Method and device for identifying water outlet position and water outlet mode of horizontal well based on distributed optical fiber temperature sensing temperature profile features

Abstract

The invention discloses a method and device for recognizing the water outlet position and the water outlet mode of a horizontal well based on DTS temperature profile features. Comprising the following steps: acquiring distributed optical fiber temperature sensing (DTS) temperature profile data along a horizontal shaft; establishing a multiphase flow horizontal well temperature prediction composite model as a forward model; an inversion model is established, and stratum permeability distribution and an oil-water output profile are obtained; according to the well section with the water phase liquid production capacity exceeding a preset threshold value in the production profile, the water outlet position of the horizontal well is recognized; and determining the water outlet mode of the horizontal well by combining the analysis result of the multiphase flow horizontal well temperature prediction composite model on the temperature response in different water outlet modes according to the temperature change characteristics presented at the identified water outlet position by the DTS temperature profile. Compared with the prior art, the water outlet position can be more accurately recognized, the water outlet mode can be effectively distinguished, the monitoring cost is reduced, and powerful technical support is provided for water control and oil stabilization of the horizontal well.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas field development, and in particular to a method and device for identifying a water outlet position and water outlet mode of a horizontal well based on temperature profile characteristics of distributed optical fiber temperature sensing. Background Art

[0002] Most oilfields in my country have entered the development phase of medium-to-high water cuts. Horizontal wells are widely used due to their advantages, such as their large reservoir contact area. However, rapid production decline, uneven fluid production leading to premature gas-water breakthrough, and rapid increases in water cuts from horizontal wells severely impact their development efficiency. Accurately identifying the water production location and implementing targeted water control measures are major challenges facing oilfields today.

[0003] The existing solutions mainly include:

[0004] Conventional production logging technologies, such as indirect testing methods like C / O testing and PNN testing, or direct testing technologies like Schlumberger and Sondex, are used. However, the complex multiphase flow patterns and undulating wellbore trajectories in horizontal wellbores make conventional logging difficult, costly, and subject to significant errors. The accuracy of test results is particularly difficult to guarantee in situations with low fluid production rates and irregular wellbores.

[0005] Theoretical model predictions: Production profiles are predicted using semi-analytical models that couple reservoir flow with wellbore flow, or using multiphase numerical simulation software (such as ECL) based on black oil models. These models typically assume isothermal processes, or, while capable of simulating multiphase production, remain based on isothermal assumptions. More importantly, these models rely on accurate data on the distribution of permeability along the wellbore, which is lacking for most horizontal wells in my country, making direct predictions of the actual production profile impractical.

[0006] Temperature and pressure profile inversion: The production profile is derived by directly measuring the temperature and pressure profiles of horizontal wells. Distributed fiber temperature measurement (DTS) technology provides continuous and accurate temperature data in real time, enabling the detection of minute temperature variations, making this approach feasible. However, formation temperature variations around horizontal wells are small, and wellbore temperature variations are primarily governed by microthermal effects such as thermal expansion, viscous dissipation, and heat conduction, making interpretation challenging. Currently, there is limited research on this topic abroad, and it is largely unavailable domestically, lacking mature theories and interpretation methods.

[0007] Therefore, there is an urgent need for a technical solution that can accurately and economically identify the water production location and water production pattern of horizontal wells to effectively guide water control and oil stabilization operations in oil fields. Summary of the Invention

[0008] The purpose of the present invention is to provide a method and device for identifying the water outlet position and water outlet pattern of a horizontal well based on the temperature profile characteristics of distributed optical fiber temperature sensing, so as to solve the problems in the prior art of difficulty in determining the water outlet position of a horizontal well, difficulty in judging the water outlet pattern, high cost of finding and controlling water, and poor effect.

[0009] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a method for identifying the water output position and water output pattern of a horizontal well based on the temperature profile characteristics of distributed optical fiber temperature sensors, characterized in that it includes the following steps: obtaining distributed optical fiber temperature sensor temperature profile data along the horizontal wellbore; establishing a multiphase flow horizontal well temperature prediction composite model as a forward model, the multiphase flow horizontal well temperature prediction composite model is based on the principles of conservation of mass, conservation of momentum and conservation of energy, and takes into account the reservoir seepage, wellbore multiphase flow and coupled heat transfer process between the wellbore of the horizontal well and the oil reservoir, the coupled heat transfer process takes into account the influence of microthermal effects and formation damage, the microthermal effects include thermal expansion, heat conduction, heat convection and viscous dissipation; establishing an inversion model, the inversion model uses the multiphase flow horizontal well temperature prediction composite model as a forward model, uses the formation permeability along the horizontal well as a parameter to identify the target, and iteratively adjusts the formation permeability through an inversion algorithm. , to minimize the fitting evaluation objective function between the predicted temperature profile calculated by the multiphase flow horizontal well temperature prediction composite model and the obtained distributed optical fiber temperature sensor temperature profile data, thereby obtaining the formation permeability distribution and production profile along the horizontal well, the production profile including the oil phase liquid production and water phase liquid production along the horizontal well; identifying the water production position of the horizontal well according to the well section in which the water phase liquid production exceeds a preset threshold in the production profile; and determining the water production mode of the horizontal well according to the temperature change characteristics presented by the distributed optical fiber temperature sensor temperature profile data at the identified water production position, combined with the analysis results of the temperature response under different water production modes by the multiphase flow horizontal well temperature prediction composite model, wherein, when the temperature change characteristic is a temperature increase, the water production mode is determined to be bottom water breakthrough in a bottom water reservoir, and when the temperature change characteristic is a temperature decrease, the water production mode is determined to be oil and water production in the same layer.

[0010] As a preferred solution, the reservoir model in the multiphase flow horizontal well temperature prediction composite model is a three-dimensional unsteady multiphase flow model, and is solved using an implicit pressure and explicit saturation method.

[0011] As a preferred solution, the wellbore model in the multiphase flow horizontal well temperature prediction composite model is a steady-state multiphase flow wellbore model, including a wellbore pressure gradient equation based on conservation of mass and momentum, and a wellbore temperature equation based on conservation of energy and taking into account the Joule-Thomson effect, convective heat exchange between the wellbore and the reservoir, and the influence of gravity.

[0012] As a preferred solution, the multiphase flow horizontal well temperature prediction composite model, while considering the influence of formation damage, solves the flow from the reservoir to the damage zone and the flow from the damage zone to the wellbore simultaneously, and the damage zone is characterized by the contamination radius and the damage zone permeability.

[0013] As a preferred solution, the coupled heat transfer process is realized by coupling the reservoir temperature model and the wellbore temperature model, wherein the temperature of the fluid flowing into the wellbore is obtained by solving the simplified radial steady-state temperature equation at the sand surface between the reservoir and the wellbore, and the simplified radial steady-state temperature equation takes into account the heat convection term, the viscous dissipation term and the heat conduction term.

[0014] As a preferred solution, the inversion algorithm is a Markov Chain Monte Carlo inversion method, which uses random walk sampling to generate candidate formation permeabilities.

[0015] As a preferred solution, it also includes obtaining distributed optical fiber pressure sensing pressure profile data along the horizontal wellbore; and the fitting evaluation objective function also includes the difference term between the predicted pressure profile calculated according to the multiphase flow horizontal well temperature prediction composite model and the obtained distributed optical fiber pressure sensing pressure profile data, and the fitting evaluation objective function is a weighted least squares function.

[0016] As a preferred solution, before establishing the inversion model, the horizontal wellbore is partitioned according to the obtained distributed optical fiber temperature sensor temperature profile data and the changing trend of its temperature derivative, and a unified formation permeability is set in each partition as an adjustment parameter of the inversion algorithm.

[0017] To achieve the above-mentioned purpose, the present invention also provides a device for identifying the water output position and water output pattern of a horizontal well based on the temperature profile characteristics of distributed optical fiber temperature sensors, which is characterized by comprising: a data acquisition module for acquiring distributed optical fiber temperature sensor temperature profile data along the horizontal wellbore; a temperature prediction module for establishing a multiphase flow horizontal well temperature prediction composite model as a forward model, wherein the multiphase flow horizontal well temperature prediction composite model is based on the principles of conservation of mass, conservation of momentum and conservation of energy, and takes into account the reservoir seepage, wellbore multiphase flow and coupled heat transfer process between the wellbore of the horizontal well and the oil reservoir, wherein the coupled heat transfer process takes into account the influence of micro-thermal effects and formation damage, wherein the micro-thermal effects include thermal expansion, heat conduction, heat convection and viscous dissipation; an inversion calculation module for establishing an inversion model, wherein the inversion model uses the multiphase flow horizontal well temperature prediction composite model as a forward model, uses the formation permeability along the horizontal well as a parameter to identify the target, and iteratively adjusts the formation permeability through an inversion algorithm. permeability, to minimize the fitting evaluation objective function between the predicted temperature profile calculated by the multiphase flow horizontal well temperature prediction composite model and the obtained distributed optical fiber temperature sensor temperature profile data, thereby obtaining the formation permeability distribution and production profile along the horizontal well, the production profile including the oil phase liquid production and water phase liquid production along the horizontal well; a position and pattern recognition module, for identifying the water production position of the horizontal well according to the well section in which the water phase liquid production exceeds a preset threshold in the production profile; and determining the water production mode of the horizontal well according to the temperature change characteristics presented by the distributed optical fiber temperature sensor temperature profile data at the identified water production position, combined with the analysis results of the temperature response under different water production modes by the multiphase flow horizontal well temperature prediction composite model, wherein, when the temperature change characteristic is a temperature increase, the water production mode is determined to be bottom water breakthrough in a bottom water reservoir, and when the temperature change characteristic is a temperature decrease, the water production mode is determined to be oil and water production in the same layer.

[0018] Furthermore, the reservoir model in the multiphase flow horizontal well temperature prediction composite model established by the temperature prediction module is a three-dimensional unsteady multiphase flow model, and is solved using an implicit pressure and explicit saturation method.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] 1. This invention constructs a refined temperature prediction model that takes into account microthermal effects, formation damage, and wellbore-reservoir coupling. Combined with an inversion algorithm, it can more accurately invert formation permeability and production profiles, thereby precisely identifying water production locations. This overcomes the limitations of conventional methods that are affected by wellbore conditions and model simplification.

[0021] 2. This invention not only identifies the water-breakout location but also effectively distinguishes whether it is a bottom-water breakthrough in a bottom-water reservoir or oil-water production in the same layer based on the specific temperature change characteristics (increase or decrease) of the DTS temperature profile at the water-breakout location, combined with model analysis. This provides a key basis for formulating differentiated water control measures.

[0022] 3. The present invention utilizes DTS for continuous, real-time temperature monitoring, combined with the interpretation method proposed in the present invention, to avoid or reduce expensive and time-consuming conventional production logging operations, thereby reducing operational risks and costs and improving monitoring and decision-making efficiency.

[0023] 4. The present invention enhances the model's adaptability and reliability: The established composite model comprehensively considers reservoir seepage, wellbore multiphase flow, and complex heat transfer mechanisms, and optimizes parameters through inversion algorithms such as MCMC, making the model more adaptable to actual well conditions and the interpretation results more reliable.

[0024] 5. Accurate production profiles, water production locations, and pattern information provide direct and accurate technical support for oilfields to adjust production parameters, optimize completion methods, and implement targeted water plugging measures, helping to achieve stable oil and water control and improve the overall production efficiency of oilfields. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solution of the present invention, the following briefly describes the drawings used in implementing the embodiments of the present invention or the prior art:

[0026] Figure 1 This is a flow chart of an embodiment of a method for identifying the water outlet position and water outlet pattern of a horizontal well based on DTS temperature profile characteristics of the present invention;

[0027] Figure 2 This is a schematic diagram of the module structure of an embodiment of a device for identifying the water outlet position and water outlet pattern of a horizontal well based on DTS temperature profile characteristics of the present invention;

[0028] Figure 3 Schematic diagram of the reservoir seepage model in an embodiment of the present invention;

[0029] Figure 4 Schematic diagram of a micro-element section of a wellbore in an embodiment of the present invention;

[0030] Figure 5 A schematic diagram of a grid for coupling a wellbore and an oil reservoir in an embodiment of the present invention;

[0031] Figure 6 This is a schematic diagram of the formation damage zone in an embodiment of the present invention;

[0032] Figure 7 This is the drilling trajectory of the horizontal section of the example well;

[0033] Figure 8 is the wellbore temperature observation data of the example well;

[0034] Figure 9 is the wellbore pressure observation data of the example well;

[0035] Figure 10 This is the inversion result of the wellbore temperature of the example well. DETAILED DESCRIPTION

[0036] To make the objectives, technical solutions, and advantages of the present invention more clear, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the scope of protection of the present invention. When writing the examples, the language should be clear, complete, and accurate, and should support each technical feature of the claims so that those skilled in the art can understand and implement the present invention.

[0037] Example 1: Method for identifying the water outlet position and water outlet pattern of a horizontal well based on temperature profile characteristics of distributed optical fiber temperature sensing

[0038] This embodiment aims to specifically disclose a method for identifying the water outlet position and water outlet pattern of a horizontal well based on the temperature profile characteristics of distributed optical fiber temperature sensing. Figure 1 As shown, the method may specifically include the following steps:

[0039] Step S101: Obtaining distributed optical fiber temperature sensing temperature profile data along the horizontal wellbore.

[0040] Specific Operation: A distributed fiber optic sensing system is installed in a horizontal well to collect temperature data along the length of the wellbore in real time or periodically. The fiber optic sensor can be permanently installed on the outside of the production string or temporarily lowered into the wellbore via coiled tubing for short-term testing. The raw data is optical, and after processing by a surface demodulation unit, it is converted into a temperature sequence with meter or even sub-meter accuracy.

[0041] Data characteristics: The acquired temperature profile data is a continuous or quasi-continuous temperature value along the well depth (or wellbore length), reflecting the temperature distribution of the wellbore fluid and the near-wellbore formation. This data is highly sensitive to small temperature changes caused by fluid inflow.

[0042] Optional: Preferably, pressure profile data along the horizontal wellbore is acquired simultaneously using a distributed fiber optic pressure sensing (DPS) system. Pressure data can provide additional constraints, particularly a detailed description of the wellbore pressure distribution and pressure drop characteristics, which helps improve the accuracy and reliability of subsequent inversion.

[0043] Step S102: Establish a composite model for multiphase flow horizontal well temperature prediction as a forward model. This composite model is based on the principles of conservation of mass, momentum, and energy, and comprehensively considers the interaction between the horizontal wellbore and the reservoir, including reservoir seepage, multiphase flow within the wellbore, and coupled heat transfer processes. The coupled heat transfer process is particularly critical, as it carefully accounts for the impact of microthermal effects and formation damage on the temperature field. Microthermal effects include multiple physical mechanisms such as thermal expansion, heat conduction, heat convection, and viscous dissipation.

[0044] Reservoir model: The reservoir model describes the flow and heat distribution of the fluid in the reservoir. Preferably, the reservoir model is a three-dimensional, non-steady-state, multi-phase (such as oil-water two-phase or oil-gas-water three-phase) flow and thermal model. Figure 3 As shown in Figure 2, the reservoir flow model can be composed of the mass conservation equation and Darcy's law within the control volume unit. For the oil-water two-phase, the continuity equation can be expressed as:

[0045]

[0046]

[0047] Among them, ρ o ,ρ w are the densities of the oil phase and the water phase, respectively; u o ,u w are the Darcy flow velocity vectors of the oil phase and water phase in the porous medium respectively; φ is the porosity of the reservoir rock; S o ,S w are the saturations of the oil phase and the water phase, respectively; t is the production time. The seepage velocity is described by the extended Darcy law:

[0048]

[0049] Where i represents the oil phase or water phase, k is the absolute permeability of the reservoir rock, and k ri is the relative permeability of phase i relative to the absolute permeability, μ i is the viscosity of phase i fluid, p i is the pressure of phase i, and g is the gravitational acceleration vector. Auxiliary equations such as the capillary pressure equation p are also required. c =p o -p w and saturation relationship S o +S w = 1. Substitute Equation 3 into Equation 1 and Equation 2, and introduce the compression coefficient The basic differential equation for oil-water two-phase flow can be obtained. It is solved using the finite difference method and the Implicit Pressure Explicit Saturation (IMPES) method. The continuity equation is differentiated within a small rectangular control volume. Taking the oil phase as an example, the right-hand side of Equation 1 can be transformed into:

[0050]

[0051] in, (In the IMPES method, it is assumed that p c It remains unchanged within Δt, so ). Substitute the expression of Darcy's law into the left side of the continuity equation and perform finite difference processing. For example, the difference form in the z direction is:

[0052]

[0053] Use upstream weighted processing By substituting and sorting the various differential equations, we can finally obtain the finite difference equations for the oil phase and the finite difference equations for the water phase. These equations constitute the difference equations for the seepage in a three-dimensional oil-water two-phase reservoir, which are solved iteratively using the IMPES method.

[0054] The reservoir thermal model is an energy conservation equation based on the reservoir flow model. It accounts for heat changes caused by heat conduction, heat convection, fluid throttling (Joule-Thomson effect), and viscous dissipation within the reservoir. The energy conservation equation for the entire reservoir, including the rock and multiphase fluid, can be described as energy change equal to energy transport plus energy generation. Ignoring kinetic energy changes, the energy change within the reservoir control volume V can be expressed as:

[0055]

[0056] The energy transport term includes two parts: heat convection and heat conduction:

[0057] E cv,res =∑ i ρ i u i (H i +gD)·A Formula 7

[0058]

[0059] Where λ represents the thermal conductivity of rock. In any controlled volume V of the reservoir where there is no source / sink term, the energy generated is zero. Substitute 6, 7, and 8 into the energy conservation principle and arrange them, using internal energy U = Hp / ρ and rock internal energy U r ≈C pr dT resThe definition of , combined with the continuity equation, can be used to obtain the reservoir temperature equation. Finally, the reservoir temperature equation can be expressed as a seven-diagonal coefficient matrix with AT = B for solution.

[0060] Wellbore model: The wellbore model describes the flow and heat distribution of the fluid in the wellbore. Preferably, the wellbore model is modeled as a one-dimensional, steady-state multiphase flow model along the wellbore direction, but can handle radial inflow along the wellbore. Figure 4 In the wellbore micro-element shown, the fluid inflow includes the flow along the wellbore axis and the flow from the wellbore wall radially. In addition, in order to describe different completion methods (such as open hole completion and perforation completion), the wellbore opening degree is defined. The wellbore flow model is based on the principles of conservation of mass and momentum, and the pressure gradient equation in the wellbore is derived. Under steady-state conditions, ignoring the second-order derivative of velocity, the momentum conservation equation in the wellbore can be rearranged to obtain the pressure gradient equation:

[0061]

[0062] Among them, p wb is the fluid pressure in the wellbore, x is the distance along the horizontal wellbore axis, R is the wellbore radius, ρ m is the density of the mixed fluid, f is the friction coefficient (depending on the flow pattern and completion method), v m is the velocity of the mixed fluid, g is the acceleration due to gravity, and θ is the well inclination. The first term is the friction pressure drop, the second term is the acceleration pressure drop, and the third term is the gravity pressure drop. The radial inflow term is calculated by the total mass conservation equation of the mixed fluid. Coupled with the reservoir model, where A is the wellbore cross-sectional area, q inflow,mass is the radial inflow mass flow rate per unit length of the wellbore. By using the finite difference method, 8 is discretized to obtain the difference equation of the wellbore flow model.

[0063] The wellbore thermal model is based on the principle of energy conservation and considers the effects of fluid flow within the wellbore, radial inflow from the formation, and heat exchange with the surrounding formation on the wellbore temperature. It carefully considers the combined effects of multiple microthermal effects, including the Joule-Thomson effect (temperature change caused by fluid expansion and pressure reduction), viscous dissipation (heat generated by fluid friction), and convective heat transfer (heat carried by fluid inflowing from the formation, and heat exchange between the wellbore fluid and the surrounding formation through the wellbore wall). Under steady-state conditions, ignoring the wellbore kinetic energy term and viscous shear term, the energy conservation equation can be expanded and converted into a multiphase flow wellbore temperature equation:

[0064]

[0065] Among them, T wb is the wellbore fluid temperature, K JTis the Joule-Thomson coefficient of the mixed fluid, α T,I =γ(ρvC p ) T,1 +(1-γ)α is the effective heat transfer parameter including the wellbore opening degree γ and the total heat transfer coefficient α, T inflow is the temperature of the fluid flowing from the formation into the wellbore, (ρvC p ) T are equivalent parameters of the mixed fluid under different physical meanings. The first term represents the temperature change due to the Joule-Thomson effect, the second term represents the heat exchange caused by convection between the wellbore and the reservoir, and the third term represents the temperature change due to gravity. Using the finite difference method, 9 is discretized to obtain the difference equation for the wellbore thermal model shown below.

[0066] Wellbore and reservoir coupled heat transfer and formation damage: There is a mutual coupling relationship between the wellbore model and the reservoir model, especially the mutual influence between the wellbore pressure and temperature and the reservoir boundary pressure and temperature, as well as the influence of the flow rate and temperature of the reservoir into the wellbore on the wellbore flow and heat transfer. Figure 5 The schematic diagram of the wellbore-reservoir coupling grid shown in the figure introduces the concept of equivalent radius to couple the three-dimensional reservoir model with the wellbore through a radial flow model.

[0067] Coupled heat transfer process, especially the temperature T of the fluid flowing into the wellbore inflow The determination is done by solving the equivalent radius r of the reservoir e Radius to wellbore r w The radial steady-state temperature equation between the two is realized. The reservoir temperature equation is simplified to a second-order ordinary differential equation

[0068]

[0069] Where r is the radial distance, T is the radial temperature, and C1, C2, and C3 are constant coefficients. The general solution of this equation is: Where l1, l2, n1, n2, and b are constant coefficients. Solve this radial temperature equation at r = r w The temperature at T inflow .

[0070] Reference Figure 6 The schematic diagram of the formation damage zone shown in the figure shows that when the influence of formation damage is considered, there is a contaminated zone with low permeability (radius r d , permeability k d ) around the wellbore, the radial temperature equation needs to be solved in different regions (from r e to r d Using the reservoir permeability k, from r d to r w Use damage band penetration k d), and when r=r d The continuity conditions of temperature and heat flow are met at r = r e The temperature at the reservoir is equal to the reservoir grid temperature T e , when r=r w The boundary conditions for convective heat transfer with the wellbore fluid are met. Determination of coefficients c1, c2, c3, and c4 requires simultaneous boundary and continuity conditions. By solving the general temperature solution for each region, the temperature profile can be calculated in the presence of formation damage.

[0071] The reservoir temperature equation, wellbore temperature equation and inflow temperature equation are mutually coupled nonlinear equations and need to be solved iteratively.

[0072] Step S103: Establish an inversion model to obtain the formation permeability distribution and production profile. The inversion model uses the multiphase flow horizontal well temperature prediction composite model established in step S102 as the forward model. The formation permeability k(x) along the horizontal wellbore is used as the parameter to identify the target. The core of the inversion is to find the optimal formation permeability distribution so that the difference between the predicted temperature profile (and optional pressure profile) calculated by the forward model based on this permeability is minimized and the measured DTS temperature profile (and optional pressure profile) data obtained in step S101 is minimized.

[0073] Preliminary processing (optional): To reduce the complexity and computational effort of the inversion problem, especially for long horizontal wells, the horizontal wellbore can be partitioned along the axis based on the changing trends of the original DTS temperature profile data and its temperature derivative (the rate of temperature change along the wellbore). Significant changes in the temperature or temperature derivative curve often correspond to changes in formation permeability, fluid type, or fluid inflow intensity. Within each partitioned area, the formation permeability is assumed to be a uniform value. In this way, the parameters to be inverted are simplified from the permeability continuously distributed along the wellbore to the representative permeability values of each area, greatly reducing the number and improving the inversion efficiency and stability.

[0074] Fitting evaluation objective function: The formation permeability is iteratively adjusted using the inversion algorithm to minimize a fitting evaluation objective function. The objective function quantifies the degree of agreement between the forward calculation results and the measured data. The objective function is usually a least squares function that represents the difference between the observed and calculated values:

[0075]

[0076] Where x is the parameter (formation permeability), d is the observed value (temperature and / or pressure data), g(x) is the predicted value calculated based on the forward model, and C mis a diagonal matrix that stores the weight of each observation value. If pressure data is obtained at the same time, preferably, the fitting evaluation objective function is a weighted least squares function, taking into account the fitting errors of temperature and pressure:

[0077]

[0078] Among them, T wb,calc (k,·) and p wb,calc (k,·) are the predicted temperature and pressure profiles calculated by the forward model based on the current permeability estimate k; T wb,obs (·) and p wb,obs (·) are the actual DTS temperature and pressure profile data (at discrete measurement points x j ,x l W T and W P is the corresponding weight factor used to balance the contribution of temperature and pressure data to the objective function. T and N P are the number of temperature and pressure data points, respectively. The goal of the inversion is to find the permeability distribution k that minimizes J(k). The choice of weights will affect the convergence of the objective function. The weights of temperature and pressure can be approximately expressed as

[0079] Inversion algorithm: The inversion algorithm can be any optimization algorithm suitable for solving such nonlinear inversion problems. Commonly used methods include gradient-based methods (such as the Levenberg-Marquardt method LM) and stochastic methods (such as the Markov Chain Monte Carlo method MCMC). In the gradient-based LM method, the Hessian matrix of the objective function f(x) can be approximately expressed as H = J T J, where J is the Jacobian matrix of the error vector e. J is related to the sensitivity matrix of the forward model Related:

[0080]

[0081] Where ξ is the damping factor, w = J T e is the gradient of f(x). It is a component of the Hessian matrix H. The objective function converges by iteratively adjusting the damping factor and parameter increments.

[0082] Preferably, the inversion algorithm is a Markov chain Monte Carlo (MCMC) inversion method. Compared with the LM method, the MCMC method is insensitive to the initial value and can better avoid falling into the local optimal solution, thereby obtaining the global optimal or near-global optimal inversion result. The MCMC method realizes efficient parameter space search and sampling by constructing a Markov chain with a posterior probability density function (related to the objective function) of the parameter to be estimated as a stationary distribution. Specifically, the Markov chain Monte Carlo inversion method uses random walk sampling to generate candidate formation permeabilities. In each iteration, the permeability state k is obtained from the current permeability state k. n According to the proposed distribution q(k n ,k * ) Generate a new candidate state k * The acceptance probability is then calculated according to the Metropolis-Hastings criterion:

[0083]

[0084] Where f(k) is the probability density function related to the target function (for example, it can be taken as f(k)∝exp(-J(k))), q(k n ,k * ) is from state k n to k * For random walk sampling, the proposed distribution is usually symmetric about the current state, that is, q(k n ,k * )=q(k * ,k n ). According to the acceptance probability, a random number u is drawn from the uniform distribution U(0,1), and the new candidate state k is accepted or rejected with this probability * After a large number of iterations and a “burn-in” phase, the state sequence of the Markov chain will converge to the target posterior distribution, whose mean or mode can be used as the best estimate for the inversion.

[0085] By iteratively running the inversion algorithm, the formation permeability parameters in the divided area are continuously adjusted, substituted into the forward model to calculate the predicted profile, and then compared with the measured profile to calculate the objective function until the objective function reaches a suitable minimum value or meets other convergence criteria (such as the parameter change is less than the threshold, or the number of iterations reaches the upper limit). The permeability distribution obtained at the end of the inversion is the formation permeability distribution along the horizontal well. Based on the permeability distribution obtained by the inversion, the production profile along the horizontal well can be calculated through the forward model. The production profile includes the oil phase liquid production q along each well section of the horizontal well. o (x) and aqueous phase liquid production q w (x).

[0086] Step S104: identifying the water-producing position of the horizontal well according to the well section in the production profile where the water-phase liquid production exceeds a preset threshold.

[0087] Operation: Analyze the production profile along the horizontal wellbore obtained by inversion in step S103, especially focusing on the water phase production q w The distribution of (x). Set a preset threshold (the threshold can be determined based on actual experience, measurement accuracy or economic benefits, such as greater than zero, greater than the minimum detectable flow rate, or greater than a percentage of the total liquid production). The total water phase liquid production along the wellbore q w (x) The continuous well section that is greater than the preset threshold is identified as the water-producing position of the horizontal well (i.e., the water-bearing well section).

[0088] Step S105: Determine the water discharge mode of the horizontal well based on the temperature change characteristics of the temperature profile data of the distributed optical fiber temperature sensor at the identified water discharge position and the analysis results of the temperature response under different water discharge modes by the multiphase flow horizontal well temperature prediction composite model.

[0089] Operation: For each well section with water production identified in step S104, trace back and analyze the temperature variation characteristics of the original DTS temperature profile data obtained in step S101 within that well section. Compare the temperature of that well section with the temperature of the immediately upstream well section (non-water production section) or the temperature background trend of the entire horizontal well to observe whether the temperature shows a local increase or decrease.

[0090] Judgment basis and determination mode:

[0091] (a) When the temperature change characteristic presented by the DTS temperature profile data at the identified water outlet position is a temperature increase:

[0092] Based on the analysis of the bottom water breakthrough pattern in bottom water reservoirs using the multiphase flow horizontal well temperature prediction composite model established in step S102, bottom water typically originates from deeper areas below the underlying oil-water interface and has a higher temperature. When bottom water breaks through into the wellbore under the influence of the production pressure differential, it carries with it higher-temperature heat, causing a localized increase in wellbore temperature. The measured temperature characteristics are compared with the bottom water breakthrough temperature response predicted by the model. If they match, the water-discharge pattern is determined to be bottom water breakthrough in a bottom water reservoir.

[0093] (b) When the temperature change characteristic presented by the DTS temperature profile data at the identified water outlet position is a temperature decrease:

[0094] According to the analysis of the oil-water co-layer production pattern based on the multiphase flow horizontal well temperature prediction composite model established in step S102, when oil and water are produced from reservoirs at the same or similar depths in the formation, the production layer temperatures are usually close, but the specific heat capacity of water is greater than that of oil, and the fluid flowing into the wellbore involves micro-thermal effects such as volume expansion. In many cases, the temperature of the water phase fluid flowing into the wellbore will be lower than the temperature of the original fluid in the wellbore, or its inflow will cause the temperature of the mixed fluid to drop, resulting in a local decrease in the wellbore temperature in that section. Comparing the measured temperature characteristics with the model-predicted oil-water co-layer production temperature response, if they are consistent, the water production pattern is determined to be oil-water co-layer production.

[0095] Results Output and Application: Output identifies the water production location and its corresponding water production pattern (bottom water breakthrough or oil-water co-zone production). This information provides key input for oilfield engineers to develop precise water control measures. For example, for bottom water breakthrough, lower water plugging can be considered to prevent channeling; for oil-water co-zone production, selective water plugging or production parameter adjustments can be considered.

[0096] Working principle and mode of action (embodiment of this method)

[0097] The method of the present invention processes and interprets the fine temperature profile data measured by DTS. First, a forward model that is perfected with physical mechanisms and takes into account a variety of complex factors in actual reservoirs and wellbores (especially micro-thermal effects, formation damage, and wellbore-reservoir coupled heat transfer) is used to theoretically predict the temperature and pressure responses of horizontal wells under a given formation permeability distribution. Next, an inversion framework is constructed, using the measured temperature (pressure) data as constraints and the formation permeability as an unknown parameter. The parameter space is efficiently searched through an inversion algorithm (preferably MCMC) to find a set of optimal permeability distributions so that the forward model prediction results best match the measured data. This optimal permeability distribution then determines the oil and water production profile based on the physical model. The water-producing well section is identified by analyzing this production profile. Finally, the unique temperature characteristics (increase or decrease) presented by the DTS data itself in the identified water-producing well section are used, combined with the model's deep understanding of the temperature response of different water-producing modes, to achieve accurate judgment of the water-producing mode. The entire process, from data acquisition to output profile interpretation and pattern diagnosis, forms a relatively complete and reliable technical chain, providing a scientific basis for efficient water control in horizontal wells.

[0098] Technical Effect (Example of this Method)

[0099] By adopting the above method, it is possible to achieve:

[0100] 1. High-precision identification of horizontal well water-yielding locations: This method, based on a sophisticated physical model and data-constrained inversion, can more accurately capture formation heterogeneity and inflow characteristics, thereby precisely identifying the water-yielding well section, overcoming the limitations of traditional methods or simplified models in complex well conditions.

[0101] 2. Effectively distinguish different water-discharge modes: This method uses the specific temperature variation characteristics (warming or cooling) of the DTS temperature profile at the water-discharge location as a criterion. Combined with the physical model's description of the inherent laws of temperature response of different water-discharge mechanisms, it can accurately distinguish between the two main water-discharge modes: bottom water breakthrough and oil-water production in the same layer. This provides a key basis for formulating targeted water-control plans.

[0102] 3. Reduce water discovery and control costs and improve efficiency: The cost of using the economical and easy-to-implement DTS monitoring technology, combined with the method of this invention, is significantly reduced compared to traditional production logging. Long-term or real-time monitoring capabilities improve response speed to wellbore dynamic changes and decision-making efficiency.

[0103] 4. Enhanced model adaptability and reliability: The established composite model comprehensively considers multiple factors affecting wellbore temperature and pressure, making it closer to reality. The inversion algorithm optimizes parameters to enhance the model's applicability to specific well conditions and improve the reliability of the interpretation results.

[0104] 5. It provides strong technical support for horizontal well production optimization: Accurate information on production profiles, water production locations, and patterns can guide oilfield engineers in differentiated management, such as adjusting production allocation for each flow section, optimizing completion structure modifications, and implementing selective water shutoff. This helps achieve the goal of "stabilizing oil production and controlling water consumption" and significantly improves the overall benefits of oilfield development.

[0105] Example 2: Device for identifying the water outlet position and water outlet pattern of a horizontal well based on temperature profile characteristics of distributed optical fiber temperature sensing

[0106] This embodiment provides a device for implementing the above method. Figure 2 As shown, the device is a schematic diagram of functional modules, each of which can be an independent hardware unit, software module, or a combination of hardware and software, working together in a computing device or computing system. The device may specifically include:

[0107] Data acquisition module 10: used to acquire temperature profile data from distributed optical fiber temperature sensors along the horizontal wellbore. This module is responsible for receiving and processing the raw signals from the downhole distributed optical fiber sensors to generate temperature profile data that can be used for subsequent analysis.

[0108] Configuration example: fiber optic sensor (downhole), fiber optic cable, surface DTS demodulator (including light source, light receiver, signal processing unit), data acquisition unit, and memory and interface for receiving and storing raw temperature data.

[0109] Functionality: Downhole optical fibers sense temperature changes along the wellbore, altering their optical properties (such as the Raman scattering intensity ratio). A surface demodulator sends laser pulses to the fiber, receives the backscattered light, and uses a demodulation algorithm (such as the Raman scattering ratio method) to calculate the temperature distribution along the fiber's length, generating digital temperature profile data.

[0110] Optional: If pressure data is also acquired, the module also includes the corresponding hardware for a distributed fiber-optic pressure sensing system (such as a fiber Bragg grating (FBG) array or a Brillouin scattering-based sensor fiber) and a surface demodulation unit to acquire and process the pressure profile data along the wellbore.

[0111] Temperature prediction module 20: used to establish and run the multiphase flow horizontal well temperature prediction composite model as a forward model. This module is responsible for simulating the temperature and pressure distribution of the wellbore according to physical principles.

[0112] Example: A high-performance computing server, workstation, or cloud computing resource equipped with a software program that implements the multiphase flow horizontal well temperature prediction composite model algorithm. This software program may be composed of multiple subroutines corresponding to the computational logic for reservoir flow, reservoir thermals, wellbore flow, wellbore thermals, reservoir-wellbore coupling, and formation damage processing.

[0113] Functionality: This module receives various input parameters (such as reservoir properties, fluid properties, production regime, completion method, etc.) and the current estimated formation permeability distribution provided by the inversion calculation module. The processor executes the embedded algorithm code to numerically solve a complex set of partial differential equations (such as the reservoir and wellbore model equations described in Example 1) and calculate the predicted temperature and pressure profiles along the wellbore under these conditions.

[0114] Implementation details: Includes computational routines for handling 3D unsteady multiphase reservoir flow (e.g., IMPES method), steady multiphase flow and heat transfer in wellbores, and coupling between wellbores and reservoirs (temperature calculation for radial flows, consideration of formation damage).

[0115] Inversion calculation module 30: used to establish and execute the inversion model. This module is responsible for converting measured data into formation permeability and production profile information using an optimization algorithm.

[0116] · Configuration example: a computing device (which can be shared with the temperature prediction module) and a memory storing an inversion algorithm program code. The program implements the inversion calculation process described in step S103 in the first embodiment.

[0117] Functionality: This module receives measured DTS temperature profile data (and optional pressure data) from the data acquisition module 10 and obtains a predicted temperature (and pressure) profile based on the current permeability estimate from the temperature prediction module 20. The processor executes an inversion program, calculates a fitting objective function (e.g., weighted least squares) between the measured and predicted data, adjusts formation permeability parameters based on a selected inversion algorithm (preferably MCMC random walk sampling), and repeatedly calls the temperature prediction module for forward modeling until the objective function converges. The resulting iteratively optimized formation permeability distribution and the corresponding oil and water production profiles are ultimately output.

[0118] Implementation details: Contains the program code for the optimization algorithm (e.g., MCMC sampling, objective function calculation, sensitivity calculation (if required), etc.) and parameter update logic. Optionally, includes a preprocessing subroutine for wellbore partitioning based on temperature profile characteristics.

[0119] Position and pattern recognition module 40: This module is used to identify the water production location of the horizontal well based on the production profile and determine the water production pattern based on the characteristics of the DTS temperature data. This module is responsible for analyzing and judging the inversion results and original data to reach a final diagnosis conclusion.

[0120] Configuration example: a computing device (which can be shared with the aforementioned modules) and a memory storing analysis and judgment logic program codes.

[0121] Functional implementation: This module receives the oil and water phase production profiles output by the inversion calculation module 30. The program analyzes the distribution of water phase production along the wellbore and identifies the well section where the water phase production exceeds the preset threshold as the water production location. At the same time, the module accesses the original DTS temperature profile data provided by the data acquisition module 10 and analyzes the temperature change characteristics (local temperature rise or temperature rise) in the well section at the identified water production location. Through built-in rules or logic (based on the judgment basis described in step S105 of Example 1), combined with the model analysis conclusions, the observed temperature characteristics are matched with different water production modes to determine the water production mode of the water production location (bottom water breakthrough or oil and water production in the same layer).

[0122] Final output: The identified water-discharge location and well section (e.g., wellbore distance range) and the corresponding diagnosed water-discharge pattern are output in the form of reports, charts, or electronic data for user review and decision-making.

[0123] Working principle and mode of action (device embodiment)

[0124] The device realizes the diagnosis of the location and pattern of water production in horizontal wells through the collaborative work between modules. The data acquisition module collects real-time or historical DTS (optional DPS) data from downhole sensors. The temperature prediction module serves as the core computing engine and performs forward simulation based on physical mechanisms. The inversion calculation module drives an iterative optimization process, using measured data to constrain the forward model, thereby inverting the formation permeability and production profile that are crucial for diagnosis. Finally, the position and pattern recognition module conducts a comprehensive analysis of the inversion results (production profile) and the original characteristic data (temperature profile), accurately locates the water-producing well section, and determines the water-producing pattern based on key temperature change characteristics. The device provides an integrated solution that integrates complex physical modeling, numerical calculation, inversion optimization and feature judgment processes to achieve intelligent and quantitative diagnosis of horizontal well water production problems.

[0125] Technical Effect (Device Example)

[0126] By employing this device, similar benefits as the method described in Example 1 can be achieved. Furthermore, diagnostic conclusions regarding the location and pattern of water production in horizontal wells can be provided in an automated and efficient manner, significantly enhancing the intelligence level of oilfield operations and the scientific nature of decision-making. This device is particularly well-suited for analyzing data from large numbers of horizontal wells, providing mass technical support for the development of regional oil and water stabilization strategies.

[0127] Example 3: Example application and model verification

[0128] This embodiment is based on the theoretical method for interpreting the horizontal well production profile formed by the present invention. It interprets the production profile and determines the water output location for an actual horizontal well in a certain oil field where temperature and pressure data have been measured using DTS testing. The data is then compared with the flow rate data directly measured through conventional production logging to verify the reliability of the model.

[0129] Based on the well deviation data and geological data of the example well, the drilling trajectory of the horizontal section of the well can be drawn. For details, please refer to Figure 7 As shown in the figure, cesium point A has a measured depth of 970 m and a vertical depth of 585.5 m; cesium point B has a measured depth of 1930 m and a vertical depth of 582.6 m. The well was completed using a liner, with a horizontal section length of 960 m. Based on the geological commissioning report, development plan, test data, and high-pressure physical property data of the example well, the basic reservoir and wellbore parameters of the well are shown in Table 1.

[0130] Basic parameters of reservoir and wellbore of the example well

[0131]

[0132] The temperature and pressure data measured in the horizontal well are as follows: Figure 8 and Figure 9As shown in the figure, the wellbore temperature profile shows two temperature drops at the horizontal well sections, 1270m-1730m and 1850m-1910m, with the most significant drop near the horizontal well toe. The wellbore pressure profile shows significant fluctuations in wellbore pressure at the horizontal well toe.

[0133] Before starting the inversion interpretation process, first, it is necessary to divide the horizontal well section into six areas according to the change trend of the temperature observation data: Area 1 is 970-1270m, Area 2 is 1270m-1730m, Area 3 is 1730m-1850m, Area 4 is 1850m-1890m, Area 5 is 1890m-1910m, and Area 6 is 1910m-1930m. Figure 8 From the temperature observation data, it can be inferred that the formation permeability in area 5 should be the highest because the temperature here is the highest, and the formation permeability value in area 6 is the second lowest.

[0134] Combining the forward model and the MCMC inversion method, the formation permeability parameters of each partition of the horizontal well section are repeatedly adjusted to fit the calculated temperature and pressure values with the observed values until the objective function reaches the minimum value. The inversion results of the horizontal wellbore temperature and pressure are as follows: Figure 10 and Figure 1 As shown in the inversion results, it can be seen that the wellbore temperature fits well, and the wellbore pressure inversion value is generally close to the observed value, but there is a large difference near the toe of the horizontal well.

[0135] Under the fitted temperature and pressure conditions, the formation permeability distribution at this time is the inverted formation permeability distribution, such as Figure 2 As shown in the inversion result of formation permeability distribution, it can be seen that the permeability of the horizontal well toe is very high, which is consistent with the previous judgment (such as according to Figure 8 temperature data).

[0136] At the same time, the inversion interpretation of the horizontal well production profile is obtained, and the inversion results of oil and water flow distribution are as follows: Figure 3 and Figure 4 As shown. Flow rate testing was also performed using conventional production logging to obtain the horizontal well flow distribution (see the observed data curve in the inversion results diagram). The inversion results of the production profile show that the flow rate interpreted by inversion and the flow rate data directly measured through conventional production logging have the same trend and are relatively close, thus revealing the horizontal well flow distribution.

[0137] From the water flow distribution inversion results Figure 4 It can be seen that the water production position of the example well is basically consistent with the water production position inferred from the temperature data above, that is, water is produced near the wellbore distance of 1270m~1730m and 1850m~1910m. Figure 4 The inversion results of the water flow distribution show that almost 85% of the water is produced from the toe of the horizontal well (1850m to 1930m from the heel of the horizontal well). This section is a high-water-yield well section, while the oil production in this section is not high (see Figure 3 ), so it is recommended to directly carry out water blocking operation in this section to reduce the water content. Figure 8 From the original temperature data, it can be observed that the temperature of the well section shows a local decrease. According to the judgment basis of step S105 in the first embodiment, it can be determined that the water production mode here is oil and water in the same layer.

[0138] The inversion interpretation results verified the reliability of the theoretical method for interpreting horizontal well production profiles based on distributed optical fiber temperature testing. It can be used to analyze downhole flow conditions, quantitatively interpret the water production location of horizontal wells, and obtain horizontal well production dynamic information.

[0139] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for identifying the water outlet position and water outlet pattern of a horizontal well based on temperature profile characteristics of distributed optical fiber temperature sensing, characterized in that: The method comprises the following steps: obtaining temperature profile data of distributed optical fiber temperature sensors along the horizontal wellbore; establishing a multiphase flow horizontal well temperature prediction composite model as a forward model, wherein the multiphase flow horizontal well temperature prediction composite model is based on the principles of conservation of mass, conservation of momentum and conservation of energy, and takes into account the reservoir seepage, wellbore multiphase flow and coupled heat transfer process between the wellbore of the horizontal well and the oil reservoir, wherein the coupled heat transfer process takes into account the influence of micro-thermal effect and formation damage, wherein the micro-thermal effect includes thermal expansion, heat conduction, heat convection and viscous dissipation; establishing an inversion model, wherein the inversion model takes the multiphase flow horizontal well temperature prediction composite model as a forward model, takes the formation permeability along the horizontal well as a parameter to identify the target, and iteratively adjusts the formation permeability through an inversion algorithm to minimize the predicted temperature profile obtained by calculating the multiphase flow horizontal well temperature prediction composite model. A fitting evaluation objective function is formed between the surface and the obtained distributed optical fiber temperature sensing temperature profile data, so as to obtain the formation permeability distribution and production profile along the horizontal well, wherein the production profile includes the oil phase liquid production and the water phase liquid production along the horizontal well; according to the well section in which the water phase liquid production exceeds a preset threshold in the production profile, the water production position of the horizontal well is identified; and according to the temperature change characteristics presented by the distributed optical fiber temperature sensing temperature profile data at the identified water production position, combined with the analysis results of the temperature response under different water production modes by the multiphase flow horizontal well temperature prediction composite model, the water production mode of the horizontal well is determined, wherein, when the temperature change characteristic is a temperature increase, the water production mode is determined to be a bottom water breakthrough in a bottom water reservoir, and when the temperature change characteristic is a temperature decrease, the water production mode is determined to be oil and water production in the same layer.

2. The method according to claim 1, characterized in that The reservoir model in the multiphase flow horizontal well temperature prediction composite model is a three-dimensional unsteady multiphase flow model, and is solved using an implicit pressure and explicit saturation method.

3. The method according to claim 1, characterized in that The wellbore model in the multiphase flow horizontal well temperature prediction composite model is a steady-state multiphase flow wellbore model, which includes a wellbore pressure gradient equation based on conservation of mass and momentum, and a wellbore temperature equation based on conservation of energy and taking into account the Joule-Thomson effect, convective heat exchange between the wellbore and the reservoir, and the influence of gravity.

4. The method according to claim 1, wherein The multiphase flow horizontal well temperature prediction composite model simultaneously solves the flow from the reservoir to the damage zone and the flow from the damage zone to the wellbore when considering the influence of formation damage. The damage zone is characterized by the contamination radius and the damage zone permeability.

5. The method according to claim 1, characterized in that The coupled heat transfer process is realized by coupling the reservoir temperature model and the wellbore temperature model, wherein the temperature of the fluid flowing into the wellbore is obtained by solving the simplified radial steady-state temperature equation at the sand surface between the reservoir and the wellbore, wherein the simplified radial steady-state temperature equation takes into account the heat convection term, the viscous dissipation term and the heat conduction term.

6. The method according to claim 1, characterized in that The inversion algorithm is a Markov chain Monte Carlo inversion method, which uses random walk sampling to generate candidate formation permeabilities.

7. The method according to claim 1, characterized in that It also includes obtaining distributed optical fiber pressure sensing pressure profile data along the horizontal wellbore; and the fitting evaluation objective function also includes a difference term between the predicted pressure profile calculated according to the multiphase flow horizontal well temperature prediction composite model and the obtained distributed optical fiber pressure sensing pressure profile data, and the fitting evaluation objective function is a weighted least squares function.

8. The method according to any one of claims 1 to 7, characterized in that Before establishing the inversion model, the horizontal wellbore is partitioned according to the obtained distributed optical fiber temperature sensor temperature profile data and the change trend of its temperature derivative, and a unified formation permeability is set in each partition as an adjustment parameter of the inversion algorithm.

9. A device for identifying the water outlet position and water outlet pattern of a horizontal well based on temperature profile characteristics of distributed optical fiber temperature sensing, characterized in that: include: A data acquisition module, configured to acquire temperature profile data from distributed optical fiber temperature sensors along the horizontal wellbore; The temperature prediction module is used to establish a multiphase flow horizontal well temperature prediction composite model as a forward model. The multiphase flow horizontal well temperature prediction composite model is based on the principles of conservation of mass, conservation of momentum and conservation of energy, and takes into account the reservoir seepage, wellbore multiphase flow and coupled heat transfer process between the wellbore of the horizontal well and the reservoir. The coupled heat transfer process takes into account the influence of micro-thermal effect and formation damage. The micro-thermal effect includes thermal expansion, heat conduction, heat convection and viscous dissipation. The inversion calculation module is used to establish an inversion model. The inversion model Using the multiphase flow horizontal well temperature prediction composite model as a forward model and the formation permeability along the horizontal well as a parameter to identify a target, iteratively adjusting the formation permeability through an inversion algorithm to minimize a fitting evaluation objective function between a predicted temperature profile calculated by the multiphase flow horizontal well temperature prediction composite model and the acquired distributed optical fiber temperature sensor temperature profile data, thereby obtaining a formation permeability distribution and a production profile along the horizontal well, wherein the production profile includes oil phase liquid production and water phase liquid production along the horizontal well; The position and pattern recognition module is used to identify the water production position of the horizontal well based on the well section in which the water phase production exceeds a preset threshold in the production profile; and determine the water production pattern of the horizontal well based on the temperature change characteristics presented by the distributed optical fiber temperature sensor temperature profile data at the identified water production position, combined with the analysis results of the temperature response under different water production patterns by the multiphase flow horizontal well temperature prediction composite model, wherein when the temperature change characteristic is a temperature increase, the water production pattern is determined to be bottom water breakthrough in a bottom water reservoir, and when the temperature change characteristic is a temperature decrease, the water production pattern is determined to be oil and water production in the same layer.

10. The device according to claim 9, characterized in that The reservoir model in the multiphase flow horizontal well temperature prediction composite model established by the temperature prediction module is a three-dimensional unsteady multiphase flow model, and is solved using an implicit pressure and explicit saturation method.

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