A method and system for flow control of a pumping experiment

Abstract

This invention relates to the field of flow control and frequency domain analysis and processing of pumping test data, and discloses a flow control method and system for pumping tests. By sequentially setting a transition section, a steady-state acquisition section, and a link delay self-calibration disturbance section in each flow control stage, control commands, flow and pressure signals are simultaneously acquired and timestamped. The disturbance section is used to calibrate the equivalent time delay from control to flow and the relative time delay from flow to pressure, constructing a true flow estimate and implementing delay-compensated closed-loop flow control. Based on the calibration time delay, the steady-state signal is time-aligned, and target frequency amplitude and phase parameters are extracted, followed by reliability verification and automatic processing. This effectively eliminates the contamination of frequency domain analysis by link delay and phase distortion, improving the accuracy, stability, and reproducibility of layered parameter identification.

Description

Technical Field

[0001] This invention belongs to the field of pumping test flow control and test data frequency domain analysis and processing technology, specifically relating to a flow control method and system for pumping tests. Background Technology

[0002] Pumping tests are an indispensable core testing method in engineering fields such as hydrogeological exploration, oil and gas field and reservoir evaluation, and water-producing layer parameter identification. Its core working principle is as follows: Pre-set, precisely controllable pumping conditions (such as fixed flow rate pumping, stepped variable flow rate pumping, periodic variable flow rate pumping, etc.) are applied to a designated pumping well. Simultaneously, the response data of physical quantities such as well pressure changing over time are observed and recorded in real time. By establishing the correspondence between "input excitation (pumping flow rate) - output response (pressure change)," key parameters such as formation permeability and hydraulic conductivity are obtained using specialized algorithms, or the stratified water-producing capacity of different formations can be identified.

[0003] When frequency domain analysis techniques are introduced into pumping test data processing (e.g., using periodic pumping tests, harmonic modulation pumping tests, or extracting frequency domain features from the response data of segmented variable flow pumping), the core basis for parameter inversion and stratification identification is the amplitude relationship and phase difference between the input excitation (flow rate) and the output response (pressure) at the target frequency. In this case, any phase lag in the test system caused by non-stratum factors such as equipment characteristics or signal transmission links will be misinterpreted by the data processing algorithm as the propagation characteristics of the stratum itself, dispersion phenomena, or differences between different strata. This leads to severe distortion in the stratum parameter identification results, failing to reflect the true stratum situation.

[0004] In existing engineering practices, flow control for pumping tests typically employs a closed-loop control scheme of "variable frequency pump + flow meter feedback + PI / PID control algorithm." The core objective of this scheme is to achieve a stable output of pumping flow, focusing only on the amplitude stability of the flow rate without considering the impact of link delay and phase distortion on data quality. However, due to various objective factors, the entire test system inevitably suffers from equivalent transmission delays in the link: 1. Equipment inherent characteristics: including the mechanical inertia of the pump and motor (response delay during startup and speed change), the elastic deformation of the pipeline volume (signal delay caused by fluid compression and expansion during flow rate changes), and the dynamic response characteristics of the valves (time difference between valve opening adjustment and flow rate stabilization); 2. Signal processing and transmission factors: including the signal filtering process within the flow meter (signal smoothing to ensure measurement stability introduces delay), the communication transmission delay between the flow meter and the controller, and the signal filtering and communication transmission delay in the pressure acquisition link.

[0005] These factors collectively result in a delay between the issuance of control commands and the detection of flow changes by the flow meter, as well as a delay between the flow meter detecting flow changes and the pressure sensor detecting the response change. When pumping tests prioritize "reliable phase data" (such as in frequency domain analysis scenarios), a stable flow output alone cannot guarantee the reliability of the phase data. Furthermore, the aforementioned link delays are not fixed values ​​and dynamically drift with changes in test conditions (such as pumping flow rate and pump load) and environmental conditions (such as temperature, humidity, and pipeline pressure). Ultimately, this leads to a lack of comparability in test results from different test sites and different test batches, making it difficult to reproduce the test process and results. Summary of the Invention

[0006] The technical problem to be solved by this invention is that the transmission delay and phase distortion inevitably introduced during signal transmission and processing of the flow control link and the metering link can be misjudged by the frequency domain analysis algorithm as the characteristics of the formation itself, resulting in inaccurate frequency domain analysis results and distorted identification of layer parameters. Furthermore, the dynamic drift of the link delay will further make the test results incomparable and unreproducible.

[0007] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution: Firstly, a flow control method for pumping experiments is proposed, comprising the following steps: Step 1: Divide the pumping test into multiple flow control stages, and configure a sequential transition section, a steady-state acquisition section, and a link delay self-calibration disturbance section in each flow control stage; wherein, the link delay self-calibration disturbance section is used to superimpose a preset standardized disturbance signal on the steady-state flow. Step 2: Establish a unified time domain reference, synchronously acquire control command signal u(t), flow measurement signal Q_meas(t), and well pressure response signal P_meas(t), and mark the acquired data with a unified timestamp; Step 3: Extract the synchronous acquisition data within the self-calibration disturbance section of the link delay, calculate the equivalent transmission delay τ_uQ from the control link to the flow metering link, and calculate the cross-link relative transmission delay τ_QP from the flow metering link to the pressure acquisition link; Step 4: Using the calculated equivalent transmission delay τ_uQ of the link and the rate of change of the flow measurement signal, estimate the actual flow estimate Q_hat(t) at the current moment; calculate the deviation between the preset target flow Q_set(t) and the actual flow estimate Q_hat(t), and adjust the control command signal u(t) through the feedback control algorithm; Step 5: Based on the link equivalent transmission delay τ_uQ and the cross-link relative transmission delay τ_QP, perform time shift alignment on the flow measurement signal Q_meas(t) and the well pressure response signal P_meas(t) in the steady-state acquisition segment to eliminate the phase deviation caused by the link delay, and extract the amplitude and phase parameters at the target frequency from the aligned data. Step 6: Verify the reliability of the amplitude and phase parameters. If the data is deemed unreliable, automatically execute at least one handling strategy to obtain amplitude and phase data that meet the reliability requirements for frequency domain analysis and hierarchical parameter identification.

[0008] Secondly, a flow control system for a pumping experiment is proposed, comprising: a pumping execution module, a signal acquisition module, and a control and data processing module; The pumping execution module includes: a controllable pump and a frequency converter for outputting pumping flow rate in response to control commands; The signal acquisition module includes: a flow meter, a pressure sensor, and a multi-channel synchronous acquisition module for simultaneously acquiring flow measurement signals and well pressure response signals; The control and data processing module is used to execute the flow control method for the pumping experiment as described in the first aspect; the control and data processing module is connected to the pumping execution module and the signal acquisition module; the control and data processing module includes: The program generation unit is used to generate a flow control program that includes a link delay self-calibration disturbance segment. The delay calibration unit is used to calculate the link equivalent transmission delay τ_uQ and the cross-link relative transmission delay τ_QP based on the disturbance segment data; A closed-loop control unit is used for flow estimation and delay compensation control based on the link equivalent transmission delay τ_uQ. The frequency domain analysis unit is used to time-align the acquired data and extract amplitude and phase data based on the delay parameter.

[0009] Compared with existing technologies, this invention has the following advantages and beneficial effects: By dividing the pumping test into several flow control stages, and sequentially setting a transition section, a steady-state acquisition section, and a link delay self-calibration disturbance section in each stage, it achieves smooth switching between different flow levels and forms a stable and usable data acquisition window. Furthermore, by superimposing standardized disturbances on the steady-state flow, it provides repeatable "calibration excitation" for the dynamic characteristics of the link. Based on this, a unified time-domain reference is used to synchronously acquire control commands, flow, and pressure, and a unified timestamp is applied, reducing the pseudo-phase difference caused by asynchronous sampling from multiple devices at the source. Subsequently, the equivalent time delay from the control link to the flow metering link, and the relative time delay from the flow metering link to the pressure acquisition link are automatically calibrated in the disturbance section data, thereby objectively quantifying the "time delay and phase distortion introduced by the control / metering link" into parameters that can be used for compensation. Further, by constructing a true... The actual flow rate estimate is adjusted in a closed loop using the deviation between the preset target flow rate and the actual flow rate estimate. This compensates for the control phase lag and oscillation tendency caused by measurement lag, making the actual excitation closer to the target excitation and reducing "control link phase contamination". Then, based on the equivalent transmission delay of the link and the relative transmission delay across the link, the flow rate measurement signal and the well pressure response signal in the steady state section are time-shifted and aligned, and the amplitude and phase at the target frequency are extracted. This removes the phase deviation caused by the link delay, making the obtained amplitude and phase characteristics more reflective of the true formation response and improving the accuracy of frequency domain analysis and layer parameter identification. Finally, by verifying the credibility of the amplitude and phase results and automatically performing actions such as extending the steady state section, recalibrating, or reducing the bandwidth when unreliable, "false amplitude and phase" caused by abnormal operating conditions or link drift are prevented from entering the identification process, thereby significantly improving the stability and reproducibility of the identification results of parameters such as layer water production coefficient / effective thickness. Attached Figure Description

[0010] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of a flow control method for a pumping experiment provided in Embodiment 1 of the present invention. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. The illustrative embodiments and descriptions of this invention are for illustrative purposes only and are not intended to limit the invention. The embodiments described below are some, but not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0012] In the following description, numerous specific details are set forth to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to practice the invention. In other embodiments, well-known structures, materials, or methods are not specifically described to avoid obscuring the invention. Unless otherwise specified, the materials, instruments, and reagents used in the following embodiments are commercially available. Unless otherwise specified, the techniques used in the embodiments are conventional methods well known to those skilled in the art.

[0013] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0014] Example 1: Existing flow control schemes for pumping tests focus primarily on the stable output of flow amplitude, neglecting the equivalent transmission delay and phase distortion in the control and metering links. When test data is processed using frequency domain analysis, these link delays will superimpose "equipment phase lag" onto the "formation phase response," leading to distorted formation parameter identification results. Furthermore, the dynamic drift of link delays will cause incomparable and unreproducible test results across different test scenarios and batches.

[0015] Therefore, this embodiment provides a flow control method for a pumping experiment. The overall technical approach is as follows: In each flow control stage of the pumping experiment, three consecutive functional segments are systematically set: a transition segment, a steady-state acquisition segment, and a link delay self-calibration disturbance segment. A repeatable and accurately identifiable calibration excitation signal is generated through the link delay self-calibration disturbance segment, and multi-channel synchronous acquisition of control command signals, flow measurement signals, and pressure acquisition signals is performed. Within the link delay self-calibration disturbance segment, the equivalent transmission delay from the control link to the flow metering link and the relative transmission delay from the flow metering link to the pressure acquisition link are accurately calibrated. The calibrated equivalent transmission delay is applied to delay-compensated closed-loop control, and an algorithm is used to... The system constructs a current real traffic estimate to achieve precise traffic control. Simultaneously, it applies the equivalent transmission delay and the relative transmission delay between the traffic metering link and the pressure acquisition link to the time-shifted data of the steady-state acquisition segment, eliminating phase deviation caused by link delay. From the time-aligned steady-state data, it extracts the amplitude and phase parameters at the target frequency and establishes a multi-dimensional credibility verification mechanism to validate the effectiveness of the amplitude and phase results. When the data is deemed unreliable, it automatically executes handling strategies such as extending the steady-state acquisition segment, recalibrating the link delay, or reducing the control bandwidth. Finally, it outputs reliable frequency domain analysis foundation data with credible phase and data reliability, providing high-quality input for subsequent frequency domain analysis and hierarchical parameter identification.

[0016] Based on the above technical approach, a flow control method for a pumping experiment includes: Figure 1 The following steps are shown: Step 1: Divide the pumping test into multiple flow control stages, and configure a sequential transition section, steady-state acquisition section and link delay self-calibration disturbance section in each stage.

[0017] The transition section is used to achieve smooth switching of traffic between adjacent traffic control stages, the steady-state acquisition section is used to acquire stable excitation-response data, and the link delay self-calibration disturbance section is used to superimpose standardized disturbance signals on the steady-state traffic to provide an excitation source for link delay calibration.

[0018] The purpose of this step is to: 1. Achieve smooth flow switching through the transition section, avoiding strong transient interference caused by sudden flow changes from entering the subsequent frequency domain analysis data window, thus ensuring the stability of the frequency domain analysis data; 2. Obtain formation response data (pressure response) under stable flow conditions through the steady-state acquisition section, providing high-quality basic data for frequency domain analysis; 3. Provide repeatable and accurately identifiable calibration excitation signals through the link delay self-calibration disturbance section, enabling objective and quantitative calibration of the transmission delay of the control link and metering link during pumping tests, without the need for additional dedicated calibration test procedures; and achieve standardization and modularization of the flow control procedure through the above three stages, ensuring uniformity of test procedures in different flow control stages and improving the comparability of test results.

[0019] 1. Divide the flow control phase Based on the preset flow rate levels in the test plan, the entire pumping test is divided into N consecutive flow control stages (N≥1, the specific number is determined by the test requirements). The target flow rate for each flow control stage is denoted as Q0 (Q0 is the set flow rate for the steady-state acquisition segment of that stage; Q0 may be the same or different for different flow control stages, such as Q0 increasing or decreasing sequentially in a stepped pumping test). The duration of each flow control stage is determined by the sum of the transition segment duration, the steady-state acquisition segment duration, and the link delay self-calibration disturbance segment duration, and can be dynamically adjusted according to the test target frequency and equipment response characteristics.

[0020] 2. Determine the parameters of the transition section The core parameter of the transition section is the maximum allowable rate of flow change, R_max. Its value must simultaneously satisfy both equipment capacity and response constraints. Ultimately, R_max = min(R_pump, R_resp) is chosen to ensure that the transition process neither exceeds the equipment's operating limits nor causes excessive interference to the formation response. R_pump is the cross-correlation coefficient between the differential signal of the pump control command output by the controller and the differential signal of the actual flow rate collected by the flowmeter. R_resp is the cross-correlation coefficient between the differential signal of the actual flow rate collected by the flowmeter and the differential signal collected by the pressure sensor.

[0021] R_pump represents the equipment capacity constraint, determined by the hardware characteristics of the pumping unit. It is the upper limit of the flow rate change rate to ensure safe and stable operation of the equipment. The specific calculation method is: the allowable speed change rate of the combined pump and the frequency converter (provided by the equipment manufacturer, e.g., the maximum allowable speed change rate of a centrifugal pump is 50 r / min). The response time of the electric regulating valve (e.g., a full-stroke response time of 5 seconds) and the system inertia (including the pump's rotational inertia and the fluid's inertia in the pipeline) are used to calculate the maximum allowable flow rate change rate through a dynamic model. For example, for a centrifugal pump system, the calculated value is R_pump = 2 m³ / h. s, meaning the rate of change of flow rate within the transition section must not exceed 2 m³ / h s.

[0022] 3. Quantitative steady-state criteria The core of the steady-state acquisition segment is to accurately determine whether the flow rate and formation response have entered a steady state. Only data that meets the steady-state criteria can be used as valid data for frequency domain analysis. The steady-state criteria adopt a multi-index joint determination method, as follows: (1) Flow steady-state criterion: Within the most recent time window T_win (T_win is set according to the test scenario, usually 10-30s), the standard deviation of the fluctuation of the flow measurement signal Q_meas(t) is statistically analyzed as σ_Q_win. When σ_Q_win ≤ σ_Q_th, the flow is considered to have entered a steady state. Wherein, σ_Q_th is the flow steady-state criterion threshold, and its value is σ_Q_th = max(3σ_Q_base, ε_meter).

[0023] σ_Q_base represents the standard deviation of the flow baseline noise, obtained through statistical analysis of baseline data. Specifically, under undisturbed, stable conditions, Q_meas(t) data is collected over a period of time, and its standard deviation is calculated as σ_Q_base. Q_meas(t) data is the flow measurement signal, representing the actual pumping flow rate signal collected and output in real-time by the flow meter. It is direct measurement data reflecting the current actual flow state, and its accuracy is affected by factors such as the flow meter type, installation method, and signal filtering parameters.

[0024] ε_meter is the minimum resolvable fluctuation in flow meter resolution conversion, calculated from the flow meter's range FS and resolution bits, i.e., ε_meter = FS / (2^Bits). For example, if an electromagnetic flow meter has a range of 0 to 50 m³ / h and a resolution of 16 bits, then ε_meter = 50 / (2^16) = 50 / 65536 ≈ 0.00076 m³ / h; if σ_Q_base = 0.0005 m³ / h, then 3 × σ_Q_base = 0.0015 m³ / h, and at this time σ_Q_th = max(0.0015, 0.00076) = 0.0015 m³ / h; (2) Steady-state response criterion: To further ensure the stability of the formation response, the pressure change rate |dP / dt| can be constrained to be below the corresponding threshold. For example, if |dP / dt|_th = 0.001 MPa / s is set, the formation response is considered to have entered a steady state when |dP / dt| ≤ |dP / dt|_th. |dP / dt|_th is the pressure change rate threshold.

[0025] (3) Steady-state confirmation: Only when the steady-state criteria of flow and the steady-state criteria of response are met at the same time, and the duration is not less than 2×T_win, can the steady-state acquisition segment be confirmed and the steady-state data be recorded.

[0026] 4. Determine the form of the disturbance signal The core of the link delay self-calibration disturbance segment is to design standardized disturbance signals that can be accurately identified while avoiding excessive disturbances that could cause irreversible damage to the formation response. The form of the disturbance signal can be selected according to the test scenario, mainly including small step disturbances, short pulse disturbances, and short-time sinusoidal disturbances.

[0027] (1) Small step disturbance Signal format: Q_set(t) = Q0 + ΔQ1, t ∈ [t_start, t_start + T_step]. Where Q_set(t) is the preset target flow signal, given by the flow control program planned before the experiment, and serves as the reference signal for the entire flow control process. Its form dynamically changes according to the experimental type (step pumping, periodic pumping, etc.) and the requirements of each functional segment (transition segment, steady-state acquisition segment, disturbance segment); ΔQ1 is the step disturbance amplitude, which needs to be adaptively adjusted according to the flow baseline noise level (see "Adaptive Generation of Disturbance Parameters" later for specific adjustment methods); t represents time; t_start is the start time of the disturbance segment, and T_step is the duration of the step disturbance. It is necessary to ensure that the flow measurement signal and pressure response signal can fully respond to the step disturbance, usually taking T_step = 3 to 5 times the system response time (the system response time can be obtained through preliminary experiments or theoretical estimation, such as 10 to 20 seconds). At t = t_start + T_step, Q_set(t) recovers to Q0, completing one step disturbance.

[0028] (2) Short pulse disturbance Signal format: Q_set(t) = Q0 + ΔQ2, t ∈ [t_start, t_start + T_pulse]. Where ΔQ2 is the amplitude of the short pulse, and its value is consistent with that of a small step disturbance; T_pulse is the pulse width (usually 1–5 s, shorter than the system response time). At t = t_start + T_pulse, Q_set(t) immediately recovers to Q0, forming a short pulse signal. T_pulse needs to be adjusted according to the system inertia to ensure that the pulse disturbance can be detected by the sensor without causing continuous fluctuations in the formation response, making it suitable for test scenarios sensitive to formation disturbances.

[0029] (3) Short-time sinusoidal disturbance Signal form: Q_set(t) = Q0 + ΔQ3 sin(2πf0t), t∈[t_start,t_start+T_cal]. Where ΔQ3 is the short-time sinusoidal amplitude, and its value is consistent with that of the small step disturbance; f0 is the frequency of the sinusoidal disturbance, which should be selected to be close to the target frequency of the frequency domain analysis (e.g., if the target frequency is 0.01Hz, then f0 can be 0.01Hz) to ensure that the calibrated time delay parameters can be directly applied to the phase compensation of the target frequency; T_cal is the duration of the sinusoidal disturbance, which should contain at least 3 to 5 complete sinusoidal cycles to ensure accurate extraction of the phase information of the sinusoidal signal. For example, when f0=0.01Hz, one cycle is 100s, and T_cal can be 300 to 500s, containing 3 to 5 cycles.

[0030] 5. Adaptive generation of disturbance parameters To ensure the signal-to-noise ratio of the disturbance signal, and to avoid excessive disturbance that could damage equipment or interfere with the formation response, the disturbance parameters in the above disturbance signal are generated adaptively. The specific steps are as follows: (1) In the small-stage disturbance signal, the disturbance parameters that need to be adaptively generated include: the step disturbance amplitude ΔQ1 and the step disturbance duration T_step.

[0031] 1) Adaptive generation of step disturbance amplitude ΔQ1 The step disturbance amplitude ΔQ1 needs to be dynamically determined based on the baseline noise level of the flow measurement signal to avoid the amplitude being too small and being submerged by noise (insufficient signal-to-noise ratio) or too large and exceeding equipment / geological constraints. The specific calculation method is as follows: First, during the steady-state acquisition phase, a undisturbed baseline window (duration ≥ 60s) is extracted, and the standard deviation of the flow differential noise σ_ΔQ1 is calculated (the first difference of the flow measurement value Q_meas(t) is performed, and the standard deviation of the difference sequence is statistically analyzed to reflect the noise fluctuation intensity); then, the signal-to-noise ratio coefficient k is set (usually taken as 5~10), and ΔQ=k is initially calculated. σ_ΔQ1 ensures that the disturbance amplitude is 5 to 10 times the noise, meeting the "identifiable" requirement; finally, a safety upper limit constraint is applied: ΔQ1≤α Q0 (α is the upper limit of the proportion, usually taken as 1% to 5% of Q0, where Q0 is the steady-state flow rate at the current stage) is used to avoid excessive disturbances that could lead to pump overload, sudden rise in pipeline pressure, or formation response entering the nonlinear region.

[0032] Example: If σ_ΔQ1=0.05m³ / h, k=8, Q0=30m³ / h, α=2%, then ΔQ1=min(8×0.05=0.4m³ / h,30×2%=0.6m³ / h)=0.4m³ / h.

[0033] 2) Adaptive generation of step disturbance duration T_step The duration of the step disturbance, T_step, must ensure that the flow response is fully established (i.e., Q_meas(t) can stably reach 80%~95% of ΔQ1) to avoid insufficient delay calibration due to an excessively short duration. The specific calculation method is as follows: First, through pre-experiments or historical data, the "flow response setup time T_build" is statistically analyzed, and Q_meas(t) increases from Q0 to Q0+β. For the time ΔQ1, β is set to 0.8~0.95; then, T_step is set to 1.2~1.5×T_build, reserving a certain redundancy to ensure that there is enough data in the disturbance window for cross-correlation analysis or 50% arrival time method calibration.

[0034] Example: If T_build=15s (when ΔQ1=0.4m³ / h, the flow rate needs 15s to reach 0.36m³ / h), then T_step=1.2×15=18s.

[0035] (2) In the short pulse disturbance signal, the disturbance parameters that need to be adaptively generated include: short pulse amplitude ΔQ2 and pulse width T_pulse.

[0036] 1) Adaptive generation of short pulse amplitude ΔQ2 Since the "identifiability" of short pulse disturbances also depends on the signal-to-noise ratio, and it is necessary to avoid excessively large pulse amplitudes that could impact equipment (such as instantaneous pump overload or wear from frequent valve switching), the adaptive generation method for short pulse amplitude ΔQ2 is the same as the adaptive generation method for step disturbance amplitude ΔQ1, with only the signal morphology being different (the pulse is "short-time step + fast recovery"), and the amplitude does not require additional adjustment.

[0037] 2) Adaptive generation of pulse width T_pulse T_pulse must satisfy the condition of being detectable by the sensor without triggering a sustained formation response (distinguishing it from the "fully established response" of a step disturbance) to avoid confusion with real fault transients. The specific calculation method is as follows: First, based on the flowmeter sampling period T_s and system inertia (pump / pipeline response delay), set T_pulse ≥ 3~5 × T_s (ensuring at least 3~5 effective sampling points are collected to avoid signal filtering); then, apply an upper limit constraint: T_pulse ≤ 0.5 × T_build (T_build is the flow response establishment time), ensuring that the flow is not fully established at the end of the pulse, only generating "instantaneous fluctuations" that do not interfere with the formation steady-state response.

[0038] Example: If T_s=0.1s and T_build=15s, then T_pulse=min(5×0.1=0.5s,0.5×15=7.5s)=0.5s (taking into account both sampling effectiveness and the lack of sustained response from the formation).

[0039] (3) In the short-time sinusoidal disturbance signal, the disturbance parameters that need to be adaptively generated include: sinusoidal amplitude ΔQ3, sinusoidal duration T_cal and sinusoidal disturbance frequency f0.

[0040] 1) Adaptive generation of sinusoidal amplitude ΔQ3 Since the phase extraction of sinusoidal disturbances depends on "signal amplitude > noise level", and it is necessary to avoid ΔQ3 being too large, causing the flow rate to exceed the pump's rated range, or sinusoidal fluctuations to cause pipeline resonance, the adaptive generation method of sinusoidal amplitude ΔQ3 is the same as the adaptive generation method of step disturbance amplitude ΔQ1 and / or short pulse amplitude ΔQ2. Only additional verification is required: the fluctuation range of ΔQ3 (Q0±ΔQ3) needs to be within 10%~90% of the flow metering range to ensure measurement linearity.

[0041] 2) Adaptive generation of sinusoidal duration T_cal T_cal needs to contain a sufficient number of sinusoidal periods to ensure phase fitting accuracy (avoiding phase calculation deviations due to too few periods) and to prevent excessive experimental time consumption due to excessive duration. The specific calculation method is as follows: First, based on the target frequency f0 (usually consistent with the target frequency of frequency domain analysis, such as 0.01Hz), calculate the period T0 = 1 / f0; then, set T_cal ≥ 3~5 × T0 (ensuring that it contains 3~5 complete periods, satisfying the "number of periods requirement" for sinusoidal fitting); finally, establish a time constraint based on the response: T_cal ≥ T_build (ensuring that the flow can follow the sinusoidal fluctuations and avoiding "distorted sinusoidal response").

[0042] Example: If f0=0.01Hz (T0=100s) and T_build=15s, then T_cal=max(3×100=300s,15s)=300s (based on cycle number constraint).

[0043] 3) Adaptive generation of sinusoidal perturbation frequency f0 When the target frequency for frequency domain analysis is not fixed (e.g., when it is necessary to cover the formation response of multiple frequency bands), f0 needs to adaptively match the analysis requirements of the current experimental stage. The specific method is as follows: First, based on the "formation response sensitive frequency band" of the previous experiment (e.g., it is determined through preliminary pumping that the formation is more sensitive to signals of 0.005~0.02Hz), select f0 within this frequency band; then, ensure that f0≤1 / (2×T_s) (satisfy the sampling theorem and avoid frequency aliasing), and f0≥1 / (10×T_cal) (avoiding too few cycles).

[0044] Example: If T_s=0.1s (sampling frequency 10Hz) and T_cal=300s, then f0 can be selected as 0.01Hz (10Hz / 2=5Hz≥0.01Hz, 1 / (10×300s)=0.0003Hz≤0.01Hz).

[0045] Additional notes: Except for the step disturbance amplitude ΔQ1, step disturbance duration T_step, short pulse amplitude ΔQ2, pulse width T_pulse, sinusoidal amplitude ΔQ3, sinusoidal duration T_cal, and sinusoidal disturbance frequency f0, which require adaptive generation, other parameters such as sampling period T_s, disturbance triggering timing, and signal recovery method do not require adaptive generation. Specifically, the sampling period T_s is determined by the target frequency and the sampling theorem (usually 5~50Hz), and remains fixed throughout the test scheme. Disturbance triggering timing is always triggered after the steady-state acquisition phase satisfies "flow fluctuation ≤ σ_Q_th (3σ_ΔQ or flowmeter resolution)," and the triggering condition is fixed, requiring only steady-state determination. Signal recovery method: After the three types of disturbances end, the flow is restored to steady-state flow Q0 (step / pulse direct recovery, sinusoidal natural attenuation), and the recovery logic is fixed.

[0046] Step 1 achieves standardization and refinement of the test process by constructing a modular flow control program consisting of a "transition section, steady-state acquisition section, and link delay self-calibration disturbance section". The transition section effectively suppresses strong transient interference caused by sudden flow changes by smoothly switching the flow, preventing transient signals from entering the steady-state acquisition section and frequency domain analysis window, thus providing a stable basic data environment for subsequent frequency domain analysis. The steady-state acquisition section ensures that the acquired excitation-response data is in a stable state through quantified steady-state criteria, improving data reliability. The link delay self-calibration disturbance section provides a repeatable, high signal-to-noise ratio excitation source for the link delay quantization calibration in step 3 through standardized and adaptive disturbance signal design, enabling the delay calibration to be performed synchronously during the experiment without adding an extra calibration process, thus improving experimental efficiency. The modular design of the entire flow control program ensures the uniformity of the experimental process in different flow control stages, laying the foundation for the comparability and reproducibility of experimental results. It also provides a clear time window division and data foundation for multi-channel synchronous acquisition in step 2, delay compensation flow control in step 4, and data alignment in step 5.

[0047] Step 2: Establish a unified time-domain reference. Using a multi-channel synchronous acquisition module, synchronously sample the control command signal u(t), the flow measurement signal Q_meas(t), and the well pressure response signal P_meas(t). During the acquisition process, mark each sampling point of each signal with a unified timestamp.

[0048] The purpose of this step is to: 1. Eliminate time synchronization errors caused by independent sampling of multiple devices, avoid pseudo-phase differences caused by asynchronous sampling, and ensure the accuracy of subsequent link delay calibration results; 2. Establish a unified time axis for all acquired signals, providing a reliable time reference for subsequent link delay calculation and data time shift alignment; 3. Completely record all key signals during the test, including control commands and measurement data, to facilitate the traceability, review, and troubleshooting of subsequent test data.

[0049] 1. Configure synchronous acquisition hardware A data acquisition module with multi-channel synchronous sampling capability should be selected. This module must support all interface types of the signals to be acquired (such as analog signals 4-20mA, digital signals, RS485 communication signals, etc.), and all channels must be driven by the same system clock to ensure complete synchronization of sampling trigger times, with a time synchronization accuracy of no less than 1ms. The data acquisition module is connected to the PLC / industrial computer via a high-speed communication interface (such as Ethernet, PCIe) to ensure that the acquired data can be transmitted to the controller for processing in real time.

[0050] 2. Determine the sampling frequency The sampling frequency f_s needs to be set according to the target analysis frequency f0 of the experiment, following the sampling theorem (f_s≥2f0), while also considering the dynamic characteristics of the signal to ensure accurate capture of signal changes. The specific value selection method is as follows: First, for experimental scenarios with low target frequencies (f0≤0.01Hz), such as stepped pumping and low-frequency periodic pumping, the sampling frequency can be set to 5~20Hz, which satisfies the sampling theorem while avoiding excessive data volume. Then, for experimental scenarios with high target frequencies (f0>0.01Hz), such as harmonic pumping and high-frequency variable flow pumping, the sampling frequency can be set to 20~50Hz to ensure accurate capture of phase changes in high-frequency signals. Finally, the sampling period T_s=1 / f_s. For example, when f_s=10Hz, T_s=0.1s, meaning that all channels are sampled synchronously every 0.1s.

[0051] 3. Marking signal acquisition and timestamp The acquisition module synchronously samples the control command signal u(t), flow measurement signal Q_meas(t), and pressure response signal P_meas(t) according to the set sampling frequency. After each sampling point is completed, a timestamp is immediately generated for that sampling point using the system clock of the acquisition module (format: "year-month-day hour:minute:second.millisecond", e.g., 2024-05-2014:30:00.001). The generation of the timestamp is synchronized with the sampling process, ensuring the accuracy of the time information for each sampling point.

[0052] 4. Recording stage markers and start and end times of disturbance segments During the data acquisition process, the start and end times of each flow control phase and each functional segment (transition segment, steady-state acquisition segment, and link delay self-calibration disturbance segment) are recorded synchronously, and these phase markers are associated with and stored in relation to the acquired data. For example, a "phase identifier" field is added to the data file, with "0" representing the transition segment, "1" representing the steady-state acquisition segment, and "2" representing the disturbance segment. The start time t_start and end time t_end of the disturbance segment are also recorded to facilitate the subsequent step S3 in accurately extracting the data for delay calibration within the disturbance window.

[0053] 5. Data preprocessing and storage After the acquired data is transmitted to the PLC / industrial computer, it first undergoes simple preprocessing, including removing obvious outliers (such as data exceeding the measurement range due to sensor failure) and signal filtering (using a low-pass filter with a cutoff frequency of f_s / 2 to avoid frequency aliasing). After preprocessing, the data, along with timestamps and stage marker information, is stored in the data storage module in the common CSV format for easy subsequent data reading and processing. It also supports real-time data backup to prevent data loss.

[0054] Step 2, through multi-channel synchronous acquisition and unified timestamp marking, eliminates time errors caused by independent sampling from multiple devices, ensuring the comparability of control command signal u(t), flow measurement signal Q_meas(t), and pressure response signal P_meas(t) in the time dimension. This provides an accurate time reference for link delay calibration using methods such as cross-correlation and the 50% arrival time method in subsequent step 3. Furthermore, the unified timestamp provides a reliable time axis for data time shifting and alignment in subsequent step 5, enabling accurate calculation of the shift amounts of Q_meas(t) and P_meas(t) and ensuring the accuracy of time alignment. Additionally, stage marking and disturbance segment start and end time recording facilitate accurate extraction of data from each functional segment, avoiding errors during data extraction. Moreover, data preprocessing and standardized storage facilitate post-experiment data analysis, review, and problem troubleshooting, while providing a high-quality data foundation for potential secondary analyses.

[0055] Step 3: Extract the synchronously acquired data within the self-calibration disturbance segment of the link delay, calculate the equivalent transmission delay τ_uQ from the control link to the flow metering link, and calculate the cross-link relative transmission delay τ_QP from the flow metering link to the pressure acquisition link.

[0056] The purpose of this step is threefold: First, to accurately quantify the transmission delay introduced by the control link and the metering link, separating the "phase lag caused by the equipment and signal link" from the "true formation response phase," thus providing accurate parameter support for subsequent delay compensation and data alignment. Second, to achieve online calibration of link delay, enabling real-time tracking of delay drift caused by changes in operating conditions and environment, ensuring that delay parameters match the current test state. Third, to establish a calibration validity judgment mechanism to avoid calibration failures due to excessive noise or ineffective identification of disturbance signals, ensuring the reliability of delay parameters input to subsequent steps.

[0057] Within the self-calibration disturbance segment of the link delay, based on the synchronization data collected in step 2, a professional calibration algorithm is used to calculate two types of link delays: 1. Calculate the equivalent transmission delay τ_uQ from the control link to the flow metering link, which is the total time delay from when the controller issues a control command signal u(t) to when the flow meter detects the flow change corresponding to the control command and outputs a flow measurement signal Q_meas(t). 2. Calculate the relative transmission delay across links: which is the time delay τ_QP from when the flow meter detects a flow change (Q_meas(t) changes) to when the pressure sensor detects the corresponding pressure response (P_meas(t) changes). Simultaneously, a calibration validity judgment mechanism is established to ensure the reliability of the calibration delay parameters.

[0058] The specific method is as follows: 1. Equivalent transmission delay τ_uQ from the calibration control link to the flow metering link. Two optional methods are available for τ_uQ calibration, which can be selected based on the form of the disturbance signal, as follows: (1) Differential cross-correlation method (applicable to all types of disturbance signals) Cross-correlation analysis measures the similarity between two signals at different time offsets. When the cross-correlation coefficient reaches its maximum value, the corresponding time offset is the transmission delay between the two signals. Since the original signal contains noise, directly performing cross-correlation analysis may lead to inaccurate results. Therefore, the signal is first differentially processed to highlight the signal's variation characteristics before performing cross-correlation analysis to improve calibration accuracy.

[0059] The specific steps are as follows: First, perform data extraction—extract the control command signal u(t) and flow measurement signal Q_meas(t) within the link delay self-calibration disturbance segment from the synchronization data collected in step 2. The extraction time window is [t_start, t_end] (start and end times of the disturbance segment); then, perform signal differential processing—perform first-order differential processing on the extracted u(t) and Q_meas(t) respectively to obtain Δu(t) and ΔQ(t). The differential formula is: Δ u(t) = u(t) - u(t - T_s), ΔQ(t) = Q_meas(t) - Q_meas(t - T_s); where T_s is the sampling period; the purpose of differential processing is to highlight the edges of signal changes and suppress the influence of DC components and low-frequency noise; next, cross-correlation calculation is performed—within the preset time offset range [0, τ_max], the cross-correlation coefficient R_uQ(τ) between Δu(t) and ΔQ(t+τ) is calculated, and the formula for calculating the cross-correlation coefficient is: ; where t star The start time t of the disturbance segment end The end time of the disturbance segment is τ, which is the time offset, ranging from 0 to τ_max (τ_max is the maximum possible delay, usually 5 to 10 seconds depending on the equipment characteristics and system architecture). Finally, the delay is determined by finding the time offset τ corresponding to the maximum value of the cross-correlation coefficient R_uQ(τ), which is the equivalent transmission delay τ_uQ from the control link to the flow metering link.

[0060] Additional notes: To improve calibration accuracy, a second interpolation (such as parabolic interpolation) can be performed on the τ value near the peak of the cross-correlation coefficient to further refine the value of τ, so that the calibration accuracy can reach 1 / 10 to 1 / 5 of the sampling period (for example, when T_s=0.1s, the calibration accuracy can reach 0.01 to 0.02s).

[0061] (2) 50% arrival time method (applicable to step disturbance and short pulse disturbance signals) For step or pulse-type disturbance signals, both the control command signal u(t) and the flow measurement signal Q_meas(t) will show obvious step changes. Find the time when the step amplitude of the two signals is 50%, and the difference between the two times is τ_uQ. The specific steps are as follows: First, perform data extraction—using the same difference cross-correlation method—to extract u(t) and Q_meas(t) within the disturbance segment; then, determine the step amplitude—for the control command signal u(t), find the step start time t_u_start (the moment when u(t) begins to change significantly) within the disturbance segment, and calculate the steady-state values ​​u_prev (the steady-state value before t_u_start) and u_curr (the steady-state value after t_u_start) before and after the step, then the step amplitude Δu = u_curr - u_prev; similarly, for Q_meas(t), find its step start time t_Q_start, and calculate the steady-state values ​​Q_prev and Q_curr before and after the step, the step amplitude ΔQ = Q_curr - Q_prev; next, find the 50% arrival time—find the time when u(t) reaches u_prev + 50%. The time corresponding to Δu is t_u50, and Q_meas(t) reaches Q_prev+50%. The time t_Q50 corresponds to ΔQ; during the search process, if the signal value of a certain sampling point is exactly equal to the 50% step amplitude value, then the time of that sampling point is the arrival time; if it is not exactly equal, then the arrival time is calculated by linear interpolation; finally, the time delay is determined - τ_uQ=t_Q50-t_u50, which is the equivalent transmission time delay from the control link to the flow metering link.

[0062] 2. Calibrate the relative transmission delay τ_QP between the flow metering link and the pressure.

[0063] The calibration principle of τ_QP is similar to that of τ_uQ, and it also uses two optional methods, as follows: (1) Differential cross-correlation method (applicable to all types of disturbance signals) The specific steps are as follows: First, perform data extraction—extract the flow measurement signal Q_meas(t) and pressure response signal P_meas(t) within the disturbance section; then, perform differential processing of the mechanical energy signal—perform first-order differential processing on Q_meas(t) and P_meas(t) respectively to obtain ΔQ(t) and ΔP(t), with the differential formula referring to the differential cross-correlation method of τ_uQ; next, calculate the cross-correlation coefficient—within the preset time offset range [0, τ_max'] (τ_max' is usually taken as 10 to 20 s, because the pressure response is slow), calculate the cross-correlation coefficient R_QP(τ) between ΔQ(t) and ΔP(t+τ), with the calculation formula referring to the differential cross-correlation method of τ_uQ; finally, determine the time delay—find the time offset τ corresponding to the maximum value of R_QP(τ), which is τ_QP, and the calibration accuracy can also be improved by quadratic interpolation.

[0064] (2) Sinusoidal phase swap algorithm (applicable to short-time sinusoidal disturbance signals) For short-time sinusoidal disturbance signals, both Q_meas(t) and P_meas(t) exhibit sinusoidal variation characteristics. By performing sinusoidal fitting on the two signals, their respective phase information can be obtained. The ratio of the phase difference to the disturbance frequency is the relative transmission delay. The specific steps are as follows: First, perform a data extraction operation—extract Q_meas(t) and P_meas(t) within the disturbance segment. The extraction time window must include the complete sinusoidal disturbance period. Then, perform a sinusoidal fitting operation—perform single-frequency sinusoidal fitting on Q_meas(t). The fitting model is: Q_meas(t) = A_Q sin(2πf0t)+B_Q cos(2πf0t)+C_Q; where A_Q and B_Q are fitting coefficients, C_Q is the DC component (i.e., steady-state flow rate Q0), and f0 is the sinusoidal disturbance frequency (known); by solving the fitting coefficients using the least squares method, the phase of Q_meas(t) at f0 is obtained as φ_Q=atan2(B_Q,A_Q); similarly, a sinusoidal fitting is performed on P_meas(t), and the fitting model is: P_meas(t)=A_P sin(2πf0t)+B_P cos(2πf0t)+C_P, we obtain the phase φ_P of P_meas(t) at f0; next, we calculate the phase difference—the formula for phase difference is: Δφ=φ_P-φ_Q. Since the pressure response lags behind the flow rate change, Δφ is usually negative (indicating that the phase of P_meas(t) lags behind Q_meas(t)); finally, we convert the time delay—the relative transmission delay τ_QP=Δφ / (2πf0). Since Δφ is negative and τ_QP is positive, it conforms to the physical meaning of time delay.

[0065] 3. Judgment of Calibration Validity To avoid the distortion of calibration results caused by excessive noise, insufficient disturbance signal amplitude, equipment failures, etc., it is necessary to judge the calibration validity. The specific judgment criteria are as follows: (1)Disturbance Response Amplitude Threshold Calculate the maximum change ΔQ_peak of Q_meas(t) within the disturbance section (i.e., the difference between the maximum value and the minimum value of Q_meas(t)), and it is required that ΔQ_peak > k_peak σ_ΔQ, where k_peak is the threshold coefficient (preferably taken as 3 - 5), and σ_ΔQ is the standard deviation of the flow differential noise statistically obtained in Step 1; if ΔQ_peak ≤ k_peak σ_ΔQ, it means that the disturbance signal is submerged by noise and cannot be effectively identified, and the calibration is determined to fail.

[0066] (2)Cross - Correlation Peak Threshold (Applicable to Differential Cross - Correlation Method) It is required that the maximum value R_max of the cross - correlation coefficients R_uQ(τ) and R_QP(τ) ≥ R_th, where R_th is the cross - correlation peak threshold (preferably taken as 0.5 - 0.7); if R_max < R_th, it means that the correlation between the two signals is weak, the calibration result is unreliable, and the calibration is determined to fail.

[0067] (3)Sine Fitting Goodness - of - Fit Threshold (Applicable to Sine Phase Conversion Algorithm) Calculate the coefficient of determination R² of the sine fitting, and it is required that R² ≥ 0.85; the closer the coefficient of determination R² is to 1, the better the fitting effect; if R² < 0.85, it means that there is too much noise component in the signal or the interference of non - target frequency components is too large, and the fitting phase is unreliable, and the calibration is determined to fail.

[0068] (4)Handling of Calibration Failure If any of the above criteria is not met, it is determined that the current calibration fails, the time - delay parameters such as τ_uQ and τ_QP are not updated, and the parameters successfully calibrated last time are continued to be used, and a re - calibration or a strategy of extending the steady - state section is triggered in Step S6.

[0069] Step 3 achieves accurate quantification of link latency and reliability verification through calibration algorithms and validity judgments. Specifically, the precise output of key time delay parameters such as τ_uQ and τ_QP provides core parameter support for the delay compensation closed-loop control in step 4, enabling the delay compensation to accurately offset the phase lag of the control link. Simultaneously, it provides accurate shift amounts for the data time shift alignment in step 5, ensuring effective elimination of phase deviations across links. Furthermore, the online calibration method for link delays can track time delay drift caused by changes in operating conditions (such as flow rate switching and pump load changes) and environmental changes (such as temperature and humidity changes) in real time, ensuring that the time delay parameters always match the current test state and avoiding the problem of fixed time delay parameters becoming invalid after changes in operating conditions. Moreover, the calibration validity judgment mechanism effectively eliminates distorted calibration results under conditions such as excessive noise and invalid disturbances, ensuring the reliability of the time delay parameters input to steps 4 and 5, providing a guarantee for the smooth progress of subsequent processes. In addition, the optional design of multiple calibration algorithms allows the method to adapt to different types of disturbance signals, improving the flexibility and applicability of the method, and allowing the selection of the optimal calibration algorithm according to the test scenario and equipment conditions.

[0070] It should be noted that, to further improve the reliability and stability of the delay parameters, this method also includes delay parameter stabilization and drift monitoring. Specifically: a sliding window buffer is set up to store the delay parameters (including τ_uQ and τ_QP) from the most recent M successful calibrations; statistical analysis is performed on the delay parameter sequence within the window, and the median is taken as the stabilized delay parameter; simultaneously, the difference between the currently calibrated delay parameter and the previously stabilized delay parameter is monitored in real time. When the difference exceeds a preset drift judgment threshold, a recalibration or equipment condition check alarm is triggered.

[0071] The purpose of delay parameter stabilization and drift monitoring is: 1. To suppress the impact of occasional abnormal calibration values ​​on subsequent control and data processing, and improve the stability and reliability of delay parameters; since there may be instantaneous noise, instantaneous sensor failures, etc. during the test, single calibration results may be abnormal. By using sliding windows and median statistics, outliers can be effectively eliminated; 2. To monitor the dynamic drift of link delay in real time. When the delay parameter changes abruptly or drifts slowly beyond the allowable range, a timely handling strategy is triggered to ensure that the delay parameter is always within a reliable range, avoiding inaccurate phase compensation due to delay drift; 3. To provide early warning of equipment failure or abnormal operating conditions. When the delay parameter changes abruptly, it may indicate that equipment such as pumps, sensors, and communication links have failed, or that the test conditions have become abnormal. An alarm is triggered to prompt operators to check in time, avoiding large-scale distortion of test data.

[0072] The specific method is as follows: 1. Set up a sliding window cache Set up independent sliding window buffers for τ_uQ and τ_QP respectively. The capacity of each buffer is M (M is the preset number of buffering times, preferably 5 or 7, and the value is odd to facilitate median calculation). The buffer adopts the "first-in, first-out" principle. When a new calibration parameter is stored, if the buffer is full, the earliest stored parameter is deleted to ensure that the buffer always stores the most recent M valid calibration parameters.

[0073] 2. Calculate the stabilization delay parameters Whenever a new successfully calibrated parameter is stored in the buffer, the M latency parameters in the buffer are sorted, and the median of the sorted values ​​is taken as the stable latency parameter (τ_uQ_stab, τ_QP_stab). The median is used instead of the average value because the median has a stronger ability to resist interference from outliers and can effectively suppress the impact of occasional outlier calibration values ​​on the stabilization result. For example, if the buffer stores 5 τ_uQ calibration results [0.7s, 0.8s, 0.75s, 0.9s, 1.5s] (where 1.5s is an outlier), after sorting, it becomes [0.7s, 0.75s, 0.8s, 0.9s, 1.5s], with a median of 0.8s, avoiding the impact of the outlier 1.5s on the result; if the average value is used, the result would be (0.7+0.8+0.75+0.9+1.5) / 5=0.93s, which is more affected by outliers.

[0074] 3. Set the drift detection threshold. The drift threshold Δτ_th is used to determine whether the difference between the current calibration value and the stable value exceeds the allowable range. Its value is dynamically generated based on the dispersion of the time delay parameter sequence and the sampling resolution. The specific calculation method is as follows: First, calculate the median absolute deviation (MAD) of the M delay parameters in the buffer. The formula for calculating MAD is: MAD = median(|τ_i - median(τ_i)|), where τ_i is the i-th delay parameter in the buffer (i = 1 to M).

[0075] Then, the drift determination threshold Δτ_th is calculated using the formula: Δτ_th = k_mad MAD+k_ts T_s, where k_mad is the MAD coefficient (preferably 2 to 3), k_ts is the sampling period coefficient (preferably 1 to 2), and T_s is the sampling period.

[0076] Finally, set upper and lower limit protections for Δτ_th. The lower limit is 0.1T_s (to ensure the threshold is not too small and causes false triggering), and the upper limit is τ_max / 5 (τ_max is the maximum possible delay preset in step 3, to ensure the threshold is not too large and causes missed triggering). For example, if MAD=0.05s, T_s=0.1s, k_mad=2, k_ts=1, then Δτ_th=2×0.05+1×0.1=0.2s. If the upper and lower limit protections are 0.01s~2s, then the final Δτ_th=0.2s.

[0077] 4. Drift monitoring The difference between the current calibration delay parameter τ_curr and the stable delay parameter τ_stab is calculated in real time: Δτ = |τ_curr - τ_stab|. If Δτ≤Δτ_th, it means that the time delay parameter has no significant drift, the stabilized time delay parameter remains unchanged, and can continue to be used in subsequent steps; If Δτ > Δτ_th, it indicates a significant drift in the time delay parameter. When a significant drift occurs in the time delay parameter, recalibration should be performed or an alarm should be triggered.

[0078] (1) Recalibration: Automatically re-execute the calibration process of the link delay self-calibration disturbance segment to obtain new calibration parameters. If the difference between the new parameters and the stable value is ≤ Δτ_th, then update the stable parameters; if it still exceeds, then repeat the recalibration, up to 3 times.

[0079] (2) Alarm prompt: If Δτ still exceeds Δτ_th after 3 recalibrations, an alarm prompt will be issued to the operator. The prompt will include "abnormal drift of link delay. Please check the working condition of equipment such as pump, sensor, and communication link". At the same time, the time of the abnormality, the current calibration value, the stabilization value and other information will be recorded to facilitate subsequent troubleshooting.

[0080] Delay parameter stabilization and drift monitoring further improve the reliability and stability of delay parameters. Specifically, the stabilized time delay parameters τ_uQ_stab and τ_QP_stab have stronger anti-interference capabilities, avoiding control oscillations in step 4 and data alignment deviations in step 5 caused by occasional abnormal calibration values, thus improving the robustness of the entire system. Furthermore, the drift monitoring mechanism can track the dynamic changes of the time delay parameters in real time, promptly detect time delay drift caused by equipment failure or abnormal operating conditions, and ensure that the time delay parameters are always within a reliable range through recalibration or alarm handling, providing a guarantee for the accurate execution of steps 4 and 5. In addition, the alarm prompt function can provide early warning of equipment failure, avoiding large-scale distortion of test data caused by equipment failure, reducing test risks, and improving test reliability. Moreover, the time delay parameter stabilization and drift monitoring complement the calibration validity judgment mechanism in step 3. Step 3 mainly eliminates invalid results from a single calibration, while this time delay parameter stabilization and drift monitoring mainly suppresses occasional abnormal valid calibration results, further ensuring the stability and reliability of the time delay parameters.

[0081] Step 4: Using the calculated equivalent transmission delay τ_uQ of the link and the rate of change of the flow measurement signal, estimate the actual flow estimate Q_hat(t) at the current moment; calculate the deviation between the preset target flow Q_set(t) and the actual flow estimate Q_hat(t), and adjust the control command signal u(t) through the feedback control algorithm.

[0082] The objectives of this step are: 1. To compensate for the transmission delay of the flow measurement link, eliminate the phase lag in closed-loop control caused by delayed feedback, and solve problems such as control oscillation and slow response caused by measurement delay in traditional PI / PID control; 2. To construct Q_hat(t) that reflects the true flow state at the current moment, enabling the feedback control algorithm to adjust based on the true flow, rather than based on the delayed measured flow, thereby improving the accuracy of flow control and making the actual pumping flow closer to the preset target flow Q_set(t); 3. To reduce the impact of control link phase contamination on frequency domain analysis, ensuring that the flow excitation signal input to the formation has good phase characteristics, laying the foundation for extracting the true formation phase response in subsequent frequency domain analysis; 4. To suppress the generation of high-frequency oscillations and pseudo-frequency components, ensuring the stability of the flow signal and avoiding the introduction of additional frequency components due to control oscillations, which would interfere with the frequency domain analysis results.

[0083] The specific method is as follows: 1. Calculate the estimated actual flow rate Q_hat(t) The core of Q_hat(t) is to predict the actual flow rate at the current moment based on the delayed flow measurement signal Q_meas(t) and the flow rate of change. The specific calculation steps are as follows: First, calculate the rate of change of flow rate. At sampling time t, calculate the smoothed rate of change of flow rate. To suppress the influence of noise on the rate of change, the following method is used: The differential rate of change of the flow measurement signal Q_meas(t) is processed by moving average. The formula for calculating the differential rate of change is: [dQ / dt]_n = [Q_meas(n·T_s) - Q_meas((n-1)·T_s)] / T_s, where n is the sampling point number, and [dQ / dt]n is the differential rate of change of the nth sampling point. The differential rates of change of the most recent N_avg sampling points are then averaged to obtain the smoothed flow rate of change. ,in, The smoothed rate of change of flow rate at sampling time t, where avg denotes the average, and N avg The moving average window length is preferably 3 to 5, which can effectively smooth out noise without lagging too much behind the actual changes in flow. Let represent the rate of change of the original flow differential at the nk-th sampling point, where n is the current sampling point index and k is the summation iteration variable.

[0084] Then, the actual flow rate is estimated. This is based on the flow measurement signal Q_meas(t) and the smoothed rate of change of flow. The actual traffic Q_hat(t) at the current moment is estimated using the following formula, based on the stabilized equivalent transmission delay τ_uQ_stab: The physical meaning of the formula is as follows: Since Q_meas(t) is the measured value after a delay of τ_uQ_stab, the actual flow rate at the current moment equals the delayed measured value plus the change in flow rate over the time interval τ_uQ_stab, thus compensating for the measurement delay. For example, if Q_meas(t) = 30 m³ / h, =0.5m³ / h If s, τ_uQ_stab=0.8s, then Q_hat(t)=30+0.5×0.8=30.4m³ / h, that is, the estimated actual flow rate at the current moment is 30.4m³ / h.

[0085] 2. Feedback control algorithm Using PI (proportional-integral) or PID (proportional-integral-derivative) control algorithms, the deviation between Q_set(t) and Q_hat(t) is taken as the control input to adjust the control command signal u(t). The specific implementation is as follows: (1) Calculate the control error Calculate the deviation e(t) between the preset target flow rate Q_set(t) and the actual flow rate estimate Q_hat(t): e(t) = Q_set(t) - Q_hat(t); when e(t) > 0, it means that the actual flow rate is lower than the target flow rate, and the control command u(t) needs to be increased; when e(t) < 0, it means that the actual flow rate is higher than the target flow rate, and the control command u(t) needs to be decreased.

[0086] (2) PI control algorithm The update formula for the control command signal u(t) is: u(t) = u(t - T_s) + K_p e(t)+K_i e(t) T_s, where u(t-T_s) is the control command at the previous sampling time, K_p is the proportional coefficient, Ki is the integral coefficient, and T_s is the sampling period. The proportional coefficient K_p is used to quickly respond to control errors; the larger K_p is, the faster the response speed, but too large a value may lead to control oscillation. The integral coefficient Ki is used to eliminate static errors; the larger Ki is, the faster the static error is eliminated, but too large a value may lead to overshoot and oscillation. The values ​​of K_p and Ki need to be tuned according to the equipment characteristics and system response characteristics, for example, by trial and error or the Ziegler-Nichols tuning method. (3) PID control algorithm (optional) By adding a differential term to the PI algorithm, the updated formula is: u(t) = u(t-T_s) + K_p e(t)+K_i e(t) T_s+K_d [e(t)-e(t-T_s)] / T_s, where K_d is the differential coefficient, which is used to predict the trend of error change, suppress overshoot, and improve control stability. The value of K_d should be carefully selected, as an excessively large value may cause the system to become sensitive to noise.

[0087] (4) Integral saturation has been suppressed To avoid integral saturation (which could lead to system overshoot) caused by the continuous accumulation of integral terms after the control command reaches its upper or lower limits, the accumulation of integral terms is stopped when u(t) reaches the preset upper limit u_max or lower limit u_min, or an anti-saturation feedback mechanism is adopted (e.g., if e(t) is still positive when u(t) reaches the upper limit, integration is stopped; if e(t) becomes negative, integration is resumed).

[0088] 3. File splitting and bandwidth constraints: (1) Transition section gear shifting control Within the transition section, the preset target flow rate Q_set(t) is generated using a ramp-based method, i.e., Q_set(t) = Q0_prev + (Q0_current - Q0_prev). (t-t_trans_start) / T_trans, where t_trans_start is the start time of the transition segment and T_trans is the duration of the transition segment, ensuring that the rate of change of flow does not exceed R_max determined in step S1, and avoiding sudden changes in flow within the transition segment.

[0089] (2) Adaptive adjustment of control bandwidth The fluctuation of the flow measurement signal Q_meas(t) is monitored in real time. The standard deviation of the flow fluctuation σ_Q_vib within the most recent T_vib time window (T_vib is 5 to 10 s) is calculated. If σ_Q_vib > σ_Q_vib_th (σ_Q_vib_th is the oscillation judgment threshold, which is 2 to 3 times σ_Q_base), it indicates that the system has a high-frequency oscillation trend. At this time, the control bandwidth is adaptively reduced, that is, the proportional coefficient K_p and integral coefficient Ki are reduced according to the graded proportional factor η (0 < η < 1, preferably 0.7 to 0.9) until σ_Q_vib ≤ σ_Q_vib_th, so as to suppress the pseudo-frequency component introduced by control oscillation.

[0090] Step 4 improves the accuracy and phase consistency of flow control through delay-compensated closed-loop control. Specifically, the introduction of the actual flow estimate Q_hat(t) effectively compensates for the transmission delay of the flow measurement link, enabling the feedback control algorithm to adjust based on the actual flow state at the current moment. This solves the problems of slow response and control oscillation caused by measurement delay in traditional control, improving the accuracy of flow control and making the actual pumping flow closer to the preset target flow Q_set(t). Furthermore, delay compensation and adaptive adjustment of control bandwidth reduce "control link phase pollution" from the source, ensuring that the flow excitation signal input to the formation has stable amplitude and phase characteristics, providing a basis for frequency domain amplitude and phase extraction in Step 5. It provides high-quality excitation data; and the integral saturation suppression and transition ramp setting method further improve the stability of flow control, avoid flow mutation and overshoot, and ensure the smoothness of the transition section. This echoes the transition section design in step 1 and jointly suppresses the impact of transient interference on frequency domain analysis. In addition, the stable flow signal and the true flow estimate Q_hat(t) output in step 4 provide a reliable excitation data basis for data time shift alignment and amplitude and phase extraction in step 5, and at the same time provide core data (RMSE of Q_set(t) and Q_hat(t)) for excitation consistency verification in step 6.

[0091] Step 5: Based on the link equivalent transmission delay τ_uQ and the cross-link relative transmission delay τ_QP, perform time shift alignment on the flow measurement signal Q_meas(t) and the well pressure response signal P_meas(t) in the steady-state acquisition segment to eliminate the phase deviation caused by the link delay, and extract the amplitude and phase parameters at the target frequency from the aligned data.

[0092] The purpose of this step is to: 1. Eliminate phase deviations caused by transmission delays introduced by the control link, metering link, and acquisition link, so that the time-aligned Q_meas(t) and P_meas(t) are completely synchronized in the time dimension, ensuring that the phase difference used in frequency domain analysis can truly reflect the response characteristics of the formation, rather than the lag of the equipment and link; 2. Accurately extract the amplitude and phase parameters at the target frequency, ensuring the accuracy and reliability of the amplitude and phase data, and providing high-quality input data for subsequent frequency domain analysis (such as amplitude attenuation analysis and phase lag analysis) and stratification parameter identification; 3. Unify the methods and standards for amplitude and phase extraction, ensuring the comparability of amplitude and phase data from different flow control stages and different test batches, and improving the reproducibility of test results.

[0093] The specific method is as follows: 1. Time translation alignment The core of time shift alignment is to shift Q_meas(t) and P_meas(t) forward according to their respective time delay parameters, eliminating transmission delay and ensuring that the time base of all signals is consistent with that of the control command signal u(t). The specific steps are as follows: First, determine the alignment reference.

[0094] Using the time axis of the control command signal u(t) as a unified reference, the time shift of all signals is based on the time of u(t).

[0095] Then, translate Q_meas(t) and P_meas(t).

[0096] Since Q_meas(t) has a transmission delay of τ_uQ_stab relative to u(t), Q_meas(t) is shifted forward along the time axis by τ_uQ_stab to obtain the aligned flow signal Q_align(t) = Q_meas(t + τ_uQ_stab). For example, if the time corresponding to Q_meas(t) at a certain sampling point is t = 10.0s and τ_uQ_stab = 0.8s, then the aligned time is t = 9.2s, that is, Q_align(9.2s) = Q_meas(10.0s).

[0097] Since P_meas(t) has a transmission delay of τ_QP_stab relative to Q_meas(t), and Q_meas(t) has a delay of τ_uQ_stab relative to u(t), the total delay of P_meas(t) relative to u(t) is τ_uQ_stab + τ_QP_stab. By shifting P_meas(t) forward along the time axis by τ_uQ_stab + τ_QP_stab, we obtain the aligned pressure signal P_align(t) = P_meas(t + τ_uQ_stab + τ_QP_stab).

[0098] Next, interpolation resampling is performed.

[0099] Since τ_uQ_stab and τ_QP_stab are not necessarily integer multiples of the sampling period T_s, the shifted signal sampling points may not coincide with the original sampling points. Therefore, time alignment is achieved through interpolation resampling. Linear interpolation or spline interpolation can be used as the interpolation method. (1) Linear interpolation For the shifted time t_align, if t_align is located between the original sampling points t_k and t_{k+1} (t_k=kT_s, t_{k+1}=(k+1)T_s), then the aligned signal value is: x_align(t_align)=x_meas(t_k)+(x_meas(t_{k+1})-x_meas(t_k)) (t_align-t_k) / T_s, where x_meas is the original measurement signal (Q_meas, P_meas).

[0100] (2) Spline interpolation The cubic spline interpolation method is adopted to construct a smooth interpolation function through the original sampling points, and then calculate the interpolation result based on t_align. Compared with linear interpolation, spline interpolation has higher accuracy and is suitable for experimental scenarios with high alignment accuracy requirements.

[0101] Finally, extract the aligned data.

[0102] After the time shift is completed, the overlapping time window after all signals are aligned is taken as the effective analysis window. The incomplete parts of the signal caused by the shift are removed to ensure that all signals in the effective analysis window have a complete time correspondence.

[0103] 2. Extract frequency domain amplitude and phase Within the aligned effective analysis window, frequency domain analysis is performed on Q_align(t) and P_align(t) to extract the amplitude and phase parameters at the target frequency f0 using two methods: (1) Method 1: Single-frequency sine fitting Assuming that the aligned signal mainly exhibits sinusoidal characteristics at the target frequency f0, the amplitude, phase, and DC component of the sinusoidal signal are fitted using the least squares method, thereby accurately extracting the amplitude and phase parameters.

[0104] The specific steps are as follows: First, establish a fitting model.

[0105] For any aligned signal x_align(t) (x can be Q or P), establish a single-frequency sine fitting model: x_align(t) = A sin(2πf0t)+B cos(2πf0t)+C, where A and B are fitting coefficients, C is the DC component (corresponding to the steady-state value of the signal), and f0 is the target frequency (known and preset by the experimental scheme).

[0106] Then, the fitting coefficients are solved.

[0107] Solve for A, B, and C using the least squares method to minimize the sum of squares of the fitting residuals: S = Σ[x_align(t_i) - (A sin(2πf0t_i)+B cos(2πf0t_i)+C)]², where t_i is the sampling time point within the effective analysis window, and the summation range is all sampling points; by taking the partial derivatives of A, B, and C with respect to S and setting them equal to 0, a system of linear equations is obtained, and solving this system of equations will yield the optimal estimates of A, B, and C.

[0108] Next, calculate the amplitude and phase parameters (including amplitude and phase).

[0109] Calculate the amplitude: This reflects the oscillation intensity of the signal at the target frequency f0; Phase calculation: φ=atan2(B,A), which reflects the phase state of the signal at the target frequency f0, and its value range is [-π,π].

[0110] Finally, Q_align(t) and P_align(t) are fitted to obtain the flow amplitude-phase parameters (Amp_Q, φ_Q) and pressure amplitude-phase parameters (Amp_P, φ_P). Here, Amp_Q represents the flow amplitude, and φ_Q represents the flow phase; Amp_P represents the pressure amplitude, and φ_P represents the pressure phase. The flow amplitude-phase parameters reflect the true amplitude and phase state of the pumping flow excitation signal at the target frequency f0 after delay compensation and time alignment; the pressure amplitude-phase parameters correspond to the formation's true response characteristics to this flow excitation. The difference between these two parameters and the flow amplitude-phase parameters is the core analytical indicator—the excitation-response phase difference Δφ_P = φ_P at the target frequency. φ_Q and the amplitude ratio K_P=Amp_P / Amp_Q are the core input data for subsequent frequency domain analysis and layer parameter identification, and are the basis for inverting key parameters such as formation permeability and hydraulic conductivity.

[0111] (2) Method 2: Discrete Fourier Transform Besides the single-frequency sine fitting method, the discrete Fourier transform (DFT) method can also be used to extract amplitude and phase parameters, which is suitable for scenarios requiring simultaneous analysis of multiple frequency components. The specific steps are as follows: First, zero-padding is performed on the alignment signal x_align(t) within the effective analysis window, resulting in a data length of 2. N (N is an integer) to improve the frequency resolution and computational efficiency of the DFT.

[0112] Then, a DFT transformation is performed on the padded data to obtain the frequency domain signal X(k), where k is the frequency point number.

[0113] Next, the frequency resolution Δf = f_s / L is calculated based on the sampling frequency f_s and the data length L, and the frequency point k0 = round(f0 / Δf) corresponding to the target frequency f0 is found.

[0114] Next, the amplitude and phase are extracted from X(k0).

[0115] Amplitude Amp = |X(k0)| × 2 / L (multiplying by 2 / L is to normalize the DFT amplitude to the original signal amplitude; this correction is required for single-channel signals); Phase φ = arg(X(k0)) (i.e., the argument of X(k0)).

[0116] Finally, the amplitude and phase parameters of Q_align(t) and P_align(t) at the target frequency f0 are calculated respectively to obtain the corresponding phase difference and amplitude ratio.

[0117] It should be noted that the DFT method is more efficient in multi-frequency component analysis, but its single-frequency accuracy is lower than that of the sine fitting method. Therefore, in scenarios where only a specific target frequency is of interest, the single-frequency sine fitting method should be preferred.

[0118] To ensure the reliability of the amplitude and phase parameters extracted in step 5 and to prevent "false phase" (such as false phase differences caused by equipment malfunction, control oscillation, or transient interference) from entering the subsequent identification model, it is necessary to verify the credibility of the data and process it automatically, and to establish a multi-dimensional verification mechanism and closed-loop processing strategy.

[0119] Step 6: Verify the reliability of the amplitude and phase parameters. If the data is deemed unreliable, automatically execute at least one handling strategy to obtain amplitude and phase data that meet the reliability requirements for frequency domain analysis and hierarchical parameter identification.

[0120] The purpose of this step is to: 1. Eliminate "false phase" and "false amplitude" data in the amplitude and phase data extracted in step 5 caused by non-geological factors such as equipment malfunction (sensor failure, unstable pump operation), control oscillation (flow fluctuations caused by PI / PID parameter mismatch), and transient interference (pipeline pressure shock, electromagnetic interference), ensuring that the data output to the frequency domain analysis and layered parameter identification stages has high reliability; 2. Solve the problems of insufficient steady-state time and time delay calibration drift that occur during the test through automatic processing strategies, reduce the frequency of manual intervention, and improve the automation and stability of the test process; 3. Establish a "reliability label" system for test data to provide a quality basis for subsequent data traceability and secondary analysis, and avoid the misuse of unreliable data that leads to parameter identification distortion.

[0121] The specific method is as follows: 1. Verify credibility across multiple dimensions.

[0122] We employ a dual-core indicator parallel verification process, supplemented by correlation verification, to ensure comprehensive verification.

[0123] First, the fitting residuals are checked. Fitting residuals are a core indicator of data quality. For the amplitude and phase data obtained using single-frequency sine fitting in step 5, the fitting residuals *res* are calculated using the following formula: , where x i To effectively analyze the original aligned data (Q_align(t), P_align(t)) of the i-th sampling point within the window, x_fit,i represents the fitted data of the i-th sampling point output by the sine fitting model. M represents the mean of the original aligned data, and M represents the total number of sampling points within the effective analysis window (exactly the same as the number of sampling points used for amplitude and phase extraction). The residual threshold res_th is dynamically generated: first, the noise threshold res_noise = 3σ_ΔQ / mean(x) is calculated based on the baseline noise of the steady-state acquisition segment (mean(x) is the mean of the original aligned data, used for normalization); then, the signal threshold res_signal = 0.05 × (Amp / mean(x)) is determined by combining the target frequency signal strength (Amp is the extracted signal amplitude; the threshold can be appropriately relaxed for stronger signals); finally, res_th = max(res_noise, res_signal), while setting a protection upper limit of 0.2 (i.e., res_th ≤ 0.2) to avoid unreliable data being missed due to an excessively large threshold. When res > res_th, the fitting quality is deemed substandard, and the data is considered unreliable.

[0124] Next, incentive consistency is checked. Incentive consistency is a core indicator of flow control accuracy. The root mean square error (RMSE) between the preset target flow rate Q_set(t) and the estimated actual flow rate Q_hat(t) within the effective analysis window is calculated using the following formula: N represents the number of data points within the effective analysis window. The error threshold ε_Q_th directly reuses the steady-state judgment threshold from step 1, i.e., ε_Q_th = max(3σ_Q_base, ε_meter), ensuring consistency of the indicators. When RMSE > ε_Q_th, it indicates that the flow control accuracy has not met the steady-state requirements, the excitation signal deviates too much from the preset target, and the amplitude and phase data extracted based on this excitation signal are unreliable; at the same time, it can help calculate the correlation coefficient r between Q_set(t) and Q_hat(t). When r < 0.95, it further confirms the poor consistency of flow excitation and the unreliability of the data.

[0125] 2. Determine the credibility of the results.

[0126] The "full compliance means reliable" judgment rule is set: the current amplitude and phase data is judged to be reliable and directly output to the subsequent frequency domain analysis stage only when the fitting residual check meets the standard (res≤res_th), the excitation consistency check meets the standard (RMSE≤ε_Q_th and r≥0.95), and the auxiliary correlation verification is normal. If any core check index fails to meet the standard or the auxiliary correlation verification is abnormal, the data is judged to be unreliable and enters the automatic processing procedure.

[0127] 3. Implement tiered automatic processing.

[0128] Based on the results of identifying unreliable causes, corresponding handling strategies are matched, as follows: Strategy 1: Extend the steady-state acquisition period The appropriate scenario for handling strategy 1 is when the fitting residual slightly exceeds the threshold and the flow fluctuation is small, which is determined to be due to insufficient steady-state time causing data fluctuation.

[0129] Extend the steady-state acquisition period by an integer multiple of the target frequency period T0, with an increment of ΔT=2T0 (to ensure that the new data contains the complete target frequency period and improve the fitting stability). After the extension, re-execute the time alignment and amplitude extraction in step 5, and then backtrack to step 6 for re-verification. Repeat the process ≤3 times. If it still does not meet the standard, switch to the handling strategy 2.

[0130] Solution 2: Recalibrate the link delay.

[0131] The appropriate scenario for handling strategy 2 is: the fitting residual meets the standard, but the auxiliary correlation verification is abnormal, or it still does not meet the standard after extending the steady state period again, which is determined to be due to the time alignment deviation caused by the drift of the time delay calibration parameter.

[0132] Re-execute the link delay self-calibration disturbance segment in step 3, synchronously collect disturbance data and recalculate the stabilization delay parameters (τ_uQ_stab, τ_QP_stab), update the parameters, re-execute the time alignment and amplitude extraction operation in step 5, and then backtrack to step 6 for re-verification. Repeat the process ≤ 2 times. If it still does not meet the standard, switch to handling strategy 3.

[0133] Strategy 3: Reduce control bandwidth.

[0134] The appropriate scenario for handling strategy 3 is: the excitation consistency check fails to meet the standard, and high-frequency fluctuations in Q_meas(t) are detected, which is determined to be due to control oscillation caused by PI / PID control parameter mismatch. The proportional coefficient K_p of the PI / PID control algorithm is lowered according to the graded proportional factor of 0.7 to 0.9 (prioritizing lowering to reduce response speed and suppress oscillation). If oscillation still exists after lowering, the integral coefficient K_i is lowered simultaneously. After adjustment, the delay compensation closed-loop control operation in step 4 and the data alignment and amplitude extraction operation in step 5 are re-executed. Then, the process is backtracked to step 6 for re-verification. The number of repetitions is ≤2 times. If the target is still not met, the process is switched to the handling strategy 4.

[0135] Handling strategy 4: Equipment failure alarm.

[0136] The appropriate scenario for handling strategy 4 is: if the data is still unreliable after all three handling strategies have been executed, or if abnormalities such as sensor output signals exceeding the range or communication interruption are detected, the device is determined to be faulty.

[0137] The controller triggers a local alarm (indicator flashing + buzzer), and simultaneously sends alarm information to the host computer via the communication module, including the alarm type (such as "pressure sensor signal abnormal" or "flow control oscillation cannot be suppressed"), the current test stage, and the reliability label of the collected data. After the alarm, the system pauses the current flow control stage and waits for the operator to troubleshoot the equipment before manually restarting the test process.

[0138] Step 6 effectively eliminates unreliable data, reducing the misjudgment rate of "false phase" data and ensuring the quality of data input to the frequency domain analysis stage, indirectly improving the accuracy of formation parameter identification. Furthermore, it enhances the stability of experimental automation, reducing the number of manual interventions through graded automatic processing, which is particularly suitable for long-term continuous pumping test scenarios. In addition, it establishes a data quality traceability system, which can quickly distinguish data quality levels through credibility tags, providing convenience for subsequent data screening and secondary analysis.

[0139] Step 6 is the "closed-loop guarantee link" of the entire technical process, forming a close two-way connection with the preceding steps: 1. The verification data of step S6 is directly derived from the amplitude and phase extraction results of step S5. The verification thresholds (such as ε_Q_th, res_noise) reuse the parameters (σ_Q_base, σ_ΔQ, ε_meter) defined in steps 1 and 3 to ensure parameter consistency and logical coherence; 2. The reliable data output of step 6 is sent to the subsequent frequency domain analysis and hierarchical parameter identification links, and is the core data input of this link; 3. When automatic processing is triggered, step 6 can drive the repeated execution of step 3 (recalibration), step 4 (adjustment of control parameters), and step 5 (re-extraction), forming a complete closed loop of "acquisition-processing-verification-optimization-reprocessing" to ensure the stability of the entire experimental process and the controllability of data quality.

[0140] Through the continuous execution of steps 1 to 6, and the close connection between each step—step 1 provides a standardized framework for subsequent processes, step 2 provides a synchronous data foundation, step 3 and optional steps provide stable and reliable time delay parameters, step 4 achieves precise flow control, step 5 extracts real amplitude and phase data, and step 6 ensures data credibility—the final output is frequency domain analysis basic data with reliable phase and compliant accuracy, providing high-quality support for stratigraphic parameter inversion and stratigraphic identification.

[0141] Example 2: Corresponding to Example 1, this example provides a flow control system for a pumping experiment, including: a pumping execution module, a signal acquisition module, and a control and data processing module; The pumping execution module includes: a controllable pump and a frequency converter for outputting pumping flow rate in response to control commands; The signal acquisition module includes: a flow meter, a pressure sensor, and a multi-channel synchronous acquisition module for simultaneously acquiring flow measurement signals and well pressure response signals; The control and data processing module is used to execute the flow control method for the pumping experiment as described in any one of claims 1 to 8; the control and data processing module is connected to the pumping execution module and the signal acquisition module; the control and data processing module includes: The program generation unit is used to generate a flow control program that includes a link delay self-calibration disturbance segment. The delay calibration unit is used to calculate the link equivalent transmission delay τ_uQ and the cross-link relative transmission delay τ_QP based on the disturbance segment data; A closed-loop control unit is used for flow estimation and delay compensation control based on the link equivalent transmission delay τ_uQ. The frequency domain analysis unit is used to time-align the acquired data and extract amplitude and phase data based on the delay parameter.

[0142] It has a credibility verification unit, which is used to monitor the consistency between the fitting residual and the excitation of the frequency domain extraction result in real time, and send a recalibration command to the program generation module or a parameter adjustment command to the closed-loop control module when the index exceeds the limit.

[0143] It should be understood that the terms "system," "device," "unit," and / or "module" as used in this specification are a method of distinguishing different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0144] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0145] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment 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 within the scope of protection of the present invention.

[0146] It should be noted that the structures, proportions, sizes, etc., illustrated in the accompanying drawings are merely for illustrative purposes to aid those skilled in the art and are not intended to limit the scope of the invention. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in proportions, or adjustments to size, without affecting the effectiveness and purpose of the invention, should still fall within the scope of the disclosed technical content. Furthermore, terms such as "upper," "lower," "left," "right," and "middle" used in this specification are merely for clarity and not intended to limit the scope of the invention. Changes or adjustments to their relative relationships, without substantially altering the technical content, should also be considered within the scope of the invention.

Claims

1. A flow control method for a pumping experiment, characterized in that, Includes the following steps: Step 1: Divide the pumping test into multiple flow control stages, and configure a sequential transition section, a steady-state acquisition section, and a link delay self-calibration disturbance section in each flow control stage; wherein, the link delay self-calibration disturbance section is used to superimpose a preset standardized disturbance signal on the steady-state flow. Step 2: Establish a unified time domain reference, synchronously acquire control command signal u(t), flow measurement signal Q_meas(t), and well pressure response signal P_meas(t), and mark the acquired data with a unified timestamp; Step 3: Extract the synchronous acquisition data within the self-calibration disturbance section of the link delay, calculate the equivalent transmission delay τ_uQ from the control link to the flow metering link, and calculate the cross-link relative transmission delay τ_QP from the flow metering link to the pressure acquisition link; Step 4: Using the calculated equivalent transmission delay τ_uQ of the link and the rate of change of the flow measurement signal, estimate the actual flow estimate Q_hat(t) at the current moment; calculate the deviation between the preset target flow Q_set(t) and the actual flow estimate Q_hat(t), and adjust the control command signal u(t) through the feedback control algorithm; Step 5: Based on the link equivalent transmission delay τ_uQ and the cross-link relative transmission delay τ_QP, perform time shift alignment on the flow measurement signal Q_meas(t) and the well pressure response signal P_meas(t) in the steady-state acquisition segment to eliminate the phase deviation caused by the link delay, and extract the amplitude and phase parameters at the target frequency from the aligned data. Step 6: Verify the reliability of the amplitude and phase parameters. If the data is deemed unreliable, automatically execute at least one handling strategy to obtain amplitude and phase data that meet the reliability requirements for frequency domain analysis and hierarchical parameter identification.

2. The flow control method for a pumping experiment according to claim 1, characterized in that, Step 1 involves generating parameters for the standardized perturbation signal, including the following steps: Baseline window data is extracted within the steady-state acquisition segment, and the standard deviation of flow differential noise σ_ΔQ is statistically analyzed. Set the disturbance amplitude ΔQ = k·σ_ΔQ; where k is the signal-to-noise ratio coefficient; The disturbance amplitude is limited to a range not exceeding the upper limit α of the current steady-state flow rate; where the upper limit α is determined based on the pumping unit capacity, well / pipeline safety constraints, and sensor range. Set the disturbance duration T_cal; the disturbance duration T_cal is greater than or equal to the setup time T_build determined based on the flow response in this stage; where the setup time T_build is the response time corresponding to the flow response reaching the threshold ratio β of the disturbance amplitude ΔQ; the threshold ratio β is taken as 0.8 to 0.

95.

3. The flow control method for a pumping experiment according to claim 1, characterized in that, In step 1, the form of the normalized disturbance signal is selected from any one of small step disturbance, short pulse disturbance, or short-time sinusoidal disturbance; In step 3, the methods for calculating the link equivalent transmission delay τ_uQ include: differential cross-correlation method or 50% arrival time method; The differential cross-correlation method includes the following steps: performing differential operations on the control command signal u(t) and the flow measurement signal Q_meas(t) within the link delay self-calibration disturbance segment; calculating the cross-correlation coefficient of the two differential signals, and selecting the time difference corresponding to the maximum value of the cross-correlation coefficient as τ_uQ; The 50% arrival time method includes the following steps: identifying the arrival time t_u50 of the 50% amplitude of the step change of the control command signal and the arrival time t_Q50 of the 50% amplitude of the step change of the flow measurement signal, and calculating the difference between t_u50 and t_Q50 as the link equivalent transmission delay τ_uQ.

4. The flow control method for a pumping experiment according to claim 3, characterized in that, In step 3, the method for calculating the cross-link relative transmission delay includes: Differential operations are performed on the flow measurement signal Q_meas(t) and the well pressure response signal P_meas(t) within the link delay self-calibration disturbance section respectively; the cross-correlation coefficient of the two differential signals is calculated, and the time difference corresponding to the maximum value of the cross-correlation coefficient is selected as the cross-link relative transmission delay τ_QP; or, When the standardized disturbance signal is a short-time sinusoidal disturbance, sinusoidal fitting is performed on the flow measurement signal and the well pressure response signal respectively, the phase difference between the two at the calibration frequency is extracted, and the cross-link relative transmission delay τ_QP is calculated based on the phase difference.

5. The flow control method for a pumping experiment according to claim 1, characterized in that, Also includes: Delay parameter stabilization steps: The delay parameter stabilization steps include: Set up a sliding window buffer; the sliding window buffer is used to store the delay parameters of the most recent M successful calibrations; The median of the delay parameter sequence in the buffer is used as the current stable delay parameter for control and data processing. Simultaneously monitor the difference between the current calibration delay parameter and the previous stable delay parameter. If the difference exceeds the drift judgment threshold calculated based on the discreteness of the delay parameter sequence and the sampling period, recalibration or an alarm will be triggered.

6. The flow control method for a pumping experiment according to claim 1, characterized in that, In step 4, the formula for calculating the actual flow estimate Q_hat(t) is: ;in, The instantaneous rate of change of flow rate after smoothing and filtering; the feedback control algorithm uses PI control or PID control, and the control error input term is e(t) = Q_set(t). Q_hat(t).

7. The flow control method for a pumping experiment according to claim 1, characterized in that, Step S6, data reliability verification and automatic processing, includes: fitting residual verification and excitation consistency verification; The fitting residual verification includes the following steps: calculate the sinusoidal fitting residual when extracting the amplitude and phase in step 5; if the residual exceeds the residual threshold generated based on the baseline noise, the data is deemed unreliable. The incentive consistency verification includes the following steps: calculating the root mean square error and / or correlation between the preset target flow Q_set(t) and the actual flow estimate Q_hat(t) in step 4; wherein, the consistency threshold is determined by the baseline noise statistics and the measurement resolution; if the consistency threshold is not met, the data is deemed unreliable; When the data is determined to be unreliable, at least one of the following handling strategies: 1 to 3 shall be executed. The handling strategy 1 is to extend the duration of the steady-state acquisition segment; the handling strategy 2 is to re-execute the link delay self-calibration disturbance segment; the handling strategy 3 is to reduce the proportional gain and integral gain of the feedback control algorithm.

8. A flow control method for a pumping experiment according to any one of claims 1-7, characterized in that, In step 5, the specific method for time translation alignment is as follows: Using the time of the control command signal u(t) as a reference, the flow measurement signal Q_meas(t) is shifted forward and translated by τ_uQ on the time axis using linear interpolation or spline interpolation algorithms to obtain the aligned flow signal; The well pressure response signal P_meas(t) is shifted forward and translated by τ_QP on the time axis to obtain the aligned pressure signal; The phase difference between the aligned flow signal and the pressure signal is used to characterize the true phase response of the formation after removing the link delay.

9. A flow control system for a pumping experiment, characterized in that, include: Pumping execution module, signal acquisition module, control and data processing module; The pumping execution module includes: a controllable pump and a frequency converter for outputting pumping flow rate in response to control commands; The signal acquisition module includes: a flow meter, a pressure sensor, and a multi-channel synchronous acquisition module for simultaneously acquiring flow measurement signals and well pressure response signals; The control and data processing module is used to execute the flow control method for the pumping experiment as described in any one of claims 1 to 8; the control and data processing module is connected to the pumping execution module and the signal acquisition module; the control and data processing module includes: The program generation unit is used to generate a flow control program that includes a link delay self-calibration disturbance segment. The delay calibration unit is used to calculate the link equivalent transmission delay τ_uQ and the cross-link relative transmission delay τ_QP based on the disturbance segment data; A closed-loop control unit is used for flow estimation and delay compensation control based on the link equivalent transmission delay τ_uQ. The frequency domain analysis unit is used to time-align the acquired data and extract amplitude and phase data based on the delay parameter.

10. The flow control system for a pumping experiment according to claim 9, characterized in that, The control and data processing module also includes: It has a credibility verification unit, which is used to monitor the consistency between the fitting residual and the excitation of the frequency domain extraction result in real time, and send a recalibration command to the program generation module or a parameter adjustment command to the closed-loop control module when the index exceeds the limit.

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