A hydrate blockage prediction and prevention method and system for a deepwater gas well testing string

Abstract

The present application relates to the technical field of deepwater oil and gas well flow safety, in particular to a hydrate blockage prediction and prevention method and system for deepwater gas well testing pipe column. The method comprises the following steps: combining the multiphase flow in the pipe column, the heat and mass transfer law of the wellbore and the formation, obtaining the temperature field and the pressure field at different well depths and different times; introducing the local supercooling degree positive feedback mechanism brought by hydrate generation and heat release, correcting the effective supercooling degree, and solving the real-time generation rate of hydrate; combining the agglomeration kernel function and the broken kernel function under the action of turbulent flow, obtaining the Sauter mean diameter and the particle wall collision probability; using the Lagrangian method to solve the particle motion trajectory, combining the collision kinetic energy and the adhesion work to calculate the particle wall adhesion probability, and counting the hydrate deposition flux of each pipe column grid unit; based on the mass conservation, calculating the deposition layer thickness change to solve the deposition layer permeability, combining the critical pipe diameter criterion to calculate the blockage time of each unit, the global safe operation time window and the high-risk blockage position.

Description

Technical Field

[0001] This invention relates to the field of deepwater oil and gas well flow safety technology, specifically to a method and system for predicting and preventing hydrate blockage in deepwater gas well test tubing. Background Technology

[0002] Deepwater oil and gas resources are a core follow-up area for global oil and gas exploration and development, and deepwater gas well testing is a crucial process before a gas field is officially put into production. The low temperature and high pressure characteristics of the deepwater environment can cause natural gas hydrates to form inside the wellbore tubing, resulting in solid blockage. This can lead to testing interruptions and reduced production, or even serious safety accidents such as tubing blockage, tool damage, and blowouts. Therefore, accurate prediction and efficient prevention and control of hydrate blockage are the core challenges of deepwater gas well testing operations.

[0003] Current technologies mainly fall into three categories: first, risk warning of hydrate formation based on thermodynamic phase equilibrium; second, hydrate inhibition through continuous injection of thermodynamic or kinetic inhibitors; and third, assessment of the degree of tubing blockage based on the assumption of uniform deposition. However, existing technologies have many fundamental flaws: Ignoring the strong exothermic characteristics of hydrate formation and failing to model the positive feedback self-reinforcing effect of local supercooling leads to an overly optimistic prediction of blockage time and a large error in the safe operating window. Assuming a fixed hydrate particle size and ignoring the dynamic equilibrium of particle aggregation and breakup in the flow field leads to distortion of the deposition flux calculation results. Treating the hydrate deposit layer as a dense, impenetrable solid without considering its porous media properties makes it impossible to characterize gas flow penetration, intralayer secondary hydrate formation, and additional pressure drop phenomena. It fails to distinguish between three completely different clogging modes with different physical mechanisms: wall deposition thickening, bridging clogging, and liquid plugging. It adopts a single prediction and control strategy, which cannot be adapted to complex working conditions. The inhibitor uses a crude approach of continuous injection at high concentrations, without considering the time-scale competition between hydrate formation and inhibitor diffusion. This not only results in large inhibitor usage and high operating costs, but also poses a risk of marine environmental pollution.

[0004] In summary, existing technologies mostly apply conventional wall deposition system theories such as wax and scale formation directly, which cannot be adapted to the unique physicochemical properties of natural gas hydrates. They have low prediction accuracy and poor economic efficiency in prevention and control schemes, making it difficult to meet the safe and efficient operation requirements of deep-water gas well testing. Summary of the Invention

[0005] To address the shortcomings of existing methods and the needs of practical applications, this invention provides a method for predicting and preventing hydrate blockage in deepwater gas well test tubing, comprising the following steps: A multiphase flow temperature and pressure field coupled model of a deepwater gas well test string was constructed. Combining the multiphase flow within the string and the heat and mass transfer laws between the wellbore and the formation, energy and momentum conservation equations for the fluid, the pipe wall, and the formation were established to obtain the temperature and pressure fields at different well depths and times. A hydrate formation kinetic model considering the exothermic self-enhancing effect was established, introducing a positive feedback mechanism of local undercooling caused by the exothermic effect of hydrate formation to correct the effective undercooling and solve for the real-time hydrate formation rate. Based on the population equilibrium model, a dynamic particle size evolution model of hydrate particle aggregation and fragmentation was established, combined with the effects of turbulence. Aggregation kernel function and fragmentation kernel function were used to obtain the Sottle mean diameter and particle wall collision probability; a particle transport and deposition dynamics model was built, and the particle trajectory was solved using the Lagrangian method. The particle wall adhesion probability was calculated by combining collision kinetic energy and adhesion work, and the hydrate deposition flux of each pipe grid unit was statistically analyzed; a hydrate deposition layer blockage evolution model considering the characteristics of porous media was established, and the deposition layer permeability was solved by calculating the deposition layer thickness change based on mass conservation. The blockage time of each unit and the safe operation time window of the whole area were calculated by combining the critical pipe diameter criterion, and the high-risk blockage location was located.

[0006] Optionally, the establishment of a hydrate formation kinetic model considering the exothermic self-enhancing effect, introducing a positive feedback mechanism of local supercooling caused by the exothermic effect of hydrate formation, correcting the effective supercooling, and solving for the real-time hydrate formation rate includes the following steps: A basic hydrate formation rate equation is established based on the intrinsic dynamics and mass transfer coupling theory. The flow pattern inside the tube is determined according to the apparent gas velocity and apparent liquid velocity. For the annular mist flow, the liquid film thickness and droplet size distribution are calculated, and the liquid film surface area and the total droplet surface area are solved separately. The total gas-liquid contact area is obtained by superimposing them. The heat released by the hydrate formation reaction is calculated. The heat feedback coefficient, the deposition layer thickness, and the effective thermal conductivity of the deposition layer are introduced. The local effective undercooling is calculated and replaced with the original undercooling in the basic formation rate equation to achieve the coupling of the exothermic self-enhancing effect.

[0007] Optionally, the basic hydrate formation rate equation satisfies:

[0008] in, Indicates the mass of hydrate formation. Indicates time, This represents the kinetic constant for hydrate formation. Indicates the effective gas-liquid contact area. Indicates supercooling. This represents the exponential term.

[0009] Optionally, the local effective subcooling satisfies:

[0010] in, Indicates the local effective subcooling. Indicates the global supercooling. Indicates the thermal feedback coefficient. This indicates that the formation of hydrates releases heat. Indicates the current thickness of the sedimentary layer. This represents the effective thermal conductivity of the deposited layer.

[0011] Optionally, the dynamic particle size evolution model for hydrate particle agglomeration and fragmentation based on the population equilibrium model satisfies:

[0012] in, Represents the particle size distribution function. Indicates particle size, Indicates time scale, Denotes the divergence operator, Indicates particle velocity. Indicates a term generated by a reunion. This indicates the end of a reunion. Indicates a broken generation term. This indicates a broken or destroyed item.

[0013] Optionally, the process of constructing a particle transport and deposition dynamics model, using the Lagrange method to solve for particle trajectories, calculating particle wall adhesion probability by combining collision kinetic energy and adhesion work, and statistically analyzing the hydrate deposition flux of each column grid cell includes the following steps: The particle motion equations, which include drag, lift, gravity, and turbulent diffusion forces, are established to solve for the particle trajectory. The critical adhesion work is simplified based on the Johnson-Kendall-Roberts contact theory, and the particle adhesion probability is obtained by combining the particle normal collision kinetic energy. The number of particle collisions per unit time, the mass of a single particle, and the corresponding adhesion probability are statistically analyzed. The deposition flux of each grid cell wall is calculated according to three sources: gravity settling, turbulent diffusion, and inertial collision.

[0014] Optionally, the establishment of a hydrate deposition layer clogging evolution model considering the characteristics of porous media, calculating the deposition layer permeability based on mass conservation to determine the deposition layer thickness change, calculating the clogging time of each unit and the safe operation time window of the entire area in conjunction with the critical pipe diameter criterion, and locating high-risk clogging locations includes the following steps: The dynamic changes in sediment thickness are calculated based on the mass conservation equation; the sediment permeability is calculated based on the sediment porosity and Sottle mean diameter, and the gas flow velocity is solved using Darcy's law; the rate of secondary hydrate formation inside the sediment layer is calculated based on the gas-liquid contact area within the pores, and the sediment porosity is dynamically updated; the additional pressure drop generated by gas flow penetrating the sediment layer is calculated and superimposed on the total flow pressure drop of the tubing string; based on the relationship between the effective flow diameter and the critical blockage diameter, the blockage time of a single grid cell is determined, the minimum value of the blockage time of all cells is taken as the overall safe operating time window of the wellbore, and the critical blockage location is located.

[0015] Optionally, the method for predicting and preventing hydrate blockage in deepwater gas well test tubing further includes the following steps: A timescale for hydrate formation and an effective timescale for inhibitors are constructed. The numerical relationship between the timescale for hydrate formation and the effective timescale for inhibitors is compared, and the inhibitor concentration is dynamically updated at the next moment using a concentration adjustment formula. The inhibitor injection rate is calculated based on the inhibitor concentration, aqueous phase volumetric flow rate, and inhibitor density. The total cumulative amount of inhibitor is minimized with the constraint that the safe operating time window is not less than the sum of the target operating time and the safety margin. Before operation, an offline mapping relationship between the timescale and the inhibitor concentration is established. During operation, parameters are collected in real time by downhole sensors, and the injection parameters are dynamically adjusted. An early warning is issued when the predicted safety window is insufficient.

[0016] Optionally, the concentration adjustment formula satisfies:

[0017] in, express Inhibitor concentration at time, express Inhibitor concentration at time, This represents the timescale of hydrate formation at time t. This represents the effective timescale of the inhibitor at time t. This represents the adjustment coefficient.

[0018] Secondly, to efficiently execute the method for predicting and controlling hydrate blockage in deep-water gas well test tubing provided by this invention, this invention also provides a system for predicting and controlling hydrate blockage in deep-water gas well test tubing, comprising: an input device, an output device, a processor, and a memory, wherein the input device, output device, processor, and memory are interconnected, and the memory stores program instructions used in the method for predicting and controlling hydrate blockage in deep-water gas well test tubing. The system for predicting and controlling hydrate blockage in deep-water gas well test tubing provided by this invention has a compact structure and stable performance, and can stably execute the method for predicting and controlling hydrate blockage in deep-water gas well test tubing provided by this invention, further enhancing the overall applicability and practical application capability of this invention.

[0019] This invention first constructs a multiphase flow temperature and pressure field coupling model for the tubing string to obtain real-time temperature and pressure parameters across the entire wellbore. Secondly, it introduces the exothermic self-enhancing effect of hydrate formation to establish a kinetic model, combining this with a population equilibrium model to characterize the dynamic particle size changes caused by particle agglomeration and breakup. Next, it calculates the wall deposition flux using a particle transport and deposition model, treating the deposition layer as a porous medium and considering gas flow penetration, intralayer secondary generation, and additional pressure drop effects. Subsequently, it identifies three types of blockage modes: wall deposition thickening, bridging blockage, and liquid plugging, and matches corresponding prediction models to accurately determine the blockage location, time, and safe operating window. Finally, based on the time-scale competition between hydrate formation and inhibitor diffusion, a closed-loop dynamic injection control system is built to optimize inhibitor usage. This solves problems such as large prediction deviations, lack of differentiation between blockage modes, and significant inhibitor waste in existing technologies. It effectively reduces the prediction error of the safe operating window and lowers inhibitor usage, making it suitable for accurate prediction and intelligent control of hydrate blockage in various deepwater gas well testing operations. Attached Figure Description

[0020] Figure 1 A flowchart illustrating a method for predicting and preventing hydrate blockage in a deepwater gas well test tubing, provided in an embodiment of the present invention. Figure 2 This is a framework diagram of a deepwater gas well test tubing hydrate blockage prediction and control system provided in an embodiment of the present invention. Detailed Implementation

[0021] Specific embodiments of the present invention will now be described in detail. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to practice the invention. In other instances, well-known circuits, software, or methods have not been specifically described to avoid obscuring the invention.

[0022] Throughout this specification, references to "an embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with that embodiment or example is included in at least one embodiment of the invention. Therefore, the phrases "in an embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily refer to the same embodiment or example. Furthermore, specific features, structures, or characteristics can be combined in one or more embodiments or examples in any suitable combination and / or sub-combination. Moreover, those skilled in the art will understand that the illustrations provided herein are for illustrative purposes and are not necessarily drawn to scale.

[0023] Please see Figure 1 This invention provides a method for predicting and preventing hydrate blockage in deepwater gas well test tubing, comprising the following steps: S1. Construct a multiphase flow temperature and pressure field coupling model for deep water gas well test tubing. Combine the multiphase flow within the tubing and the heat and mass transfer laws between the wellbore and the formation to establish energy conservation equations and momentum conservation equations for the fluid, tubing wall, and formation, and obtain the temperature and pressure fields at different well depths and times.

[0024] In this embodiment, the tubing string is divided into several uniform grid cells along the well depth. Taking into account the gas-liquid multiphase flow inside the tubing string and the heat and mass transfer processes between the wellbore and the formation, energy conservation equations and momentum conservation equations are established for the fluid inside the drill string, the annular fluid, the tubing wall, and the formation, respectively. Then, the fully implicit finite difference method is used to discretize and solve the equations to obtain the temperature T(z,t) and pressure P(z,t) distribution of each grid cell inside the wellbore at different times. This temperature and pressure field distribution provides basic environmental parameters for subsequent hydrate formation kinetic calculations.

[0025] S2. Establish a hydrate formation kinetic model that considers the exothermic self-enhancing effect, introduce a positive feedback mechanism of local supercooling caused by the exothermic effect of hydrate formation, correct the effective supercooling, and solve for the real-time hydrate formation rate.

[0026] First, based on the theory of coupling intrinsic kinetics and mass transfer, a fundamental hydrate formation rate equation is established, satisfying:

[0027] in, Indicates the mass (kg) of hydrate formation. Indicates time, This represents the kinetic constant for hydrate formation. Indicates the effective gas-liquid contact area. Indicates supercooling. This represents the exponent (usually 1.0-2.0).

[0028] From the above equation, it can be seen that the gas-liquid contact area It is a key parameter that determines the hydrate formation rate. Its value depends on the flow state and liquid phase distribution characteristics of the fluid in the tubing. The specific calculation method is as follows.

[0029] Then, the flow pattern inside the tube is determined based on the apparent gas velocity and the apparent liquid velocity. For the annular mist flow, the liquid film thickness and droplet size distribution are calculated. The surface area of ​​the liquid film and the total surface area of ​​the droplets are solved separately and then superimposed to obtain the total gas-liquid contact area.

[0030] Specifically, the apparent gas flow rate = / A, apparent flow rate of liquid = For the annular fog flow transition boundary / A, the following conditions are met:

[0031] when Furthermore, when the liquid flow rate is low, it is determined to be annular mist flow; if it is not annular mist flow, the contact area model corresponding to the flow pattern is used.

[0032] The following steps are performed for annular mist flow. Under annular mist flow conditions, some liquid forms an annular liquid film along the pipe wall. First, the thickness of the liquid film is calculated, satisfying the following:

[0033] in, Indicates the thickness of the liquid film. Indicates the inner diameter of the tubular string. Indicates gas density, Indicates the density of the liquid. It represents the apparent flow rate of gas (gas phase volumetric flow rate / pipe cross-sectional area). Indicates the gas-liquid mixing velocity. , This indicates the apparent flow rate of a liquid.

[0034] Besides the liquid film on the wall, the droplets entrained in the gas core also provide the gas-liquid contact area. Their size distribution directly affects the total surface area of ​​the droplets. The droplet size follows an upper limit log-normal distribution, with the median diameter at the top being [missing information]. ,satisfy:

[0035] Where σ is the gas-liquid interfacial tension, C is an empirical constant (taken as 0.3-0.5), and the complete particle size distribution is... Depend on and geometric standard deviation (Usually determined by 1.5-2.5) the maximum droplet diameter =(0.1-0.5)D.

[0036] Furthermore, the particle size distribution probability density function satisfies:

[0037] In the embodiments, the surface area of ​​the liquid film ,satisfy:

[0038] in, Indicates the length of the discrete mesh element. This represents the amplification factor of the ripples on the liquid film surface (considering that the liquid film is not smooth but fluctuates, it is usually taken as 1.2-2.0).

[0039] Therefore, the total surface area of ​​the droplet is:

[0040] The total surface area of ​​the droplet :

[0041] Total gas-liquid contact area It consists of two parts: liquid film contribution and droplet contribution.

[0042] Will The hydrate formation rate at the current moment can be calculated by substituting back into the formation rate equation. Based on this, the present invention further considers the self-enhancing effect brought about by the exothermic reaction of hydrate formation.

[0043] In the embodiment, the formation of hydrates releases heat, satisfying the following:

[0044] Furthermore, an effective subcooling is constructed to satisfy:

[0045] in, Indicates the local effective subcooling. Indicates the global supercooling. This represents the thermal feedback coefficient (experimentally calibrated, 0.05-0.3). This indicates that the formation of hydrates releases heat. Indicates the current thickness of the sedimentary layer. This represents the effective thermal conductivity of the deposited layer (which varies with porosity, typically 0.5-2.0).

[0046] Substituting the effective supercooling into the generation rate equation, replacing the original supercooling, we get:

[0047] It is understandable that the thicker the sediment layer, the more significant the heat release accumulation, the greater the local supercooling, the faster the hydrate formation, and the more positive feedback self-reinforcing.

[0048] Through the above corrections, the hydrate formation rate and amount considering the exothermic self-enhancing effect were obtained. At this time, the generated hydrates are suspended in the gas core in the form of a large number of tiny particles, and their particle size distribution will have an important impact on the subsequent deposition behavior.

[0049] S3. Based on the population equilibrium model, a dynamic particle size evolution model of hydrate particle aggregation-fragmentation is established. By combining the aggregation kernel function and the fragmentation kernel function under turbulence, the Sottle average diameter and the collision probability of the particle wall are obtained.

[0050] In this embodiment, the dynamic particle size evolution model of hydrate particle agglomeration-fragmentation is used to describe the dynamic changes in the particle size distribution of hydrate particles in the gas core, providing particle size input parameters for subsequent deposition flux calculation. Let... Let be the particle size distribution function, satisfying:

[0051] in, Represents the particle size distribution function. Let s represent the divergence operator (1 / m). Indicates particle velocity. Indicates a term generated by a reunion. This indicates the end of a reunion. Indicates a broken generation term. This indicates a broken or destroyed item.

[0052] For the aggregation kernel function, the collision frequency between two particles satisfies:

[0053] in For turbulent dissipation rate, This refers to kinematic viscosity.

[0054] For adhesion efficiency, the following conditions must be met:

[0055] For liquid bridge force, It is a destructive force of turbulence.

[0056] For the breakup kernel function, breakup occurs when the turbulent shear stress exceeds the tensile strength of the particles:

[0057] The breakage coefficient (experimentally calibrated) is the coefficient of mass loss. The critical stress, This refers to turbulent stress.

[0058] Furthermore, the particle size range is discretized into several intervals (such as 1-10μm, 10-50μm, 50-200μm, 200-500μm, etc.), and the PBM equation is solved in each grid cell and at each time step to obtain the number of particles in each interval, and the Sottle mean diameter is output. Used for sedimentation flux calculation and particle collision wall probability distribution.

[0059] S4. Construct a dynamic model of particle transport and deposition, use the Lagrange method to solve for particle trajectories, combine collision kinetic energy and adhesion work to calculate the adhesion probability of particle walls, and statistically analyze the hydrate deposition flux of each column grid cell.

[0060] After obtaining the particle size distribution, it is necessary to further determine whether these particles will reach the wall and deposit. Using the particle size distribution as input, the Lagrange method is used to solve for the particle trajectory. The equation of motion for a single spherical particle is as follows:

[0061] Among them, drag force: , This is the drag coefficient (related to the particle Reynolds number). For the projected area of ​​the particle, the lift is: ,gravity: Turbulent diffusion force By superimposing random walk models.

[0062] Numerical integration is performed on each particle using the above force equations, based on the flow field velocity. The particle trajectory is obtained from the turbulent kinetic energy k and the dissipation rate ε. (t) determines when and where the pipe wall comes into contact.

[0063] Then, the wall collision and adhesion probabilities are calculated, satisfying: Normal collision kinetic energy: , Critical adhesion work (simplified based on JKR theory (Johnson-Kendall-Roberts contact theory)): , The adhesion work at the hydrate-pipe wall interface (value ranges from 0.01 to 0.1).

[0064] Furthermore, the adhesion probability satisfies:

[0065] Finally, by tracking a large number of particles (thousands to tens of thousands), the average deposition flux on the wall of each grid cell is calculated, and the deposition flux of each grid cell i is determined. The contributions are respectively from gravitational settling, turbulent diffusion, and inertial collision.

[0066] Specifically, the mass of a single collision deposition satisfies: , Deposition flux of grid cell i ,satisfy:

[0067] in, This represents the wall area of ​​grid cell i. For unit length, Indicates the statistical time interval. Indicates time The total number of collisions occurring on the wall of element i. This represents the mass of the particle in the j-th collision. express The adhesion probability of the second collision.

[0068] Furthermore, the sedimentation flux is decomposed into three sources:

[0069] This indicates that it is caused by gravity settling, and mainly occurs under conditions of low flow velocity and large particles. This indicates that it is caused by turbulent diffusion, resulting from the random walk of turbulent diffusion forces. This indicates an inertial collision, where particles with excessive momentum directly impact the wall.

[0070] S5. Establish a hydrate deposition layer blockage evolution model that considers the characteristics of porous media, calculate the deposition layer thickness change based on mass conservation to solve the deposition layer permeability, combine the critical pipe diameter criterion to calculate the blockage time of each unit and the safe operation time window of the whole area, and locate the high-risk blockage location.

[0071] After obtaining the wall deposition flux of each grid cell, hydrates will gradually accumulate on the wall to form a deposition layer, causing the pipe diameter to gradually shrink. It is also necessary to establish a deposition layer blockage evolution model, using the deposition flux of each grid cell as the driving input, to calculate the dynamic growth of the deposition layer over time and its impact on the pipe diameter.

[0072] In this embodiment, the deposited layer is considered as a porous medium through which gas flow can partially penetrate, causing secondary generation and additional pressure drop within the layer.

[0073] For the evolution of sedimentary layer thickness, mass conservation satisfies:

[0074] The above formula gives the thickness growth rate of the sediment layer. However, hydrate sediment layers are not dense solids, but have the characteristics of porous media. Their pore structure allows some gas flow to pass through, which in turn affects the physical properties of the sediment layer and the clogging process. Therefore, it is necessary to further calculate the permeability of the sediment layer.

[0075] Regarding the permeability of the sedimentary layer and gas flow penetration, and the porosity of the sedimentary layer... (0.3~0.6), Kozeny-Carman permeability model, satisfies:

[0076] Therefore, according to Darcy's law (penetration velocity):

[0077] Here is the gas viscosity (Pa·s). The axial pressure difference is expressed in Pa.

[0078] The penetrating gas continues to generate hydrates within the pores of the deposition layer, leading to a decrease in porosity and consequently affecting permeability, thus creating a closed feedback loop. Therefore, it is necessary to calculate the intralayer secondary generation rate. The intralayer generation rate uses the same kinetic equation, but the contact area is replaced by the pore surface area (from...). (and specific surface area estimation), porosity is dynamically updated, satisfying:

[0079] Furthermore, the additional pressure drop, i.e. the additional pressure drop of the airflow passing through the sediment layer, is calculated, satisfying:

[0080] This additional pressure drop is added to the flow pressure drop for clogging detection.

[0081] In the embodiment, the effective pipe diameter is also calculated and the blockage time is determined.

[0082] The effective flow diameter satisfies:

[0083] in, Indicates the effective flow diameter, usually taken as ,in , The critical blockage ratio is set at 0.1-0.3, determined by field experience or experiments. A sudden increase in pressure drop can also be used as a criterion.

[0084] In the embodiment, the blocking time of unit i :

[0085] The safe operating time window for the entire wellbore (short-board effect):

[0086] The most likely location for blockage:

[0087] The above criteria are applicable to the prediction of blockage caused by uniform deposition and thickening on the wall surface. In actual operating conditions, blockage may also occur in other modes such as bridging or liquid plugging, and their physical mechanisms are different from the critical criteria. Therefore, this invention further sets up a multimodal recognition mechanism.

[0088] Blockage mode identification and differential prediction. In a preferred embodiment, three blockage modes are automatically identified based on flow parameters, and the corresponding prediction sub-model is invoked. For type 1 (wall deposition thickening type), the discrimination condition is: <0.05, We<10; This type is slow blockage, the main mechanism being layer-by-layer deposition on the wall. ≤ Congestion is determined on time. For type 2 (bridging congestion), the determination condition is: And located at bends / changes in pipe diameter / wellheads, this type of blockage mechanism involves particles bridging at abrupt changes in pipe diameter, forming a blockage. A bridging probability model is used for prediction. A value >0.8 indicates bridging blockage; for type 3 hydraulic plug sealing, the criteria are: >0.3, <0.1m / s, The volume fraction of the droplet is denoted as . This type of clogging mechanism involves the rapid growth of hydrates inside the liquid plug, eventually leading to complete blockage. A liquid plug reactor model is used for prediction.

[0089] In the embodiment, the bridging probability model satisfies:

[0090] in, The bridging factor is 0.1-0.5 (experimentally calibrated).

[0091] For type III (liquid plug hydrate sealing type): when ≥ When the crit is between 0.3 and 0.5, it is considered completely blocked.

[0092] Treating the liquid plug as a batch reactor, the evolution of hydrate volume fraction:

[0093] This represents the gas-liquid interface area within the liquid plug.

[0094] In addition, the safe operating time window must meet the following requirements:

[0095] Through steps S1 to S5 described above, quantitative prediction of the location and timing of hydrate blockage and the safe operating time window is achieved. Building upon this, to further reduce inhibitor dosage and improve the economic efficiency of control, this invention further constructs a dynamic inhibitor injection control method based on time-scale competition.

[0096] In a preferred embodiment, a minimum injection strategy is achieved by not only targeting the safety window but also considering the competition between the inhibitor diffusion and mixing timescale and the hydrate formation timescale.

[0097] Construct a hydrate formation timescale that satisfies:

[0098] The total mass of hydrates required to reach critical clogging conditions; And, the effective timescale of the inhibitor satisfies:

[0099] in, The distance from the injection point to the target well depth. This refers to the mixing velocity (i.e., the gas-liquid mixing velocity). The diffusion characteristic length (usually taken as several times the pipe diameter). The effective diffusion coefficient of the inhibitor is given.

[0100] After obtaining the quantitative values ​​of the two time scales mentioned above, the concentration control decision can be made by comparing their magnitudes.

[0101] Then set competition control rules and compare. and : like < Hydrate formation occurs faster than the inhibitor takes effect, requiring higher concentrations or earlier injection. like > Excess inhibitors can be reduced in concentration. If the two are close: maintain the current concentration.

[0102] The concentration adjustment formula satisfies:

[0103] in, express Inhibitor concentration at time, express Inhibitor concentration at time, This represents the timescale of hydrate formation at time t. This represents the effective timescale of the inhibitor at time t. This represents the adjustment coefficient.

[0104] In the embodiments, the injection rate and total dosage are also optimized, including: Inhibitor injection rate (volume flow rate):

[0105] in, This refers to the volumetric flow rate of the water phase inside the wellbore. This represents the inhibitor density.

[0106] Total cumulative usage:

[0107] Optimization goal: To ensure ≥ Under the premise of minimizing .

[0108] In the embodiments, prior to operation, based on geological parameters, tubing structure, and predicted production, offline simulations were conducted to model the blockage evolution of various modes under different inhibitor concentrations, establishing... , Mapping relationship.

[0109] Initial injection with ≈ The corresponding concentration is injected, and downhole sensors (pressure, temperature, differential pressure, flow rate) collect data in real time, updating the temperature and pressure field and flow pattern every 5 to 15 minutes.

[0110] Dynamic control calculation , And safety windows, adjusted according to competition rules. If prediction If the remaining operation time is less than the required time, an alarm will be issued, prompting the user to increase the inhibitor concentration or terminate the test early. In typical deepwater gas well tests, the total inhibitor dosage was reduced by 50%–70%, the safety window prediction deviation was less than 10%, and sudden bridging blockage could be identified and warned of. This completes the entire closed-loop control chain, from temperature and pressure field prediction, hydrate formation kinetics, particle size dynamic evolution, deposition flux calculation, blockage evolution prediction to intelligent inhibitor regulation.

[0111] It should be noted that the specific implementation methods described above, such as image processing, numerical simulation, and the construction and training of machine learning models, can all be accomplished by the processor by calling the corresponding computer program instructions stored in memory. Those skilled in the art can implement the above functions using algorithms and tools known in the prior art, according to actual needs.

[0112] Please see Figure 2 In this embodiment, to efficiently execute the method for predicting and preventing hydrate blockage in deep-water gas well test tubing provided by this invention, the present invention also provides a system for predicting and preventing hydrate blockage in deep-water gas well test tubing, comprising: an input device 1, an output device 2, a processor 3, and a memory 4. The input device 1, output device 2, processor 3, and memory 4 are interconnected. The memory 4 stores program instructions used to execute the steps of the method for predicting and preventing hydrate blockage in deep-water gas well test tubing. The system for predicting and preventing hydrate blockage in deep-water gas well test tubing of this invention has a compact structure and stable performance, and can stably execute the method for predicting and preventing hydrate blockage in deep-water gas well test tubing of this invention, further improving the overall applicability and practical application capability of this invention.

[0113] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the present invention.

Claims

1. A method for predicting and preventing hydrate blockage in deepwater gas well test tubing, characterized in that, Includes the following steps: A coupled temperature and pressure field model of multiphase flow in deep water gas well test tubing was constructed. Combining the multiphase flow in the tubing and the heat and mass transfer laws between the wellbore and the formation, the energy conservation equations and momentum conservation equations of the fluid, the tubing wall and the formation were established to obtain the temperature field and pressure field at different well depths and at different times. A hydrate formation kinetic model considering the exothermic self-enhancing effect is established. A positive feedback mechanism of local supercooling caused by the exothermic hydrate formation is introduced to correct the effective supercooling and solve the real-time hydrate formation rate. A dynamic particle size evolution model of hydrate particle aggregation-fragmentation was established based on the population equilibrium model. By combining the aggregation kernel function and the fragmentation kernel function under turbulence, the Sottle mean diameter and the collision probability of the particle wall were obtained. A particle transport and deposition dynamics model was constructed, the Lagrange method was used to solve the particle trajectory, the particle wall adhesion probability was calculated by combining collision kinetic energy and adhesion work, and the hydrate deposition flux of each column grid cell was statistically analyzed. A hydrate deposition layer clogging evolution model considering the characteristics of porous media was established. Based on the mass conservation, the deposition layer thickness variation was calculated to solve the deposition layer permeability. Combined with the critical pipe diameter criterion, the clogging time of each unit and the safe operation time window of the whole area were calculated, and the high-risk clogging location was located. The establishment of a hydrate formation kinetic model considering the exothermic self-enhancing effect, the introduction of a positive feedback mechanism of local supercooling caused by the exothermic hydrate formation, the correction of effective supercooling, and the solution of the real-time hydrate formation rate include the following steps: A basic hydrate formation rate equation is established based on the theory of intrinsic dynamics and mass transfer coupling. The flow pattern inside the tube is determined based on the apparent gas velocity and the apparent liquid velocity. For the annular mist flow, the liquid film thickness and droplet size distribution are calculated. The surface area of ​​the liquid film and the total surface area of ​​the droplets are solved separately and then superimposed to obtain the total gas-liquid contact area. The heat released by the hydrate formation reaction is calculated, and the thermal feedback coefficient, deposition layer thickness, and effective thermal conductivity of the deposition layer are introduced. The local effective undercooling is calculated and replaced with the original undercooling in the basic formation rate equation to achieve the coupling of the exothermic self-enhancing effect. The process of constructing a particle transport and deposition dynamics model, using the Lagrange method to solve for particle trajectories, calculating particle adhesion probability by combining collision kinetic energy and adhesion work, and statistically analyzing hydrate deposition flux in each column grid cell includes the following steps: Establish particle motion equations that include drag, lift, gravity, and turbulent diffusion forces to solve for particle trajectories; The critical adhesion work is simplified based on the Johnson-Kendall-Roberts contact theory, and the particle adhesion probability is obtained by combining the particle normal collision kinetic energy. The number of collisions between pipe wall particles per unit time, the mass of a single particle, and the corresponding adhesion probability are statistically analyzed. The deposition flux on the wall surface of each grid cell is calculated based on three sources: gravity settling, turbulent diffusion, and inertial collision.

2. The method for predicting and preventing hydrate blockage in deepwater gas well test tubing according to claim 1, characterized in that, The basic hydrate formation rate equation satisfies: in, Indicates the mass of hydrate formation. Indicates time, This represents the kinetic constant for hydrate formation. Indicates the effective gas-liquid contact area. Indicates supercooling. This represents the exponential term.

3. The method for predicting and preventing hydrate blockage in deepwater gas well test tubing according to claim 1, characterized in that, The local effective subcooling satisfies: in, Indicates the local effective subcooling. Indicates the global supercooling. Indicates the thermal feedback coefficient. This indicates that the formation of hydrates releases heat. Indicates the current thickness of the sedimentary layer. This represents the effective thermal conductivity of the deposited layer.

4. The method for predicting and preventing hydrate blockage in deepwater gas well test tubing according to claim 1, characterized in that, The dynamic particle size evolution model for hydrate particle aggregation and breakup, established based on the population equilibrium model, satisfies: in, Represents the particle size distribution function. Indicates particle size, Indicates time scale, Denotes the divergence operator, Indicates particle velocity. Indicates a term generated by a reunion. This indicates the end of a reunion. Indicates a broken generation term. This indicates a broken or destroyed item.

5. The method for predicting and preventing hydrate blockage in deepwater gas well test tubing according to claim 1, characterized in that, The establishment of a hydrate deposition layer clogging evolution model considering the characteristics of porous media, calculating the deposition layer permeability based on mass conservation of deposition layer thickness variation, and combining the critical pipe diameter criterion to calculate the clogging time of each unit and the safe operation time window of the entire area, and locating high-risk clogging locations, includes the following steps: The dynamic changes in the thickness of the sedimentary layer are calculated based on the mass conservation equation; The permeability of the sedimentary layer is calculated based on the porosity of the sedimentary layer and the Sottle mean diameter, and the gas flow penetration velocity is solved by combining Darcy's law. The formation rate of secondary hydrates inside the sedimentary layer is calculated based on the gas-liquid contact area within the pores, and the porosity of the sedimentary layer is dynamically updated. Calculate the additional pressure drop caused by the gas flow penetrating the sediment layer and add it to the total flow pressure drop in the tubing; Based on the relationship between the effective flow diameter and the critical blockage diameter, the blockage time of a single grid unit is determined, and the minimum value of the blockage time of all units is taken as the overall safe operating time window of the wellbore, and the critical blockage location is located.

6. The method for predicting and preventing hydrate blockage in deepwater gas well test tubing according to claim 1, characterized in that, It also includes the following steps: Constructing a timescale for hydrate formation and an effective timescale for inhibitors; By comparing the numerical relationship between the hydrate formation timescale and the inhibitor's effective timescale, the inhibitor concentration at the next moment is dynamically updated using a concentration adjustment formula. The inhibitor injection rate is calculated based on the inhibitor concentration, aqueous phase volumetric flow rate, and inhibitor density. The total cumulative amount of inhibitor is minimized with the constraint that the safe operating time window is not less than the sum of the target operating time and the safety margin. Before the operation, an offline mapping relationship between the time scale and the inhibitor concentration is established. During the operation, parameters are collected in real time and injection parameters are dynamically adjusted by relying on downhole sensors. When the predicted safety window is insufficient, an early warning is issued.

7. The method for predicting and preventing hydrate blockage in deepwater gas well test tubing according to claim 6, characterized in that, The concentration adjustment formula satisfies: in, express Inhibitor concentration at time, express Inhibitor concentration at time, This represents the timescale of hydrate formation at time t. This represents the effective timescale of the inhibitor at time t. This represents the adjustment coefficient.

8. A system for predicting and preventing hydrate blockage in deepwater gas well test tubing, characterized in that, The deepwater gas well test string hydrate blockage prediction and control system includes: an input device, an output device, a processor, and a memory, wherein the input device, output device, processor, and memory are interconnected, and the memory stores program instructions, which are used to execute the deepwater gas well test string hydrate blockage prediction and control method according to any one of claims 1-7.

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