Oil field productivity classification method, device and equipment based on Monte Carlo method

Abstract

The invention relates to an oil field productivity classification method, device and equipment based on a Monte Carlo method, and the method comprises the following steps: obtaining actual productivity data and actual productivity factor data, and carrying out the calculation based on a preset productivity calculation formula to obtain comprehensive coefficient data; according to the actual capacity factor data and the comprehensive coefficient data, carrying out statistical analysis on the capacity factors of each type of reservoir to obtain actual distribution characteristics of each capacity factor of each type of reservoir; determining the optimal sampling frequency of each productivity factor of each type of reservoir on the basis of the actual distribution characteristics; determining Monte Carlo simulation times according to the optimal sampling times of each productivity factor of each type of reservoir; according to a random number obtained after sampling of the actual productivity factor data and the comprehensive coefficient data, productivity data of each type of reservoir is obtained through calculation based on a preset productivity calculation formula, and the number of the productivity data is equal to the Monte Carlo simulation times; and obtaining a productivity probability distribution curve of the type of reservoir according to the productivity data of each type of reservoir so as to determine a productivity classification boundary.

Description

Technical Field

[0001] The present invention relates to the technical field of oilfield productivity classification, and in particular to an oilfield productivity classification method, device and equipment based on the Monte Carlo method. Background Art

[0002] Oilfield capacity analysis is a very important research topic in oilfield development, and capacity classification can better evaluate the capacity, mining value and economic benefits of different oilfields, and guide the formulation of scientific development plans.

[0003] The traditional oilfield capacity classification method is to perform regression analysis on measured capacity data to form multiple groups of capacity regression curves. The horizontal axis represents the factors affecting capacity, and the vertical axis represents capacity. Each group represents a reservoir type. The disadvantages of this method are: ① The regression curve obtained due to the small number of measured data points has limited representativeness; ② Multiple capacity data can be calculated for the same horizontal axis value, which is not unique. Its uniqueness is determined by the reservoir type. When there is little data and the reservoir type is not clearly understood, this method is difficult to uniquely determine the capacity; ③ It does not take into account the uncertainty in the reservoir understanding process.

[0004] Oilfield production practice has confirmed that the productivity of oil wells is affected by many uncertain factors, such as uncertainty in seepage mechanism, uncertainty in reservoir parameters, uncertainty in inter-well interference, and energy supply issues. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide an oilfield capacity classification method, device and equipment based on the Monte Carlo method to address the defects of unreasonable capacity classification caused by human subjectivity and boundary uncertainty in related capacity classification technologies.

[0006] The technical solution adopted by the present invention to solve the technical problem is: a method for oil field productivity classification based on the Monte Carlo method, comprising the following steps:

[0007] S1. Obtaining actual capacity data and actual capacity factor data, and calculating comprehensive coefficient data based on a preset capacity calculation formula; wherein the comprehensive coefficient is a type of capacity factor used to characterize multiple uncertain factors affecting capacity;

[0008] S2. performing statistical analysis on the productivity factors of each type of reservoir based on the actual productivity factor data and the comprehensive coefficient data to obtain actual distribution characteristics of each productivity factor of each type of reservoir;

[0009] S3. Sampling the actual productivity factor data and the comprehensive coefficient data, and determining the optimal sampling number for each productivity factor of each type of reservoir based on the actual distribution characteristics;

[0010] S4. Determine the number of Monte Carlo simulations based on the optimal number of sampling for each productivity factor of each type of reservoir;

[0011] S5. Calculate the productivity data of each type of reservoir based on the random numbers obtained by sampling the actual productivity factor data and the comprehensive coefficient data and the preset productivity calculation formula, where the number of productivity data is equal to the number of Monte Carlo simulations;

[0012] S6. Obtain a productivity probability distribution curve for each type of reservoir based on the productivity data of that type of reservoir, and determine productivity classification boundaries based on the productivity probability distribution curves of all reservoirs.

[0013] Furthermore, in the oil field productivity classification method based on the Monte Carlo method described in the present invention, in step S2, each productivity factor of each type of reservoir obeys a normal distribution.

[0014] Furthermore, in the oilfield productivity classification method based on the Monte Carlo method of the present invention, in step S3 of sampling the actual productivity factor data and the comprehensive coefficient data, the following steps are included:

[0015] The capacity factor that follows the normal distribution is sampled using the following formula:

[0016]

[0017] Where x is the production capacity factor; x i is the i-th data value of the capacity factor; μ' is the mean of the normal distribution of the capacity factor; σ is the standard deviation of the normal distribution of the capacity factor; m is the actual number of samples; is a uniformly distributed random number in the interval [0, 1]; m1 is the sample size for each sampling.

[0018] Furthermore, in the oilfield productivity classification method based on the Monte Carlo method of the present invention, in step S3, determining the optimal number of sampling times for each productivity factor of each type of reservoir based on the actual distribution characteristics includes:

[0019] A statistical analysis is performed on the random numbers obtained for each productivity factor of each type of reservoir under different sampling times, and the corresponding simulated distribution characteristics are calculated. The simulated distribution characteristics are then compared with the actual distribution characteristics to determine the sampling number that approximates the actual situation and use it as the optimal sampling number.

[0020] Furthermore, in the oil field productivity classification method based on the Monte Carlo method of the present invention, step S4 includes:

[0021] Compare the optimal sampling times of each productivity factor for each type of reservoir, select the maximum sampling times and determine it as the number of Monte Carlo simulations.

[0022] Step S6 includes:

[0023] The productivity data of each type of reservoir is collected to obtain the productivity probability distribution curve of the reservoir type. The number of productivity probability distribution curves corresponds to the number of reservoir types.

[0024] The productivity probability distribution curves of various reservoirs are plotted into a cluster of productivity probability distribution curves, and the intersection points of the curves of adjacent reservoirs are determined in the cluster of productivity probability distribution curves;

[0025] The intersection point of the curves is used as the capacity classification limit value; or

[0026] Before performing statistical analysis on the productivity factor of each type of reservoir in step S2, the method further includes:

[0027] Reservoirs are classified according to reservoir permeability classification standards.

[0028] Furthermore, in the oil field productivity classification method based on the Monte Carlo method described in the present invention, the preset productivity calculation formula is:

[0029]

[0030] Among them, Q is the oil well production, m 3 / d; K is permeability, mD; h is effective thickness of oil layer, m; Δp is production pressure difference, MPa; r e is the well control radius, m; r w is the radius of the oil well, m; μ is the viscosity of the formation crude oil, mPa·s; B is the crude oil volume coefficient; and C is the comprehensive coefficient of the impact of multiple uncertain factors on productivity, including seepage mechanism, inter-well interference, energy supply, reservoir transformation or pollution.

[0031] Furthermore, in the oilfield productivity classification method based on the Monte Carlo method described in the present invention, for low-permeability sandstone reservoirs, the seepage mechanism includes the starting pressure gradient λ and the stress sensitivity γ. If the starting pressure gradient λ and the stress sensitivity γ are known, and the skin coefficient caused by reservoir transformation or pollution is also known, the comprehensive coefficient C is:

[0032] C=C λ ·C γ ·C S C1

[0033] C λ =1-λ(r e -r w ) / Δp

[0034] C γ =e -γΔp

[0035] C S=1 / (1+S / ln(r e / r w ))

[0036] Among them, C λ is the influencing factor of the starting pressure gradient on the production capacity; λ is the starting pressure gradient, MPa / m; C γ is the factor affecting stress sensitivity on production capacity; C S is the impact factor of reservoir transformation or pollution on productivity; C1 is the impact factor of other uncertain parameters on productivity.

[0037] Furthermore, in the oil field productivity classification method based on the Monte Carlo method described in the present invention, for medium to extra-high permeability reservoirs, the following are obtained:

[0038] λ=0,γ=0,C λ =1, C γ =1.

[0039] In addition, the present invention also provides an oil field productivity classification device based on the Monte Carlo method, comprising:

[0040] A comprehensive coefficient calculation module is used to obtain actual production capacity data and actual production capacity factor data, and calculate comprehensive coefficient data based on a preset production capacity calculation formula; wherein the comprehensive coefficient is a type of production capacity factor used to represent multiple uncertain factors affecting production capacity;

[0041] an actual distribution characteristic analysis module, configured to perform statistical analysis on the productivity factors of each type of reservoir based on the actual productivity factor data and the comprehensive coefficient data, so as to obtain the actual distribution characteristics of each productivity factor of each type of reservoir;

[0042] an optimal sampling number determination module, configured to sample the actual productivity factor data and the comprehensive coefficient data, and determine the optimal sampling number for each productivity factor of each type of reservoir based on the actual distribution characteristics;

[0043] A Monte Carlo simulation number determination module is used to determine the Monte Carlo simulation number according to the optimal sampling number of each productivity factor of each type of reservoir;

[0044] a production capacity calculation module, configured to calculate production capacity data for each type of reservoir based on a random number obtained by sampling the actual production capacity factor data and the comprehensive coefficient data, and based on the preset production capacity calculation formula, wherein the number of production capacity data is equal to the number of Monte Carlo simulations;

[0045] The capacity classification boundary determination module is used to obtain the capacity probability distribution curve of each type of reservoir based on the capacity data of the reservoir, and to determine the capacity classification boundary based on the capacity probability distribution curves of all reservoirs.

[0046] In addition, the present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the steps of the above-mentioned Monte Carlo method-based oil field productivity classification method by calling the computer program stored in the memory.

[0047] The Monte Carlo-based oilfield productivity classification method, device, and equipment of the present invention have the following beneficial effects: The present invention combines many uncertain factors into a single variable to describe the uncertainties that cannot be quantified, improving the oil well production calculation formula and simplifying the complex calculations caused by various uncertainties. The Monte Carlo method is used to realistically describe the data distribution characteristics of the main control factors of productivity, effectively resolving the subjectivity and boundary uncertainty of productivity classification caused by the lack of measured productivity sample data. Random sampling maximizes the objectivity of the values of production-influencing parameters, improving the scientific nature and rationality of the oilfield productivity classification results, overcoming the shortcomings and drawbacks of traditional deterministic methods, and providing new options and new ideas for oilfield productivity classification. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0049] Figure 1 1 is a flow chart of an oil field productivity classification method based on the Monte Carlo method according to an embodiment of the present invention;

[0050] Figure 2 1 is another flow chart of the oil field productivity classification method based on the Monte Carlo method according to an embodiment of the present invention;

[0051] Figure 3 This is a distribution diagram of actual data of crude oil viscosity in a Class A reservoir formation according to an embodiment of the present invention;

[0052] Figure 4 7 is a curve showing the change of the mean viscosity of the crude oil in the Class A reservoir formation with the number of samplings according to an embodiment of the present invention;

[0053] Figure 5 7 is a graph showing the change in viscosity standard deviation of crude oil in a Class A reservoir formation versus sampling times according to an embodiment of the present invention;

[0054] Figure 6 7 is a probability density distribution curve diagram of the Class A reservoir productivity in an embodiment of the present invention;

[0055] Figure 7 This is a low permeability reservoir productivity classification boundary diagram according to an embodiment of the present invention;

[0056] Figure 8 is a comparison chart of the actual productivity data of the low-permeability reservoir and the productivity classification limit value in an embodiment of the present invention;

[0057] Figure 9 Schematic diagram of the structure of an oil field productivity classification device based on the Monte Carlo method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, the specific embodiments of the present invention are now described in detail with reference to the accompanying drawings. In the following description, it should be understood that the directions or positional relationships indicated by "front", "back", "up", "down", "left", "right", "longitudinal", "horizontal", "vertical", "horizontal", "top", "bottom", "inside", "outside", "head", "tail", etc. are based on the directions or positional relationships shown in the accompanying drawings and are constructed and operated in specific directions. They are only for the convenience of describing the technical solution and do not indicate that the devices or components referred to must have specific directions. Therefore, they should not be understood as limiting the present invention.

[0059] It should also be noted that, in the embodiment of the present invention, the capacity factor is also the capacity influencing factor, and the capacity grading limit value is also the capacity classification result.

[0060] In the following description, specific details such as particular system structures and techniques are provided for purposes of illustration, not limitation, to facilitate a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

[0061] like Figure 1 As shown, in a preferred embodiment, the oil field productivity classification method based on the Monte Carlo method of this embodiment includes the following steps:

[0062] S1. Obtain actual capacity data and actual capacity factor data, and calculate comprehensive coefficient data based on a preset capacity calculation formula. The comprehensive coefficient is a type of capacity factor used to characterize various uncertain factors that affect capacity.

[0063] It is understandable that oil well productivity is affected by many uncertain factors, such as uncertainty in flow mechanisms, uncertainty in reservoir parameters, uncertainty in interwell interference, and energy supply issues. These uncertainties are often difficult to quantify. To account for their impact on productivity, embodiments of the present invention propose integrating these uncertainties into a single variable (i.e., a comprehensive coefficient) to describe the system of unquantifiable uncertainties, thereby improving the oil well productivity calculation formula. This approach, using a single comprehensive coefficient, simplifies the complex calculations caused by various uncertainties and imbues the improved oil well calculation formula with new physical meaning. In other words, the complex underground environment increases the difficulty of quantitatively characterizing oilfield productivity classification. Traditional oilfield productivity classification methods rely on regression analysis based on limited measured productivity data, which has few sample points and does not consider the uncertainty of factors affecting productivity. To characterize these difficult-to-quantify factors affecting oil well productivity, such as uncertainty in flow mechanisms, uncertainty in reservoir parameters, uncertainty in interwell interference, and energy supply, a comprehensive coefficient is proposed to describe these uncertainties. The oil well productivity calculation formula is then improved, and this improved productivity calculation formula is used to quantify this comprehensive coefficient. It not only simplifies the complex calculations caused by various uncertain factors, but also gives new physical meaning to the improved oil well calculation formula.

[0064] The improved formula is the preset capacity calculation formula:

[0065]

[0066] Among them, Q is the oil well production, m 3 / d; K is permeability, mD; h is effective thickness of oil layer, m; Δp is production pressure difference, MPa; r e is the well control radius, m; r w is the radius of the oil well, m; μ is the viscosity of the formation crude oil, mPa·s; B is the crude oil volume coefficient; and C is the comprehensive coefficient of the impact of various uncertain factors on productivity, such as seepage mechanism, inter-well interference, energy supply, reservoir transformation or pollution, etc.

[0067] In some embodiments, for low permeability sandstone reservoirs, the seepage mechanism includes the starting pressure gradient λ and the stress sensitivity γ. If the starting pressure gradient λ and the stress sensitivity γ are known, and the skin coefficient caused by reservoir transformation or contamination is also known, the comprehensive coefficient C is:

[0068] C=C λ ·C γ ·C S C1

[0069] C λ =1-λ(r e -r w ) / Δp

[0070] C γ =e -γΔp

[0071] C S =1 / (1+S / ln(r e / r w ))

[0072] Among them, C λ is the influencing factor of the starting pressure gradient on the production capacity; λ is the starting pressure gradient, MPa / m; C γ is the factor affecting stress sensitivity on production capacity; C S is the factor affecting reservoir stimulation or contamination on productivity; C1 is the factor affecting productivity due to other uncertain parameters (such as well-to-well interference and energy supply). By combining actual productivity data and other conventional productivity factors with the improved formula, a series of coefficients for C1 and a series of comprehensive coefficients for C can be calculated.

[0073] In some embodiments, for medium to very high permeability reservoirs, there are:

[0074] λ=0,γ=0,C λ =1, C γ =1.

[0075] In other words, for medium to extra-high permeability reservoirs, the comprehensive coefficient can also be used to independently characterize the relevant production capacity influencing factors.

[0076] S2. Statistically analyzing the productivity factors of each reservoir type based on the actual productivity factor data and the comprehensive coefficient data to obtain the actual distribution characteristics of each productivity factor of each reservoir type. Optionally, before performing statistical analysis on the productivity factors of each reservoir type in step S2, the method further includes classifying the reservoirs according to a reservoir permeability classification standard. Specifically, in this embodiment of the present invention, each productivity factor of each reservoir type follows a normal distribution.

[0077] S3. Sampling the actual productivity factor data and the comprehensive coefficient data, and determining the optimal number of samplings for each productivity factor for each reservoir type based on the actual distribution characteristics. Specifically, sampling the actual productivity factor data and the comprehensive coefficient data is performed based on the Monte Carlo method, and determining the optimal number of samplings for each productivity factor for each reservoir type based on the actual distribution characteristics.

[0078] The Monte Carlo method is a numerical calculation method based on probability and statistics theory. Its theoretical basis is the law of large numbers. Its core goal is to obtain numerical solutions or probability estimates through a large number of random experiments. The greater the number of random experiments, the closer the simulation results are to reality. The Monte Carlo method maximizes the objectivity of the values of production-influencing parameters through random sampling, improving the scientific and rationality of oilfield capacity classification results. It addresses the shortcomings of traditional deterministic methods, providing new options and exploring new approaches for oilfield capacity classification.

[0079] In some embodiments, through statistical analysis of historical data of the capacity factors in the improved capacity calculation formula, it is found that each capacity factor substantially obeys a normal distribution, and thus each capacity factor satisfies the following formula:

[0080]

[0081] In step S3, when sampling the actual capacity factor data and the comprehensive coefficient data, the capacity factor that obeys the normal distribution is sampled using the following formula:

[0082]

[0083] Where x is the production capacity factor. i is the i-th data value of the capacity factor. μ' is the mean of the normal distribution of the capacity factor. σ is the standard deviation of the normal distribution of the capacity factor. m is the actual number of samples. is a uniformly distributed random number in the interval [0, 1]. m1 is the sample size for each sampling.

[0084] In step S3, based on the actual distribution characteristics, the optimal sampling number for each productivity factor of each reservoir type is determined. A statistical analysis is performed on the random numbers obtained for each productivity factor of each reservoir type at different sampling times to calculate the corresponding simulated distribution characteristics. The simulated distribution characteristics are then compared and analyzed with the actual distribution characteristics to determine the sampling number that approximates the actual situation, which is used as the optimal sampling number. It should be noted that in the embodiment of the present invention, approximating the actual situation means that the consistency between the simulated distribution characteristics and the actual distribution characteristics is within a preset allowable deviation range, indicating that the simulated distribution characteristics obtained at the current sampling number approximate the actual situation.

[0085] S4. Determine the number of Monte Carlo simulations based on the optimal sampling number for each productivity factor of each reservoir type. Specifically, in this step, the optimal sampling number for each productivity factor of each reservoir type is compared, and the maximum sampling number is selected and determined as the number of Monte Carlo simulations.

[0086] It can be understood that the theoretical basis of the Monte Carlo method is to approximate the capacity factor according to the law of large numbers. When the number of simulations is large enough, the simulated capacity factor is closer to the real value. In order to achieve good consistency between the simulated data and the actual data of the capacity factor, reduce the random error of the capacity factor, improve the stability and reliability of the simulated data, and ensure more accurate capacity simulation results, this solution comprehensively determines the sampling number of the capacity factor through the trend of the change of the random number characteristic values (such as mean, standard deviation, etc.) under different simulation times. Each capacity factor has a sampling number, and the maximum sampling number max (n1, n2, ..., n j ) is the number of simulations n for the production capacity Q. The formula is as follows:

[0087] n=max(n1,n2,...,n j )

[0088] S5. The random numbers obtained after sampling the actual production capacity factor data and the comprehensive coefficient data are used to calculate the production capacity data of each type of reservoir based on the preset production capacity calculation formula, and the number of production capacity data is equal to the number of Monte Carlo simulations.

[0089] S6. Obtain a productivity probability distribution curve for each type of reservoir based on the productivity data of that type of reservoir, and determine productivity classification boundaries based on the productivity probability distribution curves of all reservoirs.

[0090] For example, if the number of Monte Carlo simulations is 10,000, then for each type of reservoir, the random numbers obtained after sampling according to the statistical distribution characteristics of each production capacity factor are used to calculate the production capacity Q using the improved production capacity formula. The simulation is performed 10,000 times, and the results are statistically analyzed to obtain the production capacity probability distribution curve of this type of reservoir.

[0091] In this embodiment, for the multiple sets of regression curves formed by the traditional method, the same horizontal axis value has non-unique production capacity data, and different reservoir levels may also have the same oil well production capacity. In this case, the researchers' selection of the production capacity classification boundaries is subjective. At the same time, due to the limited number of oil well production capacity samples, the production capacity classification boundaries will also be uncertain. The embodiment of the present invention combines many uncertain factors into one variable to describe the uncertainty factors that cannot be quantified, improves the oil well production calculation formula, simplifies the complex calculations brought about by various uncertain factors, and realistically describes the data distribution characteristics of the main control factors of production capacity. It can effectively solve the problems of human subjectivity and boundary uncertainty in production capacity classification caused by the small amount of measured production capacity sample data. By random sampling, the objectivity of the production influencing parameter values is maximized, the scientificity and rationality of the oil field production capacity classification results are improved, the deficiencies and shortcomings of the traditional deterministic method are compensated, and new options and new ideas are provided for oil field production capacity classification.

[0092] In some embodiments, step S6 includes: statistically analyzing the productivity data for each reservoir type to obtain a productivity probability distribution curve for that reservoir type, with the number of productivity probability distribution curves corresponding to the number of reservoir types. In other words, the productivity data for each reservoir type is statistically analyzed separately to obtain a productivity probability distribution curve corresponding to each reservoir type. Next, the productivity probability distribution curves for each reservoir type are plotted into a cluster of productivity probability distribution curves, and the intersection points of adjacent reservoir curves within the cluster are determined. These intersection points are used as productivity classification thresholds.

[0093] This example uses reservoir permeability classification standards to identify the primary factors influencing productivity within the standards and their data distribution characteristics, obtaining a probability distribution function for these factors. Monte Carlo analysis is then used to maximize random sampling of these factors, calculate and establish a cluster of productivity probability distribution curves, and determine productivity classification boundaries. This ultimately results in a set of technical processes and modules suitable for offshore oilfield productivity classification. These processes comprehensively account for the uncertainties affecting productivity, mitigate researcher subjectivity, and address the classification boundaries inherent in limited sample sizes of measured productivity data.

[0094] In one embodiment, reference Figure 2 Based on the oil field productivity classification application method established by the present invention, combined with the analysis of offshore oil fields and low permeability oil fields, the implementation steps and processes of this method are further described in detail below.

[0095] (1) Calculation of comprehensive coefficient C

[0096] We collected actual oilfield production capacity factor data and, based on the improved production capacity calculation formula, calculated a comprehensive coefficient C representing all uncertainty factors. The results are shown in Table 1. In other words, by obtaining actual production capacity data and actual production capacity factor data and using the preset production capacity calculation formula, we can calculate a series of comprehensive coefficient data, as shown in Table 1.

[0097] Table 1 Calculation results of comprehensive coefficient C of uncertainty factors of productivity of offshore low permeability sandstone reservoirs

[0098]

[0099] (2) Reservoir classification

[0100] Through research, offshore low-permeability sandstone reservoirs are divided into four categories: A, B, C, and D, as shown in Table 2.

[0101] Table 2 Classification of offshore low-permeability sandstone reservoirs

[0102] Reservoir type Permeability, mD A K∈[20，50) B K∈[10，20) C K∈[5，10) D K∈[1，5)

[0103] (III) Statistical distribution characteristics of production capacity factors

[0104] According to reservoir classification and actual oilfield productivity factor data, the productivity factor of each type of reservoir was statistically analyzed. The statistical results were analyzed and it was found that each productivity factor basically obeyed the normal distribution characteristics. The statistical results of the productivity factor of type A reservoir are shown in Table 3. In the table, N means that the productivity factor obeys the normal distribution.

[0105] Table 3 Statistical distribution characteristics of productivity factors of Class A reservoirs

[0106] Capacity Factor Class A reservoir Permeability, mD <![CDATA[N(38,5 2 )]]> Effective thickness, m <![CDATA[N(12,5 2 )]]> Production pressure difference, MPa <![CDATA[N(9.5,1.4 2 )]]> Formation crude oil viscosity, mPa·s <![CDATA[N(0.8,0.37 2 )]]> Crude oil volume coefficient <![CDATA[N(1.3,0.15 2 )]]> Well control radius, m <![CDATA[N(55,20 2 )]]> Comprehensive influence coefficient of other production capacity factors <![CDATA[N(0.5,0.01 2 )]]>

[0107] (4) Determine the number of Monte Carlo simulations

[0108] (1) Determine the number of sampling times for capacity factors

[0109] In order to make the productivity simulation results more accurate, the random numbers obtained at different sampling times for each productivity factor of each type of reservoir are statistically calculated to calculate their characteristic values and probability distribution, and compared with the statistical characteristic values of the productivity factors actually collected. The sampling times that are close to the actual situation are selected to achieve a good consistency between the simulated data and the actual data of the productivity factors. Taking the mean and standard deviation of the viscosity of the crude oil in the formation of Class A reservoir as an example, the change curves under different sampling times are shown in Figure 2. Figure 4 and Figure 5 , comprehensively determine the number of sampling times. Figure 3 This is the actual data distribution diagram of crude oil viscosity in Class A reservoir formation.

[0110] (2) Determine the number of Monte Carlo simulations

[0111] After obtaining the sampling times of each productivity factor for each type of reservoir, the maximum sampling times was selected as the simulation times of productivity, which was 10,000.

[0112] (V) Calculating the probability distribution of production capacity

[0113] For each type of reservoir, the random numbers obtained by sampling according to the statistical distribution characteristics of each productivity factor are used to calculate the productivity using the improved productivity formula. The simulation is repeated 10,000 times, and the results are statistically analyzed to obtain the productivity probability distribution curve of this type of reservoir, as shown in the following example: Figure 6 shown.

[0114] (6) Determine the capacity classification boundaries

[0115] The four types of low permeability reservoirs are drawn into a cluster of productivity probability distribution curves to classify oilfield productivity. The classification results are shown in Figure 7 The actual capacity data is compared with the capacity classification limit value. Figure 8 , the two are consistent, indicating that this technology is reasonable and reliable.

[0116] The embodiment of the present invention implements oilfield productivity classification corresponding to reservoir classification, providing a reasonable and reliable calculation method for oilfield productivity classification. It uses a comprehensive coefficient to characterize all uncertain factors of productivity, thereby improving the productivity calculation formula. The Monte Carlo method is used to comprehensively determine the number of simulations for each productivity factor of each reservoir type by using the mean change trend, standard deviation change trend, and probability distribution curve under different simulation times to approximate the actual productivity factor value, ensuring good consistency between the simulated data and the actual productivity factor data. The productivity probability distribution curves of each reservoir type are calculated separately to form a cluster of productivity probability distribution curves for each reservoir type, thereby determining the boundaries of oilfield productivity classification. This method overcomes problems such as a small number of actual sample points, the potential lack of uniqueness in productivity classification caused by multiple groups of productivity regression curves, the inability to consider certain productivity influencing factors, and the subjectivity of researchers, ensuring more accurate productivity simulation results. Furthermore, the method can also randomly regenerate relevant productivity factor data according to different reservoir classifications, and has strong applicability. A set of technical processes and calculation modules suitable for oilfield productivity classification has been formed, providing a convenient and fast application tool for oilfield managers or researchers to better evaluate the productivity of different types of reservoirs in different oilfields, improve work efficiency, reduce production costs, and guide the formulation of scientific development plans.

[0117] refer to Figure 9 In another preferred embodiment, the oil field productivity classification device based on the Monte Carlo method of this embodiment includes:

[0118] The comprehensive coefficient calculation module is used to obtain actual production capacity data and actual production capacity factor data, and calculate the comprehensive coefficient data based on the preset production capacity calculation formula. The comprehensive coefficient is a type of production capacity factor used to represent various uncertain factors that affect production capacity.

[0119] The actual distribution characteristic analysis module is used to perform statistical analysis on the productivity factors of each type of reservoir based on the actual productivity factor data and the comprehensive coefficient data, so as to obtain the actual distribution characteristics of each productivity factor of each type of reservoir.

[0120] The optimal sampling number determination module is used to sample the actual productivity factor data and comprehensive coefficient data, and determine the optimal sampling number of each productivity factor for each type of reservoir based on the actual distribution characteristics.

[0121] The Monte Carlo simulation number determination module is used to determine the Monte Carlo simulation number according to the optimal sampling number of each productivity factor of each type of reservoir.

[0122] The capacity calculation module is used to calculate the capacity data of each type of reservoir based on the random numbers obtained after sampling the actual capacity factor data and the comprehensive coefficient data, and based on the preset capacity calculation formula. The number of capacity data is equal to the number of Monte Carlo simulations.

[0123] The capacity classification boundary determination module is used to obtain the capacity probability distribution curve of each type of reservoir based on the capacity data of the reservoir, and to determine the capacity classification boundary based on the capacity probability distribution curves of all reservoirs.

[0124] This embodiment provides a convenient and fast application tool for oilfield managers or researchers to better evaluate the productivity of different types of reservoirs in different oilfields, thereby improving work efficiency, reducing production costs, and guiding the formulation of scientific development plans.

[0125] In another preferred embodiment, the computer device of this embodiment includes a memory and a processor, the memory stores a computer program, and the processor executes the steps of the oil field productivity classification method based on the Monte Carlo method as in the above embodiment by calling the computer program stored in the memory.

[0126] The computer-readable storage medium of the present invention can be any computer-readable storage medium that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk.

[0127] The processor of the present invention is used to provide computing and control capabilities to support the operation of the entire device. It should be understood that in the embodiments of the present application, the processor can be a central processing unit (CPU), and the processor can also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0128] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0129] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0130] It is understandable that the above embodiments only express the preferred implementation modes of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the patent scope of the present invention. It should be pointed out that for ordinary technicians in this field, without departing from the concept of the present invention, the above technical features can be freely combined, and several deformations and improvements can be made, all of which fall within the scope of protection of the present invention. Therefore, all equivalent changes and modifications made to the scope of the claims of the present invention should fall within the scope of coverage of the claims of the present invention.

Claims

1. A method for oil field productivity classification based on Monte Carlo method, characterized in that: The following steps are involved: S1. Obtaining actual capacity data and actual capacity factor data, and calculating comprehensive coefficient data based on a preset capacity calculation formula; wherein the comprehensive coefficient is a type of capacity factor used to characterize multiple uncertain factors affecting capacity; S2. performing statistical analysis on the productivity factors of each type of reservoir based on the actual productivity factor data and the comprehensive coefficient data to obtain actual distribution characteristics of each productivity factor of each type of reservoir; S3. Sampling the actual productivity factor data and the comprehensive coefficient data, and determining the optimal sampling number for each productivity factor of each type of reservoir based on the actual distribution characteristics; S4. Determine the number of Monte Carlo simulations based on the optimal number of sampling for each productivity factor of each type of reservoir; S5. Calculate the productivity data of each type of reservoir based on the random numbers obtained by sampling the actual productivity factor data and the comprehensive coefficient data and the preset productivity calculation formula, where the number of productivity data is equal to the number of Monte Carlo simulations; S6. Obtain a productivity probability distribution curve for each type of reservoir based on the productivity data of that type of reservoir, and determine productivity classification boundaries based on the productivity probability distribution curves of all reservoirs.

2. The oilfield productivity classification method based on the Monte Carlo method according to claim 1, characterized in that: In step S2, each productivity factor of each type of reservoir obeys a normal distribution.

3. The oilfield productivity classification method based on the Monte Carlo method according to claim 2, characterized in that: In step S3, sampling the actual capacity factor data and the comprehensive coefficient data includes: The capacity factor that follows the normal distribution is sampled using the following formula: Where x is the production capacity factor; x i is the i-th data value of the capacity factor; μ' is the mean of the normal distribution of the capacity factor; σ is the standard deviation of the normal distribution of the capacity factor; m is the actual number of samples; is a uniformly distributed random number in the interval [0, 1; m1 is the sample size for each sampling.

4. The oilfield productivity classification method based on the Monte Carlo method according to claim 1 or 3, characterized in that: In step S3, based on the actual distribution characteristics, determining the optimal sampling number for each productivity factor of each type of reservoir includes: A statistical analysis is performed on the random numbers obtained for each productivity factor of each type of reservoir under different sampling times, and the corresponding simulated distribution characteristics are calculated. The simulated distribution characteristics are then compared with the actual distribution characteristics to determine the sampling number that approximates the actual situation and use it as the optimal sampling number.

5. The oilfield productivity classification method based on the Monte Carlo method according to claim 1, characterized in that: Step S4 includes: Comparing the optimal sampling times of each productivity factor of each type of reservoir, selecting the maximum sampling time and determining it as the number of Monte Carlo simulations; or Step S6 includes: The productivity data of each type of reservoir is collected to obtain the productivity probability distribution curve of the reservoir type. The number of productivity probability distribution curves corresponds to the number of reservoir types. The productivity probability distribution curves of various reservoirs are plotted into a cluster of productivity probability distribution curves, and the intersection points of the curves of adjacent reservoirs are determined in the cluster of productivity probability distribution curves; The intersection point of the curves is used as the capacity classification limit value; or Before performing statistical analysis on the productivity factor of each type of reservoir in step S2, the method further includes: Reservoirs are classified according to reservoir permeability classification standards.

6. The oilfield productivity classification method based on the Monte Carlo method according to claim 1, characterized in that: The preset capacity calculation formula is: Among them, Q is the oil well production, m 3 / d; K is permeability, mD; h is effective thickness of oil layer, m; Δp is production pressure difference, MPa; r e is the well control radius, m; r w is the radius of the oil well, m; μ is the viscosity of the formation crude oil, mPa·s; B is the crude oil volume coefficient; and C is the comprehensive coefficient of the impact of multiple uncertain factors on productivity, including seepage mechanism, inter-well interference, energy supply, reservoir transformation or pollution.

7. The oilfield productivity classification method based on the Monte Carlo method according to claim 1, characterized in that: For low-permeability sandstone reservoirs, the seepage mechanism includes the starting pressure gradient λ and stress sensitivity γ. If the starting pressure gradient λ and stress sensitivity γ are known, and the skin coefficient caused by reservoir transformation or pollution is also known, the comprehensive coefficient C is: C=C λ ·C γ ·C S ·C1 C λ =1-λ(re-rw) / Δp C γ =e-γΔp C S =1 / (1+S / ln(re / rw)) Among them, C λ is the influencing factor of the starting pressure gradient on the production capacity; λ is the starting pressure gradient, MPa / m; C γ is the factor affecting stress sensitivity on production capacity; C S is the impact factor of reservoir transformation or pollution on productivity; C1 is the impact factor of other uncertain parameters on productivity.

8. The oilfield productivity classification method based on the Monte Carlo method according to claim 7, characterized in that: For medium to extra-high permeability reservoirs, there are: λ=0, γ=0, C λ =1,C γ =1.

9. An oilfield productivity classification device based on the Monte Carlo method, characterized in that: include: A comprehensive coefficient calculation module is used to obtain actual production capacity data and actual production capacity factor data, and calculate comprehensive coefficient data based on a preset production capacity calculation formula; wherein the comprehensive coefficient is a type of production capacity factor used to represent multiple uncertain factors affecting production capacity; an actual distribution characteristic analysis module, configured to perform statistical analysis on the productivity factors of each type of reservoir based on the actual productivity factor data and the comprehensive coefficient data, so as to obtain the actual distribution characteristics of each productivity factor of each type of reservoir; an optimal sampling number determination module, configured to sample the actual productivity factor data and the comprehensive coefficient data, and determine the optimal sampling number for each productivity factor of each type of reservoir based on the actual distribution characteristics; A Monte Carlo simulation number determination module is used to determine the Monte Carlo simulation number according to the optimal sampling number of each productivity factor of each type of reservoir; a production capacity calculation module, configured to calculate production capacity data for each type of reservoir based on a random number obtained by sampling the actual production capacity factor data and the comprehensive coefficient data, and based on the preset production capacity calculation formula, wherein the number of production capacity data is equal to the number of Monte Carlo simulations; The capacity classification boundary determination module is used to obtain the capacity probability distribution curve of each type of reservoir based on the capacity data of the reservoir, and to determine the capacity classification boundary based on the capacity probability distribution curves of all reservoirs.

10. A computer device, characterized in that: The method comprises a memory and a processor, wherein a computer program is stored in the memory, and the processor executes the steps of the oil field productivity classification method based on the Monte Carlo method as described in any one of claims 1 to 8 by calling the computer program stored in the memory.

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