Fracturing slickwater evaluation method

Abstract

The invention discloses a fracturing slickwater evaluation method. The method comprises the following steps: S1, obtaining a fracturing slickwater sample; s2, extracting types and adding proportions of a drag reducer, a cleanup additive and a stabilizer in the sample; s3, carrying out simulation calculation on the sample to obtain the viscosity, friction resistance, filtration loss and sand-carrying capacity of the sample; s4, constructing a training set by using the data obtained in S2 and S3, and training the decision tree model to obtain an evaluation model; and S5, predicting the viscosity, the friction resistance, the filtration property and the sand-carrying capacity of the fracturing slickwater to be evaluated by utilizing the evaluation model. The method can greatly shorten the evaluation period and reduce the evaluation cost.

Description

Technical Field

[0001] The present invention relates to fracturing slick water, and more particularly to a fracturing slick water evaluation method. Background Art

[0002] In the field of coalbed methane mining, hydraulic fracturing technology is one of the core means to achieve efficient development of coalbed methane. As a widely used working fluid in hydraulic fracturing operations, the performance of fracturing slick water plays a decisive role in the fracturing effect and gas production efficiency of coalbed methane. At present, the evaluation of coalbed methane fracturing slick water mainly adopts experimental testing methods, such as determining the basic physical and chemical parameters of slick water, such as viscosity and surface tension, through indoor experiments, and using simulation experiments to evaluate its drag reduction, sand carrying and other properties. Obviously, the experimental method has significant defects. First, the experimental process is cumbersome and the cycle is long, requiring a lot of manpower, material resources and funds; second, the experimental environment is quite different from the actual working conditions of the fracturing operation, resulting in a significant deviation between the evaluation results and the actual situation, making it difficult to effectively guide on-site operations.

[0003] Therefore, it is necessary to design a technical solution that can overcome the above-mentioned defects. Summary of the invention

[0004] An object of the present invention is to provide a method for evaluating fracturing slick water, which can significantly shorten the evaluation period and reduce the evaluation cost.

[0005] In order to achieve these purposes and other advantages of the present invention, according to one aspect of the present invention, the present invention provides a method for evaluating fracturing slick water, comprising: S1: obtaining a fracturing slick water sample; S2: extracting the types and addition ratios of drag reducers, drainage aids, and stabilizers in the sample; S3: performing simulation calculations on the sample to obtain the viscosity, friction, filtration loss, and sand carrying capacity of the sample; S4: constructing a training set using the data obtained in S2 and S3, and training a decision tree model to obtain an evaluation model; S5: using the evaluation model to predict the viscosity, friction, filtration loss, and sand carrying capacity of the fracturing slick water to be evaluated; wherein, in the step of training the decision tree model: the maximum depth of the decision tree is determined by: setting the upper limit of the maximum depth to 12-16; each time a node splitting operation is performed When the information entropy difference before and after the split is calculated, the information entropy difference is obtained by the formula "information entropy difference = information entropy of the node before the split - the sum of the information entropy of the child nodes after the split"; the initial information entropy difference threshold is set to 0.2-0.3. During the decision tree construction process, when the information entropy difference after a node split is less than the current information entropy difference threshold, the node is stopped from being split; and after every 4-6 node splits, the information entropy difference threshold is adjusted according to the change in the number of nodes: if the number of nodes increases by more than or equal to 20% in two consecutive adjustment cycles, the information entropy difference threshold is increased to 0.25; if the number of nodes increases by less than 20% in two consecutive adjustment cycles, the information entropy difference threshold is reduced to 0.15, so as to dynamically determine the maximum depth of the decision tree.

[0006] Furthermore, the step of training the decision tree model also includes: using the information gain ratio as a metric, during the decision tree construction process, each time selecting the feature with the largest information gain ratio for node splitting until the following stopping conditions are met: when the information gain ratios of all remaining selectable features are less than 0.01-0.02, the splitting is stopped; at the same time, a minimum sample splitting number is set, and when the number of samples on the node is less than this value, no more splitting is performed.

[0007] Furthermore, the step of training the decision tree model also includes: dividing the collected sample data into 70% training set, 15% validation set, and 15% test set ratios; using the training set data to train the decision tree, and during the training process, for each branch formed after the node splits, continuously calculating the information gain ratio of each feature under the branch, and selecting the feature with the largest information gain ratio to continue splitting the node; each time the training is completed, evaluating the performance indicators such as the mean square error and accuracy of the model on the validation set, adjusting the hyperparameters according to the evaluation results, and retraining the model until the performance on the validation set no longer improves or reaches a preset number of training rounds; after the training is completed, using the test set data to evaluate the mean square error between the predicted value and the true value of the viscosity, friction, filtration loss and sand carrying capacity of the model, and calculating the determination coefficient to measure the goodness of fit of the model; if the performance of the model on the test set does not meet expectations, starting from the leaf node, the sub-branch is pruned and replaced with the leaf node, and the comprehensive score S is selected by comparing the mean square error and determination coefficient indicators of the model on the validation set before and after pruning. i The higher model is taken as the final model.

[0008] Furthermore, the comprehensive score S i It is calculated as follows: Setting the weight of mean square error The weight of the determination coefficient is set to 0.4-0.5 is 0.5-0.6 and satisfies ; For each model i, obtain its mean square error MSE on the validation set i and the coefficient of determination ; By formula Calculate the comprehensive score S for each model i .

[0009] Furthermore, step S2 includes: using HPLC to analyze the components of the drag reducer and drainage aid in the sample, and determining the types of the drag reducer and drainage aid by comparing the retention time and peak area based on known drag reducer and drainage aid standards, and calculating their contents; using GC-MS to separate the stabilizer in the sample, and determining the chemical structure and type of the stabilizer by comparing the mass spectrum with the standard spectral library, and then quantifying its content by the external standard method; wherein, when performing HPLC analysis, the following steps are also included: first preparing an initial mixed solvent in a volume ratio of 3:2 between water and methanol, and then adding N,N-dimethylformamide accounting for 1% of the total volume of the initial mixed solvent, and dimethyl sulfoxide accounting for 1% of the total volume of the initial mixed solvent to obtain a final mixed solvent; adding the sample to the final mixed solvent in a ratio of 1g sample: 30-50ml final mixed solvent, accelerating dissolution by stirring, and finally using the dissolved solution for HPLC analysis.

[0010] Furthermore, the simulation calculation of viscosity includes: according to the type and addition ratio obtained in step S2, a non-Newtonian fluid viscosity calculation model in the fluid mechanics simulation software ANSYS Fluent is used to establish a virtual model of fracturing slick water, and the temperature parameters are set to 70-80°C and the pressure is set to 30-35MPa, and a virtual viscosity value A of the fracturing slick water sample is obtained through iterative calculation; a rotational viscometer is used to measure the measured viscosity value of the sample, and the measurement is repeated three times to obtain the average measured viscosity value B; the virtual viscosity value is compared with the average measured viscosity value, and if the difference is: (AB) / B>±5%, then another non-Newtonian fluid viscosity calculation model is used, and the virtual viscosity value of the fracturing slick water sample is obtained through iterative calculation again. , compare the newly obtained virtual viscosity value with the average measured viscosity. If the difference: (AB) / B is not greater than ±5%, the replaced type of non-Newtonian fluid viscosity calculation model is selected as the annual calculation model. If the difference of all types of non-Newtonian fluid viscosity calculation models is greater than ±5%, the one with the smallest absolute value of the difference is selected; the non-Newtonian fluid viscosity calculation models include power law model, Herschel-Bulkley model, Bingham plasticity model, and Carreau model; and the iterative calculation is set with an upper limit of 1000 times.

[0011] Furthermore, in S3, the method for simulating and calculating the sample friction resistance includes: according to the actual fracturing operation, using CFD software to establish a pipeline geometry model, divide the grid, set fluid properties and flow channel model, define boundary conditions, solve the Navier-Stokes equation to simulate the flow of fracturing slick water in the pipeline, obtain the pressure values ​​at the pipeline inlet and outlet, and calculate the friction resistance using the pressure difference method.

[0012] Furthermore, in the S3, the method for simulating and calculating the filtration loss of the sample includes: according to the actual fracturing operation, using CFD software to construct a porous medium model, determining the interaction parameters between fracturing slick water and rock, setting initial conditions and boundary conditions, combining Darcy's law, solving the continuity equation, simulating the seepage process of fracturing slick water in the porous medium, setting a monitoring surface in the porous medium model, calculating the filtration rate of the fracturing slick water flow through the surface, and then calculating the filtration rate and filtration amount.

[0013] Furthermore, in S3, the method for simulating and calculating the sand carrying capacity of the sample includes: using Fluent software to construct a geometric model, setting parameters of fracturing slick water and proppant, dividing the grid, setting boundary conditions, and using the Eulerian-Eulerian model to simulate the two-phase flow of fracturing slick water and proppant. After the simulation, obtaining the proppant velocity data at the preset monitoring point and calculating the sedimentation velocity.

[0014] Furthermore, it also includes: using real data to verify the viscosity, friction, filtration loss and sand carrying capacity obtained by simulation calculation, if the verification fails, re-simulation calculation; wherein, the Pearson correlation coefficient and consistency limit of the simulation calculation data and the real data are calculated, if the Pearson correlation coefficient and the consistency limit are both within the preset range, it is judged that the verification is passed.

[0015] The present invention has at least the following beneficial effects: The present invention uses simulation calculation to obtain key performance indicators such as fracturing slick water viscosity, friction, filtration loss and sand carrying capacity. Combined with the types and addition ratios of drag reducers, drainage aids and stabilizers in fracturing slick water, an evaluation model is constructed with the help of a decision tree model. The performance of the slick water to be evaluated can be quickly predicted, the evaluation cycle can be greatly shortened, the evaluation cost can be reduced, and the deep relationship between the proportion of additives and performance can be explored, which is helpful to optimize the additive formula.

[0016] Other advantages, objectives and features of the present invention will be embodied in part through the following description, and in part will be understood by those skilled in the art through study and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of an embodiment of the present application. DETAILED DESCRIPTION

[0018] The present invention is further described in detail below in conjunction with the accompanying drawings so that those skilled in the art can implement the invention with reference to the description.

[0019] It should be understood that the terms such as "having", "including" and "comprising" used in the embodiments of the present application do not exclude the existence or addition of one or more other elements or their combinations. All directional indications (such as up, down, left, right, front, back...) in the embodiments of the present application are only used to explain the relative position relationship, movement, etc. between the components in a certain specific posture. If the specific posture changes, the directional indication will also change accordingly. When an element is referred to as "fixed on" or "set on" another element, it can be directly on the other element or there may be a centering element at the same time. When an element is referred to as "connecting" another element, it can be directly connected to another element or it can be indirectly connected to another element through a centering element. The description of "first", "second", etc. in the embodiments of the present application is only for descriptive purposes, and cannot be understood as indicating or implying its relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" can explicitly or implicitly include at least one of the features.

[0020] It should be noted that the technical solutions between the various embodiments of the present application can be combined with each other, but it must be based on the fact that ordinary technicians in the field can implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0021] like Figure 1 As shown, the embodiment of the present application provides a method for evaluating fracturing slickwater, comprising: S1: Obtain fracturing slickwater samples; Specifically, 50 groups of different fracturing slickwater samples were collected or purchased, and information such as the source and formula composition of each sample was recorded to ensure that the samples were broadly representative and could reflect various situations in actual applications.

[0022] S2: Types and addition ratios of drag reducers, drainage aids, and stabilizers in the extracted samples; If the formula is unclear, use HPLC to analyze the components of the drag reducer and drainage aid in the sample. According to the known drag reducer and drainage aid standards, the types of drag reducers and drainage aids are determined by comparing the retention time and peak area, and their contents are calculated. Use GC-MS to separate the stabilizer in the sample, and determine the chemical structure and type of the stabilizer by comparing the mass spectrum with the standard library, and then quantify its content by the external standard method. Wherein, when carrying out HPLC analysis, also comprise the following steps: First, an initial mixed solvent is prepared according to a volume ratio of water to methanol of 3:2, and then 1% of N,N-dimethylformamide and 1% of dimethyl sulfoxide are added to the total volume of the initial mixed solvent to obtain a final mixed solvent; Add the sample to the final mixed solvent at a ratio of 1g sample to 30-50ml final mixed solvent, accelerate dissolution by stirring, and finally use the dissolved solution for HPLC analysis; Generally, drag reducers include polyacrylamide drag reducers, guar gum drag reducers, and surfactant drag reducers; drainage aids include fluorocarbon surfactant drainage aids, polyether drainage aids, and alkylphenol polyoxyethylene ether drainage aids; and stabilizers include organic phosphonate stabilizers, polymer stabilizers, and borate stabilizers.

[0023] S3: simulate and calculate the sample to obtain the viscosity, friction, filtration loss and sand carrying capacity of the sample; S31: A viscosity calculation method is; According to the type and addition ratio obtained in step S2, a non-Newtonian fluid viscosity calculation model in the fluid mechanics simulation software ANSYS Fluent is used to establish a virtual model of fracturing slick water, and the temperature parameters are set to 70-80°C and the pressure is set to 30-35MPa. The virtual viscosity value A of the fracturing slick water sample is obtained through iterative calculation; Use a rotational viscometer to measure the viscosity of the sample, repeat the measurement three times, and take the average value B of the measured viscosity; Compare the virtual viscosity value with the average measured viscosity. If the difference (AB) / B>±5%, change the non-Newtonian fluid viscosity calculation model, and iterate again to get the virtual viscosity value of the fracturing slick water sample. If the difference (AB) / B between the newly obtained virtual viscosity value and the average measured viscosity is not greater than ±5%, select the changed non-Newtonian fluid viscosity calculation model as the annual calculation model. If the difference of all non-Newtonian fluid viscosity calculation models is greater than ±5%, select the one with the smallest absolute difference. The non-Newtonian fluid viscosity calculation model includes a power law model, a Herschel-Bulkley model, a Bingham plasticity model, and a Carreau model; The upper limit of the number of iterations is set to 1000.

[0024] Another way to calculate viscosity is: With the help of MaterialsStudio molecular modeling software, the molecular structure and simulation box of each component are constructed for each group of samples according to the chemical composition and molecular composition of the samples; the temperature T (unit: K) and pressure P (unit: Pa) during the actual fracturing operation are obtained to set the simulation conditions; the COMPASS force field is used to describe the interaction between molecules, which can accurately reflect the physical and chemical properties of molecules; The molecular dynamics method is used for simulation. During the simulation process, the position information of the molecules is recorded at certain time intervals, and the displacement of the molecules is obtained by analyzing the position information. The displacement data of a large number of molecules are statistically analyzed, and the diffusion coefficient D is calculated using the Einstein diffusion formula; ; Where N is the number of molecules, is the position vector of the i-th molecule at time t, and the molecular radius R is determined by referring to relevant literature; finally, according to the Einstein relationship Calculate the viscosity η, where k B is the Boltzmann constant.

[0025] S32: Friction calculation; Using ANSYS Fluent software, a pipeline geometry model is established based on the diameter d, length L, material and other parameters of the pipeline in the actual fracturing operation. The model is meshed, and the mesh is appropriately encrypted at key locations such as the pipeline wall, elbows, and reducers to improve the calculation accuracy.

[0026] Set the fluid properties. The dynamic viscosity μ is determined based on the viscosity value calculated previously. The density is obtained through laboratory measurements. Determine whether the fluid is in a turbulent or laminar state based on the actual situation and select an appropriate flow channel model. If the Reynolds number (v is the average flow velocity of the fluid), it is judged as turbulent flow and a suitable turbulence model is selected; if it is less than 2000, it is laminar flow and a laminar flow model is selected.

[0027] Define boundary conditions. The inlet is set as velocity boundary condition. The velocity v is determined according to the actual injection rate of the fracturing pump. The outlet is set as pressure boundary condition. The pressure value P out Refer to the formation pressure of coalbed methane field.

[0028] Solve the following Navier-Stokes equations; ; Where ρ is the fluid density, is the local rate of change of fluid velocity, is the convection acceleration, is the fluid velocity vector (unit: m / s), P is the pressure (unit: Pa), is the body force (unit: N / m³), which mainly considers gravity. is the pressure gradient force, is the viscous force and μ is the dynamic viscosity.

[0029] Through simulation calculation, we get the pipeline inlet pressure P1 and outlet pressure P2, and the friction resistance △P=P1-P2.

[0030] S33: Filtration calculation; Based on the geological exploration data of coalbed methane fields, such as the porosity Φ, permeability K, rock type and other information of coal seams, a porous media model was constructed using CFD software. Through interfacial tension measurement experiments and contact angle measurement experiments, the interaction parameters between fracturing slick water and coal seam rocks, such as interfacial tension σ and contact angle θ, were determined.

[0031] Set the initial conditions, the initial pressure P0 and initial flow rate v0 of the fracturing slick water, and refer to the initial settings of the actual fracturing operation. Set the boundary conditions, and the pressure Pb and flow rate Qb at the model boundary are set according to the actual situation of the coalbed methane field.

[0032] Combined with Darcy's law below; , where Q is the flow rate, K is the permeability, and A is the cross-sectional area. is the pressure gradient, μ is the fluid dynamic viscosity; The following continuity equation is discretized by finite difference method or finite element method; ,in, is the rate of change of fluid density with time t, is the divergence of mass flux, is the velocity vector of the fluid, ▽ is the Hamiltonian operator; A monitoring surface is set in the porous media model, and the flow rate Q is monitored in real time by the flow sensor at the monitoring surface to obtain the filtration rate. The filtration rate V is calculated by numerical integration. Calculation is performed using the trapezoidal integration method or the Simpson integration method, where t is the simulation time.

[0033] S34: Calculation of sand carrying capacity; Fluent software was used to construct a geometric model including wellbore and fractures, considering the wellbore diameter D well Length L well As well as actual factors such as the width ω, height h and extension direction of the crack. Set the detailed parameters of fracturing slick water and proppant. The parameters of fracturing slick water are determined according to the previous calculation and measurement results. The particle size d of the proppant p The density ρ is determined by sieving experiment. p The shape factor Ψ was determined by microscopic observation and image analysis.

[0034] The model is meshed, and the mesh is appropriately refined in areas where proppant movement and fluid flow are intense. Boundary conditions are set, and the injection velocity v of fracturing slick water and proppant is set at the inlet. in and concentration C in The injection rate is determined according to the actual injection capacity of the fracturing pump, and the concentration is determined according to the design requirements in the actual operation; the pressure condition is set at the outlet, and the pressure value P out Refer to the formation pressure of coalbed methane field.

[0035] The Eulerian-Eulerian model is used to simulate the two-phase flow of fracturing slick water and proppant. The model takes into account the interaction between the two phases, such as drag and lift. During the simulation, the continuity equation and momentum equation of the two phases are solved. After the simulation, the velocity data of the proppant are obtained at the preset monitoring points, focusing on the vertical velocity v z , sedimentation velocity v s That is v z .

[0036] S35: Verification; The viscosity, friction, filtration loss and sand carrying capacity data of each group of samples obtained by simulation calculation are compared with the real data obtained by actual laboratory measurement. The Pearson correlation coefficient r is calculated as follows: , where x i For simulation calculation data, y i For real data, and are the average values ​​of simulated data and real data respectively, and n is the number of samples.

[0037] At the same time, calculate the mean of the difference between the simulated value and the true value and standard deviation , determine the consistency limit as If the Pearson correlation coefficient and the consistency limit are both within the preset range (e.g., the Pearson correlation coefficient is greater than 0.8, and the consistency limit is within ±10%), the simulation calculation verification of the sample is judged to have passed; if it fails to pass the verification, the sample is re-simulated and the simulation parameters or model settings are adjusted until the verification passes.

[0038] This step uses advanced molecular modeling software and CFD software, comprehensively considering the actual temperature, pressure, geological structure and other factors of the coalbed methane field, to accurately simulate the real state of fracturing slick water in the well, and obtain accurate viscosity, friction, filtration and sand carrying capacity data; compared with traditional laboratory experimental methods, simulation calculations can complete the performance evaluation of a large number of samples in a shorter time, greatly shortening the evaluation cycle, and as long as the simulation parameters and conditions remain consistent, the calculation can be repeated to ensure the stability and reliability of the results; By calculating the Pearson correlation coefficient and consistency limit, the simulation data is quantitatively compared with the real data, which can accurately determine the degree of fit between the simulation results and the actual situation. If the verification is passed, it means that the model and parameter settings used in the simulation process are reasonable and effective, and the obtained data such as viscosity, friction, filtration and sand carrying capacity can truly reflect the performance of fracturing slick water in actual coalbed methane production. Accurate and reliable simulation data can enable engineers to more accurately evaluate the performance of fracturing slick water with different formulations, so as to train a more accurate evaluation model and reduce mining costs. If the verification fails, problems in the simulation calculation can be discovered in time. By re-simulating and adjusting parameters or model settings, the simulation process can be gradually optimized to make the simulation results closer to the actual situation, thereby improving the performance and practicality of the entire evaluation system. S4: Use the data obtained in S2 and S3 to construct a training set, and train the decision tree model to obtain an evaluation model; Encode the types of additives in S2, such as using different numbers to represent different types of drag reducers, drainage aids, and stabilizers. Calculate the sum feature M+N+P of the addition ratio, the product feature M×N×P of the addition ratio, the sum logarithm feature ln(M+N+P) of the addition ratio, and the product logarithm feature ln(M×N×P) of the addition ratio.

[0039] These data features are integrated with the viscosity, friction, filtration and sand carrying capacity data of each group of samples calculated by S3 simulation to form a complete data set.

[0040] In the step of training the decision tree model: the maximum depth of the decision tree is determined by the following method: setting the upper limit of the maximum depth to 12-16; calculating the information entropy difference before and after the split each time a node split operation is performed, and the information entropy difference is obtained by the formula "information entropy difference = information entropy of the node before split - the sum of the information entropy of the child nodes after splitting"; setting the initial information entropy difference threshold to 0.2-0.3, during the decision tree construction process, when the information entropy difference after a node split is less than the current information entropy difference threshold, stop splitting the node; and after completing 4-6 node splits, The information entropy difference threshold is adjusted according to the change in the number of nodes: if the number of nodes increases by more than or equal to 20% in two consecutive adjustment cycles, the information entropy difference threshold is increased to 0.25; if the number of nodes increases by less than 20% in two consecutive adjustment cycles, the information entropy difference threshold is reduced to 0.15, so as to dynamically determine the maximum depth of the decision tree; through the above method of dynamically determining the maximum depth of the decision tree, it is possible to give full play to the learning ability of the decision tree model while avoiding overfitting, so that it can better adapt to different data sets and improve the generalization performance and prediction accuracy of the model.

[0041] The calculation formula of information entropy is: , H(X) is the information entropy of the random variable X (data characteristics and viscosity, friction, filtration loss and sand carrying capacity data of each group of samples obtained by simulation calculation), n is the number of all possible values ​​of the random variable X, p(x i ) is the random variable X taking value x i The probability of The step of training the decision tree model also includes: using the information gain ratio as a metric, during the decision tree construction process, each time selecting the feature with the largest information gain ratio for node splitting, until the following stop condition is met: when the information gain ratios of all remaining selectable features are less than 0.01-0.02, stop splitting; at the same time, set the minimum sample splitting number, and when the number of samples on the node is less than this value, no more splitting; the calculation formula is as follows: Where D represents the data set, Di is the sample subset corresponding to the i-th value of feature A, and |D| and |Di| are the number of samples; is the information gain, is the information gain ratio, is the entropy of feature A itself; The steps of training the decision tree model also include: dividing the collected sample data into 70% training set, 15% validation set and 15% test set; using the training set data to train the decision tree, during the training process, for each branch formed after the node splits, continuously calculating the information gain ratio of each feature under the branch, and selecting the feature with the largest information gain ratio to continue splitting the node; after each training, evaluating the performance indicators such as the mean square error and accuracy of the model on the validation set, adjusting the hyperparameters according to the evaluation results, and retraining the model until the performance on the validation set no longer improves or reaches the preset number of training rounds; after the training is completed, using the test set data to evaluate the mean square error between the predicted value and the true value of the viscosity, friction, filtration loss and sand carrying capacity of the model, and calculating the determination coefficient to measure the goodness of fit of the model; if the performance of the model on the test set does not meet expectations, starting from the leaf node, the sub-branch is pruned and replaced with the leaf node, and the comprehensive score S is selected by comparing the mean square error and determination coefficient indicators of the model on the validation set before and after pruning. i The higher model is taken as the final model; Comprehensive score i It is calculated as follows: Setting the weight of mean square error The weight of the determination coefficient is set to 0.4-0.5 is 0.5-0.6 and satisfies ; For each model i, obtain its mean square error MSE on the validation set i and the coefficient of determination ; By formula Calculate the comprehensive score S for each model i ; S5: Use the evaluation model to predict the viscosity, friction, filtration and sand carrying capacity of the fracturing slick water to be evaluated.

[0042] Get a new sample of fracturing slick water to be evaluated, extract the type and proportion of additives according to the S2 method, extract data features, input the extracted data features into the trained evaluation model, and the model predicts the viscosity, friction, filtration and sand carrying capacity of the fracturing slick water to be evaluated based on the learned rules. The prediction results provide a scientific basis for selecting appropriate fracturing slick water for actual fracturing operations in coalbed methane fields, which helps to optimize the fracturing plan and improve the efficiency of coalbed methane extraction; In this step, by integrating data features such as additive type and addition ratio with the slick water performance indicators obtained by simulation calculation, the decision tree model can learn the complex nonlinear relationship between the two. After training with a large amount of sample data and evaluation and optimization of the test set, the model can accurately predict key performance indicators such as viscosity, friction, filtration loss and sand carrying capacity of the fracturing slick water to be evaluated, providing a scientific basis for the design and optimization of fracturing schemes. Traditional fracturing slick water evaluation methods often require a large number of experiments and tests, which consume a lot of time and resources. When using a trained decision tree model, only the additive information of the sample to be evaluated needs to be input to quickly obtain the performance prediction results, which greatly improves the evaluation efficiency and reduces the evaluation cost. By extracting the sum, product, logarithm and other features of the additive ratio, the deeper relationship between the additive and the slick water performance is excavated, enriching the input information of the model, so that the model can more comprehensively and accurately grasp the influence of various factors on the slick water performance.

[0043] The above-mentioned embodiments are used to predict and test the viscosity, friction, filtration loss and sand carrying capacity of sample 1, sample 2 and sample 3. The results are shown in the table below.

[0044] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the implementation modes, and they can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the illustrations shown and described herein.

Claims

1. A method for evaluating fracturing slick water, characterized in that: include: S1: Obtain fracturing slickwater samples; S2: extracting the types and addition ratios of drag reducers, drainage aids and stabilizers in the sample; S3: performing simulation calculation on the sample to obtain the viscosity, friction, filtration loss and sand carrying capacity of the sample; S4: Use the data obtained in S2 and S3 to construct a training set, and train the decision tree model to obtain an evaluation model; S5: using the evaluation model to predict the viscosity, friction, filtration loss and sand carrying capacity of the fracturing slick water to be evaluated; Among them, in the step of training the decision tree model: The maximum depth of the decision tree is determined by the following method: setting the upper limit of the maximum depth to 12-16; calculating the information entropy difference before and after the split each time a node split operation is performed, and the information entropy difference is obtained by the formula "information entropy difference = information entropy of the node before split - sum of information entropy of the child nodes after split"; The initial information entropy difference threshold is set to 0.2-0.

3. During the decision tree construction process, when the information entropy difference after a node split is less than the current information entropy difference threshold, stop splitting the node; and after every 4-6 node splits, adjust the information entropy difference threshold according to the change in the number of nodes: if the number of nodes increases by more than or equal to 20% in two consecutive adjustment cycles, increase the information entropy difference threshold to 0.25; if the number of nodes increases by less than 20% in two consecutive adjustment cycles, reduce the information entropy difference threshold to 0.15, so as to dynamically determine the maximum depth of the decision tree.

2. The fracturing slick water evaluation method according to claim 1, characterized in that: The steps of training a decision tree model also include: Using information gain ratio as the metric, during the decision tree construction process, the feature with the largest information gain ratio is selected each time for node splitting until the following stop condition is met: when the information gain ratios of all remaining features are less than 0.01-0.02, the splitting stops; At the same time, set the minimum number of sample splits. When the number of samples on a node is less than this value, it will no longer split.

3. The fracturing slick water evaluation method according to claim 1, characterized in that: The steps of training a decision tree model also include: The collected sample data is divided into 70% training set, 15% validation set, and 15% test set; Use the training set data to train the decision tree. During the training process, for each branch formed after the node splits, continuously calculate the information gain ratio of each feature under the branch, and select the feature with the largest information gain ratio to continue splitting the node; After each training, the mean square error and accuracy performance indicators of the model are evaluated on the validation set, and the hyperparameters are adjusted based on the evaluation results. The model is retrained until the performance on the validation set no longer improves or the preset number of training rounds is reached; After training, the test set data was used to evaluate the mean square error between the model's predicted values ​​and the true values ​​for viscosity, friction, filtration loss, and sand carrying capacity, and the coefficient of determination was calculated to measure the goodness of fit of the model; If the model performance on the test set does not meet expectations, start pruning the sub-branch from the leaf node and replace it with the leaf node. By comparing the mean square error and determination coefficient of the model on the validation set before and after pruning, select the comprehensive score S i The highest model is taken as the final model.

4. The fracturing slick water evaluation method according to claim 3, characterized in that: Comprehensive score i It is calculated as follows: Setting the weight of mean square error The weight of the determination coefficient is set to 0.4-0.5 is 0.5-0.6 and satisfies ; For each model i, obtain its mean square error MSE on the validation set i and the coefficient of determination ; By formula Calculate the comprehensive score S for each model i .

5. The method for evaluating fracturing slick water according to claim 1, wherein: Step S2 includes: HPLC was used to analyze the components of drag reducers and drainage aids in the samples. Based on known standards of drag reducers and drainage aids, the types of drag reducers and drainage aids were determined by comparing retention time and peak area, and their contents were calculated. GC-MS was used to separate the stabilizer in the sample, and the chemical structure and type of the stabilizer were determined by comparing the mass spectrum with the standard library, and then the content was quantified by the external standard method. Wherein, when carrying out HPLC analysis, also comprise the following steps: First, an initial mixed solvent is prepared according to a volume ratio of water to methanol of 3:2, and then 1% of N,N-dimethylformamide and 1% of dimethyl sulfoxide are added to the total volume of the initial mixed solvent to obtain a final mixed solvent; Add the sample to the final mixed solvent in a ratio of 1 g sample to 30-50 ml final mixed solvent, accelerate dissolution by stirring, and finally use the dissolved solution for HPLC analysis.

6. The fracturing slick water evaluation method according to claim 1, characterized in that: The viscosity simulation calculation includes: According to the type and addition ratio obtained in step S2, a non-Newtonian fluid viscosity calculation model in the fluid mechanics simulation software ANSYS Fluent is used to establish a virtual model of fracturing slick water, and the temperature parameters are set to 70-80°C and the pressure is set to 30-35MPa. The virtual viscosity value A of the fracturing slick water sample is obtained through iterative calculation; Use a rotational viscometer to measure the viscosity of the sample, repeat the measurement three times, and take the average value B of the measured viscosity; Compare the virtual viscosity value with the average measured viscosity. If the difference (AB) / B>±5%, change the non-Newtonian fluid viscosity calculation model, and iterate again to get the virtual viscosity value of the fracturing slick water sample. If the difference (AB) / B between the newly obtained virtual viscosity value and the average measured viscosity is not greater than ±5%, select the changed non-Newtonian fluid viscosity calculation model as the annual calculation model. If the difference of all non-Newtonian fluid viscosity calculation models is greater than ±5%, select the one with the smallest absolute difference. The non-Newtonian fluid viscosity calculation model includes a power law model, a Herschel-Bulkley model, a Bingham plasticity model, and a Carreau model; The upper limit of the number of iterations is set to 1000.

7. The method for evaluating fracturing slick water according to claim 1, wherein: In S3, the method of simulating and calculating the friction resistance of the sample includes: According to the actual fracturing operation, CFD software is used to establish the pipeline geometry model, divide the grid, set the fluid properties and flow channel model, define the boundary conditions, solve the Navier-Stokes equation to simulate the flow of fracturing slick water in the pipeline, obtain the pressure values ​​at the pipeline inlet and outlet, and calculate the friction resistance using the pressure difference method.

8. The method for evaluating fracturing slick water according to claim 1, wherein: In S3, the method of simulating and calculating the filtration loss of the sample includes: According to the actual fracturing operation, the porous media model is constructed using CFD software, the interaction parameters between fracturing slick water and rock are determined, the initial conditions and boundary conditions are set, and the continuity equation is solved in combination with Darcy's law to simulate the seepage process of fracturing slick water in porous media. A monitoring surface is set in the porous media model, and the flow rate of fracturing slick water passing through the surface is calculated to obtain the filtration rate, and then the filtration rate and filtration amount are calculated.

9. The method for evaluating fracturing slick water according to claim 1, wherein: In S3, the method of simulating and calculating the sand carrying capacity of the sample includes: Fluent software was used to construct the geometric model, set the parameters of fracturing slick water and proppant, divide the grid, set the boundary conditions, and use the Eulerian-Eulerian model to simulate the two-phase flow of fracturing slick water and proppant. After the simulation, the proppant velocity data at the preset monitoring point was obtained and the sedimentation velocity was calculated.

10. The method for evaluating fracturing slick water according to claim 1, wherein: Also includes: Use real data to verify the viscosity, friction, filtration and sand carrying capacity obtained by simulation. If the verification fails, re-simulate the calculation; Among them, the Pearson correlation coefficient and consistency limit of the simulated data and the real data are calculated. If the Pearson correlation coefficient and the consistency limit are both within the preset range, the verification is judged to be passed.

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