Intelligent layered large-displacement water injection and oil production sliding sleeve

Abstract

The present application belongs to the technical field of petroleum operation equipment, and discloses an intelligent layered large-displacement water injection and oil production sliding sleeve, which comprises an outer sleeve A, an outer sleeve B, an upper joint, a lower joint, an electronic cabin pipe, an inner sleeve, a bearing support and a sliding sleeve and the like. The sliding sleeve is provided with a water injection hole A, and the outer sleeve B is provided with a water injection hole B matched therewith. The sliding of the sliding sleeve is controlled by a driving assembly to realize the opening, closing and flow regulation of the water injection channel. The electronic cabin pipe is integrated with an electric control PCB, an ultrasonic flowmeter and a pressure gauge, which can monitor fluid parameters in real time, and intelligently calculate the optimal water injection flow based on multiple factors such as fluid characteristics, geological conditions, well pipe state and pressure difference in combination with a flow optimization system, so as to realize dynamic regulation. The present application solves the problems that the conventional sliding sleeve cannot be adjusted in real time and needs to be shut down for operation, and has the advantages of accurate water injection, fast response, strong adaptability, high reliability and the like, which significantly improves the oilfield development efficiency and operation safety.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of oil operation equipment, and particularly relates to an intelligent layered large-displacement water injection and oil production sliding sleeve. BACKGROUND

[0002] With the gradual transformation of oilfield development to unconventional operation, the intelligent water injection completion technology plays an increasingly important role in oil-water well operation, and is particularly suitable for complex oil reservoirs with multiple characteristics.

[0003] The traditional operation on the market has many deficiencies and cannot be turned on at any time, only can be stopped for production and injection, which increases the cost and production maintenance, and cannot achieve large flow of water injection. When performing deployment, testing and other operations in these layers, the application of the intelligent water injection completion technology ensures that the water injection process can be more accurately controlled during oilfield development, improves the recovery efficiency of the oilfield, and provides a solid technical guarantee for the development of unconventional oil and gas resources. It is not only not limited and interfered by the external environment, but also avoids the cumbersome process of stopping production and injection in traditional operation, and does not need to use large equipment and complex tools for operation. The sliding sleeve can effectively perform layered production, reduce the bottom hole back pressure, promote the ejection of oil and gas, improve the initial production, and reduce the processing cost during operation and production. SUMMARY

[0004] In view of the deficiencies of the prior art, the application provides an intelligent layered large-displacement water injection and oil production sliding sleeve, which solves the above problems.

[0005] To achieve the above purpose, the following technical scheme is adopted: an intelligent layered large-displacement water injection and oil production sliding sleeve, comprising an inner sleeve, a sliding sleeve, an outer sleeve A and an outer sleeve B which can be detachably installed, the outer sleeve A and the outer sleeve B are respectively provided with an upper joint and a lower joint at the end, the inner sleeve is provided with an electronic cabin pipe which is detachably connected with the inner sleeve, the electronic cabin pipe is detachably connected with the upper joint, one end of the electronic cabin pipe is detachably connected with one end of the inner sleeve, a pressure gauge which is electrically connected with an electric control PCB is detachably connected on the electronic cabin pipe, the sliding sleeve is in sliding fit with the lower joint, a plurality of water injection holes A are arranged in a linear array along the length direction of the sliding sleeve, a plurality of water injection holes B are arranged on the outer sleeve B and matched with the water injection holes A, and the sliding sleeve further comprises a driving assembly which is installed on the inner sleeve and used for driving the sliding sleeve to slide relative to the outer sleeve.

[0006] On the basis of the above technical scheme, the following optional technical schemes are provided:

[0007] Further technical solutions: The electronic cabin is arranged between the electronic cabin pipe and the outer sleeve pipe, the electric control PCB, the flow meter and the pressure gauge are arranged in the electronic cabin, the flow meter is an ultrasonic flow meter, and the pressure gauge is detachably connected with the electronic cabin pipe.

[0008] Further technical solutions: The drive assembly includes a motor and a speed reducer, the motor is detachably connected with the inner sleeve pipe, the output shaft of the motor is fixedly connected with the input shaft of the speed reducer, the speed reducer is fixedly connected with a lead screw arranged on the inner sleeve pipe, the lead screw is threadedly connected with a nut, the nut is fixedly connected with a sliding sleeve, the speed reducer is detachably connected with a bearing support arranged on the lead screw and located in the outer sleeve pipe A, and the sliding sleeve is in sliding fit with a limiting groove arranged on the outer sleeve pipe B.

[0009] Further technical solutions: Further comprising: a flow optimization system for dynamically adjusting the water injection flow, comprising:

[0010] A fluid analysis module calculates fluid characteristic coefficients based on fluid viscosity, fluid temperature and sand content through a fluid characteristic model;

[0011] A geological characteristic analysis module calculates geological characteristic coefficients based on formation permeability (the ability of rock to allow fluid to pass through) and formation porosity (the percentage of pore volume in rock to total volume) through a geological characteristic model;

[0012] A well pipe condition analysis module calculates well pipe condition coefficients based on vertical well depth (vertical depth from wellhead to target layer), well inclination angle (included angle between wellbore track and vertical line) and casing inner diameter (size of inner sleeve inner diameter) through a well pipe condition model;

[0013] A pressure regulation analysis module calculates pressure regulation coefficients based on pipe internal pressure and pipe external pressure under fluid characteristic coefficients and geological characteristic coefficients through an internal-external pressure regulation model;

[0014] A flow optimization module calculates and outputs target water injection flow based on pressure regulation coefficients, well pipe regulation coefficients and preset reference water injection flow through a flow optimization model.

[0015] Further technical solutions: The flow optimization model is represented as:

[0016] ;

[0017] Wherein, represents the target water injection flow, represents the reference water injection flow, represents the pressure regulation coefficient, represents the well pipe condition coefficient, Maximal flow regulation upper limit (used to limit the maximum multiple of flow amplification, to ensure water injection safety).

[0018] Further technical solutions: the steps of the pressure regulation analysis module are:

[0019] Obtain the injection pressure difference based on the internal pressure and the external pressure of the pipe , the , wherein represents the internal pressure of the pipe, represents the external pressure of the pipe;

[0020] Construct an optimal injection pressure difference model based on the reference starting pressure difference, the fluid characteristic coefficient, and the geological characteristic coefficient to output an optimal injection pressure difference, and the injection pressure difference model is represented as:

[0021] ;

[0022] , wherein represents the optimal injection pressure difference, represents the reference starting pressure difference, represents the pressure difference adjustment coefficient, represents the fluid characteristic coefficient, represents the geological characteristic coefficient;

[0023] Construct an internal-external pressure regulation model based on the injection pressure difference and the optimal injection pressure difference to output a pressure regulation coefficient, and the internal-external pressure regulation model is represented as:

[0024] ;

[0025] , wherein represents the pressure regulation coefficient, represents the injection pressure difference, represents the optimal injection pressure difference, represents the regulation sensitivity coefficient;

[0026] If , it indicates that the injection pressure difference is equal to the optimal injection pressure difference, and the reference flow rate is recommended;

[0027] If , it indicates that the injection pressure difference is greater than the optimal injection pressure difference, indicating that the bottom layer absorption capacity is better than expected or the injection pressure has a margin, and it is recommended to positively regulate (amplify) the flow rate, and the greater the pressure regulation coefficient, the higher the process;

[0028] If , it indicates that the injection pressure difference is less than the optimal injection pressure difference, indicating that the formation absorption capacity is poor or the injection pressure is insufficient, and it is recommended to inversely regulate (reduce) the flow rate to match the formation capacity or avoid ineffective water injection, and the smaller the pressure regulation coefficient, the higher the reduction degree.

[0029] Further technical solutions: the step of the well pipe condition analysis module is:

[0030] The vertical well depth (vertical depth from wellhead to target horizon), well inclination angle (included angle between wellbore trajectory and vertical line) and casing inner diameter are subjected to maximum-minimum normalization processing to obtain well depth index, inclination angle index and inner diameter index;

[0031] The well pipe condition model is constructed based on the well depth index, inclination angle index and inner diameter index to output well pipe condition coefficient, and the well pipe condition model is represented as:

[0032] ;

[0033] Among them, represents the well pipe condition coefficient, represents the well depth index, represents the inclination angle index, represents the inner diameter index, represents the well depth weight coefficient, represents the inclination angle weight coefficient, and The greater the value, the smaller the fluid flow resistance.

[0034] Further technical solutions: the step of the geological property analysis module is:

[0035] The formation permeability and bottom porosity are subjected to maximum-minimum normalization processing to obtain permeability index and porosity index;

[0036] The geological property model is constructed based on the permeability index and porosity index to output geological property coefficient, and the geological property model is represented as:

[0037] ;

[0038] Among them, represents the geological property coefficient, represents the permeability index, represents the porosity index, represents the total weight coefficient, and The greater the value, the stronger the formation's ability to absorb fluid.

[0039] Further technical solutions: the step of the fluid analysis module is:

[0040] The fluid viscosity and sand content are subjected to maximum-minimum normalization processing to obtain fluid viscosity index and sand content index;

[0041] The absolute difference between fluid temperature and optimal temperature and the allowed value of deviation from optimal temperature are subjected to ratio processing to obtain temperature index; ​

[0042] A fluid property model is constructed based on a fluid viscosity index, a sand content index, and a temperature index to output a fluid property coefficient, and the fluid property model is expressed as:

[0043] ;

[0044] wherein, represents the fluid property coefficient, represents the fluid viscosity index, represents the sand content index, represents the temperature index, and the greater the value, the better the fluid state (the better the injectability).

[0045] Advantages

[0046] The present application provides an intelligent layered large displacement water injection and oil production sliding sleeve, which has the following advantages compared with the prior art:

[0047] 1. The present application realizes real-time and accurate adjustment of water injection flow rate without stopping production, avoids the problems of production interruption and cost increase caused by frequent wellhead shutdown in traditional operations, greatly improves the efficiency and economic benefit of oilfield development, and through the integration of an electric control system and an intelligent optimization algorithm of multi-parameter fusion, dynamically calculates and controls the water injection flow rate according to multi-dimensional data such as fluid properties, geological conditions, well pipe state and real-time pressure difference, significantly improves the water injection accuracy and formation adaptability, and optimizes the water injection effect.

[0048] 2. The present application not only realizes real-time adjustment of water injection flow rate but also adjusts oil production flow rate, and the cooperation of the motor and the reducer can bring large thrust to the sliding sleeve, thereby sliding the sliding sleeve concentrically arranged with the outer sleeve pipe to realize the closing and opening of the water injection (oil production) channel, and the water injection holes A arranged in a linear array along the length direction of the sliding sleeve cooperate with the water injection holes B opened on the outer sleeve pipe B to realize multi-stage flow regulation and improve the drainage and oil production efficiency.

[0049] 3. In the present application, the ultrasonic flowmeter adopts a non-contact measurement method, transmits sound wave signals through the pipe wall, avoids the problems of scaling and corrosion caused by direct contact with the fluid, has the advantages of high precision, wide range, easy installation, etc., and can maintain long-term stable measurement accuracy in high temperature and high pressure environments. BRIEF DESCRIPTION OF DRAWINGS

[0050] Fig. 1 is a three-dimensional structural schematic diagram of the present application.

[0051] Fig. 2 is a whole structural schematic diagram of the present application.

[0052] Fig. 3The structural schematic diagram of the electronic cabin pipe of the application.

[0053] Fig. 4 The structural schematic diagram of the driving assembly of the application.

[0054] The figure mark annotation: 1, outer sleeve A; 2, outer sleeve B; 3, upper joint; 4, lower joint; 5, electronic cabin pipe; 6, inner sleeve; 7, motor; 8, speed reducer; 9, screw rod; 10, nut; 11, sliding sleeve; 12, pressure gauge; 13, flow meter; 14, electric control PCB; 15, water injection hole A; 16, water injection hole B. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical scheme and advantages of the application more clear, the application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the application and do not limit the application.

[0056] The specific implementation of the application is described in detail below in combination with specific examples.

[0057] Please refer to Figs. 1-3 According to one embodiment of the application, an intelligent layered large displacement water injection and oil production sliding sleeve is provided, which comprises an inner sleeve 6, a sliding sleeve 11, an outer sleeve A1 and an outer sleeve B2 detachably installed thereon, the outer sleeve A1 and the outer sleeve B2 are respectively provided with an upper joint 3 and a lower joint 4 at the end, the outer sleeve A1 is provided with an electronic cabin pipe 5 detachably connected thereto, the electronic cabin pipe 5 is detachably connected with the upper joint 3, an electronic cabin is arranged between the electronic cabin pipe 5 and the outer sleeve A1, the electronic cabin is provided with an electric control PCB 14 and a flow meter 13 electrically connected with the electric control PCB 14, the flow meter 13 is an ultrasonic flow meter 13, one end of the electronic cabin pipe 5 is detachably connected with one end of the inner sleeve 6, the electronic cabin pipe 5 is detachably connected with a pressure gauge 12 electrically connected with the electric control PCB 14, the sliding sleeve 11 is in sliding fit with the lower joint 4, the sliding sleeve 11 is provided with water injection holes A 15 arranged in a circular linear array along the length direction of the sliding sleeve 11, the outer sleeve B2 is provided with water injection holes B 16 matched with the water injection holes A 15, and further comprising:

[0058] A driving assembly is installed on the inner sleeve 6 and used to drive the sliding sleeve 11 to slide relative to the outer sleeve.

[0059] The driving assembly comprises a motor 7 sleeved on the inner sleeve 6 and detachably connected with the inner sleeve 6 and a speed reducer 8, the output shaft of the motor 7 is fixedly connected with the input shaft of the speed reducer 8, the speed reducer 8 is fixedly connected with a lead screw 9 sleeved on the inner sleeve 6, the lead screw 9 is threadedly connected with a nut 10, the nut 10 is fixedly connected with a sliding sleeve 11, the speed reducer 8 is detachably connected with a bearing support sleeved on the lead screw 9 and located in the outer sleeve A1, and the sliding sleeve 11 is in sliding fit with a limiting groove (not marked in the figure) formed in the outer sleeve B2.

[0060] In the embodiment of the present application, the ultrasonic flowmeter 13 adopts a non-contact measurement mode, transmits the acoustic wave signal through the pipe wall, avoids the problems such as scaling and corrosion caused by direct contact with the fluid, has the advantages of high precision, wide range, convenient installation and the like, and can maintain long-term stable measurement precision in a high-temperature and high-pressure environment, the probe of the ultrasonic flowmeter 13 is electrically connected with the electric control PCB 14, the pressure gauge 12 is used for measuring the internal pressure and external pressure of the container and has a measurement function, the pressure gauge 12 is connected with the electric control PCB 14 through a sensor line and realizes rapid data acquisition and transmission, the motor 7 can drive the speed reducer 8, the speed reducer 8 converts the high-speed low-torque output of the motor 7 into low-speed high-torque output, converts the control signal emitted by the electric control PCB 14 into physical movement of the sliding sleeve 11, so as to ensure that the lead screw 9 can still rotate smoothly under high pressure resistance, the lead screw 9 drives the nut 10 threadedly connected therewith to move along the axial direction smoothly, the nut 10 is firmly connected with the sliding sleeve 11, the axial displacement of the nut 10 directly drives the sliding sleeve 11 to realize switching between full closing, half opening and full opening, so as to control the size of the oil flow during water injection (oil extraction), thereby realizing opening, closing and opening degree adjustment of the water injection (oil extraction) channel, in the working process, the driving mechanism can automatically adjust the opening degree of the sliding sleeve 11 according to the real-time data of the flowmeter 13 and the pressure gauge 12, so as to realize accurate control and dynamic optimization of the water injection (oil extraction) amount, thereby improving the oil reservoir development efficiency and operation safety.

[0061] Further comprising: a flow optimization system for dynamically adjusting the water injection flow, comprising:

[0062] A fluid analysis module calculates a fluid characteristic coefficient based on fluid viscosity, fluid temperature and sand content through a fluid characteristic model;

[0063] A geological characteristic analysis module calculates a geological characteristic coefficient based on formation permeability (the ability of rock to allow fluid to pass through) and formation porosity (the percentage of pore volume in the total volume of rock) through a geological characteristic model;

[0064] The well pipe condition analysis module calculates a well pipe condition coefficient based on a vertical well depth (a vertical depth from a well head to a target layer), a deviation angle (an angle between a wellbore trajectory and a vertical line), and a casing inner diameter (a size of an inner casing inner diameter) through a well pipe condition model;

[0065] The pressure regulation analysis module calculates a pressure regulation coefficient based on the in-pipe pressure and the out-pipe pressure under the fluid characteristic coefficient and the geologic characteristic coefficient through an in-pressure-out-pressure regulation model;

[0066] The flow optimization module calculates and outputs a target water injection flow rate based on the pressure regulation coefficient, the well pipe regulation coefficient, and a preset reference water injection flow rate through a flow optimization model.

[0067] Specifically, the flow optimization system realizes closed-loop regulation of the water injection flow rate through multi-dimensional parameter coupling. The fluid analysis module converts fluid viscosity, temperature, and sand content into a fluid characteristic coefficient, dynamically reflecting changes in fluid flow resistance. The geologic characteristic analysis module generates a geologic characteristic coefficient based on formation permeability and porosity, and real-time assesses formation absorption capacity. The well pipe condition analysis module calculates a well pipe condition coefficient through the vertical well depth, the deviation angle, and the casing inner diameter, quantifying the influence of the wellbore structure on flow. The pressure regulation analysis module compares the actual water injection pressure difference with the optimal pressure difference, generating a pressure regulation coefficient to match the formation pressure condition. The flow optimization module combines the above coefficients with the reference flow rate, dynamically calculates the target flow rate through a nonlinear model, and realizes real-time adaptation of the water injection parameters to the formation conditions.

[0068] Compared with the prior art, the traditional water injection technology only relies on static geologic data and fixed water injection parameters, and cannot respond to changes in fluid viscosity, differences in flow resistance caused by the deviation angle, or dynamic changes in formation porosity. The present scheme introduces real-time analysis of fluid characteristics, geologic characteristics, and well pipe conditions, combines a pressure dynamic regulation mechanism, and constructs a multi-parameter coupled closed-loop optimization model, solving the problem of low water injection efficiency caused by parameter mismatch in the traditional method.

[0069] Through the above technical scheme, the present application realizes dynamic matching of the water injection flow rate and the formation absorption capacity, avoids invalid water injection caused by sudden changes in fluid viscosity or restrictions of the well pipe structure, optimizes the adaptability of the water injection pressure and the formation pressure, and significantly improves the water injection efficiency and resource utilization rate under complex reservoir conditions.

[0070] Preferably, the steps of the fluid analysis module are:

[0071] The fluid viscosity and the sand content are subjected to maximum-minimum normalization processing to obtain a fluid viscosity index and a sand content index;

[0072] The absolute difference between the fluid temperature and the optimal temperature is subjected to ratio processing with the allowable deviation value of the optimal temperature to obtain a temperature index;

[0073] The fluid property model is constructed based on the fluid viscosity index, sand content index, and temperature index to output a fluid property coefficient, and is expressed as:

[0074]

[0075] wherein, represents the fluid property coefficient, represents the fluid viscosity index, represents the sand content index, represents the temperature index, and The greater the value, the better the fluid state (the better the injectability).

[0076] The maximum-minimum normalization processing refers to mapping the original data of fluid viscosity and sand content to the 0-1 interval, so that the viscosity and sand content data have comparability. The temperature index is used to quantify the influence of temperature anomalies on fluid mobility. The fluid property model refers to the nonlinear superposition of viscosity, sand content, and temperature index through an exponential function. Specifically, the function form can be used. When any parameter deviates from the normal range, the model output value rapidly decays, thereby sensitively reflecting the negative impact of composite parameter anomalies on water injection effect.

[0077] Specifically, the fluid viscosity and sand content are converted into standardized indexes after normalization processing, eliminating the interference of different dimension parameters on the evaluation results. The temperature index quantifies the influence of temperature anomalies on fluid flow resistance by calculating the relative deviation of the actual temperature from the optimal temperature. Subsequently, the three indexes are nonlinearly superimposed through an exponential function model to generate a characteristic coefficient that comprehensively reflects the injectability of the fluid. When the viscosity increases, the sand content increases, or the temperature deviates from the optimal range, the characteristic coefficient presents an exponential decline, triggering the flow optimization module to dynamically adjust the water injection parameters. The model converts the complex fluid state into a quantifiable evaluation index through a multi-parameter synergistic mechanism, providing accurate basis for subsequent flow optimization.

[0078] In some specific embodiments, the parameter range in the maximum-minimum normalization processing can be dynamically adjusted according to the fluid properties of different oilfields, for example, the maximum viscosity can be set to 500 mPa·s and the minimum value to 10 mPa·s. The allowable deviation from the optimal temperature can be set based on the fluid type, for example, for high condensate oil, the allowable deviation from the optimal temperature can be set to 15°C for water injection hole A. The exponential superposition in the exponential function can further introduce a weight factor, for example, adjusted to wherein the weight factor can be set according to empirical values or obtained through field data training.

[0079] ​Compared with the prior art, the traditional method usually only adjusts water injection based on a single parameter (such as viscosity or pressure), and cannot handle the complex fluid state under the coupling of multiple parameters. In the prior art, the influence of temperature is usually handled by a linear correction coefficient, which is difficult to accurately reflect the nonlinear influence of temperature deviation on the fluid mobility. The present scheme can more accurately quantify the influence degree of abnormal composite parameters on water injection efficiency by establishing a multi-parameter collaborative evaluation system and combining the nonlinear characteristics of the exponential function.

[0080] Through the above technical scheme, the present application realizes dynamic quantitative evaluation of the state of water injection fluid, and solves the problem of low water injection efficiency caused by differences in fluid characteristics in complex reservoirs. The dimensional differences of multiple parameters are eliminated through standardization processing, the influence of temperature abnormality is captured through temperature deviation calculation, and the abnormality of composite parameters is sensitively reflected through an exponential model, thereby finally improving the adaptability of the water injection system to different fluid characteristics. The scheme can automatically optimize the water injection parameters according to the real-time fluid state, avoiding the risk of water injection deficiency or formation damage caused by incomplete parameter evaluation in the traditional method.

[0081] Preferably, the steps of the geological property analysis module are:

[0082] The formation permeability and the bottom porosity are maximum-minimum normalized to obtain a permeability index and a porosity index;

[0083] A geological property model is constructed based on the permeability index and the porosity index to output a geological property coefficient, and the geological property model is represented as:

[0084] ;

[0085] wherein, represents the geological property coefficient, represents the permeability index, represents the porosity index, represents the total weight coefficient, and , the The greater the value, the stronger the ability of the formation to absorb fluid.

[0086] The formation permeability refers to the ability of rock to allow fluid to pass through, which can be obtained by core experiment measurement or logging data inversion method, and is used to represent the efficiency of the formation in conducting fluid. The bottom layer porosity refers to the percentage of pore volume in the total volume of rock, which can be obtained by core analysis or logging interpretation method, and is used to represent the scale of the reservoir space. The permeability index refers to the normalized formation permeability parameter, which is used to quantify the relative level of rock permeability. The porosity index refers to the normalized bottom layer porosity parameter, which is used to quantify the relative scale of the reservoir space. The weight coefficient refers to the linear combination proportion of the permeability index and the porosity index, which can be determined by expert experience method or machine learning optimization method, and is used to adjust the contribution of different parameters to the formation absorption capacity.

[0087] Specifically, by normalizing the formation permeability and porosity respectively, the influence of different dimension parameters on the model is eliminated, and the permeability index and the porosity index are in the same order of magnitude range. The permeability index reflects the ability of rock to conduct fluid, and the porosity index reflects the space capacity of the reservoir fluid. Both of them represent the formation characteristics from two dimensions of flow efficiency and storage scale. The geological characteristic coefficient is constructed by linear weighted combination, and the contribution proportion of permeability and porosity to the formation absorption capacity is adjusted by adjusting the weight coefficient, so that the model can flexibly adapt to the difference in importance of parameters under different geological conditions. The sum of the weight coefficients is constrained to be 1, which ensures the mathematical stability of the model parameters and avoids the deviation of the calculation results from the actual physical meaning due to the imbalance of weight distribution. The finally output geological characteristic coefficient varies continuously in the range of 0-1, and the larger the value is, the stronger the comprehensive absorption capacity of the formation is, which provides a quantitative basis for subsequent water injection flow optimization.

[0088] Compared with the prior art, the traditional method usually only uses a single parameter to evaluate the formation absorption capacity, such as relying only on permeability or porosity for rough judgment, without considering the comprehensive influence of the synergistic effect of the two on fluid absorption. The parameter normalization process is missing in the prior art, which makes it difficult to directly combine different dimension parameters by weighting.

[0089] Through the above technical solutions, the present application solves the problem of inaccurate water injection flow adjustment caused by the inability to accurately evaluate the formation absorption capacity in complex oil reservoirs. Through the synergistic quantitative analysis of permeability and porosity, the actual potential of the formation to absorb fluid can be more comprehensively reflected, and the deviation caused by single parameter evaluation can be eliminated. Through the normalization process and weight constraint mechanism, the interpretability and calculation stability of the model parameters are ensured, providing reliable geological characteristic input parameters for dynamic adjustment of water injection flow.

[0090] Preferably, the step of the well pipe condition analysis module is:

[0091] The vertical well depth (vertical depth from the wellhead to the target layer), the deviation angle (the angle between the well trajectory and the vertical line), and the casing inner diameter are maximum-minimum normalized to obtain the well depth index, the deviation angle index, and the inner diameter index;

[0092] The well pipe condition model is constructed based on the well depth index, the deviation angle index, and the inner diameter index to output the well pipe condition coefficient, and the well pipe condition model is expressed as:

[0093] ;

[0094] wherein, represents the well pipe condition coefficient, represents the well depth index, represents the deviation angle index, represents the inner diameter index, represents the well depth weight coefficient, represents the deviation angle weight coefficient, and the greater the value, the smaller the fluid flow resistance.

[0095] The vertical well depth refers to the vertical depth from the wellhead to the target layer, which can be measured by a laser range finder or a pressure sensor, and is used to reflect the cumulative effect of the fluid flow resistance. The deviation angle refers to the angle between the well trajectory and the vertical line, which can be measured by a gyroscope or an inertial navigation system, and is used to represent the additional flow resistance generated when the well trajectory deviates from the vertical direction. The casing inner diameter refers to the size of the inner casing inner diameter, which can be measured by an ultrasonic thickness gauge or an inner diameter gauge, and is used to directly associate the influence of the pipe flow area on the flow efficiency. The maximum-minimum normalization refers to linear transformation of the original data to the [0, 1] interval, which is used to eliminate the incomparability of different dimension parameters. The well depth index, the deviation angle index, and the inner diameter index refer to the standardized parameters after normalization, which can be generated by a computing unit after the sensor collects the original data, and are used to quantify the influence of the well depth, the deviation angle, and the inner diameter on the flow resistance. The well pipe condition model refers to a mathematical expression that comprehensively considers the well depth index, the deviation angle index, and the inner diameter index, and is used to dynamically calculate the coefficient reflecting the well pipe flow condition. The well depth weight coefficient and the deviation angle weight coefficient refer to the parameters for adjusting the contribution of the well depth and the deviation angle to the resistance, which can be determined by empirical values or machine learning algorithm optimization, and are used to adjust the model output precision according to the actual working conditions.

[0096] Specifically, the three parameters of vertical well depth, well inclination angle and casing inner diameter are converted into well depth index, inclination angle index and inner diameter index through maximum-minimum normalization to eliminate the dimensional differences and form the standardized input. The well depth index and the inclination angle index are multiplied by the weight coefficient to form the denominator item, reflecting the cumulative effect of the along-the-way resistance caused by the increase of the well depth and the additional resistance caused by the deviation of the well inclination angle from the vertical direction. The inner diameter index is multiplied by the reciprocal of the denominator item to reflect the effect of the increase of the casing inner diameter on the flow area. The well pipe condition coefficient is calculated by the model, and its value range is limited in the interval [0, 1], and the larger the value is, the smaller the hindering effect of the well pipe condition on the fluid flow is. The weight coefficient can be dynamically adjusted according to different oilfield geological characteristics, for example, the weight coefficient of the well depth is increased in deep well operation to strengthen the influence of the well depth on the resistance.

[0097] Compared with the prior art, the traditional method usually only considers a single well pipe parameter or uses static empirical values to evaluate the flow resistance, and cannot quantify the synergistic effect of the well depth, the well inclination angle and the inner diameter. In the prior art, the influence of the well inclination angle on the flow resistance is often ignored, and the lack of normalization processing leads to difficulties in comprehensive modeling of multiple parameters. The present scheme eliminates the dimensional differences by introducing maximum-minimum normalization, constructs a dynamic model containing a weight coefficient, and realizes accurate quantitative evaluation of the multi-dimensional well pipe condition.

[0098] Through the above technical scheme, the present application solves the problem of difficult quantitative evaluation of the fluid flow resistance caused by the complex well pipe condition in the traditional water injection technology, realizes accurate quantification of the influence of the well pipe condition on the flow resistance by normalizing the vertical well depth, the well inclination angle and the casing inner diameter and constructing a dynamic model. The well pipe condition coefficient serves as a dynamic adjustment basis, so that the water injection flow rate can be optimized in real time according to the actual well pipe condition, avoiding the risk of low water injection efficiency or equipment overload caused by inaccurate resistance evaluation, and improving the safety and controllability of the water injection process.

[0099] Preferably, the step of the pressure regulation analysis module is:

[0100] Obtaining the water injection pressure difference based on the pressure inside the pipe and the pressure outside the pipe , the , wherein, represents the pressure inside the pipe, represents the pressure outside the pipe;

[0101] An optimal water injection pressure difference model is constructed based on the reference starting pressure difference, the fluid characteristic coefficient and the geological characteristic coefficient to output the optimal water injection pressure difference, and the water injection pressure difference model is represented as:

[0102] ;

[0103] wherein, represents the optimal water injection pressure difference, represents the reference starting pressure difference, This represents the differential pressure adjustment coefficient. Represents fluid characteristic coefficients. Indicates geological characteristic coefficients;

[0104] Based on the injection pressure difference and the optimal injection pressure difference, an internal pressure-external pressure regulation model is constructed to output the pressure regulation coefficient. The internal pressure-external pressure regulation model is expressed as follows:

[0105] ;

[0106] in, Indicates the pressure regulation coefficient. Indicates the water injection pressure difference. This indicates the optimal injection pressure differential. This indicates the adjustment sensitivity coefficient;

[0107] like This indicates that the injection pressure difference is equal to the optimal injection pressure difference, and it is recommended to use the baseline flow rate.

[0108] like This indicates that the injection pressure difference is greater than the optimal injection pressure difference, which means that the bottom absorption capacity is better than expected or that there is a margin in the injection pressure. It is recommended to adjust (increase) the flow rate in the positive direction. The larger the pressure adjustment coefficient, the higher the method.

[0109] like This indicates that the injection pressure difference is less than the optimal injection pressure difference, suggesting poor formation absorption capacity or insufficient injection pressure. It is recommended to adjust (reduce) the flow rate in the opposite direction to match the formation capacity or avoid ineffective injection. The smaller the pressure adjustment coefficient, the greater the reduction.

[0110] The injection pressure differential refers to the difference between the pressure inside and outside the pipe, which can be measured in real time by a pressure gauge 12 installed inside the casing. The pressure differential adjustment coefficient in the optimal injection pressure differential model controls the pressure differential correction range; for example, it can be an empirical value between 0.1 and 0.3, balancing the relationship between formation absorption capacity and injection pressure. The adjustment sensitivity coefficient in the pressure regulation coefficient controls the flow rate regulation rate; for example, it can be set to a value between 0.5 and 1.5, directly affecting the system's response speed to pressure differential changes. The three-state regulation strategy forms discrete control logic through threshold division of the pressure regulation coefficient, establishing a clear criterion for judging the direction of flow rate regulation.

[0111] Specifically, the actual injection pressure difference is calculated by real-time acquisition of the pressure inside and outside the pipe, and the optimal injection pressure difference target value is dynamically generated in combination with the formation absorption capacity represented by the fluid property coefficient and the geological property coefficient. When the actual pressure difference is higher than the optimal value, it indicates that the formation has additional absorption potential, and the injection efficiency is improved by increasing the flow rate; when the actual pressure difference is lower than the optimal value, it indicates that the formation absorption capacity is limited, and invalid injection is avoided by reducing the flow rate. The exponential operation design of the adjustment sensitivity coefficient makes the flow rate adjustment range and the pressure difference deviation degree present a nonlinear relationship, which can quickly respond to significant pressure difference changes and suppress frequent adjustments caused by small fluctuations.

[0112] Compared with the prior art, the traditional sliding sleeve injection system usually uses a fixed pressure difference threshold for flow control, which cannot adapt to the dynamic changes of formation permeability and the differences in fluid properties. The present scheme solves the problem of low injection efficiency caused by ignoring the dynamic characteristics of the formation by establishing a fluid-geology coupled pressure difference optimization model, quantifying the formation absorption capacity as a calculable correction parameter, and combining real-time pressure difference monitoring to build a closed-loop adjustment mechanism.

[0113] Through the above technical scheme, the present application realizes the dynamic matching of injection flow rate and actual absorption capacity of the formation, which can avoid the risk of formation rupture caused by excessive injection pressure and prevent invalid injection caused by insufficient pressure, and significantly improves the injection operation precision and resource utilization rate under complex reservoir conditions.

[0114] Preferably, the flow rate optimization model is represented as:

[0115] ;

[0116] wherein, represents the target injection flow rate, represents the reference injection flow rate, represents the pressure adjustment coefficient, represents the well pipe condition coefficient, represents the maximum flow rate adjustment upper limit (used to limit the maximum multiple of flow rate amplification to ensure injection safety).

[0117] Wherein, the target injection flow rate refers to the injection flow rate dynamically adjusted according to the real-time working condition, which can be realized by cooperating the flow meter 13 and the electric control PCB 14, and is used to match the formation absorption capacity. The reference injection flow rate refers to the preset initial injection flow rate, which can be set by historical data or experience value, and is used as the adjustment reference. The pressure regulation coefficient refers to the proportional factor reflecting the relationship between the injection pressure difference and the optimal pressure difference, which can be calculated by the pressure sensor and the pressure regulation module, and is used to dynamically adjust the flow amplification or reduction degree. The well pipe condition coefficient refers to the influence factor quantifying the effect of well depth, well inclination angle and casing inner diameter on fluid flow resistance, which can be realized by normalizing the well pipe parameters and weighted calculation, and is used to correct the flow regulation amplitude. The maximum flow regulation upper limit refers to the safety threshold limiting the flow amplification multiple, which can be realized by presetting a fixed value or dynamically adjusting, and is used to prevent the injection flow rate from exceeding the equipment or formation bearing capacity.

[0118] Specifically, the flow optimization model calculates the target injection flow rate based on the reference injection flow rate, combined with the pressure regulation coefficient and the well pipe condition coefficient. The pressure regulation coefficient is dynamically adjusted according to the ratio of the injection pressure difference to the optimal pressure difference. When the injection pressure difference is greater than the optimal pressure difference, the pressure regulation coefficient is greater than 1, allowing flow amplification. When the injection pressure difference is less than the optimal pressure difference, the pressure regulation coefficient is less than 1, requiring flow reduction. The well pipe condition coefficient reflects the influence of well pipe structure on flow resistance. The greater the coefficient, the smaller the flow resistance, allowing a higher flow regulation amplitude. The maximum flow regulation upper limit constrains the product of the pressure regulation coefficient and the well pipe condition coefficient. When the product exceeds the upper limit, the target injection flow rate is limited to the product of the reference flow rate and the upper limit value, avoiding the risk of over-injection due to excessive pressure margin.

[0119] Compared with the prior art, the traditional injection flow rate regulation relies on manual experience or static parameter setting, and cannot respond to real-time changes in formation pressure and differences in well pipe conditions. The present scheme dynamically combines the pressure regulation coefficient, the well pipe condition coefficient and the safety threshold by establishing a mathematical correlation model, realizing the automation and safety of flow regulation. For example, the prior art lacks systematic quantitative analysis of well inclination angle and casing inner diameter, while the present scheme incorporates these parameters into the calculation through the well pipe condition coefficient, improving the regulation accuracy.

[0120] Through the above technical scheme, the present application can dynamically adjust the injection flow rate according to the real-time injection pressure difference and well pipe conditions, automatically match the formation absorption capacity, and avoid low injection efficiency due to insufficient pressure or formation damage caused by excessive pressure. At the same time, the maximum flow regulation upper limit limits the flow amplification multiple, preventing equipment overload or over-injection risk, and ensuring the safety and stability of the injection process.

[0121] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting; it is not intended to exclude myriad other embodiments of the present application that other inventors can develop based on the same general inventive concepts embodied by the described embodiments. That is, although the present application is described in terms of particular embodiments and illustrative figures, it should be apparent that the scope of the present application is not limited to these specific embodiments.

[0122] While the embodiments of the application have been shown and described herein, it will be understood by those skilled in the art that many changes, modifications, substitutions and alterations to these embodiments can be made without departing from the principles and spirits of the application, and it is intended that the scope of the application be limited solely by the scope of the appended claims and the equivalents thereof.

Claims

1. A smart stratified high-volume water injection and oil production sliding sleeve, comprising an inner sleeve, a sliding sleeve, an outer sleeve A, and an outer sleeve B detachably installed thereon, characterized in that, The outer sleeve A and outer sleeve B are respectively detachably equipped with an upper connector and a lower connector at their ends. An electronic compartment tube is detachably connected to the outer sleeve A. The electronic compartment tube is detachably connected to the upper connector, and one end of the electronic compartment tube is detachably connected to one end of the inner sleeve. A pressure gauge electrically connected to the electronic control PCB is detachably connected to the electronic compartment tube. The sliding sleeve is slidably engaged with the lower connector. The sliding sleeve has a ring-shaped linear array of water injection holes A along its length. The outer sleeve B has a water injection hole B that mates with the water injection holes A. The system also includes: The drive assembly, mounted on the inner sleeve, is used to drive the sliding sleeve to slide relative to the outer sleeve; It also includes: a flow optimization system for dynamically adjusting the water injection flow rate, including: The fluid analysis module calculates fluid characteristic coefficients based on fluid viscosity, fluid temperature, and sand content using a fluid characteristic model. The geological characteristic analysis module calculates geological characteristic coefficients based on formation permeability and formation porosity using a geological characteristic model. The well casing condition analysis module calculates well casing condition coefficients based on the vertical well depth, well inclination angle, and casing inner diameter using a well casing condition model. The pressure regulation analysis module calculates the pressure regulation coefficient based on the internal and external pressures under fluid characteristic coefficients and geological characteristic coefficients using an internal-external pressure regulation model. The flow optimization module calculates and outputs the target injection flow rate based on the pressure regulation coefficient, the well casing regulation coefficient, and the preset benchmark injection flow rate through the flow optimization model.

2. The intelligent layered high-volume water injection and oil production sliding sleeve according to claim 1, characterized in that, An electronic compartment is provided between the electronic compartment tube and the outer tube. The electronic compartment contains an electronic control PCB and a flow meter and a pressure gauge electrically connected to the electronic control PCB. The flow meter is an ultrasonic flow meter, and the pressure gauge is detachably connected to the electronic compartment tube.

3. The intelligent layered high-volume water injection and oil production sliding sleeve according to claim 2, characterized in that, The drive assembly includes a motor and a reducer that are sleeved on and detachably connected to the inner sleeve. The output shaft of the motor is fixedly connected to the input shaft of the reducer. The reducer is fixedly connected to a lead screw sleeved on the inner sleeve. The lead screw is threadedly connected to a lead nut. The lead nut is fixedly connected to a sliding sleeve. The reducer is detachably connected to a bearing bracket sleeved on the lead screw and located inside the outer sleeve A. The sliding sleeve is slidably engaged with a limiting groove formed on the outer sleeve B.

4. The intelligent layered high-volume water injection and oil production sliding sleeve according to claim 1, characterized in that, The traffic optimization model is expressed as follows: ； in, Indicates the target water injection flow rate. Indicates the baseline injection flow rate. Indicates the pressure regulation coefficient. This represents the well casing condition coefficient. This indicates the maximum flow rate adjustment limit.

5. The intelligent layered high-volume water injection and oil production sliding sleeve according to claim 4, characterized in that, The steps of the pressure regulation and analysis module are as follows: The injection pressure difference is obtained based on the pressure inside and outside the pipe. The ,in, Indicates the internal pressure of the pipe. Indicates external pressure; Based on the baseline starting pressure difference, fluid characteristic coefficients, and geological characteristic coefficients, an optimal water injection differential model is constructed to output the optimal water injection pressure difference. The water injection pressure difference model is expressed as follows: ； in, This indicates the optimal injection pressure differential. Indicates the baseline starting pressure differential. This represents the differential pressure adjustment coefficient. Represents fluid characteristic coefficients. Indicates geological characteristic coefficients; Based on the injection pressure difference and the optimal injection pressure difference, an internal pressure-external pressure regulation model is constructed to output the pressure regulation coefficient. The internal pressure-external pressure regulation model is expressed as follows: ； in, Indicates the pressure regulation coefficient. Indicates the water injection pressure difference. This indicates the optimal injection pressure differential. This indicates the adjustment sensitivity coefficient; like This indicates that the injection pressure difference is equal to the optimal injection pressure difference, and it is recommended to use the baseline flow rate. like This indicates that the injection pressure difference is greater than the optimal injection pressure difference, suggesting that the bottom absorption capacity is better than expected or that there is a margin in the injection pressure. It is recommended to adjust the flow rate in the positive direction. The larger the pressure adjustment coefficient, the greater the amplification. like This indicates that the injection pressure difference is less than the optimal injection pressure difference, suggesting poor formation absorption capacity or insufficient injection pressure. It is recommended to adjust the flow rate in the opposite direction to match the formation capacity or avoid ineffective injection. The smaller the pressure adjustment coefficient, the greater the reduction.

6. The intelligent layered high-volume water injection and oil production sliding sleeve according to claim 5, characterized in that, The steps of the well casing condition analysis module are as follows: The depth index, inclination index, and inner diameter index are obtained by performing maximum-min normalization on the vertical well depth, well inclination angle, and casing inner diameter. A well casing condition model is constructed based on the well depth index, inclination angle index, and inner diameter index, and the well casing condition coefficients are output. The well casing condition model is expressed as follows: ； in, This represents the well casing condition coefficient. represents the well depth index, Indicates the diagonal index, Indicates the inner diameter index. Indicates the well depth weighting coefficient. Indicates the diagonal weighting coefficient, the Furthermore, the larger the value, the smaller the fluid flow resistance.

7. The intelligent layered high-volume water injection and oil production sliding sleeve according to claim 5, characterized in that, The steps of the geological characteristic analysis module are as follows: The permeability index and porosity index are obtained by performing maximum-min normalization on the formation permeability and the bottom porosity. A geological characteristic model is constructed based on the permeability index and porosity index, and geological characteristic coefficients are output. The geological characteristic model is expressed as follows: ； in, Represents geological characteristic coefficients. This represents the penetration rate index. Indicates the porosity index. Represents the full weight coefficient and The The higher the value, the stronger the formation's ability to absorb fluids.

8. The intelligent layered high-volume water injection and oil production sliding sleeve according to claim 5, characterized in that, The steps of the fluid analysis module are as follows: The fluid viscosity and sand content are obtained by performing maximum-min normalization on the fluid viscosity and sand content; The temperature index is obtained by comparing the absolute difference between the fluid temperature and the optimal temperature with the allowable deviation from the optimal temperature. A fluid characteristic model is constructed based on the fluid viscosity index, sand content index, and temperature index to output fluid characteristic coefficients. The fluid characteristic model is expressed as follows: ； in, Represents fluid characteristic coefficients. Indicates the fluid viscosity index. Indicates the sand content index. Indicates the temperature index, the The larger the value, the better the fluid state.

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