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US 11,686,871 B2Granted
Stimulated rock volume analysis
AssigneeConocoPhillips Company
InventorsKevin T. Raterman, Helen E. Farrell, Kyle Friehauf, Raymond R. Reid, Jr., Ge Jin, Baishali Roy, Dana M. Jurick, Seth Busetti
Life of this patent
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Abstract
A data acquisition program, which includes core, image log, microseismic, DAS, DTS, and pressure data, is described. This program can be used in conjunction with a variety of techniques to accurately monitor and conduct well stimulation.
Claims23
A method of recovering hydrocarbons from a hydrocarbon-containing reservoir comprising:
- a) drilling at least one producer well;
- b) drilling at least one observation well;
- c) installing a plurality of sensors for distributed acoustic sensing, microseismic monitoring and a plurality of pressure gauges in each observation well;
- d) obtaining, microseismic, pressure, and DAS data from said observation well to form a pre-stimulation data set;
- e) while fracturing at least one producer well in a first fracture stimulation stage according to pre-determine fracturing parameters to form a set of fractures;
- f) obtaining, microseismic, pressure and DAS data from said observation well to form a stimulation data set;
- g) identifying said set of fractures formed in said fracturing step by comparing said pre-stimulation data set and post-stimulation data;
- wherein formation deformation is mechanically coupled with strain rate during hydraulic fracturing and formation compression through stress shadowing;
- h) characterizing the complexity of said set of fractures;
- i) updating said pre-determined fracturing parameters based on said characterizing step;
- j) performing a second fracturing stimulation stage;
- and,
- k) producing hydrocarbons.
10 more elements
A computer-implemented method for modeling the stimulated reservoir volume (SRV) of a hydrocarbon-containing reservoir, the method comprising:
- a) drilling at least one producer well into an area of said reservoir to be stimulated;
- b) drilling at least one observation monitoring well in said reservoir;
- c) installing a plurality of sensors for microseismic monitoring and a plurality of pressure gauges in each observation well;
- d) installing one or more fiber optic cables for Distributed Acoustic Sensing (DAS) in said observation wells, wherein said fiber optic cables are attached to interrogators;
- e) obtaining, before stimulation image log data, microseismic, pressure and DAS data from said observation well to form a pre-stimulation data set;
- f) while fracturing at least one producer well in a first fracture stimulation stage according to pre-determine fracturing parameters to form a set of fractures;
- g) obtaining during stimulation image log data, microseismic, pressure and DAS data from said observation well to form a pre-stimulation data set;
- h) identifying said set of fractures formed in said fracturing step by comparing said pre-stimulation data set and post-stimulation data;
- wherein formation deformation is mechanically coupled with strain rate during hydraulic fracturing and formation compression through stress shadowing;
- i) characterizing the complexity, dip, orientation, length, branching, or density of said set of fractures;
- and,
- j) modeling said SRV using said characterization of said set of fractures.
9 more elements
A computer-implemented method for modeling the stimulated reservoir volume (SRV) of a hydrocarbon-containing reservoir, the method comprising:
- a) drilling at least one producer well into an area of said reservoir to be stimulated;
- b) installing one or more fiber optic cables for Distributed Acoustic Sensing (DAS) in said observation wells, wherein said fiber optic cables are attached to interrogators;
- c) installing one or more fiber optic cables for Distributed Temperature Sensing (DTS) in said observation wells, wherein said fiber optic cables are attached to interrogators;
- d) obtaining one or more datasets for before stimulation log data, microseismic, pressure, DTS or DAS data to form a pre-stimulation data set;
- e) fracturing at least one producer well in a first fracture stimulation stage according to pre-determine fracturing parameters to form a set of fractures;
- f) obtaining during stimulation at least one of DAS or DTS data from one or more producer wells;
- g) monitoring DAS or DTS data during warmback to determine fracture spatial efficiency or volume distribution efficiency;
- h) identifying said set of fractures formed in said fracturing step by comparing said pre-stimulation data set and post-stimulation data;
- wherein formation deformation is mechanically coupled with strain rate during hydraulic fracturing and formation compression through stress shadowing;
- i) characterizing the complexity, length, branching, or density of said set of fractures;
- and,
- j) modeling said SRV using said characterization of said set of fractures.
9 more elements
Dependencies are inferred from the claim text.