Shallow heavy oil well deep thermal field cooperative sand prevention and yield increase technology

Abstract

The invention discloses a shallow heavy oil well deep thermal field collaborative sand prevention and yield increase technical process, which comprises the following steps of: selecting coating sand particle size to prepare mortar based on a sand production particle size median value by using an algorithm, injecting the mortar into a near-wellbore area through forward / reverse circulation, and closing a well to cure a cementing agent into a high-permeability sand blocking layer; nitrogen is heated to 350 DEG C by adopting an eddy current heating device and a special crystal nano heat conduction material to be injected, and a dual heating field is constructed. The sand blocking barrier keeps high permeability at high temperature, and the stratum temperature rise promotes secondary curing of the cementing agent to enhance the strength of the sand blocking layer. The proportion of the cementing agent is optimized through a multi-factor algorithm, and nitrogen injection parameters are adjusted in a self-adaptive mode through a dynamic regulation and control algorithm according to real-time data in the production period. The whole process realizes sand prevention and yield increase synergy, and effectively improves the recovery efficiency and benefits of shallow thickened oil.

Description

Technical Field

[0001] The present invention relates to the field of oil production technology, and in particular to a technical process for sand control and production increase in a shallow heavy oil well using a deep thermal field synergistic method. Background Art

[0002] Shallow heavy oil, due to its high viscosity and poor fluidity, is prone to sand production during extraction, severely impacting well productivity and production cycles, increasing extraction costs and maintenance difficulties. As energy demand continues to rise, the need for efficient and stable extraction of shallow heavy oil is becoming increasingly urgent. Traditional extraction methods are no longer able to meet the needs of industry development, and innovative technologies and processes are urgently needed to overcome the current difficulties.

[0003] Currently, shallow heavy oil production primarily relies on mechanical and chemical sand control technologies. Mechanical sand control uses mechanical devices such as screens within the wellbore to block sand particles. However, long-term exposure to high temperatures, high pressures, and fluid erosion can cause screens to clog and break, rendering sand control ineffective. Chemical sand control relies on injecting chemical binders into the formation to solidify the sand particles. However, chemical binders are unstable in high-temperature environments and can degrade and fail, making them ineffective in forming a durable and effective sand barrier.

[0004] In terms of production enhancement technology, while conventional thermal stimulation methods can reduce the viscosity of heavy oil, they struggle to create a stable, efficient thermal field deep within the reservoir. Heat is lost during the transfer process, resulting in uneven heating of the reservoir and difficulty in effectively displacing distant reservoirs. This limits oil recovery and hinders the goal of large-scale economic production of shallow heavy oil. Summary of the Invention

[0005] In order to overcome the shortcomings and deficiencies of the existing technology, the present invention provides a technical process for sand control and production increase by synergizing deep thermal fields in shallow heavy oil wells.

[0006] The technical solution adopted by the present invention is a technical process for sand control and production increase by using a deep thermal field in a shallow heavy oil well, comprising the following steps:

[0007] Step S1. Based on the median distribution of sand particle sizes produced by oil and gas wells, a particle size matching algorithm is used to select a coating sand particle size within a range of 1.0-1.5 times the median sand size. The coating sand is then coated with a special high-temperature resistant binder to form a mortar system with a sand concentration in the range of 40%-60%.

[0008] Step S2. Use the positive circulation or reverse circulation fluid delivery method to inject the above-configured coated sand mortar into the near-wellbore area. During the injection process, the injection pressure is controlled not to exceed 80% of the formation fracture pressure, and the displacement is set at 2-5m 3 / h interval, so that the sand control section is in a uniform and stable filling state;

[0009] Step S3. After the mortar injection process is completed, the well is shut in and left to stand for 3-5 hours to allow the binder in the coated sand to cure at a reservoir temperature of 30°C-350°C, thereby constructing a high-permeability sand retaining layer near the wellbore with a permeability of not less than 500 mD and a uniaxial compressive strength of not less than 20.7 MPa at 180°C for 24 hours.

[0010] Step S4. Using a high-temperature, high-pressure nitrogen heating device based on eddy current heating principles and filled with a special crystalline nano-thermal conductive material, the nitrogen is heated to 350°C. The heating device has performance characteristics such as a heating efficiency of not less than 95%, a temperature control accuracy within a range of ±2°C, a housing temperature not exceeding 30°C, and a heating / cooling rate of not less than 20°C / s. The nitrogen temperature is continuously adjustable between 50°C and 350°C.

[0011] Step S5. Nitrogen gas heated to 350°C is heated at a rate of 5-10×10 4 m 3 / well injection volume, injection pressure of 5-20MPa and 500-1200m 3 / h of displacement is injected into the oil well, creating a dual heating field effect of near-well high-temperature viscosity reduction and remote thermal displacement in the oil well;

[0012] Step S6. Under high temperature conditions of 350°C, the formed sand barrier maintains a performance index of no less than 90% permeability, thereby ensuring the smooth migration of nitrogen within the oil well;

[0013] Step S7. The increase in formation temperature triggers a secondary solidification process of the cement in the sand barrier, thereby increasing the compressive strength of the sand barrier by 10%-15%;

[0014] Step S8. With sand retaining accuracy, i.e., particle size matching, as the core prerequisite, a multi-factor collaborative optimization algorithm is used to systematically adjust the binder ratio and optimize the compressive strength and permeability of the sand retaining layer to maximize the cost-effectiveness of sand control.

[0015] Step S9. During the production cycle after nitrogen injection is completed, based on the real-time monitoring data of oil well liquid production, sand production, and bottom hole pressure, a dynamic control algorithm is used to adaptively adjust the parameters such as the timing, injection volume, injection pressure, and displacement of subsequent nitrogen supplementary injection.

[0016] Furthermore, in step S1, the particle size matching algorithm combines the reservoir pore structure parameters, fluid flow characteristic parameters and the probability density function of the sand particle size distribution to construct a multidimensional parameter optimization model to determine the optimal particle size of the coated sand, while meeting the sand retaining accuracy and minimizing the flow resistance of the fluid through the sand retaining layer.

[0017] Furthermore, in step S2, during the injection process of the coating sand mortar, non-invasive ultrasonic monitoring technology is used to obtain in real time parameters such as the flow velocity distribution, concentration distribution and front advancement distance of the mortar in the wellbore and near-wellbore area. Based on the real-time monitoring parameters, the fuzzy control algorithm is used to dynamically adjust the injection method, injection pressure and displacement to ensure the uniform filling quality of the sand control section.

[0018] Furthermore, in step S3, during the curing period of the binder, distributed optical fiber sensing technology is used to perform real-time monitoring of the three-dimensional spatial distribution of the reservoir temperature field, pressure field and internal stress field of the sand retaining layer, and a binder curing kinetic model under the temperature-pressure-stress coupling effect is established. The waiting time is dynamically adjusted according to the model calculation results to ensure that the sand retaining layer achieves the optimal curing effect.

[0019] Furthermore, in step S4, the crystalline nano-thermal conductive material inside the high-temperature and high-pressure nitrogen heating device has its nanoparticle surface specially chemically modified to form composite particles with a core-shell structure, wherein the core portion is a high-thermal-conductivity nano-crystalline material and the shell portion is a high-temperature-resistant and antioxidant coating, and the nanoparticles form a three-dimensional network-connected structure inside the heating device in a self-assembly manner based on the principle of Brownian motion.

[0020] Furthermore, in step S5, during the high-temperature nitrogen injection process, an optimization method based on finite element numerical simulation is used for different well types, including vertical wells, directional wells or horizontal wells, and oil layer distribution characteristics, to establish a reservoir-wellbore-fluid multi-physics field coupling model. By performing numerical calculations and parameter sensitivity analysis on the model, the optimal direction, angle and injection profile of nitrogen injection are determined, and the thermal field is efficiently constructed and evenly distributed in the oil layer.

[0021] Furthermore, in step S6, the high-temperature resistant binder adopts a multi-component composite system, which includes polymer molecular segments with temperature-sensitive self-repairing function. Under a high-temperature environment of 350°C, when the pore structure of the sand retaining barrier is damaged due to thermal stress or fluid scouring, the temperature-sensitive polymer segments undergo conformational changes, triggering a self-repairing reaction, thereby maintaining the permeability of the sand retaining barrier at a performance level of not less than 90%.

[0022] Furthermore, in step S7, the chemical reaction between the binder and the formation sand particles that accompanies the secondary solidification of the binder due to the formation heating involves a chemical bonding reaction mechanism based on surface active sites. By introducing specific functional groups into the binder molecular structure, chemical reactions occur between the binder and the surface active sites of the formation sand particles to form covalent bonds, thereby enhancing the interfacial bonding strength and overall mechanical stability of the sand retaining layer and the formation.

[0023] Furthermore, in step S8, the multi-factor collaborative optimization algorithm constructs a multi-dimensional decision space including parameters such as reservoir temperature, pressure, fluid viscosity, mineral composition, and sand control period, and uses a hybrid intelligent optimization algorithm that combines genetic algorithm and particle swarm optimization algorithm to search for the optimal solution of the proportion of each component of the binder in the multi-dimensional decision space, and comprehensively optimizes the sand retaining layer performance and sand control cost.

[0024] Furthermore, in step S9, the dynamic control algorithm is based on the long short-term memory network model in machine learning, which studies and extracts features from historical data and real-time monitoring data during the oil well production process, establishes an oil well production dynamic prediction model, plans the parameters of nitrogen supplementary injection in advance according to the prediction results, and performs intelligent dynamic control of the oil well production process.

[0025] Beneficial effects: The present invention proposes a technical process for sand control and production increase in shallow heavy oil wells using a deep thermal field. In terms of sand control, this technical process uses a particle size matching algorithm to accurately select the coating sand particle size, and mixes a mortar with a specific sand concentration. Combined with a positive circulation or reverse circulation injection method and strict pressure and displacement control, a high-permeability sand retaining layer is constructed in the near-well area. The high-temperature resistant binder in the sand retaining layer not only solidifies at conventional reservoir temperatures, but also secondary solidifies when the formation temperature rises. Combined with temperature-sensitive self-healing functional polymer segments, it solves the problems of easy clogging and damage of traditional mechanical sand control and failure of chemical sand control at high temperatures, so that the permeability of the sand retaining barrier is not less than 90% at a high temperature of 350°C, and the compressive strength is increased by 10%-15%. In terms of production increase, a high-temperature and high-pressure nitrogen heating device based on the eddy current heating principle and filled with special crystalline nano-thermal conductive materials heats nitrogen to 350°C and injects it into the oil well, constructing a dual heating field of high-temperature viscosity reduction near the well and remote thermal displacement. By optimizing nitrogen injection parameters for different well types and reservoir distributions and establishing a multi-physics field coupling model using finite element numerical simulation, we avoid the heat loss and uneven heating issues associated with conventional thermal stimulation, achieving efficient construction and uniform distribution of the thermal field. Furthermore, we utilize real-time monitoring data and a dynamic control algorithm to adaptively adjust nitrogen supplemental injection parameters throughout the well's production cycle, ultimately achieving synergistic optimization of sand control performance and production enhancement, significantly improving the efficiency and economic benefits of shallow heavy oil production. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of the overall steps of the present invention. DETAILED DESCRIPTION

[0027] It should be noted that, unless there is a conflict, the embodiments and features in the embodiments of this application can be combined with each other. The present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0028] like Figure 1As shown, a deep thermal field synergistic sand control and production increase technology process for shallow heavy oil wells includes the following steps:

[0029] Step S1. Based on the median distribution of sand particle sizes produced by oil and gas wells, a particle size matching algorithm is used to accurately select the coating sand particle size within a range of 1.0-1.5 times the median sand size. The coating sand is then coated with a special high-temperature resistant binder to form a mortar system with a sand concentration in the range of 40%-60%.

[0030] Specifically, the particle size matching algorithm constructs a multidimensional parameter optimization model by analyzing reservoir pore structure parameters (such as porosity and permeability), fluid flow characteristics (such as viscosity and velocity), and the probability density function of sand production particle size. This model uses sand retention accuracy and minimization of fluid resistance as objective functions, and iteratively calculates the optimal particle size within a range of 1.0-1.5 times the median sand production value. The special high-temperature-resistant binder utilizes an organic-inorganic hybrid system, whose molecular structure contains siloxane bonds and aromatic groups, ensuring chemical stability within the temperature range of 30°C-350°C. The mortar sand concentration is selected based on particle grading theory. By adjusting the ratio of coating sand of different particle sizes, the mortar forms the most compact structure during injection, ensuring both the permeability and compressive strength of the sand retaining layer. When the sand concentration is below 40%, the resulting sand retaining layer has excessive porosity, resulting in reduced sand retention effectiveness. When the sand concentration exceeds 60%, the mortar fluidity is significantly reduced, which can easily lead to wellbore blockage.

[0031] Step S2. Use the positive circulation or reverse circulation fluid delivery method to inject the above-configured coated sand mortar into the near-wellbore area. During the injection process, the injection pressure is strictly controlled not to exceed 80% of the formation fracture pressure, and the displacement is set at 2-5m 3 / h interval, so that the sand control section can achieve a uniform and stable filling state;

[0032] Specifically, during positive circulation injection, the mortar is injected from the oil pipe, circulated through the bottom of the well to the casing and returned to the surface; reverse circulation is the opposite, the mortar is injected from the casing and returned through the oil pipe. The choice of injection method should be determined based on factors such as the completion method of the oil well and the heterogeneity of the formation permeability. The injection pressure is controlled within 80% of the formation fracture pressure, which can not only ensure that the mortar effectively enters the formation pores, but also avoid the failure of sand control caused by fracturing the formation. The displacement is set at 2-5m 3 / h range. Based on the seepage mechanics of fluids in porous media, this ensures that the slurry forms a uniform filter cake layer near the wellbore, while preventing uneven particle deposition caused by excessively high flow rates. During the injection process, by real-time monitoring of wellhead pressure, displacement changes, and the sand content of the return fluid, injection parameters are dynamically adjusted to ensure that the uniformity of the filling density in the sand control section does not exceed ±5%.

[0033] Step S3. After the mortar injection process is completed, the well is shut in and left to stand for 3-5 hours to allow the binder in the coated sand to cure at a reservoir temperature of 30°C-350°C, thereby constructing a high-permeability sand retaining layer structure near the wellbore with a permeability of not less than 500 mD and a uniaxial compressive strength of not less than 20.7 MPa at 180°C for 24 hours.

[0034] Specifically, the shut-in static time is determined based on the curing kinetics curve of the cementing agent, which reflects the relationship between the viscosity of the cementing agent and temperature and time. Within the temperature range of 30°C-350°C, the cementing agent undergoes two stages: physical cross-linking and chemical cross-linking. The physical cross-linking stage forms an initial network structure, providing initial strength; the chemical cross-linking stage forms a three-dimensional network structure through free radical polymerization, giving the sand retaining layer its ultimate strength. The permeability requirement of not less than 500mD ensures that the sand retaining layer effectively blocks sand particles while allowing crude oil to pass smoothly. The uniaxial compressive strength of not less than 20.7MPa at 180°C for 24 hours ensures that the sand retaining layer can operate stably and long-term under high temperature and high pressure environments, resisting formation stress and fluid scouring without being damaged.

[0035] Step S4. Using a high-temperature, high-pressure nitrogen heating device based on the eddy current heating principle and filled with a special crystalline nano-thermal conductive material, the nitrogen is heated to 350°C. The heating device has the following performance characteristics: a heating efficiency of not less than 95%, a temperature control accuracy within a range of ±2°C, a shell temperature not exceeding 30°C, and a heating / cooling rate of not less than 20°C / s. This allows for continuous adjustment of the nitrogen temperature between 50°C and 350°C.

[0036] Specifically, the eddy current heating device uses the principle of electromagnetic induction to generate an eddy current thermal effect in the metal pipe to achieve rapid heating of nitrogen. The core-shell structure design of the special crystalline nano-thermal conductive material uses silicon carbide nanoparticles with high thermal conductivity (average particle size 50nm) in the core, and the outer shell is covered with a 5-10nm thick silicon dioxide high-temperature resistant coating, which not only ensures thermal conductivity but also improves material stability. The three-dimensional mesh interconnected structure is formed through a self-assembly process, which enables heat to be transferred quickly and achieves a heating efficiency of more than 95%. The temperature control accuracy of ±2°C is achieved through a closed-loop control system, which monitors the temperature of the heating chamber in real time and adjusts the power supply power through feedback. The design of the shell temperature not exceeding 30°C is achieved through a double-layer casing structure and an intermediate layer of aerogel insulation material, which effectively reduces heat loss. The heating / cooling rate of not less than 20°C / s meets the need for rapid adjustment of thermal parameters during oil well operations.

[0037] Step S5. Nitrogen gas heated to 350°C is heated at a rate of 5-10×10 4 m 3 / well injection volume, injection pressure of 5-20MPa and 500-1200m 3 / h of displacement is injected into the oil well, creating a dual heating field effect of near-well high-temperature viscosity reduction and remote thermal displacement in the oil well;

[0038] Specifically, the nitrogen injection amount is determined based on the reservoir volume, crude oil viscosity and heat loss calculation model. 4 m 3 / Within the range of wells, it can ensure sufficient heat transfer to the oil layer while avoiding energy waste. The injection pressure of 5-20MPa is determined based on the burial depth of the oil layer, the formation pressure coefficient and the wellbore friction, ensuring that the nitrogen can effectively break through the contaminated zone near the wellbore and enter the remote oil layer. Displacement 500-1200m 3 The choice of 1000 rpm / h balances heat transfer efficiency and formation flushing. The near-wellbore high-temperature viscosity reduction effect reduces crude oil viscosity through heat conduction, improving the fluidity of heavy oil. The distal thermal displacement effect utilizes nitrogen's expansiveness and thermal convection to push crude oil toward the production well. The synergistic effect of the dual heating fields expands the thermal impact range and increases oil recovery.

[0039] Step S6. Under high temperature conditions of 350°C, the formed sand barrier maintains a performance index of no less than 90% permeability, thereby ensuring smooth migration of nitrogen within the oil well;

[0040] Specifically, in the multi-component composite system of the high-temperature resistant binder, the thermosensitive self-healing functional polymer chain segments are made of shape-memory polymer materials. When the temperature reaches 350°C, the material's glass transition temperature is activated, and the molecular segments transform from a glassy state to a highly elastic state, undergoing a conformational change. This change enables the polymer segments to fill microcracks caused by thermal stress or fluid erosion, restoring the pore structure of the sand barrier. By adding 10%-15% of thermosensitive polymer to the binder, the permeability retention rate of the sand barrier at high temperatures can be increased to over 90%. The achievement of this performance indicator ensures the stability of the nitrogen flow resistance in the oil well, avoiding uneven thermal field distribution due to blockage of the sand retaining layer, which affects the production increase effect.

[0041] Step S7. The increase in formation temperature triggers a secondary solidification process of the binder in the sand barrier, thereby increasing the compressive strength of the sand barrier by 10%-15%;

[0042] Specifically, the secondary curing reaction triggered by the formation temperature rise is based on the latent curing groups in the molecular structure of the binder. These groups are stable at room temperature, but are activated under high temperature (>150°C) conditions, causing a cross-linking reaction. By introducing silane coupling agents and aromatic amine curing agents into the binder, when the formation temperature rises, the alkoxy groups of the silane coupling agent react with the hydroxyl groups on the surface of the formation sand particles to form Si-O-Si covalent bonds; the aromatic amine curing agent reacts with the epoxy resin groups in the binder to further enhance the cross-linking density. This dual cross-linking mechanism increases the compressive strength of the sand retaining layer by 10%-15%, improves the ability of the sand retaining layer to resist formation stress and fluid erosion, and extends the effective period of sand control.

[0043] Step S8. With sand retaining accuracy, i.e., particle size matching, as the core prerequisite, a multi-factor collaborative optimization algorithm is used to systematically adjust the binder ratio to achieve an optimal combination of compressive strength and permeability of the sand retaining layer, thereby maximizing the cost-effectiveness of sand control.

[0044] Specifically, the multi-dimensional decision space constructed by the multi-factor collaborative optimization algorithm includes 12 key parameters, including reservoir temperature, pressure, fluid viscosity, mineral composition, and sand control period. The genetic algorithm searches for the global optimal solution in the decision space by simulating the biological evolution process; the particle swarm optimization algorithm accelerates convergence through collaboration and information sharing between particles. The combination of the two algorithms improves optimization efficiency while ensuring computational accuracy. The optimization objective function combines the three dimensions of the sand retaining layer's compressive strength, permeability, and material cost, and achieves a balance between performance and cost by setting different weighting coefficients. For example, in high-temperature, high-stress reservoirs, the weighting of compressive strength is increased; in low-permeability reservoirs, the weighting of permeability is increased. This algorithm can ensure that the performance of the sand retaining layer meets the production requirements of the oil well while reducing material costs by 15%-20%.

[0045] Step S9. During the production cycle after nitrogen injection is completed, based on real-time monitoring data such as oil well liquid production, sand production, and bottom hole pressure, a dynamic control algorithm is used to adaptively adjust parameters such as the timing, injection volume, injection pressure, and displacement of subsequent nitrogen supplementary injections.

[0046] Specifically, during the production cycle, high-precision sensors are used to monitor key data such as oil well liquid production, sand production, and bottom hole pressure in real time. Liquid production is monitored using a Coriolis mass flowmeter with a measurement accuracy of ±0.1%, which can accurately capture subtle changes in production; sand production is monitored using an ultrasonic sand detector with an accuracy of 0.01-100kg / m 3 The measurement error is ±3% within the measurement range; bottomhole pressure monitoring uses an electronic pressure gauge with an accuracy of ±0.05% FS. These real-time data are collected and transmitted to the data processing center at a frequency of 1 second.

[0047] The dynamic control algorithm is based on the long short-term memory (LSTM) model in machine learning. The model's input layer contains eight neurons, corresponding to characteristic data such as liquid production, sand production, bottomhole pressure, production time, cumulative oil production, temperature, water content, and gas content. The hidden layer has three layers of LSTM units, each containing 64 neurons, to extract long-term data dependencies. The output layer outputs the optimal timing and injection rate of nitrogen supplementation within the next 3-7 days (ranging from 1 to 5×10 4 m 3 / times), injection pressure (5-15MPa) and displacement (300-800m 3 / h). During model training, mean square error (MSE) was used as the loss function, Adam optimizer was used for parameter update, and the learning rate was set to 0.001. After 500 rounds of iterative training, the prediction error was controlled within ±5% of liquid production, ±8% of sand production, and ±3% of bottom hole pressure. According to the prediction results, when the sand production exceeded the set threshold of 0.5kg / m for three consecutive hours, the prediction error was within ±5% of liquid production, ±8% of sand production, and ±3% of bottom hole pressure. 3 , or when the liquid production drops by more than 15%, it automatically triggers nitrogen supplementary injection. Compared with traditional manual experience judgment, it can increase the average daily oil production of the oil well by 20%, effectively extending the high-yield period of the oil well.

[0048] Preferably, in step S1, the particle size matching algorithm is combined with the reservoir pore structure parameters, fluid flow characteristic parameters and sand particle size distribution probability density function to construct a multidimensional parameter optimization model to determine the optimal particle size of the coated sand, while meeting the sand retaining accuracy and minimizing the flow resistance of the fluid through the sand retaining layer.

[0049] Specifically, the multi-dimensional parameter optimization model constructed by the particle size matching algorithm comprehensively considers the reservoir pore structure parameters (such as porosity and permeability variation coefficient), fluid flow characteristic parameters (such as Reynolds number and mobility ratio) and the probability density function of sand particle size distribution. Through the Monte Carlo simulation method, 10 4 The optimal particle size was determined through random sampling calculations over 100 times, with a sand retention efficiency of ≥95% and a flow resistance coefficient of ≤0.8 as constraints. For example, for a heterogeneous reservoir with a porosity of 30% and a permeability coefficient of variation of 0.6, the model calculated that the optimal particle size for the coated sand was 1.3 times the median sand production value. This reduced the flow resistance of fluid through the sand retention layer by 20% compared to conventional methods, significantly improving well productivity.

[0050] Preferably, in step S2, during the injection of the coating sand mortar, non-invasive ultrasonic monitoring technology is used to obtain in real time parameters such as the flow velocity distribution, concentration distribution and front advancement distance of the mortar in the wellbore and near-wellbore area. Based on these real-time monitoring parameters, a fuzzy control algorithm is used to dynamically adjust the injection method, injection pressure and displacement to ensure uniform filling quality of the sand control section.

[0051] Specifically, 16 evenly distributed ultrasonic transducers are arranged outside the wellbore to transmit ultrasonic signals with a frequency of 2-5MHz. Based on the linear relationship between sound speed and mortar concentration (correlation coefficient ≥ 0.98) and the Doppler frequency shift principle, the mortar flow velocity distribution, concentration distribution and front advancement distance are obtained in real time. The fuzzy control algorithm uses injection pressure and displacement as input variables and filling uniformity index as output variable to establish a control rule library containing 49 fuzzy rules. When the mortar concentration deviation in the near-well area exceeds ±5%, the algorithm automatically adjusts the injection pressure by ±0.5MPa and the displacement by ±0.3m 3 / h, so that the uniformity deviation of the final sand control section filling density is controlled within ±3%.

[0052] Preferably, in step S3, during the curing of the binder, distributed optical fiber sensing technology is used to perform real-time monitoring of the three-dimensional spatial distribution of the reservoir temperature field, pressure field and internal stress field of the sand retaining layer, and a binder curing kinetic model under the temperature-pressure-stress coupling effect is established. The waiting time is dynamically adjusted according to the model calculation results to ensure that the sand retaining layer achieves the optimal curing effect.

[0053] Specifically, the application of distributed fiber optic sensing technology in step S3 uses a high-temperature resistant (400°C) distributed fiber optic temperature / strain sensor with an outer diameter of 0.9mm. A measurement point is set every 0.1m along the axial direction of the wellbore to achieve three-dimensional real-time monitoring of the reservoir temperature field (measurement accuracy ±0.5°C), pressure field (measurement accuracy ±0.1MPa) and stress field (measurement accuracy ±10με). The established temperature-pressure-stress coupling dynamic model solves the partial differential equations through the finite difference method to predict the change of the curing degree of the binder over time. When the deviation between the time required for the model to predict the curing degree to reach 90% and the actual monitoring data exceeds 10%, the well shut-in time is automatically extended by 30-60min to ensure that the uniaxial compressive strength of the sand retaining layer reaches more than 22MPa.

[0054] Preferably, in step S4, the surface of the nanoparticles of the crystalline nano-thermal conductive material inside the high-temperature and high-pressure nitrogen heating device is specially chemically modified to form composite particles with a core-shell structure, wherein the core is a high-thermal-conductivity nano-crystalline material and the shell is a high-temperature-resistant and anti-oxidation coating. The nanoparticles form a three-dimensional network-connected structure inside the heating device in a self-assembly manner based on the principle of Brownian motion, which significantly enhances the thermal conductivity and high-temperature stability of the material.

[0055] Specifically, the crystalline nano-thermal conductive material inside the high-temperature, high-pressure nitrogen heating device uses silicon carbide nanocrystals with an average particle size of 80nm in the core and a 5nm thick zirconium dioxide high-temperature resistant coating in the shell. Through a chemical self-assembly process, the nanoparticles form a three-dimensional network structure in a 150°C temperature field and a 0.5T magnetic field environment. The network porosity is 45% and the specific surface area reaches 120m 2 This structure increases the thermal conductivity of the material by 180% compared to traditional thermal conductive materials. After continuous operation at 350°C for 100 hours, the thermal conductivity attenuation rate is less than 5%, ensuring that the heating device maintains a heating efficiency of ≥95% during long-term operation.

[0056] Preferably, in step S5, during the high-temperature nitrogen injection process, an optimization method based on finite element numerical simulation is used for different well types (vertical wells, directional wells or horizontal wells) and oil layer distribution characteristics to establish a reservoir-wellbore-fluid multi-physical field coupling model. By performing numerical calculations and parameter sensitivity analysis on the model, the optimal direction, angle and injection profile of nitrogen injection are determined to achieve efficient construction and uniform distribution of the thermal field in the oil layer.

[0057] Specifically, the reservoir-wellbore-fluid multi-physics coupling model established in step S5 takes into account the interaction of fluid seepage, heat conduction, thermal convection, and rock deformation for different well types and reservoir distribution characteristics. Numerical simulations were performed using COMSOL Multiphysics software, with calculations performed at a grid cell of 1 million, achieving a simulation accuracy of 0.01°C. For horizontal wells, sensitivity analysis determined that the optimal injection angle was 30° relative to the horizontal section. At this point, the effective radius of the thermal field increased by 25% compared to conventional injection methods, and the reservoir temperature distribution coefficient decreased from 0.35 to 0.18, significantly improving the thermal displacement effect.

[0058] Preferably, in step S6, the high-temperature resistant binder adopts a multi-component composite system, which includes polymer molecular segments with temperature-sensitive self-repairing function. Under a high-temperature environment of 350°C, when the pore structure of the sand retaining barrier is damaged due to thermal stress or fluid scouring, the temperature-sensitive polymer segments undergo conformational changes, triggering a self-repairing reaction, thereby maintaining the permeability of the sand retaining barrier at a performance level of not less than 90%.

[0059] Specifically, in the multi-component composite system of the high-temperature resistant binder in step S6, the temperature-sensitive self-healing functional polymer segment adopts a block copolymer of polyetheretherketone (PEEK) and polyimide (PI). When the temperature rises to 350°C, the glass transition temperature of the material (Tg = 143°C) is activated, the molecular segments change from a glassy state to a highly elastic state, and the segment mobility increases by 3 orders of magnitude. At the microcracks, the segments achieve self-repair through diffusion and entanglement, and the repair efficiency reaches more than 85%. By adding 12% of this polymer to the binder, the permeability retention rate of the sand retaining barrier still reaches 92% after 10 thermal cycles at 350°C.

[0060] Preferably, in step S7, the chemical reaction between the binder and the formation sand particles that accompanies the secondary solidification of the binder due to the formation heating involves a chemical bonding reaction mechanism based on surface active sites. By introducing specific functional groups into the binder molecular structure, chemical reactions occur between the binder and the surface active sites of the formation sand particles to form covalent bonds, thereby significantly enhancing the interfacial bonding strength and overall mechanical stability of the sand retaining layer and the formation.

[0061] Specifically, the chemical bonding reaction mechanism between the binder and the formation sand particles in step S7 is achieved by introducing the silane coupling agent KH-560 (γ-glycidyloxypropyltrimethoxysilane) into the molecular structure of the epoxy resin. At a high temperature of 180°C, the trimethoxy group of the silane coupling agent is hydrolyzed to form a silanol group (Si-OH), which undergoes a condensation reaction with the hydroxyl group (Si-OH) on the surface of the formation sand particles to form a Si-O-Si covalent bond. At the same time, the amine curing agent reacts with the epoxy group of the epoxy resin to form a three-dimensional network structure, which increases the interface bonding strength between the sand retaining layer and the formation from 5MPa to 12MPa. A triaxial compression test was carried out under a confining pressure of 30MPa, and the interface shear failure strength was increased by 60% compared with that of traditional binders.

[0062] Preferably, in step S8, a multi-factor collaborative optimization algorithm constructs a multi-dimensional decision space including parameters such as reservoir temperature, pressure, fluid viscosity, mineral composition, and sand control period, and uses a hybrid intelligent optimization algorithm that combines a genetic algorithm with a particle swarm optimization algorithm to search for the optimal solution for the proportion of each component of the binder in the multi-dimensional decision space, thereby achieving comprehensive optimization of the sand retaining layer performance and sand control cost.

[0063] Specifically, the multi-factor collaborative optimization algorithm in step S8 constructs a multi-dimensional decision space containing 12 parameters, each parameter is set at 5 levels, forming 5 12= 244,140,625 combinations. The genetic algorithm population size was set to 200, with a crossover probability of 0.8 and a mutation probability of 0.05. The particle swarm algorithm had an inertia weight of 0.7 and an acceleration constant c1 = c2 = 1.5. Through mixed optimization, the optimal binder ratio for a high-temperature, high-salinity reservoir was determined to be 45% epoxy resin E-51, 30% phenolic resin, 5% silane coupling agent, and 20% curing agent. This resulted in a sand retaining layer with a permeability of 650 mD and a compressive strength of 25 MPa. The material cost was reduced by 18% compared to the conventional formulation, achieving an optimal balance between performance and cost.

[0064] Preferably, in step S9, the dynamic control algorithm is based on the long short-term memory network (LSTM) model in machine learning, and learns and extracts features from historical data and real-time monitoring data during the oil well production process, establishes an oil well production dynamic prediction model, and plans the parameters of nitrogen supplementary injection in advance according to the prediction results, thereby realizing intelligent dynamic control of the oil well production process.

[0065] Specifically, the dynamic control algorithm of step S9 is based on the LSTM model. The input layer contains 8 features such as liquid production, sand production, bottom hole pressure, etc. The hidden layer sets 3 LSTM unit layers, each layer contains 50 neurons, and the output layer is the prediction parameter for the next 7 days. The model uses the Adam optimizer, with a learning rate of 0.001, a batch size of 32, and 100 training iterations. In the actual application of a certain oil well, the model's prediction error for liquid production is <5%, and the prediction error for sand production is <8%. According to the prediction results, when the sand production exceeds 0.3kg / m 3 When the oil well is running low, the nitrogen supplement injection is automatically triggered, and the injection volume is reduced by 15% compared with the conventional experience method, while the stable production period of the oil well is extended by 20%.

[0066] The present invention proposes a technical process for sand control and production increase in shallow heavy oil wells using a deep thermal field. In terms of sand control, this technical process breaks through traditional limitations. Through a particle size matching algorithm, an optimization model is constructed by integrating multiple parameters such as reservoir pore structure and fluid characteristics, and the particle size of the coated sand is accurately selected. Compared with the problem that traditional mechanical sand control screens are prone to clogging and damage, this method can better fit the actual sand production situation of the oil well and improve the sand control accuracy from the source. A mortar with a specific sand concentration is formulated, and positive or reverse circulation injection is adopted. Combined with dynamic adjustment of strict pressure and displacement control and ultrasonic monitoring, it ensures that the sand control section is evenly filled. At the same time, the high-temperature resistant binder solidifies at the reservoir temperature to form a high-permeability sand retaining layer, and can be secondary solidified when the formation temperature rises. Combined with the temperature-sensitive self-healing functional polymer chain segment, it solves the problem of high-temperature degradation and failure of chemical sand control, so that the permeability of the sand retaining barrier is not less than 90% at a high temperature of 350°C, and the compressive strength is increased by 10%-15%, forming a long-lasting and stable sand retaining effect.

[0067] At the production-increasing level, this technology has achieved an innovative upgrade in thermal field construction. Based on the eddy current heating principle and filled with special crystalline nano-thermal conductive materials, the high-temperature, high-pressure nitrogen heating device has efficient heating and precise temperature control capabilities. It heats nitrogen to 350°C and injects it into the oil well, creating a dual heating field for near-well high-temperature viscosity reduction and remote thermal displacement. Finite element numerical simulation is used to establish a multi-physics field coupling model for different well types and reservoir distributions, optimizing the direction, angle, and profile of nitrogen injection. This changes the heat loss and uneven heating conditions of conventional thermal production stimulation, enabling the thermal field to efficiently and evenly cover the oil layer, effectively reducing the viscosity of heavy oil, achieving effective displacement of remote reservoirs, and significantly improving crude oil recovery.

[0068] Furthermore, this technology offers the advantage of intelligent dynamic control. By real-time monitoring of oil well liquid production, sand production, bottomhole pressure, and other data, a dynamic control algorithm is employed, using a machine learning-based long-short-term memory network model to predict oil well production dynamics and adaptively adjust nitrogen injection parameters. This intelligent control throughout the oil well production cycle not only enables timely response to various changes in the production process but also further optimizes sand control and production increase. This achieves the coordinated optimization of sand control and production increase during shallow heavy oil production, effectively improving production efficiency and economic benefits, and comprehensively overcoming the shortcomings of existing technologies.

[0069] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "disposed," "installed," "connected," "connected," and "fixed" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to mechanical connections or electrical connections; they may refer to direct connections or indirect connections through an intermediate medium; and they may refer to internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0070] Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and variations may be made to these embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A deep thermal field synergistic sand control and production increase technology process for shallow heavy oil wells, characterized in that: The following steps are involved: Step S1. Based on the median distribution of sand particle sizes produced by oil and gas wells, a particle size matching algorithm is used to select a coating sand particle size within a range of 1.0-1.5 times the median sand size. The coating sand is then coated with a special high-temperature resistant binder to form a mortar system with a sand concentration in the range of 40%-60%. Step S2. Use the positive circulation or reverse circulation fluid delivery method to inject the above-configured coated sand mortar into the near-wellbore area. During the injection process, the injection pressure is controlled not to exceed 80% of the formation fracture pressure, and the displacement is set at 2-5m 3 / h interval, so that the sand control section is in a uniform and stable filling state; Step S3. After the mortar injection process is completed, the well is shut in and left to stand for 3-5 hours to allow the binder in the coated sand to cure at a reservoir temperature of 30°C-350°C, thereby constructing a high-permeability sand retaining layer near the wellbore with a permeability of not less than 500 mD and a uniaxial compressive strength of not less than 20.7 MPa at 180°C for 24 hours. Step S4. A high-temperature, high-pressure nitrogen heating device based on the eddy current heating principle and filled with a special crystalline nano-thermal conductive material is used to heat the nitrogen to raise the nitrogen temperature to 350°C. The heating device has performance characteristics such as a heating efficiency of not less than 95%, a temperature control accuracy within the range of ±2°C, a shell temperature not higher than 30°C, and a heating / cooling rate of not less than 20°C / s. The nitrogen temperature is continuously adjustable between 50-350°C.

2. The deep thermal field synergistic sand control and production increase technology process for shallow heavy oil wells according to claim 1 is characterized in that: The process also includes: Step S5. Nitrogen gas heated to 350°C is heated at a rate of 5-10×10 4 m 3 / well injection volume, injection pressure of 5-20MPa and 500-1200m 3 / h of displacement is injected into the oil well, creating a dual heating field effect of near-well high-temperature viscosity reduction and remote thermal displacement in the oil well; Step S6. Under high temperature conditions of 350°C, the formed sand barrier maintains a performance index of no less than 90% permeability, thereby ensuring the smooth migration of nitrogen within the oil well; Step S7. The increase in formation temperature triggers a secondary solidification process of the cement in the sand barrier, thereby increasing the compressive strength of the sand barrier by 10%-15%; Step S8. With sand retaining accuracy, i.e., particle size matching, as the core prerequisite, a multi-factor collaborative optimization algorithm is used to systematically adjust the binder ratio and optimize the compressive strength and permeability of the sand retaining layer to maximize the cost-effectiveness of sand control. Step S9. During the production cycle after nitrogen injection is completed, based on the real-time monitoring data of oil well liquid production, sand production, and bottom hole pressure, a dynamic control algorithm is used to adaptively adjust the timing, injection volume, injection pressure, and displacement parameters of subsequent nitrogen supplementary injection.

3. The deep thermal field synergistic sand control and production increase technology process for shallow heavy oil wells according to claim 1 is characterized in that: In step S1, the particle size matching algorithm combines reservoir pore structure parameters, fluid flow characteristic parameters, and sand particle size distribution probability density function to construct a multi-dimensional parameter optimization model to determine the optimal particle size of the coating sand, while meeting the sand retention accuracy and minimizing the flow resistance of the fluid through the sand retention layer; In step S2, during the injection process of the coating sand mortar, non-invasive ultrasonic monitoring technology is used to obtain in real time the flow velocity distribution, concentration distribution and front advancement distance parameters of the mortar in the wellbore and near-wellbore area. Based on the real-time monitoring parameters, a fuzzy control algorithm is used to dynamically adjust the injection method, injection pressure and displacement to ensure uniform filling quality of the sand control section.

4. The deep thermal field synergistic sand control and production increase technology process for shallow heavy oil wells according to claim 1 is characterized in that: In step S3, during the curing period of the binder, distributed optical fiber sensing technology is used to perform real-time monitoring of the three-dimensional spatial distribution of the reservoir temperature field, pressure field, and internal stress field of the sand retaining layer, and a binder curing kinetic model under the temperature-pressure-stress coupling effect is established. The waiting time is dynamically adjusted according to the model calculation results to ensure that the sand retaining layer achieves the optimal curing effect.

5. The deep thermal field synergistic sand control and production increase technology process for shallow heavy oil wells according to claim 1 is characterized in that: In step S4, the crystalline nano-thermal conductive material inside the high-temperature and high-pressure nitrogen heating device is heated, and the surface of the nanoparticles is specially chemically modified to form composite particles with a core-shell structure, wherein the core portion is a high-thermal-conductivity nano-crystalline material and the shell portion is a high-temperature-resistant and antioxidant coating. The nanoparticles form a three-dimensional network-connected structure inside the heating device in a self-assembly manner based on the principle of Brownian motion.

6. The deep thermal field synergistic sand control and production increase technology process for shallow heavy oil wells according to claim 2 is characterized in that: In step S5, during the high-temperature nitrogen injection process, an optimization method based on finite element numerical simulation is used to establish a reservoir-wellbore-fluid multi-physics field coupling model for different well types, including vertical wells, directional wells, or horizontal wells, and oil layer distribution characteristics. By performing numerical calculations and parameter sensitivity analysis on the model, the optimal direction, angle, and injection profile of nitrogen injection are determined, and the thermal field is efficiently constructed and evenly distributed in the oil layer.

7. The deep thermal field synergistic sand control and production increase technology process for shallow heavy oil wells according to claim 2 is characterized in that: In step S6, the high-temperature resistant binder adopts a multi-component composite system, which includes polymer molecular segments with temperature-sensitive self-repairing function. Under a high-temperature environment of 350°C, when the pore structure of the sand retaining barrier is damaged due to thermal stress or fluid scouring, the temperature-sensitive polymer segments undergo conformational changes, triggering a self-repairing reaction, thereby maintaining the permeability of the sand retaining barrier at a performance level of not less than 90%.

8. The deep thermal field synergistic sand control and production increase technology process for shallow heavy oil wells according to claim 2 is characterized in that: In step S7, the chemical reaction between the binder and the formation sand particles that accompanies the secondary solidification of the binder due to the formation heating involves a chemical bonding reaction mechanism based on surface active sites. By introducing specific functional groups into the binder molecular structure, the binder chemically reacts with the surface active sites of the formation sand particles to form covalent bonds, thereby enhancing the interfacial bonding strength and overall mechanical stability of the sand retaining layer and the formation.

9. The deep thermal field synergistic sand control and production increase technology process for shallow heavy oil wells according to claim 2 is characterized in that: In step S8, a multi-factor collaborative optimization algorithm is used to construct a multi-dimensional decision space including reservoir temperature, pressure, fluid viscosity, mineral composition, and sand control cycle parameters, and a hybrid intelligent optimization algorithm combining a genetic algorithm and a particle swarm optimization algorithm is used to search for the optimal solution for the proportion of each component of the binder in the multi-dimensional decision space, thereby comprehensively optimizing the sand retaining layer performance and sand control cost.

10. The deep thermal field synergistic sand control and production increase technology process for shallow heavy oil wells according to claim 2, characterized in that: In step S9, the dynamic control algorithm is based on the long short-term memory network model in machine learning, which learns and extracts features from historical data and real-time monitoring data during the oil well production process, establishes an oil well production dynamic prediction model, plans nitrogen supplementary injection parameters in advance based on the prediction results, and performs intelligent dynamic control of the oil well production process.

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