Method and system for screening chemical composite cold oil reservoir

Abstract

The invention discloses a method and system for screening chemical composite cold production oil reservoirs, and the method comprises the steps: classifying target oil fields according to the evaluation factors of the target oil fields and the adaptation degrees of different blocks and chemical composite cold production; an oil reservoir numerical simulation model of each type of blocks is established, chemical composite cold production simulation is carried out, and dynamic development indexes of each type of blocks under different oil reservoir characteristic and injection parameter combination conditions are calculated; establishing a development index prediction model according to different combination condition data and corresponding dynamic development indexes; predicting the development index of the to-be-evaluated region by using the development index prediction model, and calculating the yield; and according to the recovery ratio and the yield rate, calculating an adaptation coefficient for representing the suitability degree of the area for chemical composite cold production and a safety coefficient for representing the risk resistance capability of the area, so as to diagnose whether the area is a chemical composite cold production potential block or not.

Description

Technical Field

[0001] The invention relates to the technical field of chemical composite cold production oil reservoir development, and in particular to a method and system for screening chemical composite cold production oil reservoirs. Background Art

[0002] After multiple rounds of steam stimulation in heavy oil reservoirs, it is difficult to further increase crude oil production capacity by continuing the next round of steam stimulation. New development methods need to be found. Common methods include steam drive, multi-element thermal fluid stimulation, thermochemical composite drive, etc. These methods all involve the problem of energy loss during the heat injection process. Unlike thermal oil recovery, chemical composite cold recovery in heavy oil reservoirs uses water drive, injecting foam, viscosity reducer, oil displacement agent and other chemical agents to achieve the effect of emulsification and viscosity reduction, thereby driving the heavy oil.

[0003] Chemical composite cold production of heavy oil is used in many oil fields in my country, including Shengli Oilfield. It has high requirements on the physical properties of heavy oil reservoirs. Screening of reservoirs suitable for chemical composite cold production of heavy oil is an urgent problem to be solved in on-site heavy oil development.

[0004] In the prior art, the screening of heavy oil reservoirs for chemical composite cold recovery is qualitative, but lacks a quantitative characterization method. Summary of the invention

[0005] The purpose of the present invention is to provide a scheme capable of quantitatively screening heavy oil reservoirs suitable for chemical composite cold recovery, thereby achieving simultaneous evaluation of the adaptability and safety of chemical composite cold recovery of heavy oil reservoirs.

[0006] In order to solve the above technical problems, an embodiment of the present invention provides a method for screening chemical composite cold production reservoirs, comprising: classifying the target oil fields according to the evaluation factors of the target oil fields and the degree of adaptability of different blocks to chemical composite cold production; establishing a reservoir numerical simulation model for each type of block and performing chemical composite cold production simulation, and calculating the dynamic development index of each type of block under different reservoir characteristics and injection parameter combination conditions; establishing a development index prediction model according to different combination condition data and the corresponding dynamic development index; using the development index prediction model of the block to which the area to be evaluated belongs, predicting the development index of the current area, thereby calculating the corresponding rate of return; according to the recovery rate and the rate of return, calculating, for the current area, an adaptation coefficient used to characterize the suitability of the area for chemical composite cold production and a safety factor used to characterize the risk resistance of the area, thereby diagnosing whether the current area is a potential block for chemical composite cold production.

[0007] Preferably, in the step of classifying the target oilfield according to the evaluation factors of the target oilfield and the degree of adaptability of different blocks to chemical composite cold production, it includes: performing dimensionless range transformation on the original data of multiple evaluation factors corresponding to each sample point in the target oilfield; determining the weight of each evaluation factor, the evaluation factors including crude oil viscosity, effective thickness, permeability and mineralization; calculating the trajectory coordinates of each sample point according to the new data after transformation of each sample point and the weight of each evaluation factor; drawing a constellation diagram according to the trajectory coordinates of each sample point, so as to divide the target oilfield into multiple types of blocks according to the distribution characteristics of each point in the constellation diagram and the degree of adaptability of different blocks to chemical composite cold production.

[0008] Preferably, the following expression is used to perform dimensionless range transformation on the original data of multiple evaluation factors corresponding to each sample point:

[0009] Q ij =(X ij -X jmin )*π / (X jmax -X jmin )

[0010] Among them, Q ij represents the new data of the jth evaluation factor corresponding to the i-th sample point after range transformation, X ij represents the original data of the jth evaluation factor corresponding to the i-th sample point, X jmin , X jmax They represent the minimum and maximum values ​​of the jth evaluation factor in all samples, respectively, where the trajectory coordinates of each sample point are:

[0011] O i (W1cosQ i1 +W2cosQ i2 +W3cosQ i3 +W4cosQ i4 ,W1sinQ i2 +W2sinQ i2 +W3sinQ i3 +W4sinQ i4 )

[0012] Among them, O i represents the coordinates of the i-th sample point, W1, W2, W3 and W4 represent the weights of crude oil viscosity, effective thickness, permeability and salinity, respectively, and Q i1 , Q i2 , Q i3 and Q i4 They respectively represent the new data of crude oil viscosity, effective thickness, permeability and salinity corresponding to the i-th sample point.

[0013] Preferably, in the process of constructing the development indicator prediction model, it includes: establishing a basic neural network model, using the different combination condition data as training input data and the dynamic development indicator as training output data, using a machine learning algorithm, and performing sample training on the basic neural network model, so as to obtain the corresponding development indicator prediction model for each type of block, wherein the basic neural network model is a BP neural network.

[0014] Preferably, in the step of diagnosing whether the current area to be evaluated is a potential block for chemical composite cold production, it includes: comparing the adaptation coefficient with a preset adaptation threshold, and the safety factor with a preset safety threshold, respectively, and determining whether the current area is suitable for chemical composite cold production based on the comparison results of the two coefficients, wherein, when the adaptation coefficient is greater than the adaptation threshold and the safety factor is less than the safety threshold, it is determined to be suitable; when the adaptation coefficient is less than or equal to the adaptation threshold and the safety factor is greater than or equal to the safety threshold, it is determined to be unsuitable.

[0015] Preferably, the adaptation coefficient and the safety factor are calculated using the following expressions:

[0016] F=RY * ·ω RY +IRR * ·ω IRR

[0017]

[0018]

[0019]

[0020] Among them, F represents the adaptation coefficient, RY represents the recovery factor, IRR represents the rate of return, and RY * Represents the normalized recovery factor, IRR * represents the normalized rate of return, ω RY represents the weight of the recovery factor, ω IRR Represents the weight of the rate of return, RY max RY min Respectively represent the maximum and minimum recovery factors of all the areas to be evaluated, IRR max 、IRR min They represent the maximum and minimum returns of all the areas to be evaluated, S represents the safety factor, and n represents the number of areas to be evaluated. Represents the average rate of return of each area to be evaluated, IRR k Represents the rate of return of the kth sample in the area to be evaluated.

[0021] Preferably, the weight of the crude oil viscosity is 0.3, the weight of the effective thickness is 0.3, the weight of the permeability is 0.2, and the weight of the mineralization is 0.2.

[0022] Preferably, the step of dividing the target oil field into multiple types of blocks according to the distribution characteristics of each point in the constellation diagram and the degree of adaptability of different blocks to chemical composite cold production includes: in the constellation diagram, according to the areas where points with similar parameter values ​​gather together, circling the boundaries of different constellation areas, thereby forming different types of oil field blocks, wherein each type of block represents a different degree of adaptability to chemical composite cold production construction.

[0023] Preferably, the adaptation coefficient ranges from 0 to 1, wherein the adaptation coefficient is proportional to the actual cold production effect; and the safety factor is proportional to the risk level.

[0024] On the other hand, an embodiment of the present invention provides a computer-readable storage medium comprising a series of instructions for executing the method steps as described above.

[0025] In addition, an embodiment of the present invention further provides a system for screening chemical composite cold production reservoirs, comprising: an oilfield classification module, which is configured to classify the target oilfield according to the evaluation factors of the target oilfield and the degree of adaptability of different blocks to chemical composite cold production; a numerical model calculation module, which is configured to respectively establish a reservoir numerical simulation model for each type of block and perform chemical composite cold production simulation, and calculate the dynamic development index of each type of block under different reservoir characteristics and injection parameter combination conditions; a prediction model construction module, which is configured to establish a development index prediction model according to different combination condition data and the corresponding dynamic development index; an evaluation parameter calculation module, which is configured to use the development index prediction model of the block to which the area to be evaluated belongs to predict the development index of the current area, thereby calculating the corresponding rate of return; a potential block diagnosis module, which is configured to calculate, for the current area, a fitness coefficient used to characterize the suitability of the area for chemical composite cold production and a safety factor used to characterize the risk resistance of the area according to the recovery rate and the rate of return, thereby diagnosing whether the current area is a potential block for chemical composite cold production.

[0026] Compared with the prior art, one or more embodiments of the above scheme may have the following advantages or beneficial effects:

[0027] The present invention proposes a method and system for screening chemical composite cold production reservoirs. The method and system include: (1) establishing a constellation diagram method for cluster analysis of chemical composite cold production reservoirs to classify blocks suitable for chemical composite cold production; (2) establishing a reservoir numerical simulation model based on typical blocks; (3) selecting development dynamic indicators and establishing a machine learning sample set; (4) selecting a machine learning algorithm for sample training; (5) predicting the development dynamic indicators of the block and calculating the internal rate of return; (6) establishing an evaluation index for the applicability and risk capacity of chemical composite cold production of oil reservoirs; (7) evaluating the adaptation coefficient and safety factor of the oil field. The present invention is based on four parameters of crude oil viscosity, effective thickness, permeability, and salinity of heavy oil reservoirs, combined with technical indicators (enhanced recovery rate RY) and economic indicators (internal rate of return IRR), in order to continuously improve the recovery rate of oil fields, to form a screening scheme for heavy oil chemical composite cold production reservoirs, so that when performing chemical composite cold production simulation calculations, a plurality of simulation models are established by using a reservoir numerical simulation method to form a development dynamic indicator learning sample set under different conditions and different injection parameters. In this way, the present invention can be widely applied to the screening of chemical composite cold production in heavy oil reservoirs.

[0028] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0030] Figure 1 Schematic diagram of the steps of the method for screening chemical composite cold recovery reservoirs according to an embodiment of the present application.

[0031] Figure 2 This is an example diagram of a constellation diagram in a method for screening chemically composite cold recovery reservoirs according to an embodiment of the present application.

[0032] Figure 3 This is a module block diagram of a system for screening chemical composite cold recovery reservoirs according to an embodiment of the present application. DETAILED DESCRIPTION

[0033] The following will describe the implementation methods of the present invention in detail with reference to the accompanying drawings and embodiments, so that the implementation process of how the present invention applies technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly. It should be noted that as long as there is no conflict, the various embodiments of the present invention and the various features in the embodiments can be combined with each other, and the technical solutions formed are all within the protection scope of the present invention.

[0034] In addition, the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions. Also, although a logical sequence is shown in the flowchart, in some cases, the steps shown or described can be performed in a sequence different from that here.

[0035] The terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments. Unless the context clearly indicates otherwise, the singular forms "one", "one" and "item" used herein are also intended to include plural numbers. It should also be understood that the terms "include" and / or "comprise" used herein specify the existence of stated features, integers, steps, operations, units and / or components, without excluding the existence or addition of one or more other features, integers, steps, operations, units, components and / or combinations thereof.

[0036] After multiple rounds of steam stimulation in heavy oil reservoirs, it is difficult to further increase crude oil production capacity by continuing the next round of steam stimulation. New development methods need to be found. Common methods include steam drive, multi-element thermal fluid stimulation, thermochemical composite drive, etc. These methods all involve the problem of energy loss during the heat injection process. Unlike thermal oil recovery, chemical composite cold recovery in heavy oil reservoirs uses water drive, injecting foam, viscosity reducer, oil displacement agent and other chemical agents to achieve the effect of emulsification and viscosity reduction, thereby driving the heavy oil.

[0037] Chemical composite cold production of heavy oil is used in many oil fields in my country, including Shengli Oilfield. It has high requirements on the physical properties of heavy oil reservoirs. Screening of reservoirs suitable for chemical composite cold production of heavy oil is an urgent problem to be solved in on-site heavy oil development.

[0038] In the prior art, the screening of heavy oil reservoirs for chemical composite cold recovery is qualitative, but lacks a quantitative characterization method.

[0039] In order to solve the technical problems in the above-mentioned background technology, the embodiment of the present application proposes a method and system for screening chemical composite cold production reservoirs. The method and system are based on four parameters such as crude oil viscosity, effective thickness, permeability, and salinity of heavy oil reservoirs, combined with technical indicators (enhanced recovery rate) and economic indicators (internal rate of return), and can achieve the purpose of continuously improving the recovery rate of oil fields, thereby forming a screening scheme for heavy oil chemical composite cold production reservoirs. In this way, the present invention solves the problem that the prior art lacks a quantitative method for screening heavy oil reservoirs for chemical composite cold production and cannot evaluate the adaptability and safety of chemical composite cold production of heavy oil reservoirs.

[0040] Embodiment 1

[0041] Figure 1 This is a schematic diagram of the steps of the method for screening chemical composite cold recovery reservoirs according to an embodiment of the present application. Figure 1 The specific steps of the method for screening chemical composite cold recovery oil reservoirs (also referred to as "reservoir screening method") described in the embodiment of the present invention are described below:

[0042] Step S110 classifies the current target oil field according to the multiple evaluation factors of the target oil field and the degree of adaptability of different blocks to chemical composite cold production. It should be noted that the target oil field described in the embodiment of the present invention includes different types of oil field blocks. Specifically, the target oil field includes multiple types of blocks with different degrees of adaptability to chemical composite cold production construction.

[0043] In step S110, the target oil field is classified into blocks with different suitability for chemical composite cold production by using a cluster analysis constellation diagram method.

[0044] Further, multiple evaluation factors include crude oil viscosity, effective thickness, permeability and salinity.

[0045] Specifically, step S110 first needs to obtain the original data of multiple evaluation factors corresponding to each sample point in the target oil field (different oil field locations are used as sample points), and perform dimensionless range transformation on the original data of multiple evaluation factors corresponding to each sample point, so as to obtain the new data of each evaluation factor corresponding to each sample point after the original data is transformed, so that the value of the new data of each evaluation factor corresponding to each sample point falls within [0, π]. The following expression is used to perform dimensionless range transformation on the original data of multiple evaluation factors corresponding to each sample point:

[0046] Q ij =(X ij -X jmin )*π / (X jmax -X jmin ) (1)

[0047] Among them, Q ij It represents the new data of the jth evaluation factor corresponding to the i-th sample point after range transformation, expressed in radians; X ij represents the original data of the jth evaluation factor corresponding to the i-th sample point, X jmin , X jmax They represent the minimum and maximum values ​​of the jth evaluation factor in all samples respectively.

[0048] Then, the weight value of each evaluation factor is determined. It should be noted that the weight of each evaluation factor can be set according to the actual classification requirements, and the embodiment of the present invention does not specifically limit these weight values.

[0049] In one embodiment, the weight of crude oil viscosity is 0.3, the weight of effective thickness is 0.3, the weight of permeability is 0.2, and the weight of salinity is 0.2.

[0050] Next, step S110 further calculates the trajectory coordinates of each sample point according to the transformed new data of each sample point and the weight of each evaluation factor.

[0051] Since the classification process of the embodiment of the present invention is implemented by cluster analysis constellation diagram method, it is necessary to calculate the coordinate value of each sample point. The constellation diagram is the distribution of each point in the semicircular interval, so the trajectory coordinates of the i-th sample point are:

[0052] O i (W1 cosQ i1 +W2cosQ i2 +W3cosQ i3 +W4 cosQ i4 ,W1 sinQ i2 +W2 sinQ i2 +W3 sinQ i3 +W4sinQ i4 )

[0053] Among them, O i represents the coordinates of the i-th sample point, W1, W2, W3 and W4 represent the weights of crude oil viscosity, effective thickness, permeability and salinity, respectively, and Q i1 , Q i2 , Q i3 and Q i4 They respectively represent the new data of crude oil viscosity, effective thickness, permeability and salinity corresponding to the i-th sample point after range transformation.

[0054] Finally, step S110 will also draw a constellation diagram according to the trajectory coordinates of each sample point, so as to divide the current target oil field into multiple types of blocks according to the distribution characteristics of each point in the current constellation diagram and the degree of adaptability of different blocks to chemical composite cold production.

[0055] Specifically, in the process of drawing the constellation diagram, first draw an upper semicircle with a radius of 1, take the center of the circle as the coordinate origin, and the diameter of the upper semicircle as the X-axis to establish a rectangular coordinate system; then, according to the trajectory coordinates of each sample point, determine the position of each sample point in the semicircle, thereby forming a corresponding constellation diagram, such as Figure 2 shown.

[0056] In the drawn constellation diagram, the boundaries of different constellation areas are circled according to the areas where points with similar parameter values ​​gather together, thus forming different types of oil field blocks. Among them, each type of block represents a different degree of adaptability to chemical composite cold production construction. Specifically, refer to Figure 2 ,In the constellation diagram, points with similar parameter values ​​gather together to form a “constellation”, thereby ,circling the boundaries of different constellations and obtaining different types of oil field blocks, ,thus dividing multiple blocks with different adaptabilities to chemical ,composite cold production construction.

[0057] Step S120 establishes a reservoir numerical simulation model for each type of block, performs chemical composite cold production simulation on different types of reservoir numerical simulation models, and calculates the dynamic development index of each type of block under different reservoir characteristics and injection parameter combinations.

[0058] In step S120, first, for different types of heavy oil reservoirs, considering the static and dynamic main controlling factors of the corresponding types of reservoirs, the reservoir numerical simulation models for different types of heavy oil reservoirs are established based on the typical blocks in each type of reservoir. Then, the chemical composite cold production simulation is carried out for the reservoir numerical simulation model of each type of reservoir, so as to calculate the dynamic development index of each type of block under different reservoir characteristics and injection parameter combination conditions.

[0059] In an embodiment of the present invention, since the simulation process of the reservoir numerical simulation model for each type of oil reservoir is similar, the present invention only takes one type of model as an example to illustrate the chemical composite cold production simulation process. For a certain type of block, it is necessary to first determine the reservoir characteristic condition index and injection parameter condition index required for the chemical composite cold production simulation, and then, according to these condition indexes, input a combination of actual data suitable for this type of oil reservoir, so as to calculate the corresponding dynamic development index under the combination of condition indexes of different actual data. In an embodiment of the present invention, the dynamic development index includes but is not limited to cumulative oil production and recovery rate. Thus, the actual data of different condition combinations and the dynamic development index obtained by corresponding calculations are formed into a learning sample set (i.e., a training sample set).

[0060] Furthermore, in step S120, corresponding training sample sets are obtained for different reservoir types.

[0061] Step S130 establishes a development index prediction model for each type of block according to different combination condition data and corresponding dynamic development indexes.

[0062] In step S130, a development index prediction model of the corresponding type of oil reservoir is established based on the training sample set of each type of oil reservoir. The development index prediction model of each type of oil reservoir is an intelligent model for predicting the development index of the corresponding type of oil reservoir block. Since the construction process of the development index prediction model of each type of oil reservoir is similar, the present invention only takes the prediction model of one type of oil reservoir as an example to illustrate the construction process of the development index prediction model.

[0063] In one embodiment, a basic neural network model is first established; actual data of different combination conditions are used as training input data, and corresponding dynamic development indicators are used as training output data, and a machine learning algorithm is used to perform sample training on the established basic neural network model. After the training is completed, a development indicator prediction model for the current type of block is obtained. Further, step S130 obtains a corresponding development indicator prediction model for each type of block.

[0064] In a specific embodiment, the basic neural network model is a BP neural network.

[0065] After the development index prediction model of each type of reservoir block is constructed, the process proceeds to step S140.

[0066] Step S140 uses the development index prediction model of the block to which the to-be-evaluated area belongs to predict the development index of the current to-be-evaluated area, thereby calculating the (internal) rate of return of the to-be-evaluated area.

[0067] It should be noted that the area to be evaluated refers to the oilfield area that currently needs to be evaluated for chemical composite cold recovery suitability, and the area is part of the target oilfield. In addition, in the embodiment of the present invention, the area to be evaluated can be one or more.

[0068] In step S140, first determine which type of reservoir block the area belongs to based on the actual data of the evaluation factors of the current area to be evaluated, then use the preset model of the development index corresponding to this block, and obtain the development index of the current area to be evaluated based on the actual data of the dynamic and static main control factors of the current area, and further obtain the recovery rate index of the current area to be evaluated. Thus, the corresponding internal rate of return index can be calculated based on the predicted development index of the current area to be evaluated, and then enter step S150.

[0069] Step S150 calculates the adaptation coefficient used to characterize the suitability of the current area to be evaluated for chemical composite cold production and the safety factor used to characterize the risk resistance of the current area to be evaluated based on the recovery factor index and the yield index calculated in step S140, so as to diagnose whether the current area to be evaluated is a potential block for chemical composite cold production.

[0070] In step S150, firstly, according to the recovery factor index and the rate of return index of each area to be evaluated, the adaptation coefficient and the safety factor are calculated respectively according to the following expressions:

[0071] F=RY * ·ω RY +IRR * ·ω IRR (2)

[0072]

[0073]

[0074]

[0075] Among them, F represents the adaptation coefficient, RY represents the recovery factor index of each area to be evaluated, IRR represents the rate of return index of the area to be evaluated, and RY * represents the normalized recovery factor of the area to be evaluated (i.e., calculated using expression (3) based on the recovery factor data of all the areas to be evaluated), IRR * represents the normalized rate of return of the area to be evaluated (i.e., calculated using expression (4) based on the rate of return data of all the areas to be evaluated), ω RY represents the weight of the recovery factor, ω IRR Represents the weight of the rate of return, RY max RY min Respectively represent the maximum and minimum recovery factors of all the areas to be evaluated, IRR max 、IRR min They represent the maximum and minimum returns of all the areas to be evaluated, S represents the safety factor, and n represents the number of areas to be evaluated. Represents the average rate of return of each area to be evaluated, IRR k Represents the rate of return of the kth sample in the area to be evaluated.

[0076] In addition, it should be noted that if there is only one area to be evaluated, there is no need to normalize the enhanced oil recovery index and the rate of return index. The values ​​of these two indicators can be directly substituted into RY in expression (2): * 、IRR * , the adaptation coefficient can be obtained.

[0077] Next, based on the adaptation coefficient and safety factor of each area to be evaluated, it is evaluated whether each area to be evaluated is a potential block for chemical composite cold mining.

[0078] Specifically, the adaptation coefficient of the area to be evaluated is compared with the preset adaptation threshold, and the safety factor of the area to be evaluated is compared with the preset safety threshold, and whether the current area is suitable for chemical composite cold production is determined based on the comparison results of the two coefficients. In the embodiment of the present invention, the adaptation threshold is preferably 0.55, and the safety factor is preferably 0.45.

[0079] In the first embodiment, when the adaptation coefficient is greater than the adaptation threshold and the safety factor is less than the safety threshold, the current area to be evaluated is determined to be a potential block suitable for chemical composite cold mining.

[0080] In the second embodiment, when the adaptation coefficient is less than or equal to the adaptation threshold and the safety factor is greater than or equal to the safety threshold, it is determined that the current area to be evaluated is a block that is not suitable for chemical composite cold mining.

[0081] In the embodiment of the present invention, the value of the adaptation coefficient F is between 0 and 1, and the adaptation coefficient is proportional to the actual cold recovery effect. When F is close to 1, the chemical composite cold recovery effect of the reservoir is better, and vice versa. The safety factor S is the degree of influence of different oil prices on the internal rate of return, and the safety factor is proportional to the risk level. The larger S is, the greater the risk level is, the greater the impact of oil prices on the change of the internal rate of return is, and the higher the risk to be borne at this time; vice versa.

[0082] Embodiment 2

[0083] Based on the above-mentioned embodiment 1, the above-mentioned reservoir screening method is applied to a certain oil field, and the implementation process and application effect of the present invention are specifically described below.

[0084] (1) Establish a constellation diagram method for cluster analysis of chemical composite cold production reservoirs to classify the blocks suitable for chemical composite cold production:

[0085] The four parameters of crude oil viscosity, effective thickness, permeability and salinity at different locations in the target oil field are statistically analyzed, and the target oil field is divided into multiple types of reservoirs according to the constellation diagram method of chemical composite cold recovery reservoir cluster analysis. Figure 2 ;

[0086] (2) Establishing a reservoir numerical simulation model based on typical blocks:

[0087] For different types of heavy oil reservoirs, considering their corresponding static and dynamic main controlling factors, a reservoir numerical simulation model is established based on typical blocks;

[0088] (3) Select and develop dynamic indicators and establish a machine learning sample set:

[0089] Conduct chemical composite cold production simulation calculations on numerical simulation models of different types of reservoirs, determine the cumulative oil production and recovery rate as development indicators, and form a learning sample set of development dynamic indicators under different conditions and different injection parameters;

[0090] (4) Select a machine learning algorithm for sample training:

[0091] For different types of reservoirs, the BP neural network model is selected for sample training with dynamic and static main control factors as input parameters and dynamic indicators of development effect as output parameters;

[0092] (5) Predict the development dynamic indicators of the block and calculate the internal rate of return:

[0093] According to the actual data of the evaluation factors of area A (see Table 1), a certain area A in the target oilfield is determined to be a Class I reservoir. The trained BP neural network model corresponding to this type of reservoir is selected to predict the development dynamic indicators of area A, and the internal rate of return is calculated on this basis.

[0094] Table 1 Reservoir parameter values ​​for area A

[0095]

[0096] (6) Establish evaluation indicators for the applicability and risk capacity of reservoir chemical composite cold recovery

[0097] Combined with the enhanced oil recovery factor RY of 0.076 and the internal rate of return IRR of 0.884 in area A, an evaluation index F that comprehensively reflects the applicability of chemical composite cold recovery in the reservoir is established. Considering the sensitivity of the impact of oil prices on the internal rate of return of each block, an evaluation index S that reflects the risk resistance of the reservoir to possible oil price fluctuations is established.

[0098] (7) Evaluate the adaptation factor F and safety factor S of oil field A:

[0099]

[0100]

[0101] F=RY * ·ω RY +IRR * ·ω IRR =0.3491*0.3+0.3268*0.7=0.3335<0.55

[0102]

[0103] According to the limits of chemical composite cold production indicators, the A oilfield's suitability coefficient is less than 0.55, which is not suitable for chemical composite cold production.

[0104] Embodiment 3

[0105] Based on the above reservoir screening method, an embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, and the computer program is executed to run a method for screening chemical composite cold recovery reservoirs. The computer program can run computer instructions, and the computer instructions include computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc.

[0106] Computer-readable storage media may include: any entity or device that can carry computer program code, recording media, USB flash drives, mobile hard disks, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0107] It should be noted that the content contained in computer-readable storage media can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, the content contained in computer-readable storage media does not include electrical carrier signals and telecommunication signals.

[0108] Embodiment 4

[0109] In addition, based on the method for screening chemical composite cold production reservoirs described in the above-mentioned embodiment 1 and embodiment 2, an embodiment of the present invention also provides a system for screening chemical composite cold production reservoirs (also referred to as "reservoir screening system").

[0110] Figure 3 Module block diagram of the system for screening chemical composite cold recovery reservoirs according to the embodiment of the present application. Figure 3 As shown, the production situation evaluation system described in the embodiment of the present invention includes: an oil field classification module 31, a numerical model calculation module 32, a prediction model construction module 33, an evaluation parameter calculation module 34 and a potential block diagnosis module 35.

[0111] Specifically, the oilfield classification module 31 is implemented according to the method described in step S110, and is configured to classify the target oilfield according to the evaluation factors of the target oilfield and the degree of adaptability of different blocks to chemical composite cold production; the numerical model calculation module 32 is implemented according to the method described in step S120, and is configured to respectively establish a reservoir numerical simulation model for each type of block and perform chemical composite cold production simulation, and calculate the dynamic development index of each type of block under different combinations of reservoir characteristics and injection parameters; the prediction model construction module 33 is implemented according to the method described in step S130, and is configured to calculate the dynamic development index of each type of block according to different combination condition data and corresponding dynamic The evaluation parameter calculation module 34 is implemented according to the method described in step S140, and is configured to use the development indicator prediction model of the block to which the area to be evaluated belongs to predict the development indicators of the current area, so as to calculate the corresponding rate of return; the potential block diagnosis module 35 is implemented according to the method described in step S150, and is configured to calculate, for the current area, according to the recovery factor and the rate of return, an adaptation coefficient used to characterize the suitability of the area for chemical composite cold mining and a safety factor used to characterize the risk resistance of the area, so as to diagnose whether the current area is a potential block for chemical composite cold mining.

[0112] The present invention discloses a method and system for screening chemical composite cold production reservoirs. The method and system include: (1) establishing a constellation diagram method for cluster analysis of chemical composite cold production reservoirs to classify blocks suitable for chemical composite cold production; (2) establishing a reservoir numerical simulation model based on typical blocks; (3) selecting development dynamic indicators and establishing a machine learning sample set; (4) selecting a machine learning algorithm for sample training; (5) predicting the development dynamic indicators of the block and calculating the internal rate of return; (6) establishing an evaluation index for the applicability and risk capacity of chemical composite cold production of oil reservoirs; (7) evaluating the adaptation coefficient and safety factor of the oil field. The present invention is based on four parameters of crude oil viscosity, effective thickness, permeability, and salinity of heavy oil reservoirs, combined with technical indicators (enhanced recovery rate RY) and economic indicators (internal rate of return IRR), in order to continuously improve the recovery rate of oil fields, to form a screening scheme for heavy oil chemical composite cold production reservoirs, so that when performing chemical composite cold production simulation calculations, a plurality of simulation models are established by using a reservoir numerical simulation method to form a development dynamic indicator learning sample set under different conditions and different injection parameters. In this way, the present invention can be widely applied to the screening of chemical composite cold production in heavy oil reservoirs.

[0113] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any person familiar with the technology within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0114] In the description of the present invention, unless otherwise specified, "plurality" means two or more than two; the orientations or positional relationships indicated by the terms "upper", "lower", "left", "right", "inner", "outer", "front end", "rear end", "head", "tail", etc. are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0115] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "connected" and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0116] It should be understood that the embodiments disclosed in the present invention are not limited to the specific structures, processing steps or materials disclosed herein, but should be extended to equivalent substitutions of these features understood by ordinary technicians in the relevant field. It should also be understood that the terms used herein are only used for the purpose of describing specific embodiments and are not meant to be limiting.

[0117] The "one embodiment" or "embodiment" mentioned in the specification means that the specific features, structures or characteristics described in conjunction with the embodiment are included in at least one embodiment of the present invention. Therefore, the phrases "one embodiment" or "embodiment" appearing in various places throughout the specification do not necessarily refer to the same embodiment.

[0118] Although the embodiments disclosed in the present invention are as above, the contents described are only embodiments adopted for facilitating the understanding of the present invention and are not intended to limit the present invention. Any technician in the technical field to which the present invention belongs can make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in the present invention, but the patent protection scope of the present invention shall still be subject to the scope defined in the attached claims.

Claims

1. A method for screening chemical composite cold recovery oil reservoirs, characterized in that: include: According to the evaluation factors of the target oil fields, the target oil fields are classified according to the degree of compatibility of different blocks with chemical composite cold recovery; Establish a reservoir numerical simulation model for each type of block and conduct chemical composite cold production simulation to calculate the dynamic development index of each type of block under different reservoir characteristics and injection parameter combinations; Establishing a development indicator prediction model according to different combination condition data and the corresponding dynamic development indicators; Using the development index prediction model of the block to which the area to be evaluated belongs, the development index of the current area is predicted, thereby calculating the corresponding rate of return; According to the recovery factor and the yield rate, the adaptation coefficient used to characterize the suitability of the area for chemical composite cold production and the safety factor used to characterize the risk resistance of the area are calculated for the current area, so as to diagnose whether the current area is a potential block for chemical composite cold production.

2. The method according to claim 1, characterized in that: The step of classifying the target oil fields according to the evaluation factors of the target oil fields and the degree of compatibility of different blocks with chemical composite cold production includes: Performing dimensionless range transformation on the original data of multiple evaluation factors corresponding to each sample point in the target oil field; Determining the weight of each evaluation factor, wherein the evaluation factors include crude oil viscosity, effective thickness, permeability and salinity; Calculate the trajectory coordinates of each sample point according to the transformed new data of each sample point and the weight of each evaluation factor; A constellation diagram is drawn according to the trajectory coordinates of each sample point, so that the target oil field is divided into multiple types of blocks according to the distribution characteristics of each point in the constellation diagram and the degree of adaptability of different blocks to chemical composite cold production.

3. The method according to claim 2, characterized in that The following expression is used to perform dimensionless range transformation on the original data of multiple evaluation factors corresponding to each sample point: Q ij =(X ij -X jmin )*π / (X jmax -X jmin ) Among them, Q ij represents the new data of the jth evaluation factor corresponding to the i-th sample point after range transformation, X ij represents the original data of the jth evaluation factor corresponding to the i-th sample point, X jmin , X jmax They represent the minimum and maximum values ​​of the jth evaluation factor in all samples, respectively, where the trajectory coordinates of each sample point are: O i (W1cosQ i1 +W2cosQ i2 +W3cosQ i3 +W4cosQ i4 ,W1sinQ i2 +W2sinQ i2 +W3sinQ i3 +W4sinQ i4 ) Among them, O i represents the coordinates of the i-th sample point, W1, W2, W3 and W4 represent the weights of crude oil viscosity, effective thickness, permeability and salinity, respectively, and Q i1 , Q i2 , Q i3 and Q i4 They respectively represent the new data of crude oil viscosity, effective thickness, permeability and salinity corresponding to the i-th sample point.

4. The method according to any one of claims 1 to 3, characterized in that The process of constructing the development indicator prediction model includes: A basic neural network model is established, the different combination condition data are used as training input data and the dynamic development indicators are used as training output data, and a machine learning algorithm is used to perform sample training on the basic neural network model, so as to obtain the corresponding development indicator prediction model for each type of block, wherein the basic neural network model is a BP neural network.

5. The method according to any one of claims 1 to 4, characterized in that: The steps of diagnosing whether the current area to be evaluated is a potential block for chemical composite cold mining include: The adaptation coefficient is compared with the preset adaptation threshold, and the safety coefficient is compared with the preset safety threshold, and whether the current area is suitable for chemical composite cold production is determined according to the comparison results of the two coefficients, wherein: When the adaptation coefficient is greater than the adaptation threshold and the safety factor is less than the safety threshold, determining that the device is suitable; When the adaptation coefficient is less than or equal to the adaptation threshold and the safety factor is greater than or equal to the safety threshold, it is determined to be unsuitable.

6. The method according to any one of claims 1 to 5, characterized in that: The adaptation factor and the safety factor are calculated using the following expressions: F=RY * ·oh RY +IRR * ·oh IRR Among them, F represents the adaptation coefficient, RY represents the recovery factor, IRR represents the rate of return, and RY * Represents the normalized recovery factor, IRR * represents the normalized rate of return, ω RY represents the weight of the recovery factor, ω IRR Represents the weight of the rate of return, RY max RY min Respectively represent the maximum and minimum recovery factors of all the areas to be evaluated, IRR max 、IRR min They represent the maximum and minimum returns of all the areas to be evaluated, S represents the safety factor, and n represents the number of areas to be evaluated. Represents the average rate of return of each area to be evaluated, IRR k Represents the rate of return of the kth sample in the area to be evaluated.

7. The method according to claim 2 or 3, characterized in that: The weight of the crude oil viscosity is 0.3, the weight of the effective thickness is 0.3, the weight of the permeability is 0.2, and the weight of the mineralization is 0.

2.

8. The method according to claim 2, 3 or 7, characterized in that: The step of dividing the target oil field into multiple types of blocks according to the distribution characteristics of each point in the constellation diagram and the degree of adaptability of different blocks to chemical composite cold production includes: In the constellation diagram, the boundaries of different constellation areas are circled according to the areas where points with similar parameter values ​​gather together, thereby forming different types of oil field blocks, wherein each type of block represents a different degree of adaptability to chemical composite cold production construction.

9. The method according to any one of claims 1 to 8, wherein the range of the adaptation coefficient is between 0 and 1, wherein: The adaptation coefficient is proportional to the actual cold production effect; The safety factor is proportional to the degree of risk.

10. A computer-readable storage medium, characterized in that: It contains a series of instructions for executing the method steps as claimed in any one of claims 1 to 9.

11. A system for screening chemical composite cold recovery reservoirs, characterized in that: include: An oilfield classification module, which is configured to classify the target oilfields according to the evaluation factors of the target oilfields and the degree of compatibility of different blocks with chemical composite cold production; A numerical model calculation module is configured to establish a reservoir numerical simulation model for each type of block and perform chemical composite cold production simulation to calculate the dynamic development index of each type of block under different reservoir characteristics and injection parameter combination conditions; A prediction model building module, configured to build a development indicator prediction model according to different combination condition data and the corresponding dynamic development indicators; An evaluation parameter calculation module, configured to use the development index prediction model of the block to which the area to be evaluated belongs to predict the development index of the current area, thereby calculating the corresponding rate of return; The potential block diagnosis module is configured to calculate, for the current area, an adaptation coefficient used to characterize the suitability of the area for chemical composite cold mining and a safety factor used to characterize the risk resistance of the area based on the recovery rate and the yield rate, so as to diagnose whether the current area is a potential block for chemical composite cold mining.

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