, wherein each grouping corresponds to a respective bin in the histogram.

12. The computing system of claim 9 , wherein the operations further comprises defining, independent of the data points, the maximum extent of variation and the minimum number of data points, and wherein the maximum extent of variation comprises a maximum standard deviation.

13. A non-transitory computer-readable medium storing instructions that, when executed by data processing apparatus, cause the data processing apparatus to perform operations comprising:

identifying groupings of data points, the data points based on microseismic data from a subterranean region; and

constraining the identification of the groupings such that each grouping includes at least a minimum number of the data points, and such that the data points in each grouping have at most a maximum extent of variation, wherein:

identifying the groupings comprises:

identifying a first grouping containing the minimum number of data points;

adding additional data points to the first grouping as long as the data points in the first grouping have at most the maximum extent of variation when accounting for the additional data points; and

identifying subsequent groupings by an iterative process that includes, for each subsequent grouping:

identifying the subsequent grouping containing the minimum number of data points; and

adding additional data points to the subsequent grouping as long as the data points in the subsequent grouping have at most the maximum extent of variation when accounting for the additional data points;

the identified constrained groupings of data points generate, in real-time, updated fracture planes induced by a fracturing treatment; and

the fracturing treatment is modified, in real-time, by the updated fracture planes.

14. The non-transitory computer-readable medium of claim 13 , wherein the data points represent basic planes, each defined by a coplanar subset of microseismic events and having an orientation relative to a common axis, and the operations comprise:

identifying the basic planes from the microseismic data;

identifying the number of basic planes in each of the groupings; and

identifying a dominant orientation based on the number of basic planes in one or more of the groupings.

15. The non-transitory computer-readable medium of claim 13 , wherein the operations further comprise generating a histogram based on the groupings, wherein each grouping corresponds to a respective bin in the histogram.

16. The non-transitory computer-readable medium of claim 13 , wherein the operations further comprise defining, independent of the data points, the maximum extent of variation and the minimum number of data points, and wherein the maximum extent of variation comprises a maximum standard deviation.