EP2414631A1 - Procédé de modélisation d'un réseau de fractures et de la croissance d'un réseau de fractures pendant la stimulation de formations souterraines - Google Patents

Procédé de modélisation d'un réseau de fractures et de la croissance d'un réseau de fractures pendant la stimulation de formations souterraines

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Publication number
EP2414631A1
EP2414631A1 EP10759553A EP10759553A EP2414631A1 EP 2414631 A1 EP2414631 A1 EP 2414631A1 EP 10759553 A EP10759553 A EP 10759553A EP 10759553 A EP10759553 A EP 10759553A EP 2414631 A1 EP2414631 A1 EP 2414631A1
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EP
European Patent Office
Prior art keywords
fracture
stimulation
simulated
fractures
computer
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP10759553A
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German (de)
English (en)
Inventor
Susan Petty
Matthew Clyne
Trenton Cladouhos
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Altarock Energy Inc
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Altarock Energy Inc
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Filing date
Publication date
Application filed by Altarock Energy Inc filed Critical Altarock Energy Inc
Publication of EP2414631A1 publication Critical patent/EP2414631A1/fr
Withdrawn legal-status Critical Current

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]

Definitions

  • the field of the invention relates generally to computer modeling systems.
  • the present invention is directed to a method for modeling fracture network, and fracture network growth during stimulation in subsurface formations.
  • Numerical models assist in the design of hydraulic stimulations used to enhance or develop the permeability of a natural fracture system.
  • the goal of the numerical modeling is to model multiple design scenarios and arrive at an optimal rate, pressure and volume for each stimulation.
  • a computer implemented method comprises receiving data comprising characteristics of a subsurface formation, generating simulated fractures based upon the characteristics of the subsurface formation, simulating stimulation of the simulated fracture by creating a plurality of injection points and stimulating from every injection point of the plurality of injection points simultaneously. Simulation results are output and displayed, the simulation results including at least one of fluid volume, fluid pressure, three dimensional geometry of a stimulated volume, potential permeability enhancement, and simulated seismic activity.
  • Figure 1 illustrates an exemplary computer architecture for use with the present system, according to one embodiment.
  • Figure 2 illustrates an exemplary shear dilation.
  • Figure 3 illustrates an exemplary fractured reservoir.
  • Figure 4 illustrates an exemplary process for generating a single fracture network realization within the present system, according to one embodiment.
  • Figure 5 illustrates an exemplary calculation process for modeling within the present system, according to one embodiment.
  • Figure 6 illustrates an exemplary architecture of the present system, according to one embodiment.
  • a computer implemented method comprises receiving data comprising characteristics of a subsurface formation, generating simulated fractures based upon the characteristics of the subsurface formation, simulating stimulation of the simulated fracture by creating a plurality of injection points and stimulating from every injection point of the plurality of injection points simultaneously. Simulation results are output and displayed, the simulation results including at least one of fluid volume, fluid pressure, three dimensional geometry of a stimulated volume, potential permeability enhancement, and simulated seismic activity.
  • the present system includes a software modeling tool that simulates the creation of an engineered reservoir or the enhancement of a naturally fractured low permeability reservoir.
  • the software modeling tool simulates the creation of the engineered reservoir by stochastically modeling naturally occurring fractures in a subsurface formation and then modeling the propagation of those fractures via hydraulic stimulation.
  • the model for use within the present system utilizes the fracture modeling algorithm described by Willis Richards et al. (Willis-Richards, J.. K. Watanabe, and H. Takahashi (1996), Progress toward a stochastic rock mechanics model of engineered geothermal systems, J. Geophys. Res., 1 O1 (B8), 17,481 - 17,496).
  • the fracture modeling algorithm approach proposes the use of several equations to model various facets of stimulation.
  • Original fracture modeling algorithm equations used in the present system include the following:
  • the fracture modeling algorithm approach can reasonably be considered a scoping tool that enables the rapid testing and evaluation of a large number of stimulation scenarios.
  • the output from the model allows the uncertainty in the stimulation process to be assessed and some key engineering decisions to be made, such as the potential variability in the stimulation fluid volume and the hydraulic and thermal performance of the reservoir.
  • the fracture modeling algorithm approach does not treat the stimulation as a dynamic, hydraulic process. Rather it considers a series of static assumptions of the pressure field within a rock mass. This simplification reduces the execution time for each realization dramatically and enables the investigation of a statistically meaningful number of realizations.
  • the present system includes three components:
  • the present system enables rapid testing and evaluation of a large number of different stimulation scenarios.
  • the present system accounts for the uncertainty inherent in trying to describe a natural rock mass system and aims to capture the approximate hydro-mechanical behavior of the fracture system during stimulation.
  • the present system does not. however, account for the dynamics of fluid flow in the fracture network, but instead models the stimulation in a s of static steps.
  • the present system can be utilized to produce estimates for water volume, pump rate, pumping pressure, and hydraulic horsepower to aid in mitigating risk inherit in stimulation.
  • Outputs of the present system include but are not limited to
  • EPM Equivalent Porous Medium
  • Stimulation fluid volume provides the range of fluid volumes and pressures that might be required to achieve the target stimulated volume and/or well separation. This aids the planning of the injection interval lengths, water supply, and scheduling of operations.
  • 3D reservoir geometry Captures the potential variation in 3D geometry of the stimulated volume, such as the tendency for upwards, downwards and/or asymmetric horizontal growth. This information helps in planning the subsurface and surface position of production wells, and in defining the stages in a multi-stage stimulation.
  • Circulation model input Provides a population of stimulated fracture networks that represent the variability in the outcome of the stimulation process. These can be used in an Equivalent Porous Medium (EPM) model to investigate circulation and long term thermal recovery.
  • EPM Equivalent Porous Medium
  • Simulated micro seismic clouds Provides a statistical estimate of the micro seismic event cloud that might be generated during the stimulation. This is useful in designing the resolution and sensitivity of the micro seismic monitoring system.
  • modeling using the present system, can also be a collaborative process. Utilizing Visual Basic to interact directly with Microsoft Excel, multiple fracture network databases generate and process all data with relative ease, for example. Monte Carlo simulation techniques are used to acquire vast amounts of reservoir data for finding best fits, means, medians, and averages for similar fracture network databases. Using these techniques, the present system accurately predicts the behavior of a given formation bod) under stimulation in the least amount of time possible.
  • the present system includes a user-friendly interface, incorporating user familiarity with Microsoft Excel and a friendly programming interface.
  • the present system includes data management (data input and output) and the tile structure of solutions that allow visualization and manipulation of the data in meaningful ways.
  • the present system creates accurate input meshes for use in TO UGH 2 modeling.
  • TOUGH2 is a general-purpose numerical simulation program for multi-phase fluid and heat flow in porous and fractured media. The inclusion of this output enables the dynamics of fluid flow in the fracture network produced by the present system to be analyzed.
  • the present system models the simultaneous stimulation of two or more wells.
  • the present system allows for the linear summation of the pressures within the separate stimulation boundaries from each injection well by updating the relative permeability tensor and stimulation boundaries in the model.
  • the present system defines no upper limit to the number of simultaneous injection points.
  • One objective of a stimulation is to enhance the permeability within a specified target rock volume (i.e. m 3 ) that will then form all or part of a subsurface reservoir. This volume is most frequently expressed in terms of the injector and producer well separation required to achieve the target circulation volume.
  • the primary stimulation design parameters are the fluid pressure and the fluid volume (i.e. flow rate and duration) that will achieve this stimulated volume.
  • Other stimulation parameters such as the fluid density and viscosity, also have an effect, but these are essentially additional controls on the pressure field and injected fluid volume.
  • assessments provided by the present system include:
  • Fluid volume i.e. flow rate and time
  • pressure required to achieve the target stimulated volume and/or well separation
  • forward modeling creates a first look at fracture network characteristics and a stress regime prior to generating a fracture model.
  • the data can be collected during operations, compiled from previous ventures, and summarized from coring and televiewer data. Fracture characteristics that are collected include orientation, size. spacing, and aperture. Stress state and rock mechanics are also collected. A numerical model representing the naturally occurring fractures is created.
  • Input parameters for fracture generation include data about the model region (e.g. size and center point), formation stresses and alignment, fracture classes (e.g. strike, dip, radius). and a number of models to generate.
  • fluid is injected into a formation at a pressure less than or equal to the minimum effective stress (s' min). Fluid migrates from the injection borehole through the fracture system causing the fractures to: a. Open elastically in a direction normal to the fracture surface (normal compliance), and b. If the pressure is sufficient to overcome the frictional strength of the fracture, the fracture will also fail in shear. During shearing the asperities (roughness) on the fracture surface result in an irreversible normal deformation known as "shear dilation.” The misalignment of the "saw-tooth” asperities acts as "self-propping" that holds the fracture surfaces apart.
  • the formation composition consists of a relatively impermeable matrix intersected by a network of interconnected faults, joints and fractures -hereafter termed "fractures.”
  • fracture stimulation input parameters include boundaries, injectors, and legs (legs determine accuracy of the pressure boundary growth).
  • Stimulation Design Uncertainties [0038] Uncertainties in stimulation modeling and design include:
  • Fracture distribution including the spatial distribution, size, orientation, apertures and mechanical properties of the individual fractures and fracture families:
  • Stimulation modeling is therefore considered a "data limited” problem, where the specific outcome of any stimulation cannot be accurately predicted beforehand.
  • the present stochastic stimulation design model is therefore useful in determining: 1 ) The range of fluid volumes and pressures that are required to achieve the target stimulated volume and/or well separation. Specifically the minimum, maximum and most likely fluid volumes required. This aids in the planning of the injection interval lengths, surface plant, water supply and scheduling of operations.
  • a population of stimulated fracture networks i.e. permeability fields that represent the variability in the outcome of the stimulation process. These are valuable in assessing the potential variation in the hydraulic performance during circulation, and in particular the potential for short circuiting. This is then taken into account by allowing for increasing the well spacing and/or open hole lengths.
  • the present system implements a stochastic model to provide the stimulation performance criteria described above as its primary output.
  • the present system generates a fracture growth model and computes seismicity.
  • the present system provides a statistical representation of the micro seismic cloud generated during stimulation and provides input into a flow model, such as TOUGH2, to evaluate the performance under circulation.
  • simplifications in the model include:
  • the present system includes three software modules:
  • output from the present system is converted to useful formats for further reviewing and modeling. Multiple fracture models are analyzed for determining error and means using Monte Carlo methods. Seismic output can be visualized for easy visual model verification. Validation and Verification
  • Fracture classes in the 8,000 km 3 region are modeled. Predictions of micro seismicity generated by the present system are compared to actual data. Sensitivity analysis identifies limits for field parameters for optimal stimulation design. Parameters
  • Input and output parameters of the present system include but are not limited to: [0053] Fracture generation Inputs: Run Parameters:
  • Termination Volume (stimulated volume matched)
  • Termination Length maximum boundary leg length reached
  • Termination Cycles Module has executed a particular number of "cycles '"
  • Model Volume total volume of model region as defined in first module
  • the present method and system also relates to apparatus for performing the operations herein.
  • This apparatus may be specially constructed for the required purposes, or il may comprise a general-purpose computer selectively activated or reconfigured by a computer program stored in the computer.
  • a computer program may be stored in a computer readable storage medium, such as, but is not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, and magnetic-optical disks, read-only memories (“ROMs”), random access memories (“RAMs”), EPROMs, EEPROMs, magnetic or optical cards, or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus.
  • ROMs read-only memories
  • RAMs random access memories
  • EPROMs electrically erasable programmable read-only memory
  • EEPROMs electrically erasable programmable read-only memory
  • magnetic or optical cards or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus.
  • FIG. 1 illustrates an exemplary computer architecture for use with the present system, according to one embodiment.
  • One embodiment of architecture 100 comprises a system bus 120 for communicating information, and a processor 1 10 coupled to bus 120 for processing information.
  • Architecture 100 further comprises a random access memory (RAM) or other dynamic storage device 125 (referred to herein as main memory), coupled to bus 120 for storing information and instructions to be executed by processor 1 10.
  • Main memory 125 also may be used for storing temporary variables or other intermediate information during execution of instructions by processor 1 10.
  • Architecture 100 also may include a read only memory (ROM) and/or other static storage device 126 coupled to bus 120 for storing static information and instructions used by processor 1 10.
  • ROM read only memory
  • a data storage device 125 such as a magnetic disk or optical disc and its corresponding drive may also be coupled to computer system 100 for storing information and instructions.
  • Architecture 100 can also be coupled to a second I/O bus 150 via an I/O interface 130.
  • a plurality of I/O devices may be coupled to I/O bus 150, including a display device 143, an input device (e.g., an alphanumeric input device 142 and/or a cursor control device 141 ).
  • the communication device 140 allows for access to other computers (servers or clients) via a network.
  • the communication device 140 may comprise one or more modems, network interface cards, wireless network interfaces or other well known interface devices, such as those used for coupling to Ethernet, token ring, or other types of networks.
  • Figure 2 illustrates an exemplary shear dilation.
  • fluid enters the fracture 201, applying force in the direction normal to the fractures face 202. If the stimulation pressure is great enough to overcome the friction on the face of the fracture, shearing will occur 203. As shearing occurs, the faces of the fracture will move from their original position 204 where the fracture is very tight, to their new position 205.
  • the increase in the fracture ' s aperture 206 is known as shear dilation, and is a lasting effect.
  • Figure 3 illustrates an exemplary fractured reservoir.
  • the reservoir in a subsurface formation 301 exists in the gaps between relatively impermeable masses 302. It is easiest to understand when simplified in the manner of Figure 3.
  • the matrix represents the relatively impermeable mass bodies, and the fractures are represented by the spaces between.
  • FIG. 4 illustrates an exemplary process for generating a single fracture network realization within the present system, according to one embodiment.
  • a process for generating a single fracture network realization 400 begins with loading a statistical description of the fracture network including strike, dip, radius, and mechanical properties 401 .
  • Deterministic fracture planes are added to the model 404 and a new random seed is selected 405.
  • a new stochastic fracture is generated 406 and the cumulative fracture surface area is calculated 407.
  • the equivalency of the model fracture density and the observed fracture density is tested 408 and if they are not equal the process 400 returns to generating a new stochastic fracture 406.
  • a fracture aperture scaling factor ( ⁇ ) is estimated to match in-situ permeability 409. Fracture aperture distribution is evaluated under undisturbed conditions 410 and the process is terminated and results are output 41 1 . [0067] The fracture generation process continues until the fracture density within the modeled region matches field observations. The fracture density is estimated by summing the total fracture area generated within the model (Spr2) and dividing by the total model volume (Vm Lx x Ly x Lz).
  • the initial fracture apertures are calibrated against the measured (or assumed) undisturbed permeability of the system. This process uses a scaling factor ⁇ . which is derived under the reasonable assumption of an approximately parallel plate fracture distribution.
  • ⁇ is derived under the reasonable assumption of an approximately parallel plate fracture distribution.
  • specific deterministic (i.e. known or assumed) fractures are added to the model. This addition is useful for incorporating:
  • Figure 5 illustrates an exemplary calculation process for modeling within the present system, according to one embodiment.
  • a network of circular fractures is generated by randomly sampling the defined distribution of fracture location, orientation, radius, in-situ aperture and mechanical properties. Fractures continue to be generated within the model volume until the total number of fractures matches the known or estimated fracture plane density. For a 3D representation of the fracture network this can be quantified in terms of the mean facture surface area per unit volume of rock.
  • the overpressure within the open hole section is assumed to be constant during the entire stimulation operation.
  • the stimulation proceeds in a series of discrete spatial steps, through which the boundary of the stimulation propagates out through the fracture network.
  • An exemplary calculation process 500 begins with loading a fracture network database and stress field 501. Injection points, pressure, and backstress are initialized 502 and the fracture database is sorted by increasing distance between injection point and fracture center 503.
  • the stimulation boundary is initialized or updated 504 and a fracture is selected from the sorted list 505.
  • the fracture is tested, whether it is within the current stimulation boundary 506. If it is not, another fracture is selected 504 and the process 500 continues. I f it is within the current stimulation boundary 506, the pressure and stresses acting on the fracture surface are calculated 507.
  • the new fracture's apertures are calculated 508, including compliance, shear, and jacking contributions.
  • the local and average reservoir backstresses are updated 509, and the fracture aperture is tested for significant change in the iteration 510. If the fracture aperture has changed significantly, then the process 500 returns to calculate the pressure and stresses acting on the fracture surface 507 and continues. If the fracture aperture has not changed significantly, the list is checked for more fractures 51 1.
  • STOP criterion include:
  • the mechanical deformation is calculated for all fractures contained within the current stimulation volume at each step in the calculation process 500. This includes changes in fracture aperture due to normal compliance, shearing and also jacking, which is tensile opening at zero effective stress. Every stimulated fracture contributes to an average elastic backstress, which is a compression of the reservoir due to the sum of all additional fracture apertures. This backstress is used to correct the principal stress components and fracture apertures, such that they are in equilibrium.
  • an apparent permeability tensor is updated after every step in the calculation process 500. This describes the relative improvement in conductivity in all directions within the 3D reference frame.
  • the permeability tensor is used to define the stimulation boundary for the next stimulation step. The extent of the stimulation boundary in any direction is directly proportional to the relative permeability.
  • the growth of the present stimulation mimics the way in which actual stimulations are controlled by the interaction of the fracture network and stresses.
  • Sorting the fracture database by the distance from the injection point means that the stimulation progresses in a logical fashion away from the injection borehole.
  • time can be saved by ignoring all fractures in the list after a fracture is determined to be beyond the longest leg of the stimulation area. This is particular! ⁇ effective in decreasing computation in the early iterations of the stimulation module as the stimulation boundaries are relatively small compared to the total volume.
  • the stresses acting on the individual fracture surfaces are resolved and then the deformation is calculated, including testing for shearing and shear dilation. Any change in fracture aperture results in a change in the backstress acting on the fracture itself and also in the overall backstress generated by the inflated fracture system.
  • the reservoir backstress converges to a stable value and the requirement for iteration is significantly reduced. At this point, computation power can be conserved by adopting a static value for backstress.
  • the calculation process 500 updates the relative permeability tensor and the stimulation boundary.
  • Several approaches have been adopted ranging from a regular (i.e. ellipsoidal) boundary increment described by described by Willis Richards et al. (Willis-Richards, J., K. Watanabe, and H. Takahashi (1996), Progress toward a stochastic rock mechanics model of engineered geothermal systems, J. Geophys. Res., 101 (B8), 17,481-17,496) to a non-uniform envelope described by Kohl and Megel (Kohl T. and Megel T., 2005 "Numerical modelling of hydraulic stimulations at Soultz-sous-Forets").
  • the non-uniform approach is chosen as it allows for asymmetric growth of the stimulation, and hence is a more realistic representation of spatial uncertainty.
  • the non-uniform boundary can introduce significant computational complexity as it requires the tracking of an asymmetric boundary condition and the testing of whether the latest fracture falls within the boundary.
  • the complexity of this approach has been mitigated some by the approach adopted by this method.
  • the boundary at each injection point is represented by an assignable number of "spider legs" which can be incremented in length separately.
  • each fracture is assigned to the leg which it is closest too, and only has to test whether its distance from the injection point is less than the length of the leg to determine if it falls within the boundary.
  • the increase in leg length is determined by evaluating the degree to which fractures in the leg ' s boundary are stimulated and their orientation relative to the vector of the leg.
  • FIG. 6 illustrates an exemplary architecture of the present system, according to one embodiment.
  • a server 601 is in communication with a network 603, and a database 602 is in communication with the network 603.
  • a client device 604, having modeling software 605 installed thereon, is in communication with the network 603.
  • the client device 604 receives input 606.
  • the input 606 can be received from a microseismic monitoring system as well as from pump trucks and other instrumentation through an I/O system, according to one embodiment.
  • the client device can also compare output from the modeling software 605 to the input 606 and control a stimulation process accordingly.
  • the server 601 has modeling software installed thereon as well.
  • the client device 604, server 601 , and database 602 have architectures similar to that described in Figure 1 , according to one embodiment.

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Abstract

L'invention concerne un procédé de modélisation d'un réseau de fractures et de la croissance d'un réseau de fractures pendant la stimulation de formations souterraines. Dans un mode de réalisation, le procédé mis en œuvre par ordinateur comprend les étapes consistant: à recevoir des données comprenant des caractéristiques d'une formation souterraine; à produire des fractures simulées sur la base des caractéristiques de la formation souterraine; à simuler une stimulation des fractures simulées par la production d'une pluralité de points d'injection; et à produire une stimulation simultanée à partir de chaque point d'injection de la pluralité des points d'injection. Les résultats de simulation sont produits et affichés, les résultats de simulation comprenant au moins une des valeurs suivantes: volume de fluide, pression de fluide, géométrie tridimensionnelle d'un volume stimulé, augmentation potentielle de la perméabilité et activité sismique simulée.
EP10759553A 2009-04-03 2010-04-05 Procédé de modélisation d'un réseau de fractures et de la croissance d'un réseau de fractures pendant la stimulation de formations souterraines Withdrawn EP2414631A1 (fr)

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US16638309P 2009-04-03 2009-04-03
PCT/US2010/030013 WO2010115211A1 (fr) 2009-04-03 2010-04-05 Procédé de modélisation d'un réseau de fractures et de la croissance d'un réseau de fractures pendant la stimulation de formations souterraines

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WO2017030725A1 (fr) * 2015-08-18 2017-02-23 Schlumberger Technology Corporation Simulations de réservoir incluant des réseaux de fracture
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US10947841B2 (en) 2018-01-30 2021-03-16 Baker Hughes, A Ge Company, Llc Method to compute density of fractures from image logs
US20200341167A1 (en) * 2019-04-29 2020-10-29 Halliburton Energy Services, Inc. Complexity Index Optimizing Job Design
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US20070272407A1 (en) * 2006-05-25 2007-11-29 Halliburton Energy Services, Inc. Method and system for development of naturally fractured formations

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