A multi-injection parameter collaborative optimization method for efficient coal-based carbon fixation and production increase

Abstract

The present invention discloses a multi-injection parameter collaborative optimization method for efficient coal-based carbon fixation and production increase, which belongs to the field of natural gas mining technology. The method comprises: measuring, collecting and fitting the physical property parameters of unrecoverable coal seams at different reservoir depths; constructing a mixture gas injection production increase and carbon fixation scheme based on physical property parameters and a ComsolMultiphysics numerical calculation model, forming a breakthrough time, CH corresponding to different injection parameters. 4 Cumulative harvest, N 2 Cumulative injection volume, CO 2 A database of numerical solutions such as cumulative injection volume; an economic objective function including gas supply and compression costs, carbon sequestration and methane output benefits, injection pressure and other factors is designed; based on the above database and economic objective function, combined with BP neural network and genetic algorithm, the optimal injection parameter combination, sensitivity and weight of influencing factors under different reservoir parameters are determined. The present invention can achieve efficient coal-based carbon sequestration and increase recovery rate, and reduce carbon fixation and production increase costs and energy consumption.

Description

Technical Field

[0001] The present invention relates to the technical field of natural gas exploitation, and in particular to a method for collaboratively optimizing multiple injection parameters for efficient coal-based carbon fixation and production increase. Background Art

[0002] Coalbed methane (CBM) is the main hazard factor that causes a series of disasters such as gas explosions, coal and gas outbursts, and is also a source of environmental pollution. The greenhouse effect of its emissions in the air is 25 times that of carbon dioxide. However, this unconventional natural gas can be used as a non-renewable clean energy. The conventional CBM recovery rate is poor. In order to improve mining efficiency, related technologies such as fracturing permeability enhancement and heat injection production increase have been rapidly developed.

[0003] Unmineable coal seams have the characteristics of difficult reservoir transformation and low CH4 production, and usually cannot obtain economic benefits through conventional mining technology. When CO2 is injected alone, it is easy to cause the pore structure of the coal seam to expand and the permeability to decrease, thus affecting the desorption and output of methane. N2 injection can not only avoid the decrease in coal seam permeability, but also displace methane through pressure difference, increase the desorption rate of methane, and thus increase coalbed methane production. The use of CO2 / N2 mixed gas injection technology has unique advantages. It can not only seal CO2 in the coal seam and reduce greenhouse gas emissions, but also increase coalbed methane production. Therefore, the mixed injection of CO2 and N2 forms a relatively optimized coal-based production increase and carbon fixation pathway, achieving the dual goals of resource and environmental benefits.

[0004] The current means of increasing production are a series of conceptual carbon fixation and production increase methods proposed with the core goal of improving recovery rate or increasing carbon storage, such as the Chinese patent with publication number "CN115539130A" and name "A method for enhancing coalbed methane extraction and CO2 storage in unmineable coal seams". This invention first fractures the unmineable coal seams by CO2 foam pulsating fracturing to provide a broad adsorption space for CO2 storage, and then begins the CO2 injection to displace the coalbed methane enhanced extraction process to achieve CO2 storage and increase recovery rate. This method only empirically gives relevant technical parameters such as the corresponding fracturing parameters, the spacing between injection wells and production wells, and does not fully consider the matching of actual reservoir conditions and technical parameters. Its additional fracturing will increase costs. The Chinese patent with the publication number "CN118167250A" and the name "A method for extracting gas from high-gas low-permeability coal seams" optimizes the key parameters of CH4 extraction from coal seams by gas injection, establishes key control parameters, and optimizes the extraction method based on sensitivity analysis. Although this method provides a basis for the design of CO2-promoted CH4 extraction and injection schemes for coal seams, it focuses more on coal seam gas recovery and does not consider the impact of cost.

[0005] The coordinated optimization of multiple injection parameters refers to the realization of a cooperative balance between multiple injection parameters and reservoir physical parameters in the process of coal-based carbon fixation and production increase by adjusting the ratio of injected gas mixture, injection pressure, temperature, well spacing, etc., so as to achieve a win-win situation of carbon fixation and production increase. Therefore, how to adapt the optimal injection parameters according to the physical parameters to achieve a balance between cost-efficiency and recovery rate and increase carbon storage is a problem that needs to be solved. Summary of the invention

[0006] In order to overcome the defects in the prior art, the present invention proposes a multi-injection parameter collaborative optimization method for efficient coal-based carbon fixation and production increase. Through the collaborative optimization of injection parameters, efficient coal-based carbon sequestration and increased recovery rate are achieved, and costs and energy consumption are reduced.

[0007] The technical solution adopted by the present invention is: a method for collaborative optimization of multiple injection parameters for efficient coal-based carbon fixation and production increase, comprising the following steps:

[0008] Step S1, measuring, collecting and fitting the physical property parameters of the unmineable coal seam at different reservoir depths H; the physical property parameters include reservoir temperature T0, reservoir pressure p0, reservoir permeability k0 and vertical ground stress ;

[0009] Step S2, establishing a conventional extraction numerical model that meets the on-site reservoir parameters and production conditions, and comparing it with the on-site gas production rate to verify the reliability of the model;

[0010] Step S3: After the model is verified, a corresponding mixed gas injection plan is formulated according to the reservoir characteristics of different reservoir depths H, where the injection parameters include the injection pressure P inj , mixed gas ratio c, injection temperature T inj Well spacing ;

[0011] Step S4, establishing a single variable control numerical calculation model for injection parameters according to the corresponding mixed gas injection scheme;

[0012] Step S5: Count the numerical calculation results under each injection scheme, including the breakthrough time , Cumulative CH4 recovery , N2 cumulative injection amount , Cumulative CO2 injection volume and cumulative CO2 recovery , and then build a database;

[0013] Step S6: Design a breakthrough time The economic objective function , the economic objective function Including CO2 supply costs , Nitrogen supply cost , Mixture compression cost Well construction cost , injection cost 、CH4 income , Carbon storage benefits and gas separation costs ; The objective function formula is as follows:

[0014]

[0015] In the formula, Indicates the amount of gas, m 3 , the subscript p represents production, inj represents injection, h represents reservoir thickness, and m represents meters; represents the breakthrough time, d represents days; Represents the total amount of produced gas, m 3 ;

[0016] Step S7: Taking the reservoir depth H and the injection parameters under the corresponding injection scheme as independent variables and the numerical calculation result data under each injection scheme as dependent variables, construct and verify the BP neural network; write a genetic algorithm to seek the optimal economic objective function , and determine the optimal injection parameter combination and the influence weight of each factor under different reservoir depth H conditions.

[0017] As a further improvement of the present invention, in step S1, the physical property parameters of the unmineable coal seams at different reservoir depths H are measured, collected and fitted, specifically:

[0018] Step S11, establishing a coordinate system with reservoir depth as the horizontal coordinate and physical property parameters as the vertical coordinate;

[0019] Step S12: plot the tested and collected physical property parameters in a coordinate system and perform parameter fitting.

[0020] As a further improvement of the present invention, in step S2, in establishing a conventional extraction numerical model that meets the field reservoir parameters and production conditions, the production conditions include the production well pressure and temperature, the production well pressure is 0.15Mpa, the production temperature is the initial reservoir temperature, and the average error rate between the numerical solution of the gas production rate and the actual field value is no more than 15% as the model verification threshold.

[0021] As a further improvement of the present invention, in step S3, the injected mixed gas is CO2 and N2, and the injection scheme is specifically as follows:

[0022] Step S31, the reservoir depth H is 300-1200m, and the increment step is a1;

[0023] Step S32: injection pressure P injThey are 1.1~2.0 times of the reservoir pressure P0, respectively, and the increase step is a2;

[0024] Step S33, the proportion of CO2 in the CO2 / N2 mixture c, where c is between 0.0 and 1.0, and the increment step is a3;

[0025] Step S34, injection temperature T inj is greater than or equal to the reservoir temperature T0, the difference between the two does not exceed 30°C of the reservoir temperature, and the increase step is a4;

[0026] Step S35: Well spacing Between 150 and 500 m, increase the step length to a5;

[0027] As a further improvement of the present invention, the values ​​of a1, a2, a3, a4, and a5 in the increasing step size are 50, 0.1, 0.1, 5, and 50, respectively.

[0028] As a further improvement of the present invention, the breakthrough time is set in the numerical calculation model of step S4 , specifically:

[0029] Step S41: To ensure that there is sufficient concentration of CH4 on the production well side, the breakthrough time is set at a ratio of the mixed gas production rate to the CH4 yield of 50%. ;

[0030] Step S42: When production > 7000d or CH4 production rate < 1000m 3 / d, the corresponding production time is mandatory to be set as the breakthrough time ,d stands for day.

[0031] As a further improvement of the present invention, in step S6, the injection cost Including injection pressure costs and injection temperature cost , the specific formula is as follows:

[0032] =10+2(T inj -T0)($ / d)

[0033] =10+50(p-p0)($ / d)

[0034] Where $ / d is US dollars per day.

[0035] As a further improvement of the present invention, in step S7, the BP neural network is specifically: the BP neural network includes 10 hidden layers, the number of iterations is 100, the iteration is terminated when the mean square error is less than 0.1, the model training group is 80% of the number of all injection schemes, and the prediction group is 20% of the number of all injection schemes.

[0036] The beneficial effects of the present invention are:

[0037] (1) Based on the synergistic effect of multiple injection parameters, the present invention takes into account the differences in the influence of different injection parameters on the basis of reservoir parameters, and realizes the cooperative balance among the injection parameters under the constraints of the economic objective function including different categories of cost, benefit and other factors, thereby ensuring the maximization of the benefits of increased production and coal-based carbon sequestration, further reducing the energy consumption of injection and production processes, and saving energy and protecting the environment;

[0038] (2) The present invention has strong applicability and scalability. When designing and planning reservoir exploitation, the injection parameters of different regions, including injection pressure, mixed gas ratio, injection temperature, and well spacing, can be zoned according to the non-uniformity of reservoir parameters. Injection parameters can be collaboratively optimized during reservoir exploitation design and planning, and can be optimized once or multiple times during production according to changes in reservoir parameters, such as once every 500 or 1,000 days of production. At the same time, the economic objective function The cost parameters involved are not fixed and may change with market fluctuations. For example, changes in factors such as different CH4 prices and CO2 storage benefits will lead to different optimal injection plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0040] Figure 1 It is a process flow chart of the multi-injection parameter collaborative optimization method for efficient coal-based carbon fixation and production increase of the present invention;

[0041] Figure 2 is a diagram showing the relationship between reservoir physical property parameters and burial depth in the embodiment;

[0042] Figure 3 Figure 1 is a numerical calculation model diagram of the mixed gas injection to increase carbon fixation in the embodiment;

[0043] Figure 4 Figure II is a numerical calculation model diagram of the mixed gas injection to increase carbon fixation in the embodiment;

[0044] Figure 5 This is a model verification diagram for gas production rate at different well spacings in the embodiment;

[0045] Figure 6 It is the BP neural network partial prediction-actual verification diagram;

[0046] Figure 7 The parameter combination diagram injected under the optimal objective function in the embodiment;

[0047] Figure 8 This is a diagram of injection parameter combinations under different CH4 prices in the embodiment. DETAILED DESCRIPTION

[0048] The multi-injection parameter collaborative optimization method for efficient coal-based carbon fixation and production increase of the present invention is described in detail below with a specific embodiment.

[0049] Based on the unmineable coal seams with a depth of about 800m in my country, the specific physical properties are based on the fitting parameters. Ordinary coalbed methane extraction in the area generally adopts vertical gas wells with a bottom hole pressure of 0.15MPa and a well spacing of 300~500m.

[0050] like Figure 1 As shown, the multi-injection parameter collaborative optimization method for efficient coal-based carbon fixation and production increase of the present invention has the following specific steps:

[0051] Step S1, measure, collect and fit the basic physical property parameters of unconventional reservoirs at different reservoir depths H, mainly including: reservoir temperature T0, reservoir pressure p0, reservoir permeability k0 and vertical ground stress At the same time, a coordinate system is established to draw a physical property parameter image, with the reservoir depth as the horizontal coordinate and the main physical property parameters as the vertical coordinate, such as Figure 2 shown. Figure 2 In the figure, (a) is the relationship between temperature and burial depth, (b) is the relationship between permeability and burial depth, (c) is the relationship between pressure and burial depth, and (d) is the relationship between vertical stress and burial depth. The main physical property parameters at different reservoir depths H are fitted.

[0052] In order to ensure the simplicity of the invention, linear fitting is used, where:

[0053] The relationship between reservoir temperature and burial depth is: y = 0.0257x + 13.522;

[0054] The relationship between reservoir pressure and burial depth is: y = 0.0066x + 0.262;

[0055] The relationship between reservoir permeability and burial depth is y = -0.1173x + 104.04;

[0056] Relationship between vertical stress and burial depth: y = 0.0208x + 3.7876.

[0057] The basic physical parameters corresponding to the burial depth of 800m are shown in Table 1.

[0058]

[0059] Formulate corresponding injection scheme according to reservoir characteristics. Injection parameters include injection pressure p, mixture ratio c, injection temperature T inj , well spacing and reservoir depth H, injection pressure p is 1.1~2.0 times of reservoir pressure p0, with a step interval of 0.1; CO2 proportion c in CO2 / N2 mixture, c is 0.0~1.0, with a step interval of 0.1; injection temperature T inj It should be greater than or equal to the reservoir temperature T0, and the difference between the two should not exceed 30℃ of the reservoir temperature. The step length is 5; well spacing , that is, the projection distance between the injection well and the production well is between 150 and 500 m, with a step size of 50, and a total of 6160 numerical calculation schemes.

[0060] The injection scheme parameters are shown in Table 2.

[0061]

[0062] Table 2 shows the parameters corresponding to the injection scheme, which adopts the single variable control method, that is, there are 10 levels of injection pressure, 11 levels of mixture ratio, 7 levels of injection temperature, 8 levels of well spacing, and 1 level of reservoir burial depth in the embodiment. Therefore, the total number of numerical model schemes is 10×11×7×8×1=6160.

[0063] Step S2: Use Comsol Multiphysics software to establish a conventional extraction numerical model that meets actual conditions, such as Figure 3 and Figure 4 As shown in the figure, for the case without injection wells, the production well pressure is 0.15 MPa, the production time is 6000 days, and the error rate between the numerical solution of the recovery rate and the actual solution is no more than 15%. Figure 5 shown.

[0064] Step S3: After the model is verified, a corresponding mixed gas injection plan is formulated according to the reservoir characteristics at different reservoir depths H. The injection parameters include the injection pressure P inj , mixed gas ratio c, injection temperature T inj和 Well spacing .

[0065] Step S4: establishing a single variable control numerical calculation model for injection parameters according to the corresponding mixed gas injection scheme.

[0066] To ensure that there is sufficient concentration of CH4 on the production well side, the breakthrough time is set at a ratio of N2+CO2 production rate to CH4 production rate of 50%. At the same time, production > 7000d or CH4 production rate < 1000m 3 / d Forced well closure, d represents days.

[0067] Step S5: Statistically summarize the breakthrough time under different injection schemes , Cumulative CH4 recovery , N2 cumulative injection amount , Cumulative CO2 injection volume , Cumulative CO2 recovery .

[0068] Step S6: Design an economic objective function to achieve breakthrough time , including the cost of CO2 supply , Nitrogen supply cost , Mixture compression cost Well construction cost , injection cost 、CH4 income , Carbon storage benefits , Gas separation costs Etc. The specific formula is:

[0069]

[0070] In the formula, Indicates the amount of gas, m 3 , the subscript represents production, inj represents injection, h represents reservoir thickness, m, represents the breakthrough time, and d represents days.

[0071] Step S7, injection pressure p, mixture ratio c, injection temperature T inj , well spacing , reservoir depth H as independent variable, objective function The variables involved are dependent variables, including breakthrough time , Cumulative CH4 recovery , N2 cumulative injection amount , Cumulative CO2 injection volume , Cumulative CO2 recovery , construct a BP neural network. The BP neural network contains 10 hidden layers, the number of iterations is 100, and the iteration is terminated when the mean square error is less than 0.1. The model training group is 80% of the total number of solutions, that is, 4928, and the prediction group is 20% of the total number of solutions, that is, 1232. The variance of the predicted value and the actual value of each dependent variable is used to verify the effectiveness of the model. Due to the large number of prediction groups, some prediction results are shown in Figure 6 As shown, R 2 It is above 0.96, indicating the effectiveness of the model. Figure 6In the figure, (a) is the breakthrough time corresponding to some samples, (b) is the cumulative CH4 production corresponding to some samples, (c) is the cumulative CO2 injection amount corresponding to some samples, and (d) is the cumulative N2 injection amount corresponding to some samples.

[0072] On the basis of verifying the correctness of BP neural network, the optimal objective function value is sought through Matlab's own module and legacy algorithm. The genetic algorithm learning rate is 1e-3, the number of iterations is 500, and the calculation tolerance is 1e -6 。 Collect and organize the economic objective function proposed in step S6 The cost parameters involved are shown in Table 3.

[0073]

[0074] To ensure the correctness of the optimal objective function value, the optimization algorithm of each solution is executed 1000 times, and the economic objective function is taken Maximum, CH4, CO2 prices are 0.5$ / m 3 1$ / m 3 , the economic objective function obtained The maximum value is 26.28$ / m 3 .

[0075] Economic objective function determined by genetic algorithm The maximum value is obtained by using the back propagation BP neural network to predict the optimal injection parameter combination and the weight of influencing factors under different reservoir depths H. The injection pressure, well spacing, temperature, and mixture ratio are 10.0 MPa, 175.6, 34 °C, and 0.97, respectively, and the weights are 0.30, 0.20, 0.15, and 0.45, respectively. The corresponding recovery factor is 79.86%. Figure 7 As shown, it can provide a basis for the selection of main control parameters when optimizing injection parameters.

[0076] At the same time, the model can also analyze the impact of different CH4 prices on the optimal combination. The carbon storage price is fixed at 0.4$ / m 3 , CH4 prices are 0.2, 0.5, 1$ / m 3 The optimal injection parameter combinations corresponding to different CH4 prices are injection pressure, well spacing, temperature, and mixture ratio of 10MPa-219.2m-309.5K or 36.4℃-100%, 10MPa-187.5m-309.7K or 36.5℃-100%, and 10MPa-174.8m-305.5K or 32.4℃-83.8%, respectively. The CH4 recovery factors are 55.94%, 77.76%, and 80.00%, respectively. The objective functions are 3.36, 8.74, and 16.63$ / m3 ,like Figure 8 shown.

[0077] Under different injection parameter combinations, CO2 may reach a supercritical state, and the synergistic optimization effect of injection parameters under different phase conditions can be further analyzed.

[0078] The multi-injection parameter collaborative optimization method for efficient coal-based carbon fixation and production increase of the present invention significantly improves the recovery rate and carbon fixation rate, reduces the occurrence of very high fuel gas, reduces greenhouse gas emissions, and achieves high efficiency, energy saving, and environmental protection. At the same time, the economic objective function The cost parameters involved are not fixed and can change with market fluctuations. In addition, the injection parameters can be optimized before mining or adjusted during mining. The specific method remains unchanged, only the initial physical property parameters are changed, which reflects the reliability and scalability of this method.

[0079] The embodiments of the present invention are described in detail above in conjunction with the accompanying drawings, but the present invention is not limited thereto. Various changes that can be made within the knowledge scope of technicians in the relevant technical field without departing from the spirit of the present invention are all within the protection scope of the claims of the present invention.

Claims

1. A method for the coordinated optimization of multiple injection parameters for efficient coal-based carbon fixation and production increase, characterized in that: The following steps are involved: Step S1, measuring, collecting and fitting the physical property parameters of the unmineable coal seam at different reservoir depths H; the physical property parameters include reservoir temperature T0, reservoir pressure p0, reservoir permeability k0 and vertical ground stress σ 垂直 ; Step S2, establishing a conventional extraction numerical model that meets the on-site reservoir physical property parameters and production conditions, and comparing it with the on-site gas production rate to verify the reliability of the model; Step S3: After the model is verified, a corresponding mixed gas injection plan is formulated according to the reservoir characteristics of different reservoir depths H, where the injection parameters include the injection pressure P inj , mixed gas ratio c, injection temperature T inj and well spacing S inj ; Step S4, establishing a single variable control numerical calculation model for injection parameters according to the corresponding mixed gas injection scheme; Step S5: Count the numerical calculation results under each injection scheme, including the breakthrough time t occ , Cumulative CH4 recovery Cumulative N2 injection Cumulative CO2 injection and cumulative CO2 recovery Then build the database; Step S6: Design a breakthrough time t occ The economic objective function P j , the economic objective function P j Including CO2 supply cost $ CO2 , Nitrogen supply cost $ N2 , Mixed gas compression cost $ comp , Well construction cost $ well , injection cost $ injtem 、CH4 income $ CH4 、Carbon sequestration benefits $ CO2credit and gas separation cost $ sep ; The objective function formula is as follows: In the formula, Q represents the amount of gas, m 3 , the subscript p represents production, inj represents injection, h represents reservoir thickness, and m represents meters; t occ Indicates breakthrough time, d represents days; Q ptotal Represents the total amount of produced gas, m 3 ; Step S7: Taking the reservoir depth H and the injection parameters under the corresponding injection scheme as independent variables and the numerical calculation result data under each injection scheme as dependent variables, construct and verify the BP neural network; write a genetic algorithm to find the optimal economic objective function P j , and determine the optimal injection parameter combination and the influence weight of each factor under different reservoir depth H conditions.

2. The method for collaborative optimization of multiple injection parameters for efficient coal-based carbon fixation and production increase according to claim 1 is characterized in that: In step S1, the physical property parameters of unmineable coal seams at different reservoir depths H are measured, collected and fitted, specifically: Step S11, establishing a coordinate system with reservoir depth as the horizontal coordinate and physical property parameters as the vertical coordinate; Step S12: plot the tested and collected physical property parameters in a coordinate system and perform parameter fitting.

3. The method for collaborative optimization of multiple injection parameters for efficient coal-based carbon fixation and production increase according to claim 1 is characterized in that: In step S2, a conventional extraction numerical model that meets the field reservoir parameters and production conditions is established. The production conditions include the production well pressure and temperature. The production well pressure is 0.15 MPa, and the production temperature is the initial reservoir temperature. The average error rate between the numerical solution of the gas production rate and the actual field value is no more than 15% as the model verification threshold.

4. The method for collaborative optimization of multiple injection parameters for efficient coal-based carbon fixation and production increase according to claim 1 is characterized in that: In step S3, the injected mixed gas is CO2 and N2, and the injection scheme is specifically as follows: Step S31, the reservoir depth H is 300-1200m, and the increment step is a1; Step S32: injection pressure P inj They are 1.1 to 2.0 times of the reservoir pressure P0, respectively, and the increase step is a2; Step S33, the proportion of CO2 in the CO2 / N2 mixed gas c, where c is between 0.0 and 1.0, and the increment step is a3; Step S34, injection temperature T inj is greater than or equal to the reservoir temperature T0, the difference between the two does not exceed 30°C of the reservoir temperature, and the increase step is a4; Step S35, well spacing S inj Between 150 and 500 m, increase the step size to a5.

5. The method for collaborative optimization of multiple injection parameters for efficient coal-based carbon fixation and production increase according to claim 4 is characterized in that: The values ​​of a1, a2, a3, a4, and a5 in the increasing step are 50, 0.1, 0.1, 5, and 50 respectively.

6. The method for collaborative optimization of multiple injection parameters for efficient coal-based carbon fixation and production increase according to claim 1 is characterized in that: In the numerical calculation model of step S4, the breakthrough time t is set occ , specifically: Step S41: To ensure that there is sufficient concentration of CH4 on the production well side, the ratio of the mixed gas production rate to the CH4 yield is equal to 50% as the breakthrough time t occ ; Step S42: When production > 7000d or CH4 production rate < 1000m 3 / d, the corresponding production time is mandatory to be set as the breakthrough time t occ ,d stands for day.

7. The method for collaborative optimization of multiple injection parameters for efficient coal-based carbon fixation and production increase according to claim 1 is characterized in that: In step S6, the cost $ injtem Including injection pressure cost $ injp and injection temperature cost $ injt , the specific formula is as follows: $ injt =10+2(T inj -T0)($ / d)(Formula 2) $ injp =10+50(p inj -p0)($ / d)(Formula 3) Where $ / d is US dollars per day.

8. The method for collaborative optimization of multiple injection parameters for efficient coal-based carbon fixation and production increase according to claim 1 is characterized in that: In step S7, the BP neural network is specifically as follows: the BP neural network includes 10 hidden layers, the number of iterations is 100, the iteration is terminated when the mean square error is less than 0.1, the model training group is 80% of all injection schemes, and the prediction group is 20% of all injection schemes.

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