# US 10,120,089 B2 · Identifying correlations between stimulated reservoir volume parameters and fracture network parameters > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,120,089 B2 - Document ID: US10120089B2 - Status: Granted - Kind: B2 - Application number: 15/129,768 - Filed: 2014-04-30 - Published: 2017-05-11 - Granted: 2018-11-06 - Earliest claimed: 2014-04-30 - Assignees: Halliburton Energy Services, Inc. - Applicants: Halliburton Energy Services, Inc. - Inventors: Jianfu Ma; Avi Lin; Baidurja Ray - Web page: https://patentgrep.com/patent/US10120089B2 ## CPC - G01V 1/288: {Event detection in seismic signals, e.g. microseismics (G01V1/36 takes precedence)} - G01V 2210/646: Fractures - G01V: GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS (means for indicating the location of accidentally buried, e.g. snow-buried, persons A63B29/02) ## Abstract In some aspects, a stimulated reservoir volume (SRV) parameter for a stimulation treatment applied to a subterranean region is identified. A parameter of a fracture-plane network generated by application of the stimulation treatment is identified. A correlation between the SRV parameter and the fracture-plane network parameter is identified. In some implementations, the SRV and the fracture-plane network are computed based on microseismic event data associated with the stimulation treatment. ## Claims ### Claim 1 A method of analyzing a stimulation treatment of a subterranean region, the method comprising: identifying, by operation of a computer system, a stimulated reservoir volume (SRV) parameter of a stimulation treatment applied to a subterranean region based on microseismic event data associated with the stimulation treatment; identifying, by operation of the computer system, a fracture-plane network parameter of a fracture-plane network generated by application of the stimulation treatment based on the microseismic event data; identifying a correlation between the SRV parameter and the fracture-plane network parameter; and using the correlation to analyze the stimulation treatment. ### Claim 2 (depends on 1) The method of claim 1, wherein the SRV parameter and the fracture-plane network parameter are associated with an individual stage of a multi-stage stimulation treatment of the subterranean region. ### Claim 3 (depends on 1) The method of claim 1, further comprising identifying a stimulation effectiveness of the stimulation treatment. ### Claim 4 (depends on 1) The method of claim 1, further comprising identifying a contribution of an individual stage of a multi-stage stimulation treatment based on a ratio of an effective SRV of the individual stage to a total effective SRV of the multi-stage stimulation treatment. ### Claim 5 (depends on 1) The method of claim 1, wherein the SRV parameter comprises a spatial extent of the SRV, the fracture-plane network parameter comprises a spatial extent of the fracture-plane network, and identifying the correlation comprises identifying a proportionality between the spatial extent of the SRV and the spatial extent of the fracture-plane network. ### Claim 6 (depends on 5) The method of claim 5, wherein the spatial extent of the SRV comprises a length or height of the SRV, and the spatial extent of the fracture-plane network comprises a length or height of the fracture-plane network. ### Claim 7 (depends on 1) The method of claim 1, wherein the SRV parameter comprises a width of the SRV, the fracture-plane network parameter comprises a number of fractures in the fracture-plane network, and identifying the correlation comprises identifying a correlation between the number of fractures and the width of the SRV. ### Claim 8 (depends on 1) The method of claim 1, wherein the SRV parameter comprises an orientation of the SRV, the fracture-plane network parameter comprises an orientation of the fracture-plane network, and identifying the correlation comprises identifying a correlation between the orientation of the SRV and the orientation of the fracture-plane network. ### Claim 9 (depends on 1) The method of claim 1, wherein the SRV parameter comprises a fracture complexity index, the fracture-plane network parameter comprises a probability distribution of orientations of the fracture-plane network, and identifying the correlation comprises identifying a correlation between a number of peaks of the probability distribution and the fracture complexity index. ### Claim 10 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 a stimulated reservoir volume (SRV) parameter of a stimulation treatment applied to a subterranean region based on microseismic event data associated with the stimulation treatment; identifying a fracture-plane network parameter of a fracture-plane network generated by application of the stimulation treatment based on the microseismic event data; identifying a correlation between the SRV parameter and the fracture-plane network parameter; and using the correlation to analyze the stimulation treatment. ### Claim 11 (depends on 10) The computer-readable medium of claim 10, wherein the SRV parameter comprises a spatial extent of the SRV, the fracture-plane network parameter comprises a spatial extent of the fracture-plane network, and identifying the correlation comprises identifying a proportionality between the spatial extent of the SRV and the spatial extent of the fracture-plane network. ### Claim 12 (depends on 10) The computer-readable medium of claim 10, wherein the SRV parameter comprises a width of the SRV, the fracture-plane network parameter comprises a number of fractures in the fracture-plane network, and identifying the correlation comprises identifying a correlation between the number of fractures in the fracture-plane network and the width of the SRV. ### Claim 13 (depends on 10) The computer-readable medium of claim 10, wherein the SRV parameter comprises an orientation of the SRV, the fracture-plane network parameter comprises an orientation of the fracture-plane network, and identifying the correlation comprises identifying a correlation between the orientation of the SRV and the orientation of the fracture-plane network. ### Claim 14 (depends on 10) The computer-readable medium of claim 10, wherein the SRV parameter comprises a fracture complexity index, the fracture-plane network parameter comprises a probability distribution of orientations of the fracture-plane network, and identifying the correlation comprises identifying a correlation between a number of peaks of the probability distribution and the fracture complexity index. ### Claim 15 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 a stimulated reservoir volume (SRV) parameter of a stimulation treatment applied to a subterranean region based on microseismic event data associated with the stimulation treatment; identifying a fracture-plane network parameter of a fracture-plane network generated by application of the stimulation treatment based on the microseismic event data; identifying a correlation between the SRV parameter and the fracture-plane network parameter; and using the correlation to analyze the stimulation treatment. ### Claim 16 (depends on 15) The computing system of claim 15, the operations further comprising identifying a stimulation effectiveness of the stimulation treatment of the subterranean region. ### Claim 17 (depends on 15) The computing system of claim 15, the operations further comprising identifying a contribution of an individual stage of a multi-stage stimulation treatment based on a ratio of an effective SRV of the individual stage to a total effective SRV of the multi-stage stimulation treatment. ### Claim 18 (depends on 15) The computing system of claim 15, wherein the SRV parameter comprises a width of the SRV, the fracture-plane network parameter comprises a number of fractures in the fracture-plane network, and identifying the correlation comprises identifying a correlation between the number of fractures and the width of the SRV. ### Claim 19 (depends on 15) The computing system of claim 15, wherein the SRV parameter comprises a fracture complexity index, the fracture-plane network parameter comprises a probability distribution of orientations of the fracture-plane network, identifying the correlation comprises identifying a correlation between a number of peaks of the probability distribution and the fracture complexity index, and the computing system comprises a display device operable to display a rose diagram representing the probability distribution.