Oil-water well liquid production profile recognition and inversion method, system and program product

Abstract

The invention discloses an oil-water well liquid production profile identification and inversion method, system and program product, relates to the technical field of oil-gas field development, and solves the problems that in an existing liquid production profile monitoring method based on DAS and DTS technologies, DAS signals are prone to being interfered by fluid flow, the signal quality is poor, liquid point information cannot be accurately reflected, and the reliability is poor. The problems that liquid production profile information of DTS signal inversion lacks enough spatial constraint conditions, and the inversion result is large in deviation are solved. According to the method, the local liquid production characteristics in the DAS signals are extracted through blind source separation, the liquid outlet point positions are effectively recognized in combination with a statistical screening method, the recognized liquid outlet point positions serve as constraint conditions to be introduced into the DTS inversion process, and the precision and stability of the inversion result are effectively improved; the method not only overcomes the limitation of a traditional F-K spectral analysis method in an oil-water well environment, but also can introduce effective spatial constraints in DTS inversion, significantly reduces inversion errors, and improves the interpretation precision of a liquid production profile.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas field development, and more particularly to an oil and water well production fluid profile identification and inversion method, system and program product. Background Art

[0002] During oil and gas field development, accurately identifying the wellbore's fluid production profile along its depth is crucial for optimizing injection and production strategies, improving reservoir recovery, and extending the field's production life. Fluid production profile monitoring helps engineers understand the fluid distribution within the wellbore in real time, enabling effective adjustments to production strategies. This provides precise data support, particularly for managing multi-well, multi-layer reservoirs.

[0003] With the advancement of distributed fiber optic sensing technology, the combined application of distributed acoustic sensing (DAS) and distributed temperature sensing (DTS) has become a key trend in fluid production profile monitoring. DAS technology monitors the propagation of sound waves within the wellbore, acquiring real-time acoustic signals along the wellbore depth. DTS, on the other hand, uses temperature changes to reflect fluid movement and heat conduction. The combination of these two technologies provides powerful data support for monitoring fluid production profiles. However, in practical applications, the integration of DAS and DTS still faces a number of technical challenges.

[0004] First, there are significant challenges in the application of DAS signals in oil and water wells. Oil and water wells typically operate at low flow rates, and the fluid in the well is a two-phase mixture of oil and water with a complex flow state. In this environment, DAS signals are often affected by the complex acoustic wave interference caused by fluid disturbances, resulting in poor signal quality. Especially when oil and water are mixed, the properties and fluctuations of the fluid will cause strong interference, affecting the propagation characteristics of the sound waves, making it impossible for the DAS signal to accurately reflect the liquid point information. In addition, due to the low flow rate, the propagation characteristics of the sound waves are more complex, and the traditional frequency-wavenumber (FK) analysis method has limited applicability in the oil and water well environment. This method is usually used for frequency analysis of sound waves in high-speed gas wells, but it cannot effectively extract clear sound velocity characteristics in oil and water wells, resulting in the inability to accurately identify the liquid production area.

[0005] Secondly, there are problems with the DTS signal in the inversion process of the liquid production profile. The inversion process of DTS technology is used to convert the temperature data measured by the fiber optic sensor into liquid production profile information. However, the inversion problem itself is seriously ill-posed and lacks sufficient spatial constraints, which can easily lead to large deviations in the inversion results. Specifically, in the absence of sufficient spatial constraints, the inversion results may have multiple solutions or be unstable, making it difficult to provide an accurate and reliable interpretation of the liquid production profile. Therefore, the DTS inversion process lacks a clear identification of the actual liquid outlet point location, and may lead to inconsistent or even erroneous inversion results due to misjudgment or lack of constraints.

[0006] To this end, the present application proposes a method, system and program product for identifying and inverting the production profile of an oil and water well to solve the problems in the above background technology. Summary of the Invention

[0007] The purpose of this application is to provide a method, system and program product for identifying and inverting the production profile of oil and water wells, so as to solve the problems in the existing production profile monitoring methods based on DAS and DTS technologies, that is, the DAS signal is easily affected by fluid flow interference, the signal quality is poor, and it cannot accurately reflect the liquid point information, and the production profile information inverted by the DTS signal lacks sufficient spatial constraints, and the inversion results have large deviations; the present application extracts the local production characteristics in the DAS signal through blind source separation, and effectively identifies the location of the liquid point in combination with a statistical screening method, and further introduces the identified liquid point location as a constraint condition into the DTS inversion process, thereby effectively improving the accuracy and stability of the inversion results; the present application not only overcomes the limitations of the traditional FK spectrum analysis method in the oil and water well environment, but also can introduce effective spatial constraints in the DTS inversion, significantly reducing the inversion error and improving the interpretation accuracy of the production profile.

[0008] This paper first provides a method for identifying and inverting the production profile of oil and water wells, including: step S1, obtaining DAS signals and DTS signals based on a distributed optical fiber system arranged in the wellbore, and preprocessing and low-frequency extraction of the DAS signals; step S2, performing blind source separation on the extracted DAS signals to extract each independent source signal; step S3, identifying the location of potential liquid production points based on each independent source signal combined with statistical screening; step S4, performing DTS inversion using the potential liquid production point location as a constraint, and obtaining the final interpretation result according to the forward solution strategy.

[0009] In one possible implementation, step S1 includes: discretely sampling the acoustic wave signal at spatial intervals and time intervals to obtain a DAS signal; decomposing the DAS signal into signals of different frequencies based on a two-dimensional space-time Fourier transform; and filtering the signals of different frequencies to extract signals in the low-frequency band.

[0010] In one possible implementation, step S2 includes: performing de-averaging processing on the extracted DAS signal, and performing whitening processing on the de-averaged signal; judging the number of independent sources based on the whitened signal as the number of independent liquid outlet points; bringing the number of independent sources into the independent component analysis algorithm, performing blind source separation on the extracted signal, and obtaining each independent source signal.

[0011] In one possible embodiment, step S3 includes: calculating the low-frequency energy index or amplitude index of each independent source signal, distributing the low-frequency energy index or amplitude index along the depth direction, and obtaining the sound wave intensity distribution along the depth; based on the sound wave intensity distribution along the depth combined with the set statistical threshold or hypothesis testing method, screening out the spatial position where the sound wave intensity exceeds the sound wave intensity threshold as the potential liquid outlet point position.

[0012] In one possible embodiment, step S4 includes: step S41, constructing a mathematical model of wellbore-reservoir fluid and heat conduction coupling to describe the temperature and pressure changes of the fluid in the wellbore, as well as the flow and heat conduction process of the multiphase fluid inside the reservoir; step S42, constructing a single well spatial grid model and initializing it, setting the grid well index of the potential liquid production point position identified by blind source separation to 1, and setting the remaining positions to 0 as subsequent simulation boundary conditions; step S43, simulating the wellbore temperature and oil and water production profile production at each moment based on the forward solution strategy, extracting the actually observed wellbore temperature and oil and water production profile production based on the DTS signal, and optimizing the model parameters by comparing the simulation results with the actual observation results; step S44, calculating the final horizontal well oil and water production profile production based on the model after parameter optimization.

[0013] In a possible implementation, step S41 includes: constructing a wellbore fluid thermodynamic behavior model to describe changes in fluid temperature and pressure in the wellbore; and constructing a reservoir multiphase seepage model and a reservoir heat conduction process model.

[0014] In a possible embodiment, step S42 includes: collecting various data related to the reservoir and the well; performing spatial coordinate conversion on the structural surface and the well trajectory data, and after the conversion, the well trajectory is located in the same In the plane, a regular hexahedral grid is generated based on the structural surface after spatial coordinate transformation to simulate the three-dimensional space of the reservoir and wellbore; an initial attribute value is assigned to each grid cell; the grid well index of the potential liquid production point position identified by blind source separation is set to 1, and the remaining positions are set to 0 as the boundary conditions for subsequent simulations.

[0015] In one possible embodiment, step S43 includes: performing a forward modeling solution based on a mathematical model of wellbore-reservoir fluid and heat conduction coupling and a single-well spatial grid model to simulate the wellbore temperature, oil and water production profile production at each moment; extracting the wellbore temperature, oil and water production profile production at the corresponding moment based on the DTS signal and the DAS signal interception segment as actual observation results; and comparing the results obtained by the forward modeling solution with the results obtained by actual observation based on an integrated progressive data assimilation method to optimize the model parameters.

[0016] The present application also provides a water well production profile identification and inversion system for implementing the oil and water well production profile identification and inversion method shown above; the system includes: a signal acquisition module for acquiring DAS signals and DTS signals based on a distributed optical fiber system arranged in the wellbore, and preprocessing and low-frequency extraction of the DAS signals; a signal separation module for performing blind source separation on the extracted DAS signals to extract each independent source signal; a liquid outlet point identification module for identifying the potential liquid outlet point location based on each independent source signal combined with statistical screening; a DTS inversion module for performing DTS inversion using the potential liquid outlet point location as a constraint, and obtaining the final interpretation result according to the forward solution strategy.

[0017] The present application also provides a computer program product. When the computer program product is run on an electronic device, the electronic device executes the oil and water well production profile identification and inversion method shown above.

[0018] Compared with the existing technology, the present application has the following beneficial effects: the oil and water well production profile identification and inversion method, system and program product provided by the present application extract local (low frequency, ultra-low frequency) production signals through blind source separation of DAS signals, and identify the liquid point position by statistical screening, which is then used as a constraint condition to guide the distributed temperature sensing DTS inversion, thereby realizing the joint and accurate interpretation of the oil and water well production profile; the present invention is not only suitable for the accurate identification and interpretation of the production profiles of various oil and water wells, but can also be applied to oil and water wells with different flow levels and different fluid types, and has great technical promotion value and market potential; it is particularly suitable for oil-water two-phase wells and oil-water-gas composite wells with low flow rates, and has unique advantages in accurate monitoring in such wells. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings: Figure 1 A flow chart of a method for identifying and inverting a production profile of an oil and water well provided in an embodiment of the present application; Figure 2 A waterfall diagram of the DAS response during the entire water injection process (logarithm of intensity to base 2) provided in an embodiment of the present application. Figure 3 The DAS spatiotemporal Fourier transform and frequency extraction results provided in the embodiments of this application; Figure 4 This is the 100Hz spatiotemporal distribution diagram after DAS decomposition provided in the embodiment of the present application; Figure 5 This is the 10Hz spatiotemporal distribution diagram after DAS decomposition provided in the embodiment of this application; Figure 6This is the 3Hz spatiotemporal distribution diagram after DAS decomposition provided in the embodiment of this application; Figure 7 This is the 1Hz spatiotemporal distribution diagram after DAS decomposition provided in the embodiment of the present application; Figure 8 This is the 0.5Hz spatiotemporal distribution diagram after DAS decomposition provided in the embodiment of this application; Figure 9 A schematic diagram of a local acoustic signal obtained after a certain depth blind source separation provided in an embodiment of the present application; Figure 10 A spatiotemporal distribution diagram of local sound waves obtained after DAS blind source separation provided in an embodiment of the present application; Figure 11 A schematic diagram of a three-dimensional grid of a single-well coupled numerical model established in an embodiment of the present application; Figure 12 A schematic diagram of the wellbore temperature profile distribution of each model at a certain moment after data assimilation provided in the embodiment of this application; Figure 13 A schematic diagram of a horizontal section water absorption profile obtained by explaining the embodiments of the present application; Figure 14 This is a structural diagram of the oil and water well production profile identification and inversion system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] Hereinafter, the terms "include" or "may include" as used in various embodiments of the present application indicate the presence of the claimed function, operation, or element, and do not limit the addition of one or more functions, operations, or elements. In addition, as used in various embodiments of the present application, the terms "include", "have" and their cognates are intended only to indicate specific features, numbers, steps, operations, elements, components, or combinations of the foregoing, and should not be understood as first excluding the presence of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing or the possibility of adding one or more features, numbers, steps, operations, elements, components, or combinations of the foregoing.

[0021] The terms used in the various embodiments of the present application are only used for the purpose of describing specific embodiments and are not intended to limit the various embodiments of the present application. As used herein, the singular form is intended to also include the plural form, unless the context clearly indicates otherwise. Unless otherwise specified, all terms used herein (including technical terms and scientific terms) have the same meaning as those generally understood by those skilled in the art to which the various embodiments of the present application belong. Terms (such as those defined in generally used dictionaries) 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 present application.

[0022] In order to make the objectives, technical solutions and advantages of this application more clear, the present application is further described in detail below in conjunction with examples and drawings. The schematic implementation methods of this application and their descriptions are only used to explain this application and are not intended to limit this application.

[0023] See Figure 1 As shown, Figure 1 This is a flow chart of a method for identifying and inverting the production profile of oil and water wells. The method includes: Step S1: Acquiring DAS and DTS signals using a distributed optical fiber system deployed in the wellbore, preprocessing the DAS signals and performing low-frequency extraction; Step S2: Performing blind source separation on the extracted DAS signals to extract individual independent source signals; Step S3: Identifying potential production points based on statistical screening of the individual independent source signals; Step S4: Using the potential production point locations as constraints, performing DTS inversion and obtaining the final interpretation using a forward solution strategy.

[0024] Specifically, the present application identifies the location of the liquid production point of oil and water wells based on blind source separation and statistical screening of DAS signals, and introduces liquid production point location constraints based on DTS signals to invert and interpret the liquid production profile information of oil and water wells.

[0025] The improvement of this application lies in extracting local fluid production signals through blind source separation (BSS) of DAS signals, and combining statistical screening to identify the location of fluid points. This is then used as a constraint to guide distributed temperature sensing (DTS) inversion, thereby achieving a joint and accurate interpretation of the fluid production profile of oil and water wells. This application not only overcomes the limitations of traditional FK spectrum analysis methods in oil and water well environments, but also introduces effective spatial constraints into DTS inversion, significantly reducing inversion errors and improving the interpretation accuracy of fluid production profiles. The ability to accurately extract DAS signal features related to fluid production activity in oil and water wells solves the problem of traditional methods being unable to adapt to the complex flow environments of oil and water wells. It also improves the accuracy of DTS inversion and ensures high precision and stability in the interpretation of fluid production profiles.

[0026] In one possible implementation, step S1 includes: discretely sampling the acoustic wave signal at spatial intervals and time intervals to obtain a DAS signal; decomposing the DAS signal into signals of different frequencies based on a two-dimensional space-time Fourier transform; and filtering the signals of different frequencies to extract signals in the low-frequency band.

[0027] Specifically, first, the acoustic wave signal is collected in a discrete manner in space and time through a distributed optical fiber system. The collected signal is expressed as: ; in: is the spatial sampling point number (there are spatial sampling points); is the time sampling point number (there are time sampling points); the spatial interval of sampling is The sampling time interval is ; is the position coordinate of the mth spatial sampling point along the optical fiber axis; is the moment corresponding to the nth time sampling point.

[0028] Then, the collected discrete signal is subjected to a two-dimensional Fourier transform to convert the signal from the space-time domain to the space-frequency domain. The formula for the two-dimensional Fourier transform is: ; in, is the spatial frequency index (corresponding to ), is the time frequency index (corresponding to ). Through the above two-dimensional space-time Fourier transform, the original space-time signal is decomposed into multiple space-time fields with specific frequency characteristics. These frequency fields can include but are not limited to 0.01Hz, 0.02Hz, 1Hz, 100Hz, etc., depending on the time-frequency index .

[0029] Finally, the separated frequency band signals are processed to remove interference such as isolated spike noise and instrument drift, improving signal quality and reliability and making subsequent analysis and applications more accurate. For distributed fiber optic systems, the primary consideration is extracting signals in low-frequency and even ultra-low-frequency bands to enhance local acoustic signals related to fluid production and improve the signal-to-noise ratio.

[0030] In one possible implementation, step S2 includes: performing de-averaging processing on the extracted DAS signal, and performing whitening processing on the de-averaged signal; judging the number of independent sources based on the whitened signal as the number of independent liquid outlet points; bringing the number of independent sources into the independent component analysis algorithm, performing blind source separation on the extracted signal, and obtaining each independent source signal.

[0031] Specifically, blind source separation (BSS) refers to the process of recovering the original source signal as much as possible by observing the mixed signal when the details of the mixing process and the characteristics of the source signal are unknown. The DAS signal in this application is applicable to this process. The acoustic waves generated by each outlet point propagate along the wellbore, and the collected DAS signal is a mixed observation signal from each outlet point. Therefore, it is necessary to perform blind source separation on the extracted mixed observation signal to separate the independent source signals from the mixed observation signal. Each independent source signal generates an acoustic wave signal at a specific depth underground.

[0032] First, the extracted mixed observation signal is de-meaned. The mean of each dimension is 0; then the signal after de-meaning is whitened. Become isotropic, that is, make the covariance matrix of the signal become the unit matrix. Specifically, principal component analysis (PCA) can be used: Let the covariance matrix be ;in is the mixed observation signal, Indicates the expected value, yes The transpose of Perform eigenvalue decomposition or singular value decomposition (SVD): on the covariance matrix Perform eigenvalue decomposition and get ;in is the eigenvector matrix, whose column vectors are The eigenvector of is a diagonal matrix, and the elements on the diagonal are The eigenvalues ​​of , and arranged in descending order; is the transposed matrix; Then the whitening matrix is: ; The signal after whitening: ; After this processing, the whitened signal The covariance matrix of ,Right now .

[0033] Next, the number of independent sources is determined based on the whitened signal. Eigenvalue decomposition is performed on the whitened signal obtained through PCA processing. The resulting eigenvalues ​​correspond to the energies of each principal component. The number of sources is typically determined by the inflection point at which the eigenvalue energy decreases. Methods include knee point detection, automatic determination using information criteria (such as AIC and BIC), and energy accumulation methods. For example, the number of valid principal components (i.e., sources) is determined when the total energy accumulates to at least 95%.

[0034] Finally, blind source separation is performed on the extracted DAS signal based on the independent component analysis (ICA) algorithm. The mathematical model for the solution is established: yes DAS signals from independent outlet point locations, yes Mixed observation signals. The mixing process is modeled as linear instantaneous mixing: ,in: , is the unknown mixing matrix; is time. The solution goal is to estimate an unmixing matrix , such that: . That is, through the unmixing matrix Mixing the observed signal Converted into separated signals ,make As close to the original signal as possible . That is, each Corresponding to an independent source of the liquid outlet position. Independent component analysis (ICA) can be used to solve the unmixing matrix : Independent component analysis (ICA) is a signal processing technique that aims to recover the original independent signals from a mixture of signals. The commonly used optimization criterion is to maximize non-Gaussianity (or minimize mutual information) because, according to the central limit theorem, the mixed signal tends to be closer to a Gaussian distribution. A common loss function is Negentropy, which is used to measure the non-Gaussianity of the signal. Negentropy is defined as: ; in: is entropy, a measure of the uncertainty of the signal; are Gaussian variables with the same covariance; It is the signal processed by ICA algorithm; The bigger, The more non-Gaussian; Solve using the FastICA algorithm, Infomax algorithm, JADE algorithm, etc. Here we take the FastICA algorithm as an example, based on the iterative formula (taking the logarithmic cosh function as an example) that maximizes non-Gaussianity (such as using the logarithmic cosh function or the kurtosis function): ; Among them: Normalization is required after each iteration ( ). is the updated unmixing matrix; is the mixed solution matrix before updating; is the input signal vector; is a nonlinear function (e.g. ), used to approximate non-Gaussianity; yes The derivative of represents the expected value, that is, for all input signals average.

[0035] As you can see, step S2 uses the DAS low-frequency signal for blind source separation, successfully overcoming the poor applicability of traditional FK spectroscopy in oil and water wells. DAS signals in oil and water wells are often affected by acoustic interference, making traditional FK analysis methods ineffective in extracting useful information. Blind source separation technology can extract a clear local production signal from complex interference signals, providing reliable input for further analysis.

[0036] In one possible embodiment, step S3 includes: calculating the low-frequency energy index or amplitude index of each independent source signal, distributing the low-frequency energy index or amplitude index along the depth direction, and obtaining the sound wave intensity distribution along the depth; based on the sound wave intensity distribution along the depth combined with the set statistical threshold or hypothesis testing method, screening out the spatial position where the sound wave intensity exceeds the sound wave intensity threshold as the potential liquid outlet point position.

[0037] Specifically, potential liquid outflow points are identified based on the results of blind source separation. Based on blind source separation technology, independent source signals are extracted from the mixed signal. These independent source signals correspond to local signals at different spatial locations and depths. Their low-frequency energy index or amplitude index is calculated to obtain the sound wave intensity distribution along the depth. By setting a statistical threshold (such as the mean + 3 times the standard deviation) or using a hypothesis testing method (such as a significance test based on the p-value), spatial locations with statistically significant and abnormally enhanced sound wave intensity are screened out and identified as potential liquid outflow points.

[0038] As can be appreciated, step S3 uses statistical testing to select spatial locations with significant changes in acoustic wave intensity as the liquid outlet locations, effectively preventing misidentifications due to noise or accidental disturbances. Statistical methods include setting a threshold (e.g., mean + 3 standard deviations) or employing hypothesis testing (e.g., significance testing based on p-values) to ensure high accuracy in liquid outlet identification.

[0039] In one possible embodiment, step S4 includes: step S41, constructing a mathematical model of wellbore-reservoir fluid and heat conduction coupling to describe the temperature and pressure changes of the fluid in the wellbore, as well as the flow and heat conduction process of the multiphase fluid inside the reservoir; step S42, constructing a single well spatial grid model and initializing it, setting the grid well index of the potential liquid production point position identified by blind source separation to 1, and setting the remaining positions to 0 as subsequent simulation boundary conditions; step S43, simulating the wellbore temperature and oil and water production profile production at each moment based on the forward solution strategy, extracting the actually observed wellbore temperature and oil and water production profile production based on the DTS signal, and optimizing the model parameters by comparing the simulation results with the actual observation results; step S44, calculating the final horizontal well oil and water production profile production based on the model after parameter optimization.

[0040] Specifically, based on the blind source separation results, the DTS inversion is performed with the location of the liquid outlet constrained. The specific steps are as follows: Step S41 constructs a mathematical model of the wellbore-reservoir fluid and heat conduction coupling. Furthermore, step S41 includes: constructing a wellbore fluid thermodynamic behavior model to describe the changes in fluid temperature and pressure within the wellbore; and constructing a reservoir multiphase seepage model and a reservoir heat conduction process model.

[0041] Specifically, a thermodynamic behavior model of wellbore fluid is constructed: ; in, is the temperature of the fluid in the tube, is the initial formation temperature, A is the relaxation distance, is the mass flow rate, is the specific heat capacity of the mixed fluid, is the Joule-Thomson coefficient, is the density of the oil-water mixture, is the flow rate, For pressure, is the well trajectory inclination, is the friction coefficient, is the depth of the wellbore, is the acceleration due to gravity; represents the differential operator.

[0042] Relaxation distance A The empirical formula obtained by Shiu and Begg based on statistical regression is: ; in, arrive is the empirical coefficient, For time, is the wellbore radius, is the density of formation oil, is the specific gravity of formation gas, is the density of the liquid in the wellbore, is the wellhead pressure.

[0043] arrive The specific values ​​are shown in the following table: .

[0044] Constructing a reservoir multiphase flow model: The flow of oil, water and gas multiphases within the reservoir satisfies the mass conservation equation: ; in, is the porosity of the reservoir, For the Prime Minister (such as oil phase, water phase, gas phase) density, For the Prime Minister saturation, For components c In the phase The quality score in is the absolute permeability, For the Prime Minister The severity (equal to The relative permeability Divide by the fluid viscosity ), For the Prime Minister pressure, For the Prime Minister The fluidity, For components c In the phase The source and sink terms in represents the gradient operator.

[0045] Constructing a reservoir heat conduction process model: ; in, For the Prime Minister The specific heat capacity, For the Prime Minister The isobaric thermal expansion coefficient, For the Prime Minister The flow rate, is the rock density, is the specific heat capacity of rock, is the temperature, For depth, is the thermal conductivity of rock.

[0046] Step S42 initializes the single well spatial grid model. Further, step S42 includes: collecting various data related to the reservoir and the well; performing spatial coordinate conversion on the structural surface and the well trajectory data, and after the conversion, the well trajectory is located in the same In the plane, a regular hexahedral grid is generated based on the structural surface after spatial coordinate transformation to simulate the three-dimensional space of the reservoir and wellbore; an initial attribute value is assigned to each grid cell; the grid well index of the potential liquid production point position identified by blind source separation is set to 1, and the remaining positions are set to 0 as the boundary conditions for subsequent simulations.

[0047] Specifically, prepare basic data: collect reservoir top and bottom structural surfaces, well trajectories, logging interpretations (porosity, permeability, water saturation), fluid PVT parameters, wellbore structure data, fiber optic temperature profiles, and wellhead production data. Spatial coordinate transformation: perform spatial coordinate transformation on structural surfaces and well trajectory data so that the well trajectories are located in the same position as much as possible. Simplify modeling in the plane. Generate a regular hexahedron mesh based on the converted construction surface: 1. The directional grid boundary fits the top and bottom structural surfaces; 2. Directional grid size ≤ 1 meter, Direction 10–20 meters, direction ≤5 meters; 3. Consider the specific conditions of the reservoir, The direction extends a certain distance (such as 100 meters) to cover the effective area of ​​the reservoir. Initialization attribute assignment: 1. Same The layer assignment corresponds to porosity and permeability; 2. The same The cross-section values ​​correspond to oil / water saturation. 3. Initial pressure and temperature values ​​are calculated based on wellhead pressure and geothermal gradient. Well index setting for the perforation area: Set the grid well index for potential liquid-producing points identified by blind source separation to 1, and 0 for all other locations. This serves as the boundary condition for subsequent simulations.

[0048] Step S43: Forward numerical simulation and DTS profile fitting. Furthermore, step S43 includes: performing forward modeling based on a mathematical model of wellbore-reservoir fluid and heat conduction coupling and a single-well spatial grid model to simulate the wellbore temperature and oil and water production profile production at each moment; extracting the DTS signal and the DAS signal interception segment corresponding to the wellbore temperature and oil and water production profile production as actual observation results; and comparing the forward modeling results with the actual observation results using an integrated progressive data assimilation method to optimize model parameters.

[0049] Specifically, the forward solution strategy is as follows: 1. Spatial discretization: discretize the continuous physical space into a finite number of grid cells so that numerical calculations can be performed on these grids; use central differences for fluid dynamics equations and second-order central differences for heat conduction equations. 2. Time discretization: discretize the continuous time process into a series of time steps to facilitate numerical simulation of time evolution; the Crank-Nicolson method can be used for time discretization. 3. Coupled numerical solution: In the coupled problem of fluid flow and heat conduction, the flow of the fluid affects the temperature distribution, and the temperature change affects the density and viscosity of the fluid, thereby affecting the flow; therefore, a coupling strategy of alternating iterations of flow and heat transfer is used. The alternating iterative coupling strategy gradually approximates the solution of the coupled system by alternately solving the flow equation and the heat conduction equation in each time step. 4. Output results, which are the goal of the numerical simulation; during the numerical simulation process, calculate and record the wellbore temperature and oil and water production profile of the well at each time step.

[0050] Temperature and yield inversion based on the ES-MDA method: Update model parameters through multiple rounds of assimilation and fit DTS observation data. The specific steps are as follows: 1. Select actual observation data: Select the temperature profile (to get the wellbore temperature) and wellhead metering data (to get the oil and water production profile) at the time (can be multiple times) corresponding to the DAS signal interception segment from the DTS signal, and set it as the actual observation data vector ; 2. Initialize model parameters: In the initialized single well space grid model, set The permeability and water saturation in the direction are used as adjustable variables, and the initial model parameter vector is constructed. ; 3. Assimilation steps and expansion coefficient settings: Set the total number of data assimilation times , and in Expansion coefficient during secondary assimilation , satisfying the following relationship: ; 4. Number and definition of model sets: Define the number of model sets , and record the model set after each assimilation as: ; in, ;when hour, According to the prior distribution The initial model parameters are obtained by sampling from hour, Indicates the After the first assimilation The parameters of the model; 5. Carry out forward calculation and disturbance processing: arrive , perform the following steps: A. Forward model calculation: Based on the established multi-field coupling calculation model, forward calculation is performed on each model in the model set to obtain a set of prediction results: ; in, According to the model A vector consisting of the wellbore temperature and horizontal well oil and water production profile data obtained after forward calculation; B. Perturb the actual observation data: Perturb the actual observation data and calculate the corrected observation data vector: ; in, is the vector of actual observation data; To introduce Gaussian distribution random errors, usually from the standard normal distribution Extract from is the identity matrix, indicating that the errors are independent and identically distributed; is the autocovariance matrix of the observation error; C. Model update: Based on the forward calculation results, each model in the model set is updated according to the following formula: ; in, For the After the first assimilation The parameter vector of the model; For the After the first assimilation The parameter vector of the model; is the covariance matrix of the model parameters; is the covariance matrix of the data; is the autocovariance matrix of the observation error; 6. After assimilation, the model parameters are averaged: The model ensemble after assimilation , and calculate the mean of the model parameters based on this set: ; in, is the total number of models in the model set; For the After the first assimilation, The parameter vector of the model, From 1 to , represents each model in the model set. The purpose of this formula is to calculate After data assimilation iterations, the average value of all model parameters in the model ensemble is calculated by adding the parameter vectors of all models and then dividing by the total number of models. The average parameter vector obtained is It can be used as the final model parameter estimate to optimize the model.

[0051] Finally, the forward model is used to obtain the final interpretation results: according to the mean of the model parameters Optimize model parameters and use forward modeling to calculate the final horizontal well production profile based on the optimized model .

[0052] It can be understood that step S4 introduces spatial position constraints based on the blind source analysis results into the DTS inversion, significantly reducing inversion uncertainty and improving the consistency and accuracy of the analytical results. By using the location of the liquid outlet point extracted from the DAS signal as a constraint in the inversion process, the inversion results of the temperature profile are guaranteed to conform to the physical characteristics of the actual liquid production area.

[0053] See Figure 2-13 As shown, the embodiment of the present application also provides specific application examples to help those skilled in the art fully understand the implementation process of the present application. Taking the water absorption profile of a water injection open hole horizontal well in the Middle East as an example, the following is performed: Figure 1 The method for identifying and inverting the production profile of an oil and water well shown in the figure includes: Step S1: Processing DAS data and identifying the location of the liquid point based on the results of blind source analysis. A distributed optical fiber system is arranged in the horizontal wellbore to obtain DAS signals and DTS signals. The well section depth ranges from 2400 meters to 3100 meters, and the well trajectory is basically horizontal. The reservoir porosity averages 25%, the permeability varies greatly, and there is heterogeneity. From the DAS signal response waterfall diagram of the entire water injection process (such as Figure 2 As shown in the figure, the horizontal axis represents time, the vertical axis represents depth, and the color represents signal amplitude; the figure shows the change of the signal root mean square RMS with time and depth in the frequency range of 1.0 Hz to 10.0 Hz) After selecting the stable water injection section from the continuously collected DAS data, the spatiotemporal Fourier transform (such as Figure 3 As shown in the figure), extract the signals of each frequency, and the time-space distribution of 100Hz, 10Hz, 3Hz, and 1Hz is as follows Figure 4 、 5 , 6, 7 ( Figure 4-7 The horizontal axis represents the equivalent period T(s) in the frequency domain after Fourier transform, that is, the inverse of the frequency. The vertical axis represents the depth, and the color represents the signal amplitude. It can be seen that the low frequency (1Hz) has a more obvious spatial distribution unevenness. After comprehensive analysis, the 0.5Hz spatiotemporal distribution diagram is extracted, as shown in the figure. Figure 8 As shown (the horizontal axis in the figure represents the equivalent period T(s) in the frequency domain after Fourier transform, that is, the inverse of the frequency, the vertical axis represents the depth, and the color represents the signal amplitude).

[0054] Step S2: The extracted spatiotemporal signals are subjected to blind source separation analysis to obtain local acoustic wave signals at each depth, such as Figure 9 As shown in the figure, the horizontal axis represents the frequency domain after Fourier transform, and the vertical axis represents the signal amplitude. The red color represents the interference (measured) result, and the blue color represents the extracted local acoustic wave signal.

[0055] Step S3: further forming Figure 10 As shown in the figure (the horizontal axis represents the equivalent period T(s) in the frequency domain after Fourier transform, that is, the inverse of the frequency, the vertical axis represents the depth, and the color represents the signal amplitude), the spatiotemporal distribution of local sound waves at each depth is obtained after DAS blind source separation. The location of the liquid outlet point is determined based on the 0.5Hz sound wave amplitude intensity and the statistical test method (assuming that the sound wave amplitude intensity at this depth is abnormal).

[0056] Step S4: Construct a numerical simulation model. Construct a single well geological model and a numerical simulation model. The geological model includes formation pressure, porosity, permeability, water / oil saturation, and temperature. Import parameters such as PVT and phase permeability curves to establish a single well pipe flow-seepage numerical simulation model, such as Figure 11 The perforation section is set based on the locations of the liquid outlet points identified by the blind source analysis results and the statistical analysis results.

[0057] The ES-MDA data assimilation method is used to fit the temperature profile and wellhead water content. Select the profile data corresponding to the observation time on the DTS temperature waterfall chart, and use the ES-MDA method to assimilate the observation data at the selected time, such as Figure 12 As shown in the figure (the horizontal axis represents temperature and the vertical axis represents depth), the black dots represent the observed true values, the red x represents the average or best estimate calculated by the ES-MDA method, the gray dots represent the predicted values ​​of each model in the ensemble, and the red part is the DTS temperature curve calculated under the average parameters. After data assimilation, the water absorption profile calculated by the final average model is as follows: Figure 13 As shown, it includes the cumulative water absorption profile and the water absorption along the wellbore profile. The vertical axis is the depth along the horizontal well. The horizontal axis on the right represents the total injection volume at that depth during the observation time. The horizontal axis on the left represents the accumulation of the total injection volume of each point further calculated along the depth direction (from bottom to top). It is worth noting that the outlet (maximum value) is the total injection volume of the entire well during the observation period.

[0058] It can be understood that the oil and water well production profile identification and inversion method provided in this application extracts local (low-frequency, ultra-low-frequency) production signals through blind source separation of DAS signals, and identifies the liquid point position by statistical screening, which is then used as a constraint condition to guide the distributed temperature sensing DTS inversion, thereby achieving a joint and accurate interpretation of the oil and water well production profile; the present invention is not only suitable for the accurate identification and interpretation of various oil and water well production profiles, but can also be applied to oil and water wells with different flow levels and different fluid types, and has great technical promotion value and market potential; it is especially suitable for oil-water two-phase wells and oil-water-gas composite wells with low flow rates, and has unique advantages in accurate monitoring of such wells.

[0059] See Figure 14 As shown, Figure 14 The structural diagram of the oil and water well production profile identification and inversion system provided in the embodiment of the present application. The water well production profile identification and inversion system is used to achieve the following Figure 1 The oil and water well production profile identification and inversion method shown in the figure; the system includes: a signal acquisition module, which is used to obtain DAS signals and DTS signals based on a distributed optical fiber system arranged in the wellbore, and preprocess and low-frequency extract the DAS signals; a signal separation module, which is used to perform blind source separation on the extracted DAS signals and extract each independent source signal; a liquid point identification module, which is used to identify the potential liquid point location based on each independent source signal combined with statistical screening; a DTS inversion module, which is used to perform DTS inversion using the potential liquid point location as a constraint, and obtain the final interpretation result according to the forward solution strategy.

[0060] The embodiment of the present application also provides a computer program product, which, when executed on an electronic device, enables the electronic device to execute the following Figure 1 The oil and water well production profile identification and inversion method is shown.

[0061] 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 method for identifying and inverting the production profile of an oil and water well, characterized in that: include: Step S1, acquiring DAS signals and DTS signals based on a distributed optical fiber system arranged in a wellbore, and performing preprocessing and low-frequency extraction on the DAS signals; Step S2: performing blind source separation on the extracted DAS signal to extract each independent source signal; Step S3: Identify potential liquid outlet locations based on individual source signals combined with statistical screening; Step S4: Perform DTS inversion using the potential outlet point as a constraint, and obtain the final interpretation result according to the forward solution strategy.

2. The method for identifying and inverting the production profile of an oil and water well according to claim 1, characterized in that: Step S1 includes: The DAS signal is obtained by discretely sampling the acoustic wave signal at spatial intervals and time intervals; Based on two-dimensional space-time Fourier transform, the DAS signal is decomposed into signals of different frequencies; Filter the signals of different frequencies and extract the signals in the low-frequency band.

3. The method for identifying and inverting the production profile of an oil and water well according to claim 1, characterized in that: Step S2 includes: Performing de-averaging processing on the extracted DAS signal and performing whitening processing on the de-averaging signal; The number of independent sources is determined based on the whitened signal, which is used as the number of independent liquid outlet points. The number of independent sources is brought into the independent component analysis algorithm, and the extracted signals are subjected to blind source separation to obtain the signals of each independent source.

4. The method for identifying and inverting the production profile of an oil and water well according to claim 1, wherein: Step S3 includes: Calculating the low-frequency energy index or amplitude index of each independent source signal, distributing the low-frequency energy index or amplitude index along the depth direction, and obtaining the sound wave intensity distribution along the depth; According to the sound wave intensity distribution along the depth combined with the set statistical threshold or hypothesis testing method, the spatial position where the sound wave intensity exceeds the sound wave intensity threshold is screened out as the potential liquid outlet point position.

5. The method for identifying and inverting the production profile of an oil and water well according to claim 1, characterized in that: Step S4 includes: Step S41: constructing a mathematical model of wellbore-reservoir fluid and heat conduction coupling to describe the temperature and pressure changes of the fluid in the wellbore, as well as the flow and heat conduction process of the multiphase fluid in the reservoir; Step S42: construct and initialize a single-well spatial grid model, set the grid well index of the potential liquid-producing point identified by blind source separation to 1, and set the index of the remaining locations to 0, as the boundary conditions for subsequent simulations; Step S43: simulating the wellbore temperature and oil and water production profile production at each moment based on the forward solution strategy, extracting the actually observed wellbore temperature and oil and water production profile production based on the DTS signal, and optimizing the model parameters by comparing the simulation results with the actual observation results; Step S44: Calculate the final horizontal well oil and water production profile based on the model after parameter optimization.

6. The method for identifying and inverting the production profile of an oil and water well according to claim 5, characterized in that: Step S41 includes: Construct a thermodynamic behavior model of wellbore fluid to describe the changes in fluid temperature and pressure in the wellbore; Construct reservoir multiphase seepage model and reservoir heat conduction process model.

7. The method for identifying and inverting the production profile of an oil and water well according to claim 5, characterized in that: Step S42 includes: Collect various data related to reservoirs and wells; The structural surface and well trajectory data are transformed into spatial coordinates. After the transformation, the well trajectory is located in the same In-plane; Generate a regular hexahedral mesh based on the structural surface after spatial coordinate transformation to simulate the three-dimensional space of the reservoir and wellbore; Assign initial attribute values ​​to each grid cell; The grid well index of the potential liquid outlet point identified by blind source separation is set to 1, and the other locations are set to 0, which serve as the boundary conditions for subsequent simulations.

8. The method for identifying and inverting the production profile of an oil and water well according to claim 5, characterized in that: Step S43 includes: Based on the mathematical model of wellbore-reservoir fluid and heat conduction coupling and the single-well spatial grid model, forward modeling is performed to simulate the wellbore temperature, oil and water production profile and production at each moment; The wellbore temperature, oil and water production profile and production at the corresponding time of the DTS signal extraction and DAS signal interception segment are used as the actual observation results; Based on the integrated progressive data assimilation method, the results obtained by forward modeling are compared with those obtained by actual observations to optimize the model parameters.

9. An oil and water well production profile identification and inversion system, characterized in that: A method for identifying and inverting a production profile of an oil-water well as claimed in any one of claims 1 to 8, comprising: A signal acquisition module, configured to acquire DAS signals and DTS signals based on a distributed optical fiber system arranged in the wellbore, and perform pre-processing and low-frequency extraction on the DAS signals; A signal separation module is used to perform blind source separation on the extracted DAS signal and extract each independent source signal; The liquid outlet point identification module is used to identify the potential liquid outlet point location based on each independent source signal combined with statistical screening; The DTS inversion module is used to perform DTS inversion using the potential outlet point location as a constraint and obtain the final interpretation result based on the forward solution strategy.

10. A computer program product, characterized in that When the computer program product is run on an electronic device, the electronic device executes the method for identifying and inverting the production profile of an oil-water well as described in any one of claims 1 to 8.

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