Hydrofracture process optimization

Abstract

A method (100) for optimizing a hydromechanical fracturing process, the method (100) comprising the steps of: a. Loading static and dynamic data (10); b, loading a pressure-fracture aperture relation curve (20); and c. Generating a hydraulic fracturing process model (30) by repeating the following steps c1 to c3 using the static and dynamic data: c1. Solving a discrete fracture network (DFN) flow model (32); c2. Updating the fracture aperture (34) of the discrete fracture network (DFN) using the pressure-fracture aperture relationship curve; and c3. Historical matching (36) of the reservoir data by updating the pressure-fracture aperture relationship curve; d. Simulating hydromechanical fracturing of a particular reservoir based on the hydrofracture process model to generate optimized process parameters (40); and e. Performing hydraulic mechanical fracturing (50) using the optimized process parameters.

Description

Technical Field

[0001] The present invention relates to the field of extraction of underground media, such as oil or gas reservoirs, in particular by hydraulic fracturing. The present invention relates to modeling of hydraulic fracturing processes, in particular in tight reservoirs, for optimization of hydraulic fracturing processes in order to extract underground resources. Background Art

[0002] "Tight reservoir" is a term generally used to refer to low permeability reservoirs, and such reservoirs are often naturally fractured. Wells in tight reservoirs generally produce smaller amounts of hydrocarbons than wells in conventional reservoirs. In order to achieve commercial production rates of hydrocarbons, it is necessary to increase the permeability of the reservoir. To this end, different promotion processes can be used, for example, hydraulic fracturing promotion.

[0003] The production of hydrocarbons trapped in unconventional reservoirs (i.e., where the reservoir permeability is very low) depends on the ability to effectively and safely drain the wellbore area within the tight formation. To date, hydraulic fracturing is the primary operating technique used to convert tight reservoirs into economically recoverable resources. The injection of high-pressure fluids induces new pathways in the rock and increases the ultimate recovery of hydrocarbons.

[0004] However, the results of such techniques remain difficult to assess and model due to the complexity of the mechanisms involved and the heterogeneity that governs flow in such environments. Considering the amount of data available such as microseismic event records, it becomes apparent that pre-existing geological features that can be classified as weak points in the geological rock system, for example, natural fractures and stratigraphic interfaces, are reactivated during such hydraulic fracturing operations.

[0005] "Hydraulic fracturing" (also known as "hydraulic fracking" or "hydrofracking") is a process designed to release hydrocarbons trapped in unconventional / tight reservoir formations for production. The process promotes the mobilization of hydrocarbons by injecting a fluid (usually water mixed with other components) to create new fractures in the formation and / or reactivate existing fractures.

[0006] Previously, underground media, in particular hydrocarbon fields and tight / shale reservoirs, have been studied before production in order to find the best locations for hydraulic fracturing. Such studies require the construction of models, which are broadly referred to as "geological models". For example, these geological models make it possible to determine technical parameters related to the investigation, study or exploitation of hydrocarbon reservoirs. Therefore, the geological model represents to a certain extent the structure of the reservoir and its characteristics (including the dynamic response of the model). Typically, such a geological model can be a numerical model represented on a computer.

[0007] Such geological models can also be used to simulate the hydraulic fracturing process. To this end, the promotion process modeling includes the laws of elastic-plastic geomechanics, where the existing fracture network under the field stress tensor is reactivated under the drive of pressure accumulation. The hydraulic fracturing process aims to make this promotion irreversible by mixing the injected water with other components (chemicals and / or proppants) and reaching the Mohr-Coulomb failure criterion to induce a plastic irreversible response.

[0008] The created geological model may be history matched (ie, assimilated with the data) using dynamic data, such as well pressure and / or microseismicity signatures, as well as other data types as they exist.

[0009] This history matching is performed by seeking to minimize the error between the observed pressure curve and the simulated pressure curve from the geological numerical model. However, the uncertainties affecting the fracture properties and the local field stress tensor combined with the complexity of the geomechanical model reduce the chances of matching the observed data in a satisfactory manner and, therefore, reduce the chances of obtaining a reliable model.

[0010] Such a geomechanical model is described in the following paper: Delorme, M., Daniel, J.-M., Kada-Kloucha, C., Khvoenkova, N., Schueller, S., & Souque, C. (August 12, 2013): "An Efficient Model to Simulate Reservoir Stimulation and Induced Microseismic Events on 3D Discrete Fracture Network for Unconventional Reservoirs", Society of Petroleum Engineers. DOI: 10.1190 / URTEC2013-146 and Delorme, M., Mota, R.O., Khvoenkova, N., Fourno, A., & Noetinger, B. (2013): “A methodology to characterize fractured reservoirs constrained by statistical geological analysis and production: a real field case study”, Geological Society, London, Special Publications, 374, SP374-14.

[0011] The paper Quinghua Lei, John-Paul Latham, Chin-Fu Tsang (January 2, 2017): “The use of discrete fracture networks for modelling coupled geomechanical and hydrological behavior of fractured rocks”, Elsevier Ldt. discloses a method for calibrating measured fracture apertures using coupled dynamic / geomechanical models for classical and unconventional reservoirs.

[0012] Document US2017 0370197A1 discloses hydraulic fracturing in kerogen-rich unconventional formations and discloses different approaches for characterizing discrete fracture networks (DFNs) of unconventional reservoirs based on geomechanical models.

[0013] Document CN 107545113 A discloses a method for simulating the formation process of complex fracture networks in hydraulic fracturing of unconventional oil and gas reservoirs.

[0014] Facilitating good modeling and simulation of processes contributes to improved reservoir utilization and optimization of oil field production.Therefore, there is a need for effective modeling of the process of recovering hydrocarbons via fracturing. Summary of the invention

[0015] The above-mentioned problems and other problems are solved by a method according to claim 1 .

[0016] In particular, the above mentioned problem is solved by a method for optimizing a hydromechanical fracturing process, the method comprising the following steps:

[0017] a. Load static and dynamic data, preferably in a geological numerical model;

[0018] b. Loading pressure-fracture aperture relationship curve; and

[0019] c. Generate a hydraulic fracturing process model by repeating the following steps c1 to c3 using static and dynamic data:

[0020] c1. Solve the discrete fracture network (DFN) flow model;

[0021] c2. Using the pressure-fracture aperture relationship curve to update (34) the fracture aperture of the discrete fracture network (DFN);

[0022] c3. Perform history matching on reservoir data by updating the pressure-fracture diameter relationship curve;

[0024] d. simulating hydraulic mechanical fracturing of a specific reservoir based on a hydraulic fracturing process model to generate optimized process parameters; and

[0025] e. Performing hydromechanical fracturing using optimized process parameters.

[0026] The present application relates to hydraulic fracturing, the process of introducing fractures into underground formations by pumping fluid into a wellbore at a specific pressure. To characterize underground media, geological models are usually used, while the present invention contemplates the use of pressure / fracture-aperture curves.

[0027] The present invention simplifies the modeling of hydraulic fracturing processes in the case of unconventional reservoirs for hydromechanical fracturing processes. The modeling framework provides optimized process parameters that are then used during actual hydromechanical fracturing of hydrocarbon reservoirs. In the present invention, the hydraulic fracturing process is simulated by generating a hydraulic fracturing process model using pressure / fracture-aperture curves.

[0028] The method uses loaded static and dynamic data in generating a hydraulic fracturing process model.

[0029] The DFN flow model is a simulation method used to model flow in a DFN (discrete fracture network). Various methods can be used to simulate flow and transport in fractured reservoirs. These methods can be divided into two categories: 1-DFN flow model and 2-equivalent continuum model (ECM). The DFN flow model explicitly represents individual fractures, while the ECM uses fracture properties to determine the equivalent continuum parameters.

[0030] Therefore, static data may include all data used to characterize a reservoir. Static data preferably includes rock properties, rock type distribution, phase, initial fluid distribution, etc. Rock properties include porosity, permeability, and compressibility. Based on such data, static models such as fracture models and rock property models are established. Static data can be obtained from seismic data, well cores, outcrop data, and other similar methods.

[0031] Dynamic data preferably includes production data, fluid distribution and geomechanical data. Production data may include production rate (volume / time), well pressure curve, fluid saturation, etc. Geomechanical data may include stress and strain data. Such dynamic data may be obtained from well tests, production history, 4D seismic data and other similar methods. Therefore, such dynamic data may include historical data and actual data from actual sites.

[0032] Steps c1, c2 and c3 are repeated several times. The minimum and / or maximum number of iterations and the tolerance error can be defined by the user. If the result is within the tolerance error range, the hydraulic fracturing process model converges. The maximum number of iterations can preferably be set to hundreds of times.

[0033] The step of history matching the reservoir data by updating the pressure-fracture diameter relationship curve preferably comprises calculating the reservoir data according to the DFN flow model and comparing it with the actual measured reservoir data.

[0034] By using optimized hydraulic fracturing process parameters, underground resources can be better exploited. The information gathered from this optimization method is used to design and implement the most favorable underground economic intervals to achieve targeted production of hydrocarbon resources (including conventional and unconventional reservoirs) and can also be used for the promotion of enhanced geothermal systems.

[0035] Preferably, step c. of generating a hydraulic fracturing process model further comprises the following step: c4. matching an existing leakage test by updating a pressure-fracture aperture relationship curve.

[0036] Preferably, the step of loading the pressure-fracture aperture relationship curve includes determining the pressure-fracture aperture relationship curve: based on laboratory experiments or outcrop studies; and / or based on the pressure-fracture aperture relationship curve of similar reservoirs; and / or based on an artificial intelligence model; and / or based on analysis of geological outcrop studies.

[0037] Pressure / pore diameter curves are associated to each fracture family. These curves can be obtained numerically by using geomechanical models of selected wells with similar characteristics or via testing in the laboratory. Optionally, the use of artificial intelligence (AI) algorithms will simplify the hydraulic fracturing process modeling and reduce uncertainty and require less data.

[0038] Preferably, the reservoir data includes pressure data and / or microseismic data.

[0039] Preferably, the optimized process parameters include injection fluid pressure and / or injection fluid rate and / or injection duration.

[0040] Preferably, the DFN flow model is a static model describing the fracture network.

[0041] Preferably, the DFN flow model is a randomly generated model.

[0042] Preferably, the DFN flow model is generated based on fracture medium parameters.

[0043] Preferably, the fracture medium parameters include one or more of the following:

[0044] The number of cracks per volume (i.e., crack density),

[0045] Average length,

[0046] average height,

[0047] Average pore size,

[0048] Average conductivity,

[0049] Average orientation.

[0050] Preferably, the DFN flow model includes using static data to represent a discrete fracture network (DFN) of the subsurface. The DFN is a static model that describes a fracture network. The DFN is typically generated randomly based on fracture medium parameters, which are fracture density (number of fractures / volume), average length, average height, average pore size, average conductivity, and average orientation.

[0051] Preferably, the step (c1.) of solving the DFN flow model generates a map of the pressure and saturation distribution of the well.

[0052] Preferably, in the step (c3.) of history matching, reservoir data are calculated based on the DFN flow model and compared with the actually measured reservoir data.

[0053] Preferably, the step (b.) of loading the pressure-fracture aperture relationship curve and the step (c.) of generating the hydraulic fracturing process model include the following steps:

[0054] -Upload the initial pressure-fracture aperture curve;

[0055] -Solve the DFN flow model and calculate the pressure;

[0056] - Compare the calculated pressure with the actual pressure data;

[0057] - The fracture aperture is updated for each time step based on the pressure-fracture aperture curve and the calculated pressure.

[0058] Therefore, the pressure-fracture aperture curve is updated by calibration of the parameters controlling the pressure-fracture aperture curve equation.

[0059] Preferably, the step (e.) of performing hydromechanical fracturing using optimized process parameters includes promoting or enhancing the production of oil or gas through preferentially induced fracture pathways in the subterranean rock. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In the following, preferred embodiments of the present invention are described with reference to the accompanying drawings, which show:

[0061] Figure 1 is an exemplary pressure-fracture aperture relationship curve during the pressure rise stage;

[0062] Figure 2 is an exemplary pressure-fracture aperture relationship curve during the pressure drop stage;

[0063] Figure 3A is the sign convention for the multi-crack group;

[0064] Figure 3B is the transformation equation for the multi-crack group;

[0065] Figure 4 is a flow chart of an exemplary embodiment of an algorithm for generating a hydraulic fracturing process model;

[0066] Figure 5 is an exemplary plot of fracture aperture versus time step;

[0067] Figure 6 is an exemplary plot of well pressure versus time step;

[0068] Figure 7is an example of a pressure map generated by solving the DFN flow model;

[0069] Figure 8 is an example of a randomly generated DFN;

[0070] Fig. 9 is a schematic diagram of generating a hydraulic fracturing process model by using DFN; and

[0071] Fig.10 is a schematic illustration of an embodiment of a method for optimizing a hydromechanical fracturing process.

[0072] The present invention relates to a method 100 for exploiting underground media, in particular hydrocarbon reservoirs. The term "hydrocarbons" within the meaning of the present invention should be understood as, for example, oil-containing products, such as petroleum or crude oil, gasoline or extra heavy oil, asphaltene sands, asphaltene shales and gases present in underground formations. The method 100 according to the present invention is also suitable for exploiting gas storage reservoirs containing gases such as CO2. The underground medium is fractured (or faulted) and is penetrated by at least one well for exploiting it.

[0073] A fault (or fracture) is a surface created by shear rupture that separates rock by creating a drop between two adjacent blocks. Three types of faults can be identified in reservoirs. Therefore, the term "fracture family" (i.e., a group of fractures with the same geological origin) is used analogously for the fault types:

[0074] Earthquake faults are large attributes visible on earthquake detectors. These are objects of large size (from hundreds of meters to thousands of meters). Earthquake faults can be modeled explicitly, i.e. non-stochastically.

[0075] Subseismic faults are properties whose size is not large enough to be visible on seismic images. This involves fracturing at a very variable scale (on the order of meters); this type of fracture can be modeled by stochastic methods.

[0076] Diffusive faults correspond to small-scale (meter-scale) fractures and can be modeled by a stochastic approach, i.e., by a set of distribution law parameters.

[0077] A so-called fractured geological model (or reservoir model) is a representation of the subsurface medium in which the geometry of the faults is usually represented by Boolean objects. In two dimensions, the faults are represented by contours, and in three dimensions, the faults are represented by surfaces. Properties such as porosity, permeability, effective pore size, etc. are associated with each object.

[0078] Diffuse fractures are added by so-called "probabilistic" construction methods due to the limitations of available information (limited number of wells, etc.). For this reason, geological models (or reservoir models) constructed according to these probabilistic methods are called "stochastic models". Diffuse fractures are grouped into different fracture families.

[0079] A fracture family is a group of fractures represented by the same fracture geometry and dynamic properties (density, length, height, aperture, orientation and permeability). Figure 1 and Figure 2 The pressure / fracture-pore diameter curve shown is used to illustrate the method of hydraulic fracturing process.

[0080] like Figure 1 As described, in geomechanics, a fracture subjected to an increase in pressure exhibits two behaviors (elastic and plastic), and the fracture aperture will increase. Figure 2 As described and shown, after stopping the water injection, the pressure will drop and the fracture aperture will decrease but it will not return to the initial aperture (it will follow a different path).

[0081] A pressure / pore size curve (similar to the KrPc / hysteresis curve) is associated to each fracture cluster. These curves can be obtained numerically using a complete geomechanical model around the selected well or via laboratory testing. Since fractures within the same cluster (same orientation) in the same rock type respond similarly to changes in pressure, the curves can be used. Figure 3A The symbol convention and Figure 3B The transformation equations generalize these curves to the field, and these curves can be history-matched more easily.

[0082] There are different approaches for modeling flow in fractured reservoirs. One of them is the discrete fracture network (DFN) model. The idea is to simulate the flow in fractures and rock simultaneously, with an exchange term between the two media in the model.

[0083] According to one embodiment, a method 100 for optimizing a hydromechanical fracturing process is Figure 4 and Fig.10 The method comprises the following steps:

[0084] a. Load static and dynamic data 10;

[0085] b. Loading pressure-fracture aperture relationship curve 20; and

[0086] c. Generate a hydraulic fracturing process model 30 by repeating the following steps for each time step using static and dynamic data:

[0087] c1. Solve the discrete fracture network (DFN) flow model 32;

[0088] c2. Using the pressure-fracture aperture relationship curve to update the discrete fracture network (DFN) fracture aperture 34; and

[0089] c3. Perform history matching 36 on reservoir data by updating the pressure-fracture diameter relationship curve.

[0090] d. simulating hydraulic mechanical fracturing of a specific reservoir based on the hydraulic fracturing process model to generate optimized process parameters 40; and

[0091] e. Performing hydromechanical fracturing 50 using optimized process parameters.

[0092] First, a discrete fracture network (DFN) is generated using static data to represent the subsurface. This network is then coupled to a flow simulator that solves a multiphase flow problem in order to predict the flow of subsurface fluids through the existing fracture network.

[0093] The DFN flow model is solved using well-known state-of-the-art equations, such as Darcy's law and Forcheimer's equation.

[0094] Figure 4 The details of the generation step 30 of the hydraulic fracturing process model are shown. First, in step c1 / 32, the discrete fracture network (DFN) flow model is solved. Then, in step c2 / 34, the fracture aperture of the discrete fracture network (DFN) is updated by calculating the new fracture aperture and using the pressure-fracture aperture relationship curve. Then, in step 31, it is determined whether the calculated pressure minus the tested well pressure is greater than the threshold value ε (|P 计算出的 -–P 井测试 |>ε), and if so, the method 30 continues to step c3 / 36 to perform history matching on the reservoir data by updating the pressure-fracture aperture relationship curve. If the history matching c3 / 36 is completed, the method continues to step c1 / 32.

[0095] If in step 31, the calculated pressure minus the tested well pressure is less than or equal to the threshold ε, the method proceeds to determine whether the necessary iterations have been performed (time_step ≤ final_time_step). If the necessary iterations have not been performed, the method 30 proceeds to step 35, which increases the time step by 1. The method 30 then proceeds to solve step c1 / 32. However, if the necessary iterations have been performed, the method 30 ends, and the method 100 proceeds to simulate step d / 40.

[0096] The output of solving the DFN flow model is a plot of the pressure and saturation distribution at each time step. Figure 7An exemplary pressure diagram is shown in .

[0097] Obtaining the sequence of step c for generating a hydraulic fracturing process model provides an evolving pressure-fracture aperture curve.

[0098] Figure 8 An example of a randomly generated DFN is shown in .

[0099] Fig. 9 A schematic diagram showing the generation of a hydraulic fracturing process model by using DFN is shown.

[0100] The history matching algorithm has been tested and validated on a comprehensive case study as follows:

[0101] Step 1:

[0102] a) Run the DFN flow model using one injection well

[0103] b) 10m for 25 hours 3 Water is injected at a rate of / min

[0104] c) Crack aperture along Figure 5 Open / close the dashed curve in (the dashed curve is input)

[0105] d) Record the pressure at the wellhead ( Figure 6 The dashed curve in

[0106] Step 2:

[0107] a) Run the history matching algorithm on the same cases

[0108] b) Through calibration Figure 5 The crack-aperture / pressure curve in Figure 6 Pressure curve in

[0109] Wire

[0110] c) Figure 5 The point-like crack-aperture / pressure curve is the output of the calibration.

[0111] Figure 6 It shows that the history matching algorithm can predict the fracture-aperture / pressure curve with high accuracy. The dashed well pressure curve perfectly covers the dotted history matching well pressure curve.

[0112] Therefore, by simulating the hydromechanical fracturing for a specific reservoir based on the hydraulic fracturing process model, optimized process parameters can be generated. Based on the present invention, such optimized process parameters for hydromechanical fracturing, such as injection fluid pressure, injection fluid rate, injection duration, can be determined and used to perform hydromechanical fracturing. This optimizes the production at the site.

Claims

1. A method (100) for optimizing a hydromechanical fracturing process, the method comprising the following steps: a. Load static and dynamic data (10); b. Loading pressure-fracture aperture relationship curve (20); and c. Generate a hydraulic fracturing process model (30) by repeating the following steps c1 to c3 using the static and dynamic data: c1. Solve the discrete fracture network (DFN) flow model (32); c2. using the pressure-fracture aperture relationship curve to update the fracture aperture (34) of the discrete fracture network (DFN); and c3. Performing history matching of reservoir data by updating the pressure-fracture aperture relationship curve (36); d. simulating hydraulic mechanical fracturing for a specific reservoir based on the hydraulic fracturing process model to generate optimized process parameters (40); and e. Performing hydromechanical fracturing using the optimized process parameters (50).

2. The method for optimizing a hydraulic mechanical fracturing process according to claim 1, wherein: The step (c.) of generating the hydraulic fracturing process model (30) further comprises the following steps: c4. Match the existing leak test by updating the pressure-fracture aperture relationship curve.

3. The method for optimizing a hydromechanical fracturing process according to claim 1 , wherein: The step (b.) of loading the pressure-fracture aperture relationship curve (20) comprises determining the pressure-fracture aperture relationship curve as follows: a. Based on laboratory experiments; and / or b. Based on the pressure-fracture diameter relationship curves of similar reservoirs; and / or c. Based on an AI model; and / or d. Based on analysis of geological outcrop studies.

4. Method for optimizing a hydromechanical fracturing process according to one of claims 1 to 3, wherein: The reservoir data includes pressure data.

5. Method for optimizing a hydromechanical fracturing process according to one of claims 1 to 4, wherein: The reservoir data includes microseismic data.

6. Method for optimizing a hydromechanical fracturing process according to one of claims 1 to 5, wherein: The optimized process parameters include injection fluid pressure and / or injection fluid rate and / or injection duration.

7. Method for optimizing a hydromechanical fracturing process according to one of claims 1 to 6, wherein: The DFN flow model is a static model that describes the fracture network.

8. The method for optimizing a hydromechanical fracturing process according to claim 7, wherein: The DFN flow model is a randomly generated model.

9. The method for optimizing a hydromechanical fracturing process according to claim 8, wherein: The DFN flow model is generated based on fracture medium parameters.

10. The method for optimizing a hydromechanical fracturing process according to claim 9, wherein: The fracture medium parameters include one or more of the following: The number of cracks per volume (crack density), Average length, average height, Average pore size, Average conductivity, Average orientation.

11. Method for optimizing a hydromechanical fracturing process according to one of claims 1 to 10, wherein: The DFN flow model includes representing a discrete fracture network (DFN) of the subsurface using the static data.

12. Method for optimizing a hydromechanical fracturing process according to one of claims 1 to 11, wherein: The step (c1.) of solving the DFN flow model (32) generates a map of the pressure and saturation distribution of the well.

13. Method for optimizing a hydromechanical fracturing process according to one of claims 1 to 12, wherein: In step (c3.) of history matching (36), the reservoir data is calculated based on the DFN flow model and compared with the actual measured reservoir data.

14. Method for optimizing a hydromechanical fracturing process according to one of claims 1 or 13, wherein: The step (b.) of loading the pressure-fracture aperture relationship curve (20) and the step (c.) of generating the hydraulic fracturing process model (30) include the following steps: -Upload the initial pressure-fracture aperture curve; -Solve the DFN flow model and calculate the pressure; - Compare the calculated pressure with the actual pressure data; - For each time step, the fracture aperture is updated based on the pressure-fracture aperture curve and the calculated pressure.

15. Method for optimizing a hydromechanical fracturing process according to one of claims 1 to 14, wherein: The step (e.) of performing hydromechanical fracturing (50) using the optimized process parameters includes promoting or enhancing the production of oil or gas through preferentially induced fracture pathways in the subterranean rock.

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