Joint inversion method for wellbore fluid production profile based on DTS and DAS data

Abstract

The invention discloses a shaft fluid production profile joint inversion method based on DTS and DAS data, and relates to the technical field of oil and gas exploitation monitoring. According to the technical key points, a DTS forward model and a DAS forward model are established respectively, a multi-target optimization model is established based on the minimum joint error of model calculation data and measured data of DTS and DAS, DTS and DAS signal joint inversion is performed through a multi-target optimization algorithm, the optimal parameters of the wellbore liquid production profile are obtained, and the interpretation precision of the underground injection-production profile is improved.

Description

Technical Field

[0001] The invention relates to the technical field of oil and gas production monitoring, and in particular to a joint inversion method of a wellbore fluid production profile based on DTS and DAS data. Background Art

[0002] Distributed temperature sensing (DTS) and distributed acoustic sensing (DAS) technologies are widely used to provide continuous and accurate monitoring of reservoir conditions and have become an important part of real-time downhole monitoring. Accurate inversion of DTS and DAS data is essential for identifying key water injection channels, optimizing the utilization of remaining oil reserves, and accurately predicting production indicators. However, the task of inverting subsurface properties and fluid dynamics information using DAS and DTS data is very complex, and existing inversion algorithms often show inefficiency and lack of robustness when dealing with forward models with a large number of parameters and high computational costs.

[0003] With the rapid development of artificial intelligence technology, many machine learning methods have been applied to the field of reservoir development, and significant progress has been made in integrating machine learning and intelligent algorithms in inverting DTS and DAS data. However, existing studies usually rely only on DTS or DAS data, ignoring the potential connection between DAS and DTS data, and have not yet achieved simultaneous inversion of these two types of data. The accuracy of interpretation of downhole injection and production profile information still needs to be further improved. Summary of the invention

[0004] The present invention provides a joint inversion method for a wellbore fluid production profile based on DTS and DAS data, so as to solve the problem that the inversion of single DAS or DTS data has insufficient accuracy in diagnosing a wellbore water injection profile.

[0005] The present invention is achieved through the following technical solutions:

[0006] A first aspect of the present invention provides a joint inversion method for a wellbore fluid production profile based on DTS and DAS data, comprising:

[0007] Establishing a DTS forward model and a DAS forward model respectively, wherein the DTS forward model is used to simulate the temperature and pressure distribution in the wellbore, and the DAS forward model is used to simulate the flow distribution in the wellbore;

[0008] Based on the goal of minimizing the joint error between the error between the DTS calculated data and the measured data and the error between the DAS calculated data and the measured data, a multi-objective optimization model is established, wherein the DTS calculated data is calculated according to the DTS forward model, and the DAS calculated data is calculated according to the DAS forward model;

[0009] The multi-objective optimization model is solved by a multi-objective optimization algorithm to obtain the optimal parameters of the wellbore fluid production profile.

[0010] The present invention establishes a forward model of distributed temperature sensing (DTS) and distributed acoustic sensor (DAS) signals, combines multi-objective optimization problems, and establishes a joint inversion multi-objective optimization model of DTS and DAS data for solving the production profile. The mutual influence between low-frequency acoustic signals and temperature signals is considered, and combined with a multi-objective optimization algorithm, the DTS and DAS data are inverted at the same time. Compared with the inversion of a single DTS or DAS signal, the water injection profile diagnosis accuracy of horizontal water injection wells in the reservoir is improved, thereby optimizing the oilfield development strategy and improving the oil and gas recovery rate.

[0011] Furthermore, the DTS forward model includes a wellbore flow model and a wellbore thermal model; the wellbore flow model is expressed as:

[0012]

[0013] Where P is the wellbore pressure, x is the wellbore depth, ρ w is the density of the fluid in the wellbore, f is the friction coefficient of the wellbore wall, v w is the velocity of injected water in the wellbore, θ is the wellbore inclination angle, g is the gravitational acceleration, and R is the flow coefficient;

[0014] The wellbore thermal model is expressed as:

[0015]

[0016] Where T is the temperature of the reservoir wall, K JT is the Joule-Thomson coefficient, β is the correction coefficient, ρ is the wellbore density, C p is the specific heat capacity of the wellbore, α w is the total heat transfer coefficient of injected water from the wellbore to the wellbore wall, ρ w is the density of the fluid in the wellbore, v w is the velocity of injected water in the wellbore, R is the flow coefficient, and T1 is the initial temperature.

[0017] Furthermore, the method of establishing the DAS forward model includes:

[0018] S3-1, establish a quantitative model of DAS signal and flow rate, expressed as:

[0019]

[0020] Among them, RMS f [i,m] is the DAS signal strength generated by the pipe flow at time m and wellbore section i, RMS p [i,m] is the DAS signal intensity generated along the reservoir direction at time m and wellbore section i, θ1 and θ2 are unknown coefficients, Qi is the pipe flow rate at wellbore section i, q j is the flow rate in the reservoir direction, κ j is the flow coefficient in the reservoir direction;

[0021] S3-2, establish the parameter optimization objective function, expressed as:

[0022]

[0023] Among them, RMS true [i,m] is the DAS measured signal strength at time m and wellbore section i, Q is the production profile vector, Q trial is the given value of the production profile quantity;

[0024] S3-3, based on the given production profile vector, solve the parameter optimization objective function to obtain the values ​​of the unknown coefficients θ1 and θ2, and substitute the solved θ1 and θ2 into the DAS signal and flow rate quantitative model to obtain the DAS forward model.

[0025] Furthermore, when solving the parameter optimization objective function, the simulated annealing algorithm, the particle swarm optimization algorithm or the genetic algorithm is used to perform random perturbations on the undetermined coefficients θ1 and θ2, where θ1 is in [e -6 ,e -1 ] range, θ2 is randomly perturbed in [e -8 ,e -4 ] until the values ​​of θ1 and θ2 satisfy the optimization objective function.

[0026] Furthermore, the multi-objective optimization model is expressed as:

[0027] minimize(F(x)=(f1(x), f2(x)));

[0028] Among them, f1(x) is the error between the DAS calculated data and the DAS measured data at the wellbore depth x, f2(x) is the error between the DTA calculated data and the DTA measured data at the wellbore depth x, and F(x) is the joint error of f1(x) and f2(x).

[0029] Furthermore, solving the multi-objective optimization model by a multi-objective optimization algorithm includes:

[0030] S6-1, Initialize the particle swarm: Each particle represents a set of DAS and DTS fitting parameters, and randomly initialize the position x of a group of particles in the search space i (0) and speed v i (0), where i = 1, 2, ..., N, N is the number of particles;

[0031] S6-2, calculate the fitness of each particle, based on the Pareto dominance criterion, using Pareto rank and crowding distance to evaluate the fitness;

[0032] S6-3, update the optimal position p of each particle i And the global optimal position g:

[0033] p i =x i (t),if f j (x i )<f j (p i (t-1));

[0034] g = x i (t),if f j (x i )<f j (g(t-1));

[0035] Among them, p i represents the updated position of the ith particle, x i (t) represents the position of the i-th particle in the t-th iteration, f j (x i ) indicates that the i-th particle is at its current position x i The jth objective function value at j (P i (t-1)) represents the position of the i-th particle at its individual optimal position P i The jth objective function value at (t-1) (i.e., the best position in the t-1th iteration), g represents the global optimal position of the particle swarm, and f j (g(t-1)) represents the jth objective function value at the global optimal position g(t-1) (i.e., the global optimal position in the t-1th iteration);

[0036] S6-4, update the particle's velocity and position:

[0037] v i (t+1)=ωv i (t)+c1r1(p i (t)-x i (t))+c2r2(p i (t)-x i (t));

[0038] x i (t+1)=x i (t)+v i (t+1);

[0039] Among them, v i(t+1) is the velocity of the ith particle in the t+1th iteration, v i (t) is the velocity of the ith particle in the tth iteration, ω is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers in the interval [0,1], and p i (t) is the optimal position of the ith particle in the tth iteration, x i (t+1) is the position of the ith particle in the t+1th iteration, x i (t) is the position of the i-th particle in the t-th iteration;

[0040] S6-5, combined with NSGA-Ⅱ algorithm, introduces new solutions through crossover and mutation operations to update the particle swarm;

[0041] S6-6, using the DAS forward model and the DTS forward model to evaluate the fitness of the particles in the updated particle swarm, and selecting the non-dominated solution set to update the Pareto frontier;

[0042] S6-7, repeat steps S6-2 to S6-6 until the maximum number of iterations is reached, and output the final Pareto front as the non-dominated solution set of DAS and DTS fitting parameters.

[0043] Furthermore, before solving, the flow parameters in the fitting parameters are subjected to dimensionality reduction processing through an autoencoder to obtain low-dimensional flow features. When solving, the low-dimensional flow features are used for iterative calculation, and the feature dimension of the low-dimensional flow features is less than or equal to 64.

[0044] Furthermore, the autoencoder adopts a bidirectional long short-term memory network as a basic framework, including an encoder and a decoder; flow samples with different production rates distributed along the wellbore space are generated by Perlin noise as a training set, and the encoder and decoder are jointly trained. The trained encoder is used to perform dimensionality reduction processing on flow parameters.

[0045] A second aspect of the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for joint inversion of wellbore fluid production profile based on DTS and DAS data as described in any one of the first aspects of the present invention is implemented.

[0046] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the joint inversion method for wellbore fluid production profile based on DTS and DAS data as described in any one of the first aspects of the present invention.

[0047] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0048] A multi-objective optimization model for joint inversion is established by combining the forward models of DTS and DAS, and a multi-objective optimization algorithm is used to solve the DTS and DAS fitting parameters. Due to the integration of the mutual relationship between DTS and DAS data, the profile interpretation accuracy is improved compared with single DTS or DAS inversion.

[0049] A DTS forward model for simulating the temperature and pressure distribution in the wellbore and a DAS forward model for simulating the flow distribution in the wellbore are proposed. Through joint inversion, the downhole injection and production profile information considering different signals is derived. An innovative and effective method is given for the interpretation of the horizontal well water absorption profile, and a solution is provided for the joint quantitative use of DTS and DAS data.

[0050] A solution method combining NSGA-Ⅱ algorithm and hybrid multi-objective particle swarm optimization is proposed. New solutions are introduced through hybrid strategies to prevent the algorithm from falling into local optimality, ensure the diversity and good distribution of the solution set, and select the best fitting parameters based on the optimization results.

[0051] Combined with deep learning, the flow is reduced from the original dimension to a feature space of 64 dimensions or even lower dimensions. In the subsequent steps, the optimization algorithm is solved for the feature space, which speeds up the inversion speed and improves the optimization robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without creative work. In the drawings:

[0053] Figure 1 is a DTS and DAS joint inversion flow chart of an embodiment of the present invention;

[0054] Figure 2 is a diagram of an autoencoder network structure according to an embodiment of the present invention;

[0055] Figure 3 is a selected DAS data graph for inversion according to an embodiment of the present invention;

[0056] Figure 4 is a selected DTS data graph for inversion according to an embodiment of the present invention;

[0057] Figure 5 is a result of Pareto front multi-objective optimization in an embodiment of the present invention;

[0058] Figure 6It is a schematic diagram of a DAS data decomposed into flow along the wellbore and flow in the reservoir according to an embodiment of the present invention;

[0059] Figure 7 is a comparison diagram of real DAS data and calculated DAS data in an embodiment of the present invention;

[0060] Figure 8 is a comparison diagram of real DTS data and calculated DTS data according to an embodiment of the present invention;

[0061] Fig. 9 is a schematic diagram of a profile interpretation method dominated by DAS data according to an embodiment of the present invention;

[0062] Fig.10 It is a schematic diagram of a profile interpretation method dominated by DTS data according to an embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0064] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to or inherent to other steps or units of the device.

[0065] The terms used in the various embodiments of the application are only used to describe the purpose of specific embodiments and are not intended to limit the various embodiments of the application. As used herein, the singular form is intended to also include the plural form, unless the context clearly indicates otherwise. Unless otherwise limited, all terms used here (including technical terms and scientific terms) have the same meaning as the meanings commonly understood by ordinary technicians in the field of the various embodiments of the application. The terms (such as the terms defined in the dictionary generally used) will be interpreted as having the same meaning as the contextual meaning in the relevant technical field and will not be interpreted as having an idealized meaning or an overly formal meaning, unless clearly defined in the various embodiments of the application.

[0066] An embodiment of the present invention provides a joint inversion method for wellbore fluid production profile based on DTS and DAS data, which is used to simultaneously invert distributed temperature sensing (DTS) and distributed acoustic sensing (DAS) data to improve the injection profile diagnostic accuracy of horizontal injection wells in carbonate reservoirs, optimize oilfield development strategies, and improve oil and gas recovery rates.

[0067] See also Figure 1 The DTS and DAS joint inversion flow chart shown in the figure proposes an innovative and efficient method system, which greatly improves the diagnostic accuracy of the water injection profile and the oil and gas recovery rate. The core innovations of the present invention include the construction of the forward model, the DAS signal quantitative model, the Auto-encoder flow distribution model and the hybrid multi-objective optimization algorithm.

[0068] The present invention firstly provides a forward model of DTS and DAS signals, establishes a multi-objective model for joint inversion of DTS and DAS data for solving water absorption profiles, and provides a solution method. Based on this set of processes, the present invention provides an innovative and effective method for the interpretation of horizontal well water absorption profiles, and provides a solution for the joint quantitative use of DTS and DAS data.

[0069] One implementation method includes the following steps:

[0070] S1, establish DTS forward model and DAS forward model respectively;

[0071] S2, based on the goal of minimizing the joint error between the error between the DTS calculated data and the measured data and the error between the DAS calculated data and the measured data, a multi-objective optimization model is established, wherein the DTS calculated data is calculated according to the DTS forward model, and the DAS calculated data is calculated according to the DAS forward model.

[0072] S3, solves the multi-objective optimization model of S2 through a multi-objective optimization algorithm to obtain the optimal parameters of the wellbore fluid production profile.

[0073] Among them, the DTS forward model is used to simulate the temperature and pressure distribution in the wellbore, including the wellbore flow model and the wellbore thermal model, and the DAS forward model is used to simulate the flow distribution in the wellbore.

[0074] Furthermore, based on the principles of conservation of mass, momentum and energy, a wellbore flow model for injected water and a wellbore thermal model are established. The flow of the fluid includes the flow along the wellbore axis and the radial flow into the wellbore flow model.

[0075] 1. Wellbore flow model.

[0076] According to the law of conservation of mass and momentum, the pressure gradient equation of the injected water wellbore is expressed as:

[0077]

[0078] Where P is the wellbore pressure, x is the wellbore depth, ρ w is the density of the fluid in the wellbore, f is the friction coefficient of the wellbore wall, v wis the velocity of injected water in the wellbore, θ is the wellbore inclination angle, g is the gravitational acceleration, and R is the flow coefficient;

[0079] 2. Wellbore thermal model.

[0080] Under steady-state conditions, the steady-state wellbore temperature expression is established as:

[0081]

[0082] Where T is the temperature of the reservoir wall, K JT is the Joule-Thomson coefficient, β is the correction coefficient, ρ is the wellbore density, C p is the specific heat capacity of the wellbore, α w is the total heat transfer coefficient of injected water from the wellbore to the wellbore wall, ρ w is the density of the fluid in the wellbore, v w is the velocity of injected water in the wellbore, R is the flow coefficient, and T1 is the initial temperature.

[0083] Finally, the fourth-order Runge-Kutta format is used to solve the system of equations consisting of the pressure gradient equation and the temperature gradient equation at one time. The solution result is the temperature profile in the wellbore, that is, the temperature profile calculated by the forward model, which is used to compare with the actual monitored DTS data to realize the inversion of reservoir parameters.

[0084] The methods for establishing DAS forward model include:

[0085] S3-1, establish the quantitative model of DAS signal and flow rate, which can be expressed as:

[0086]

[0087]

[0088] Among them, RMS f [i,m] is the DAS signal strength generated by the pipe flow at time m and wellbore section i, RMS p [i,m] is the DAS signal intensity generated along the reservoir direction at time m and wellbore section i, θ1 and θ2 are unknown coefficients, Q i is the pipe flow rate, q j is the flow rate in the reservoir direction, κ j is the flow coefficient in the reservoir direction.

[0089] Specifically, the DAS signal is obtained by intercepting the relatively stable DAS data S of the horizontal section of the wellbore within a certain period of time at the end of the injection test, and performing short-time Fourier transform (STFT) processing with the same parameters on the data S at each depth of the wellbore. By adjusting the length parameter of the window function, the frequency resolution is made higher, and the general frequency resolution is higher than 0.5 Hz, that is, the two-dimensional time-frequency representation of the acoustic wave data of the wellbore at the i-th depth is obtained:

[0090]

[0091] Where X[i,m,k] is the spectrum representation at borehole depth index i, time index m, and frequency index k; N is the time window length; S[n] is the discrete DAS signal, n is the time index of the discrete point; ω[nm] is the window function of length N, centered at time m, e -j2πkn / N It is the complex exponential representation of frequency k, where k = 0, 1, …, N.

[0092] Considering that the response of DAS to fluid is concentrated in low frequency, a low frequency interval k1-k2 is selected for each depth index i, such as 1-50 Hz amplitude, and the corresponding root mean square (RMS) is calculated without being limited to this interval. The calculation formula is as follows:

[0093]

[0094] Wherein, K is the number of frequency points in the frequency interval, K=k2-k1+1.

[0095] S3-2, establish parameter optimization objective function,

[0096] Consider the minimum error between the calculated value and the actual value: RMS true [i,m]=RMS f [i,m]+RMS p [i,m], where RMS true [i,m] is obtained by processing the measured signal data, and the parameter optimization objective function is established, which is expressed as:

[0097]

[0098] Among them, RMS true [i,m] is the DAS measured signal strength at time m and wellbore section i, Q is the production profile vector, Q trial is the given value of the production profile quantity.

[0099] S3-3, based on the given production profile vector, solve the parameter optimization objective function to obtain the values ​​of the unknown coefficients θ1 and θ2, and substitute the solved θ1 and θ2 into the DAS signal and flow rate quantitative model to obtain the DAS forward model.

[0100] Furthermore, in the process of solving the objective function, based on the given production profile vector Q = Q trial , the optimization problem can be solved by using simulated annealing algorithm, particle swarm optimization algorithm or genetic algorithm, and the variables θ1 and θ2 are perturbed within the response frequency band amplitude range, such as θ1 in [e -6 ,e -1 ] range, θ2 is randomly perturbed in [e -8 ,e -4 ] range, but not limited to this. The interval range may need to be selected according to the selected response segment amplitude. Substitute variables θ1 and θ2 into the objective function, and update the parameters according to the corresponding algorithm until the values ​​of θ1 and θ2 satisfy the calculated values ​​and the measured values ​​are equal or within a certain error range, and the unknown parameters θ1 and θ2 are obtained.

[0101] Finally, the response of the current production profile to DAS is calculated using the solved parameters θ1 and θ2, i.e., the DAS forward model. This model is denoted as DAS calculated (Q).

[0102] In the present invention, the goal of inversion is to find the minimum value of the fitting error between the original DTS and DAS data and the forward model data. The fitting error between the original DAS data and the forward model data can be expressed as:

[0103] f1(x)=min(DAS true -DAS calculated ) 2

[0104] Among them, DAS true It is measured data, DAS calculated It is the model calculation data.

[0105] The fitting error between the original DTS data and the forward model data can be expressed as:

[0106] f2(x)=min(DTS true -DTS calculated ) 2

[0107] Among them, DTS true It is measured data, DTS calculated It is the model calculation data.

[0108] The joint inversion model is described as follows:

[0109] minimize(F(x)=(f1(x),f2(x)))

[0110] Among them, f1(x) is the error between the DAS calculated data and the DAS measured data at the wellbore depth x, f2(x) is the error between the DTA calculated data and the DTA measured data at the wellbore depth x, and F(x) is the objective function vector, which is the joint error of f1(x) and f2(x), expressed as the vector form of two errors. The constraint condition is x∈Ω, Ω is the feasible region of the decision space, which is restricted by the physical conditions of the wellbore.

[0111] Furthermore, the present invention adopts a hybrid multi-objective particle swarm optimization (HMPSO) algorithm to solve the multi-objective optimization model. In HMPSO, a group of particles (candidate solutions) move in the search space, adjust their positions based on their own experience and the experience of their neighbors, and gradually approach the optimal solution. Multi-objective optimization involves optimizing multiple conflicting objective functions at the same time, and the Pareto front is usually used to represent the non-dominated solution set. On this basis, combined with the non-dominated sorting genetic algorithm II (NSGA-Ⅱ), new solutions are introduced through crossover and mutation operations to prevent the algorithm from falling into the local optimum. The optimization steps are as follows:

[0112] S6-1, Initialize the particle swarm: Each particle represents a set of DAS and DTS fitting parameters, and randomly initialize the position x of a group of particles in the search space i (0) and speed v i (0), where i = 1, 2, ..., N, and N is the number of particles.

[0113] S6-2, calculate the fitness of each particle Fitness(x i ), based on the Pareto dominance criterion, using the Pareto level (P l ) and crowding distance (C d ) to evaluate fitness.

[0114] S6-3, update the optimal position p of each particle i And the global optimal position g:

[0115] p i =x i (t),if f j (x i )<f j (p i (t-1))

[0116] g = x i (t),if f j (x i )<f j(g(t-1))

[0117] Among them, p i represents the updated position of the ith particle, x i (t) represents the position of the i-th particle in the t-th iteration, f j (x i ) indicates that the i-th particle is at its current position x i The jth objective function value at j (P i (t-1)) represents the position of the i-th particle at its individual optimal position P i The jth objective function value at (t-1) (i.e., the best position in the t-1th iteration), g represents the global optimal position of the particle swarm, and f j (g(t-1)) represents the jth objective function value at the global optimal position g(t-1) (i.e., the global optimal position in the t-1th iteration). The objective function value of each particle, that is, the fitting error represented by each particle, the fitness function takes the inverse of the fitting error. The smaller the error, the higher the fitness.

[0118] According to the fitness evaluation results, the most suitable group of particles is selected in the current iteration step as the parent of the next iteration for the next optimization iteration.

[0119] S6-4, update the particle's velocity and position:

[0120] v i (t+1)=ωv i (t)+c1r1(p i (t)-x i (t))+c2r2(p i (t)-x i (t))

[0121] x i (t+1)=x i (t)+v i (t+1)

[0122] Among them, v i (t+1) is the velocity of the ith particle in the t+1th iteration, v i (t) is the velocity of the ith particle in the tth iteration, ω is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers in the interval [0,1], and p i (t) is the optimal position of the ith particle in the tth iteration, x i (t+1) is the position of the ith particle in the t+1th iteration, x i (t) is the position of the ith particle at the tth iteration.

[0123] S6-5, combined with the NSGA-Ⅱ algorithm, introduces new solutions through crossover and mutation operations and updates the particle swarm.

[0124] S6-6, the DAS forward model and the DTS forward model are used to evaluate the fitness of the particles in the updated particle swarm, and the non-dominated solution set is selected to update the Pareto front to ensure the diversity and good distribution of the solution set.

[0125] S6-7, repeat steps S6-2 to S6-6 until the maximum number of iterations is reached, and output the final Pareto front PF* as the non-dominated solution set of DAS and DTS fitting parameters.

[0126] Finally, according to actual needs and actual reservoir conditions, the solution in the final Pareto front PF* is selected as the fitting parameter for the joint inversion of DTS and DAS.

[0127] Furthermore, before solving the problem, the flow parameters in the fitting parameters are reduced in dimension through the autoencoder to obtain low-dimensional flow features. When solving the problem, the low-dimensional flow features are used for iterative calculation.

[0128] In the above-mentioned solution process using the HMPSO algorithm, the fitting parameters of DAS and DTS are the feature vectors of the reservoir parameters after compression and dimensionality reduction by the neural network, that is, the latent variable features output by the auto-encoder, which are the feature vectors of the reservoir parameters after compression and dimensionality reduction by the neural network.

[0129] In the present invention, the flow rate at each depth point is actually the object that needs to be solved, but the dimension of the flow solution space is too large (often thousands), so the present invention introduces an autoencoder to reduce the flow rate from the original dimension to a feature space of 64 dimensions or even lower dimensions. In the subsequent steps, an optimization algorithm is performed on the feature space to speed up the inversion speed and improve the optimization robustness.

[0130] Furthermore, considering that the traffic should show a certain but limited spatial continuity, which means that there is a sequential relationship between adjacent grid points. Therefore, the bidirectional long short-term memory network (Bi-LSTM) framework is adopted as the basic architecture of the Auto-encoder. Figure 2 As shown in the autoencoder network structure diagram, the auto-encoder is divided into two main modules: encoder and decoder.

[0131] The encoder and decoder parts of the Auto-encoder are used to establish the mapping x = f encoder (Q) and Q = f decoder (x), where x is the latent variable output by the encoder and Q is the traffic data.

[0132] When solving multi-objective problems, the forward model of DTS and DAS is used to calculate the DTS and DAS data, and the HMPSO multi-objective optimization algorithm is used to calculate and measure the DTS and DAS in the feature space x to perform error minimization fitting and solution.

[0133] During model training, flow samples with different production rates and spatial distribution along the wellbore are generated using Perlin noise as training sets, and the encoder and decoder are jointly trained. The trained encoder is used to perform dimensionality reduction processing on the flow parameters.

[0134] In order to train a robust Auto-encoder, a large number of samples are required. It is not feasible to use simple random noise to generate flow rates at various wellbore depths because noise may seriously destroy continuity and generate meaningless samples. In order to increase the diversity of samples and the diversity of spatial relationships, the present invention uses Perlin noise to generate samples with different but reasonable production rates distributed along the wellbore space.

[0135] Through the above-mentioned joint inversion method, the present invention conducts a joint inversion experiment of wellbore fluid production profile based on DTS and DAS data.

[0136] 1. First, establish the DTS and DAS forward models.

[0137] This step is based on the principles of conservation of mass, momentum and energy. Through the laws of conservation of mass and momentum, the pressure gradient and temperature distribution in the wellbore are calculated to obtain the theoretical model of DTS data. By processing the DAS data, the short-time Fourier transform (STFT) is used to perform spectrum analysis on the DAS signal, extract the amplitude characteristics of the low-frequency range, capture the movement of the fluid in the wellbore, and establish a quantitative relationship between the DAS signal and the flow rate. Figure 3 Figure 1 shows selected DAS data used for inversion. Figure 4 Figure 2 shows selected DTS data used for inversion, where the short-time Fourier transformed DAS data are used for subsequent model optimization.

[0138] 2. Establish a multi-objective optimization model.

[0139] After the forward model of DTS and DAS is established, the two types of data, DTS and DAS, are combined and jointly inverted using a multi-objective optimization algorithm.

[0140] 3. Application of encoder-decoder neural network.

[0141] In order to effectively process high-dimensional traffic data, the Auto-encoder model is introduced to compress high-dimensional traffic distribution data into a low-dimensional feature space, reducing the computational complexity of the optimization solution.

[0142] 4. Apply multi-objective optimization algorithm.

[0143] After completing the construction of the forward model and flow distribution model of DTS and DAS data, the hybrid multi-objective particle swarm optimization algorithm (HMPSO) is used to jointly invert the DTS and DAS data to minimize the data fitting error.

[0144] During the inversion process, the fitting errors of DTS and DAS data are calculated and the minimum value of the error is sought in the optimization algorithm. Figure 5 The Pareto front in shows the results of multi-objective optimization, and each point represents a different set of inversion solutions. The optimization process combines the non-dominated sorting genetic algorithm (NSGA-Ⅱ) to introduce new solutions through hybrid strategies.

[0145] 5. Result analysis and optimization.

[0146] After the optimization process is completed, the inversion results are compared with the real data to evaluate the accuracy of the model. Figure 6 The DAS data are decomposed into independent contributions of flow along the wellbore and flow in the reservoir, and the real DAS data are compared with the calculated DAS data. Figure 7 The accuracy of the inversion results can be evaluated through error analysis. Figure 8 The comparison between the real DTS data and the DTS data calculated by the forward model is shown to ensure that the temperature curve is consistent with the actual situation and to verify the accuracy of the model.

[0147] After obtaining reliable inversion results, Fig. 9 The DAS data-driven interpretation approach shown and Fig.10 The DTS data-driven interpretation method shown is used to analyze the water injection profile and determine the distribution of fluids in the horizontal well.

[0148] In summary, the present invention introduces the Auto-encoder technology to compress the high-dimensional space of flow distribution into a low-dimensional feature space, and then uses the optimization algorithm to solve it, thereby improving the efficiency and robustness of the inversion. The bidirectional long short-term memory network framework is used as the basic architecture of the Auto-encoder to ensure the spatial continuity and optimization effect of the flow distribution. In the joint inversion process, a hybrid multi-objective particle swarm optimization algorithm is adopted. By introducing the hybrid strategy of the NSGA-Ⅱ algorithm and combining the multi-objective optimization model, the error minimization fitting of the DTS and DAS data is achieved, forming a complete set of processes to achieve efficient joint inversion of DTS and DAS data, and significantly improving the diagnostic accuracy of the horizontal well water absorption profile.

[0149] An embodiment of the present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for joint inversion of wellbore fluid production profile based on DTS and DAS data as described in any one of the above embodiments of the present invention is implemented.

[0150] As a computer-readable storage medium, the memory can be used to store software programs, computer executable programs and modules. The processor executes various functional applications and data processing of the electronic device by running the software programs, instructions and modules stored in the memory. The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network and a combination thereof.

[0151] The embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the method for joint inversion of wellbore fluid production profile based on DTS and DAS data of any embodiment of the present invention is implemented. The storage medium may be ROM / RAM, a magnetic disk, an optical disk, etc.

[0152] An embodiment of the present invention further provides a computer program product. When the computer program product is run on a computer, the computer executes the joint inversion method of wellbore fluid production profile based on DTS and DAS data according to any of the above embodiments of the present invention.

[0153] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. 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 joint inversion method for wellbore fluid production profile based on DTS and DAS data, characterized in that: include: Establishing a DTS forward model and a DAS forward model respectively, wherein the DTS forward model is used to simulate the temperature and pressure distribution in the wellbore, and the DAS forward model is used to simulate the flow distribution in the wellbore; A multi-objective optimization model is established based on the goal of minimizing the joint error between the error between the DTS calculated data and the measured data and the error between the DAS calculated data and the measured data, wherein the DTS calculated data is calculated according to the DTS forward model, and the DAS calculated data is calculated according to the DAS forward model; The multi-objective optimization model is solved by a multi-objective optimization algorithm to obtain the optimal parameters of the wellbore fluid production profile.

2. The joint inversion method of wellbore fluid production profile based on DTS and DAS data according to claim 1 is characterized in that: The DTS forward model includes a wellbore flow model and a wellbore thermal model; the wellbore flow model is expressed as: Where P is the wellbore pressure, x is the wellbore depth, ρ w is the density of the fluid in the wellbore, f is the friction coefficient of the wellbore wall, v w is the velocity of injected water in the wellbore, θ is the wellbore inclination angle, g is the gravitational acceleration, and R is the flow coefficient; The wellbore thermal model is expressed as: Where T is the temperature of the reservoir wall, K JT is the Joule-Thomson coefficient, β is the correction coefficient, ρ is the wellbore density, C p is the specific heat capacity of the wellbore, α w is the total heat transfer coefficient of injected water from the wellbore to the wellbore wall, ρ w is the density of the fluid in the wellbore, v w is the velocity of injected water in the wellbore, R is the flow coefficient, and T1 is the initial temperature.

3. The joint inversion method of wellbore fluid production profile based on DTS and DAS data according to claim 1, characterized in that: The methods for establishing DAS forward model include: S3-1, establish a quantitative model of DAS signal and flow rate, expressed as: Among them, RMS f [i,m] is the DAS signal strength generated by the pipe flow at time m and wellbore section i, RMS p [i,m] is the DAS signal intensity generated along the reservoir direction at time m and wellbore section i, θ1 and θ2 are unknown coefficients, Q i is the pipe flow rate at wellbore section i, q j is the flow rate in the reservoir direction, κ j is the flow coefficient in the reservoir direction; S3-2, establish the parameter optimization objective function, expressed as: Among them, RMS true [i,m] is the DAS measured signal strength at time m and wellbore section i, Q is the production profile vector, Q trial is the given value of the production profile quantity; S3-3, based on the given production profile vector, solve the parameter optimization objective function to obtain the values ​​of the unknown coefficients θ1 and θ2, and substitute the solved θ1 and θ2 into the DAS signal and flow rate quantitative model to obtain the DAS forward model.

4. The joint inversion method of wellbore fluid production profile based on DTS and DAS data according to claim 3 is characterized in that: When solving the parameter optimization objective function, the simulated annealing algorithm, particle swarm optimization algorithm or genetic algorithm are used to perform random perturbations on the undetermined coefficients θ1 and θ2, where θ1 is in [e -6 ,e -1 ] range, θ2 is randomly perturbed in [e -8 ,e -4 ] until the values ​​of θ1 and θ2 satisfy the optimization objective function.

5. The joint inversion method of wellbore fluid production profile based on DTS and DAS data according to claim 1, characterized in that: The multi-objective optimization model is expressed as: minimize(F(x)=(f1(c),d2(x))); Among them, f1(x) is the error between the DAS calculated data and the DAS measured data at the wellbore depth x, f2(x) is the error between the DTA calculated data and the DTA measured data at the wellbore depth x, and F(x) is the joint error of f1(x) and f2(x).

6. The joint inversion method of wellbore fluid production profile based on DTS and DAS data according to claim 1, characterized in that: Solving the multi-objective optimization model by a multi-objective optimization algorithm includes: S6-1, Initialize the particle swarm: Each particle represents a set of DAS and DTS fitting parameters, and randomly initialize the position x of a group of particles in the search space i (0) and speed v i (0), where i = 1, 2, ..., N, N is the number of particles; S6-2, calculate the fitness of each particle, based on the Pareto dominance criterion, using the Pareto rank and crowding distance to evaluate the fitness; S6-3, update the optimal position p of each particle i And the global optimal position g: p i =x i (t),if f j (x i )<f j (p i (t-1)); g=x i (t),if f j (x i )<f j (g(t-1)); Among them, p i represents the updated position of the ith particle, x i (t) represents the position of the i-th particle in the t-th iteration, f j (x i ) indicates that the i-th particle is at its current position x i The jth objective function value at j (P i (t-1)) represents the position of the i-th particle at its individual optimal position P i The jth objective function value at (t-1) (i.e., the best position in the t-1th iteration), g represents the global optimal position of the particle swarm, and f j (g(t-1)) represents the jth objective function value at the global optimal position g(t-1) (i.e., the global optimal position in the t-1th iteration); S6-4, update the particle's velocity and position: v i (t+1)=ωv i (t)+c1r1(p i (t)-x i (t))+c2r2(p i (t)-x i (t)); x i (t+1)=x i (t)+v i (t+1); Among them, v i (t+1) is the velocity of the ith particle in the t+1th iteration, v i (t) is the velocity of the ith particle in the tth iteration, ω is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers in the interval [0,1], and p i (t) is the optimal position of the ith particle in the tth iteration, x i (t+1) is the position of the ith particle in the t+1th iteration, x i (t) is the position of the i-th particle in the t-th iteration; S6-5, combined with NSGA-Ⅱ algorithm, introduces new solutions through crossover and mutation operations to update the particle swarm; S6-6, using the DAS forward model and the DTS forward model to evaluate the fitness of the particles in the updated particle swarm, and selecting the non-dominated solution set to update the Pareto frontier; S6-7, repeat steps S6-2 to S6-6 until the maximum number of iterations is reached, and output the final Pareto front as the non-dominated solution set of DAS and DTS fitting parameters.

7. The joint inversion method of wellbore fluid production profile based on DTS and DAS data according to claim 6, characterized in that: Before solving, the flow parameters in the fitting parameters are subjected to dimensionality reduction processing through the autoencoder to obtain low-dimensional flow features. When solving, the low-dimensional flow features are used for iterative calculation, and the feature dimension of the low-dimensional flow features is less than or equal to 64.

8. The joint inversion method of wellbore fluid production profile based on DTS and DAS data according to claim 7, characterized in that: The autoencoder adopts a bidirectional long short-term memory network as a basic framework, including an encoder and a decoder; flow samples with different production rates distributed along the wellbore space are generated by Perlin noise as a training set, and the encoder and decoder are jointly trained. The trained encoder is used to perform dimensionality reduction processing on flow parameters.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for joint inversion of wellbore fluid production profile based on DTS and DAS data as described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for joint inversion of wellbore fluid production profile based on DTS and DAS data as described in any one of claims 1 to 8 is implemented.

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