Reservoir gas-water phase state productivity evaluation method and device

Abstract

The application provides a reservoir gas-water phase state productivity evaluation method and device, and belongs to the technical field of oil and gas exploration and development. The method comprises the following steps: obtaining a gas production and water production time sequence of a target drainage well in a drainage process; converting the gas production and water production time sequence into respective corresponding power spectra and performing energy decomposition to obtain a depletion energy and an intermittent energy corresponding to the gas production time sequence, and a depletion energy and an intermittent energy corresponding to the water production time sequence; determining a reservoir productivity evaluation result of the target drainage well according to the above parameters; wherein the energy decomposition steps corresponding to different power spectra are as follows: performing frequency weighting accumulation on power spectrum values in a first frequency interval of the power spectrum to obtain the depletion energy; in a second frequency interval of the power spectrum, screening out significant spectral peaks with a false alarm probability less than a preset significance threshold, and accumulating power spectrum values corresponding to the significant spectral peaks to obtain the intermittent energy. The application can improve the accuracy of reservoir productivity evaluation.

Description

Technical Field

[0001] This application belongs to the field of oil and gas exploration and development technology, and more specifically, it relates to a method and apparatus for evaluating the production capacity of reservoir gas-water phase. Background Technology

[0002] Unconventional gas reservoirs (including coalbed methane, shale gas, and tight gas) have become a core replacement area for increasing natural gas reserves and production in my country. The development of unconventional gas reservoirs highly depends on the gradual release and rational control of reservoir energy during long-term continuous drainage. During drainage, reservoir energy sources are complex, including both long-term macroscopic energy released from natural steady-state depletion and short-term intermittent energy triggered by fracturing, formation stress adjustment, human intervention during drainage, and non-steady-state events such as slug breakthrough, fracture opening and closing, and coal dust migration. Simultaneously, the energy transfer and interaction between the gas and water phases within the reservoir and wellbore directly affect the production capacity and stable production cycle of gas wells. Therefore, accurately quantifying the contribution of different energy sources to production capacity and objectively evaluating the gas-water phase coupling state are of great significance for optimizing drainage systems, delaying reservoir energy depletion, and improving recovery rates.

[0003] Existing dynamic analysis methods for coalbed methane drainage convert time-domain signals into frequency-domain energy spectra, revealing the periodic characteristics and energy distribution patterns hidden behind complex fluctuations. Different frequency spectral components correspond to geological and development processes at different physical scales. Among these, extremely low-frequency components typically reflect the overall energy depletion trend of the reservoir, while low- to mid-frequency components may correspond to intermittent events such as the dynamic evolution of fracturing fractures, the formation and release of water locks, and sudden increases in gas desorption. High-frequency components are mostly measurement noise and random interference. However, the application of existing dynamic analysis methods based on frequency-domain analysis technology in oil and gas field development still faces many challenges. First, field-collected data commonly suffers from uneven sampling intervals and missing data. Directly performing a Fast Fourier Transform requires uniform resampling interpolation, which introduces spurious frequencies, leading to severe spectral leakage and frequency confusion, and the loss of intrinsic periodic information from the original data. Second, the power spectrum exhibits a natural tendency to increase in the low-frequency range. Directly integrating the power spectrum would overestimate the reservoir depletion energy. Current technologies lack effective low-frequency energy compensation methods, resulting in distorted quantitative calculations of depletion energy.

[0004] Therefore, there is an urgent need for a method to evaluate the productivity of reservoir gas-water phases in order to solve the above-mentioned technical problems. Summary of the Invention

[0005] The purpose of this application is to provide a method and apparatus for evaluating the productivity of reservoir gas-water phases, so as to reduce spectral leakage and frequency confusion caused by uniform sampling and improve the accuracy of reservoir productivity evaluation.

[0006] A first aspect of this application provides a method for evaluating the productivity of reservoir gas-water phases, including: Obtain the gas production time series and water production time series of the target drainage well during the drainage process; wherein, both the gas production time series and the water production time series are non-uniform sampling time series; The gas production time series and water production time series are converted into their respective power spectra, which are either the gas production power spectrum or the water production power spectrum. Energy decomposition was performed on the gas production power spectrum and the water production power spectrum to obtain the gas production exhaustion energy and gas production intermittent energy corresponding to the gas production time series, and the water production exhaustion energy and water production intermittent energy corresponding to the water production time series. The gas production exhaustion energy and water production exhaustion energy both represent the driving energy continuously released by the reservoir, and the gas production intermittent energy and water production intermittent energy both represent the energy released by non-steady-state drainage events that exceeds the energy threshold. The energy of gas production depletion, the energy of gas production intermittent, the energy of water production depletion, and the energy of water production intermittent are used as the reservoir productivity evaluation results of the target drainage well. The energy decomposition steps corresponding to different power spectra are as follows: Frequency-weighted accumulation of power spectrum values ​​in the first frequency range yields the decay energy. Within the second frequency range of the power spectrum, significant spectral peaks with a false alarm probability less than a preset significance threshold are selected, and the power spectrum values ​​corresponding to each significant spectral peak are summed to obtain the intermittent energy.

[0007] A second aspect of this application provides a reservoir gas-water phase productivity evaluation device, comprising: The data acquisition module is used to acquire the gas production time series and water production time series of the target drainage well during the drainage process; wherein, both the gas production time series and the water production time series are non-uniform sampling time series; The energy conversion module is used to convert the gas production time series and water production time series into their respective power spectra, which are either the gas production power spectrum or the water production power spectrum. The energy decomposition module is used to decompose the gas production power spectrum and the water production power spectrum into energy, respectively, to obtain the gas production exhaustion energy and gas production intermittent energy corresponding to the gas production time series, and the water production exhaustion energy and water production intermittent energy corresponding to the water production time series. The gas production exhaustion energy and water production exhaustion energy both represent the driving energy continuously released by the reservoir, and the gas production intermittent energy and water production intermittent energy both represent the energy released by non-steady-state drainage events that exceeds the energy threshold. The evaluation module is used to evaluate the reservoir productivity of the target well by taking the energy of gas production depletion, the energy of gas production intermittent, the energy of water production depletion, and the energy of water production intermittent. Specifically, the energy decomposition module is used for: Frequency-weighted accumulation of power spectrum values ​​in the first frequency range yields the decay energy. Within the second frequency range of the power spectrum, significant spectral peaks with a false alarm probability less than a preset significance threshold are selected, and the power spectrum values ​​corresponding to each significant spectral peak are summed to obtain the intermittent energy.

[0008] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the above-described reservoir gas-water phase productivity evaluation method.

[0009] In a fourth aspect of this application, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the above-described reservoir gas-water phase productivity evaluation method.

[0010] The beneficial effects of the reservoir gas-water phase productivity evaluation method and apparatus provided in this application are as follows: This application's embodiments can directly perform unbiased spectral estimation and energy decomposition on non-uniformly sampled drainage data without interpolation resampling, avoiding spectral leakage and frequency confusion introduced by uniform resampling in Fast Fourier Transform, and fully preserving intrinsic periodic information. Furthermore, this application's embodiments also divide production fluctuations into exhaustion energy characterizing macroscopic driving capabilities and intermittent energy characterizing the excitation of non-steady-state events through energy decomposition. This allows the evaluation results to simultaneously reflect the steady-state release level and transient disturbance level of the reservoir, overcoming the problems of overestimation of exhaustion energy due to low-frequency natural enhancement and interference from numerous spurious spectral peaks. Therefore, it provides accurate and reliable frequency domain quantitative basis for reservoir productivity evaluation. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A schematic flowchart of a reservoir gas-water phase productivity evaluation method provided in an embodiment of this application; Figure 2 Gas production data provided in one embodiment of this application; Figure 3 Water production data provided in one embodiment of this application; Figure 4 This is a gas production power spectrum corresponding to a gas production time series provided in an embodiment of this application; Figure 5 This is a production water power spectrum corresponding to a production water time series provided in an embodiment of this application; Figure 6 A structural block diagram of a reservoir gas-water phase productivity evaluation device provided in an embodiment of this application; Figure 7 This is a schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0013] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0014] To facilitate understanding, the main terms used in this application will be explained first.

[0015] Target drainage well: refers to a gas well that produces gas by draining water, such as a coalbed methane well, shale gas well or tight gas well, whose gas production and water production change dynamically with the drainage time.

[0016] Non-uniform sampling time series: refers to a production record sequence in which the intervals between sampling times are not equal and there may be missing data, as opposed to uniform sampling with equal intervals.

[0017] Power spectrum: The energy distribution function with frequency as the independent variable obtained by transforming the time-domain output signal using the Lomb-Scargle periodogram method. Its value is dimensionless after variance normalization.

[0018] Energy decomposition: The process of dividing the power spectrum into energy components with different physical meanings according to frequency range and statistical significance.

[0019] Exhaustion energy: Characterizes the intensity of macroscopic driving energy continuously and stably released from the reservoir, corresponding to an extremely low frequency range.

[0020] Intermittent energy: Characterizes the intensity of short-term intermittent energy triggered by non-steady-state drainage events, corresponding to the low-to-medium frequency range.

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0022] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a reservoir gas-water phase productivity evaluation method provided in an embodiment of this application. The method can be executed by electronic equipment and includes: S101: Obtain the gas production time series and water production time series of the target drainage well during the drainage process.

[0023] Among them, both the gas production time series and the water production time series are non-uniformly sampled time series.

[0024] In this embodiment, the target drainage well continuously produces gas from the date of commissioning. The gas production (unit: m³ / d) and water production (unit: m³ / d) are read daily by a wellhead metering device, where d represents the number of days. For example... Figure 2 and Figure 3 The figures show the gas production and water production data recorded when the production period was 500 days. Figure 2 and Figure 3 The horizontal axis represents the gas production time series and water production time series, respectively. These time series can be stored in the relational database of the field data acquisition system in the form of structured data tables, indexed by well number and timestamp.

[0025] In this embodiment, due to holidays, equipment maintenance, and communication interruptions, the actual records are not strictly recorded at equal daily intervals, and the interval between adjacent records fluctuates between 1 and 7 days. Before evaluating the production capacity of the target well, the water production or gas production can be cleaned and obvious outliers (such as negative production or zero value jumps) can be removed before direct recall, without interpolation to complete the missing time periods, thus fully preserving the unequal interval characteristics of the original sampling.

[0026] S102: Convert the gas production time series and water production time series into their respective power spectra.

[0027] The power spectrum is either the gas production power spectrum or the water production power spectrum.

[0028] In this embodiment, the two non-uniform sequences obtained in step S101 are directly subjected to spectral estimation at their natural sampling times using the Lomb-Scargle periodogram method to obtain the gas production power spectrum P_gas(f) with the sampling frequency f as the independent variable (see reference). Figure 4 ) and the water production power spectrum P_water(f) (reference) Figure 5 ).

[0029] This step does not perform any equally spaced resampling or interpolation, thus avoiding the introduction of spurious frequency components. The specific spectral estimation analytical formula and normalization method can be found in subsequent embodiments, but here it is implemented according to the general Lomb-Scargle method.

[0030] S103: Perform energy decomposition on the gas production power spectrum and the water production power spectrum respectively to obtain the gas production exhaustion energy and gas production intermittent energy corresponding to the gas production time series, and the water production exhaustion energy and water production intermittent energy corresponding to the water production time series.

[0031] Both gas production depletion energy and water production depletion energy represent the driving energy continuously released from the reservoir, while both gas production intermittent energy and water production intermittent energy represent the energy released by non-steady-state drainage events that exceeds the energy threshold.

[0032] In this embodiment, the energy decomposition steps corresponding to different power spectra are as follows: Frequency-weighted accumulation of power spectrum values ​​in the first frequency range yields the decay energy. Within the second frequency range of the power spectrum, significant spectral peaks with a false alarm probability less than a preset significance threshold are selected, and the power spectrum values ​​corresponding to each significant spectral peak are summed to obtain the intermittent energy.

[0033] In this embodiment, the energy decomposition methods for both the gas production power spectrum and the water production power spectrum can adopt the above scheme. If the gas production power spectrum is decomposed, the gas production exhaustion energy and gas production intermittent energy are obtained; if the water production power spectrum is decomposed, the water production exhaustion energy and water production intermittent energy are obtained.

[0034] The frequencies within the first frequency range are lower than the frequencies within the second frequency range. The first frequency range can be a preset extremely low frequency range, for example, [ f min ,f low The specific values ​​for the [0.001, 0.01] Hz range need to be determined based on actual drainage data and are not limited here; this is merely an example. The second frequency range can be a preset low-to-medium frequency range. The steps for energy decomposition of the gas production power spectrum and water production power spectrum in this embodiment can refer to the methods given in the above embodiments.

[0035] In this embodiment, the preset significance threshold (FAP) for false alarm probability can be 0.01. Non-steady-state drainage events refer to all production events that cause short-term, sudden, non-periodic strong fluctuations in gas / water production due to unconventional factors such as downhole operations, reservoir mutations, multiphase flow regime transitions, or surface disturbances during the drainage process, thereby deviating from the steady state of continuous natural depletion of reservoir energy. These events include, but are not limited to, bubble drainage operations, plunger gas lift cycles, wellbore fluid accumulation and drainage recovery, bottom water intrusion, pulsation of fracture conductivity, and interference from fracturing in adjacent wells.

[0036] In one implementation, frequency-weighted accumulation of power spectral values ​​located in a first frequency range in the power spectrum is performed to obtain the attenuation energy, including: When the power spectrum is a continuous spectrum, the power spectrum value in the first frequency range is integrated with frequency weight to obtain the decay energy; When the power spectrum is discrete, the power spectrum value corresponding to each discrete frequency point in the first frequency interval is multiplied by the frequency width to obtain the attenuation energy.

[0037] In this embodiment, a continuous spectrum refers to an idealized representation of the power spectrum as a continuous function on the frequency axis rather than discrete sampling points; a discrete spectrum refers to a power spectrum with a finite number of discrete frequency points. and its corresponding power spectral values The result presented in form.

[0038] In this embodiment, the reservoir depletion process is mainly characterized by the continuous release of energy in the first frequency range (extremely low frequency band) in the frequency domain. To quantitatively characterize the macroscopic driving energy intensity corresponding to water or gas production, the obtained power spectrum needs to be analyzed within the specified first frequency range. f min ,f low Weighted integration is performed within [the specified range].

[0039] If the data sampling is relatively complete and the frequency resolution meets the requirements, the power spectrum can be regarded as a continuous spectrum. In this case, the decay energy can be calculated using continuous integral form, as shown in the following formula:

[0040] In the formula, For depleted energy, For at frequency point f The power spectrum value calculated at [location] It is a frequency-weighted function. (0<α≤1) is used to compensate for the natural increase in the power spectral density of the drainage curve as the frequency decreases.

[0041] If the frequency resolution of the data does not meet the requirements and the power spectrum is discrete, then the following formula should be used for discrete summation:

[0042] In the formula, The frequency value of the kth discrete frequency point; For this frequency point f The power spectrum value calculated at [location]; The frequency width (interval between adjacent points) represented by the k-th point.

[0043] This embodiment distinguishes between the continuous and discrete forms of the power spectrum, and calculates the attenuation energy using frequency-weighted integration and frequency width multiplication and accumulation respectively, thus matching the calculation method with the original form of the spectral data. Using a single calculation method would inevitably introduce deviations. For the continuous spectrum, integration completely preserves the continuous variation information of the spectral function on the frequency axis, resulting in high theoretical accuracy. For the discrete spectrum, the power spectral value at each discrete frequency point is directly multiplied and accumulated with the corresponding frequency width, avoiding interpolation errors and spectral distortion introduced by interpolating and resampling the discrete spectrum to adapt to integration.

[0044] In this embodiment, intermittent energy It can be calculated using the following formula:

[0045] m is the index number of the m-th significant spectral peak after screening by the preset significance threshold. It is a count variable with a value range of m=1,2,…,M; M is the total number of significant spectral peaks after screening by the preset significance threshold. This represents the frequency value corresponding to the m-th significant spectral peak. For at this frequency The calculated power spectrum value.

[0046] S104: Gas production depletion energy, gas production intermittent energy, water production depletion energy, and water production intermittent energy are used as the reservoir productivity evaluation results of the target drainage well.

[0047] As can be seen from the above, the embodiments of this application can directly perform unbiased spectral estimation and energy decomposition on non-uniformly sampled drainage data without interpolation resampling, avoiding spectral leakage and frequency confusion introduced by uniform resampling in Fast Fourier Transform, and completely preserving intrinsic periodic information. In addition, the embodiments of this application also divide production fluctuations into exhaustion energy, which characterizes macroscopic driving capacity, and intermittent energy, which characterizes the excitation of non-steady-state events, through energy decomposition. This allows the evaluation results to simultaneously reflect the steady-state release level and transient disturbance level of the reservoir, overcoming the problems of overestimation of exhaustion energy caused by low-frequency natural enhancement and interference from a large number of spurious spectral peaks, thereby providing an accurate and reliable frequency domain quantitative basis for reservoir productivity evaluation.

[0048] In one embodiment of this application, the gas production time series and water production time series are converted into their respective corresponding power spectra, including: For each time series in the gas production time series and water production time series, perform the following transformation steps: Determine the time offset based on each sampling time in the time series; Based on the time offset, the power spectrum values ​​of the time series at different sampling frequencies are determined, and the power spectrum corresponding to the time series is obtained. The time offset satisfies the following formula:

[0049] In the formula, This is the time offset. It is a time series. angular frequency , f The sampling frequency.

[0050] In one implementation, the power spectral values ​​of the time series at different frequencies are determined based on the following formula:

[0051] In the formula, (f) is the sampling frequency f The power spectral value at that location, For the time series of sampling, t i For the first time series i The sampling time corresponding to each sampling point For the first i The gas or water production corresponding to each sampling point This represents the average gas or water production rate corresponding to all sampling points in the time series. Let V be the variance of this time series. N This represents the total number of sampling points in the time series.

[0052] This embodiment presents the specific analytical formula and parameter determination method for Lomb-Scargle spectral estimation. By introducing a time offset τ determined by the sampling time distribution, the power spectrum remains an unbiased estimate even under non-uniform sampling, fundamentally avoiding spectral leakage and spurious frequencies introduced by Fast Fourier Transform resampling. The power spectrum is normalized to a dimensionless quantity after variance normalization, making the spectral energy of different wells and different drainage stages comparable.

[0053] In one embodiment of this application, the reservoir productivity evaluation result also includes the reservoir energy depletion rate; The reservoir energy depletion rate is calculated based on the following method: For each type of depletion energy in the gas production depletion energy and water production depletion energy: The first energy value corresponding to the first mining time window and the second energy value corresponding to the second mining time window are determined respectively; wherein, the first mining time window is earlier than the second mining time window; Based on the first energy value and the second energy value, determine the reservoir energy attenuation coefficient corresponding to this type of depletion energy; Based on the reservoir energy decay coefficient, the reservoir energy depletion rate of the target drainage well is determined.

[0054] In this embodiment, the reservoir energy attenuation coefficient β satisfy:

[0055] In the formula, The first energy value, The second energy value, This refers to the sampling time corresponding to the first sampling time window. This refers to the sampling time corresponding to the second sampling time window.

[0056] In this embodiment, a larger β value indicates faster energy decay, suggesting insufficient formation pressure replenishment after fracturing fluid discharge, potentially leading to a rapid transition to a low-production phase. This embodiment can also determine a warning threshold based on historical data from multiple wells in the same area. When β exceeds the warning threshold, rapid depletion can be identified, triggering a warning.

[0057] In one embodiment of this application, the reservoir productivity evaluation results further include the product gas centroid frequency and the product water centroid frequency.

[0058] Wherein, the centroid frequency is the weighted average of each frequency value in the corresponding power spectrum, weighted by its respective power spectrum value. And it is determined by the following formula:

[0059] In the formula, For the mass center frequency of the product or the mass center frequency of the product water, is the power spectrum of gas production or water production, and f is the sampling frequency.

[0060] In this embodiment, the centroid frequency is used to characterize the frequency scale on which the energy centroid of the entire power spectrum falls, whether it is concentrated in the low frequency range (long-term, gentle macroscopic drive) or diffused to the mid-to-high frequency range (short-term, active fluctuation components). In this embodiment, the dominant gas production mechanism of the target drainage well can be determined based on the direction of the shift of the gas production centroid frequency with the drainage time. The dominant gas production mechanism is either a matrix desorption-diffusion dominant mechanism or a free gas active recharge dominant mechanism.

[0061] When the centroid frequency decreases with drainage time, energy concentrates at lower frequencies, indicating a weakening of free gas drive and the dominance of matrix desorption, entering a long-term stable production period. At this time, the dominant gas production mechanism is matrix desorption-diffusion. When the centroid frequency increases with drainage time, energy diffuses to higher frequencies, indicating that new adsorbed gas is converted into free gas replenishment or that human intervention in drainage causes dynamic activity. At this time, the dominant gas production mechanism is active free gas replenishment.

[0062] In one embodiment of this application, the reservoir productivity evaluation result further includes the gas-water phase coupling state, which is determined based on the following method: The frequency corresponding to the strongest spectral peak shared by the gas production power spectrum and the water production power spectrum is taken as the common dominant frequency. The fitted phases of the gas production time series and the water production time series at the common dominant frequency are determined respectively, and the difference between the fitted phase of the gas production time series at the common dominant frequency and the fitted phase of the water production time series at the common dominant frequency is determined as the gas-water phase difference at the common dominant frequency. The energy transfer efficiency between gas and water is determined based on the cosine value of the gas-water phase difference.

[0063] Based on the gas-water energy transmission efficiency, the gas-water phase coupling state of the target drainage well is determined.

[0064] In this embodiment, the gas production time series Water production time series The fitted phases corresponding to the common dominant frequency f are:

[0065]

[0066] The phase difference between air and water is:

[0067] The cosine of the gas-water phase difference, which is also the gas-water energy transfer efficiency, is:

[0068] In one implementation, determining the gas-water phase coupling state of the target drainage well based on the gas-water energy transfer efficiency includes: When the gas-water energy transfer efficiency is greater than zero, the gas-water phase coupling state is determined to be a mixed-phase driven state where gas and water are in the same phase; when the gas-water energy transfer efficiency is less than zero, the gas-water phase coupling state is determined to be a state where water phase inhibits gas phase release; when the gas-water energy transfer efficiency is equal to zero, the gas-water phase coupling state is determined to be a critical state for gas-water energy conversion.

[0069] This invention classifies the gas-water energy transfer efficiency into three coupling states—mixed-phase driven, water-phase suppressed, and critical—based on positive and negative zero values. This transforms frequency domain coupling diagnosis from abstract numerical values ​​into qualitative results that can guide on-site operations. The mixed-phase driven state indicates that gas and water are in synergy and can maintain stable production; the water-phase suppressed state can provide timely warnings of water lock or wellbore fluid accumulation risks, suggesting adjustments to the drainage system or enhanced drainage; the critical state identifies the energy conversion inflection point, facilitating early monitoring to prevent abrupt shifts to the suppressed state.

[0070] Corresponding to the reservoir gas-water phase productivity evaluation method in the above embodiments, Figure 6 This is a structural block diagram of a reservoir gas-water phase productivity evaluation device provided in one embodiment of this application. For ease of explanation, only the parts relevant to the embodiment of this application are shown. References Figure 6 The reservoir gas-water phase productivity evaluation device 20 includes: a data acquisition module 21, an energy conversion module 22, an energy decomposition module 23, and an evaluation module 24.

[0071] The data acquisition module 21 is used to acquire the gas production time series and water production time series of the target drainage well during the drainage process; wherein, both the gas production time series and the water production time series are non-uniform sampling time series. The energy conversion module 22 is used to convert the gas production time series and the water production time series into their respective power spectra, which are either the gas production power spectrum or the water production power spectrum. The energy decomposition module 23 is used to decompose the gas production power spectrum and the water production power spectrum respectively to obtain the gas production exhaustion energy and gas production intermittent energy corresponding to the gas production time series, and the water production exhaustion energy and water production intermittent energy corresponding to the water production time series; the gas production exhaustion energy and water production exhaustion energy both represent the driving energy continuously released by the reservoir, and the gas production intermittent energy and water production intermittent energy both represent the energy released by non-steady-state drainage events that is greater than the energy threshold. Evaluation module 24 is used to evaluate the reservoir productivity of the target drainage well by taking the energy of gas production depletion, the energy of gas production intermittent, the energy of water production depletion, and the energy of water production intermittent. Specifically, the energy decomposition module 23 is used for: Frequency-weighted accumulation of power spectrum values ​​in the first frequency range yields the decay energy. Within the second frequency range of the power spectrum, significant spectral peaks with a false alarm probability less than a preset significance threshold are selected, and the power spectrum values ​​corresponding to each significant spectral peak are summed to obtain the intermittent energy.

[0072] In one embodiment of this application, the energy decomposition module 23 is specifically used for: When the power spectrum is a continuous spectrum, the power spectrum value in the first frequency range is integrated with frequency weight to obtain the decay energy; When the power spectrum is discrete, the power spectrum value corresponding to each discrete frequency point in the first frequency interval is multiplied by the frequency width to obtain the attenuation energy.

[0073] In one embodiment of this application, the energy conversion module 22 is specifically used for: For each time series in the gas production time series and water production time series, perform the following transformation steps: Determine the time offset based on each sampling time in the time series; Based on the time offset, the power spectrum values ​​of the time series at different sampling frequencies are determined, and the power spectrum corresponding to the time series is obtained. The time offset satisfies the following formula:

[0074] In the formula, This is the time offset. It is a time series. angular frequency , f The sampling frequency.

[0075] In one embodiment of this application, the power spectral values ​​of the time series at different frequencies are determined based on the following formula:

[0076] In the formula, (f) is the sampling frequency f The power spectral value at that location, For the time series of sampling, t i For the first time series i The sampling time corresponding to each sampling point For the first i The gas or water production corresponding to each sampling point This represents the average gas or water production rate corresponding to all sampling points in the time series. Let V be the variance of this time series. N This represents the total number of sampling points in the time series.

[0077] In one embodiment of this application, the reservoir productivity evaluation result also includes the reservoir energy depletion rate; The reservoir energy depletion rate is calculated based on the following method: For each type of depletion energy in the gas production depletion energy and water production depletion energy: The first energy value corresponding to the first mining time window and the second energy value corresponding to the second mining time window are determined respectively; wherein, the first mining time window is earlier than the second mining time window; Based on the first energy value and the second energy value, determine the reservoir energy attenuation coefficient corresponding to this type of depletion energy; Based on the reservoir energy decay coefficient, the reservoir energy depletion rate of the target drainage well is determined.

[0078] In one embodiment of this application, the reservoir energy attenuation coefficient β satisfy:

[0079] In the formula, The first energy value, The second energy value, This refers to the sampling time corresponding to the first sampling time window. This refers to the sampling time corresponding to the second sampling time window.

[0080] In one embodiment of this application, the reservoir productivity evaluation results further include the product gas centroid frequency and the product water centroid frequency.

[0081] Wherein, the centroid frequency is the weighted average of each frequency value in the corresponding power spectrum, weighted by its respective power spectrum value. And it is determined by the following formula:

[0082] In the formula, For the mass center frequency of the product or the mass center frequency of the product water, is the power spectrum of gas production or water production, and f is the sampling frequency.

[0083] In one embodiment of this application, the reservoir productivity evaluation result further includes the gas-water phase coupling state, which is determined based on the following method: The frequency corresponding to the strongest spectral peak shared by the gas production power spectrum and the water production power spectrum is taken as the common dominant frequency. The fitted phases of the gas production time series and the water production time series at the common dominant frequency are determined respectively, and the difference between the fitted phase of the gas production time series at the common dominant frequency and the fitted phase of the water production time series at the common dominant frequency is determined as the gas-water phase difference at the common dominant frequency. The energy transfer efficiency between gas and water is determined based on the cosine value of the gas-water phase difference.

[0084] Based on the gas-water energy transmission efficiency, the gas-water phase coupling state of the target drainage well is determined.

[0085] In one embodiment of this application, determining the gas-water phase coupling state of the target drainage well based on the gas-water energy conduction efficiency includes: When the gas-water energy transfer efficiency is greater than zero, the gas-water phase coupling state is determined to be a mixed-phase driven state where gas and water are in the same phase; when the gas-water energy transfer efficiency is less than zero, the gas-water phase coupling state is determined to be a state where water phase inhibits gas phase release; when the gas-water energy transfer efficiency is equal to zero, the gas-water phase coupling state is determined to be a critical state for gas-water energy conversion.

[0086] See Figure 7 , Figure 7This is a schematic block diagram of an electronic device provided according to an embodiment of this application. Figure 7 The electronic device 300 in this embodiment may include one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memories 304 store computer programs, including program instructions. The processors 301 execute the program instructions stored in the memories 304. Specifically, the processors 301 are configured to invoke the program instructions to perform the functions of the modules in the aforementioned device embodiments, for example... Figure 6 The functions of the data acquisition module 21, energy conversion module 22, energy decomposition module 23, and evaluation module 24 are shown.

[0087] It should be understood that, in the embodiments of this application, the processor 301 may be a central processing unit (CPU), but it may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0088] Input device 302 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 303 may include a display (LCD, etc.), a speaker, etc.

[0089] The memory 304 may include read-only memory and random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory.

[0090] In specific implementations, the processor 301, input device 302, and output device 303 described in the embodiments of this application can execute the implementation method described in the reservoir gas-water phase productivity evaluation method provided in the embodiments of this application, or they can execute the implementation method of the electronic device described in the embodiments of this application, which will not be repeated here.

[0091] In another embodiment of this application, a computer-readable storage medium is provided. This computer-readable storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions implement all or part of the processes in the methods described above. Alternatively, the computer program can instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0092] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device. Furthermore, the computer-readable storage medium can include both internal and external storage units of the electronic device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0093] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0094] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the electronic devices and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0095] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces or units, or it may be an electrical, mechanical, or other form of connection.

[0096] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0097] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0098] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for evaluating the productivity of reservoir gas-water phases, characterized in that, include: Obtain the gas production time series and water production time series of the target drainage well during the drainage process; wherein, the gas production time series and the water production time series are both non-uniform sampling time series; The gas production time series and the water production time series are respectively converted into their respective power spectra, and the power spectra are either gas production power spectra or water production power spectra. Energy decomposition is performed on the gas production power spectrum and the water production power spectrum to obtain the gas production exhaustion energy and gas production intermittent energy corresponding to the gas production time series, and the water production exhaustion energy and water production intermittent energy corresponding to the water production time series; the gas production exhaustion energy and the water production exhaustion energy both represent the driving energy continuously released by the reservoir, and the gas production intermittent energy and the water production intermittent energy both represent the energy greater than the energy threshold released by non-steady-state drainage events; The energy of gas production depletion, the energy of gas production intermittent, the energy of water production depletion, and the energy of water production intermittent are used as the reservoir productivity evaluation results of the target drainage well. The energy decomposition steps corresponding to different power spectra are as follows: Frequency-weighted accumulation of the power spectrum values ​​located in the first frequency range in the power spectrum is performed to obtain the attenuation energy; Within the second frequency range of the power spectrum, significant spectral peaks with a false alarm probability less than a preset significance threshold are selected, and the power spectrum values ​​corresponding to each significant spectral peak are accumulated to obtain intermittent energy.

2. The method for evaluating reservoir gas-water phase productivity as described in claim 1, characterized in that, The step of frequency-weighted accumulation of power spectrum values ​​located in the first frequency range to obtain the attenuation energy includes: When the power spectrum is a continuous spectrum, the power spectrum value in the first frequency range is integrated with frequency weight to obtain the attenuation energy; When the power spectrum is a discrete spectrum, the power spectrum value corresponding to each discrete frequency point in the first frequency interval is multiplied by the frequency width to obtain the attenuation energy.

3. The method for evaluating reservoir gas-water phase productivity as described in claim 1, characterized in that, The step of converting the gas production time series and the water production time series into their respective corresponding power spectra includes: For each time series in the gas production time series and the water production time series, the following transformation steps are performed: Determine the time offset based on each sampling time in the time series; Based on the time offset, the power spectrum values ​​of the time series at different sampling frequencies are determined, and the power spectrum corresponding to the time series is obtained. The time offset satisfies the following formula: In the formula, This is the time offset. It is a time series. angular frequency , f The sampling frequency.

4. The method for evaluating reservoir gas-water phase productivity as described in claim 3, characterized in that, The power spectral values ​​of this time series at different frequencies are determined based on the following formula: In the formula, (f) is the sampling frequency f The power spectral value at that location, For the time series of sampling, t i For the first time series i The sampling time corresponding to each sampling point For the first i The gas or water production corresponding to each sampling point This represents the average gas or water production rate corresponding to all sampling points in the time series. Let V be the variance of this time series. N This represents the total number of sampling points in the time series.

5. As claimed in claim 1 The method for evaluating the productivity of reservoir gas-water phases is characterized in that, The reservoir productivity evaluation results also include the reservoir energy depletion rate; The reservoir energy depletion rate is calculated based on the following method: For each of the gas production depletion energy and the water production depletion energy: Determine the first energy value of the type of depletion energy corresponding to the first drainage time window and the second energy value corresponding to the second drainage time window; wherein the first drainage time window is earlier than the second drainage time window. Based on the first energy value and the second energy value, determine the reservoir energy attenuation coefficient corresponding to this type of depletion energy; Based on the reservoir energy decay coefficient, the reservoir energy depletion rate of the target drainage well is determined.

6. The method for evaluating reservoir gas-water phase productivity as described in claim 5, characterized in that, The reservoir energy attenuation coefficient β satisfy: In the formula, The first energy value, The second energy value, This refers to the sampling time corresponding to the first sampling time window. This refers to the sampling time corresponding to the second sampling time window.

7. The method for evaluating reservoir gas-water phase productivity as described in claim 1, characterized in that, The reservoir productivity evaluation results also include the gas production center frequency and the water production center frequency. Wherein, the centroid frequency is the weighted average of each frequency value in the corresponding power spectrum, weighted by its respective power spectrum value. And it is determined by the following formula: In the formula, For the mass center frequency of the product or the mass center frequency of the product water, is the power spectrum of gas production or water production, and f is the sampling frequency.

8. The method for evaluating reservoir gas-water phase productivity as described in claim 1, characterized in that, The reservoir productivity evaluation results also include the gas-water phase coupling state, which is determined based on the following method: The frequency corresponding to the strongest spectral peak shared by the gas production power spectrum and the water production power spectrum is taken as the common dominant frequency. The fitted phases of the gas production time series and the water production time series at the common dominant frequency are determined respectively, and the difference between the fitted phase of the gas production time series at the common dominant frequency and the fitted phase of the water production time series at the common dominant frequency is determined as the gas-water phase difference at the common dominant frequency. The gas-water energy transfer efficiency is determined based on the cosine value of the gas-water phase difference. Based on the gas-water energy conduction efficiency, the gas-water phase coupling state of the target drainage well is determined.

9. The method for evaluating reservoir gas-water phase productivity as described in claim 8, characterized in that, The determination of the gas-water phase coupling state of the target drainage well based on the gas-water energy conduction efficiency includes: When the gas-water energy conduction efficiency is greater than zero, the gas-water phase coupling state is determined to be a mixed-phase driven state where gas and water are in the same phase; when the gas-water energy conduction efficiency is less than zero, the gas-water phase coupling state is determined to be a state where water phase inhibits gas phase release; when the gas-water energy conduction efficiency is equal to zero, the gas-water phase coupling state is determined to be a critical state for gas-water energy conversion.

10. A reservoir gas-water phase productivity evaluation device, characterized in that, include: The data acquisition module is used to acquire the gas production time series and water production time series of the target drainage well during the drainage process; wherein, the gas production time series and the water production time series are both non-uniform sampling time series; An energy conversion module is used to convert the gas production time series and the water production time series into their respective corresponding power spectra, wherein the power spectra are either gas production power spectra or water production power spectra. An energy decomposition module is used to decompose the gas production power spectrum and the water production power spectrum into energy, respectively, to obtain the gas production exhaustion energy and gas production intermittent energy corresponding to the gas production time series, and the water production exhaustion energy and water production intermittent energy corresponding to the water production time series; the gas production exhaustion energy and the water production exhaustion energy both represent the driving energy continuously released by the reservoir, and the gas production intermittent energy and the water production intermittent energy both represent the energy released by non-steady-state drainage events that exceeds the energy threshold; The evaluation module is used to take the gas production depletion energy, the gas production intermittent energy, the water production depletion energy, and the water production intermittent energy as the reservoir productivity evaluation results of the target drainage well; Specifically, the energy decomposition module is used for: Frequency-weighted accumulation of the power spectrum values ​​located in the first frequency range in the power spectrum is performed to obtain the attenuation energy; Within the second frequency range of the power spectrum, significant spectral peaks with a false alarm probability less than a preset significance threshold are selected, and the power spectrum values ​​corresponding to each significant spectral peak are accumulated to obtain intermittent energy.

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