WO2022149976A1 - Method and system for estimating an effective leak-off coefficient of natural fractures in a naturally fractured reservoir - Google Patents
Method and system for estimating an effective leak-off coefficient of natural fractures in a naturally fractured reservoir Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 60
- 238000004088 simulation Methods 0.000 claims abstract description 89
- 230000003993 interaction Effects 0.000 claims abstract description 32
- 239000012530 fluid Substances 0.000 claims description 111
- 238000002347 injection Methods 0.000 claims description 72
- 239000007924 injection Substances 0.000 claims description 72
- 238000012360 testing method Methods 0.000 claims description 34
- 239000011435 rock Substances 0.000 claims description 28
- 239000011159 matrix material Substances 0.000 claims description 26
- 238000004590 computer program Methods 0.000 claims description 13
- 230000001419 dependent effect Effects 0.000 claims description 12
- 230000000977 initiatory effect Effects 0.000 claims description 12
- 238000013500 data storage Methods 0.000 claims description 9
- 230000015556 catabolic process Effects 0.000 claims description 7
- 206010017076 Fracture Diseases 0.000 description 165
- 208000010392 Bone Fractures Diseases 0.000 description 40
- 230000006399 behavior Effects 0.000 description 21
- 238000010586 diagram Methods 0.000 description 19
- 239000000463 material Substances 0.000 description 15
- 230000035699 permeability Effects 0.000 description 15
- 238000013461 design Methods 0.000 description 5
- 238000002405 diagnostic procedure Methods 0.000 description 5
- 238000009533 lab test Methods 0.000 description 5
- 239000011148 porous material Substances 0.000 description 5
- 230000009977 dual effect Effects 0.000 description 4
- 238000013459 approach Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000004075 alteration Effects 0.000 description 2
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- 230000003936 working memory Effects 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 239000000470 constituent Substances 0.000 description 1
- 238000013499 data model Methods 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
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Classifications
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP 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
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B2200/00—Special features related to earth drilling for obtaining oil, gas or water
- E21B2200/20—Computer models or simulations, e.g. for reservoirs under production, drill bits
Abstract
A method for obtaining an effective leak-off coefficient of natural fractures in a naturally fractured reservoir is described. In an embodiment, the method comprises: (i) receiving empirical data associated with a parameter characterizing an interaction between the natural fractures and hydraulic fractures formed by hydraulic fracturing the naturally fractured reservoir; (ii) iteratively performing simulation for hydraulic fracturing the naturally fractured reservoir by varying a leak-off coefficient of the natural fractures to obtain an optimized simulated data, the optimized simulated data matching the empirical data associated with the parameter; and (iii) estimating the effective leak-off coefficient, the effective leak-off coefficient being a value of the leak-off coefficient used in the simulation for generating the optimized simulated data.
Description
Method and system for estimating an effective leak-off coefficient of natural fractures in a naturally fractured reservoir
Technical Field The present disclosure relates to a method and system for estimating a leak-off coefficient, particularly, an effective leak-off coefficient of natural fractures in a naturally fractured reservoir.
Background
Leak-off of hydraulic fracturing fluid in a reservoir with little or no natural fracture occurs through a porous rock medium of the reservoir, and can be characterized by the Carter’s leak-off coefficient of the porous rock medium. The Carter’s leak-off coefficient, CL, is given by CL = ^DRf/2m)1/2, where k is the permeability of the rock medium (in m2), DR is the pressure difference between hydraulic fractures and the rock medium (in Pa), f is the porosity of the reservoir (unitless), and m is the viscosity of the hydraulic fracturing fluid (in Pa-s). Experimentally, the Carter’s leak-off coefficient CL can be estimated from hydraulic fracturing diagnostic tests such as a fracture diagnostic test (i.e. a minifractest) or a diagnostic fracture injection test (DFIT). However, the Carter’s leak-off coefficient characterizes a leak-off behavior of the porous rock medium with the assumption that there is little or no natural fractures in the reservoir. To account for a leak-off behavior of a reservoir with natural fractures, the concept of an apparent total leak-off coefficient of a naturally fractured reservoir has been proposed. The total leak-off coefficient, CP is given by CP = C0eb PNF, where C0 is the matrix leak- off coefficient (i.e. the Carter’s leak-off coefficient of the matrix), b is the pressure- dependent leak-off factor (1/Pa), and APNF is the natural fracture opening pressure (Pa). However, the pressure-dependent leak-off factor b and the natural fracture opening pressure APNF are difficult to obtain experimentally, and there is no industry-wide recognized method for obtaining these values.
Estimating an effective or characteristic leak-off coefficient for natural fractures in naturally fractured reservoirs are important for hydraulic fracturing simulations and reservoir simulations (e.g. dual porosity and dual permeability). Particularly, during hydraulic fracturing, hydraulic fractures can either be arrested by the natural fractures or intersect with the natural fractures depending on the leak-off characteristics of the natural
fractures. In other words, the type of interaction between the hydraulic fractures and the natural fractures are highly dependent on the leak-off characteristic of the natural fractures. This therefore has a profound effect on hydraulic fracturing simulation as well as for obtaining optimized conditions for performing hydraulic fracturing in naturally fractured reservoirs.
Laboratory tests such as a fracture conductivity test are typically used for estimating leak-off characteristics of natural fractures. However, these laboratory tests are often expensive and time consuming to perform. Further, the results of these tests are often not representative of the scale of the effective leak-off characteristics of these natural fractures. Therefore, in real applications, the effective leak-off coefficient of natural fractures in naturally fractured reservoirs are often speculated or estimated, for example, to be 100 times higher than a leak-off coefficient of a porous rock matrix of the naturally fractured reservoirs. This is highly inaccurate and affects an effectiveness of any hydraulic fracturing simulation performed on naturally fractured reservoirs.
It is therefore desirable to provide a method and system for estimating an effective leak- off coefficient of natural fractures in a naturally fractured reservoir which address the aforementioned problems or provide a useful alternative. Further, other desirable features and characteristics will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and this background.
Summary
Aspects of the present application relate to a method and system for estimating an effective leak-off coefficient of natural fractures in a naturally fractured reservoir.
In accordance with a first aspect, there is provided a method for obtaining an effective leak-off coefficient of natural fractures in a naturally fractured reservoir. The method comprises: (i) receiving empirical data associated with a parameter characterizing an interaction between the natural fractures and hydraulic fractures formed by hydraulic fracturing the naturally fractured reservoir; (ii) iteratively performing simulation for hydraulic fracturing the naturally fractured reservoir by varying a leak-off coefficient of the natural fractures to obtain an optimized simulated data, the optimized simulated data matching the empirical data associated with the parameter; and (iii) estimating the
effective leak-off coefficient, the effective leak-off coefficient being a value of the leak-off coefficient used in the simulation for generating the optimized simulated data.
By iteratively performing simulation for hydraulic fracturing the naturally fractured reservoir with a varying leak-off coefficient of the natural fractures, the optimized simulated data associated with a parameter characterizing an interaction between the natural fractures and hydraulic fractures and which matches a corresponding empirical data can be identified, and the effective leak-off coefficient of the natural fractures can be estimated. The present method therefore provides a systematic way of estimating the leak-off coefficient using empirical data obtained from relevant fracturing tests performed on the naturally fractured reservoir. This thereby eliminates the need of guessing a value for the leak-off coefficient of the natural fractures based on a leak-off coefficient of the rock matrix of the reservoir which can be highly inaccurate.
The method may comprise determining if the interaction between the natural fractures and the hydraulic fractures includes a leak-off behavior of a fracturing fluid used in forming the hydraulic fractures using the experimental fracturing test, the interaction is determined to include the leak-off behavior if no breakdown pressure is observed in a bottom hole pressure plot associated with the fracturing fluid or if a pressure dependent leak-off characteristic is observed in a G-function analysis plot. This determining step may be performed before (ii) iteratively performing simulation to determine if it is in fact critical to determine the estimated leak-off coefficient for the naturally fractured reservoir.
Where (ii) iteratively performing simulation for hydraulic fracturing the naturally fractured reservoir by varying the leak-off coefficient of the natural fractures may comprise: initiating a finite element method (XFEM) model for simulating the hydraulic fracturing; creating a mesh for the naturally fractured reservoir, the mesh comprises a first type of reservoir region representing a rock matrix and a second type of reservoir region representing the natural fractures; defining a perforation location in the mesh for initiating hydraulic fracturing; and simulating the hydraulic fracturing in the naturally fractured reservoir using the XFEM model.
The method may comprise: using the effective leak-off coefficient as an input parameter for the natural fractures in the XFEM model; and obtaining parameters associated with the hydraulic fracturing for maximizing a stimulated reservoir volume and a fracture conductivity of the hydraulic fracturing. These steps improve an effectiveness of the hydraulic fracturing design by determining the parameters for use in hydraulic fracturing
so as to maximize the stimulated reservoir volume and the fracture conductivity of the hydraulic fracturing.
In accordance with a second aspect, there is provided a computer readable medium storing processor executable instructions which when executed on a processor cause the processor to carry out any of the preceding method.
In accordance with a third aspect, there is provided a system for obtaining an effective leak-off coefficient of natural fractures in a naturally fractured reservoir, the system comprising a processor and a data storing computer program instructions operable to cause the processor to: receive empirical data associated with a parameter characterizing an interaction between the natural fractures and hydraulic fractures formed by hydraulic fracturing the naturally fractured reservoir; iteratively perform simulation for hydraulic fracturing the naturally fractured reservoir by varying a leak-off coefficient of the natural fractures to obtain an optimized simulated data, the optimized simulated data matching the empirical data associated with the parameter; and estimate the effective leak-off coefficient, the effective leak-off coefficient being value of the leak- off coefficient used in the simulation for generating the optimized simulated data.
The empirical data associated with the parameter may be obtained from an experimental fracturing test performed on a sample of the naturally fractured reservoir.
The experimental fracturing test may comprise a minifrac test or a diagnostic fracture injection test (DFIT). The minifrac test or the DFIT are routinely performed. This means that empirical data from these tests are easy to obtain and can be used extensively for estimating the leak-off coefficient in the present method and system.
The data storage may store computer program instructions operable to cause the processor to: determine if the interaction between the natural fractures and the hydraulic fractures includes a leak-off behavior of a fracturing fluid used in forming the hydraulic fractures using the experimental fracturing test, the interaction is determined to include the leak-off behavior if no breakdown pressure is observed in a bottom hole pressure plot associated with the fracturing fluid or if a pressure dependent leak-off characteristic is observed in a G-function analysis plot.
The parameter may comprise a fluid pressure of an injection fluid for hydraulic fracturing the naturally fractured reservoir.
The data storage may store computer program instructions operable to cause the processor to: initiate an extended finite element method (XFEM) model for simulating the hydraulic fracturing; create a mesh for the naturally fractured reservoir, the mesh comprises a first type of reservoir region representing a rock matrix and a second type of reservoir region representing the natural fractures; define a perforation location in the mesh for initiating hydraulic fracturing; and simulate the hydraulic fracturing in the naturally fractured reservoir using the XFEM model.
The data storage may store computer program instructions operable to cause the processor to: use the effective leak-off coefficient as an input parameter for the natural fractures in the XFEM model; and obtain parameters associated with the hydraulic fracturing for maximizing a stimulated reservoir volume and a fracture conductivity of the hydraulic fracturing.
It should be appreciated that features relating to one aspect may be applicable to the other aspects. Embodiments therefore provide a method and system for estimating an effective leak-off coefficient of natural fractures in a naturally fractured reservoir. By iteratively performing simulation for hydraulic fracturing the naturally fractured reservoir with a varying leak-off coefficient of the natural fractures, the optimized simulated data associated with a parameter characterizing an interaction between the natural fractures and hydraulic fractures and which matches a corresponding empirical data can be identified, and the effective leak-off coefficient of the natural fractures can be estimated. The present method therefore provides a systematic way of estimating the leak-off coefficient using empirical data obtained from relevant fracturing tests performed on the naturally fractured reservoir. This eliminates the need of guessing a value for the leak- off coefficient of the natural fractures based on a leak-off coefficient of the rock matrix of the reservoir which can be highly inaccurate. This provides a holistic and accurate approach in an effective leak-off coefficient of natural fractures in a naturally fractured reservoir, which is critical in the optimization of hydraulic fracturing design. Although not explicitly described above, it will be appreciated that the method and system is applicable to the oil and gas industry.
Brief description of the drawings
Embodiments will now be described, by way of example only, with reference to the following drawings, in which:
Figure 1 shows a block diagram of a system for estimating an effective leak-off coefficient of natural fractures in a naturally fractured reservoir in accordance with an embodiment;
Figure 2 is a flowchart showing steps of a method for estimating an effective leak-off coefficient of natural fractures in a naturally fractured reservoir, using the system of Figure 1 , in accordance with an embodiment;
Figure 3 is a flowchart showing steps of a method for simulating hydraulic fracturing in the naturally fractured reservoir for use in the method of Figure 2 in accordance with an embodiment;
Figure 4 is a flowchart showing steps of a method for obtaining parameters associated with the hydraulic fracturing for maximizing a stimulated reservoir volume and a fracture conductivity of the hydraulic fracturing in accordance with an embodiment;
Figures 5A, 5B and 5C show diagrams of a mesh used in simulating hydraulic fracturing using the method of Figure 3 in accordance with an embodiment, where Figure 5A shows a schematic of the mesh comprising a first type of reservoir region representing a rock matrix and a second type of reservoir region representing natural fractures, Figure 5B shows a schematic of the mesh with an introduction of the perforation locations for initiating hydraulic fracturing, and Figure 5C shows a schematic of the mesh with the simulated hydraulic fracturing;
Figure 6 shows a schematic diagram for illustrating injection fluid flow of hydraulic fracturing used in simulating hydraulic fracturing in accordance with an embodiment;
Figure 7 shows a schematic diagram for illustrating normal flow of the injection fluid through permeable layers used in simulating hydraulic fracturing in accordance with an embodiment;
Figure 8 shows a typical plot of bottomhole pressure versus time illustrating typical pressure behaviors of hydraulic fracturing in a naturally fractured reservoir;
Figure 9 shows a typical plot of pressure dependent leak-off of a G-function analysis plot;
Figures 10A and 10B show examples of plots used in history matching for obtaining an effective leak-off coefficient of natural fractures in accordance with an embodiment, where Figure 10A shows a plot of an empirical flow rate of an injection fluid versus time used as inputs to the hydraulic fracturing simulation and Figure 10B shows a plot of
simulated injection pressure for use in matching empirical data obtained in a hydraulic fracturing diagnostic test;
Figure 11 show a schematic diagram of a core shale sample used in a laboratory hydraulic fracturing test in accordance with an embodiment;
Figure 12 shows plots of an injection fluid flow rate and its corresponding fluid injection pressure versus time obtained using the core shale sample in the laboratory hydraulic fracturing test of Figure 11 ;
Figures 13A and 13B show diagrams of hydraulic fracturing simulation outputs assuming a value of the effective leak-off coefficient for the natural fractures of about 100 times higher than a rock matrix permeability of the naturally fractured reservoir in accordance with an embodiment, where Figure 13A shows a plot of simulated injection fluid pressure versus time and Figure 13B shows a diagram of a simulation output of the injection fluid pressure; and
Figures 14A and 14B show diagrams of hydraulic fracturing simulation outputs assuming a value of the effective leak-off coefficient for the natural fractures of about 10,000 times higher than a rock matrix permeability of the naturally fractured reservoir in accordance with an embodiment, where Figure 14A shows a plot of simulated injection fluid pressure versus time and Figure 14B shows a diagram of a simulation output of the injection fluid pressure.
Detailed description
Exemplary embodiments relate to a method and system for obtaining an effective leak- off coefficient, particularly, an effective leak-off coefficient of natural fractures in a naturally fractured reservoir.
Figure 1 shows a block diagram of a system 100 for obtaining an effective leak-off coefficient of natural fractures in a naturally fractured reservoir in accordance with an embodiment.
As shown in Figure 1 , the system 100 is a computer system with memory that stores computer program modules which implement a computer-implemented method for obtaining an effective leak-off coefficient of natural fractures in a naturally fractured reservoir. The system 100 comprises a processor 102, a working memory 104, an input
module 106, an output module 108, a user interface 110, program storage 112 and data storage 114. The processor 102 may be implemented as one or more central processing unit (CPU) chips. The program storage 112 is a non-volatile storage device such as a hard disk drive which stores computer program modules such as a simulation module 116 and an analysis module 118. The computer program modules 116, 118 are loaded into the working memory 104 for execution by the processor 102. The input module 106 is an interface which allows data, for example (a) empirical data associated with a parameter (such as injection fluid pressure) characterizing an interaction between the natural fractures and hydraulic fractures formed by hydraulic fracturing the naturally fractured reservoir, (b) data in relation to formation of a mesh used in hydraulic fracturing simulation, (c) data associated with material properties of different regions of the mesh etc., to be received by the system 100. The output module 108 is an output device which allows data and results of calculated model parameters by the system 100 to be output. The output module 108 may be coupled to a display device or a printer. The user interface 110 allows a user of the system 100 to input selections and commands and may be implemented as a graphical user interface.
The program storage 112 stores the simulation module 116 and the analysis module 118. The simulation module 116 and the analysis module 118 cause the processor 102 to execute various simulation and analysis processes which are described in more detail below. The program storage 112 may be referred to in some contexts as computer readable storage media and/or non-transitory computer readable media. As depicted in Figure 1 , the computer program modules 116, 118 are distinct modules which perform respective functions implemented by the system 100. It will be appreciated that the boundaries between these modules 116, 118 are exemplary only, and that alternative embodiments may merge modules or impose an alternative decomposition of functionality of modules. For example, the modules discussed herein may be decomposed into sub-modules to be executed as multiple computer processes, and, optionally, on multiple computers. Moreover, alternative embodiments may combine multiple instances of a particular module or sub-module. It will also be appreciated that, while a software implementation of the computer program modules is described herein, these may alternatively be implemented as one or more hardware modules (such as field-programmable gate array(s) or application-specific integrated circuit(s)) comprising circuitry which implements equivalent functionality to that implemented in software.
The data storage 114 stores various model data and model parameters. As shown in Figure 1 , the data storage 114 has storage for a simulation model 120 and an analysis model 122 for use with their corresponding modules 116, 118. In the present embodiment, the simulation model 120 comprises a three-dimensional (3D) mesh model 124, a fluid injection model 126, physical models 128 and model parameters 130. Data entered by the user in relation to creating a 3D mesh for simulating hydraulic fracturing is stored in the 3D mesh model 124. Similarly, data entered in relation to a fluid injection model for simulating hydraulic fracturing is stored in the fluid injection model 126. The physical models 128 and the model parameters 130 store data in relation to physical models (e.g. existing physical and mathematical models used for simulating hydraulic fracturing) and model parameters (e.g. various parameters used in relation to material properties etc.) respectively. The analysis model 122 includes a historical data model 132 which stores data in relation to simulation and analysis performed for identifying an optimized data which matches the received empirical data associated with the parameter characterizing the interaction between the natural fractures and the hydraulic fractures formed by hydraulic fracturing.
Figure 2 is a flowchart showing steps of a method 200 for estimating an effective leak- off coefficient of natural fractures in a naturally fractured reservoir in accordance with an embodiment. The method 200 can be performed using the system 100 as described in relation with Figure 1.
In a step 202, the system 100 receives, via the input module 106, empirical data associated with a parameter characterizing an interaction between the natural fractures and hydraulic fractures formed by hydraulic fracturing the naturally fractured reservoir. The empirical data associated with the parameter is obtained from experimental fracturing tests performed on the naturally fractured reservoir. Experimental fracturing tests can be at least one of, for example, a minifrac test or a diagnostic fracture injection test (DFIT). The parameter includes a fluid pressure of an injection fluid for hydraulic fracturing the naturally fractured reservoir.
In a step 204, the simulation module 116 is executed by the processor 102 to iteratively perform simulation for hydraulic fracturing the naturally fractured reservoir by varying a leak-off coefficient of the natural fractures to obtain an optimized simulated data which matches the empirical data. In the present embodiment, the simulation module 216 is configured to determine an initial value of the leak-off coefficient of the natural fractures
by using a historical value associated with a naturally fractured reservoir in a similar region or geographical area as the naturally fractured reservoir in question. Alternatively, the initial value of the leak-off coefficient of the natural fractures can be a guess value based on existing conventional protocols, for example, as described in the background section (e.g. a value of 100 times that of a leak-off coefficient of the rock matrix of the naturally fractured reservoir). Once the initial value is determined, the simulation module 116 is configured to iteratively perform simulation for hydraulic fracturing by varying the leak-off coefficient of the natural fractures, while keeping other parameters used in the simulation constant. In an embodiment, this includes receiving, by the simulation module 116, successive inputs of estimated leak-off coefficient of the natural fractures from the user of the system 100 (e.g. by trial and error) in order to achieve a simulated data which best fits or matches the empirical data (i.e. any difference between the simulated data and the empirical data is minimized).
In a step 206, the analysis module 118 is executed by the processor 102 to estimate the effective leak-off coefficient of the natural fractures where the effective leak-off coefficient is a value of the leak-off coefficient of the natural fractures which is used to generate the optimized simulated data. In other words, once the optimized simulated data is obtained, the value of the leak-off coefficient of the natural fractures used for generating the optimized simulated data can be extracted as the effective leak-off coefficient of the natural fractures of the naturally fractured reservoir.
The method 200 as described above can be used in conjunction with any simulation programs or methods that take into account a leak-off coefficient of natural fractures in a naturally fractured reservoir as an input to the simulation program, and generate an output associated with a parameter characterizing an interaction between the natural fractures and the hydraulic fractures which can be used in matching with empirical data associated with the parameter.
In an embodiment, an extended Finite Element Method (XFEM) based 3D hydraulic fracturing model is used in simulating hydraulic fracturing of the naturally fractured reservoir. In the XFEM model, a natural fracture can be modelled as a thin weak material with high fluid leak-off characteristics, which emulates the natural fracture with weak cohesion (strength) and high fluid permeability in real life. This method is also known as a continuum natural fracture approach. Using this approach, as discussed below in
relation to Figures 5A-5C, the fluid leak-off characteristics through the natural fractures associated with their interference with hydraulic fractures can be simulated.
XFEM has been implemented in multiple commercial FEM software including Abaqus. Abaqus version 6.9 (2009) introduces the ability to model cracks in the FEM framework by using a version of the enriched element formulation to incorporate XFEM into the traditional FEM framework. The Abaqus software has the capacity to simulate full 3D poro-elastic behaviors of reservoirs (i.e. pore pressure alteration due to fluid injection and hydraulic fracture propagation) as well as tangential and normal fluid flow in XFEM cracks. Physical and mathematical models used in XFEM modeling (e.g. in the Abaqus simulation program) are not described here for clarity and succinctness.
Figure 3 shows a flowchart showing steps of a method 300 for simulating hydraulic fracturing in a naturally fractured reservoir in accordance with an embodiment. The method 300 is carried out on the system 100. The method 300 is performed during the step 204 of the method 200 for use in iteratively simulating hydraulic fracturing in the naturally fractured reservoir.
In a step 302, the simulation module 116 is executed by the processor 102 to initiate an extended finite element method (XFEM) model for simulating the hydraulic fracturing.
In a step 304, the simulation module 116 is executed by the processor 102 to create a mesh for the naturally fractured reservoir, using data and/or parameters from the 3D mesh model 124 and the model parameters 130. The mesh comprises a first type of reservoir region representing a rock matrix having a first material property and a second type of reservoir region representing natural fractures having a second material property. The first material property being different from the second material property. In this step 304, a value of the leak-off coefficient of the natural fractures, among other input parameters for the second material property, can be input. Further detail of creating a mesh for the naturally fractured reservoir is discussed below in relation to Figures 5A.
In a step 306, the simulation module 116 is executed by the processor 102 to define a perforation location in the mesh for initiating hydraulic fracturing. This is discussed in detail in relation to Figure 5B below.
In a step 308, the simulation module 116 is executed by the processor 102 to define a plurality of parameters associated with an injection fluid for the hydraulic fracturing, the plurality of parameters include an injection rate of the fluid, a volume of the fluid, an
injection duration of the fluid, a component of the fluid, a viscosity of the fluid and a density of the fluid. The plurality of parameters defined are stored in the fluid injection model 126 for use in the simulation of the hydraulic fracturing by the simulation module 116.
In a step 310, the simulation module 116 is executed by the processor 102 to simulate the hydraulic fracturing in the naturally fractured reservoir using the simulation model 120. The simulation of the hydraulic fracturing takes into account the created mesh using data stored in the 3D mesh model 124, the data stored in relation to the fluid injection model 126, and the physical models 128 and the model parameters 130. This is discussed in more detail in relation to Figure 5C.
Figure 4 shows a flowchart showing steps of a method 400 for obtaining parameters associated with the hydraulic fracturing for maximizing a stimulated reservoir volume and a fracture conductivity of the hydraulic fracturing in accordance with an embodiment. The method 400 is carried out on the system 100.
In a step 402, the simulation module 116 is executed by the processor 102 to use the effective leak-off coefficient estimated in the step 206 as an input parameter associated with the natural fractures in the XFEM model.
In a step 404, the simulation module 116 is executed by the processor 102 to obtain parameters associated with the hydraulic fracturing for maximizing a stimulated reservoir volume (SRV) and a fracture conductivity of the hydraulic fracturing. In an embodiment, this involves the simulation module 116 being configured to optimize the plurality of parameters associated with fluid injection using the fluid injection model 126 in tandem with the simulated results (e.g. the simulated reservoir volume and the fracture conductivity) generated by the simulation module 116 to achieve a maximum stimulated reservoir volume and/or a maximum fracture conductivity given the created mesh and the perforation location.
Figures 5A, 5B and 5C show diagrams of a mesh used in simulating hydraulic fracturing using the method of Figure 3 in accordance with an embodiment.
Figure 5A shows a schematic of the mesh 500 comprising a first type of reservoir region 502 representing a rock matrix and a second type of reservoir region 504 representing a natural fracture, in accordance with an embodiment. Once the XFEM model is initiated by the XFEM module 216, creation of the XFEM modelling begins with the creation of
the mesh 500. The mesh 500 represents the geometry of the rock matrix and the natural fracture in the naturally fractured reservoir. As shown in Figure 5A, the mesh is initially drawn in two dimensions and can be later extruded to form the 3D mesh.
The physical properties of the rock matrix and the natural fracture can then be introduced. For example, parameters of a material property of the rock matrix and the parameters of a material property of the natural fracture can be defined. The parameters of the material property of the rock matrix and/or the natural fractures includes: a Young’s modulus, a density, a Poisson’s ratio, a cohesive fracture energy, a porosity, a permeability, a leakoff coefficient and/or a damage criterion. Each of the regions of the mesh 500 can be defined to have different material properties and can be named accordingly. For example, as shown in Figure 5A, the first type of reservoir region 502 representing the rock matrix can be assigned a first material property and the second type of reservoir region 504 representing the natural fracture can be assigned a second material property.
An example for defining the parameters of the two different types of regions is presented below:
‘Damage Initiation, criterion=MAXPS, tolerance=0.01 , growth tolerance=0.01
*** pg
1 0e+6,
‘Damage Evolution, type=ENERGY, mixed mode behavior=BK, power=2.0 “* J/m2, J/m2, J/m2 25, 25, 25
‘Elastic “* Pa, unitless 4.0e+10, 0.25 ‘Fluid Leakoff *“ m3/Pa-s
1 e-12, 1e-12 ‘Gap Flow *“ Pa-s 5e-04, ‘Permeability, specific=9.8e+02 “* m/s, unitless 1e-08, 0.05 f *o **r * n **a *t *u *r *a **l * f *ra **c *t *u *r *e *s *** (i *. *e *. * s **e *c **o *n *d ** t *y **p *e ** o *f ** r *e *s *ervoir region 504)
‘Material, name=Natural_Fracture
‘Damage Initiation, criterion=MAXPS, tolerance=0.01 , growth tolerance=0.01
*** pg
1 0e+4,
‘Damage Evolution, type=ENERGY, mixed mode behavior=BK, power=2.0 *** J/m2, J/m2, J/m2 2.5, 2.5, 2.5 ‘Elastic
“* Pa, unitless 4.0e+08, 0.35 ‘Fluid Leakoff “* m3/Pa-s 1 e-10, 1e-10
‘Gap Flow *“ Pa-s 5e-04,
‘Permeability, specific=9.8e+02 “* m/s, unitless
1e-06, 0.25
The model is then assembled. Within assembly, all the parts are combined to produce a single simulation system where all its constituents will be simulated.
After the mesh 500 is well defined and assembled, perforation locations for initiating the hydraulic fracturing can be defined.
Figure 5B shows a schematic of the mesh 500 with an introduction of the perforation locations 506 for initiating hydraulic fracturing. The location and other characteristics of the perforations can be defined in the XFEM model.
Following the definition of the perforations and their locations, a fluid injection treatment at the perforations can be defined. Defining the fluid injection treatment includes defining a rate of injection of the fluid (in L/min) and/or a volume of the fluid (in L) injected with time (in minutes). The fluid injection treatment can also include one or more stages where an injection rate of the fluid can be defined for each stage of the treatment. Other parameters associated with the injection fluid for the hydraulic fracturing can also be defined. These other parameters include an injection duration of the fluid, a component of the fluid, a viscosity of the fluid and a density of the fluid. Further, a proppant used, a size of the proppant, a concentration of the proppant, a volume of the proppant, and a mass of the proppant for the fluid injection treatment can also be defined.
Following the definition of the fluid treatment step, interaction between the components of the simulation system can be introduced. The fracture surface and propagation algorithm are defined, and the enrichment nodes within the simulation are also defined. Although Figure 5 shows the mesh 500, the actual meshing of the model may be applied at this point. For simulation of the hydraulic fracturing using XFEM modelling, a hex type
mesh can be implemented. Lastly, a job file is created. Simulation of the hydraulic fracturing is then performed by using the parameters and/or input data defined in the previous steps. For the simulation of the hydraulic fracturing, the user may also define a step size and a step number to be simulated.
Figure 5C shows a schematic of the mesh 500 with the simulated hydraulic fracturing. Three different interactions between the hydraulic fractures and the natural fractures are shown in Figure 5C. The line 508 shows a hydraulic fracture being arrested by the natural fracture. The line 510 shows a hydraulic fracture being diverted by the natural fracture. The line 512 shows a hydraulic fracture crossing the natural fracture.
The XFEM-based 3D hydraulic fracturing model as described above has a capacity to simulate full 3D poro-elastic behaviors of reservoirs (e.g. pore pressure alteration due to fluid injection and hydraulic fracture propagation) as well as tangential and normal fluid flow in XFEM cracks. Figure 6 shows a schematic diagram 600 for illustrating injection fluid flow within a cracked enriched element used in simulating hydraulic fracturing in accordance with an embodiment. As shown in Figure 6, an arrow 602 illustrates a direction for tangential flow of the injection fluid, and arrows 604 illustrate directions for normal flow of the injection fluid in the cracked enriched element.
Figure 7 shows a schematic diagram 700 for illustrating normal injection fluid flow through permeable layers of a cracked enriched element used in simulating hydraulic fracturing in accordance with an embodiment. As shown in Figure 7, the cracked enriched element has a top surface 702 and a bottom surface 704. The top and bottom surfaces 702, 704 each includes a permeable layer.
The flow rate of injection fluid through the top and bottom surfaces 702, 704 of the cracked enriched element can be defined by the pressure differences across each of these surfaces, for example, using the following equations: qt = Ct (Pi - Pt); and
C|b — ¾ (Pi — Pb) where qt and qb are the flow rates (m3/s) through the top and bottom surfaces 702, 704 of the cracked enriched element, respectively; P, is the pressure (Pa) at the phantom node located at the cracked element edge; Pt and Pb are the pore pressures (Pa) on the top and bottom surfaces of a cracked element, respectively; and ct and Cb are the fluid
leak-off parameter (m3/Pa-s) of the top and bottom surfaces 702, 704 of the cracked element, respectively. The fluid leak-off parameters (e.g. ct and Cb) as utilized in the XFEM modelling are different from the Carter’s leak-off coefficient (CL). The fluid leak off parameters ct and cb can however be associated with the Carter’s leak-off (CL) coefficient using other parameters such as formation permeability and fracture fluid viscosity.
From the above equations, it is therefore clearthat the fluid pressure of the injection fluid (or bottomhole pressure) is a parameter which takes into account a leak-off characteristic of the natural fractures and characterizes an interaction between the natural fractures and the hydraulic fractures. Simulation data in relation to the fluid pressure of the injection fluid (or the bottomhole pressure) can therefore be used for comparing with the corresponding empirical data for estimating the effective leak-off coefficient of the natural fractures.
Figure 8 shows a typical plot 800 of bottomhole pressure versus time illustrating typical pressure behavior of hydraulic fracturing in a naturally fractured reservoir. The bottomhole pressure is associated with a fracturing fluid used in the hydraulic fracturing of the naturally fractured reservoir. A plot 802 shows a typical pressure behavior of hydraulic fracturing with a clear break down pressure, while a plot 804 shows a leak-off behavior with no breakdown pressure observed in the bottomhole pressure plot due to fracturing fluid leak-off potentially through natural fractures of the naturally fractured reservoir.
Figure 9 shows a typical plot 900 of pressure dependent leak-off of a G-function analysis plot. Two characteristics of the plot 900 can be used to determine if the interaction between the natural fractures of the naturally fractured reservoir and the hydraulic fractures exhibits leak-off behavior. A first characteristic is a pressure dependent leak- off characteristic 902 observed in a G d/dG plot and a second characteristic is a measure of a pressure dependent leak-off (PDL) net pressure 904. As shown in this example, the PDL net pressure is about 330 psi.
Using either one of the above experimental plots 800, 900, in an embodiment, the analysis module 118 is executed by the processor 102 to determine if the interaction between the natural fractures and the hydraulic fractures includes a leak-off behavior of a fracturing fluid used in forming the hydraulic fractures. The interaction is determined to include the leak-off behavior if no breakdown pressure is observed in a bottom hole
pressure plot 800 associated with the fracturing fluid or a pressure dependent leak-off characteristic is observed in a G-function analysis plot 900.
In an embodiment, the determination of a leak-off characteristic of the interaction between the natural fractures and the hydraulic fractures provides a first step for identifying if an effective leak-off coefficient of natural fractures in a naturally fractured reservoir is critical in a simulation of hydraulic fracturing of the reservoir. In an embodiment, if substantially no leak-off behavior of the fracturing fluid used in forming the hydraulic fractures is observed, the effective leak-off coefficient of natural fractures may not be a critical parameter and the Carter’s leak-off coefficient CL may be sufficient as an input for the hydraulic fracturing simulation of the reservoir. Alternatively, if there is leak-off behavior observed in the experimental plots 800, 900 as described above, the effective leak-off coefficient of natural fractures becomes critical in the simulation of hydraulic fracturing of the reservoir and the method 200 can be used to estimate the effective leak-off coefficient as described above. It will be appreciated that the determination of the leak-off characteristic of the interaction between the natural fractures and the hydraulic fractures is optional and in some embodiments, the method 200 can be performed regardless of whether this determination was carried out.
In the present embodiment, once it is determined that the leak-off behavior is observed in the reservoir, iterative simulation of hydraulic fracturing of the reservoir can be performed. Particularly, a parameter characterizing an interaction between the natural fractures and hydraulic fractures is chosen, and empirical data associated with this parameter can be used to compare with simulation data for estimating the effective leak- off coefficient of the natural fractures as described in the method 200. This is termed history matching. In the present embodiment, a fluid pressure of the injection fluid or the bottomhole pressure associated with the injection fluid for hydraulic fracturing is chosen.
Figures 10A and 10B show examples of diagrams used in history matching for obtaining an effective leak-off coefficient of natural fractures in accordance with an embodiment. Figure 10A shows a plot 1000 of an actual or empirical flow rate of an injection fluid versus time for use as inputs to the hydraulic fracturing simulation, while Figure 10B shows a plot 1010 of simulated fluid pressure of the injection fluid for use in matching empirical data obtained in a hydraulic fracturing diagnostic test. By using empirical inputs such as the actual or empirical flow rate of the injection fluid as shown in Figure 10A, fluid pressure of the injection fluid (or injection pressure) can be simulated as shown in
Figure 10B. The simulated injection pressure of Figure 10B can then be compared or matched to empirical bottom hole pressure obtained in a hydraulic fracturing test performed on the reservoir (or naturally fractured reservoir) of interest using the same empirical flow rate of the injection fluid as shown in Figure 10A. To achieve this, iteratively simulation of hydraulic fracturing of the reservoir is performed by keeping other inputs to the simulation module 116 constant, while varying a value of the leak-off coefficient of natural fractures for use as an input for performing the hydraulic fracturing simulation. The effective leak-off coefficient of the natural fractures can then be estimated to be a value of the leak-off coefficient of natural fractures used in the simulation which provides an optimized simulated data of the bottomhole pressure that matches the empirical data of the bottomhole pressure obtained in the hydraulic fracturing diagnostic test performed on the reservoir.
Figure 11 show a schematic diagram 1100 of a core shale sample 1102 used in a laboratory hydraulic fracturing test in accordance with an embodiment. The core shale sample 1102 is extracted from the reservoir of interest and is preferably representative of the reservoir of interest. Two major weak beddings were identified in the core shale sample 1102. The extracted core shale sample 1102 was cut to an 8 cm x 8 cm x 8 cm cube to simulate a true triaxial condition (sn, sH, ah). A small hole of 5 mm diameter 1104 was drilled to the centre of the cube in order to inject fluid for performing micro hydraulic fracturing.
In an embodiment, the laboratory hydraulic fracturing test was performed on anisotropic shale samples with weak bedding planes obtained from the reservoir. A range of shale samples can be used for the hydraulic fracturing test and a representative empirical bottomhole pressure data can be obtained, for example, by averaging out corresponding empirical bottomhole pressure data obtained from each of these shale samples.
Figure 12 shows plots 1202, 1204 of an injection fluid flow rate and its corresponding fluid injection pressure versus time obtained using the core shale sample 1102 in the laboratory hydraulic fracturing test of Figure 11. The plot of injection fluid flow rate is shown as 1202 while its corresponding plot of injection fluid pressure is shown as 1204.
From the plot 1204, strong effects of leak-off through the weak beddings or natural fractures of the core shale sample 1102 can be observed. It can be concluded from the polyaxial hydraulic fracturing laboratory tests performed that substantial fluid leak-off through the weak bedding of the anisotropic shale occurred and limited hydraulic fracture
growth is obtained (i.e. hydraulic fractures induced in the hydraulic fracturing laboratory tests were “arrested” due to the substantial fluid leak-off). The observed injection fluid pressure data from the plot 1204 also demonstrated typical leak-off behaviors in hydraulic fracturing operation (see for example Figure 8 above) as no distinctive breakdown pressure and a low net pressure are observed.
Hydraulic fracturing simulations are then performed following obtaining of the empirical data. In the present embodiment, two values of the leak-off parameter of the natural fractures in the simulation were used to demonstrate the use of the method 200. It should however be understood that iterative simulation involving two or more simulations can be performed for estimating the effective leak-off coefficient. In some embodiments, instead of performing the hydraulic fracturing simulation iteratively or sequentially, a range of values of the leak-off parameter of the natural fractures can be used for performing the hydraulic fracturing simulation in parallel. The value of the leak-off coefficient which provides a simulated data of the bottomhole pressure or injection fluid pressure that best matches the corresponding empirical data may be estimated to be the effective leak-off coefficient of the natural fractures.
In another embodiment, this initial value obtained in the range of values is not taken to be the effective leak-off coefficient. Instead, once the value of the leak-off coefficient (“initial best value”) that best matches the corresponding empirical data has been identified using this initial range of values of the leak-off parameter, a second range of values of the leak-off parameter of the natural fractures may be used to narrow down on a better estimate of the effective leak-off coefficient. For example, a narrow range of values with a smaller increment between each value within the range and having a center at the initial best value may be used as inputs for performing a second round of hydraulic fracturing simulations. By using this narrow range of values of the leak-off parameter of the natural fractures, further corresponding simulated data of the bottomhole pressure or injected fluid pressure can be obtained and a further value of the leak-off coefficient among this second range of values that best matches the corresponding empirical data may be estimated to be the effective leak-off coefficient of the natural fractures. It should be appreciated that this process as described can be further iterated to obtain even a closer estimate for a value of the effective leak-off coefficient of the natural fractures.
In the present case as described above, two values of the leak-off parameter of the natural fractures in the simulation were used to demonstrate the use of the method 200.
The first value of the leak-off parameter used has a value of 100 times higher than that of the rock matrix (i.e. 100 nD) of the reservoir, while the second value of the leak-off parameter used has a value of 10,000 times higher than that of the rock matrix. The simulation results obtained for the first value are shown in relation to Figures 13A and 13B, while the simulation results obtained for the second value are shown in relation to Figures 14A and 14B. It is noted that simulations performed for both values applied the same input parameters for modeling except the leak-off parameter of the natural fractures, since it had not been measured through laboratory tests.
Figures 13A and 13B show diagrams of hydraulic fracturing simulation outputs assuming an effective leak-off coefficient for the natural fractures having a value of about 100 times higher than a rock matrix permeability of the naturally fractured reservoir in accordance with an embodiment. Figure 13A shows a plot 1300 of simulated injection fluid pressure versus time, while Figure 13B shows a diagram 1310 of a simulation output of the injection fluid pressure. The values provided in Figure 13B are related to pore pressure and are given in the unit of MPa.
As shown in Figure 13A, a net injection fluid pressure of around 20 MPa was simulated in the plot 1300. This value does not match the empirical value of the net injection fluid pressure of about 6-7 MPa as shown in the plot 1204 of Figure 12.
Figures 14A and 14B show diagrams of hydraulic fracturing simulation outputs assuming an effective leak-off coefficient for the natural fractures having a value of about 10,000 times higher than a rock matrix permeability of the naturally fractured reservoir in accordance with an embodiment. Figure 14A shows a plot 1400 of simulated injection fluid pressure versus time and Figure 14B shows a diagram 1410 of a simulation output of the injection fluid pressure. The values provided in Figure 14B are related to pore pressure and are given in the unit of MPa.
In this case, by using a value for the leak-off coefficient of about 10,000 times higher than a rock matrix permeability of the naturally fractured reservoir, a net injection fluid pressure was simulated to be around 6-7 MPa which matches empirical value of the net injection pressure of about 6-7 MPa as shown in the plot 1204 of Figure 12. The effective leak-off coefficient of natural fractures in the naturally fractured reservoir can then be estimated to be 1 mD in permeability (i.e. 10,000 times higher than the matrix permeability of 100 nD).
The effective leak-off coefficient obtained can be used as a critical input in future hydraulic fracturing simulations and reservoir simulations (e.g. dual porosity and dual permeability). Particularly, it can be used in hydraulic fracturing simulations and reservoir simulations for optimizing hydraulic fracturing design. For example, optimizing hydraulic fracturing design generally aims for maximizing stimulated reservoir volume (SRV) and fracture conductivity (i.e. popped opening). The SRV and the fracture conductivity can be optimized by controlling parameters in relation to the fluid injection treatment for hydraulic fracturing operation. For example, an arresting interaction typically provides a low SRV while a crossing interaction typically provides a high SRV. Therefore, in order to maximize the SRV by having the crossing interaction with the hydraulic fracture intersecting the natural fractures, for example, the injection rate could be increased and the proppant concentration could be decreased (i.e. increasing the fluid velocity). Further, perforation locations for optimizing stress shadowing effects can also be used for optimizing hydraulic fracturing design. Although only certain embodiments of the present invention have been described in detail, many variations are possible in accordance with the appended claims. For example, features described in relation to one embodiment may be incorporated into one or more other embodiments and vice versa.
Claims
1. A method for estimating an effective leak-off coefficient of natural fractures in a naturally fractured reservoir, the method comprising: receiving empirical data associated with a parameter characterizing an interaction between the natural fractures and hydraulic fractures formed by hydraulic fracturing the naturally fractured reservoir; iteratively performing simulation for hydraulic fracturing the naturally fractured reservoir by varying a leak-off coefficient of the natural fractures to obtain an optimized simulated data, the optimized simulated data matching the empirical data associated with the parameter; and estimating the effective leak-off coefficient, the effective leak-off coefficient being a value of the leak-off coefficient used in the simulation for generating the optimized simulated data.
2. The method of claim 1 , wherein the empirical data associated with the parameter is obtained from an experimental fracturing test performed on the naturally fracture reservoir.
3. The method of claim 2, wherein the experimental fracturing test comprises a minifrac test or a diagnostic fracture injection test (DFIT).
4. The method of claim 2 or claim 3, further comprising determining if the interaction between the natural fractures and the hydraulic fractures includes a leak-off behavior of a fracturing fluid used in forming the hydraulic fractures using the experimental fracturing test, the interaction is determined to include the leak-off behavior if no breakdown pressure is observed in a bottom hole pressure plot associated with the fracturing fluid or if a pressure dependent leak-off characteristic is observed in a G-function analysis plot.
5. The method of any one of claims 1 to 4, wherein the parameter comprises a fluid pressure of an injection fluid for hydraulic fracturing the naturally fractured reservoir.
6. The method of any preceding claim, wherein iteratively performing simulation for hydraulic fracturing the naturally fractured reservoir comprises:
initiating a finite element method (XFEM) model for simulating the hydraulic fracturing; creating a mesh for the naturally fractured reservoir, the mesh comprises a first type of reservoir region representing a rock matrix and a second type of reservoir region representing the natural fractures; defining a perforation location in the mesh for initiating hydraulic fracturing; and simulating the hydraulic fracturing in the naturally fractured reservoir using the XFEM model.
7. The method of claim 6, further comprising: using the effective leak-off coefficient as an input parameter for the natural fractures in the XFEM model; and obtaining parameters associated with the hydraulic fracturing for maximizing a stimulated reservoir volume and a fracture conductivity of the hydraulic fracturing.
8. A computer readable medium storing processor executable instructions which when executed on a processor cause the processor to carry out a method according to any one of claims 1 to 7.
9. A system for estimating an effective leak-off coefficient of natural fractures in a naturally fractured reservoir, the system comprising a processor and a data storing computer program instructions operable to cause the processor to: receive empirical data associated with a parameter characterizing an interaction between the natural fractures and hydraulic fractures formed by hydraulic fracturing the naturally fractured reservoir; iteratively perform simulation for hydraulic fracturing the naturally fractured reservoir by varying a leak-off coefficient of the natural fractures to obtain an optimized simulate data, the optimized simulated data matching the empirical data associated with the parameter; and estimate the effective leak-off coefficient, the effective leak-off coefficient being a value of the leak-off coefficient used in the simulation for generating the optimized simulated data.
10. The system of claim 9, wherein the empirical data associated with the parameter is obtained from an experimental fracturing test performed on a sample of the naturally fracture reservoir.
11 . The system of claim 10, wherein the experimental fracturing test comprises a minifrac test or a diagnostic fracture injection test (DFIT).
12. The system of claim 2 or claim 3, wherein the data storage further stores computer program instructions operable to cause the processor to: determine if the interaction between the natural fractures and the hydraulic fractures includes a leak-off behavior of a fracturing fluid used in forming the hydraulic fractures using the experimental fracturing test, the interaction is determined to include the leak-off behavior if no breakdown pressure is observed in a bottom hole pressure plot associated with the fracturing fluid or if a pressure dependent leak-off characteristic is observed in a G-function analysis plot.
13. The system of any one of claims 9 to 12, wherein the parameter comprises a fluid pressure of an injection fluid for hydraulic fracturing the naturally fractured reservoir.
14. The system of any one of claims 9 to 13, wherein the data storage further stores computer program instructions operable to cause the processor to: initiate an extended finite element method (XFEM) model for simulating the hydraulic fracturing; create a mesh for the naturally fractured reservoir, the mesh comprises a first type of reservoir region representing a rock matrix and a second type of reservoir region representing the natural fractures; define a perforation location in the mesh for initiating hydraulic fracturing; and simulate the hydraulic fracturing in the naturally fractured reservoir using the XFEM model.
15. The system of claim 14, wherein the data storage further stores computer program instructions operable to cause the processor to: use the effective leak-off coefficient as an input parameter for the natural fractures in the XFEM model; and
obtain parameters associated with the hydraulic fracturing for maximizing a stimulated reservoir volume and a fracture conductivity of the hydraulic fracturing.
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| CA3199121A CA3199121A1 (en) | 2021-01-11 | 2022-01-10 | Method and system for estimating an effective leak-off coefficient of natural fractures in a naturally fractured reservoir |
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| CN117077577A (en) * | 2023-10-17 | 2023-11-17 | 中国石油大学(华东) | Rapid simulation and optimization method suitable for low-permeability fractured reservoir |
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| US5305211A (en) * | 1990-09-20 | 1994-04-19 | Halliburton Company | Method for determining fluid-loss coefficient and spurt-loss |
| WO2003067025A2 (en) * | 2002-02-01 | 2003-08-14 | Regents Of The University Of Minnesota | Interpretation and design of hydraulic fracturing treatments |
| US20130332129A1 (en) * | 2012-06-07 | 2013-12-12 | Dassault Systemes Simulia Corp. | Hydraulic fracture simulation with an extended finite element method |
| US20150176400A1 (en) * | 2013-12-23 | 2015-06-25 | Dassault Systems Simulia Corp. | Wellbore modeling |
| WO2020060729A1 (en) * | 2018-09-21 | 2020-03-26 | Landmark Graphics Corporation | Well operations involving synthetic fracture injection test |
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- 2022-01-10 WO PCT/MY2022/050001 patent/WO2022149976A1/en unknown
- 2022-01-10 AU AU2022205899A patent/AU2022205899A1/en active Pending
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| US5305211A (en) * | 1990-09-20 | 1994-04-19 | Halliburton Company | Method for determining fluid-loss coefficient and spurt-loss |
| WO2003067025A2 (en) * | 2002-02-01 | 2003-08-14 | Regents Of The University Of Minnesota | Interpretation and design of hydraulic fracturing treatments |
| US20130332129A1 (en) * | 2012-06-07 | 2013-12-12 | Dassault Systemes Simulia Corp. | Hydraulic fracture simulation with an extended finite element method |
| US20150176400A1 (en) * | 2013-12-23 | 2015-06-25 | Dassault Systems Simulia Corp. | Wellbore modeling |
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| CN117077577A (en) * | 2023-10-17 | 2023-11-17 | 中国石油大学(华东) | Rapid simulation and optimization method suitable for low-permeability fractured reservoir |
| CN117077577B (en) * | 2023-10-17 | 2024-02-02 | 中国石油大学(华东) | Rapid simulation and optimization method suitable for low-permeability fractured reservoir |
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