WO2016140982A1 - Prédiction de comportement microsismique - Google Patents

Prédiction de comportement microsismique Download PDF

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Publication number
WO2016140982A1
WO2016140982A1 PCT/US2016/020293 US2016020293W WO2016140982A1 WO 2016140982 A1 WO2016140982 A1 WO 2016140982A1 US 2016020293 W US2016020293 W US 2016020293W WO 2016140982 A1 WO2016140982 A1 WO 2016140982A1
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WIPO (PCT)
Prior art keywords
stage
stimulation
microseismicity
candidate factor
pattern
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PCT/US2016/020293
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English (en)
Inventor
Simon H. Bittleston
Ian David Richard BRADFORD
Michael John Williams
Original Assignee
Schlumberger Technology Corporation
Schlumberger Canada Limited
Services Petroliers Schlumberger
Schlumberger Technology B.V.
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Application filed by Schlumberger Technology Corporation, Schlumberger Canada Limited, Services Petroliers Schlumberger, Schlumberger Technology B.V. filed Critical Schlumberger Technology Corporation
Publication of WO2016140982A1 publication Critical patent/WO2016140982A1/fr

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/40—Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B43/00—Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells
    • E21B43/25—Methods for stimulating production
    • E21B43/26—Methods for stimulating production by forming crevices or fractures
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/288—Event detection in seismic signals, e.g. microseismics
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/10—Aspects of acoustic signal generation or detection
    • G01V2210/12—Signal generation
    • G01V2210/123—Passive source, e.g. microseismics
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/10—Aspects of acoustic signal generation or detection
    • G01V2210/12—Signal generation
    • G01V2210/123—Passive source, e.g. microseismics
    • G01V2210/1234—Hydrocarbon reservoir, e.g. spontaneous or induced fracturing

Definitions

  • Embodiments of the present disclosure relate to a method of predicting microseismic behavior, the method being of the type that, for example, employs physical analysis techniques.
  • Embodiments of the present disclosure also relate to a multi-stage stimulation process microseismicity predication apparatus of the type that, for example, comprises a rules engine and a knowledge base in order to predict microseismic behavior.
  • microseismic analysis is in hydrocarbon exploration, hydrocarbon drilling, hydrocarbon production, hydrocarbon management and/or the like.
  • each well has a section located within hydrocarbon bearing formation(s), which is termed the lateral section, which is stimulated along a significant proportion of its length using multiple hydraulic fractures.
  • the operation of stimulation of the geological formation(s) surrounding a single predetermined length of the wellbore results in the creation of a "fracture stage".
  • the wells are stimulated using a "zipper frac" methodology in which fracture stages are interdigitated between two or more of the wellbores.
  • hydraulic fracturing involves the injection of a fluid, or a mixture of fluid and solid particles, the latter being known as proppant, into the surrounding geological formation via a wellbore with the intention of initiating fracture(s).
  • a fluid or a mixture of fluid and solid particles, the latter being known as proppant
  • proppant a fluid, or a mixture of fluid and solid particles, the latter being known as proppant
  • elastic waves are produced as the material in the path of fracture propagation, and surrounding the fracture, fails.
  • Such seismic events are detected and are referred to as microseismic events due to the low magnitude of sound typically emitted.
  • Hydraulic fracture monitoring is employed in underground oil and gas wellbores in order to provide an understanding of the geometry of the hydraulic fractures so as to enable: optimize well spacing, better completion and stimulation design, reliable production predictions, real-time operational decisions during the treatment or stimulation of the wellbore itself and/or the like.
  • seismic traces are recorded, which include both the longitudinal and transverse waves travelling through the formation. These traces are recorded at a number of locations, typically using one or more lines of receivers that may be disposed in one or more monitoring wells or at the surface. From these traces, it is possible to locate at least the origin of the microseismic event in space and time, and possibly also, depending on the geometry of the receivers, to determine the source characteristics of the microseismic event.
  • a number of techniques are known that use the determined locations of microseismic events in order to make operational decisions. Such decisions include the cessation of pumping in order to avoid excessive fracture height growth or avoidance of induced seismicity, amendment to the number of planned fracture stages, fracture placement, reduced/increased pumping and/or the like.
  • microseismic events in order to derive spatio-temporal evolution of source characteristics, derive geometrical features from microseismic event "clouds", such as pre-existing faults and newly created fracture conduits, characterize the nature of microseismic event clusters, refining or calibrating properties of some or all of a known set of geometrical features, and quantifying the perturbations to the stress state due to the appearance of, and/or change of state of, some or all of the set of geometrical features that are independently identified using microseismic data.
  • clouds such as pre-existing faults and newly created fracture conduits
  • a method of predicting microseismic behavior in a multi-stage stimulation process in respect of a geological formation comprises identifying a primary factor contributing to a type and pattern of observed microseismicity that is associated with a stimulation stage.
  • a candidate factor contributing to the type and pattern of the observed microseismicity associated with the stimulation stage is postulated.
  • a physical analysis technique is used to test the candidacy of the postulated candidate factor by analyzing the contribution of the postulated candidate factor to the type and pattern of the observed microseismicity.
  • Results of the prediction analysis may be displayed or sent to a processor controlling the stimulation process. Processing may be iterative with respect to a number of different postulated candidate factors and a most likely postulated candidate factor may be detremined from the iterative process.
  • the analysis technique may be a geophysical analysis technique.
  • the method may further comprise: repeating the identification of the primary factor in respect of another candidate factor; and generating a set of primary factors determined to contribute to the type and pattern of the observed microseismicity.
  • the method may further comprise: determining a sequence of the primary factors of the set of primary factors.
  • the method may further comprise: identifying a spatio-temporal pattern change in the observed microseismicity associated with the stimulation stage.
  • the method may further comprise: accessing pre-execution data associated with the multi-stage stimulation process; and postulating the candidate factor and/or testing the candidacy of the candidate factor using the pre-execution data.
  • the method may further comprise: accessing post-execution data and/or execution parameter data associated with execution of the stimulation stage of the multistage stimulation process; and postulating the candidate factor and/or testing the candidacy of the candidate factor using the post-execution data and/or the execution parameter data.
  • the post-execution data may comprise microseismic event data corresponding to observed microseismicity responsive to the stimulation stage.
  • the method may further comprise: accessing execution parameter data associated with execution of the stimulation stage of the multi-stage stimulation process; and postulating the candidate factor and/or testing the candidacy of the candidate factor using the execution parameter data.
  • the execution parameter data may comprises at least one of: a pump pressure; a pumping rate; and/or a pumping duration.
  • the method may further comprise: accessing formation type identification data associated with execution of the stimulation stage of the multi-stage stimulation process; and postulating the candidate factor and/or testing the candidacy of the candidate factor using the formation type identification data.
  • the postulation of the candidate factor contributing to the type and pattern of the observed microseismicity may comprise: determining the nature of a change to the observed microseismicity.
  • the determining whether the candidate factor contributes to the type and pattern of the observed microseismicity comprises: determining a position of the candidate factor in a sequence of influence of the primary factors.
  • the method may further comprise: determining in respect of the fracture stage a variation in at least one of: an event pattern; a rate; and/or a source characteristic.
  • the method may further comprise: determining a variation between fracture stages of at least one of: an event pattern; a rate of microseismic events; and/or a source characteristic.
  • Testing the candidacy of the candidate factor may comprise: using the physical analysis technique to quantify the effect of the candidate factor, the effect having a correlation with the observed microseismicity.
  • the method may further comprise: storing the primary factors identified.
  • the multi-stage stimulation process may be a hydraulic fracturing process.
  • a method of treating a geological formation in which a wellbore extends comprises the method of predicting microseismicity in a multi-stage stimulation process as set forth above in relation to the first aspect.
  • a multi-stage stimulation process microseismicity prediction apparatus comprising: a rules engine arranged to identify a primary factor contributing to a type and pattern of observed microseismicity associated with a stimulation stage of a geological formation, the rules engine having access to a knowledge base in order to: postulate a candidate factor contributing to the type and pattern of the observed microseismicity associated with the stimulation stage; and an analysis algorithm engine arranged to cooperate with the rules engine in order to determine whether the candidate factor contributes to the type and pattern of the expected microseismicity using a physical analysis technique.
  • Figure 1 is a schematic diagram of a hydrocarbon field, for example a gas field, having a formation being treated in accordance with some embodiments of the present disclosure
  • Figure 2 is a schematic diagram of a fracture analysis apparatus in accordance with some embodiments of the present disclosure.
  • Figure 3 is a schematic diagram of an architectural stack supported by the apparatus of Figure 2;
  • Figure 4 is a schematic diagram of application software in accordance with some embodiments of the present disclosure.
  • Figure 5 is a flow diagram of a method of predicting microseismic behavior in accordance with some embodiments of the present disclosure
  • Figure 6 is a flow diagram of a first part of a method of determining a change in nature of microseismicity as used in the method of Figure 5 in accordance with some embodiments of the present disclosure
  • Figure 7 is a flow diagram of a second part of the method of Figure 6;
  • Figure 8 is a chart of a distribution of microseismic events in respect of a first stage and a second stage of a treatment of a first wellbore and a second wellbore;
  • Figure 9 is a chart of distribution of microseismic events in respect of a third stage and a fourth stage of treatment of the first and second wellbores;
  • Figure 10 is a chart of distribution of microseismic events in respect of a fifth stage and a sixth stage of treatment of the first and second wellbores;
  • Figure 11 is a chart of distribution of microseismic events in respect of a seventh, eighth stage and ninth stage of treatment of the first wellbore and a seventh stage of treatment of the second wellbore;
  • Figure 12 is a screen shot of plots of z-component microseismic data generated by an array of geophones recording microseismic events, where the z-component is parallel to the trajectory of the first borehole;
  • Figures 13(a)-(c) are graphs of minimum moment magnitude versus b-value, distance versus moment magnitude, and moment magnitude versus probability;
  • Figure 14 is a flow diagram of a method of testing candidacy of a candidate factor employed in the method of Figure 5;
  • Figure 15 is a spatial plot of effective normal stress corresponding to the charts of Figures 8 to 1 1 and used with the method of Figures 5 and 14;
  • Figures 16 to 19 are a set of screen shots generated by a candidacy testing application with the method of Figures 5 and 14;
  • Figure 20 is a schematic diagram of a multi-stage stimulation process microseismicity prediction apparatus in accordance with some embodiments of the present disclosure.
  • similar components and/or features may have the same reference label.
  • various components of the same type may be distinguished by following the reference label by a dash and a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label.
  • the embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged.
  • a process is terminated when its operations are completed, but could have additional steps not included in the figure.
  • a process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination corresponds to a return of the function to the calling function or the main function.
  • the term “storage medium” may represent one or more devices for storing data, including read only memory (ROM), random access memory (RAM), magnetic RAM, core memory, magnetic disk storage mediums, optical storage mediums, flash memory devices and/or other machine readable mediums for storing information.
  • ROM read only memory
  • RAM random access memory
  • magnetic RAM magnetic RAM
  • core memory magnetic disk storage mediums
  • optical storage mediums flash memory devices and/or other machine readable mediums for storing information.
  • computer-readable medium includes, but is not limited to portable or fixed storage devices, optical storage devices, wireless channels and various other mediums capable of storing, containing or carrying instruction(s) and/or data.
  • embodiments may be implemented by hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof.
  • the program code or code segments to perform the necessary tasks may be stored in a machine readable medium such as storage medium.
  • a processor(s) may perform the necessary tasks.
  • a code segment may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements.
  • a code segment may be coupled to another code segment or a hardware circuit by passing and/or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, etc.
  • first and second features are formed in direct contact
  • additional features may be formed interposing the first and second features, such that the first and second features may not be in direct contact.
  • a hydrocarbon field 100 comprises a wellbore 102 having a vertical portion 104 and, in the example of so-called unconventional resource access, a plurality of lateral portions, for example a first lateral portion 106 and a second lateral portion 108 extending in a geological formation 1 10 known to contain hydrocarbon resources, for example gas, liquid or a combination thereof.
  • the first and second lateral portions are disposed in spaced relation to each other.
  • the formation 1 10 is treated by means of hydraulic stimulation in order to create hydraulic fractures.
  • a first hydraulic fracture stage 1 12 has been formed about the first lateral portion 106 and a second hydraulic fracture stage 1 14 has been formed about the second lateral portion 108.
  • a third hydraulic fracture stage 1 16 has been formed about the first lateral portion 106 and a fourth hydraulic fracture stage 1 18 has been formed about the second lateral portion 108.
  • a fifth hydraulic fracture stage 120 has been formed about the first lateral portion 106, but further stimulation is yet to occur, for example in respect of the second lateral portion 108, which would be to the left of the fourth fracture stage 1 18.
  • the treatment of the wellbore 102 is designed to create a so-called zipper fracture pattern between the first and second lateral portions 106, 108 by virtue of the first, third and fifth fracture stages 1 12, 1 16, 120 being longitudinally spaced along the first lateral portion 106 at predetermined locations and the second and fourth fracture stages 1 14, 1 18 being longitudinally spaced along the second longitudinal portion 108 at predetermined locations such that the first, third and fifth fracture stages 1 12, 1 16, 120 are interdigitated with the second and fourth fracture stages 1 14, 1 18.
  • a further vertical wellbore 122 is provided in which an array of geophones are disposed in order to detect microseismic events.
  • each hydraulic fracture created in the formation 1 10 results in microseismicity, i.e. the generation of microseismic events.
  • the wellbore 122 has been described as vertical, the skilled person will appreciate that alternative, non-vertical, wellbore trajectories are contemplated.
  • geophysical measurement devices can be employed at the surface 124, in shallow boreholes and/or in the borehole 102 to be treated.
  • the geophysical monitoring devices can be employed with a controlled, active, source located either in a shallow borehole, or in the borehole 102 and/or the further borehole 122 or on the surface 124.
  • the interpretation of the acquired data to characterize one or more fracture stages can use alternate data analysis techniques such as, but not limited to, the analysis of cross-well geophysical data, optionally combined with the analysis of the microseismic data.
  • Non-geophysical monitoring techniques can also be employed using, for example, tiltmeters, which measure deformation at the surface 124, in shallow boreholes and/or in the borehole 102 and/or the further 122.
  • Microseismic data generated by the array of geophones is provided to a data analysis center (not shown) that serves as a data processing environment, including providing for example a processing workstation constituting a hydraulic fracture analysis apparatus 124 ( Figure 2).
  • the fracture analysis apparatus 124 is used, as described later below, to interpret the various data, including microseismic data, collected and provided to the fracture analysis apparatus 124 in order to identify primary factors attributable to the microseismicity observed and/or expected in respect of the treatment of the formation 1 10 by stimulation of the formation 1 10. Such factors may be used to understand the activities of a stimulation engineer as well as predict the nature of the fractures produced.
  • the nature of the fractures refers, for example, to the fracture geometry and/or whether the fractures are hydraulically connected, for example where a set of fractures contain fluid that is able to move between the fractures, a set of fractures that are not hydraulically connected, and/or the location of proppant within the fractures.
  • the nature of the fractures is derived using the pattern of microseismic events, which refers to the distribution of microseismic events in space and time, possibly in conjunction with the style of the microseismic events, which refers to the deformation associated with a microseismic event, for example a deformation that is shearing and/or opening or closing.
  • the block diagram of the fracture analysis apparatus 124 is not inclusive of all components of the processing workstation, but is only representative of many example components.
  • the workstation is located within a housing (not shown).
  • the processing workstation can be, for example, a general- purpose computing apparatus, for example a Personal Computer (PC), or any other suitable computing device.
  • the fracture analysis apparatus 124 includes, in this example, a processing resource, for example a processor 200, coupled to an input device 202 via an input device interface (not shown) and a display device, for example a display screen 204 via a display driver (also not shown).
  • the input device 202 represents any number of input devices, including a keyboard device, mouse, trackball, voice input device, touch panel and/or any other known input device utilized to input information.
  • the display screen 204 can include any type of display screen, for example a Liquid Crystal Display (LCD).
  • the processor 200 supports a Graphical User Interface (GUI) that operates in conjunction with the input device 202 and the display screen 204.
  • GUI Graphical User Interface
  • the processor 200 is operably coupled to and capable of receiving input data from input device 202 via a connection 206, and operatively connected to the display screen 204 and optionally to an output device 208, via respective output connections 212, to output information thereto.
  • the output device 208 is, for example, an audible output device, such as a loudspeaker.
  • the processor 200 is operably coupled to a memory resource 214 via internal connections 216, for example address and data buses, and is further adapted to receive/send information from/to input/output (I/O) ports 218 via connection 220.
  • the memory resource 214 comprises, for example, a volatile memory, such as a Random Access Memory (RAM) and a non-volatile memory, for example a digital memory, such as a flash memory.
  • RAM Random Access Memory
  • non-volatile memory for example a digital memory, such as a flash memory.
  • a storage device for example a hard disc drive 222, or a solid state drive is also operably coupled to the processor 200 to provide high- capacity data storage capabilities.
  • the processor 200 of the fracture analysis apparatus 124 loads an operating system 230 from the memory resource 214 and/or the hard drive 222 for execution by functional hardware components 232, which provides an environment in which application software 234 can run.
  • the operating system 230 serves to control the functional hardware components 232 and resides between the application software 234 and the functional hardware components 232.
  • the application software 234 provides an operational environment including the GUI 236 that supports core functions of the fracture analysis apparatus 124.
  • the operational environment supports application software.
  • the performance of primary factor identification is described in the context of a desktop analysis where a human operator, for example an analyst, uses the application software 234 in combination with knowledge and experience to postulate candidates for the primary factors mentioned above and then to test these postulations in order to determine whether the candidate factors are, in fact, primary factors.
  • identification of a primary factor comprises postulating a candidate factor that contribute to the type and pattern of observed microseismicity for a given stimulation stage and then it is determined whether the candidate factor contributes to the type and pattern of observed microseismicity using one or more physical analysis techniques, for example a geophysical analysis technique. The candidacy of the candidate primary factor is thereby tested.
  • the application software 234 supported by the fracture analysis apparatus 124 comprises a plurality of candidate selection analysis applications 250.
  • the application software 234 also comprises a plurality of candidate testing applications 252.
  • Each of the candidate selection analysis applications 250 and the candidate testing applications 252 are capable of accessing with a database of microseismic data 254 stored by the hard disc drive 222 and a database of other treatment-related data 256 stored by the hard disc drive 222.
  • the application software 234 further comprises a factor sequence recordal application 260 that is capable of accessing a factor sequence store 258, which is also stored by the hard disc drive 222.
  • the factor sequence recordal application 260 can be any suitable application for recording the primary factors and a sequence thereof, for example a spreadsheet application, such as Excel ® available from Microsoft Corporation.
  • the candidate selection analysis applications 250 can be any suitable analysis application that implements one or more analysis techniques, which enables an analyst, using the fracture analysis apparatus 124, to identify one or more candidate factors.
  • any one more of the techniques set out in the disclosures referenced below can be employed, the contents of which are hereby incorporated by reference in their entirety.
  • the candidate testing applications 252 can be any suitable analysis application that implements one or more analysis techniques that can be used to confirm whether a candidate factor postulated using the candidate selection analysis applications 250 is indeed a primary factor and also the location in a sequence of primary factors associated with the treatment of the formation 1 10.
  • the database of microseismic data 254 stores microseismic measurement data corresponding to observed microseismic events.
  • the database of other treatment-related data 256 stores, for example data relating to the formation and/or engineering parameters, such as pump pressure data, pump rate data and/or pump duration data, fluid and proppant properties, fluid and proppant injection volume rates versus time, total fluid pumped versus time, total proppant pumped versus time, measurements from inside the treatment well, such as downhole pressure versus time and temperature, measurements made using fibre-optic sensors installed in and/or around the treatment well.
  • data relating to the formation and/or engineering parameters such as pump pressure data, pump rate data and/or pump duration data, fluid and proppant properties, fluid and proppant injection volume rates versus time, total fluid pumped versus time, total proppant pumped versus time, measurements from inside the treatment well, such as downhole pressure versus time and temperature, measurements made using fibre-optic sensors installed in and/or around the treatment well.
  • An analyst selects (Step 300) an analysis technique and selects (Step 302) a candidate selection analysis application 250 that can perform the analysis technique selected.
  • the analyst selects, in this example, an interpretation software application, such as the Petrel application (available from Schlumberger Limited) or the Matlab® application (available from The MathWorks, Inc.), as the application in order to plot and visually analyze a distribution of microseismic events.
  • the interpretation software application accesses the database of microseismic data 254, but it should be understood that other applications may require additional or alternative access to the database of other treatment-related data 256.
  • the interpretation software is therefore used to identify a number of candidate factors attributable to the observed microseismicity. Depending upon the application used, one or more candidate factors may be identified. In this respect, it is desirable to monitor the style and pattern of the microseismicity, and to this end it is necessary to identify the factor or factors which are likely to contribute to and hence influence the style and pattern of the microseismicity in fracture stages yet to be implemented. In order to achieve this goal, an analyst, using the analysis technique(s) selected, identifies (Step 304) changes to the nature of observed microseismicity and, where possible, the times at which the changes occur.
  • the interpretation software 400 accesses (Step 400) a first data set relating to a first given fracture stage, which may not necessarily be the first fracture stage performed, and an analyst, using the interpretation software 400, analyses the pattern of points defined by the microseismic data accessed, constituting an example of post (stimulation stage)-execution data, in order to determine (Step 402) whether, within the stage being analyzed, a change has taken place, for example a change in a spatio- temporal pattern of the observed microseismicity associated with the stimulation stage. In the event that a change has been detected, the change is logged (Step 404) by an analyst.
  • the interpretation application is used to analyze rate data in order to determine (Step 406) whether, within the stage being analyzed, the rate of occurrence of microseismic events has changed. In the event that a change is detected, the change is logged (Step 408) by the analyst. Thereafter, the interpretation software application is used to analyze the source characteristic data in order to determine (Step 410) whether, within the stage being analyzed, the source characteristic has changed.
  • the source characteristic data comprises information defining the motion during failure of the formation, for example shear, tensile opening or closure, and the extent of the motion.
  • the source characteristic data can be any suitable quantifiable descriptor that can assist in determining the type (style) of the microseismic event.
  • the change is logged (Step 412) by the analyst.
  • An analyst determines (Step 414) whether the database of microseismic data 250 contains data relating to subsequent fracture stages.
  • the database of microseismic data contains data relating to further fracture stages and so the data relating to the next fracture stage is accessed (Step 416) and, using the newly accessed microseismic data and the previously accessed microseismic data mentioned above, the interpretation application is used to analyze the respective patterns respectively defined by each of the current and preceding microseismic data and to determine (Step 418) whether, between the fracture stages, the patterns of microseismicity have changed. In the event that the patterns have changed, the change is logged (Step 420) by the analyst. Similarly, the interpretation application is then used to analyze the rate data for each of the preceding and current fracture stages being analyzed and to determine (Step 422) whether, between the stages, the rates have changed.
  • Step 424 the change is logged (Step 424) by the analyst. Thereafter, the interpretation software application is used to analyze the source characteristic data for each of the preceding and current fracture stages being analyzed and to determine (Step 426) whether, between the stages, the source characteristics have changed. In the event that the source characteristics have changed, the change is logged (Step 428) by the analyst.
  • Steps 418, 422, 426) have been described above in a specific order, it should be appreciated that the steps can be performed in any order deemed appropriate to the specific application.
  • the analysis relating to detecting changes within the fracture stage is also performed (Steps 402 to 412) before the analyst determines (Step 414) whether data relating to further microseismic stages is stored. In the event that further data is stored, the above-described analysis can be repeated.
  • the above-described example employed a data window bounded by a preceding and immediately succeeding fracture stage. In other examples, the data window can be widened.
  • the window should not be expanded so as to be too wide, for example greater than two fracture stages in width in a given temporal direction (it is conceivable to analyze data relating to a fracture stage temporally disposed between a number of other fracture stages; this is to provide context, so that changes can be seen but are not obscured by too much data).
  • Step 430 determines whether any changes in the nature of the observed microseismic data have been logged. Referring back to Figure 5, in the event that changes have been logged, an analyst uses knowledge of the changes logged to infer (Step 306) one or more candidate factors using knowledge and experience available to the analyst. An analyst then proceeds (Step 432, Figure 6) to test (Step 308) the candidacy of the candidate factors postulated. However, if no changes have been logged, an analyst proceeds to analyze (Step 434, Figure 6) engineering parameters.
  • the analyst can analyze the engineering parameters prior to testing the candidacy of the candidate and provide a prediction of distribution of the microseismic events that may be encountered upon execution of future fracture stage.
  • the analyst can also optionally provide, where circumstances permit, a prediction of the style of the microseismic events.
  • the analyst attempts to detect (Step 450) changes in engineering parameters, constituting execution parameter data.
  • the interpretation application can be used to analyze pumping pressure, pumping rate and/or pumping duration.
  • the interpretation application can be used to analyze time sequences of these parameters.
  • the change is logged (Step 452) along with any relevant time sequence information.
  • An analyst also attempts to determine (Step 454) any changes relating to the formation 1 10 being stimulated.
  • such changes relate to the formation 1 10 including changes to pre-existing fault properties of faults in the formation 1 10, for example changes to: the stress state of the formation, for example pore pressure, mechanical properties of the formation 1 10 (for example density and/or failure properties), flow properties (for example porosity and permeability); and changes to the properties of in-situ fluids.
  • the analyst logs (Step 456) the changes identified.
  • the changes in nature to the observed microseismicity are either diagnosed directly using microseismic data alone or microseismic data in combination with other types for data, for example data indicative of changes in engineering parameters and/or the nature of the formation 1 10.
  • Step 308 the analyst proceeds (Step 308, Figure 5) to test the candidacy of the candidate factors in order to quantify the effect of the candidate factor where the effect has a correlation with the observed microseismicity.
  • the above described method will now be further exemplified by an analysis being performed in the context of the zipper fracture of Figure 1 .
  • plots of microseismic events generated by the interpretation application used by the analyst can be used to analyze the evolution of observed microseismicity during stimulation treatments in a first representation 508 of the first lateral portion 106 and a second representation 510 of the second lateral portion 108 in accordance with the method of Figures 5 and 6 described above.
  • first fault 500 a first fault 500
  • second fault 502 a second fault 502
  • Information relating to the faults is stored in the database of other treatment-related data 256.
  • Microseismic data used is obtained from data collected in a monitoring well 506, which constitutes the further borehole 122 by, in this example, the 20 deepest levels of a so-called 71 x3C seismic array located in the monitoring well 506.
  • the earliest stage of the first lateral portion 508, which is nearest to the distal ends (sometimes referred to as "toes") of the wellbore 102 comprises substantially linear fractures of expected length.
  • fracture stages further from the toe of the first lateral portion 508 and located north of the first and third faults 500, 504 comprise shorter fractures that are geometrically less linear in nature than the fractures near to the toes of the wells.
  • microseismic activity decreases to the south, although such a decrease may be attributable to suggested declines in the number of located microseismic events with increasing distance between the sources of microseismic events and the seismic array located in the monitoring well 122.
  • qualitative changes to the observed microseismicity can be analyzed, for example as described above, it should be appreciated that such analysis can be accompanied by or replaced by quantifiable measures of changes in microseismicity, for example as described in the disclosures referenced below, the contents of which are incorporated herein by reference in their entirety:
  • the candidate factor identified by the analyst using the interpretation application is the nature of the formation 1 10, namely the first, second and third faults 500, 502, 504.
  • a different candidate selection analysis application 250 for example a Seisview application (available from Lynx Information Systems, UK), is used in conjunction with the interpretation software application in order to observe changes attributable to the influence of the fracturing process on the surrounding formation, for example, in-well processes.
  • the z- component observations from a Versatile Seismic Imager (VSI) tool monitoring in the first lateral portion 106 is presented by the Seisview application mentioned above.
  • the left hand side 550 of the observations are random in nature, for example having a Poisson-type type distribution, and an exponential-type distribution of amplitudes.
  • the right hand side of the observations are substantially regular in frequency and substantially constant in amplitude.
  • This change is caused by particulates in proppant blocking casing perforations, also known as near-well “screen outs”, which in this example constitutes the candidate factor identifiable by the analyst.
  • the change can be identified using b-value analysis ( Figure 13), namely analysis of the exponent, b, associated with a Gutenberg-Richter distribution, for example as described in "Reconstructing Frequency-Magnitude Statistics from Detection Limited Microseismic Data” (Williams, M. J. and Calvez, J.
  • each fracture stage can have a respective zone of influence around itself, for example a zone within the formation that is perturbed due, for example, to perturbations to the stress state and pore pressure of the formation.
  • Individual fracture stages can therefore be placed sufficiently far apart so that their respective zones of influence do not interact each other. Despite such spacing, such zones of influence can nevertheless interact with pre-existing features in the geological environment, for example fluid and/or solids invasion into the formation matrix or into pre-existing faults, and/or remote activation of pre-existing faults by stress or pore pressure change.
  • coupling effects can occur, for example pre-existing or newly created features can interact either directly or via zones of influence and, of course, with the geological environment.
  • the changes in the observed microseismicity identified by the analyst using the candidate selection analysis application(s) 250 now allow the candidate factors to be tested (Step 308) in order to determine which of the candidate factors are primary factors attributable to the microseismicity observed.
  • an analyst selects (Step 470) a first candidate from the candidate factors identified and then an analyst selects (Step 472) a candidate test analysis technique appropriate for testing the candidacy of the candidate factor selected.
  • the analyst selects (Step 474) a candidate testing application 252 capable of implementing the technique selected. Thereafter, the software application is executed in order to test the candidacy of the candidate factor selected.
  • the analyst selects analysis of stress as the technique (Step 470).
  • the analyst selects (Step 472) the Petrel application available from Schlumberger Limited to generate a map view ( Figure 15) of effective normal stress in the z (north) direction.
  • the analyst uses the candidate testing application 252 to overlay the locations of the first, second and third faults 500, 502, 504 and the first and second lateral portions 508, 510.
  • Figure 14 "Correlating The Distribution Of Microseismic Events With The Stress State For Multi-Stage Stimulations" (Bradford, I.
  • first, second and third faults 500, 502, 504 which occurs during the early stages of a zipper fracture, creates zones of higher compression, shown as darker shading.
  • the zones of higher compression influence the pattern of observed microseismicity during the later stages of the creation of the zipper fracture.
  • the first and third faults 500, 504 do not significantly influence the in-situ stress state at locations more than 1500ft (458m) to their north.
  • Stages further from the toe of the wellbore and north of the first and third faults 500, 502 generate shorter fractures and microseismicity does not occur in the higher compression zones north of the first and third faults 500, 504.
  • a fracture stage occurs directly between the first and second faults 500, 502, but its lateral extent seems to be suppressed by the stronger compression occurring in this area.
  • South of the second and third faults 502, 504, where there is some enhanced compression there is little microseismicity.
  • the analyst confirms that the selected candidate factor, namely the first, second and third faults 500, 502, 504 are, in fact, primary factors. Moreover, this analysis enables the analyst to determine when, in time, i.e. when in a sequence of factors during treatment of the formation 1 10, the primary factor influences the fracturing of the formation 1 10.
  • the analyst determines (Step 476) whether more candidate factors were identified during the previous stage of analysis, and if more candidate factors remain for testing, the analyst selects (Step 478) the next candidate factor and repeats the above-described procedure (Steps 472 to 476) using an appropriate analysis technique and candidate testing application 252.
  • the process of identification of candidate factors which includes postulation and testing candidacy, is repeated in order to generate a set of primary factors determined to contribute to the type and pattern of observed microseismicity.
  • the analyst selects another analysis technique and a candidate testing application 252 in order to perform a geomechanical modelling exercise in order to determine whether a candidate factor previously identified is, in fact, a primary factor.
  • the candidate factor is the evolution of failure zones in the formation 1 10 on neighboring fracture stages. This is a different factor to those described above, but would be apparent to the skilled person analyzing a given formation.
  • the analyst selects (Step 474) the Petrel Reservoir Geomechanics finite-element geomechanics simulator application available from Schlumberger Limited.
  • This candidate testing application 252 is used by the analyst to review the microseismicity and development of failure zones in the formation 1 10.
  • the analyst observes that in respect of a first stage of the first lateral portion 106 there is no microseismic activity in the circled regions 600, 602.
  • failure can be seen to be developing as a result of execution of the first stage of fracturing in respect of the first lateral portion 106.
  • the microseismic response associated with the first fracturing stage of the second lateral portion 108 suggests reactivation of a failure zone by this treatment, which is an example of the analysis revealing that a pre-existing fault and/or fracture can modify the stress state of the formation 1 10, bringing it nearer to failure or taking it further away from failure, which can impact upon the development of fractures in relation to the first lateral portion 106.
  • the geomechanical model for this stage Figure 19
  • the analyst concludes that the candidate factor of the evolution of failure zones on neighboring fracture zones influences the pattern of microseismicity, is in fact a primary factor.
  • a number of techniques and types of information for example formation type identification data, are at the analyst's disposal for testing the candidacy of the candidate factor, or indeed selection of the candidate factor.
  • the testing analysis allows the analyst to determine when in a sequence of primary factors this particular primary factor is of significance in the treatment of the formation 1 10, which is noted by the analyst.
  • a multi-stage stimulation process microseismicity prediction apparatus 700 comprises a rules engine 702 capable of accessing a knowledge base 704.
  • the rules engine 702 is arranged to execute rules in order to implement a decision-making process emulating the knowledge and experience of the analyst, thereby automating the above-described methodology.
  • the rules engine 702 is operably coupled to an analysis algorithm engine 706 and both the rules engine 702 and the analysis algorithm engine are capable of accessing a data store 708, which can include the database of microseismic data 254 and/or the database of other treatment-related data 256 mentioned above.
  • the analysis algorithm engine 706 is also capable of accessing a resource of algorithms 710 for execution.
  • the analysis algorithm engine 706 also comprises a pattern recognition engine 712.
  • the analysis algorithm engine 706, based upon instructions from the rules engine 702, executes appropriate analysis algorithms in order to contribute to the implementation of the methodology described above in relation to the previous desktop analysis based examples.
  • the rules engine 702 uses the knowledge base 704 in order to make decisions concerning postulation of candidate factors contributing to a type and pattern of observed microseismicity associated with a stimulation stage of a geological formation and testing of the candidacy of the candidate factors.
  • the rules engine 702 uses results generated by the analysis algorithm engine 706, including where appropriate analysis results generated by the pattern recognition engine 712, in order to make appropriate decisions concerning the postulation of the candidate factors and/or the testing of the candidacy of the candidate factors.
  • the above-described methodology can be employed as part of a process of treating a geological formation by stimulation where a wellbore extends into the formation.
  • the primary factors identified can thus be used in order adjust engineering parameters that contribute to the stimulation of the formation.
  • the identification of the primary factors allows potential observed microseismicity to be better predicted in respect of subsequent, unexecuted/proposed, fracture stages.
  • a well site with associated wellbore and apparatus is described in order to describe an embodiment of the application.
  • an apparatus at the well site can be altered due to field considerations encountered.
  • the term “storage unit” may represent one or more devices for storing data, including read only memory (ROM), random access memory (RAM), magnetic RAM, core memory, magnetic disk storage mediums, optical storage mediums, flash memory devices and/or other machine readable mediums for storing information.
  • ROM read only memory
  • RAM random access memory
  • magnetic RAM magnetic RAM
  • core memory magnetic disk storage mediums
  • optical storage mediums flash memory devices and/or other machine readable mediums for storing information.
  • computer-readable medium includes, but is not limited to portable or fixed storage devices, optical storage devices, wireless channels and various other mediums capable of storing, containing or carrying instruction(s) and/or data.

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Abstract

L'invention concerne un comportement microsismique dans un procédé de stimulation à plusieurs étages par rapport à une formation géologique qui est prédit par l'identification d'un facteur primaire contribuant à un type et un modèle de microsismicité observée associée à un étage de stimulation. L'identification consiste à poser comme principe un facteur candidat contribuant au type et au modèle de la microsismicité observée associée à cet étage de stimulation. L'identification consiste également à déterminer si le facteur candidat contribue au type et au modèle de la microsismicité observée à l'aide d'une technique d'analyse physique, ce qui permet de tester la candidature du facteur candidat.
PCT/US2016/020293 2015-03-05 2016-03-01 Prédiction de comportement microsismique WO2016140982A1 (fr)

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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108345033A (zh) * 2018-01-26 2018-07-31 中国石油大学(华东) 一种微地震信号时频域初至检测方法
CN112696190A (zh) * 2020-12-25 2021-04-23 重庆市能源投资集团科技有限责任公司 一种地下钻孔轨迹的测定方法及煤层压裂增透方法
CN114895352A (zh) * 2022-05-20 2022-08-12 北京科技大学 一种基于微震监测的岩体失稳预测方法及装置
CN117148434A (zh) * 2023-11-01 2023-12-01 煤炭科学研究总院有限公司 基于时序贡献图分解的微震信号自适应解算方法

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110188348A1 (en) * 2010-02-02 2011-08-04 Schlumberger Technology Corporation Method and apparatus for monitoring acoustic activity in a subsurface formation
US20120179444A1 (en) * 2007-01-29 2012-07-12 Utpal Ganguly System and method for performing downhole stimulation operations
US20130075086A1 (en) * 2011-09-27 2013-03-28 Frank PRESS Using microseismic activity to facilitate hydrocarbon production in tight sand and shale reservoirs
US20140358510A1 (en) * 2013-05-29 2014-12-04 Chevron U.S.A. Inc. System and method for characterizing uncertainty in subterranean reservoir fracture networks
US20140372089A1 (en) * 2011-11-04 2014-12-18 Schlumberger Technology Corporation Method of calibrating fracture geometry to microseismic events

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120179444A1 (en) * 2007-01-29 2012-07-12 Utpal Ganguly System and method for performing downhole stimulation operations
US20110188348A1 (en) * 2010-02-02 2011-08-04 Schlumberger Technology Corporation Method and apparatus for monitoring acoustic activity in a subsurface formation
US20130075086A1 (en) * 2011-09-27 2013-03-28 Frank PRESS Using microseismic activity to facilitate hydrocarbon production in tight sand and shale reservoirs
US20140372089A1 (en) * 2011-11-04 2014-12-18 Schlumberger Technology Corporation Method of calibrating fracture geometry to microseismic events
US20140358510A1 (en) * 2013-05-29 2014-12-04 Chevron U.S.A. Inc. System and method for characterizing uncertainty in subterranean reservoir fracture networks

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108345033A (zh) * 2018-01-26 2018-07-31 中国石油大学(华东) 一种微地震信号时频域初至检测方法
CN112696190A (zh) * 2020-12-25 2021-04-23 重庆市能源投资集团科技有限责任公司 一种地下钻孔轨迹的测定方法及煤层压裂增透方法
CN114895352A (zh) * 2022-05-20 2022-08-12 北京科技大学 一种基于微震监测的岩体失稳预测方法及装置
CN117148434A (zh) * 2023-11-01 2023-12-01 煤炭科学研究总院有限公司 基于时序贡献图分解的微震信号自适应解算方法
CN117148434B (zh) * 2023-11-01 2024-01-23 煤炭科学研究总院有限公司 基于时序贡献图分解的微震信号自适应解算方法

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