Fracturing effect evaluation method and fracturing construction curve evaluation method and device
By constructing a pressure fluctuation prediction model and calculating theoretical pressure fluctuation indices using parameters such as construction displacement, sand ratio, and liquid viscosity, the problem of insufficient quantitative evaluation of the fluctuation characteristics of fracturing construction curves in existing technologies is solved, and accurate evaluation of fracturing effects and optimization of construction parameters are achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA PETROLEUM & CHEMICAL CORP
- Filing Date
- 2024-11-25
- Publication Date
- 2026-05-26
Smart Images
Figure CN122087552A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of oil and gas reservoir fracturing technology, specifically to a method for evaluating fracturing effect, a method and apparatus for evaluating fracturing operation curves. Background Technology
[0002] Fracturing operation curve analysis is an important means to optimize fracturing design and evaluate fracturing effectiveness. By analyzing the changes in parameters such as pressure and injection rate over time, key information such as fracture initiation, propagation, and sealing can be identified, the physical and mechanical properties of the formation can be characterized, fracture morphology and fracturing effect can be predicted, and the optimization and adjustment of fracturing operation parameters can be guided.
[0003] However, existing fracturing operation curve analysis methods mainly rely on qualitative analysis or simplified models, lacking quantitative evaluation of the fluctuation characteristics of fracturing operation curves, thus failing to effectively assess fracturing effects. Summary of the Invention
[0004] The purpose of this disclosure is to provide a method and apparatus for evaluating fracturing effect and fracturing operation curve, aiming to solve the technical problem of how to quantitatively evaluate the fluctuation characteristics of fracturing operation curve.
[0005] To achieve the above objectives, in a first aspect, embodiments of this disclosure provide a method for evaluating fracturing operation curves, comprising: obtaining the operation flow rate, sand ratio, liquid viscosity, and operation pressure in the fracturing operation curve; determining a first pressure fluctuation index of the fracturing operation curve based on the operation flow rate, sand ratio, and liquid viscosity in the fracturing operation curve using a pressure fluctuation prediction model; determining a second pressure fluctuation index of the fracturing operation curve based on the operation pressure in the fracturing operation curve; comparing the first pressure fluctuation index and the second pressure fluctuation index, and evaluating the fracturing operation curve based on the comparison results.
[0006] In some embodiments, the pressure fluctuation prediction model includes a preprocessing layer and a function layer; based on the fracturing operation curve, the displacement, sand ratio, and liquid viscosity in the fracturing operation curve, the pressure fluctuation prediction model is used to determine a first fluctuation index of the fracturing operation curve, including: based on the displacement, sand ratio, and liquid viscosity in the fracturing operation curve, using the preprocessing layer to determine a consistency metric index of the fracturing operation curve; based on the consistency metric index, using the function layer to determine the first fluctuation index of the fracturing operation curve, wherein the function layer includes a functional relationship between the consistency metric index and the first fluctuation index.
[0007] In some embodiments, the formula for calculating the consistency metric is:
[0008] Where Q is the construction discharge rate, S is the sand ratio, μ is the liquid viscosity, α is the weighting coefficient of the construction discharge rate, β is the weighting coefficient of the sand ratio, and γ is the weighting coefficient of the liquid viscosity.
[0009] In some embodiments, the functional relationship between the consistency metric and the first volatility metric is as follows:
[0010] Where V1 is the first volatility index, C is the consistency measure index, a and b are the coefficients of the power function, and ε is the random error term.
[0011] In some embodiments, determining a second pressure fluctuation index of the fracturing operation curve based on the operation pressure in the fracturing operation curve includes: determining the relative rate of change of the operation pressure in the fracturing operation curve based on the operation pressure in the fracturing operation curve; and determining the second fluctuation index of the fracturing operation curve based on the relative rate of change.
[0012] In some embodiments, the formula for calculating the relative rate of change is:
[0013] in, P(t) represents the relative rate of change, and P(t) represents the construction pressure at construction time t. Construction time Construction pressure, For time intervals; The formula for calculating the second volatility indicator is:
[0014] Among them, V2 is the second volatility indicator. For relative rate of change, T represents the time interval and T represents the total construction time.
[0015] In some embodiments, the comparison result is the difference between the first pressure fluctuation index and the second pressure fluctuation index; the evaluation of the fracturing construction curve based on the comparison result includes: if the difference is within a preset threshold range, the fracturing construction curve is determined to meet the construction expectations; if the difference exceeds the preset threshold range, the fracturing construction curve is determined to not meet the construction expectations.
[0016] Secondly, embodiments of this disclosure provide an evaluation device for fracturing operation curves, comprising: an acquisition module for acquiring the operation flow rate, sand ratio, liquid viscosity, and operation pressure in the fracturing operation curve; a first determination module for determining a first pressure fluctuation index of the fracturing operation curve based on the operation flow rate, sand ratio, and liquid viscosity in the fracturing operation curve using a pressure fluctuation prediction model; a second determination module for determining a second pressure fluctuation index of the fracturing operation curve based on the operation pressure in the fracturing operation curve; and an evaluation module for comparing the first pressure fluctuation index and the second pressure fluctuation index, and evaluating the fracturing operation curve based on the comparison results.
[0017] Thirdly, embodiments of this disclosure provide an electronic device comprising: a memory configured to store instructions; and a processor configured to retrieve instructions from the memory and, when executing the instructions, to implement the evaluation method for fracturing construction curves provided in the first aspect or any embodiment of the first aspect.
[0018] Fourthly, embodiments of this disclosure provide a machine-readable storage medium storing instructions for causing a machine to perform the method for evaluating fracturing operation curves provided in the first aspect or any embodiment of the first aspect.
[0019] Fifthly, embodiments of this disclosure provide a method for evaluating fracturing effect, the method comprising: obtaining a fracturing operation curve; evaluating the fracturing operation curve according to the evaluation method for the fracturing operation curve provided in the first aspect or any embodiment of the first aspect; and evaluating the fracturing effect based on the evaluation result of the fracturing operation curve.
[0020] Through the above technical solution, the evaluation method for fracturing operation curves provided in this disclosure can calculate the theoretical pressure fluctuation index based on the construction parameters related to the fracturing effect in the fracturing operation curve using a pre-constructed pressure fluctuation prediction model. Simultaneously, the actual pressure fluctuation index is calculated based on the actual recorded construction pressure in the fracturing operation curve. Furthermore, the fluctuation characteristics of the fracturing operation curve are quantitatively evaluated based on the difference between the theoretical and actual pressure fluctuation indices.
[0021] Other features and advantages of the embodiments disclosed herein will be described in detail in the following detailed description section. Attached Figure Description
[0022] The accompanying drawings are provided to further illustrate the embodiments of this disclosure and form part of the specification. They are used together with the following detailed description to explain the embodiments of this disclosure, but do not constitute a limitation thereof. In the drawings: Figure 1This is a flowchart illustrating an evaluation method for fracturing operation curves according to Embodiment 1 of this disclosure; Figure 2 This is a flowchart illustrating an evaluation method for fracturing operation curves according to Embodiment 2 of this disclosure; Figure 3 This is a flowchart illustrating an evaluation method for fracturing operation curves according to Embodiment 3 of this disclosure; Figure 4 This is a schematic flowchart of an evaluation method for fracturing operation curves provided according to Embodiment 4 of this disclosure; Figure 5 This is a schematic flowchart of a fracturing effect evaluation method provided according to an embodiment of the present disclosure; Figure 6 This is a schematic diagram of the structure of an evaluation device for fracturing operation curves provided according to an embodiment of the present disclosure. Detailed Implementation
[0023] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this disclosure and are not intended to limit the embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.
[0024] It should be noted that the acquisition, transmission, storage, use, and processing of data in this disclosed technical solution comply with the relevant provisions of national laws and regulations. In the embodiments of this disclosure, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this disclosure, and do not imply that the applicant has already used or necessarily used such solutions.
[0025] Furthermore, if the embodiments of this disclosure involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this disclosure.
[0026] Fracturing operation curve analysis is a key tool for optimizing fracturing design and evaluating fracturing effect. By studying the changes of parameters such as pressure and injection rate over time, it can accurately identify important moments such as fracture initiation, propagation and sealing, thereby characterizing the physical and mechanical properties of the formation, predicting fracture morphology and fracturing effect, and thus guiding the optimization and adjustment of fracturing operation parameters.
[0027] Fracturing operation curve analysis methods can be mainly divided into traditional graphical analysis, analytical modeling, and numerical simulation. Traditional graphical analysis relies on the engineer's experience, visually comparing the shapes of curves for parameters such as pressure and injection rate to identify critical moments in fracture activity. While simple and intuitive, it is highly dependent on the engineer's experience and has limited quantitative analysis capabilities. Analytical modeling involves establishing mechanical models, such as PKN, KGD, and Penny models, and solving for theoretical curves using actual fracturing parameters. These curves are then fitted with measured data to derive rock mechanics parameters. Although it has a certain theoretical basis, its applicability is limited by the simplification of model assumptions. Numerical simulation employs advanced computational techniques, such as finite element method and boundary element method, to simulate the fracture propagation process in detail, providing a more realistic reservoir response. However, it is computationally expensive, and parameter inversion involves uncertainties. Each method has its applicable conditions and limitations; in engineering practice, the appropriate analysis method must be selected based on the site conditions.
[0028] Although both academia and industry attach great importance to the analysis methods of fracturing operation curves and have proposed various qualitative and quantitative evaluation methods, research results on the fluctuation characteristics of fracturing operation curves are still relatively scarce. Morphological fracturing curve analysis methods mainly determine key time points such as fracture initiation, propagation, and closure by comparing the morphological characteristics of parameter curves such as pressure and injection rate (e.g., net pressure analysis). However, these methods are mostly qualitative or semi-quantitative analyses, lacking precise characterization of fluctuation features. Physical mechanism-based fracturing curve interpretation methods establish physical and mechanical models of fracturing fracture propagation, fit the fracturing curve, and invert rock physical parameters. However, these methods always focus on mechanism analysis and pay insufficient attention to pressure fluctuation characteristics. In addition, some scholars have introduced time series analysis tools, such as wavelet transform and Hilbert-Huang transform, to extract multi-scale features of pressure fluctuations, but these methods have not yet considered the correlation with operation parameters.
[0029] Based on this, this disclosure establishes a quantitative relationship between fracturing construction parameters and pressure fluctuation characteristics by constructing a consistency measurement index and a pressure fluctuation correlation model, thereby obtaining a theoretical pressure fluctuation index. Combined with the actual pressure fluctuation index, it can be used to guide the optimization of construction parameters and to quickly evaluate and compare the fracturing effect under different conditions. This provides a theoretical basis for fracturing design, improves the fracturing production enhancement effect of unconventional oil and gas reservoirs, and has important engineering application value and economic significance.
[0030] Figure 1 This is a schematic flowchart illustrating an evaluation method for fracturing operation curves according to Embodiment 1 of this disclosure. Figure 1 As shown, the evaluation method for the fracturing operation curve includes steps S101 to S104.
[0031] Step S101: Obtain the fracturing displacement, sand ratio, liquid viscosity and fracturing pressure from the fracturing operation curve.
[0032] During fracturing operations, it is necessary to record and collect various key parameters in real time to construct fracturing operation curves. These parameters include, but are not limited to, injection flow rate, proppant ratio, fluid viscosity, and injection pressure. Injection flow rate can be monitored in real time using a flow meter to record the amount of fluid injected per unit time. Propane ratio can be calculated by measuring the mass of proppant (such as silica sand) carried per unit volume of fluid using a weighing system. Fluid viscosity can be measured periodically using a viscometer to ensure it remains within a predetermined range. Injection pressure can be monitored in real time using a pressure sensor to record pressure changes during the operation.
[0033] It should be noted that existing acquisition technologies are quite mature, including high-precision flow meters, weighing systems, viscometers, and pressure sensors. These devices can provide reliable and accurate data, providing a basis for subsequent analysis. This disclosure does not specifically limit or describe these technologies.
[0034] Step S102: Based on the fracturing operation rate, sand ratio and liquid viscosity in the fracturing operation curve, the first pressure fluctuation index of the fracturing operation curve is determined using the pressure fluctuation prediction model.
[0035] It should be noted that the fracturing displacement affects the rate of pressure rise, the sand ratio affects the fracture support effect, and the liquid viscosity affects the fluid flow characteristics; these three factors collectively determine the fluctuation characteristics of the fracturing operation curve. Therefore, in this embodiment, a pressure fluctuation prediction model can be pre-constructed by analyzing the relationship between the fracturing displacement, sand ratio, liquid viscosity, and the fluctuation characteristics of the fracturing pressure. This pressure fluctuation prediction model can then be used, taking the fracturing displacement, sand ratio, and liquid viscosity as inputs, to output the first pressure fluctuation index of the fracturing operation curve. That is, the theoretical pressure fluctuation index is calculated based on the construction parameters related to the fracturing effect in the fracturing operation curve.
[0036] In this embodiment of the disclosure, the pressure fluctuation prediction model can be constructed using the following steps: 1) Data collection: Collect a large amount of historical fracturing operation data, including parameters such as operation displacement, sand ratio, liquid viscosity and operation pressure.
[0037] 2) Data preprocessing: Clean the data, remove outliers and noise, and ensure data quality.
[0038] 3) Feature extraction: Extract key features from the raw data, such as calculating consistency metrics to characterize construction discharge, sand ratio, liquid viscosity, and the relative change rate of construction pressure.
[0039] 4) Model selection: Choose a suitable machine learning model, such as neural network, support vector machine, etc.
[0040] 5) Model Training: The model is trained using the training dataset to optimize its parameters and enable it to accurately predict pressure fluctuations. Gradient descent, stochastic gradient descent, and other optimization algorithms can be used during training. By collecting a large amount of fracturing operation data, a functional relationship can be fitted between the first pressure fluctuation index V1 and the consistency metric C: V1 = f(C) + ε, where f(C) is the undetermined functional form and ε is the random error term.
[0041] 6) Model Validation: Use a validation dataset to validate the model and evaluate its prediction accuracy and generalization ability.
[0042] 7) Model Optimization: The least squares method is applied to optimize the model parameters to ensure good fitting and generalization ability. To achieve good fitting and generalization, the function form f(C) needs to be appropriately chosen, and the model parameters α, β, and γ need to be optimized. Specifically, a loss function is defined to quantify the error between the model's predicted values and the actual observed values. In the least squares method, the squared error is typically used as the loss function. For n data points, the loss function L can be expressed as: , where V i C is the actual pressure fluctuation index for the i-th data point. i The consistency metric is calculated using (Q, S, μ) of the i-th data point, where θ represents the parameters in function f(C), and L is the loss function, reflecting the total error between the model's predicted and actual values. The goal is to find the values of parameters (α, β, γ, θ) that minimize the loss function L.
[0043] 8) Model application: Apply the trained model to actual fracturing construction data to predict the first pressure fluctuation index.
[0044] Step S103: Based on the construction pressure in the fracturing construction curve, determine the second pressure fluctuation index of the fracturing construction curve.
[0045] The second pressure fluctuation index is calculated based on the actual recorded construction pressure data. That is, the actual pressure fluctuation index is calculated based on the actual construction pressure recorded in the fracturing construction curve.
[0046] This step may specifically include: extracting time series data of construction pressure from the fracturing construction curve, calculating the fluctuation characteristics of the time series data of construction pressure, such as relative rate of change, maximum value, minimum value, average value, standard deviation and frequency, and then calculating a second pressure fluctuation index based on the extracted fluctuation characteristics.
[0047] Step S104: Compare the first pressure fluctuation index and the second pressure fluctuation index, and evaluate the fracturing operation curve based on the comparison results.
[0048] The fluctuation characteristics of the fracturing operation curve are evaluated by comparing the first and second pressure fluctuation indices. Specifically, this involves a difference analysis of the first and second pressure fluctuation indices, followed by the establishment of evaluation criteria. Based on the results of the difference analysis, it is determined whether the fracturing operation curve meets the expected operation. An evaluation report can also be generated, providing further evaluation results and improvement suggestions for the fracturing effect.
[0049] The fracturing operation curve evaluation method provided in Embodiment 1 of this disclosure calculates the theoretical pressure fluctuation index using a pre-constructed pressure fluctuation prediction model and the actual pressure fluctuation index using the actual recorded operation pressure of the fracturing operation curve, thus achieving a quantitative evaluation of the fluctuation characteristics of the fracturing operation curve. This method not only improves the accuracy of fracturing effect evaluation but also provides a scientific basis for optimizing fracturing design and operation parameters.
[0050] Figure 2 This is a flowchart illustrating a method for evaluating fracturing operation curves according to Embodiment 2 of this disclosure. In this embodiment, the pressure fluctuation prediction model may include a preprocessing layer and a function layer. For example... Figure 2 As shown, in the evaluation method of the fracturing operation curve, the first pressure fluctuation index of the fracturing operation curve can be determined by using a pressure fluctuation prediction model based on the operation displacement, sand ratio and liquid viscosity in the fracturing operation curve, which may include steps S201 to S202.
[0051] Step S201: Based on the fracturing operation curve, the displacement, sand ratio and liquid viscosity, determine the consistency index of the fracturing operation curve using the pretreatment layer.
[0052] This disclosure introduces a consistency metric C to comprehensively consider the effects of factors such as construction discharge volume, sand ratio, and liquid viscosity.
[0053] In this embodiment of the disclosure, in order to comprehensively consider the impact of fracturing flow rate, sand ratio, and liquid viscosity on fracturing operations, a consistency metric can be defined. This consistency metric is calculated in the preprocessing layer of the pressure fluctuation prediction model.
[0054] In some embodiments, the formula for calculating the consistency metric is:
[0055] Where Q is the construction discharge rate, S is the sand ratio, μ is the liquid viscosity, α is the weighting coefficient of the construction discharge rate, β is the weighting coefficient of the sand ratio, and γ is the weighting coefficient of the liquid viscosity.
[0056] For example, taking well #F as an example, it is known that α=0.5, β=0.3, and γ=0.2 are obtained through fitting multiple sets of data. The construction flow rate Q of well #F is 0.85 cubic meters / minute (m³ / min), the sand ratio S is 75% (%), and the liquid viscosity μ is 0.9 poise (P). By inputting Q=0.85, S=0.75, and μ=0.9 into the pretreatment layer, the consistency metric C of well #F is calculated to be 0.85.
[0057] Step S202: Based on the consistency metric, the first fluctuation index of the fracturing operation curve is determined using the function layer, wherein the function layer includes the functional relationship between the consistency metric and the first fluctuation index.
[0058] The embodiments of this disclosure use a first pressure fluctuation index V1 to quantitatively describe the theoretical pressure fluctuation characteristics of the entire fracturing process.
[0059] By performing regression analysis on multiple sets of construction data, it was found that there is a good power function relationship between the consistency measurement index and the first fluctuation index. Therefore, the function layer of the pressure fluctuation prediction model is constructed in the form of a fuzzy function.
[0060] In some embodiments, the functional relationship between the consistency metric and the first volatility metric is as follows:
[0061] Where V1 is the first volatility index, C is the consistency measure index, a and b are the coefficients of the power function, and ε is the random error term.
[0062] For example, taking well #F as an example again. It is known that through fitting, a=42.5, b=-1.8, and ε follows a normal distribution with a mean of 0 and a variance of 0.2. Inputting C=0.85 into the function layer yields V1=14.7 for well #F.
[0063] The evaluation method for fracturing operation curves provided in Embodiment 2 of this disclosure, through a pressure fluctuation prediction model, can mathematically characterize the fluctuation features of the fracturing operation curve, revealing the intrinsic relationship between consistency metrics and pressure fluctuations, and providing a theoretical basis and quantitative indicators for fracturing operation optimization. Specifically, this method calculates the consistency metric index through a preprocessing layer, and then determines the first fluctuation index through a function layer, achieving an accurate assessment of the fluctuation characteristics of the fracturing operation curve. This quantitative evaluation method based on a pressure fluctuation prediction model can reveal the intrinsic relationship between fracturing operation parameters and pressure fluctuation characteristics, providing a theoretical basis for parameter optimization and fracturing effect evaluation in actual fracturing operations, and is of great significance for improving the development efficiency of unconventional oil and gas reservoirs.
[0064] Figure 3 This is a flowchart illustrating a method for evaluating fracturing operation curves according to Embodiment 3 of this disclosure. In this method for evaluating fracturing operation curves, determining a second pressure fluctuation index of the fracturing operation curve based on the operation pressure in the fracturing operation curve may include steps S301 and S302.
[0065] Step S301: Based on the construction pressure in the fracturing construction curve, determine the relative rate of change of the construction pressure in the fracturing construction curve.
[0066] In some embodiments, the formula for calculating the relative rate of change is:
[0067] in, P(t) represents the relative rate of change, and P(t) represents the construction pressure at construction time t. Construction time Construction pressure, For time intervals.
[0068] Step S302: Based on the relative rate of change, determine the second fluctuation index of the fracturing operation curve.
[0069] The formula for calculating the second volatility indicator is:
[0070] Among them, V2 is the second volatility indicator. For relative rate of change, T represents the time interval and T represents the total construction time.
[0071] For example, taking well #F as an example, the construction time of well #F is known to be 3 hours (T=10800 seconds), and the sampling interval of pressure data is 1 second. =1). By discretizing and summing the integrals approximating the result, and combining the formulas for calculating the relative rate of change and the second fluctuation index, V2=15.2 for well #F was obtained.
[0072] The evaluation method for fracturing operation curves provided in Embodiment 3 of this disclosure achieves a quantitative evaluation of the fluctuation characteristics of the fracturing operation curve by calculating the relative rate of change of the operation pressure and a second fluctuation index. This method not only accurately reflects the dynamic characteristics of pressure changes during operation but also provides a scientific basis for evaluating fracturing effectiveness and optimizing operation parameters. Specifically, the calculation of the relative rate of change captures the instantaneous changes in operation pressure, while the second fluctuation index comprehensively reflects the pressure fluctuations throughout the entire operation. This method helps to promptly identify and resolve abnormal problems during operation, improving the safety and efficiency of fracturing operations.
[0073] Figure 4 This is a flowchart illustrating a method for evaluating fracturing operation curves according to Embodiment 4 of this disclosure. In this method, the comparison result can be the difference between a first pressure fluctuation index and a second pressure fluctuation index. Comparing the first pressure fluctuation index and the second pressure fluctuation index, and evaluating the fracturing operation curve based on the comparison result, may include steps S401 to S402.
[0074] Step S401: If the difference is within the preset threshold range, the fracturing construction curve is determined to meet the construction expectations.
[0075] In this embodiment of the disclosure, the difference D between the first pressure fluctuation index V1 and the second pressure fluctuation index V2 is first calculated as |V1−V2|. If the difference D is within the preset threshold range, i.e. D≤threshold, then the fracturing construction curve is determined to meet the construction expectation.
[0076] Step S402: If the difference exceeds the preset threshold range, it is determined that the fracturing construction curve does not meet the construction expectations.
[0077] If the difference D exceeds the preset threshold range, i.e., D>threshold, then the fracturing construction curve is determined to be inconsistent with the construction expectations.
[0078] For example, taking well #F as an example, given that V1=14.7 and V2=15.2, the difference between V1 and V2 is calculated as D=|V1−V2|=0.5. Assuming the preset threshold is 0.75, then 0.5<0.75, indicating that the fracturing operation curve of well #F meets the expected operation.
[0079] The fracturing operation curve evaluation method provided in Embodiment 4 of this disclosure achieves a quantitative evaluation of the fluctuation characteristics of the fracturing operation curve by comparing the difference between a first pressure fluctuation index and a second pressure fluctuation index. This method can not only accurately assess the effectiveness of fracturing operations but also promptly detect anomalies during operation, providing a scientific basis for optimizing fracturing design and operation parameters. Specifically, by setting a preset threshold, it is possible to effectively determine whether the fracturing operation curve meets the expected operation, thereby improving the safety and efficiency of fracturing operations. This method helps to take necessary adjustment measures in a timely manner to ensure the smooth progress of fracturing operations.
[0080] Figure 5 This is a schematic flowchart of a fracturing effect evaluation method provided according to an embodiment of this disclosure. Figure 5 As shown, the fracturing effect evaluation method includes steps S501 to S503.
[0081] Step S501: Obtain the fracturing operation curve.
[0082] Obtain fracturing operation curves. Fracturing operation curves include various key parameters recorded during the operation, such as displacement, sand ratio, fluid viscosity, and operating pressure. These parameters can be monitored and recorded in real time using equipment such as flow meters, weighing systems, viscometers, and pressure sensors.
[0083] Step S502: Evaluate the fracturing operation curve.
[0084] Specifically, the fracturing operation curve is evaluated according to the evaluation method for the fracturing operation curve provided in the above embodiments. The specific steps can be found in the detailed description of the evaluation method for the fracturing operation curve described above, and will not be repeated here.
[0085] Step S503: Evaluate the fracturing effect based on the evaluation results of the fracturing construction curve.
[0086] Based on the evaluation results of the fracturing operation curve, the fracturing effect is comprehensively assessed. If the fracturing operation curve meets the expected results, it indicates that the pressure fluctuation characteristics during the fracturing operation are consistent with the theoretical expectations, and the fracturing effect is good. If the fracturing operation curve does not meet the expected results, it indicates that there may be abnormalities during the fracturing operation, and further analysis and adjustment of the operation parameters are needed to optimize the fracturing effect.
[0087] The fracturing effect evaluation method provided in this disclosure accurately assesses the effectiveness of fracturing operations by acquiring and quantitatively evaluating fracturing construction curves. This method not only improves the accuracy of fracturing effect evaluation but also provides a scientific basis for optimizing fracturing design and construction parameters. Specifically, by comparing theoretical and actual pressure fluctuation indicators, abnormal situations during construction can be detected in a timely manner, ensuring the safety and efficiency of fracturing operations. This method helps to take necessary adjustments in a timely manner, ensuring the smooth progress of fracturing operations and improving the overall fracturing effect.
[0088] Figure 6 This is a schematic diagram of the structure of an evaluation device for fracturing operation curves provided according to an embodiment of this disclosure. Figure 6 As shown, the evaluation device 100 for the fracturing construction curve may include an acquisition module 110, a first determination module 120, a second determination module 130, and an evaluation module 140.
[0089] The acquisition module 110 is used to acquire the construction displacement, sand ratio, liquid viscosity and construction pressure in the fracturing construction curve.
[0090] The first determination module 120 is used to determine the first pressure fluctuation index of the fracturing operation curve based on the operation displacement, sand ratio and liquid viscosity in the fracturing operation curve and using a pressure fluctuation prediction model.
[0091] The second determining module 130 is used to determine the second pressure fluctuation index of the fracturing construction curve based on the construction pressure in the fracturing construction curve.
[0092] Evaluation module 140 is used to compare the first pressure fluctuation index and the second pressure fluctuation index, and evaluate the fracturing operation curve based on the comparison results.
[0093] The beneficial effects of the fracturing operation curve evaluation device provided in this disclosure are the same as those of the fracturing operation curve evaluation method provided in the above embodiments, and other technical features in the fracturing operation curve evaluation device are the same as those disclosed in the fracturing operation curve evaluation method, and will not be repeated here.
[0094] This disclosure also provides an electronic device, which includes: a memory; and a processor configured to execute the evaluation method for fracturing operation curves provided in the above embodiments.
[0095] The beneficial effects of the electronic equipment provided in this embodiment are the same as those of the evaluation method for fracturing construction curves provided in the above embodiments, and will not be repeated here.
[0096] This disclosure also provides a machine-readable storage medium storing instructions for causing a machine to perform the method for evaluating fracturing operation curves provided in the first aspect or any embodiment of the first aspect.
[0097] The beneficial effects of the machine-readable storage medium provided in this disclosure are the same as those of the evaluation method for fracturing construction curves provided in the above embodiments, and will not be repeated here.
[0098] This disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the evaluation method for fracturing construction curves as described above.
[0099] The beneficial effects of the computer program product provided in this embodiment are the same as those of the evaluation method for fracturing construction curves provided in the above embodiments, and will not be repeated here.
[0100] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0101] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0102] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0103] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0104] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0105] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.
[0106] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.
[0107] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.
[0108] The above are merely embodiments of this disclosure and are not intended to limit the scope of this disclosure. Various modifications and variations can be made to this disclosure by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the scope of the claims of this disclosure.
Claims
1. A method for evaluating fracturing operation curves, characterized in that, include: Obtain the fracturing operation curve for displacement, sand ratio, liquid viscosity, and operation pressure; Based on the fracturing operation curve, the displacement, sand ratio, and liquid viscosity, the first pressure fluctuation index of the fracturing operation curve is determined using a pressure fluctuation prediction model. Based on the construction pressure in the fracturing construction curve, a second pressure fluctuation index of the fracturing construction curve is determined; The first pressure fluctuation index and the second pressure fluctuation index are compared, and the fracturing operation curve is evaluated based on the comparison results.
2. The evaluation method for fracturing operation curves according to claim 1, characterized in that, The pressure fluctuation prediction model includes a preprocessing layer and a function layer; based on the fracturing operation curve, the first fluctuation index of the fracturing operation curve is determined using the pressure fluctuation prediction model, including: Based on the fracturing operation curve, the displacement, sand ratio, and liquid viscosity, the consistency index of the fracturing operation curve is determined using the pretreatment layer. Based on the consistency metric, a first fluctuation index of the fracturing operation curve is determined using a function layer, wherein the function layer includes the functional relationship between the consistency metric and the first fluctuation index.
3. The evaluation method for fracturing operation curves according to claim 2, characterized in that, The formula for calculating the consistency metric is as follows: Where Q is the construction discharge rate, S is the sand ratio, μ is the liquid viscosity, α is the weighting coefficient of the construction discharge rate, β is the weighting coefficient of the sand ratio, and γ is the weighting coefficient of the liquid viscosity.
4. The evaluation method for fracturing operation curves according to claim 2, characterized in that, The functional relationship between the consistency metric and the first volatility metric is as follows: Where V1 is the first volatility index, C is the consistency measure index, a and b are the coefficients of the power function, and ε is the random error term.
5. The evaluation method for fracturing operation curves according to claim 1, characterized in that, The step of determining the second pressure fluctuation index of the fracturing operation curve based on the operation pressure in the fracturing operation curve includes: Based on the construction pressure in the fracturing construction curve, determine the relative rate of change of the construction pressure in the fracturing construction curve; Based on the relative rate of change, a second fluctuation index is determined for the fracturing operation curve.
6. The evaluation method for fracturing operation curves according to claim 5, characterized in that, The formula for calculating the relative rate of change is: in, P(t) represents the relative rate of change, and P(t) represents the construction pressure at construction time t. Construction time Construction pressure, For time intervals; The formula for calculating the second volatility indicator is: Among them, V2 is the second volatility indicator. For relative rate of change, T represents the time interval and T represents the total construction time.
7. The evaluation method for fracturing operation curves according to any one of claims 1 to 6, characterized in that, The comparison result is the difference between the first pressure fluctuation index and the second pressure fluctuation index; the evaluation of the fracturing operation curve based on the comparison result includes: If the difference is within the preset threshold range, the fracturing operation curve is determined to meet the construction expectations; If the difference exceeds the preset threshold range, the fracturing construction curve is determined to be inconsistent with the construction expectations.
8. An evaluation device for fracturing operation curves, characterized in that, include: The acquisition module is used to obtain the fracturing operation curve, including the operation displacement, sand ratio, liquid viscosity, and operation pressure. The first determining module is used to determine the first pressure fluctuation index of the fracturing operation curve based on the operation displacement, sand ratio and liquid viscosity in the fracturing operation curve using a pressure fluctuation prediction model. The second determining module is used to determine a second pressure fluctuation index of the fracturing operation curve based on the operation pressure in the fracturing operation curve. The evaluation module is used to compare the first pressure fluctuation index and the second pressure fluctuation index, and evaluate the fracturing operation curve based on the comparison results.
9. An electronic device, characterized in that, include: The memory is configured to store instructions; as well as A processor configured to retrieve the instructions from the memory and, when executing the instructions, to implement a method for evaluating fracturing operation curves according to any one of claims 1 to 7.
10. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores instructions for causing the machine to perform the evaluation method of the fracturing operation curve according to any one of claims 1 to 7.
11. A method for evaluating fracturing effect, characterized in that, include: Obtain fracturing operation curves; The fracturing operation curve is evaluated using the evaluation method according to any one of claims 1 to 7; The fracturing effect is evaluated based on the evaluation results of the fracturing operation curve.