Layer series recombination method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention relates to a strata series recombination method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring static indexes and dynamic indexes, influencing strata series recombination, of each small layer of a target reservoir; normalizing the numerical value of each evaluation index of each small layer to obtain a normalized index matrix; determining the comprehensive weight of each evaluation index based on a hierarchical improvement method and an entropy weight method, and calculating a weighted normalized index matrix based on the comprehensive weights; processing the weighted normalized index matrix based on a grey correlation method to obtain the grey correlation degree of each small layer; based on the threshold range of the grey correlation degree of each small layer, determining a preliminary strata series recombination scheme; constraining the preliminary strata series recombination scheme based on technical and economic limit conditions to obtain a final strata series recombination scheme; according to the embodiment of the invention, the influence of the dynamic and static factors, the technical policy limit and the economic constraint index which influence the strata series recombination is comprehensively considered, and the method has a better guiding effect on the efficient development of the high-water-cut-period multilayer sandstone reservoir.

Description

Technical Field

[0001] The present invention relates to the field of oil and gas development, and in particular to a stratum reorganization method, device, electronic equipment and storage medium. Background Art

[0002] At present, most sandstone reservoirs are developed by water injection. After entering the high water content stage, interlayer interference and single layer breakthrough phenomena are serious, resulting in uneven distribution of interlayer and plane residual oil. The development effect is getting worse year by year, but the remaining geological reserves are still considerable. Strata reconstruction technology plays an important role in reducing interlayer differences in reservoirs and improving the degree of water drive control and utilization, and has always been highly valued by reservoir engineers.

[0003] The stratum reorganization method in the related art is generally divided according to the physical parameters and sedimentary characteristics of the reservoir, but it is no longer suitable for the mid-to-late development adjustment of multi-layer sandstone reservoirs in the high water content period. Therefore, inventing a stratum reorganization method for multi-layer sandstone reservoirs in the mid-to-late water injection development has become a technical problem to be solved in this field. Summary of the invention

[0004] The embodiments of the present invention provide a stratum reorganization method, device, electronic device and storage medium to solve the technical problem that the current stratum reorganization technology is no longer suitable for the late stage development and adjustment of multi-layer sandstone reservoirs during water injection development.

[0005] In the first aspect, an embodiment of the present invention provides a stratum reorganization method, comprising: obtaining the values ​​of multiple evaluation indicators corresponding to each sublayer of the target reservoir, wherein the evaluation indicators include static indicators and dynamic indicators that affect the stratum reorganization; normalizing the values ​​of each evaluation indicator of each sublayer to obtain a normalized indicator matrix; determining the comprehensive weight of each evaluation indicator based on a hierarchical improvement method and an entropy weight method, and calculating a weighted normalized indicator matrix based on the comprehensive weight; processing the weighted normalized indicator matrix based on a grey correlation method to obtain the grey correlation degree of each sublayer; determining a preliminary stratum reorganization plan for each sublayer based on a threshold range of the grey correlation degree of each sublayer; determining the technical and economic boundary conditions for stratum reorganization, and constraining the preliminary stratum reorganization plan based on the technical and economic boundary conditions to obtain a final stratum reorganization plan.

[0006] In some embodiments, the static indicators include at least one of the following: oil-bearing area, effective thickness, porosity, permeability, coefficient of variation, oil layer connectivity and crude oil viscosity; the dynamic indicators include at least one of the following: formation pressure maintenance level, well network density, production degree, remaining recoverable reserves and remaining reserve abundance.

[0007] In some embodiments, the degree of recovery and crude oil viscosity are cost-type indicators, the oil-bearing area, effective thickness, oil layer connectivity, remaining recoverable reserves and remaining reserve abundance are benefit-type indicators, and the porosity, permeability, coefficient of variation, formation pressure maintenance level and well network density are interval-type indicators; the normalization of the values ​​of each evaluation indicator of each sublayer includes: for the values ​​of evaluation indicators of different indicator types of each sublayer, normalization is performed using the range transformation method corresponding to the indicator type.

[0008] In some embodiments, the method of determining the comprehensive weight of each evaluation indicator based on the hierarchical improvement method and the entropy weight method includes: processing the normalized indicator matrix based on the hierarchical improvement method to obtain the subjective weight of each evaluation indicator; determining the entropy value of each evaluation indicator based on the subjective weight of each evaluation indicator of each sublayer; determining the information utility value of each evaluation indicator based on the entropy value of each evaluation indicator; normalizing the information utility value of each evaluation indicator to obtain the objective weight of each evaluation indicator; and determining the comprehensive weight of each evaluation indicator according to the subjective weight and objective weight of each evaluation indicator.

[0009] In some embodiments, the calculation formula of the entropy value of each evaluation index is as follows:

[0010]

[0011] Among them, E i represents the entropy value of the i-th evaluation index, represents the subjective weight of the i-th evaluation index, and m represents the number of small layers.

[0012] In some embodiments, the weighted normalized indicator matrix is ​​processed based on the grey correlation method to obtain the grey correlation degree of each sublayer, including: calculating the grey correlation coefficient of the weighted normalized indicator matrix corresponding to each sublayer; and determining the grey correlation degree of each sublayer based on the grey correlation coefficient.

[0013] In some embodiments, the technical and economic boundary conditions include at least one of the following: economic limit recoverable reserves of a single well, technical and economic lower limit of reserve abundance, effective thickness of economic limit of a single well, interlayer permeability difference and span limit of layer combination.

[0014] In a second aspect, an embodiment of the present invention provides a stratum reorganization device, comprising: an acquisition module, used to obtain the values ​​of multiple evaluation indicators corresponding to each sublayer of the target reservoir, wherein the evaluation indicators include static indicators and dynamic indicators that affect the stratum reorganization; a standardization module, used to normalize the values ​​of each evaluation indicator of each sublayer to obtain a normalized indicator matrix; a weighting module, used to determine the comprehensive weight of each evaluation indicator based on the hierarchical improvement method and the entropy weight method, and calculate the weighted normalized indicator matrix based on the comprehensive weight; a processing module, used to process the weighted normalized indicator matrix based on the grey correlation method to obtain the grey correlation degree of each sublayer; a preliminary reorganization module, used to determine the preliminary stratum reorganization plan for each sublayer based on the threshold range of the grey correlation degree of each sublayer; a final reorganization module, used to determine the technical and economic boundary conditions of stratum reorganization, and constrain the preliminary stratum reorganization plan based on the technical and economic boundary conditions to obtain the final stratum reorganization plan.

[0015] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; the memory is used to store computer programs; and the processor is used to implement the steps of the hierarchical reorganization method described in any one of the first aspects when executing the program stored in the memory.

[0016] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the layer reorganization method described in any one of the first aspects.

[0017] The embodiments of the present invention provide a stratum reorganization method, device, electronic device and storage medium, the method comprising: obtaining the values ​​of multiple evaluation indicators corresponding to each sublayer of the target reservoir, wherein the evaluation indicators include static indicators and dynamic indicators that affect the stratum reorganization; normalizing the values ​​of each evaluation indicator of each sublayer to obtain a normalized indicator matrix; determining the comprehensive weight of each evaluation indicator based on the hierarchical improvement method and the entropy weight method, and calculating the weighted normalized indicator matrix based on the comprehensive weight; processing the weighted normalized indicator matrix based on the grey correlation method to obtain the grey correlation degree of each sublayer; determining the preliminary stratum reorganization plan of each sublayer based on the threshold range of the grey correlation degree of each sublayer; determining the technical and economic boundary conditions of the stratum reorganization, and constraining the preliminary stratum reorganization plan based on the technical and economic boundary conditions to obtain the final stratum reorganization plan; that is, the embodiments of the present invention comprehensively consider the influence of dynamic and static factors, technical policy boundaries and economic constraint indicators that affect the stratum reorganization on the stratum reorganization effect, and have a good guiding role in the efficient development of multi-layer sandstone reservoirs in the high water content period. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0020] Figure 1 A schematic flow chart of a layer system reorganization method provided in an embodiment of the present invention;

[0021] Figure 2 for Figure 1 A detailed flow chart of step S103 in the illustrated embodiment;

[0022] Figure 3 for Figure 1 A detailed flow chart of step S104 in the illustrated embodiment;

[0023] Figure 4 A schematic diagram of fuzzy evaluation correlation of each sub-layer of a target reservoir provided by an embodiment of the present invention;

[0024] Figure 5 A schematic diagram of a curve showing a change in the economic limit recoverable reserves of a single well at different oil prices provided by an embodiment of the present invention;

[0025] Figure 6 A schematic diagram of a curve showing a change in the technical and economic lower limit of reserve abundance at different oil prices provided by an embodiment of the present invention;

[0026] Figure 7 An effective thickness of economic limit under different oil prices provided by an embodiment of the present invention;

[0027] Figure 8a A relationship diagram between liquid production intensity and permeability difference provided in an embodiment of the present invention;

[0028] Figure 8b A relationship diagram between a water absorption uniformity coefficient and a permeability variation coefficient provided in an embodiment of the present invention;

[0029] Fig. 9 A diagram showing a relationship between a water injection span and a water absorption thickness ratio provided by an embodiment of the present invention;

[0030] Fig.10 A comparison diagram of development effects before and after layer reorganization provided by an embodiment of the present invention;

[0031] Fig.11 A target reservoir decline rate change curve provided by an embodiment of the present invention;

[0032] Fig.12 A schematic diagram of the structure of a layer system reorganization device provided by an embodiment of the present invention;

[0033] Fig.13 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] At present, most sandstone reservoirs are developed by water injection. After entering the high water cut stage, interlayer interference and single layer breakthrough phenomena are serious, resulting in uneven distribution of interlayer and plane residual oil. The development effect is getting worse year by year, but the remaining geological reserves are still considerable. Strata reorganization technology plays an important role in reducing the interlayer differences of reservoirs and improving the degree of water drive control and utilization, and has always been highly valued by reservoir engineers. For multi-layer sandstone reservoirs with many oil-bearing layers and serious reservoir heterogeneity, it is difficult to effectively tap the remaining oil with conventional oil and water well measures after entering the ultra-high water cut period. However, stratigraphic reorganization can effectively reduce interlayer contradictions and improve the development effect of multi-layer sandstone reservoirs in the high water cut period.

[0036] In related technologies, the stratigraphic reconstruction method is generally divided according to the physical parameters and sedimentary characteristics of the reservoir. However, there is currently no unified stratigraphic reconstruction evaluation index system, and the impact of changes in reservoir dynamic parameters, technical policy boundaries, and economic constraints on the stratigraphic reconstruction effect has not been fully considered. It can be said that the current stratigraphic reconstruction is a stratigraphic division method in a relatively general sense, and is no longer suitable for the development and adjustment of multi-layer sandstone reservoirs in the high water content period.

[0037] In view of the above technical problems, the technical concept of the present invention is: by comprehensively considering the dynamic and static factors affecting the reorganization of the strata, on the basis of constructing a mathematical model using multi-parameter fuzzy judgment and grey correlation methods, and combining restrictive conditions such as technical policy boundaries and economic constraints, a new stratum reorganization method is formed to achieve efficient development guidance for multi-layer sandstone oil reservoirs in the high water-cut period.

[0038] Figure 1The present invention provides a schematic flow chart of a layer reorganization method, wherein the execution subject is a layer reorganization device, which can be implemented by any software / hardware. Figure 1 As shown, the layer system reorganization method includes:

[0039] Step S101, obtaining values ​​of multiple evaluation indicators corresponding to each sublayer of the target reservoir, wherein the evaluation indicators include static indicators and dynamic indicators that affect the reorganization of the strata.

[0040] In some embodiments, the static indicators include at least one of the following: oil-bearing area, effective thickness, porosity, permeability, coefficient of variation, oil layer connectivity and crude oil viscosity; the dynamic indicators include at least one of the following: formation pressure maintenance level, well network density, production degree, remaining recoverable reserves and remaining reserve abundance.

[0041] Specifically, by comprehensively analyzing the geological characteristics and production conditions of the oil reservoir, the main factors affecting the reorganization of the strata are determined, and a comprehensive evaluation index system is established. The geological characteristics, production conditions and remaining oil potential of the oil reservoir are classified and counted, and the main factors affecting the reorganization of the strata are determined to include static factors and dynamic factors. Among them, static factors include oil-bearing area, effective thickness, porosity, permeability, coefficient of variation, connectivity, and crude oil viscosity, and dynamic factors include formation pressure maintenance level, well network density, degree of production, remaining recoverable reserves and remaining reserve abundance. In this step, the static index value and dynamic index value of each sublayer of the target reservoir can be obtained.

[0042] Step S102: normalize the value of each evaluation index of each sub-layer to obtain a normalized index matrix.

[0043] Specifically, determine the number of small layers m, denoted by P = (P1, P2, ..., Pm), which can be understood as candidate parameters, and determine n evaluation indicators, denoted by I = (I1, I2, ..., In). First, establish an evaluation matrix of candidate parameters for evaluation indicators, that is, the evaluation value of the i-th candidate parameter for the j-th evaluation indicator, denoted by x ij (i=1,2,…,m;j=1,2,…,n), forming an m×n dimensional evaluation matrix X=(x ij )m×n, and then normalize the values ​​of each evaluation index in the evaluation matrix to obtain the normalized index matrix R=(r ij )m×n.

[0044] In some embodiments, the degree of recovery and the crude oil viscosity are cost-type indicators, the oil-bearing area, effective thickness, oil layer connectivity, remaining recoverable reserves and remaining reserve abundance are benefit-type indicators, and the porosity, permeability, coefficient of variation, formation pressure maintenance level and well network density are interval-type indicators; then the value of each evaluation indicator of each sublayer in step S102 is normalized, including: for the values ​​of evaluation indicators of different indicator types of each sublayer, the range transformation method corresponding to the indicator type is used for normalization.

[0045] Specifically, the dynamic and static indicators that affect the reorganization of the formation can be divided into three types: cost type, benefit type and interval type. Among them, the cost type evaluation indicators include the degree of recovery and crude oil viscosity. The smaller the value, the better. The lower the degree of recovery, the larger the remaining reserves. The lower the crude oil viscosity, the easier the reservoir is to be water-driven. The benefit type evaluation indicators include oil-bearing area, effective thickness, oil layer connectivity, remaining recoverable reserves and remaining reserve abundance. The larger the value, the better. The larger the oil-bearing area, effective thickness and remaining recoverable reserves, the higher the remaining resources. The higher the remaining reserve abundance, the more favorable it is for well network control. The interval type evaluation indicators include porosity, permeability, coefficient of variation, pressure maintenance level and well network density. There are optimal parameter intervals, such as porosity of 25-28%, permeability of 100-200mD, coefficient of variation of 0.5-0.8, and reasonable well network density of 11.3-14.6 wells / km 2 ; The formation pressure is maintained at 72.8-76.1%.

[0046] In this embodiment, the evaluation indicators are divided into three types: cost type, benefit type and interval type, and the relevant range transformation method is used to normalize the evaluation indicator values.

[0047] Among them, for cost-type indicators, normalization is performed according to formula (1):

[0048]

[0049] For benefit-based indicators, normalization is performed according to formula (2):

[0050]

[0051] For interval indicators, normalization is performed according to formula (3):

[0052]

[0053] Among them, [q 1j ,q 2j] represents the optimal parameter interval of the interval-type evaluation index. According to formulas (1)-(3), the values ​​of the corresponding types of evaluation indicators are normalized, and the evaluation matrix X of the candidate parameters can be converted into a normalized indicator matrix R, as shown in formula (4):

[0054]

[0055] Step S103: determine the comprehensive weight of each evaluation index based on the hierarchical improvement method and the entropy weight method, and calculate a weighted normalized index matrix based on the comprehensive weight.

[0056] Specifically, the methods for determining the weight of indicators mainly include subjective weighting (including Delphi method, analytic hierarchy process and efficacy coefficient method, etc.) and objective weighting method (including entropy weight coefficient method, principal component analysis method and standard deviation coefficient method, etc.). There are certain defects in simply using subjective or objective weighting method. However, this embodiment combines analytic hierarchy process and entropy weight method, which can not only determine the subjective weight of evaluation indicators based on experience, but also make the evaluation indicator weight relatively objective, and then determine the comprehensive weight of each evaluation indicator based on the subjective weight and objective weight, and finally determine the weighted normalized indicator matrix based on the comprehensive weight.

[0057] Step S104: Process the weighted normalized index matrix based on the grey correlation method to obtain the grey correlation degree of each sub-layer.

[0058] Specifically, this embodiment calculates the grey correlation coefficient of the weighted normalized index matrix corresponding to each small layer; and determines the grey correlation degree of each small layer according to the grey correlation coefficient.

[0059] Step S105: Determine a preliminary layer system reorganization plan for each sub-layer based on the threshold range of the grey correlation degree of each sub-layer.

[0060] Specifically, the mean value of the grey correlation degree of each sublayer is calculated and used as the middle value, and the interval is divided according to the preset step size, and the sublayers in each interval are determined as the reorganized sublayers. For example, the mean value of the correlation degree is 0.5 as the middle value, and the step size is 0.1 for interval division, and the sublayers with a correlation degree greater than 0.6 are identified as the first level, the sublayers with a correlation degree between 0.5-0.6 are identified as the second level, and the sublayers with a correlation degree less than 0.5 are identified as the third level. At this time, a preliminary layer system reorganization plan is obtained.

[0061] Step S106, determining the technical and economic boundary conditions for layer reorganization, and constraining the preliminary layer reorganization plan based on the technical and economic boundary conditions to obtain a final layer reorganization plan.

[0062] Production practice shows that the reorganized layer system must have a certain material basis, economic benefits and operability. Therefore, this embodiment comprehensively considers the constraints such as technical limits and economic limits that affect the layer system reorganization, constrains the preliminary layer system reorganization plan, and determines the optimal or final layer system reorganization plan.

[0063] In some embodiments, the technical and economic boundary conditions include at least one of the following: economic limit recoverable reserves of a single well, technical and economic lower limit of reserve abundance, effective thickness of economic limit of a single well, interlayer permeability difference and span limit of layer combination.

[0064] Specifically, according to the current development status of the oil reservoir, cost per ton of oil, operating cost, etc., the limit values ​​of the reservoir that meet the conditions for stratum combination can be determined by combining technical indicators with economic indicators, such as the economic limit recoverable reserves of a single well, the technical and economic lower limit of reserve abundance, and the economic limit effective thickness of a single well. At the same time, in order to avoid inter-layer interference, the inter-layer permeability difference and the span limit of stratum reorganization are introduced for analysis and evaluation. The evaluation results are used as constraints for stratum reorganization to determine the final stratum reorganization plan.

[0065] The economic limit recoverable reserves of a single well can be understood as the cumulative oil production of the oil well during the investment recovery period under existing technical conditions. This embodiment adopts the dynamic cash flow method to calculate the economic limit recoverable reserves of a single well based on the time value of funds, and calculates the economic limit recoverable reserves of a single well based on the input-output break-even principle, as shown in formula (5):

[0066]

[0067] Among them, N R represents the economically recoverable reserves of a single well, 10 4 t; B t It is expressed as the coefficient of production change; Io is the total investment in a single well, ten thousand yuan; P is the crude oil price, yuan / t; O is the operating cost per ton of oil, yuan / t; T is the tax per ton of oil, yuan / t; Ic is the internal rate of return on investment, %; OWR is the ultimate water cut, %.

[0068] The technical and economic lower limit of reserve abundance can be calculated from two perspectives: maximum revenue per unit area and recoverable reserves controlled by a single well, as shown in formula (6):

[0069]

[0070] Among them, F lim Indicates the technical and economic lower limit of reserve abundance, 10 4 t / km 2 ; a represents oil recovery efficiency; %; b represents well pattern density, wells / km 2; P represents the crude oil sales price, RMB / t; O represents the crude oil production and operation costs, RMB / t; ID represents the average single well measures cost, RMB10,000 / well; IB represents the single well surface construction investment, RMB10,000 / well.

[0071] Regarding the effective thickness of the economic limit of a single well, due to the large interlayer interference in the development of multi-layer sandstone reservoirs, the development of a single layer must have sufficient material basis and ensure the contradiction of economic effectiveness. By studying the recoverable reserves of the economic limit of a single well, it is determined whether the reservoir has a material basis, and based on this, the minimum effective thickness of the layer subdivision is determined. The calculation formula for the effective thickness of the economic limit of a single well is shown in (7):

[0072]

[0073] Among them, N R Expressed as the economic limit recoverable reserves of a single well, 10 4 t;D i represents the original reserve single reserve coefficient; R represents the current recovery degree, %; A represents the single well controlled area, km 2 ; ΔR represents the stage-by-stage increase in recovery, %.

[0074] In view of the interlayer permeability difference, multi-layer sandstone reservoirs are generally subdivided into layers to solve the problem of interlayer heterogeneity in accordance with the principle of similar reservoir physical properties and fluid properties during water injection development. However, for reservoirs with serious interlayer heterogeneity, the water drive of medium and low permeability layers is poor, and the degree of geological reserve utilization is uneven. Therefore, the permeability difference within the layer system is a key parameter for the reorganization and development of the layer system. For example, according to the production profiles of 79 layers of 13 wells in the target reservoir, when the permeability difference is greater than 3, the interlayer interference is serious and the production intensity of the oil well is low; the larger the permeability variation coefficient, the greater the difference in water absorption profiles.

[0075] The span limit of the stratum combination refers to the maximum well section length of a set of stratum combinations, which mainly affects the stratum production by affecting the production pressure difference. Generally speaking, the larger the span value of the stratum combination, the more serious the interlayer interference caused by the permeability difference, and the worse the reservoir development effect. The statistical ratio of the span of the water injection well to the water absorption thickness of the target reservoir shows that when the span of the stratum combination is within 60 meters, the water absorption thickness ratio is higher and the water injection development effect is better.

[0076] The stratum reorganization method provided by the embodiment of the present invention comprehensively considers the dynamic and static factors affecting the stratum reorganization, such as the static factors such as oil-bearing area, effective thickness, porosity, permeability, coefficient of variation, connectivity, etc., and the dynamic factors such as the degree of production, well network density, pressure maintenance level, remaining recoverable reserves, remaining reserve abundance, etc., for the complex production situation of multi-layer sandstone reservoirs with high water content, such as multi-layer commingled production and injection, and many casing-changing and shut-down wells. The improved hierarchical analysis method and entropy weight method are used to determine the comprehensive weights of the evaluation parameters, and the advantages of cluster analysis method and grey correlation method are combined to obtain the comprehensive weights of the evaluation parameters. A weighted grey correlation comprehensive evaluation method for multiple parameter screening and evaluation was established; based on the above process and method, the reservoir classification boundaries were quantified and the same type of strata were combined; at the same time, the optimal stratum reorganization plan was determined through constraints such as the stratum combination span boundary, extreme economic indicators, permeability difference, and remaining oil potential, and the target oil reservoir was subdivided into multiple development strata, and the main strata and non-main strata formed independent development well networks, which effectively improved the reservoir utilization degree and the water drive development effect, and played a good guiding role in the efficient development of multi-layer sandstone reservoirs in the high water cut period.

[0077] Based on the above embodiments, Figure 2 for Figure 1 The detailed flow chart of step S103 in the embodiment shown is as follows: Figure 2 As shown, step S103 includes the following steps:

[0078] Step S201: Process the normalized indicator matrix based on the hierarchical improvement method to obtain the subjective weight of each evaluation indicator.

[0079] Specifically, we can first use the judgment matrix constructed by the 1-9 scale method and transform it into a consistency judgment matrix so that it naturally meets the consistency requirements. Then, we can determine the subjective weight of each evaluation index based on the consistency judgment matrix, as shown in formula (8):

[0080]

[0081] in, represents the subjective weight of the i-th evaluation index, ω i Represents the eigenvector of the consistency matrix.

[0082] Step S202: Determine the entropy value of each evaluation indicator based on the subjective weight of each evaluation indicator of each sub-layer.

[0083] In some embodiments, the entropy value of each evaluation index is as shown in formula (9):

[0084]

[0085] Among them, E iIt represents the entropy value of the i-th evaluation index, which is also the distribution entropy of the Euclidean distance. represents the subjective weight of the i-th evaluation index, and m represents the number of small layers.

[0086] Step S203: Determine the information utility value of each evaluation indicator based on the entropy value of each evaluation indicator.

[0087] Specifically, the information utility value of the evaluation index is shown in formula (10):

[0088] d i =1-E i (10)

[0089] Among them, d i It represents the information utility value of the i-th evaluation index, or is called the redundancy of the evaluation index.

[0090] Step S204: normalize the information utility value of each evaluation indicator to obtain the objective weight of each evaluation indicator.

[0091] Specifically, the objective weight of the evaluation index (also called entropy weight) reflects the importance of the evaluation index, as shown in the calculation formula (11):

[0092]

[0093] Among them, ω i ' represents the objective weight of the i-th evaluation index.

[0094] Step S205: Determine the comprehensive weight of each evaluation indicator according to the subjective weight and objective weight of each evaluation indicator.

[0095] Specifically, since there are many evaluation indicators used, in order to avoid the situation where the weights of the evaluation indicators are not much different, the multiplication synthesis method is used to comprehensively weight each evaluation indicator, as shown in formula (12):

[0096]

[0097] Among them, ω i represents the comprehensive weight of the i-th evaluation index; α, β represent weight coefficients, which respectively represent the relative importance of subjective weight and objective weight, and 0≤α, β≤1, α+β=1.

[0098] It should be noted that the entropy weight method needs to pay attention to the following points: if the entropy value of the evaluation indicator is 1, then the entropy weight is 0; if the entropy weight of the evaluation indicator is larger, it means that the evaluation indicator is more important in the evaluation indicator system; the larger the entropy weight of an evaluation indicator, it means that the evaluation indicator can provide more valuable information.

[0099] On the basis of the foregoing embodiments, the comprehensive weights of the evaluation indicators are determined by means of the hierarchical analysis method and the entropy weight method, which improves the accuracy of the indicator weights and facilitates the subsequent combination of the advantages of the cluster analysis method and the grey correlation analysis method. A grey correlation model for multiple parameter screening and evaluation is established to achieve a comprehensive evaluation of each sublayer.

[0100] Based on the above embodiments, Figure 3 for Figure 1 The detailed flow chart of step S104 in the embodiment shown is as follows: Figure 3 As shown, step S104 includes the following steps:

[0101] Step S301, calculating the grey correlation coefficient of the weighted normalized index matrix corresponding to each small layer;

[0102] Step S302: determine the grey relational degree of each sub-layer according to the grey relational coefficient.

[0103] Specifically, the grey correlation coefficient of the weighted normalized index matrix corresponding to each sub-layer is calculated, as shown in formula (13):

[0104]

[0105] Among them, △ i (k) is the absolute difference between the sample values ​​in the ith comparison series and the corresponding sample values ​​in the reference series; max ,△ min It is to compare the maximum and minimum values ​​of the absolute difference of each sample data in the series; ρ is the resolution coefficient, which is used to improve the significance of the difference between correlation coefficients. The smaller the ρ value, the greater the difference between correlation coefficients. Usually, a satisfactory resolution can be obtained when the value is 0.5.

[0106] Calculate the grey correlation degree r of each sublayer based on the grey correlation method i :

[0107]

[0108] Among them, r i is the correlation between the ith comparison sequence and the reference sequence. The greater the correlation, the more consistent the changes of the comparison sequence and the reference sequence.

[0109] On the basis of the above-mentioned embodiments, a grey correlation comprehensive evaluation method for multiple parameter screening and evaluation is established to achieve a comprehensive evaluation of each small layer, which is convenient for subsequent layer system reorganization.

[0110] In order to further understand the embodiment of the present invention, a target oil reservoir includes three original strata, wherein strata one includes I1-II5, strata two includes III 1-5, strata three includes IV 1-8, a total of 22 small layers, that is, m=22, and strata reorganization is performed, including the following steps:

[0111] Step 1: Determine the comprehensive evaluation parameters for layer reorganization.

[0112] Based on the classified statistics of the research results on the geological characteristics, production conditions and remaining oil potential of the target oil reservoirs, a database of static and dynamic indicators that affect the reorganization of the formation system is established. The static factors include oil-bearing area, effective thickness, porosity, permeability, coefficient of variation, connectivity, and crude oil viscosity; the dynamic factors include formation pressure maintenance level, well network density, recovery degree, remaining recoverable reserves and remaining reserve abundance.

[0113] Step 2: Establish a comprehensive evaluation model for layer reorganization.

[0114] First, the evaluation matrix of each sublayer for the evaluation index is determined, and the evaluation matrix is ​​normalized to obtain the normalized index matrix. The parameters of the cost evaluation index (recovery degree and crude oil viscosity) of each sublayer are as described in Table 1:

[0115] Table 1 Cost evaluation index parameter table

[0116]

[0117] The parameters of the benefit evaluation indicators (oil-bearing area, effective thickness, connectivity, remaining recoverable reserves, and remaining reserve abundance) of each sub-layer are described in Table 2:

[0118] Table 2 Benefit evaluation index parameter table

[0119]

[0120] The parameters of interval evaluation indicators (porosity, permeability, coefficient of variation, well pattern density, and pressure maintenance level) of each sublayer are described in Table 3:

[0121] Table 3 Interval type evaluation index parameter table

[0122]

[0123]

[0124] After obtaining the dynamic and static indicator data of each sub-layer, the cost-type indicators are normalized according to formula (1), the benefit-type indicators are normalized according to formula (2), and the interval-type indicators are normalized according to formula (3), so that the evaluation matrix of each sub-layer is transformed into a normalized indicator matrix R, as shown in Table 4.

[0125] Table 4 Standardized indicator matrix

[0126] 1 1 0.2943 0.0545 0.7748 0.3653 0.2052 1 1 1 0.3684 0.9860 0.5347 1 0.3003 0.6909 0.7748 0.4304 0.5555 1 1 1 0.3684 0.9860 0.7609 0.9739 0.2643 1 0.7748 1 1 1 1 1 0.3684 0.9860 0.6279 0.9739 0.3183 0.4000 0.7748 0.4000 0.4003 1 1 1 0.3684 0.9860 0.3059 0.9739 0.4805 0.1455 0.7748 0.2075 0.1075 1 1 0.1237 0.3721 0.9375 0.5391 0.9132 0.3784 0.4182 0.7748 0.5404 0.3388 1 1 1 0.0002 0.9375 0.4341 0.8945 0.3333 0.0182 0.7748 0.11404 0.0532 1 1 1 0.6224 0.9375 0 0.9113 0.4595 0.0545 0.7748 0 0 1 1 1 0.1255 0.9375 0.0428 0.9113 0.4354 0.0727 0.7748 0.0276 0.0245 1 0.6090 0.2702 0.3099 0.9375 0.3895 0.9113 0.3844 0.8000 0 0.4695 0.5736 1 0.2842 1 0.0098 0.0002 0.6241 0.9113 0.4505 0.3091 0 0.4975 0.3579 1 1 1 0.1121 0.0002 0.6989 0.9113 0.3183 0.3818 0 0.5682 0.4220 1 1 1 0.1344 0.0002 0.8781 0.9141 0.2282 0.1273 0 0.4310 0.2545 1 1 1 0.2296 0.0002 0.3110 0.9141 0.2282 0.8000 0 0.3056 0.5092 1 1 1 0.4751 0.0002 0.7438 0.9141 0 0.1091 1 0.2447 0.1909 1 1 1 1 0.4511 0.4957 0.9141 0.0661 0.1636 1 0.1498 0.1558 1 1 1 1 0.4511 0.5857 0.3193 0.0150 0.7091 1 0.4394 0.5616 1 1 1 0.9515 0.4511 0.6776 0.3772 0.6727 0.1636 1 0.3258 0.1878 1 1 1 1 0.4511 0.3239 0.6265 1 0.3455 1 0.1387 0.1843 0 1 1 1 0.4511 0.6742 0.5425 0.7297 0 1 0.2359 0.0968 1 0.8189 1 1 0.4511 0.4613 0.0392 0.2733 0.2909 1 0.2018 0.2344 1 1 0.0151 1 0.4511 0.8725 0 0.2402 0.4364 1 0.5514 0.4163 1 0.0008 1 1 0.4511

[0127] Step 3: Use the hierarchical improvement method and entropy weight method to determine the comprehensive weight of the evaluation indicators.

[0128] Specifically, the subjective weights of the evaluation indicators are first determined based on the improved analytic hierarchy process, that is, the normalized matrix shown in Table 4 is processed according to formula (8) to obtain the hierarchical subjective weights of the individual parameters, as shown in Table 5. Because the difference is small, entropy value processing is required.

[0129] Table 5 Subjective weight table of evaluation indicators

[0130]

[0131]

[0132] Then, the objective weight of the evaluation index is determined according to the entropy weight method. The entropy value of the evaluation index is calculated based on formula (9), as shown in Table 6; the information utility value of the evaluation index is calculated based on formula (10); the entropy weight of each evaluation index is calculated based on formula (11), as shown in Table 7. Entropy weight is the objective weight, which reflects the importance of the evaluation index. It can be seen from Table 7 that the objective weights are effective thickness, pressure maintenance level, remaining reserve abundance, well network density, oil layer connectivity, etc. from large to small; development dynamic indicators are dominant.

[0133] Table 6 Entropy value table of evaluation indicators

[0134]

[0135]

[0136] Table 7 Entropy weight table of evaluation indicators

[0137]

[0138] Finally, the comprehensive weight of the evaluation index is determined. Formula (12) can be used to determine the comprehensive weight of each evaluation index, as shown in Table 8. From Table 8, it can be determined that the comprehensive weights of effective thickness, formation pressure maintenance level, and remaining reserve abundance are the largest.

[0139] Table 8 Comprehensive weight table of evaluation indicators

[0140]

[0141]

[0142] Step 4: Determine the grey correlation degree of each sublayer based on the grey correlation method and sort them according to the grey correlation degree.

[0143] First, calculate the absolute difference between the sample values ​​in the ith comparison series and the reference series, as shown in Table 9.

[0144] Table 9 Differences between reference series and comparison series

[0145]

[0146] Finally, the grey correlation method is used to obtain the grey correlation degree. Based on the above research, the reference series and the comparison series difference are determined, the grey correlation coefficient is calculated based on formula (13), and the grey correlation degree of each sublayer is determined based on the grey correlation method of formula (14), as shown in Table 10.

[0147] Table 10 Grey correlation coefficient table of evaluation indicators

[0148]

[0149] The layers are reorganized according to the correlation, and the small layers with similar correlation are divided into the same set of development layers, such as Figure 4 A schematic diagram of the fuzzy evaluation correlation of each sublayer of a target reservoir provided in an embodiment of the present invention.

[0150] The above process evaluates the reservoir relevance of the stratum reorganization, that is, the stratum similarity combination. However, production practice shows that the reorganized stratum must have a certain material basis, economic benefits and operability. Therefore, it is also necessary to comprehensively consider the constraints of stratum reorganization such as technical limits and economic limits, that is, to proceed to the fifth step.

[0151] Step 5: Determine the technical and economic limits of layer reorganization

[0152] Based on formula (5), the economic limit recoverable reserves of a single well are determined. In this embodiment, the production change coefficient in formula (5) is the ratio of the production in the tth year to the production in the first year. The value of the investment recovery period in the first two years is 0.85, and the value is 0.88 thereafter. The total investment per well of the target reservoir is 5.15 million yuan, the operating cost per ton of oil is 1,220 yuan / t, the limit water cut is 98%, and the internal rate of return is 8%. Figure 5 A schematic diagram of a curve showing a change in the economic limit recoverable reserves of a single well under different oil prices provided by an embodiment of the present invention is shown in FIG. Figure 5 As shown, under the condition of oil price of US$80, the economic limit recoverable reserves of a single well in the target reservoir is 3,900 tons.

[0153] Based on formula (6), the technical and economic lower limit of reserve abundance is determined. In this embodiment, the oil displacement efficiency in formula (6) is set at a recovery factor of 57% and a well pattern density of 12 wells / km. 2The cost of single-well measures is 550,000 yuan / well, there is no single-well ground construction investment, and the operating cost is 1,220 yuan / t. Figure 6 A schematic diagram of a curve showing a change in the technical and economic lower limit of reserve abundance at different oil prices provided by an embodiment of the present invention is shown in FIG. Figure 6 As shown in Figure 2, under the condition of oil price of USD 80, the technical and economic lower limit of reserve abundance of the target reservoir is 29.46×10 4 t / km 2 .

[0154] Based on formula (7), the effective thickness of the economic limit of a single well is determined: Figure 7 The economic limit effective thickness under different oil prices provided by the embodiment of the present invention is as follows: Figure 7 As shown in the figure, under the condition of oil price of USD 80, the effective economic limit thickness of a single well in the target reservoir is 12.2 m.

[0155] According to the statistics of 79 layers of production profiles of 13 wells in the target reservoir, when the permeability difference is greater than 3, the interlayer interference is serious and the production intensity of the oil well is low; the larger the permeability variation coefficient, the greater the difference in the water absorption profile, e.g. Figure 8a A relationship diagram between liquid production intensity and permeability difference provided in an embodiment of the present invention, Figure 8b A relationship diagram between a water absorption uniformity coefficient and a permeability variation coefficient is provided for an embodiment of the present invention. Therefore, the permeability difference should be reasonably controlled within the same set of layers, and the permeability difference should be controlled within 3 as much as possible, and the permeability variation coefficient should be controlled within 0.5.

[0156] The statistical analysis of the ratio of the span of water injection wells to the water absorption thickness of the target reservoir shows that when the span of the stratum combination is within 60 meters, the water absorption thickness ratio is high and the water injection development effect is better. Fig. 9 A diagram showing the relationship between the water injection span and the water absorption thickness ratio provided in an embodiment of the present invention.

[0157] In summary, the embodiment of the present invention uses the grey correlation-ideal model comprehensive evaluation method, while considering the constraints such as technical limits and economic limits to reorganize the target oil reservoir. As shown in the comparison table of the reorganization of the layers in Table 11, the target oil reservoir is re-divided into 4 sets of layers to realize the formation of independent development well networks for the main layers and non-main layers.

[0158] Table 11 Comparison of target reservoir strata reorganization

[0159] category Layer 1 Layer II Layer three Layer 4 Before layer reorganization I1-II5 III1-5 IV1-8 After layer reorganization I1-4 II1-4 II5, III1-5 IV1-8

[0160] Fig.10A comparison chart of development effects before and after the formation reorganization provided by the embodiment of the present invention shows that after the formation optimization and reorganization of the target reservoir, the injection and production well network is more perfect, and the water drive control degree increases from 78.5% to 88.6%, an increase of 10.1 percentage points. The non-main formation is effectively used, and the water drive utilization degree increases from 45.9% to 52.5%, an increase of 6.6 percentage points.

[0161] Fig.11 This is a curve of the decline rate of a target reservoir provided by an embodiment of the present invention. In that year, 11 oil-water wells were implemented in the target reservoir, and 44 wells were used for horizontal injection and interlayer adjustment, achieving a steady recovery in oil production and a gradual decrease in water content. Compared with the previous year, the natural decline and comprehensive decline decreased by 3.51 and 8.9 percentage points respectively.

[0162] In summary, the embodiment of the present invention establishes a comprehensive evaluation method of a grey correlation model for screening and evaluating multiple parameters, comprehensively evaluates the dynamic and static factors that affect the development effect of the reservoir, and adopts a combination of technical indicators and economic indicators to jointly determine the limit value of the reservoir with the conditions for the formation combination, and determines the final formation reorganization plan based on the evaluation results as the constraint conditions for the formation reorganization. It realizes the subdivision of the target reservoir into multiple sets of development formations, and the main formation and non-main formation form an independent development well network, effectively improving the reservoir utilization degree, improving the water drive development effect, and having a good guiding role in the efficient development of multi-layer sandstone reservoirs in the high water content period.

[0163] Fig.12 A schematic diagram of the structure of a layer reorganization device provided by an embodiment of the present invention is shown in FIG. Fig.12 As shown, the layer system reorganization device includes:

[0164] The acquisition module 1201 is used to obtain the values ​​of multiple evaluation indicators corresponding to each sublayer of the target reservoir, wherein the evaluation indicators include static indicators and dynamic indicators that affect the formation reorganization; the standardization module 1202 is used to normalize the values ​​of each evaluation indicator of each sublayer to obtain a normalized indicator matrix; the weighting module 1203 is used to determine the comprehensive weight of each evaluation indicator based on the hierarchical improvement method and the entropy weight method, and calculate the weighted normalized indicator matrix based on the comprehensive weight; the processing module 1204 is used to process the weighted normalized indicator matrix based on the grey correlation method to obtain the grey correlation degree of each sublayer; the preliminary reorganization module 1205 is used to determine the preliminary formation reorganization plan of each sublayer based on the threshold range of the grey correlation degree of each sublayer; the final reorganization module 1206 is used to determine the technical and economic boundary conditions of formation reorganization, and constrain the preliminary formation reorganization plan based on the technical and economic boundary conditions to obtain the final formation reorganization plan.

[0165] In some embodiments, the dynamic indicators include at least one of the following: oil-bearing area, effective thickness, porosity, permeability, coefficient of variation, oil layer connectivity and crude oil viscosity; the static indicators include at least one of the following: formation pressure maintenance level, well network density, production degree, remaining recoverable reserves and remaining reserve abundance.

[0166] In some embodiments, the degree of recovery and the crude oil viscosity are cost-type indicators, the oil-bearing area, effective thickness, oil layer connectivity, remaining recoverable reserves and remaining reserve abundance are benefit-type indicators, and the porosity, permeability, coefficient of variation, formation pressure maintenance level and well network density are interval-type indicators; the standard module 1202 is used to: for the numerical values ​​of evaluation indicators of different indicator types for each sublayer, use the range transformation method corresponding to the indicator type for normalization processing.

[0167] In some embodiments, the weighting module 1203 is specifically used to: process the normalized indicator matrix based on the hierarchical improvement method to obtain the subjective weight of each evaluation indicator; determine the entropy value of each evaluation indicator based on the subjective weight of each evaluation indicator of each sublayer; determine the information utility value of each evaluation indicator based on the entropy value of each evaluation indicator; normalize the information utility value of each evaluation indicator to obtain the objective weight of each evaluation indicator; determine the comprehensive weight of each evaluation indicator according to the subjective weight and objective weight of each evaluation indicator.

[0168] In some embodiments, the calculation formula of the entropy value of each evaluation index is as follows:

[0169]

[0170] Among them, E i represents the entropy value of the i-th evaluation index, represents the subjective weight of the i-th evaluation index, and m represents the number of small layers.

[0171] In some embodiments, the processing module 1204 is specifically used to: calculate the grey correlation coefficient of the weighted normalized indicator matrix corresponding to each small layer; and determine the grey correlation degree of each small layer according to the grey correlation coefficient.

[0172] In some embodiments, the technical and economic boundary conditions include at least one of the following: economic limit recoverable reserves of a single well, technical and economic lower limit of reserve abundance, effective thickness of economic limit of a single well, interlayer permeability difference and span limit of layer combination.

[0173] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process and corresponding beneficial effects of the layer system reorganization device described above can refer to the corresponding process in the aforementioned method example, and will not be repeated here.

[0174] Fig.13 The hardware structure diagram of an electronic device provided by an embodiment of the present invention is shown in FIG. Fig.13 As shown, the electronic device includes a processor 1301, a communication interface 1302, a memory 1303 and a communication bus 1304, wherein the processor 1301, the communication interface 1302, and the memory 1303 communicate with each other through the communication bus 1304.

[0175] Memory 1303, used for storing computer programs;

[0176] In one embodiment of the present invention, the processor 1301 is used to implement the steps of the layer reorganization method provided by any one of the above method embodiments when executing the program stored in the memory 1303.

[0177] The implementation principle and technical effect of the electronic device provided by the embodiment of the present invention are similar to those of the above embodiment and will not be described in detail here.

[0178] The above-mentioned memory 1303 can be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read-only memory), an EPROM, a hard disk or a ROM. The memory 1303 has a storage space for program codes for executing any method steps in the above-mentioned method. For example, the storage space for program codes may include various program codes for implementing various steps in the above method respectively. These program codes can be read from or written into one or more computer program products. These computer program products include program code carriers such as hard disks, compact disks (CDs), memory cards or floppy disks. Such computer program products are usually portable or fixed storage units. The storage unit may have a storage segment or storage space arranged similarly to the memory 1303 in the above-mentioned electronic device. The program code can be compressed, for example, in an appropriate form. Generally, the storage unit includes a program for executing the method steps according to an embodiment of the present invention, that is, a code that can be read by a processor such as 1301, which, when run by an electronic device, causes the electronic device to execute various steps in the method described above.

[0179] The embodiment of the present invention further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the layer system reorganization method described above are implemented.

[0180] The computer-readable storage medium may be included in the device / apparatus described in the above embodiment; or it may exist independently without being assembled into the device / apparatus. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present invention is implemented.

[0181] According to an embodiment of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus, or device.

[0182] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0183] The foregoing is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A layer system reorganization method, characterized in that: include: Obtaining values ​​of multiple evaluation indicators corresponding to each sublayer of the target reservoir, wherein the evaluation indicators include static indicators and dynamic indicators that affect the reorganization of the strata; Normalize the values ​​of each evaluation index of each sub-layer to obtain a normalized index matrix; Determine the comprehensive weight of each evaluation indicator based on the hierarchical improvement method and the entropy weight method, and calculate a weighted normalized indicator matrix based on the comprehensive weight; Processing the weighted normalized index matrix based on the grey correlation method to obtain the grey correlation degree of each sublayer; Based on the threshold range of the grey correlation degree of each sub-layer, the preliminary stratum reorganization scheme of each sub-layer is determined; The technical and economic boundary conditions of the layer system reorganization are determined, and the preliminary layer system reorganization plan is constrained based on the technical and economic boundary conditions to obtain a final layer system reorganization plan.

2. The method according to claim 1, characterized in that The static index includes at least one of the following: oil-bearing area, effective thickness, porosity, permeability, coefficient of variation, oil layer connectivity and crude oil viscosity; The dynamic indicators include at least one of the following: formation pressure maintenance level, well pattern density, production degree, remaining recoverable reserves and remaining reserve abundance.

3. The method according to claim 2, characterized in that The recovery degree and crude oil viscosity are cost-type indicators, the oil-bearing area, effective thickness, oil layer connectivity, remaining recoverable reserves and remaining reserve abundance are benefit-type indicators, and the porosity, permeability, coefficient of variation, formation pressure maintenance level and well pattern density are interval-type indicators; the value of each evaluation indicator of each sublayer is normalized, including: The values ​​of evaluation indicators of different indicator types for each sub-layer are normalized using the range transformation method corresponding to the indicator type.

4. The method according to any one of claims 1 to 3, characterized in that: The comprehensive weight of each evaluation index is determined based on the hierarchical improvement method and the entropy weight method, including: The normalized indicator matrix is ​​processed based on a hierarchical improvement method to obtain a subjective weight of each evaluation indicator; Determine the entropy value of each evaluation indicator based on the subjective weight of each evaluation indicator of each sub-layer; Determine the information utility value of each evaluation indicator based on the entropy value of each evaluation indicator; The information utility value of each evaluation index is normalized to obtain the objective weight of each evaluation index; The comprehensive weight of each evaluation indicator is determined according to the subjective weight and objective weight of each evaluation indicator.

5. The method according to claim 4, characterized in that The calculation formula of the entropy value of each evaluation index is as follows: Among them, E i represents the entropy value of the i-th evaluation index, represents the subjective weight of the i-th evaluation index, and m represents the number of small layers.

6. The method according to any one of claims 1 to 3, characterized in that: The weighted normalized index matrix is ​​processed based on the grey correlation method to obtain the grey correlation degree of each sublayer, including: Calculate the grey correlation coefficient of the weighted normalized index matrix corresponding to each small layer; The grey relational degree of each sublayer is determined according to the grey relational coefficient.

7. The method according to any one of claims 1 to 3, characterized in that: The technical and economic boundary conditions include at least one of the following: The economic limit of recoverable reserves of a single well, the technical and economic lower limit of reserve abundance, the effective thickness of the economic limit of a single well, the permeability difference between layers and the span limit of the layer combination.

8. A layer reorganization device, characterized in that: include: An acquisition module is used to acquire the values ​​of multiple evaluation indicators corresponding to each small layer of the target reservoir, wherein the evaluation indicators include static indicators and dynamic indicators that affect the reorganization of the strata; The normalization module is used to normalize the values ​​of each evaluation index of each sub-layer to obtain a normalized index matrix; A weighting module, used to determine the comprehensive weight of each evaluation indicator based on the hierarchical improvement method and the entropy weight method, and calculate a weighted normalized indicator matrix based on the comprehensive weight; A processing module, used for processing the weighted normalized index matrix based on the grey correlation method to obtain the grey correlation degree of each sublayer; A preliminary reorganization module is used to determine the preliminary layer system reorganization scheme of each sub-layer based on the threshold range of the grey correlation degree of each sub-layer; The final reorganization module is used to determine the technical and economic boundary conditions of the layer reorganization, and constrain the preliminary layer reorganization plan based on the technical and economic boundary conditions to obtain the final layer reorganization plan.

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; Memory, used to store computer programs; The processor is used to implement the steps of the layer system reorganization method described in any one of claims 1 to 7 when executing the program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the layer system reorganization method according to any one of claims 1 to 7 are implemented.

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