A method for intelligent monitoring and failure evaluation of characteristic parameters of a hydrogen storage well cement sheath under high-frequency cyclic injection and production

Abstract

A method for intelligent monitoring and failure evaluation of cement sheath characteristic parameters in hydrogen storage wells under high-frequency cyclic injection and production is characterized by the following steps: First, the method uses full-scale experiments and distributed fiber optic sensing technology to collect strain and acoustic emission signals of the cement sheath under cyclic loading and hydrogen environment in real time, and extracts key characteristic parameters such as cumulative acoustic emission energy, strain energy density, and distribution non-uniformity index. Then, a quantitative prediction model between the aforementioned fiber optic characteristic parameters and cement sheath characteristic parameters is established using multivariate nonlinear regression analysis. Finally, the model is integrated with preset failure criteria to construct an intelligent evaluation system, enabling online, quantitative evaluation and early warning of cement sheath health status driven by real-time data. This invention overcomes the limitations of traditional static evaluation, accurately identifies failure modes, and provides key technical support for the safe operation of hydrogen storage wells. This invention is applicable to the field of oil and gas drilling and production engineering technology.

Description

Technical Field

[0001] This invention relates to the field of oil and gas drilling and production engineering technology, specifically a method for intelligent monitoring and failure evaluation of cement sheath characteristic parameters in hydrogen storage wells under high-frequency cyclic injection and production. Background Technology

[0002] With the advancement of the "dual-carbon" strategy, hydrogen energy, as a core carrier of zero-carbon energy, has seen its large-scale underground storage (hydrogen storage wells) become a key infrastructure in the energy strategy. Unlike conventional oil and gas wells, hydrogen storage wells face high-frequency, large-amplitude cyclic injection and production operations, leading to the coupling effect of alternating loads and hydrogen-induced damage on the cement sheath. Its failure mode has shifted from static strength failure to dynamic fatigue damage accumulation. The direct consequence of this damage is the synergistic deterioration of the cement sheath's mechanical properties (compressive / tensile strength, Young's modulus, interfacial bonding strength) and sealing performance (permeability, gas breakthrough pressure), ultimately forming hydrogen leakage channels and triggering serious safety accidents.

[0003] Currently, cement sheath failure evaluation technology suffers from two fundamental limitations: First, traditional logging methods (such as CBL / VDL) are offline, static assessments that cannot capture the dynamic damage evolution of the cement sheath throughout its entire lifespan. Second, some existing evaluation methods (such as the published patent CN120251191A) primarily rely on mechanical theoretical models for stress and strain calculations. However, in the complex environment of hydrogen storage wells, where high-frequency cycling and chemical corrosion are coupled, theoretical models struggle to accurately determine the material constitutive relationship and damage threshold, leading to significant discrepancies between evaluation results and actual conditions, thus hindering accurate early warning.

[0004] While distributed fiber optic sensing technology can acquire data in real time and online, accurately correlating these monitored physical signals (strain, acoustic emission) with the actual mechanical / sealing performance of cement rings represents a key technological gap in moving from "monitoring" to "diagnosis." Current technologies cannot construct such quantitative relationship maps based on real experimental data, preventing advanced sensing technologies from outputting decision-making conclusions that can be directly used for risk assessment.

[0005] Therefore, there is an urgent need for a method specifically designed for hydrogen storage wells. This method can not only utilize optical fibers for online monitoring, but also fundamentally solve the scientific problem of "how to establish a quantitative relationship between optical fiber dynamic data and cement sheath characteristic parameters based on real physical experiments." This would enable a leap from theoretical prediction to data-driven approaches, providing reliable technical support for the long-term safe operation of hydrogen storage wells. Summary of the Invention

[0006] The purpose of this invention is to provide an intelligent monitoring and failure evaluation method for the characteristic parameters of cement sheath in hydrogen storage wells under high-frequency cyclic injection and production, so as to solve the technical problem in the prior art that relies on theoretical models and is difficult to evaluate the characteristic parameters of cement sheath under the coupled effect of high-frequency cycling and hydrogen-induced damage in real time and quantitatively.

[0007] To achieve the above objectives, the present invention provides an intelligent monitoring and failure evaluation method for cement sheath characteristic parameters in hydrogen storage wells under high-frequency cyclic injection and production. The method is characterized by fully considering two failure modes (mechanical performance failure and sealing performance failure) of the cement sheath under the coupled effects of cyclic loading and hydrogen-induced damage. Through system experiments, a quantitative relationship diagram is constructed with fiber optic characteristic parameters as input and cement sheath mechanical and sealing performance indicators as output. Data monitored online using fiber optic technology is input into the diagram to achieve intelligent monitoring and failure evaluation of cement sheath characteristic parameters. The specific technical solution adopted is as follows.

[0008] Step 1: Obtain the wellbore structure, cement slurry formula, and injection / production parameters of the on-site hydrogen storage well (including temperature, pressure, loading / unloading rate, number of cycles, frequency, and peak duration).

[0009] Step 2: Based on the parameters obtained in Step 1, a full-size experimental device consisting of a "production sleeve-cement ring-technical sleeve" assembly is prepared. Before pouring the cement slurry, a corrosion-resistant distributed optical fiber sensor is tightly attached to and fixed to the outer wall of the production sleeve to ensure that there is no relative slippage between it and the cement ring formed by subsequent curing, thereby achieving direct and efficient transmission of strain and vibration signals.

[0010] Step 3: The prepared "production casing-cement ring-technical casing" assembly is divided into a blank group and a condition control group. The blank group is used to quantitatively evaluate the characteristic parameters of the cement ring under the initial state, while the condition control group is used to quantitatively evaluate the damage evolution law of the characteristic parameters of the cement ring under hydrogen exposure and alternating load.

[0011] Step 4: Refer to GB / T19139-2012 standard and perform maintenance to form a cement ring according to the actual downhole service conditions.

[0012] Step 5: Using the blank group "production casing-cement ring-technical casing" assembly prepared in Step 3, conduct interfacial mechanical property tests to obtain the initial bond strength σbond0 and initial gas breakthrough pressure Pbt0 of the casing-cement ring interface.

[0013] Step 6: The blank cement rings whose initial interface properties have been determined in Step 5 are removed without damage by a core extractor and processed into two sets of standard samples in sufficient quantities, including: (1) Group A samples for mechanical performance testing and (2) Group B samples for sealing performance testing.

[0014] Step 7: Based on Step 6, and in accordance with ASTM C39 standard, perform uniaxial compression tests and Brazilian splitting tests on the Group A specimens to obtain their initial compressive strength. and initial tensile strength .

[0015] Step 8: Based on Step 6, conduct triaxial compression tests on Group A specimens under confining pressure to obtain their complete stress-strain curves and initial Young's modulus. Initial Poisson's ratio and initial shear modulus .

[0016] Step 9: Based on Step 6, and in accordance with the API RP40 standard, measure the initial porosity of Group B samples using a gas porosimeter and a pulse attenuation permeameter. and initial penetration rate .

[0017] Step 10: Conduct cement sheath integrity experiments under different alternating loads (temperature / pressure), cycle periods, and hydrogen exposure (referred to as the condition control group). Continuously monitor and record all raw data generated by the distributed fiber optic sensor throughout the process, including: distributed strain along the fiber optic path and waveform data of acoustic emission events captured by the fiber optic sensing system.

[0018] Step 11: Process the waveform data of the acoustic emission event collected in Step 10 and calculate the quantization characteristic parameters used for modeling, including: (1) Calculate the integral of the square of the waveform signal over time as the "energy" of the event, and sum the energies of all events in the period as the cumulative acoustic emission energy. (2) Calculate the ratio of the rise time to the maximum amplitude of the waveform for each acoustic emission event, and take the average value, which is recorded as the mean acoustic emission RA value. (3) Using acoustic emission source localization technology, calculate the sum of acoustic emission events originating from the predefined spatial region near the casing-cement ring interface, and denot it as the interface acoustic emission energy. .

[0019] Step 12: Process the distributed strain data collected in Step 10 and calculate the quantitative characteristic parameters used for modeling, including: (1) Integrating the stress-strain curve of the weak point of the cement ring within a complete loading-unloading cycle to obtain the strain energy density. (2) At the peak moment of a cycle, calculate the standard deviation of the strain values ​​at different positions along the axial or circumferential direction of the cement ring, and divide it by the mean of the absolute values ​​of the strain at all positions at that moment. This is denoted as the strain distribution non-uniformity index. (3) Calculation method and The same method is used, but only the strain data measured by fiber optic sensors deployed near the interface is calculated, and this is denoted as the interfacial strain distribution non-uniformity index. .

[0020] Step Thirteen: Determination of the mechanical properties of the casing-cement ring interface in the conditional control group: Using the casing-cement ring from the conditional control group experiment conducted in Step Ten, perform interfacial mechanical tests to obtain the time-varying bond strength of the casing-cement ring interface. Breakthrough pressure of time-varying gases Among them, "time-varying" refers to "parameter values ​​measured under different alternating loads, cycle periods and hydrogen exposure conditions".

[0021] Step Fourteen: Remove the cement ring from the control group in Step Thirteen without damage and process it into two sets of standard test specimens in sufficient quantities, including: (1) Group C specimens for mechanical performance testing, and (2) Group D specimens for sealing performance testing.

[0022] Step 15: Based on Step 14, and in accordance with ASTM C39 standard, perform uniaxial compression tests and Brazilian splitting tests on the Group C specimens to obtain their time-varying compressive strength. and time-varying tensile strength .

[0023] Step 16: Based on Step 14, conduct triaxial compression tests on the C group specimens under confining pressure to obtain stress-strain curves and time-varying Young's modulus. Time-varying Poisson ratio and time-varying shear modulus .

[0024] Step 17: Based on Step 14, and in accordance with the API RP40 standard, measure the time-varying porosity of the Group D samples using a gas porosimeter and a pulse decay permeameter. and time-varying penetration .

[0025] Step 18: Using multivariate nonlinear regression analysis, the interface acoustic emission energy from Step 11 is... and the interfacial strain distribution inhomogeneity index in step twelve A correlation analysis was performed with the interfacial bonding strength indices obtained in steps five and thirteen to establish an interfacial bonding strength prediction model. ,in These are the model coefficients obtained by fitting experimental data.

[0026] Step 19: Using multivariate nonlinear regression analysis, the cumulative acoustic emission energy from Step 11 is calculated. and the strain distribution inhomogeneity index in step twelve A correlation analysis was performed with the gas breakthrough pressure indices obtained in steps five and thirteen to establish a gas breakthrough pressure prediction model. .

[0027] Step 20: Using multivariate nonlinear regression analysis, the cumulative acoustic emission energy from Step 11 is calculated. Mean acoustic emission RA value A correlation analysis was performed with the tensile strength indices obtained in steps seven and fifteen to establish a tensile strength prediction model. .

[0028] Step 21: Using multivariate nonlinear regression analysis, the strain energy density from Step 12 is... A correlation analysis was performed with the compressive strength indices obtained in steps seven and fifteen to establish a compressive strength prediction model. .

[0029] Step 22: Using multivariate nonlinear regression analysis, the mean acoustic emission (RA) value from Step 11 is calculated. and the strain distribution inhomogeneity index in step twelve A correlation analysis was performed with the Poisson's ratio index obtained in steps eight and sixteen to establish a Poisson's ratio prediction model. .

[0030] Step 23: Using multivariate nonlinear regression analysis, the strain energy density from Step 12 is... A correlation analysis was performed with the Young's modulus indices obtained in steps eight and sixteen to establish a Young's modulus prediction model. .

[0031] Step 24: Using multivariate nonlinear regression analysis, the strain energy density from Step 12 is... Accumulated energy of harmonic emission A correlation analysis was performed with the shear modulus indices obtained in steps eight and sixteen to establish a shear modulus prediction model. .

[0032] Step 25: Using multivariate nonlinear regression analysis, the cumulative acoustic emission energy from Step 11 is calculated. and the strain distribution inhomogeneity index in step twelve A correlation analysis was performed with the penetration rate indicators obtained in steps eight and sixteen to establish a penetration rate prediction model. .

[0033] Step 26: Using multivariate nonlinear regression analysis, the cumulative acoustic emission energy from Step 14 is calculated. A correlation analysis was performed with the porosity indices obtained in steps eight and sixteen to establish a porosity prediction model. .

[0034] Step 27: The prediction models for the characteristic parameters of each cement ring established in Steps 18-26, along with the coefficients determined by their fitting, are compiled into standardized algorithm functions that can be called by computer software. The input interface of this function library is a real-time optical fiber characteristic parameter array. , , , , , The output interface is the corresponding array of predicted feature parameters of the cement ring. , , , , , , , , ].

[0035] Step 28: Based on the algorithm function encapsulated in Step 27, plot a quantitative relationship curve of the evolution of cement ring characteristic parameters with optical fiber characteristic parameters.

[0036] Step 29: Based on industry safety specifications, API standards, and full-scale experimental data, define theoretical thresholds for three safety levels for various cement ring characteristic parameters: (1) Safe zone (green): ≤2.0* , ≥0.7* , ≥0.8* , ≥0.8* , ≥0.8* , ≤1.2* , ≥0.8* , ≥0.8* 0.86* ≤ ≤1.14* (2) Warning zone (yellow): 2.0* < ≤10.0* 0.33* ≤ <0.7* 0.5* ≤ <0.8* 0.6* ≤ <0.8* 0.6* ≤ <0.8* 1.2* < ≤1.4* 0.6* ≤ <0.8* 0.6* ≤ <0.8* 0.71* ≤ <0.86* Or 1.14* < ≤1.29* (3) Alert Zone (Red): >10.0* , <0.33* , <0.5* , <0.6* , <0.6* , >1.4* , <0.6* , <0.6* , <0.71* or >1.29* .

[0037] Step 30: Based on the safety level threshold defined in Step 29 and the predicted value of cement sheath characteristic parameters output in Step 27, the failure criterion for the cement sheath of the hydrogen storage well is formed: (1) When Entering the alarm zone, and When >0.25, an alarm is triggered, and the mode is "interface bonding failure and high risk of micro-annulus gap". (2) When v enters the alarm zone, and When entering the alarm zone, an alarm is triggered, and the mode is "the brittleness of the cement ring increases significantly and the ability to resist cracking decreases severely", (3) when , , , If any two or more indicators enter the warning zone or alert zone, and , , When any one of the indicators enters the warning zone or alarm zone, an alarm is triggered, and the mode is "mechanical-sealing coupling failure".

[0038] Step 31: Based on steps 27-30, integrate the data acquisition interface and human-computer interaction interface into the software to form a complete intelligent monitoring and failure evaluation system for the characteristic parameters of the cement sheath in hydrogen storage wells. This system has the capability to automatically receive fiber optic data, call upon model calculations, compare failure criteria, and output evaluation results.

[0039] Step 32: Deploy the intelligent monitoring and failure evaluation system for the characteristic parameters of the cement sheath of the hydrogen storage well constructed in Step 31 to the monitoring center of the target hydrogen storage well, and during the service of the well: (1) receive the data monitored by the downhole fiber optic sensor in real time and automatically calculate the real-time fiber optic characteristic parameters; (2) automatically call the built-in standardized algorithm function library, take the real-time characteristic parameters as input, and output the predicted values ​​of all characteristic parameters of the cement sheath; (3) automatically call the built-in failure criteria, compare and logically judge the predicted values ​​of all characteristic parameters of the cement sheath with the defined failure modes in real time; (4) automatically output the evaluation results: display "safe" (green), "warning" (yellow, and indicate the deterioration index) or "alarm" (red, and indicate the specific failure mode) on the monitoring interface. The whole process does not require manual intervention in parameter analysis and result judgment.

[0040] Step 33: If the cement ring evaluation result is "safe" or "warning", then predict the remaining service life of the cement ring; if the evaluation result is "alarm", then take corresponding emergency response measures.

[0041] The advantages of this invention are: This invention achieves online, non-destructive, quantitative diagnosis and proactive early warning of the health status of cement sheaths in hydrogen storage wells by constructing a quantitative relationship map between fiber optic monitoring data and cement sheath characteristic parameters. Its core advantage lies in the first-ever coupling of the evaluation of cement sheath mechanical properties and sealing performance. Through real-time fiber optic data, it not only determines whether performance has deteriorated, but also accurately identifies the failure mechanism, thereby breaking through the lag bottleneck of traditional destructive testing and realizing a revolutionary transformation from passive maintenance to proactive early warning, providing technical support for the long-term safe operation of hydrogen storage wells. Attached Figure Description

[0042] Figure 1 This is a technical roadmap for the present invention.

[0043] Figure 2 This is an experimental device for a combination of "production sleeve-cement ring-technical sleeve" based on optical fiber technology.

[0044] Figure 3 The standard cement stone sample was extracted without damage. Detailed Implementation

[0045] To provide a clearer understanding of the technical features, objectives, and beneficial effects of the present invention, the technical solution of the present invention will now be described in detail with reference to the accompanying drawings, but this should not be construed as limiting the scope of implementation of the present invention.

[0046] Referring to the accompanying drawings, this invention proposes an intelligent monitoring and failure evaluation method for characteristic parameters of the cement sheath in hydrogen storage wells under high-frequency cyclic injection and production, such as... Figure 1 As shown, the method mainly includes the following steps.

[0047] Step 1: Obtain the wellbore structure, cement slurry formula, and injection / production parameters (including temperature, pressure, loading / unloading rate, number of cycles, frequency, and peak duration) of the on-site hydrogen storage well.

[0048] Step Two: Based on the parameters obtained in Step One, a full-size experimental device consisting of a "production sleeve-cement ring-technical sleeve" assembly is fabricated. Before pouring the cement grout, a corrosion-resistant distributed fiber optic sensor is tightly fitted and fixed to the outer wall of the production sleeve, ensuring no relative slippage between it and the cement ring formed during subsequent curing. This allows for the direct and efficient transmission of strain and vibration signals. Figure 2 As shown.

[0049] Step 3: The prepared "production casing-cement ring-technical casing" assembly is divided into a blank group and a condition control group. The blank group is used to quantitatively evaluate the characteristic parameters of the cement ring under the initial state, while the condition control group is used to quantitatively evaluate the damage evolution law of the characteristic parameters of the cement ring under hydrogen exposure and alternating load.

[0050] Step 4: Refer to GB / T19139-2012 standard and perform maintenance to form a cement ring according to the actual downhole service conditions.

[0051] Step 5: Using the blank group "production casing-cement ring-technical casing" assembly prepared in Step 3, conduct interfacial mechanical property tests to obtain the initial bonding strength of the casing-cement ring interface as 2.1 MPa and the initial gas breakthrough pressure as 15.2 MPa.

[0052] Step 6: The blank cement rings whose initial interface properties have been determined in Step 5 are removed without damage by a core extractor and processed into two sets of standard samples in sufficient quantities, including: (1) Group A samples for mechanical performance testing and (2) Group B samples for sealing performance testing.

[0053] Step 7: Based on Step 6, according to ASTM C39 standard, uniaxial compression test and Brazilian splitting test were performed on the Group A specimens to obtain their initial compressive strength of 38.5 MPa and initial tensile strength of 3.6 MPa.

[0054] Step 8: Based on Step 6, triaxial compression tests were conducted on the Group A specimens under confining pressure to obtain their complete stress-strain curves, initial Young's modulus of 8.5 GPa, initial Poisson's ratio of 0.21, and initial shear modulus of 3.4 GPa.

[0055] Step 9: Based on Step 6, and in accordance with the API RP40 standard, the initial porosity of the B group sample was measured to be 18.5% and the initial permeability to be 0.15 μD using a gas porosimeter and a pulse decay permeometer.

[0056] Step 10: Conduct cement sheath integrity experiments under different alternating loads (temperature / pressure), cycle periods, and hydrogen exposure (referred to as the condition control group). Continuously monitor and record all raw data generated by the distributed fiber optic sensor throughout the process, including: distributed strain along the fiber optic path and waveform data of acoustic emission events captured by the fiber optic sensing system.

[0057] Step 11: Process the waveform data of the acoustic emission event collected in Step 10 and calculate the quantization characteristic parameters used for modeling, including: (1) Calculate the integral of the square of the waveform signal over time as the "energy" of the event, and sum the energies of all events in the period as the cumulative acoustic emission energy. (2) Calculate the ratio of the rise time to the maximum amplitude of the waveform for each acoustic emission event, and take the average value, which is recorded as the mean acoustic emission RA value. (3) Using acoustic emission source localization technology, calculate the sum of acoustic emission events originating from the predefined spatial region near the casing-cement ring interface, and denot it as the interface acoustic emission energy. .

[0058] Step 12: Process the distributed strain data collected in Step 10 and calculate the quantitative characteristic parameters used for modeling, including: (1) Integrating the stress-strain curve of the weak point of the cement ring within a complete loading-unloading cycle to obtain the strain energy density. (2) At the peak moment of a cycle, calculate the standard deviation of the strain values ​​at different positions along the axial or circumferential direction of the cement ring, and divide it by the mean of the absolute values ​​of the strain at all positions at that moment. This is denoted as the strain distribution non-uniformity index. (3) Calculation method and The same method is used, but only the strain data measured by fiber optic sensors deployed near the interface is calculated, and this is denoted as the interfacial strain distribution non-uniformity index. .

[0059] Step Thirteen: Determination of the mechanical properties of the casing-cement ring interface in the conditional control group: Using the casing-cement ring from the conditional control group experiment conducted in Step Ten, the interfacial mechanical properties were tested to obtain the time-varying bond strength of the casing-cement ring interface. Breakthrough pressure of time-varying gases Among them, "time-varying" refers to "parameter values ​​measured under different alternating loads, cycle periods and hydrogen exposure conditions".

[0060] Step Fourteen: Remove the cement ring from the control group in Step Thirteen without damage and process it into two sets of standard test specimens in sufficient quantities, including: (1) Group C specimens for mechanical performance testing, and (2) Group D specimens for sealing performance testing.

[0061] Step 15: Based on Step 14, and in accordance with ASTM C39 standard, perform uniaxial compression tests and Brazilian splitting tests on the Group C specimens to obtain their time-varying compressive strength. and time-varying tensile strength .

[0062] Step 16: Based on Step 14, conduct triaxial compression tests on the C group specimens under confining pressure to obtain stress-strain curves and time-varying Young's modulus. Time-varying Poisson ratio and time-varying shear modulus .

[0063] Step 17: Based on Step 14, and in accordance with the API RP40 standard, measure the time-varying porosity of the Group D samples using a gas porosimeter and a pulse decay permeameter. and time-varying penetration .

[0064] Step 18: Using multivariate nonlinear regression analysis, the interface acoustic emission energy from Step 11 is... and the interfacial strain distribution inhomogeneity index in step twelve A correlation analysis was performed with the interfacial bonding strength indices obtained in steps five and thirteen to establish an interfacial bonding strength prediction model. .

[0065] Step 19: Using multivariate nonlinear regression analysis, the cumulative acoustic emission energy from Step 11 is calculated. and the strain distribution inhomogeneity index in step twelve A correlation analysis was performed with the gas breakthrough pressure indices obtained in steps five and thirteen to establish a gas breakthrough pressure prediction model. .

[0066] Step 20: Using multivariate nonlinear regression analysis, the cumulative acoustic emission energy from Step 11 is calculated. Mean acoustic emission RA value A correlation analysis was performed with the tensile strength indices obtained in steps seven and fifteen to establish a tensile strength prediction model. .

[0067] Step 21: Using multivariate nonlinear regression analysis, the strain energy density from Step 12 is... A correlation analysis was performed with the compressive strength indices obtained in steps seven and fifteen to establish a compressive strength prediction model. .

[0068] Step 22: Using multivariate nonlinear regression analysis, the mean acoustic emission (RA) value from Step 11 is calculated. and the strain distribution inhomogeneity index in step twelve A correlation analysis was performed with the Poisson's ratio index obtained in steps eight and sixteen to establish a Poisson's ratio prediction model. .

[0069] Step 23: Using multivariate nonlinear regression analysis, the strain energy density from Step 12 is... A correlation analysis was performed with the Young's modulus indices obtained in steps eight and sixteen to establish a Young's modulus prediction model. .

[0070] Step 24: Using multivariate nonlinear regression analysis, the strain energy density from Step 12 is... Accumulated energy of harmonic emission A correlation analysis was performed with the shear modulus indices obtained in steps eight and sixteen to establish a shear modulus prediction model. .

[0071] Step 25: Using multivariate nonlinear regression analysis, the cumulative acoustic emission energy from Step 11 is calculated. and the strain distribution inhomogeneity index in step twelve A correlation analysis was performed with the penetration rate indicators obtained in steps eight and sixteen to establish a penetration rate prediction model. .

[0072] Step 26: Using multivariate nonlinear regression analysis, the cumulative acoustic emission energy from Step 14 is calculated. A correlation analysis was performed with the porosity indices obtained in steps eight and sixteen to establish a porosity prediction model. .

[0073] Step 27: The prediction models for the characteristic parameters of each cement ring established in Steps 18-26, along with the coefficients determined by their fitting, are compiled into standardized algorithm functions that can be called by computer software. The input interface of this function library is a real-time optical fiber characteristic parameter array. , , , , , The output interface is the corresponding array of predicted feature parameters of the cement ring. , , , , , , , , ].

[0074] Step 28: Based on the algorithm function encapsulated in Step 27, plot a quantitative relationship curve of the integrity index as the fiber characteristic parameters evolve.

[0075] Step 29: Based on industry safety specifications, API standards, and full-scale experimental data, define theoretical thresholds for three safety levels for various cement ring characteristic parameters: (1) Safe zone (green): ≤0.30μD, ≥10.60MPa ≥1.68MPa ≥2.88MPa ≥30.80MPa ≤22.20%, ≥6.80GPa, ≥2.72GPa, 0.18≤ ≤0.24, (2) Warning zone (yellow): 0.30μD< ≤1.50μD, 5.0MPa≤ <10.60MPa, 1.05MPa≤ <1.68MPa, 2.16MPa≤ <2.88MPa, 23.10MPa≤ <30.80MPa, 22.20%< ≤25.90%, 5.1GPa≤ <6.8GPa, 2.04GPa≤ <2.72GPa, 0.15≤ <0.18 or 0.24< ≤0.27, (3) Alert Zone (Red): >1.50μD, <5.0MPa, <2.16MPa, <2.16MPa, <23.10MPa, >25.90%, <5.10 GPa, <2.04 GPa, <0.15 or >0.27.

[0076] Step 30: Based on the safety level threshold defined in Step 29 and the predicted value of cement sheath characteristic parameters output in Step 27, the failure criterion for the cement sheath of the hydrogen storage well is formed: (1) When Entering the alarm zone, and When >0.25, an alarm is triggered, and the mode is "interfacial bonding failure and high risk of micro-annulus"; (2) when Entering the alarm zone, and When entering the alarm zone, an alarm is triggered, and the mode is "significantly increased brittleness of cement ring and severely reduced resistance to cracking"; (3) when , , , If any two or more indicators enter the warning zone or alert zone, and , , When any one of the indicators enters the warning zone or alarm zone, an alarm is triggered, and the mode is "mechanical-sealing coupling failure".

[0077] Step 31: Based on steps 27-30, integrate the data acquisition interface and human-computer interaction interface into the software to form a complete intelligent monitoring and failure evaluation system for the characteristic parameters of the cement sheath in hydrogen storage wells. This system has the capability to automatically receive fiber optic data, call upon model calculations, compare failure criteria, and output evaluation results.

[0078] Step 32: Deploy the intelligent monitoring and failure evaluation system for the characteristic parameters of the cement sheath of the hydrogen storage well constructed in Step 31 to the monitoring center of the target hydrogen storage well, and during the service of the well: (1) Obtain fiber optic data for real-time on-site monitoring: =1.2×10 6 au, =45kHz / V, =9.5kJ / m³, =0.22, =0.8×10⁶ a.u. =0.18; (2) Automatically calculated: =0.73μD =2.2MPa =27.9MPa =1.02MPa =6.8MPa =20.7%, =6.0GPa =2.3GPa, v=0.19; (3) Automatically perform criterion comparison: , , , , Located in the warning zone, , Located in the alarm zone, the other indicators are located in the safe zone; (4) Automatically output diagnostic conclusion: issue a red alarm, and indicate that the failure mode is: mechanical damage causes deterioration of sealing performance, which leads to mechanical-sealing coupling failure.

[0079] Step 33: Immediately verify the well section and take appropriate emergency response measures.

[0080] The present invention proposes an intelligent monitoring and failure evaluation method for cement sheath characteristic parameters of hydrogen storage wells under high-frequency cyclic injection and production. This method can be used to intelligently monitor the cement sheath characteristic parameters of different hydrogen storage wells under actual service conditions, and can accurately identify fundamental failure mechanisms such as "sealing failure caused by mechanical damage". Petroleum engineers can use this method to evaluate the integrity of the cement sheath, diagnose failure modes, quantitatively assess potential risks, adjust production systems and process parameters in a timely manner, and select reasonable well workover measures.

[0081] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for intelligent monitoring and failure evaluation of characteristic parameters of cement sheath in hydrogen storage wells under high-frequency cyclic injection and production, characterized in that, Includes the following steps: (1) Prepare a full-size experimental device for a "production casing-cement sheath-technical casing" assembly simulating the actual working conditions of a hydrogen storage well. Distributed fiber optic sensors are arranged on the outer wall of the production casing in a non-slip coupling manner. An alternating load simulating high-frequency cyclic injection and production and the effects of the hydrogen environment is applied to the assembly. Distributed strain and acoustic emission waveform data are continuously collected using the fiber optic sensors. The collected data are processed to extract fiber optic characteristic parameters that can characterize the damage evolution of the cement sheath. The characteristic parameters include at least the strain distribution non-uniformity index. SDI Interface strain distribution non-uniformity index Acoustic emission cumulative energy Interfacial acoustic emission energy Mean acoustic emission RA value and strain energy density ; (2) Destructive tests were conducted on cement rings at different experimental stages to obtain their initial and time-varying mechanical properties and sealing performance parameters; using the multivariate nonlinear regression analysis method, the fiber characteristic parameters extracted in (1) were used as input variables, and the measured cement ring characteristic parameters were used as output variables to establish a quantitative prediction model library between the two. (3) The prediction model library established in (2) is encapsulated into a standardized algorithm function, and combined with the preset multi-level safety threshold and combined failure criteria, it is integrated into an intelligent diagnostic system. During the service of the hydrogen storage well, the system receives downhole fiber data in real time, automatically calculates fiber characteristic parameters, calls the model library to predict all characteristic parameters of the current cement sheath, and automatically outputs diagnostic conclusions and failure modes based on the combined failure criteria.

2. The method according to claim 1, characterized in that, The strain distribution non-uniformity index in (1) The calculation method is as follows: at the peak moment of a cycle, calculate the standard deviation of the strain values ​​at different positions along the axial or circumferential direction of the cement ring, and divide it by the mean of the absolute values ​​of the strain at all positions at that moment. The non-uniformity index of interfacial strain distribution The calculation method and The same, but only strain data measured by fiber optic sensors deployed near the sleeve-cement ring interface are used.

3. The method according to claim 1, characterized in that, The interface acoustic emission energy in (1) The calculation method is as follows: using acoustic emission source localization technology, the sum of acoustic emission events originating from a pre-defined spatial region near the casing-cement ring interface is calculated; the average acoustic emission RA value is... Used to distinguish between tensile and shear crack failure mechanisms in cement rings.

4. The method according to claim 1, characterized in that, The mechanical properties mentioned in (2) include at least the interfacial bonding strength. ,tensile strength Compressive strength Young's modulus Poisson's ratio and shear modulus The sealing performance includes at least the gas breakthrough pressure. Penetration rate and porosity .

5. The method according to claim 4, characterized in that, The quantitative prediction model library in (2) specifically includes: Interfacial bonding strength prediction model: ; Gas breakthrough pressure prediction model: ; Tensile strength prediction model: ; Compressive strength prediction model: ; Poisson's ratio prediction model: ; Young's modulus prediction model: ; Shear modulus prediction model: ; Penetration prediction model: ; Porosity prediction model: ; in, It is a multivariate nonlinear function determined by fitting experimental data, and its specific form depends on the actual experimental data rather than a pre-defined theoretical formula.

6. The method according to claim 1, characterized in that, The combined failure criterion in (3) includes: First criterion, interface failure: when the interface bonding strength... The predicted value enters the preset alarm zone, and the strain distribution non-uniformity index SDI When the value exceeds the preset threshold, it is diagnosed as "interfacial bonding failure and high risk of micro-ring gaps"; The second criterion, increased brittleness: when Poisson's ratio ν and tensile strength When all predicted values ​​enter the preset alarm zone, the diagnosis is "significantly increased brittleness of cement ring and severely reduced crack resistance"; The third criterion, coupling failure: When the tensile strength... Compressive strength Young's modulus shear modulus If the predicted values ​​of any two or more indicators enter the preset early warning zone or alarm zone, and the penetration rate... Gas breakthrough pressure Porosity When the predicted value of any one of the indicators enters the warning zone or alarm zone, it is diagnosed as "mechanical-sealing coupling failure".

7. The method according to claim 1, characterized in that, The output of the intelligent diagnostic system in (3) is automated and requires no human intervention. Specifically, it includes displaying a green state representing "safety", a yellow state representing "warning" and listing the deterioration indicators that have entered the warning zone on the monitoring interface, or a red state representing "alarm" and indicating the specific failure mode.

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