Method and device for calculating sand concentration at hole inlet and storage medium

Abstract

The invention provides a method and device for calculating the sand concentration at a hole inlet and a storage medium, and belongs to the field of oil and gas exploitation. The method comprises the following steps: firstly, establishing a training sample; then, a prediction model used for predicting the sand concentration of the sand-carrying fluid of a single hole is established based on a gene expression programming algorithm by utilizing the training sample, and finally, the sand concentration of the sand-carrying fluid of each hole under different flow distribution conditions is obtained through calculation. According to the method, the influence of the following property of the proppant and the flow change in the shaft on the concentration of the proppant can be considered, the technical limitation of a traditional model is avoided, and the method is suitable for calculating the sand concentration at the hole under the horizontal well multi-cluster perforation condition so as to improve the rationality of fracturing design.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas extraction, and specifically relates to a method, device and storage medium for calculating sand concentration at the orifice inlet. Background Technology

[0002] my country has enormous shale gas resource potential, with recoverable resources estimated at 26 × 10⁻⁶. 12 m 3 The reserves are roughly equivalent to those in the United States. The Cambrian and Silurian shale formations in the Sichuan Basin alone contain 1.5 to 2.5 times the conventional natural gas resources of the basin, indicating enormous development potential. Currently, bridge plug-perforation combined operation and multi-cluster perforation fracturing have become one of the main methods for shale gas development in my country. During fracturing, each cluster is effectively transformed, forming an effective fracture network covering the entire wellbore, which is one of the important prerequisites for successful fracturing.

[0003] Calculating fracture propagation morphology under different combinations of process parameters and optimizing them to form reasonable fracturing process parameters is one of the foundations for ensuring balanced stimulation of multi-perforation clusters. Currently, many propagation models assume a uniform sand concentration in the proppant-carrying fluid at the inlet of each perforation cluster. However, due to variations in wellbore discharge, the sand inflow within the perforations is not uniformly distributed, directly leading to a non-uniform distribution of sand concentration in the fracturing fluid at the inlet of the multi-perforation cluster, resulting in a discrepancy with the assumptions. Therefore, a method for calculating the sand concentration at the perforation inlet that considers proppant follow-up is urgently needed to improve the rationality of fracturing design. Summary of the Invention

[0004] The purpose of this invention is to solve the problems existing in the prior art and provide a method, device and storage medium for calculating sand concentration at the perforation inlet. It can take into account the proppant following and the influence of flow rate changes in the wellbore on the proppant concentration, avoid the technical limitations of traditional models, and is applicable to the calculation of sand concentration at the perforation in horizontal wells with multiple perforations, so as to improve the rationality of fracturing design.

[0005] This invention is achieved through the following technical solution:

[0006] The first aspect of the present invention provides a method for calculating the sand concentration at the inlet of an orifice. First, a training sample is established; then, using the training sample, a prediction model for predicting the sand concentration of the sand-carrying liquid in a single orifice is established based on a gene expression programming algorithm; finally, the sand concentration of the sand-carrying liquid in each orifice under different flow distribution conditions is calculated.

[0007] A further improvement of the present invention is that:

[0008] The method includes the following steps:

[0009] S100, establish a prediction model for the sand inflow rate of a single hole;

[0010] S200, a prediction model for the sand concentration of the sand-carrying liquid in a single hole under different influencing factors is formed by fitting;

[0011] S300, calculate the sand concentration of the sand-carrying liquid in each orifice under different flow distribution conditions;

[0012] S400, output result.

[0013] A further improvement of the present invention is that:

[0014] In S100, a prediction model for the sand ingress rate of a single orifice is established. The specific operations include:

[0015] A proppant transport model under the influence of a single perforation hole was established through numerical modeling, which is a prediction model of the sand ingress rate of a single perforation hole. The well diameter, perforation hole diameter, and length in the model are consistent with the actual well conditions.

[0016] The flow space of the horizontal wellbore and perforation is used as a three-dimensional computational domain for solid-liquid flow. A hexahedral structured mesh is used to process the geometric model, and the mesh is refined for flow domains with large pressure and velocity variations.

[0017] The shaft inlet is the velocity inlet, the shaft outlet is the velocity outlet, and the orifice outlet is the pressure outlet.

[0018] A further improvement of the present invention is that:

[0019] The numerical model adopts the discrete element model, multiphase flow model or particle flow model.

[0020] A further improvement of the present invention is that:

[0021] In S200, a prediction model for the sand concentration of the sand-carrying liquid in a single well is formed under different influencing factors. The specific operations include:

[0022] Step 201, establish training samples;

[0023] Step 202: Using the training samples established in step 201, a prediction model for predicting the sand concentration of the sand-carrying liquid in a single hole is established based on the gene expression programming algorithm.

[0024] A further improvement of the present invention is that:

[0025] Step 201 involves creating training samples, specifically including:

[0026] The experimental influencing factors were designed using single-factor or orthogonal experimental methods, and a prediction model for the sand ingress rate of a single orifice was established using S100 to conduct the experiment. The sand ingress rate of the orifice under different influencing factors was obtained, and the ratio of the sand concentration in the orifice to that in the wellbore was calculated.

[0027] Using influencing factors as input and the ratio of sand concentration in the borehole to that in the wellbore as output, this serves as a training sample for a prediction model of sand concentration in a single borehole under different influencing factors.

[0028] A further improvement of the present invention is that:

[0029] The influencing factors include the ratio of flow rate in the perforation to flow rate in the wellbore, proppant particle size, wellbore inlet sand ratio, fracturing fluid viscosity, wellbore discharge rate, proppant apparent density, and perforation orifice direction.

[0030] A further improvement of the present invention is that:

[0031] In S300, the specific steps for calculating the sand concentration at the inlet of each orifice under different flow distribution conditions include:

[0032] The ratio of flow rate in each perforation to flow rate in the wellbore, proppant particle size, sand ratio at the wellbore inlet, fracturing fluid viscosity, wellbore discharge rate, proppant apparent density, perforation direction, and sand concentration in the wellbore are used as inputs to the prediction model obtained by fitting in step S200, and the sand concentration at the inlet of each perforation is output.

[0033] The sand concentration inside the wellbore needs to be updated based on the sand concentration at each perforation. Taking the i-th perforation as an example, it is:

[0034]

[0035] In the formula:

[0036] Q i —Flow rate in the wellbore at the i-th perforation, m 3 / s;

[0037] C i —Sand concentration in the wellbore at the i-th perforation, kg / m³ 3 ;

[0038] —Flow rate within the i-th perforation orifice, m 3 / s;

[0039] —Sand concentration in the i-th perforation hole, kg / m³ 3 .

[0040] A second aspect of the present invention provides a device for calculating sand concentration at the orifice inlet, comprising:

[0041] The single-hole model building unit is used to build a prediction model for the sand inflow rate of a single hole;

[0042] The sand concentration prediction model fitting unit is used to fit and form a prediction model of the sand concentration of the sand-carrying liquid in a single hole under different influencing factors.

[0043] The orifice inlet sand concentration calculation unit is used to calculate the sand concentration of the sand-carrying liquid in each orifice under different flow distribution conditions.

[0044] Output unit, used to output results.

[0045] A third aspect of the present invention provides a computer-readable storage medium storing at least one computer-executable program, which, when executed by the computer, causes the computer to perform the steps in the method for calculating sand concentration at the orifice inlet.

[0046] Compared with the prior art, the beneficial effects of the present invention are:

[0047] The method for calculating the sand inlet volume in the perforation of this invention can take into account the proppant following behavior and the influence of changes in flow rate in the wellbore on the proppant concentration, thus avoiding the technical limitations of traditional models. It is applicable to the calculation of sand concentration at the perforation under multi-cluster perforation conditions in horizontal wells, thereby improving the rationality of fracturing design. Attached Figure Description

[0048] Figure 1 This invention provides a flowchart of a method for calculating sand concentration at the orifice inlet;

[0049] Figure 2 This is a proppant transport model in the wellbore under the influence of perforation, established in this embodiment of the invention;

[0050] Figure 3 The mesh is refined at the intersection of the well shaft and perforations, as well as on the inlet and outlet walls;

[0051] Figure 4 Flowchart of the algorithm for programming gene expressions. Detailed Implementation

[0052] The present invention will now be described in further detail with reference to the accompanying drawings:

[0053] Current fracture propagation models all assume a uniform sand concentration at the inlet of each perforation cluster. However, due to variations in wellbore flow rate and proppant density, the sand concentration at each perforation differs, resulting in a significant non-uniform sand concentration distribution. Therefore, this invention provides a method for calculating the inlet sand concentration at perforations that considers proppant following behavior within the wellbore. This method takes into account the influence of proppant following behavior and wellbore flow rate variations on proppant concentration, thus avoiding the technical limitations of traditional models.

[0054] This invention provides a method for calculating sand concentration at the orifice inlet. First, an established prediction model for the sand inflow rate of a single orifice is used to conduct experiments to obtain the sand inflow rate under different influencing factors and calculate the ratio of the sand concentration in the orifice to that in the wellbore, which serves as a training sample. Then, using the training sample, a prediction model for predicting the sand concentration of the sand-carrying liquid in a single orifice is established based on a gene expression programming algorithm. Finally, the sand concentration of the sand-carrying liquid in each orifice under different flow distribution conditions is calculated.

[0055]

Example 1

[0056] like Figure 1 As shown, this embodiment of the invention provides a method for calculating sand concentration at the orifice inlet, including the following steps:

[0057] S100, establish a prediction model for the sand inflow rate of a single hole;

[0058] S200, a prediction model for the sand concentration of the sand-carrying liquid in a single hole under different influencing factors is formed by fitting.

[0059] S300, calculate the sand concentration of the sand-carrying liquid in each orifice under different flow distribution conditions;

[0060] S400, output result.

[0061] The method of this invention considers the proppant following behavior and the influence of flow rate changes in the wellbore on the proppant concentration, avoiding the technical limitations of traditional models. It is applicable to the calculation of sand concentration at the perforation in horizontal wells with multiple perforations, thereby improving the rationality of fracturing design.

[0062]

Example 2

[0063] S100, Establish a prediction model for sand inflow rate in a single orifice. Specific operations include:

[0064] Considering the difference in sand concentration between the proppant-carrying fluid in the perforation and the proppant-carrying fluid in the wellbore due to the apparent density of the proppant during proppant migration, a numerical model is established to predict the proppant migration within the wellbore under the influence of a single perforation, specifically a model for predicting the sand ingress rate in a single perforation. In this model, the wellbore diameter, perforation diameter, and length are kept consistent with actual well conditions. Figure 2 As shown.

[0065] The flow space of the horizontal wellbore and perforations is used as a three-dimensional computational domain for solid-liquid flow. A hexahedral structured mesh is employed to process the geometric model. Mesh refinement is applied to flow domains with significant pressure and velocity variations, such as the intersection of the wellbore and perforations, and the inlet and outlet walls. Figure 3 As shown.

[0066] The wellbore inlet is the velocity inlet, the wellbore outlet is the velocity outlet, and the orifice outlet is the pressure outlet. The numerical model can be a solid-liquid two-phase flow calculation model such as the discrete element model (DEM), multiphase flow model, or particle flow model.

[0067] By establishing a prediction model for the sand feed rate of a single orifice, the solid content at the orifice outlet is tracked, and the solid mass flow rate, i.e., the sand feed rate of the orifice, is calculated.

[0068]

Example 3

[0069] S200, a prediction model for the sand concentration of the sand-carrying liquid in a single well is formed under different influencing factors. Specific operations include:

[0070] Step 201, Create training samples.

[0071] The specific operations include:

[0072] The experimental influencing factors were designed using single-factor or orthogonal experimental methods, and a prediction model for the sand ingress rate of a single borehole was established using S100 to conduct experiments. The borehole sand ingress rate under different influencing factors was obtained, and the ratio of the sand concentration in the corresponding borehole to that in the wellbore was calculated.

[0073] Influencing factors include the ratio of flow rate in the perforation to the flow rate in the wellbore, proppant particle size, sand ratio at the wellbore inlet, fracturing fluid viscosity, flow rate in the wellbore, apparent density of proppant, and perforation direction. The numerical ranges of parameters such as proppant particle size, fracturing fluid viscosity, flow rate in the wellbore, and perforation direction can be obtained from fracturing parameters. The upper limit of the numerical range of the sand ratio parameter can be selected as more than three times the maximum value of the fracturing parameter.

[0074] Using influencing factors as input and the ratio of sand concentration in the borehole to that in the wellbore as output, this serves as a training sample for a prediction model of sand concentration in a single borehole under different influencing factors.

[0075] Step 202: Using the training samples established in Step 201, a predictive model is built based on the gene expression programming algorithm to predict the sand concentration of the sand-carrying liquid in a single hole.

[0076] like Figure 4 As shown, the specific operations include:

[0077] Step 2021: Set the relevant parameters of the gene expression programming algorithm. These parameters include population size, maximum number of iterations, function symbol set F, terminator set T, number of random numbers, gene head length, number of chromosome genes, chromosome gene connectors, mutation probability, reverse string probability, insertion string probability, root insertion string probability, single-point recombination probability, two-point recombination probability, and gene recombination probability. Genetically encode the influencing factors of the sand concentration in the wellbore carrying fluid (influencing factors include: proppant particle size, viscosity, wellbore flow rate, wellbore flow rate, sand ratio, and wellbore direction). Genetically generate an initial population through initialization operations, using this initial population as the current population. Select a suitable fitness function based on experimental results. Possible fitness functions include:

[0078]

[0079] In the formula:

[0080] f i —The fitness of the i-th individual;

[0081] P i,j —The calculated value of the j-th sample based on the expression corresponding to the i-th individual;

[0082] T j —The j-th sample value.

[0083] Step 2022: Read each gene sequentially from left to right and construct an expression tree that can express rich semantics according to the grammatical rules. Then, traverse the expression tree layer by layer in the order from top to bottom and from left to right to obtain the expression mapped by the gene.

[0084] Step 2023: Connect the genes with the chromosome gene connectors set in step 2021 to obtain the expression of each chromosome individual in the population. Substitute the training samples into the expression mapped to each individual to obtain the predicted value obtained by the calculation formula. Combine the fitness function to evaluate the fitness value of each chromosome individual in the population.

[0085] Step 2024: According to the "optimal preservation strategy", compare the best chromosome individual in the current population with the best chromosome individual in the global population. If the best chromosome individual in the current population is better than the best chromosome individual in the global population, then retain the best chromosome individual in the current population.

[0086] Step 2025: According to Darwin's principle of "survival of the fittest", eliminate the individual with the worst chromosome in the population and preserve the individual with the best chromosome in the population.

[0087] Step 2026: Perform genetic operations on the population using selection operators, mutation operators, insertion operators, reverse string operators, root insertion operators, gene transformation operators, single-point recombination operators, two-point recombination operators, and gene recombination operators to generate new offspring individuals;

[0088] Step 2027: If the maximum number of generations or the calculation accuracy is reached, the evolution ends and a prediction formula for sand concentration in sand-carrying liquid is established; otherwise, proceed to step 2022.

[0089]

Example 4

[0090] In S300, the specific steps for calculating the sand concentration at the inlet of each orifice under different flow distribution conditions include:

[0091] The ratio of flow rate in each perforation to flow rate in the wellbore, proppant particle size, sand ratio at the wellbore inlet, fracturing fluid viscosity, wellbore discharge rate, proppant apparent density, perforation direction, and sand concentration in the wellbore are used as inputs to the prediction model obtained by fitting in step S200, and the sand concentration at the inlet of each perforation is output.

[0092] The sand concentration inside the wellbore needs to be updated based on the sand concentration at each perforation. Taking the i-th perforation as an example, it is:

[0093]

[0094] In the formula:

[0095] Q i —Flow rate in the wellbore at the i-th perforation, m 3 / s;

[0096] C i —Sand concentration in the wellbore at the i-th perforation, kg / m³ 3 ;

[0097] —Flow rate within the i-th perforation orifice, m 3 / s;

[0098] —Sand concentration in the i-th perforation hole, kg / m³ 3 .

[0099] The average sand concentration in the pores of each cluster is calculated using formula (3). Taking the i-th cluster as an example, it is:

[0100]

[0101] In the formula:

[0102] —Average sand concentration within the i-th cluster of pores, kg / m³ 3 ;

[0103] n—the number of holes in the i-th cluster.

[0104]

Example 5

[0105] Taking the third section of a shale gas well in southwestern Sichuan as an example, a total of 48 perforations were formed. The flow distribution of each cluster of 70 / 140 mesh quartz sand with a sand ratio of 6% was calculated after it entered the perforations, with a maximum discharge of 20 m³ / s. 3 / min.

[0106] 1. Establish training samples

[0107] The main influencing factors include the fracturing fluid delivery ratio, proppant particle size, sand ratio, fracturing fluid viscosity, proppant carrying fluid flow rate, proppant apparent density, and perforation orifice direction. The numerical ranges for parameters such as proppant particle size, fracturing fluid viscosity, proppant carrying fluid flow rate, and perforation orifice direction can be obtained from the fracturing parameters. The upper limit of the numerical range for the sand ratio parameter can be selected as more than three times the maximum value of the fracturing parameter, i.e., 20%. Substituting these values ​​into the established proppant migration model within the wellbore under the influence of perforation, the calculation results are shown in Table 1.

[0108] Table 1 Training Samples

[0109]

[0110]

[0111]

[0112] 2. Establish a prediction model

[0113] To fully reflect the nonlinear characteristics, this paper defines a set of function symbols:

[0114]

[0115] After multiple selections, calculations, and comparisons, each chromosome in this embodiment consists of 25 genes, with each gene connected by a "+", and the gene head is 40 units long.

[0116] The parameters for running the gene expression programming algorithm are shown in Table 2:

[0117] Table 2. Parameters of the Gene Expression Programming Algorithm

[0118]

[0119] The prediction model is formed as follows:

[0120]

[0121] In the formula:

[0122] ρ—Density of fracturing fluid, g / cm³ 3 ;

[0123] μ — fracturing fluid viscosity, mPa·s;

[0124] Q perf / Q—The ratio of flow rate in the orifice to flow rate in the wellbore;

[0125] α perf —Direction of the perforation hole, °;

[0126] mesh — proppant mesh number (particle size), mesh.

[0127] 3. Calculate the sand concentration at each well.

[0128] This well consists of 3 clusters, each with a discharge capacity of 9m³. 3 / min, 8m 3 / min, 3m 3 / min, calculate the flow rate of each orifice, and combine it with formula (3) to calculate the sand ratio and average sand concentration in each cluster of orifices, as shown in Table 3.

[0129] Table 3 Calculation Results

[0130] Cluster number Sand ratio % <![CDATA[Sand concentration kg / m 3 > 1 5.88 94.14 2 7.47 119.56 3 12.83 205.25

[0131]

Example 6

[0132] This invention also provides a device for calculating sand concentration at the orifice inlet, comprising:

[0133] The single-hole model building unit is used to build a prediction model for the sand inflow rate of a single hole;

[0134] The sand concentration prediction model fitting unit is used to fit and form a prediction model of the sand concentration of the sand-carrying liquid in a single hole under different influencing factors.

[0135] The orifice inlet sand concentration calculation unit is used to calculate the sand concentration of the sand-carrying liquid in each orifice under different flow distribution conditions.

[0136] Output unit, used to output results.

[0137] The sand concentration prediction model fitting unit includes:

[0138] Training sample creation sub-units are used to create training samples;

[0139] The model fitting subunit is used to build a predictive model for predicting the sand concentration of the sand-carrying liquid in a single hole using the established training samples and a gene expression programming algorithm.

[0140] This invention can take into account the proppant following behavior and the influence of flow rate changes in the wellbore on the proppant concentration, avoiding the technical limitations of traditional models. It is applicable to the calculation of sand concentration at the perforation under multi-cluster perforation conditions in horizontal wells, thereby improving the rationality of fracturing design.

[0141]

Example 7

[0142] This invention also provides a computer-readable storage medium storing at least one computer-executable program, which, when executed by the computer, causes the computer to perform the steps in the method for calculating sand concentration at the orifice inlet.

[0143] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0144] The above technical solution is only one embodiment of the present invention. For those skilled in the art, based on the principles disclosed in the present invention, it is easy to make various types of improvements or modifications, and not limited to the technical solutions described in the specific embodiments of the present invention. Therefore, the foregoing description is only a preferred option and is not restrictive.

Claims

1. A method for calculating sand concentration at the orifice inlet, characterized in that, First, training samples are established; then, using the training samples, a prediction model for predicting the sand concentration of the sand-carrying liquid in a single orifice is established based on a gene expression programming algorithm; finally, the sand concentration of the sand-carrying liquid in each orifice under different flow distribution conditions is calculated.

2. The method for calculating sand concentration at the orifice inlet according to claim 1, characterized in that, The method includes the following steps: S100, establish a prediction model for the sand inflow rate of a single hole; S200, a prediction model for the sand concentration of the sand-carrying liquid in a single hole under different influencing factors is formed by fitting; S300, calculate the sand concentration of the sand-carrying liquid in each orifice under different flow distribution conditions; S400, output result.

3. The method for calculating sand concentration at the orifice inlet according to claim 2, characterized in that, In S100, a prediction model for the sand ingress rate of a single orifice is established. The specific operations include: A proppant transport model under the influence of a single perforation hole was established through numerical modeling, which is a prediction model of the sand ingress rate of a single perforation hole. The well diameter, perforation hole diameter, and length in the model are consistent with the actual well conditions. The flow space of the horizontal wellbore and perforation is used as a three-dimensional computational domain for solid-liquid flow. A hexahedral structured mesh is used to process the geometric model, and the mesh is refined for flow domains with large pressure and velocity variations. The shaft inlet is the velocity inlet, the shaft outlet is the velocity outlet, and the orifice outlet is the pressure outlet.

4. The method for calculating sand concentration at the orifice inlet according to claim 3, characterized in that, The numerical model adopts the discrete element model, multiphase flow model or particle flow model.

5. The method for calculating sand concentration at the orifice inlet according to claim 2, characterized in that, In S200, a prediction model for the sand concentration of the sand-carrying liquid in a single well is formed under different influencing factors. The specific operations include: Step 201, establish training samples; Step 202: Using the training samples established in step 201, a prediction model for predicting the sand concentration of the sand-carrying liquid in a single hole is established based on the gene expression programming algorithm.

6. The method for calculating sand concentration at the orifice inlet according to claim 5, characterized in that, Step 201 involves creating training samples, specifically including: The experimental influencing factors were designed using single-factor or orthogonal experimental methods, and a prediction model for the sand ingress rate of a single orifice was established using S100 to conduct the experiment. The sand ingress rate of the orifice under different influencing factors was obtained, and the ratio of the sand concentration in the orifice to that in the wellbore was calculated. Using influencing factors as input and the ratio of sand concentration in the borehole to that in the wellbore as output, this serves as a training sample for a prediction model of sand concentration in a single borehole under different influencing factors.

7. The method for calculating sand concentration at the orifice inlet according to claim 6, characterized in that, The influencing factors include fracturing fluid delivery ratio, proppant particle size, sand ratio, fracturing fluid viscosity, proppant carrying fluid discharge rate, proppant apparent density, and perforation orifice direction.

8. The method for calculating sand concentration at the orifice inlet according to claim 2, characterized in that, In S300, the specific steps for calculating the sand concentration at the inlet of each orifice under different flow distribution conditions include: The ratio of flow rate in each perforation to flow rate in the wellbore, proppant particle size, sand ratio at the wellbore inlet, fracturing fluid viscosity, wellbore discharge rate, proppant apparent density, perforation direction, and sand concentration in the wellbore are used as inputs and fed into the prediction model fitted in step S200 to obtain the sand concentration at the inlet of each perforation. The sand concentration inside the wellbore needs to be updated based on the sand concentration at each perforation. Taking the i-th perforation as an example, it is: In the formula: Q i —Flow rate in the wellbore at the i-th perforation, m 3 / s; C i —Sand concentration in the wellbore at the i-th perforation, kg / m³ 3 ; —Flow rate within the i-th perforation orifice, m 3 / s; —Sand concentration in the i-th perforation hole, kg / m³ 3 .

9. A device for calculating sand concentration at the inlet of an orifice, characterized in that, include: The single-hole model building unit is used to build a prediction model for the sand inflow rate of a single hole; The sand concentration prediction model fitting unit is used to fit and form a prediction model of the sand concentration of the sand-carrying liquid in a single hole under different influencing factors. The orifice inlet sand concentration calculation unit is used to calculate the sand concentration of the sand-carrying liquid in each orifice under different flow distribution conditions. Output unit, used to output results.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one computer-executable program, which, when executed by the computer, causes the computer to perform the steps in the method for calculating sand concentration at the orifice inlet as described in any one of claims 1-8.

Images