Method, device, equipment, medium and program product for determining anti-fouling and fouling removal measures

Abstract

The method, device, equipment, medium and program product for determining scale prevention and removal measures provided by the application relate to the technical field of scale prevention and removal of industrial pipelines. The method comprises the following steps: obtaining scale formation related dynamic parameters of a target oilfield; establishing a corresponding dynamic scale formation rate calculation model according to the scale formation related dynamic parameters; calculating a scale formation rate prediction result of the target oilfield according to the dynamic scale formation rate calculation model and the scale formation related dynamic parameters; determining a scale prevention and removal layered early warning threshold according to the scale formation rate prediction result; obtaining real-time scale formation monitoring data of the target oilfield and a plurality of preset scale prevention and removal alternative measures; determining the weight of a scale prevention evaluation index according to the scale prevention and removal layered early warning threshold and the real-time scale formation monitoring data; and determining a target scale prevention and removal measure according to the weight of the scale prevention evaluation index and each scale prevention and removal alternative measure. The method provided by the application achieves the technical effect of improving the accuracy and timeliness of scale prevention and removal decision selection.

Description

Technical Field

[0001] This application relates to the field of scale prevention and removal technology for industrial pipelines, and in particular to a method, apparatus, equipment, medium, and procedure for determining scale prevention and removal measures. Background Technology

[0002] In oilfield wellbores, gathering and transmission pipelines, and pumping stations, physical, chemical, or mechanical methods are typically used for scale prevention, and operational plans are developed based on experience or historical data.

[0003] However, such solutions are mostly based on a single factor or static data, which makes it difficult to reflect the continuous impact of changes in operating conditions such as temperature, flow rate, pH value, and ion concentration on the scaling process. This results in inaccurate scaling risk assessment, and the selection of scale prevention measures is prone to lag or redundancy. Furthermore, the objectivity and adaptability of the decision-making results are insufficient. As a result, the existing technology has technical problems such as low accuracy and poor timeliness in scale prevention and removal decision-making.

[0004] Therefore, improving the accuracy and timeliness of scaling risk assessment and scaling prevention measure selection under dynamic operating conditions has become a challenging and significant issue. Summary of the Invention

[0005] This application provides a method, apparatus, equipment, medium, and procedure for determining scale prevention and removal measures, which aims to improve the accuracy and timeliness of scale prevention and removal measure selection.

[0006] Firstly, this application provides a method for determining scale prevention and removal measures, including:

[0007] Obtain dynamic parameters related to scale formation in the target oilfield;

[0008] Based on the dynamic parameters related to scaling, a corresponding dynamic scaling rate calculation model is established;

[0009] Based on the dynamic scaling rate calculation model and scaling-related dynamic parameters, the scaling rate prediction results for the target oilfield are calculated.

[0010] Based on the scaling rate prediction results, determine the early warning threshold for scale prevention and removal stratification;

[0011] Acquire real-time scaling monitoring data of the target oilfield and pre-set multiple alternative scaling prevention and removal measures;

[0012] The weights of the scale prevention and removal stratified early warning thresholds and real-time scale monitoring data are determined based on the scale prevention assessment indicators.

[0013] Based on the weights of the scale prevention assessment indicators and each alternative scale prevention and removal measure, the target scale prevention and removal measures are determined.

[0014] Secondly, this application provides a device for determining scale prevention and removal measures, comprising:

[0015] The first acquisition module is used to acquire dynamic parameters related to scale formation in the target oilfield;

[0016] A module is established to build a corresponding dynamic scaling rate calculation model based on scaling-related dynamic parameters;

[0017] The calculation module is used to calculate the predicted scaling rate of the target oilfield based on the dynamic scaling rate calculation model and scaling-related dynamic parameters.

[0018] The first processing module is used to determine the early warning threshold for scale prevention and removal based on the scale formation rate prediction results.

[0019] The second acquisition module is used to acquire real-time scaling monitoring data of the target oilfield and a variety of preset scaling prevention and removal alternatives;

[0020] The second processing module is used to determine the weight of the scale prevention evaluation index based on the scale prevention and descaling stratification early warning threshold and real-time scale monitoring data.

[0021] The third processing module is used to determine the target scale prevention and removal measures based on the weight of the scale prevention evaluation indicators and each scale prevention and removal alternative measure.

[0022] Thirdly, this application provides a device for determining scale prevention and removal measures, including: a memory and a processor;

[0023] The memory stores instructions that the computer executes;

[0024] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0025] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible embodiments of the first aspect.

[0026] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0027] This application provides a method, apparatus, equipment, medium, and program product for determining scale prevention and removal measures. By acquiring dynamic parameters related to scale formation in the target oilfield, establishing a corresponding dynamic scale formation rate calculation model, and calculating the predicted scale formation rate of the target oilfield based on this model, the scale risk assessment can better reflect the continuous impact of dynamic operating condition changes on the scale formation process. Furthermore, by determining the scale prevention and removal stratified early warning threshold based on the scale formation rate prediction results, and combining real-time scale monitoring data of the target oilfield, multiple alternative scale prevention and removal measures, and the weights of scale prevention assessment indicators, the target scale prevention and removal measures are determined. This solves the technical problems of low accuracy and poor timeliness in the selection of scale prevention and removal measures in the prior art, and achieves the technical effect of improving the accuracy and timeliness of the selection of scale prevention and removal measures. Attached Figure Description

[0028] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0029] Figure 1 This application provides a schematic diagram of an application data processing system architecture.

[0030] Figure 2 A flowchart illustrating the method for determining scale prevention and removal measures provided in the embodiments of this application. Figure 1 ;

[0031] Figure 3 A flowchart illustrating the method for determining scale prevention and removal measures provided in the embodiments of this application. Figure 2 ;

[0032] Figure 4 A flowchart illustrating the method for determining scale prevention and removal measures provided in the embodiments of this application. Figure 3 ;

[0033] Figure 5 A flowchart illustrating the method for determining scale prevention and removal measures provided in the embodiments of this application. Figure 4 ;

[0034] Figure 6 A flowchart illustrating the method for determining scale prevention and removal measures provided in the embodiments of this application. Figure 5 ;

[0035] Figure 7 A schematic diagram of the structure of the device for determining scale prevention and removal measures provided in the embodiments of this application;

[0036] Figure 8 A schematic diagram of the structure of the device for determining the anti-scaling and descaling measures provided in the embodiments of this application.

[0037] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0038] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0039] In existing technologies, oilfield scale prevention typically employs physical, chemical, or mechanical methods. These include altering deposition conditions through ultrasound or electromagnetic means, inhibiting crystal growth by adding scale inhibitors, or reducing scale formation using metal scale inhibitors or scraping devices. While these technologies can be effective under specific conditions, their decision-making process relies heavily on human experience, historical cases, or single-indicator judgments. They are primarily static selections based on a specific point in time, rather than dynamic assessments based on the evolution of scale formation. Furthermore, traditional solutions often prioritize cost, convenience, or empirical applicability, lacking simultaneous evaluation of multiple dimensions such as safety, applicability, technical difficulty, efficiency, and economy. They also lack tiered early warning mechanisms based on scale severity, leading to the same scale prevention methods being applied in both high-risk and low-risk scenarios. This can result in decreased production capacity, increased energy consumption, and equipment wear due to delayed treatment, or excessive intervention leading to wasted reagents, increased construction frequency, and higher production costs. Therefore, existing technologies suffer from low accuracy and poor timeliness in selecting scale prevention and removal measures.

[0040] To address the aforementioned issues, this application provides a method, apparatus, equipment, medium, and program product for determining scale prevention and removal measures. By acquiring dynamic parameters related to scale formation in the target oilfield, establishing a corresponding dynamic scale formation rate calculation model, and calculating the predicted scale formation rate of the target oilfield based on this model, the scale risk assessment can better reflect the continuous impact of dynamic operating condition changes on the scale formation process. Furthermore, by determining the scale prevention and removal stratified early warning threshold based on the scale formation rate prediction results, and combining real-time scale monitoring data of the target oilfield, multiple alternative scale prevention and removal measures, and the weights of scale prevention assessment indicators, the target scale prevention and removal measures are determined. This solves the technical problems of low accuracy and poor timeliness in the selection of scale prevention and removal measures in existing technologies, and achieves the technical effect of improving the accuracy and timeliness of scale prevention and removal measure selection.

[0041] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0042] Figure 1 This is a schematic diagram of an application data processing system architecture provided in an embodiment of this application. The application data processing system is a computer device. Figure 1 As shown, the above architecture includes at least one of a data acquisition device 101, a processing device 102, and a display device 103.

[0043] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the architecture of the application data processing system. In other feasible embodiments of this application, the above architecture may include more or fewer components than illustrated, or combine some components, or split some components, or arrange different components, which can be determined according to the actual application scenario and is not limited here. Figure 1 The components shown can be implemented in hardware, software, or a combination of both.

[0044] In the specific implementation process, the data acquisition device 101 may include an input / output interface or a communication interface, and the data acquisition device 101 can be connected to the processing device through the input / output interface or the communication interface.

[0045] The treatment equipment 102 can form a complete technical route from risk prediction, threshold classification, weight adjustment to measure determination by using dynamic parameters related to scaling, dynamic scaling rate calculation model, scaling rate prediction results, stratified early warning thresholds, real-time scaling monitoring data, and the linkage between alternative measures. This means that it no longer relies solely on static experience to directly select scale prevention methods, but instead predicts scaling trends based on real-time changing production parameters, establishes a stratified early warning mechanism based on the prediction results, and dynamically adjusts the weights of scale prevention evaluation indicators in combination with on-site monitoring data. Finally, it determines the appropriate target scale prevention and removal measures from multiple alternative measures, thereby providing more accurate and timely decision support for scale control in oilfield production.

[0046] The display device 103 can also be a touch screen or the screen of a terminal device, used to receive user commands while displaying the above-mentioned content, so as to realize interaction with the user.

[0047] It should be understood that the aforementioned processing device can be implemented by a processor reading instructions from memory and executing those instructions, or it can be implemented by a chip circuit.

[0048] Furthermore, the network architecture and business scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0049] Figure 2 A flowchart illustrating the method for determining scale prevention and removal measures provided in the embodiments of this application. Figure 1 ,like Figure 2 As shown, the method for determining scale prevention and removal measures provided in this embodiment includes:

[0050] S201. Obtain dynamic parameters related to scale formation in the target oilfield.

[0051] In this embodiment, the target oilfield refers to the specific production object that requires scale risk identification, scale prevention scheme screening, and scale removal measure decision-making. It can be a single oil well, water injection well, produced fluid gathering and transportation branch, or a pumping station, metering station, combined station, or a continuous transportation system consisting of wellhead to station. Scale-related dynamic parameters refer to a set of time-varying data that can reflect the changes in scale trend during fluid transportation, heating, throttling, mixing, and residence. Among them, "dynamic" is used to characterize that the parameter is not a static single-point value, but a sequence of data that changes with time within a continuous production cycle. It can be formed by using a fixed sampling period to form time-series samples, or it can be encrypted by using an event-triggered method when the operating conditions change abruptly.

[0052] Specifically, scale-related dynamic parameters can be directly collected by field instruments, including temperature sensors, pressure transmitters, electromagnetic flow meters, ultrasonic flow meters, online pH analyzers, conductivity meters, turbidity meters, and ion analyzers. Alternatively, ionic composition, crystallization tendency, and solid content can be obtained by laboratory water sample test results. Operation events and maintenance records can also be imported through production databases, well station historical ledgers, and manual inspection terminals.

[0053] Optionally, after obtaining the scaling-related dynamic parameters of the target oilfield, the method further includes:

[0054] The scale-related dynamic parameters are processed by time alignment, missing value repair, outlier identification, unit unification and spatial assignment labeling to obtain the scale-related dynamic parameters after processing.

[0055] For example, time alignment can use a unified timestamp to resample data with different sampling frequencies to the same time scale, such as forming a standard time series at the minute, hour, or shift level; missing value repair can be based on linear interpolation of adjacent times, sliding window mean filling, or estimation based on the statistical characteristics of similar well groups; outlier identification can remove distorted data based on physical boundary conditions, equipment range, and historical fluctuation range.

[0056] For different nodes in the target oilfield, such as wellbore, gathering and transmission pipeline, valve group, pump chamber and heat exchange part, parameter mapping relationship can be established according to spatial location, and each monitoring point can be matched with a specific scaling part, so as to form a set of scaling-related dynamic parameters applicable to local equipment or the whole process unit.

[0057] Based on the above processing, a dynamic data matrix with time labels, location labels, and parameter category labels (the scale-related dynamic parameters after processing) can be generated, providing an input basis for the next step of establishing a dynamic scale rate calculation model.

[0058] Based on the above analysis, it can be seen that by continuously acquiring and standardizing the dynamic parameters related to scaling, temperature fluctuations, pressure changes, ion composition changes and particle migration states can be uniformly incorporated into the analysis framework, avoiding the risk of distortion caused by relying solely on a single detection value or human experience, and improving the accuracy and timeliness of subsequent scaling rate prediction from the source.

[0059] In one possible implementation, the scaling-related dynamic parameters of the target oilfield are obtained, including: according to the target oilfield, the scaling ion concentration, surface free energy component, production temperature, water sample pH, pipeline inner diameter, and fluid physical property parameters at the locations of the oilfield wellbore, gathering and transmission pipeline, and pump equipment; and the scaling ion concentration, surface free energy component, production temperature, water sample pH, pipeline inner diameter, and fluid physical property parameters are integrated to form scaling-related dynamic parameters.

[0060] In this embodiment, the scale-forming ion concentration is used to characterize the enrichment degree of ions in the fluid that are prone to crystallizing scale. These ions may include one or more of calcium, magnesium, barium, strontium, sulfate, and bicarbonate ions. The surface free energy component is used to calculate the interfacial interaction forces in complex interface systems, typically obtained through calculations of interfacial tension and surface tension. The production temperature is the real-time temperature of the fluid at the target location, and the water sample pH reflects the influence of the fluid chemical environment on crystallization and deposition behavior. The pipe inner diameter characterizes the flow path of wellbores, gathering and transmission pipelines, or pump chambers, and the fluid velocity parameter characterizes the current flow intensity. Fluid physical properties include at least one of the following: flow velocity, density, kinematic / dynamic viscosity, minimum equilibrium distance between the scale particles and the pipe wall interface, scale particle radius, separation distance between the two phases, equivalent radius, elastic modulus of each surface, and Poisson's ratio of each material.

[0061] After the above parameters are uniformly encoded, they are input into the dynamic parameter library to form a set of scale-related dynamic parameters (i.e., scale-related dynamic parameters) for subsequent scale rate calculation models.

[0062] It should be noted that the aforementioned sensors, sampling devices, and parameter calculation modules can be set up in the same edge computing terminal, or they can be deployed in a distributed manner and aggregated through an industrial communication network. In practical applications, other models of this component can also be selected, and this application embodiment does not limit this.

[0063] This embodiment combines chemical, thermal, and flow parameters at different locations to enable a unified description of the scaling environment at wellbore, gathering and transmission pipelines, and pump equipment.

[0064] By adopting this implementation method, the dynamic parameters related to scaling can be updated synchronously with changes in the target oilfield operating conditions, thereby improving the input accuracy and scenario adaptability of the dynamic scaling rate calculation model. Since the parameters cover scaling ions, particles, temperature, pH, and flow heat transfer characteristics, the resulting early warning thresholds and scaling prevention and removal measures can better reflect actual field conditions, reducing biases caused by experience-based judgments and helping to reduce reagent waste, delayed scaling, and unplanned equipment downtime.

[0065] S202. Based on the dynamic parameters related to scaling, establish a corresponding dynamic scaling rate calculation model.

[0066] In this embodiment, the dynamic scaling rate calculation model refers to a calculation model used to describe the comprehensive process of scaling material precipitation, adhesion, growth and local peeling under time-varying operating conditions. It can be a mathematical model, a mechanistic model, an empirical model or a hybrid model that combines mechanistic and empirical models. Its output is the amount of scale growth per unit time, the amount of deposit per unit length of pipe wall, the adhesion mass per unit heat exchange area, or a rate index that comprehensively characterizes the scaling trend.

[0067] Optionally, models can be classified based on the media type and main scaling mechanism of the target oilfield:

[0068] For operating conditions dominated by carbonate scale, crystallization driving terms can be constructed around ion supersaturation, carbon dioxide (CO2) emission effect caused by temperature and pressure changes, and pH changes.

[0069] For operating conditions where sulfate scale is the main component, a precipitation tendency term can be established based on the relationship between the product of different ion concentrations and the solubility product.

[0070] For sediments and suspended particles, sedimentation and resuspension terms can be constructed by combining flow velocity, turbulence intensity, particle size, and wall shear force.

[0071] For example, the model for calculating the dynamic scaling rate can be established by using mechanistic modeling based on thermodynamics and mass transfer theory, or by using data-driven modeling methods such as regression analysis, time series modeling, neural networks, random forests, or ensemble learning. Alternatively, a coupling of mechanistic and machine learning models can be used, with mechanistic terms ensuring interpretability and data-driven terms improving the fitting ability under complex operating conditions.

[0072] In addition, when establishing a dynamic scaling rate calculation model, sub-models can be set up for wellbore sections, surface pipeline sections, and pump station equipment sections in the target oilfield, and the scaling differences at different locations can be reflected through segmented coupling.

[0073] Optionally, after the dynamic scaling rate calculation model is established, parameter training, model calibration, and rolling updates are also included for the dynamic scaling rate calculation model.

[0074] For example, parameter training can use historical monitoring data, scale thickness measured during maintenance, cleaning records, differential pressure rise records, and heat exchange efficiency decay records as supervisory samples;

[0075] Model calibration can correct the coefficients based on actual scaling results, reducing deviations caused by regional water quality differences.

[0076] Rolling updates allow for daily or weekly re-estimation of model parameters as new data becomes available, enabling the model to continuously adapt to changes in production conditions.

[0077] This step transforms scale-related dynamic parameters into a computable dynamic scale rate model, realizing a structured expression from raw monitoring data to the law of scale evolution. This allows subsequent risk prediction to move beyond static empirical judgments and reflect the continuous change mechanism of scale formation, growth, and peeling.

[0078] S203. Based on the dynamic scaling rate calculation model and scaling-related dynamic parameters, calculate the predicted scaling rate of the target oilfield.

[0079] In this embodiment, the scaling rate prediction result refers to the numerical result obtained by calculating the scaling development status of the target oilfield at the current time and within a predetermined future time window through a dynamic scaling rate calculation model. The scaling rate prediction result can include physical quantities such as instantaneous scaling rate and cumulative scaling amount, or it can be converted into a standardized risk index to achieve a unified comparison of different locations and equipment structures.

[0080] For example, when predicting the scaling rate for a target oilfield, the dynamic parameters related to scaling are first input into the dynamic scaling rate calculation model to perform real-time calculations on the current operating conditions and obtain the current scaling rate value. Then, combined with the preset oilfield production operation calculations and operating condition change trends, the scaling rate in the future generation cycle is extended to obtain a complete scaling rate development sequence (i.e., scaling rate prediction results).

[0081] Optionally, the calculation process can cover locations prone to scaling, such as well shafts, gathering and transportation pipelines, and pumps, and simultaneously output the scaling rate of different key parts. For time-related production scenarios, a sliding time window method can be used, taking the data of the past n consecutive sampling periods as input and outputting the scaling rate of the next N periods. For production scenarios with fluctuating operating conditions, the corresponding calculation parameters can also be adjusted according to the actual operating status to improve the accuracy of the prediction results.

[0082] In one possible implementation, this step may also output a confidence interval or prediction deviation range, and recalibrate the dynamic scaling rate calculation model based on the output confidence interval or prediction deviation range.

[0083] For example, a 95% confidence band can be calculated using historical fitting errors to reflect the uncertainty of the prediction; when the prediction error exceeds a set limit, model retraining or recalibration can be triggered.

[0084] The predicted scaling rate can be expressed not only in physical quantities, such as millimeters per day or grams per square meter per hour, but also converted into a standardized risk index. For example, the rate values ​​at different locations and on different devices can be normalized to a scale of 0 to 100 to facilitate unified comparison and subsequent threshold stratification.

[0085] Based on the above analysis, it can be seen that by outputting the scaling rate prediction results through the dynamic scaling rate model, the development trend of scaling can be identified before severe deposits are formed, providing a quantitative basis for establishing stratified early warning thresholds, thereby shifting the scaling prevention and removal decision-making from post-event treatment to pre-event intervention.

[0086] S204. Based on the scaling rate prediction results, determine the early warning threshold for scale prevention and removal stratification.

[0087] In this embodiment, the tiered early warning threshold refers to a multi-level trigger limit divided according to different scaling risk levels, used to transform the continuously changing scaling rate prediction results into actionable risk levels and treatment levels. Tiered refers to classifying risks into at least several levels, including low risk, concern risk, medium risk, high risk, and emergency risk, according to their severity. Each level corresponds to different monitoring intensities, chemical dosing strategies, physical scale prevention device operating parameters, or mechanical cleaning timing.

[0088] For example, when determining the early warning threshold for scale prevention and removal stratification, the critical value of scale thickness corresponding to the wellbore, gathering and transportation pipeline, and pump equipment in the target oilfield when production energy consumption increases to a preset value is first obtained. Then, based on the scale rate prediction results, the time required to reach the critical value of scale thickness is calculated. The time value is compared with the preset first time threshold and second time threshold to classify different early warning levels, thereby determining the corresponding early warning threshold for scale prevention and removal stratification.

[0089] In practice, the correlation analysis between the predicted scaling rate and the actual consequences can be conducted first. Samples that have caused abnormal pressure drop, decreased pump efficiency, valve jamming, reduced heat exchange efficiency, increased cleaning frequency, or shutdown for maintenance can be used as the basis for risk assessment. Then, the thresholds at each level can be determined in combination with the tolerance of the current production unit.

[0090] For example, a scaling rate below the first threshold T1 can be defined as the normal operation layer, indicating that scaling is at an acceptable level, requiring only basic monitoring and routine prevention; a scaling rate between the first threshold T1 and the second threshold T2 can be defined as the early warning layer, indicating that scaling is increasing and that sampling frequency and reagent ratio need to be increased; a scaling rate between the second threshold T2 and the third threshold T3 can be defined as the control layer, indicating that equipment efficiency may be affected in a short period of time, requiring the activation of enhanced scaling inhibition or local physical scaling prevention; and a scaling rate exceeding the third threshold T3 can be defined as the intervention layer, indicating that there is a significant risk of clogging, requiring mechanical scaling removal, process switching, or high-intensity comprehensive treatment.

[0091] Optionally, the determination of the scale prevention and descaling stratification early warning threshold can be achieved by using the statistical quantile method, taking different quantile points as level boundaries based on the historical rate distribution; or by using the equipment performance backward deduction method, taking the maximum allowable scaling rate before reaching the upper limit of pressure drop, the lower limit of pump efficiency, or the lower limit of heat transfer coefficient as the threshold; or by using the multi-factor coupling method, incorporating the prediction results with production safety constraints, downtime costs, reagent costs, and treatment windows into the threshold setting.

[0092] It should be noted that, in order to improve the adaptability of the scale prevention and descaling stratification early warning threshold, the scale prevention and descaling stratification early warning threshold in this embodiment is not fixed, but can be dynamically updated according to the season, water source switching, well group water content changes, station processing capacity and equipment maintenance status.

[0093] For example, during the low-temperature transport phase in winter, the tendency for certain salts to precipitate increases, and the warning threshold can be lowered accordingly; when the equipment has just completed maintenance and the inner wall is relatively clean, the control layer boundary can be adjusted appropriately according to the actual tolerance capacity.

[0094] Optionally, the system establishes a threshold database for each monitoring location and forms a mapping table of "location-level-measure". When the prediction result enters a certain level, the corresponding treatment requirements are automatically marked.

[0095] Based on the above analysis, it can be seen that by mapping the scale formation rate prediction results to the scale prevention and removal stratified early warning threshold, the complex continuous risk can be transformed into a clear graded state, so that the scale prevention and removal strategies under different severity levels have clear triggering conditions, avoiding the long-term use of a single intensity or fixed cycle treatment method on site, thereby improving the pertinence of treatment and the efficiency of resource utilization.

[0096] S205. Obtain real-time scaling monitoring data of the target oilfield and pre-set multiple alternative measures for scale prevention and removal.

[0097] In this embodiment, real-time scaling monitoring data refers to online or near-online data that can directly or indirectly reflect the current scaling status, scaling location, and scaling change trend. This includes scale thickness monitoring values, pipe section pressure difference, flow rate attenuation rate, pump current changes, heat exchange efficiency decrease, ultrasonic echo characteristics, electrochemical impedance changes, image recognition results, pig recovery of deposits, solid phase quality collected by samplers, and feedback from manual inspections. Among these, the real-time scaling rate generated based on the established dynamic scaling rate calculation model is the core monitoring indicator that can directly quantify the scaling development status and achieve continuous monitoring of the scaling degree.

[0098] For example, real-time scaling monitoring data can be automatically uploaded to the edge gateway through on-site testing equipment, and then sent to the decision-making platform via the industrial communication network. For laboratory testing and manual inspection data, they can be supplemented and imported through mobile terminals or station input interfaces. In addition, the core generation channel for real-time scaling monitoring data is the established dynamic scaling rate calculation model. This model can not only be used for early scaling rate prediction and scale prevention and removal stratification and early warning, but also directly access real-time production parameters collected online by the oilfield, including flow rate, water sample pH, production temperature, fluid velocity, scale ion concentration, etc., to calculate the scaling rate under the current operating conditions in real time, and realize continuous dynamic monitoring of the scaling degree of wellbore, gathering and transportation pipelines and pump equipment.

[0099] Optionally, to enable data from different sources to be used within the same evaluation framework, this step also includes standardizing the mapping of real-time scaling monitoring data.

[0100] Specifically, for example, the increase in pressure difference can be converted into a blockage risk indicator, the decrease in heat transfer coefficient can be converted into a heat transfer fouling indicator, and the attachment area identified in the image can be converted into a scale coverage rate. Among them, the real-time fouling rate output by the dynamic fouling rate calculation model can serve as a unified quantitative benchmark for the degree of fouling across the entire scenario, enabling horizontal benchmarking and unified evaluation of the fouling status of different locations and different equipment.

[0101] This step establishes a correspondence between risk status and governance resources by simultaneously acquiring real-time scaling monitoring data reflecting the current status and a library of alternative measures reflecting feasibility. This enables subsequent weight assessment and measure selection to simultaneously consider the actual on-site working conditions and practical operability, avoiding the problem of having only risk assessments without any executable solutions, or measures that are feasible but do not match the current scaling status.

[0102] S206. Determine the weights of the scale prevention and removal stratification early warning thresholds and real-time scale monitoring data.

[0103] In this embodiment, the scale prevention assessment index refers to an index system used to comprehensively evaluate the adaptability of various alternative measures, including at least safety, applicability, technical difficulty, effectiveness, and economy; the weight refers to the relative importance of different assessment indicators in the current decision-making process, and its value can be expressed in a normalized form so that the sum of the weights of each indicator is 1. The core quantitative input for determining the weight is the scale rate output by the established dynamic scale rate calculation model, including the actual value of the scale rate calculated based on real-time monitoring data and the previously output scale rate prediction results. The scale rate is the core link connecting the scale prevention and descaling stratified early warning threshold, real-time scale conditions, and the weight allocation of assessment indicators.

[0104] For example, the real-time scaling monitoring data of the target oilfield is first input into the established dynamic scaling rate calculation model to calculate the actual real-time scaling rate of each scaling-prone location in the wellbore, gathering and transportation pipeline, and pump equipment. The actual scaling rate value is then used to determine the time required to reach the critical value of scaling thickness corresponding to the preset increase in production energy consumption. The current risk level is then determined by comparing the time value with the scale prevention and removal layered early warning threshold. At the same time, the actual severity, development speed, and location sensitivity of the scaling on site are identified by combining the real-time scaling rate.

[0105] If the scaling rate output by the dynamic scaling rate calculation model is at a low level, and the corresponding time value is greater than the second time threshold, the scaling risk is still in the early warning level and has a limited impact on production. In this case, the weight of the economic indicators can be relatively increased to screen low-cost and stable preventive measures.

[0106] If the scaling rate output by the dynamic scaling rate calculation model is at a high level, and the corresponding time value is less than or equal to the first time threshold, and there has been a rapid increase in differential pressure, a significant decrease in flow rate, or a decline in the efficiency of key equipment, then the weights of the performance and safety indicators will be increased accordingly to ensure that measures are taken to address urgent risks first.

[0107] Optionally, set the basic weight vector W0:

[0108]

[0109] Among them, w1 corresponds to the security weight, w2 corresponds to the applicability weight, w3 corresponds to the technical difficulty weight, w4 corresponds to the efficiency weight, and w5 corresponds to the economic weight.

[0110] Then, the basic weights are dynamically adjusted based on the risk level correction coefficient matrix to obtain the target weight vector W under the current working condition:

[0111]

[0112] Here, M represents a correction matrix related to the warning level, scaling location, real-time scaling rate, and monitoring intensity. As the risk level increases, the coefficients for effectiveness and safety in M ​​increase, while the coefficient for economy decreases appropriately. When the scaling location is downhole or in areas difficult to intervene in without interrupting production, the weights for applicability and technical difficulty increase to avoid selecting theoretically effective but impractical solutions. The core basis for the values ​​of each coefficient in the correction matrix M is the scaling rate output by the dynamic scaling rate calculation model. A higher scaling rate indicates a faster scaling development speed and a more urgent production safety risk; the coefficients for effectiveness and safety in M ​​increase accordingly, while the coefficients for economy decrease appropriately. A more stable and lower scaling rate indicates a controllable scaling development trend; the coefficients for economy in M ​​increase accordingly, achieving dynamic adaptation of weight allocation to the scaling rate.

[0113] Alternatively, weights can be determined using methods such as the analytic hierarchy process (AHP), entropy weighting, fuzzy comprehensive evaluation, and data statistical fusion. Alternatively, historical treatment effects can be used as feedback samples, and weights can be adaptively adjusted through reinforcement learning or Bayesian update methods.

[0114] In practical applications, to reduce human subjectivity, the system can quantify the contribution of each indicator to the final success rate under a certain type of working condition in historical cases, and then calculate the weight together with the current monitoring data.

[0115] Based on the above analysis, this step takes the scaling rate output by the dynamic scaling rate calculation model as the core, and combines the future scaling risk reflected by the early warning threshold with the current scaling status reflected by real-time monitoring data to affect the weight of the evaluation indicators. This transforms the logic of measure selection from fixed rules to a dynamic evaluation mechanism, which can take into account risk level, on-site limitations and governance objectives, and improve the objectivity and adaptability of measure decisions.

[0116] S207. Based on the weights of the scale prevention assessment indicators and each alternative scale prevention and removal measure, determine the target scale prevention and removal measures.

[0117] In this embodiment, the target scale prevention and removal measures refer to the most suitable implementation plan selected after comprehensive evaluation under the current target oilfield, current scale formation stage and current production constraints. It can be a single measure or a combination of measures in sequence or in parallel.

[0118] For example, the system scores each alternative measure according to the weight of the anti-scaling evaluation index, forming a measure evaluation matrix.

[0119] The score for each measure on each indicator can be derived from the preset values ​​in the knowledge base, field test data, statistical results of historical cases, and correction values ​​for the current working conditions.

[0120] For example, a certain scale inhibitor is highly applicable to calcium carbonate scale conditions, but its score may need to be lowered in scenarios with high oil content and high suspended solids due to limited dispersion effect; a certain mechanical descaling method is highly effective against thick scale blockage, but its construction risks and production downtime are significant, and its safety and economic scores are relatively low.

[0121] Subsequently, the system can use weighted summation, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) ranking, grey relational analysis, fuzzy optimization, or multi-objective optimization algorithms to calculate the comprehensive fit score of each alternative measure and rank them according to the score.

[0122] Specifically, when the highest-scoring scale prevention and removal alternative is the continuous addition of chemical scale inhibitors, and its score is significantly higher than other scale prevention and removal alternatives, this alternative can be directly identified as the target scale prevention and removal measure.

[0123] When multiple scale prevention and removal alternatives score similarly but are applicable to different stages, they can be combined into a single measure. For example, a short-cycle mechanical cleaning can be implemented to remove existing deposits, followed by the activation of continuous scale inhibitor dosing and physical scale prevention devices to inhibit subsequent re-scaling.

[0124] In one possible implementation, this step may also include setting a constraint verification mechanism to perform safety verification, equipment compatibility verification, inventory reagent availability verification, construction window verification, and production plan conflict verification for alternative scale prevention and removal measures.

[0125] If the highest-scoring anti-scaling and descaling alternative fails the constraint verification, the second-highest ranked alternative that satisfies the constraints will be automatically selected.

[0126] In one possible implementation, after the target measures are determined, the system can also automatically generate execution parameters, including reagent concentration, injection rate, duration of action, equipment start-up and shutdown time, descaling cycle and monitoring and verification nodes, and send the results to the scheduling platform or on-site execution terminal.

[0127] This step achieves closed-loop decision-making from risk identification and indicator evaluation to measure implementation by quantitatively matching dynamic weights with the attributes of alternative measures. This enables the target scale prevention and removal measures to simultaneously meet current risk control needs and on-site implementation conditions, reducing problems such as excessive chemical dosing, incorrect selection, or delayed scale removal timing, thereby improving the real-time performance, economy, and reliability of oilfield scale control.

[0128] This application provides a method for determining scale prevention and removal measures. By acquiring dynamic parameters related to scale formation in the target oilfield, establishing a corresponding dynamic scale formation rate calculation model, and calculating the scale formation rate prediction results of the target oilfield based on the dynamic scale formation rate calculation model, the scale formation risk assessment can better reflect the continuous impact of dynamic operating condition changes on the scale formation process. Furthermore, by determining the scale prevention and removal stratified early warning threshold based on the scale formation rate prediction results, and combining the real-time scale formation monitoring data of the target oilfield, multiple scale prevention and removal alternative measures, and the weights of scale prevention assessment indicators, the target scale prevention and removal measures are determined. This solves the technical problems of low accuracy and poor timeliness in the selection of scale prevention and removal measures in the prior art, and achieves the technical effect of improving the accuracy and timeliness of the selection of scale prevention and removal measures.

[0129] Figure 3 A flowchart illustrating the method for determining scale prevention and removal measures provided in the embodiments of this application. Figure 2 ,like Figure 3 As shown, this embodiment, based on the above embodiments, provides a detailed explanation of the specific establishment process of the dynamic scaling rate calculation model, including:

[0130] S301. Determine the scaling process, which includes crystallization scaling, particulate deposition, and the rolling and peeling of scale particles throughout the process.

[0131] For example, by combining the production conditions of easily scaled locations such as wellbore, gathering and transmission pipelines and pump equipment in the target oilfield, three core mechanisms of scaling in pipelines are identified from the perspective of coupling between micro-interface effects and macro-processes.

[0132] Scale formation refers to the process by which scale-forming ions in a fluid reach a supersaturated state under environmental conditions, and crystal nuclei are formed.

[0133] Particulate deposition refers to the process by which solid particles carried by fluids adhere to and aggregate on the pipe wall to form a scale layer. The intensity of this process is directly related to scale-related dynamic parameters such as scale-forming ion concentration, fluid temperature, water sample pH, and fluid properties (viscosity, flow rate, etc.).

[0134] The scale rolling and peeling effect refers to the process in which, during the continuous flow of fluid in a pipeline, the fluid force creates a removal torque on the scale particles already attached to the pipe wall. This torque competes with the resistance torque formed by interfacial adhesion. When the removal torque is dominant, the scale particles roll off. This effect accompanies the entire scaling and deposition process. The intensity of this effect is directly related to parameters such as flow shear stress, fluid velocity, scale particle size, and interfacial surface energy. It is the core inverse influencing factor that determines the final net scaling rate.

[0135] S302. Regarding the scaling effect, obtain the scale ion concentration and fluid temperature from the scaling-related dynamic parameters, convert the fluid temperature into the fluid thermodynamic temperature, and calculate the solution supersaturation based on the scale ion concentration.

[0136] For example, both the scale-forming ion concentration and the fluid temperature are derived from scale-related dynamic parameters collected on-site. The fluid temperature is the Celsius temperature measured on-site, converted to thermodynamic temperature using a unit system to provide a unified dimensional basis for subsequent temperature-related calculations of the crystallization reaction rate. The solution supersaturation is calculated based on the scale-forming ion concentration and is used to characterize the degree of supersaturation of the scale-forming components in the fluid. It is the core thermodynamic condition driving the crystallization and scale-forming reaction; the higher the supersaturation, the stronger the driving force for crystallization.

[0137] The formula for calculating the supersaturation of a solution is:

[0138]

[0139] Where S is the supersaturation of the solution. Ksp is the product of the concentrations of the scale-forming ions, and Ksp is the solubility product constant of the corresponding sparingly soluble salt, which is related to the fluid temperature.

[0140] S303. Based on the solution supersaturation, fluid thermodynamic temperature, and activation energy of the crystallization reaction, generate a calculation item for the crystal growth rate.

[0141] In this embodiment, the activation energy of the crystallization reaction is a model coefficient obtained by regression of historical production data, or it can be pre-configured by consulting relevant literature.

[0142] For example, based on the kinetic equation of calcium carbonate precipitation, the quantitative relationship between crystallization rate and solution supersaturation and reaction order is characterized. The Arrhenius equation is introduced to describe the change of the crystallization rate constant with temperature, reflecting the regulatory role of temperature on the crystallization reaction rate. The activation energy of the crystallization reaction is the core parameter characterizing the temperature sensitivity of the crystallization reaction. It is obtained by nonlinear least squares regression fitting using scaling parameters and measured scaling data from the historical production cycle of the target oilfield, adapting to the water quality and operating conditions of the target oilfield. Substituting solution supersaturation, fluid thermodynamic temperature, and activation energy of the crystallization reaction into the kinetic framework, the crystal growth rate term reflecting the contribution of crystallization scaling can be obtained.

[0143] The kinetic equation for the crystallization reaction is:

[0144]

[0145]

[0146] The Arrhenius temperature response equation is:

[0147]

[0148] Where R is the crystallization reaction rate; n is the reaction order; A is the pre-exponential factor or pre-exponential factor, also known as the apparent frequency factor; Ea is the activation energy of the crystallization reaction; R is the ideal gas constant; and T is the thermodynamic temperature.

[0149] S304. Based on the fluid velocity, pipe diameter, and fluid properties in the scaling-related dynamic parameters, calculate the Reynolds number and Schmidt number; calculate the Sherwood number based on the Reynolds number and Schmidt number; and use the Sherwood number as a mass transfer correction coefficient to generate a scaling particle mass transfer calculation term.

[0150] For example, the Reynolds number is used to characterize the flow state of fluid in a pipe, and the Schmidt number is used to characterize the relative ratio of momentum diffusion to mass diffusion of matter in the fluid. Both can be calculated from dynamic parameters related to fouling, such as fluid velocity, pipe diameter, fluid density, and dynamic viscosity, collected on-site.

[0151] The Sherwood number is a dimensionless parameter describing the ratio of convective to diffusion mass transfer. For fully developed turbulent conditions in a circular pipe, it is correlated with the Reynolds number and Schmidt number through the Dittus-Boelter mass transfer correlation to quantify the contribution of the convective mass transfer process to the rate of scale formation.

[0152] Using the Sherwood number as a mass transfer correction coefficient to generate a mass transfer term for scale particles can correct the deviation of the simple reaction kinetic model, making the crystallization rate calculation more consistent with the actual mass transfer conditions of turbulent flow in the pipeline.

[0153] The mass transfer correlation for the Sherwood number is:

[0154]

[0155] Where Sh is the Sherwood number; Re is the Reynolds number, calculated from the fluid velocity, pipe diameter, and fluid kinematic viscosity; and Sc is the Schmidt number, calculated from the fluid dynamic viscosity and the pre-configured solute diffusion coefficient.

[0156] S305. Regarding the rolling removal effect of scale particles, based on the relevant dynamic parameters of scale formation, calculate the ratio of the removal torque of the fluid acting on the scale particles to the interface adhesion resistance torque to obtain the rolling torque; based on the rolling torque and the erosion correction index, generate the scale removal rate calculation term.

[0157] In this embodiment, the erosion correction index is a model coefficient obtained by regression of historical production data. The interfacial adhesion drag torque is based on the extended Deakin-Landau-Freuth-Overbick (XDLVO) theory and is determined by the total interfacial force between the scale particles and the pipe wall.

[0158] For example, the rolling detachment of scale particles is determined by the torque balance relationship. The calculation of both the removal torque and the interfacial adhesion resistance torque is based on scale-related dynamic parameters: the removal torque is contributed by the drag force of the fluid acting on the scale particles and the Saffman lift force, and the parameters involved, such as fluid velocity, dynamic viscosity of the fluid in the pipe, and fluid density in the pipe, are all derived from the fluid physical property parameters in the scale-related dynamic parameters; the interfacial adhesion resistance torque is determined by the total interfacial force between the scale particles and the initial adhesion layer on the pipe wall, and the surface free energy component involved is derived from the scale-related dynamic parameters. The removal torque generated by the fluid-induced external force and the resistance torque formed by the interfacial adhesion are in competition, and the ratio of the two is the rolling torque, which is used to quantify the tendency of scale particles to roll off.

[0159] The total interfacial force refers to the surface interaction force between the scale particles and the pipe wall. Its calculation is based on the extended deakin-Landau-Freuth-Olfbiek (XDLVO) theory. Building upon the classical deakin-Landau-Freuth-Olfbiek (DLVO) theory, which considers the competitive balance between electrostatic double-layer repulsion and van der Waals attraction, the XDLVO theory further introduces Lewis acid-base interactions to describe short-range molecular forces such as hydrophobic / hydrophilic interactions and hydrogen bonding, thus providing a more comprehensive characterization of the interfacial adhesion strength between scale particles and the pipe wall. Before calculating the scale removal rate, the rolling torque must be calculated based on the XDLVO theory to determine the scale particle adhesion state on the wall: when the rolling torque is less than or equal to 1, the interfacial adhesion resistance torque is dominant, and the scale particles maintain their wall adhesion state. Under this premise, the quantitative calculation of the scaling rate and scale removal rate can continue. When the rolling torque is greater than 1, the external removal torque is dominant, and the scale particles roll around the contact point and detach from the pipe wall.

[0160] The contact radius and relative proximity distance between scale particles and the adhesion layer are calculated using the Dejakin-Müller-Topolov (DMT) contact mechanics theory, serving as geometric parameters for calculating the interfacial adhesion resistance torque. The erosion correction index, a model coefficient characterizing the influence of rolling torque on the peeling rate, is obtained through regression fitting of historical production data from the target oilfield. Substituting the rolling torque and erosion correction index into the calculation framework yields the scale peeling rate term, reflecting the intensity of the scale particle rolling peeling action.

[0161] The formula for calculating rolling torque is:

[0162]

[0163] Where RM is the rolling torque, F d F is the drag force acting on the scale particles. l F is the Saffman lift force acting on the scale particles. a denoted as , where is the total interfacial force between the scale particles and the pipe wall; α is the relative approach distance between the scale particles and the initial adhesion layer; and a is the contact radius between the scale particles and the initial adhesion layer. When the rolling torque RM > 1, the external removal torque dominates, allowing the scale particles to roll around the contact point and detach from the pipe wall.

[0164] The total interfacial forces consist of the superposition of Lifshitz-van der Waals forces and Lewis acid-base forces, and the calculation formula is as follows:

[0165]

[0166]

[0167]

[0168] in, This is the Lifshitz-van der Waals force. The Lewis acid-base interaction, the Lifshitz-van der Waals interaction, and the Lewis acid-base interaction can be calculated using the following formula: (The formula is missing from the provided text.)

[0169]

[0170]

[0171]

[0172] in, Let H0 be the free energy of the Lifshitz-van der Waals interface in the three-phase system of scale particles-water-pipe wall; H0 is the minimum equilibrium distance between the two phases of scale particles and pipe wall; R is the radius of scale particles; and H is the separation distance between the two phases. The Lifshitz-van der Waals component of the surface free energy of the solid phase of the scale particles; The Lifshitz-van der Waals component of the surface free energy of the aqueous medium; The Lifshitz-van der Waals component of the surface free energy of the solid phase in the tube wall. Let be the Lewis acid-base interface free energy of the scale-water-pipe wall three-phase system; λ is the correlation length of water molecules. Let be the Lewis acid-base interaction free energy per unit area at the contact distance, and exp be the natural exponential function. The electron acceptor component of the surface free energy of the solid phase of the scale particles. The electron donor component of the surface free energy of the aqueous medium; The electron acceptor component of the surface free energy of the solid phase in the tube wall. The electron acceptor component of the surface free energy of the aqueous medium; The electron donor component of the surface free energy of the solid phase of the scale particles; This represents the electron donor component of the free energy at the surface of the solid phase in the tube wall.

[0173] F d And Fl can be calculated by the following formula:

[0174]

[0175]

[0176] Where, ρ m μ represents the density of the fluid inside the pipe. m Indicates the dynamic viscosity of the fluid inside the pipe; u p It represents the relative velocity between the fluid and the condensed oil particles (sludge particles) at the particle center.

[0177] The contact radius and relative proximity distance between the fouling particles and the initial adhesion layer were calculated using the Dejakin-Muller-Topolov contact mechanics theory. This theory takes into account the adhesion forces outside the contact area and is applicable to rigid materials and small particle systems.

[0178] The formula for calculating the contact radius is:

[0179]

[0180] Where a is the actual contact radius; P is the applied load; R is the equivalent radius; E* is the composite modulus; E1 and E2 are the elastic moduli of the two surfaces, respectively; ν1 and v2 are the Poisson's ratios of the two materials; W SWP This is the work done on adhesion.

[0181] The formula for calculating the relative proximity distance α is:

[0182]

[0183] In summary, adhesion work W SWP It can be calculated using the interfacial surface tension, and the calculation formula is as follows:

[0184]

[0185] in, , , This represents the interfacial tension between scale and water, water and pipe wall, and scale and pipe wall.

[0186] Adhesion work W SWP It can also be expressed through surface free energy components, and the calculation formula is:

[0187]

[0188] The formula for calculating the applied load on scale particles is:

[0189]

[0190] Where P is the applied load.

[0191] S306. Based on the calculation of crystal growth rate, scale particle mass transfer and scale peeling rate, perform coupled calculations to obtain net scaling rate, and construct a dynamic scaling rate calculation model based on net scaling rate.

[0192] For example, the growth rate term and the mass transfer calculation term are superimposed to obtain the total deposition rate. Then, the peeling rate corresponding to the scale removal rate calculation term is subtracted to obtain the net scaling rate per unit area. This is used as the core output to construct a complete dynamic scaling rate calculation model. The net scaling rate output by the model can be updated in real time with changes in scale-related dynamic parameters, accurately reflecting the scaling rate variation under different operating conditions. This provides a quantitative calculation basis for subsequent scaling rate prediction, scale prevention and removal threshold setting, and real-time monitoring in various production stages.

[0193] The formula for calculating the net scaling rate per unit area is:

[0194]

[0195] in, denoted as net scaling rate per unit area, k0 as crystallization rate factor (obtained through regression analysis of production data), Ea as activation energy of crystallization reaction, R as ideal gas constant, T as thermodynamic temperature, S as solution supersaturation ratio, n as reaction order, Sh as Sherwood number, β as mass transfer influence weighting index (obtained through regression analysis of production data), RM as rolling torque, and α as rolling torque erosion correction index (obtained through regression analysis of production data).

[0196] In summary, this application's embodiments, targeting the on-site operating conditions of oilfield wellbores, gathering and transmission pipelines, and pump equipment, select corresponding physical parameters as unified inputs to construct calculation items for crystal growth rate, scale particle mass transfer, and scale particle peeling rate, respectively. These are then coupled to obtain the net scaling rate. The model's output is not limited to reagent dosage calculation but comprehensively serves a complete business process, including scaling rate prediction, determination of stratification warning thresholds after time-series slope correction, calculation of scale prevention evaluation index weights, and selection of multiple scale prevention and removal alternatives. Specifically, scale particle peeling adopts a modeling approach based on rolling torque and interfacial adhesion resistance torque, which differs from existing empirical formula models for overall scale peeling and is more suitable for the actual operating conditions of scale particle rolling and detachment inside oilfield pipelines.

[0197] The method for determining scale prevention and removal measures provided in this application clarifies the mechanisms of crystallization scaling, particle deposition, and scale rolling detachment, providing a theoretical support combining microscopic and macroscopic perspectives for model construction. The calculation of solution supersaturation and fluid thermodynamic temperature provides an accurate thermodynamic parameter basis for crystallization rate calculation. A crystallization growth rate calculation term is generated based on the crystallization kinetic equation and the Arrhenius temperature response law, accurately reflecting the regulatory effect of temperature and supersaturation on crystallization. A mass transfer calculation term is generated using Sherwood number scale particles, closely matching actual mass transfer conditions and describing the promoting effect of turbulent flow in the pipeline on scale adhesion. A scale detachment rate calculation term is generated based on rolling torque, accurately quantifying the reverse influence of scale rolling detachment on the scaling process. A particle deposition rate calculation term is generated by combining particle scale flow rate and flow shear stress, quantifying the coupling effect of particle carrying and flow field shear. The net scaling rate is obtained by coupling the three types of rate calculation terms, and the constructed model can be updated in real time according to operating conditions, improving the accuracy and field adaptability of scaling rate calculation.

[0198] Figure 4 A flowchart illustrating the method for determining scale prevention and removal measures provided in the embodiments of this application. Figure 3 ,like Figure 4 As shown, this embodiment, based on the above embodiments, provides a detailed explanation of the process for determining the early warning threshold for scale prevention and removal stratification, including:

[0199] S401. Obtain the critical value of scale thickness corresponding to the wellbore, gathering and transportation pipeline and pump equipment of the target oilfield when the production energy consumption increases to the preset value.

[0200] In this embodiment, the critical value of scale thickness is used to characterize the critical state in which the scale layer in the pipe wall or pump cavity thickens to the point that it causes an abnormal increase in energy consumption. It can usually be calibrated based on historical operating data, energy consumption monitoring curves, and equipment efficiency decay patterns.

[0201] For example, the critical value of scale thickness can be derived by collecting real-time data on energy consumption, current, pressure difference, and transportation efficiency of the wellbore, gathering and transportation pipeline, and pump equipment corresponding to the target oilfield during production, and combining the scale rate prediction results.

[0202] S402. Based on the predicted scaling rate, determine the time required to reach the critical scaling thickness value.

[0203] In this embodiment, the duration value is used to characterize the estimated time required for the scale layer to grow from its current thickness to the critical thickness under the current scaling rate conditions.

[0204] For example, the estimated time to reach the critical value is obtained based on the scaling rate prediction results.

[0205] S403. Obtain the preset first duration threshold and second duration threshold.

[0206] In this embodiment, the first duration threshold is less than the second duration threshold.

[0207] For example, the first and second duration thresholds are pre-set grading boundaries based on the oilfield production and maintenance cycle, equipment maintenance window, scaling hazard prevention and control requirements, and emergency response capabilities. Through two clearly defined thresholds, three scaling risk intervals with different urgency levels can be divided, providing a unified judgment standard for subsequent risk level determination and early warning threshold matching, and ensuring the standardization, consistency, and field feasibility of the entire tiered early warning system.

[0208] It should be noted that the specific values ​​of the first duration threshold and the second duration threshold can be set according to actual needs or circumstances, and this embodiment does not impose specific limitations on them.

[0209] S404. Compare the duration value with the first duration threshold and the second duration threshold, and determine the initial tiered early warning threshold based on the comparison result.

[0210] For example, the calculated duration value, which reflects the urgency of current scaling development, is compared one by one with two pre-set grading thresholds (the first duration threshold and the second duration threshold) to accurately pinpoint the numerical range of the duration value, clarify the risk level boundary corresponding to the current scaling condition, and provide a direct basis for the formal determination of the subsequent warning level and the accurate matching of the tiered warning thresholds, thus ensuring the objectivity and accuracy of risk grading.

[0211] Specifically, when the calculated duration value is less than or equal to the first duration threshold, it indicates that scaling will reach a critical state that endangers production in a very short time, which may cause equipment failure, excessive energy consumption, or even production interruption at any time. This is a high-risk warning level. At this time, the first duration threshold is used as the initial stratified warning threshold corresponding to this level, which clarifies the risk control threshold for high-risk scenarios and provides a clear trigger benchmark for the subsequent weight allocation of anti-scaling assessment indicators and the selection of emergency prevention and control measures to adapt to emergency working conditions.

[0212] When the calculated duration value is between the first duration threshold and the second duration threshold, it indicates that there is a certain buffer period for scaling development, and it will not break through the production safety threshold in the short term. However, there is a clear trend of scaling growth and production impact risk, which belongs to the medium-risk warning level. At this time, the second duration threshold is used as the initial stratified warning threshold for this working condition, which clarifies the risk control threshold for medium-risk scenarios and provides a precise quantitative basis for the subsequent weight allocation of scale prevention assessment indicators and the selection of routine prevention and control measures for medium-risk scenarios.

[0213] When the calculated duration value is greater than the second duration threshold, it indicates that the scaling development is slow and will not exceed the production safety threshold within a long production cycle. The overall scaling risk is controllable and belongs to the low-risk warning level. At this time, the second duration threshold is used as the initial stratified warning threshold corresponding to this warning level, clarifying the risk control baseline for low-risk scenarios, and providing a unified judgment standard for the subsequent weight allocation of anti-scaling assessment indicators and the selection of preventive maintenance measures to adapt to normal working conditions.

[0214] It is important to note that independent thresholds can be set for the wellbore, gathering and transmission pipeline, and pump equipment of the target oilfield, or they can be set together according to the unified operation and maintenance standards of the same oilfield to ensure that the early warning range matches the pace of on-site management.

[0215] S405. Continuously acquire the scaling rate values ​​output by the dynamic scaling rate calculation model according to the preset time period, construct a scaling rate time series based on the scaling rate values, and calculate the moving average of the scaling rate within the preset fixed time window according to the preset fixed time window.

[0216] For example, the dynamic scaling rate calculation model continuously outputs real-time scaling rate values ​​at pre-set fixed time intervals, uninterruptedly collecting all output values ​​and arranging them in the order of collection to form a continuous scaling rate time series, completely recording the trend of scaling rate changes with production conditions. A statistical time window of fixed duration is pre-defined, and all scaling rate data within the window are captured segment by segment along the time series. The arithmetic mean of all rate values ​​within the window is calculated to obtain the moving average. The moving average can filter out rate abrupt changes caused by instantaneous fluctuations in operating conditions, stably reflecting the stage-by-stage average scaling rate increase.

[0217] S406. Based on the predicted results of the sliding average value and scaling rate, determine the corresponding slope of change.

[0218] For example, using the predicted scaling rate as the baseline rate and the moving average as the staged real-time average rate, a two-dimensional correspondence is constructed with the change over time as the horizontal axis and the scaling rate value as the vertical axis. The slope is obtained by calculating the ratio of the difference between the two values ​​to the time span. The sign of the slope value represents the direction of change in the scaling rate, and the magnitude of the absolute value of the slope represents the fluctuation range of the scaling rate: a slope less than 0 indicates that the staged scaling rate has decreased compared to the baseline predicted value; a slope greater than 0 and less than 1 indicates that the scaling rate has increased slightly; and a slope greater than 1 indicates that the scaling rate has increased significantly and rapidly.

[0219] S407. Based on the changing slope and the pre-configured correction formula, the initial stratification warning threshold is corrected to obtain the scale prevention and descaling stratification warning threshold.

[0220] For example, the pre-configured correction formula includes three types of correction parameters: the effective time of emergency descaling measures, the tolerance parameter, and the amplification limit. The effective time of emergency descaling measures is less than the initial stratification warning threshold, the tolerance parameter is greater than 0, and the amplification limit restricts the maximum adjustable range of the threshold.

[0221] First, calculate the tolerance parameter s. The calculation formula is as follows:

[0222]

[0223] Where s is the tolerance parameter, used to control the response sensitivity of the threshold correction process; U is the amplification upper limit, used to limit the maximum upward adjustment of the warning threshold, U>1.

[0224] Substituting the slope of change as the independent variable x into the threshold mapping calculation formula, we obtain the corrected scale prevention and descaling stratification early warning threshold:

[0225]

[0226] in, The corrected output of the scale prevention and descaling stratification early warning threshold is defined as follows: C is the time it takes for emergency descaling measures to take effect, representing the time required for the protective effect to be achieved after emergency descaling operations are carried out on site; A is the initial stratification early warning threshold, which is the baseline threshold without dynamic correction; and x is the slope of change, representing the direction and magnitude of the fluctuation of the scaling rate relative to the baseline prediction result.

[0227] This formula is a non-linear mapping relationship, which adaptively corrects the initial stratification warning threshold based on the changing slope. When the changing slope is less than 0, it means that the actual scaling rate is lower than the baseline prediction result. At this time, the output scale prevention and descaling stratification warning threshold shifts in the amplification direction, corresponding to an extension of the risk treatment buffer period. When the changing slope is greater than 0 and the value is small, it means that the scaling rate has increased slightly, the threshold is appropriately reduced, and the warning judgment conditions are tightened. When the changing slope is greater than 1, it means that the scaling rate has increased sharply, the threshold will be significantly reduced, the warning buffer time will be shortened, and the system will trigger a higher level of scaling prevention and control treatment process.

[0228] This nonlinear correction method differs from simple linear scaling. Under the constraint of an upper limit on amplification, it enables differentiated adjustment of the slope across different intervals: the threshold amplification in the negative slope interval is constrained by the upper limit and will not increase indefinitely; when the slope increases positively, the threshold converges rapidly to near the effective time of emergency descaling measures, preventing the warning threshold from exceeding the operational response time and missing the treatment window. The scale prevention and descaling stratification warning threshold output through this step can automatically adapt to dynamic fluctuations in the scaling rate, overcoming the deficiency of static initial stratification warning thresholds that cannot adapt to changes in on-site operating conditions.

[0229] The method for determining scale prevention and removal measures provided in this application can determine the scale thickness boundary corresponding to excessive energy consumption by obtaining the critical value of scale thickness for different equipment. Based on the scale rate prediction result, the remaining buffer period before the equipment reaches the critical scale state can be obtained by converting the time value. Obtaining two-level time thresholds can establish the judgment boundary for risk interval division. Comparing the time values ​​yields the initial stratification warning threshold, completing the risk level division under static conditions. Collecting multiple sets of scale rate values ​​to construct a time series and calculating the moving average can filter out data disturbances caused by instantaneous operating conditions. Calculating the slope of change from the moving average and the scale rate prediction result can characterize the direction and amplitude of scale rate fluctuations. Based on the slope of change, the initial stratification warning threshold is nonlinearly corrected to obtain a scale prevention and removal stratification warning threshold adapted to operating condition fluctuations.

[0230] Figure 5 A flowchart illustrating the method for determining scale prevention and removal measures provided in the embodiments of this application. Figure 4 ,like Figure 5 As shown, this embodiment, based on the above embodiments, provides a detailed explanation of the process for determining the weights of the anti-scaling evaluation indicators, including:

[0231] S501. Obtain preset multi-dimensional anti-scaling technology evaluation indicators.

[0232] In this embodiment, the multi-dimensional scale prevention technology evaluation index is used to characterize the comprehensive adaptability of different scale prevention and removal measures in field applications. It can typically include dimensions such as safety, economy, applicability, technical difficulty, and effectiveness.

[0233] For example, the system can obtain multi-dimensional anti-scaling technology evaluation indicators pre-set by the system.

[0234] S502. Based on the multi-dimensional scale prevention technology evaluation indicators, construct a hierarchical analysis model with the selection of scale prevention and removal technologies as the target layer, the multi-dimensional scale prevention technology evaluation indicators as the criterion layer, and the alternative scale prevention and removal measures as the scheme layer.

[0235] In this embodiment, the alternative scale prevention and removal measures, such as ultrasonic scale prevention, electromagnetic scale prevention, metal scale inhibitor scale prevention, and scale inhibitor addition, can be configured according to the site conditions of the wellbore, gathering and transportation pipeline, or pump equipment. The hierarchical analysis model is used to structure the mapping relationship between the target layer, criterion layer, and scheme layer, enabling different scale prevention technologies to complete weight allocation within the same evaluation framework.

[0236] For example, the hierarchical analysis model can be constructed based on the Analytic Hierarchy Process (AHP) and combined with the nine-level scaling method to compare the relative importance of indicators in the criterion layer and between the criterion layer and the scheme layer, forming a positive and negative judgment matrix.

[0237] S503. Input the scale prevention and descaling stratification early warning threshold and real-time scale monitoring data into the hierarchical analysis model to determine the weight of the scale prevention evaluation index.

[0238] For example, the scale prevention and removal stratified early warning threshold is used to characterize the current scale risk range, while real-time scale monitoring data reflects current state parameters such as pipe wall scale thickness, scale growth rate, fluid turbidity, or ion concentration changes. After inputting both into the hierarchical analysis model, the model can assign higher weights to indicators such as safety and efficiency based on the current risk level, or increase the weights of economy and convenience in low-risk ranges, thereby obtaining scale prevention assessment indicator weights that match the current operating conditions. The weighted outputs can then be directly used for subsequent comprehensive ranking of various scale prevention and removal alternatives.

[0239] The method for determining scale prevention and removal measures provided in this application no longer relies solely on fixed experience in calculating the weights of scale prevention assessment indicators. Instead, it dynamically adjusts the weights by combining tiered early warning thresholds and real-time monitoring results, enabling the evaluation results to reflect real-time changes in scaling risk. This approach improves the objectivity and adaptability of scale prevention assessment indicators and helps select scale prevention and removal measures that match the on-site risks at different scaling stages. This reduces the probability of over-treatment or delayed treatment, thereby improving the accuracy and economy of oilfield scale control.

[0240] Figure 6 A flowchart illustrating the method for determining scale prevention and removal measures provided in the embodiments of this application. Figure 5 ,like Figure 6 As shown, this embodiment, based on the above embodiments, provides a detailed explanation of the specific process for obtaining target material property data, including:

[0241] S601. Based on the hierarchical analysis model, the nine-level scaling method is used to compare elements at the same level in pairs to construct a positive-negative inverse judgment matrix.

[0242] In this embodiment, the nine-level scaling method is a way to quantify the relative importance of elements at the same level, which can transform empirical judgments into computable comparison values. The positive-negative inverse judgment matrix is ​​a matrix formed by comparing each element at the same level pairwise.

[0243] For example, the weights of the scale prevention assessment indicators and various scale prevention and removal alternatives can be input into the hierarchical analysis model, and each alternative in the same level can be compared in pairs according to the dimensions of safety, applicability, implementation difficulty, treatment efficiency and economy, and the corresponding positive and negative judgment matrix can be generated according to the nine-level scaling method.

[0244] S602. Based on the positive and negative inverse judgment matrices, determine the corresponding maximum eigenvalue and the corresponding order.

[0245] For example, the eigenvalues ​​of the positive and negative inverse matrix are solved, and the largest eigenvalue and its corresponding order are extracted.

[0246] S603. Determine the consistency index based on the largest eigenvalue and the order.

[0247] In this embodiment, the consistency index is used to characterize the degree of deviation of the positive and negative counter-discrimination matrices.

[0248] For example, the consistency index can be calculated using the following formula:

[0249]

[0250] Where CI is the consistency index, and λ max is the largest eigenvalue; n is the order of the positive / negative inverse matrix.

[0251] S604. Obtain the preset random consistency index, and determine the corresponding consistency ratio based on the consistency index and the random consistency index, so as to determine whether the positive and negative counter-discrimination matrix meets the preset logical consistency requirements.

[0252] In this embodiment, the random consistency index is used to reflect the baseline consistency level of random matrices of the same order, while the consistency ratio is used to measure the degree of difference between the actual positive and negative counter-discrimination matrix and the baseline random matrix, thereby determining whether the matrix has acceptable logical consistency.

[0253] For example, when the consistency index is zero, it means that the consensus is complete. The closer the consistency index is to 0, the higher the degree of consensus. To measure the magnitude of the consistency index, a random consistency index is introduced.

[0254] Based on the consistency index, and combined with the preset random consistency index, the consistency ratio is calculated. Then, based on the consistency ratio, it is determined whether the positive and negative counter-consistency matrices meet the preset logical consistency requirements. The formula for calculating the consistency ratio is as follows:

[0255]

[0256] Wherein, CR is the consistency ratio, CI is the consistency index, and RI is the random consistency index.

[0257] S605. When the consistency ratio is less than the preset ratio threshold, the positive and negative judgment matrix is ​​determined to meet the preset logical consistency requirements, and the total ranking weight of each scale prevention and descaling alternative measure is determined according to the weight of the positive and negative judgment matrix and the scale prevention evaluation index.

[0258] For example, when the consistency ratio is less than the preset ratio threshold, it is determined that the consistency ratio meets the threshold requirement, which means that the constructed positive and negative judgment matrix does not have obvious logical conflicts and can be used for subsequent ranking calculations. On this basis, the local priority relationship reflected in the positive and negative judgment matrix is ​​combined with the weight of the scale prevention evaluation index to obtain the total ranking weight of each scale prevention and removal candidate measure.

[0259] S606. Sort each total ranking weight in descending order of weight, and determine the scale prevention and removal alternative measure with the highest weight as the target scale prevention and removal measure.

[0260] For example, priority is assigned based on the total ranking weight. Since the candidate with the highest weight matches the current scale prevention risk characteristics and evaluation indicators the best, it is identified as the target scale prevention and removal measure, thereby achieving objective screening and targeted output of on-site scale prevention strategies.

[0261] The method for determining scale prevention and removal measures provided in this application first quantifies empirical judgments into a positive-negative inverse judgment matrix through pairwise comparisons. Then, a consistency check is used to eliminate judgments with significant logical contradictions, thereby ensuring that weight calculations are based on reliable data. Subsequently, multiple candidate measures are compared under the same evaluation scale through a total ranking weight, thus avoiding bias caused by a single indicator dominating decision-making. This makes the final selected target scale prevention and removal measures more in line with the comprehensive management needs under current operating conditions, thereby improving the objectivity and consistency of scale prevention and removal measure selection, reducing subjective bias caused by manual experience selection, and enabling comparable ranking of different candidate measures under a unified weight system. Because a consistency check mechanism is introduced, erroneous decisions caused by distortion of the positive-negative inverse judgment matrix can also be effectively avoided, thereby improving the matching degree between the scale prevention and removal scheme and the on-site scaling risk, reducing reagent waste, ineffective construction, and equipment operation fluctuations, and enhancing the stability and economy of scaling management in oilfield production processes.

[0262] Figure 7 This is a schematic diagram of the device for determining scale prevention and removal measures provided in an embodiment of this application. The device in this embodiment can be in the form of software and / or hardware. For example... Figure 7 As shown in the embodiment of this application, the device 700 for determining scale prevention and removal measures includes: a first acquisition module 701, an establishment module 702, a calculation module 703, a first processing module 704, a second acquisition module 705, a second processing module 706, and a third processing module 707.

[0263] The first acquisition module 701 is used to acquire dynamic parameters related to scale formation in the target oilfield;

[0264] Module 702 is established to build a corresponding dynamic scaling rate calculation model based on scaling-related dynamic parameters.

[0265] The calculation module 703 is used to calculate the predicted scaling rate of the target oilfield based on the dynamic scaling rate calculation model and scaling-related dynamic parameters.

[0266] The first processing module 704 is used to determine the scale prevention and descaling stratification early warning threshold based on the scale formation rate prediction result.

[0267] The second acquisition module 705 is used to acquire real-time scaling monitoring data of the target oilfield and a variety of preset scale prevention and removal alternatives;

[0268] The second processing module 706 is used to determine the weight of the scale prevention evaluation index based on the scale prevention and descaling stratification early warning threshold and real-time scale monitoring data.

[0269] The third processing module 707 is used to determine the target scale prevention and removal measures based on the weight of the scale prevention evaluation index and each scale prevention and removal alternative measure.

[0270] In one possible implementation, the first acquisition module 701 is further configured to:

[0271] Based on the target oilfield, obtain the scale ion concentration, surface free energy component, production temperature, water sample pH, pipeline inner diameter, and fluid physical property parameters at the locations of the oilfield wellbore, gathering and transmission pipeline, and pump equipment.

[0272] By integrating the concentration of scale-forming ions, surface free energy components, production temperature, water sample pH, pipe inner diameter, and fluid physical properties, dynamic parameters related to scale formation are formed.

[0273] In one possible implementation, the establishment module 702 is further configured to:

[0274] The scaling process was determined to include crystallization scaling, particulate deposition, and the rolling and peeling of scale particles throughout the process.

[0275] To address the scaling effect, the concentration of scaling ions and fluid temperature are obtained from scaling-related dynamic parameters. The fluid temperature is converted into the fluid thermodynamic temperature, and the supersaturation of the solution is calculated based on the concentration of scaling ions.

[0276] Based on the solution supersaturation, fluid thermodynamic temperature, and crystallization reaction activation energy, a crystal growth rate calculation term is generated; where the crystallization reaction activation energy is a pre-configured model coefficient or a coefficient obtained by regression from historical production data.

[0277] Based on the fluid velocity, pipe diameter, and fluid properties in the scaling-related dynamic parameters, the Reynolds number and Schmidt number are calculated; the Sherwood number is calculated based on the Reynolds number and Schmidt number; and the Sherwood number is used as a mass transfer correction coefficient to generate a scaling particle mass transfer calculation term.

[0278] To address the rolling removal effect of scale particles, the ratio of the removal torque of the fluid acting on the scale particles to the interfacial adhesion resistance torque is calculated based on the relevant dynamic parameters of scale formation, thus obtaining the rolling torque. Based on the rolling torque and the erosion correction index, a scale particle removal rate calculation term is generated; where the erosion correction index is a model coefficient obtained by regression of historical production data.

[0279] The net scaling rate is obtained by coupling calculations of the crystal growth rate, scale mass transfer rate, and scale peeling rate. A dynamic scaling rate calculation model is then constructed based on the net scaling rate.

[0280] In one possible implementation, the first processing module 704 is further configured to:

[0281] Obtain the critical value of scale thickness corresponding to the wellbore, gathering and transmission pipeline and pump equipment of the target oilfield when the production energy consumption increases to the preset value;

[0282] Based on the predicted scaling rate, determine the time required to reach the critical scaling thickness value;

[0283] Obtain a preset first duration threshold and a second duration threshold, wherein the first duration threshold is less than the second duration threshold;

[0284] The duration value is compared with the first duration threshold and the second duration threshold, and the initial tiered warning threshold is determined based on the comparison results.

[0285] The scaling rate calculation model continuously acquires scaling rate values ​​output by the dynamic scaling rate calculation model according to a preset time period. Based on the scaling rate values, a scaling rate time series is constructed, and the moving average of the scaling rate within the preset fixed time window is calculated.

[0286] Based on the predicted results of the sliding average value and scaling rate, determine the corresponding slope of change;

[0287] Based on the changing slope and the pre-configured correction formula, the initial stratification warning threshold is corrected to obtain the scale prevention and descaling stratification warning threshold.

[0288] In one possible implementation, the second processing module 706 is further configured to:

[0289] Obtain preset multi-dimensional anti-scaling technology evaluation indicators;

[0290] Based on multi-dimensional scale prevention and removal technology evaluation indicators, a hierarchical analysis model is constructed, with scale prevention and removal technology selection as the target layer, multi-dimensional scale prevention and removal technology evaluation indicators as the criterion layer, and scale prevention and removal alternative measures as the scheme layer.

[0291] The scale prevention and removal stratified early warning threshold and real-time scale monitoring data are input into the hierarchical analysis model to determine the weight of the scale prevention evaluation indicators.

[0292] In one possible implementation, the third processing module 707 is further configured to:

[0293] Based on the hierarchical analysis model, the nine-level scaling method is used to compare elements at the same level pairwise to construct a positive-negative inverse judgment matrix.

[0294] Based on the positive and negative inverse matrix, determine the corresponding maximum eigenvalue and the corresponding order respectively;

[0295] The consistency index is determined based on the largest eigenvalue and the order.

[0296] Obtain the preset random consistency index;

[0297] Based on the consistency index and the random consistency index, the corresponding consistency ratio is determined to determine whether the positive and negative counter-discrimination matrix meets the preset logical consistency requirements.

[0298] When the consistency ratio is less than the preset ratio threshold, the positive and negative judgment matrix is ​​determined to meet the preset logical consistency requirements, and the total ranking weight of each scale prevention and descaling alternative measure is determined according to the weight of the positive and negative judgment matrix and the scale prevention evaluation index.

[0299] The total ranking weights are sorted in descending order of weight, and the scale prevention and removal alternative with the highest weight is determined as the target scale prevention and removal measure.

[0300] The scale prevention and descaling determination device 700 provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0301] Figure 8 A schematic diagram of the structure of the device for determining scale prevention and removal measures provided in the embodiments of this application. (See attached diagram.) Figure 8 As shown, the scale prevention and descaling measure determination device 800 provided in this embodiment includes at least one processor 801 and a memory 802. Optionally, the scale prevention and descaling measure determination device 800 further includes a communication component 803. The processor 801, memory 802, and communication component 803 are connected via a bus.

[0302] In a specific implementation, at least one processor 801 executes computer execution instructions stored in memory 802, causing at least one processor 801 to perform the above-described method.

[0303] The specific implementation process of processor 801 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0304] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0305] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0306] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0307] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0308] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0309] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0310] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0311] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0312] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0313] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0314] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0315] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0316] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for determining scale prevention and removal measures, characterized in that, include: Obtain dynamic parameters related to scale formation in the target oilfield; Based on the aforementioned scaling-related dynamic parameters, a corresponding dynamic scaling rate calculation model is established; Based on the dynamic scaling rate calculation model and the scaling-related dynamic parameters, the scaling rate prediction results for the target oilfield are calculated. Based on the scaling rate prediction results, determine the early warning threshold for scale prevention and removal stratification; Acquire real-time scaling monitoring data and multiple preset scaling prevention and removal alternatives for the target oilfield; The weights of the scale prevention and descaling stratification early warning thresholds and the real-time scale monitoring data are determined based on the scale prevention evaluation indicators. The target scale prevention and removal measures are determined based on the weights of the scale prevention assessment indicators and each of the scale prevention and removal alternative measures.

2. The method according to claim 1, characterized in that, The acquisition of scale-related dynamic parameters of the target oilfield includes: Based on the target oilfield, the concentration of scale-forming ions, surface free energy components, production temperature, water sample pH, pipeline inner diameter, and fluid physical property parameters at the locations of the oilfield wellbore, gathering and transportation pipeline, and pump equipment were obtained respectively. The scale-forming ion concentration, surface free energy component, production temperature, water sample pH, pipe inner diameter, and fluid properties are integrated to form the scale-related dynamic parameters.

3. The method according to claim 2, characterized in that, The step of establishing a corresponding dynamic scaling rate calculation model based on the scaling-related dynamic parameters includes: The scaling process was determined to include crystallization scaling, particulate deposition, and the rolling and peeling of scale particles throughout the process. Regarding the aforementioned scaling effect, the concentration of scaling ions and the fluid temperature are obtained from the scaling-related dynamic parameters. The fluid temperature is converted into a fluid thermodynamic temperature, and the supersaturation of the solution is calculated based on the concentration of scaling ions. Based on the solution supersaturation, the fluid thermodynamic temperature, and the activation energy of the crystallization reaction, a crystal growth rate calculation term is generated; wherein, the activation energy of the crystallization reaction is a pre-configured model coefficient or a model coefficient obtained by regression from historical production data; Based on the fluid velocity in the pipe, the pipe diameter, and the fluid properties in the scaling-related dynamic parameters, the Reynolds number and Schmidt number are calculated; the Sherwood number is calculated based on the Reynolds number and Schmidt number; and the Sherwood number is used as a mass transfer correction coefficient to generate a scale particle mass transfer calculation term. For the aforementioned scale removal effect, based on the scale-related dynamic parameters, the ratio of the removal torque of the fluid acting on the scale particles to the interfacial adhesion resistance torque is calculated to obtain the rolling torque; based on the rolling torque and the erosion correction index, a scale removal rate calculation term is generated; wherein, the erosion correction index is a model coefficient obtained by regression of the historical production data; The net scaling rate is obtained by coupling the crystal growth rate calculation term, the scale particle mass transfer calculation term, and the scale particle stripping rate calculation term. The dynamic scaling rate calculation model is then constructed based on the net scaling rate.

4. The method according to claim 1, characterized in that, The step of determining the scale prevention and descaling stratification early warning threshold based on the scale formation rate prediction result includes: Obtain the critical value of scale thickness corresponding to the wellbore, gathering and transportation pipeline and pump equipment of the target oilfield when the production energy consumption increases to a preset value; Based on the scaling rate prediction results, determine the time required to reach the critical scaling thickness value; Obtain a preset first duration threshold and a second duration threshold, wherein the first duration threshold is less than the second duration threshold; The duration value is compared with the first duration threshold and the second duration threshold, and the initial tiered warning threshold is determined based on the corresponding comparison results; The scaling rate values ​​output by the dynamic scaling rate calculation model are continuously acquired according to a preset time period. Based on the scaling rate values, a scaling rate time series is constructed, and the sliding average value of the scaling rate within the preset fixed time window is calculated. Based on the sliding average value and the predicted scaling rate, the corresponding slope of change is determined; Based on the change slope and the pre-configured correction formula, the initial stratification warning threshold is corrected to obtain the scale prevention and descaling stratification warning threshold.

5. The method according to any one of claims 1-4, characterized in that, The step of determining the weights of the scale prevention evaluation indicators based on the scale prevention and descaling stratification early warning threshold and the real-time scale monitoring data includes: Obtain preset multi-dimensional anti-scaling technology evaluation indicators; Based on the multi-dimensional scale prevention and removal technology evaluation indicators, a hierarchical analysis model is constructed, with the selection of scale prevention and removal technologies as the target layer, the multi-dimensional scale prevention and removal technology evaluation indicators as the criterion layer, and the alternative scale prevention and removal measures as the scheme layer. The scale prevention and descaling stratified early warning threshold and the real-time scale monitoring data are input into the hierarchical analysis model to determine the weight of the scale prevention evaluation index.

6. The method according to claim 5, characterized in that, The step of determining the target scale prevention and removal measure based on the weights of the scale prevention evaluation indicators and each of the scale prevention and removal alternative measures includes: Based on the hierarchical analysis model, the nine-level scaling method is used to compare elements at the same level pairwise to construct a positive-negative inverse judgment matrix. Based on the positive and negative inverse discrimination matrix, determine the corresponding maximum eigenvalue and the corresponding order, respectively; Based on the maximum eigenvalue and the order, a consistency index is determined; Obtain the preset random consistency index; Based on the consistency index and the random consistency index, the corresponding consistency ratio is determined to determine whether the positive and negative inverse judgment matrix meets the preset logical consistency requirements. When the consistency ratio is less than the preset ratio threshold, the positive and negative counter-judgment matrix is ​​determined to meet the preset logical consistency requirements, and the total ranking weight corresponding to each of the scale prevention and descaling alternative measures is determined according to the weight of the positive and negative counter-judgment matrix and the scale prevention evaluation index. The total ranking weights are sorted in descending order of weight, and the scale prevention and removal alternative measures with the highest weights are determined as the target scale prevention and removal measures.

7. A device for determining scale prevention and removal measures, characterized in that, include: The first acquisition module is used to acquire dynamic parameters related to scale formation in the target oilfield; A module is established to build a corresponding dynamic scaling rate calculation model based on the aforementioned scaling-related dynamic parameters; The calculation module is used to calculate the predicted scaling rate of the target oilfield based on the dynamic scaling rate calculation model and the scaling-related dynamic parameters. The first processing module is used to determine the scale prevention and descaling stratification early warning threshold based on the scale formation rate prediction result. The second acquisition module is used to acquire real-time scaling monitoring data of the target oilfield and a variety of preset scale prevention and removal alternatives; The second processing module is used to determine the weight of the scale prevention evaluation index based on the scale prevention and descaling stratification early warning threshold and the real-time scale monitoring data. The third processing module is used to determine the target scale prevention and removal measures based on the weights of the scale prevention evaluation indicators and each of the scale prevention and removal alternative measures.

8. A device for determining scale prevention and removal measures, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-6.

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