System and method for integrated passive and microseismic monitoring of reservoir integrity and velocity changes during onshore co2 injection

Abstract

A method of acquiring seismic data (1000) comprises: positioning (1100) a plurality of sensors to record microseismic signals in a predetermined observation area comprising a study volume with a plurality of study points; recording (1200) microseismic signals in the predetermined area, the microseismic signals comprising waves with a vertical component and a horizontal component; processing (1300, 2300, 3300) the recorded microseismic signals; a first modeling step (1400, 4400) for calculating expected vertical components in the predetermined area; generating (1500) a predicted subsurface earth structure by comparing the recorded and processed microseismic signals to the calculated expected vertical components in the predetermined area; a second modeling step (1600, 5600) for modeling seismic-mechanical properties for the predetermined observation area based on prior data; simulating (1700) expected microseismic responses on sensors from the study points of the study volume; locating (1800) a microseismic event; determining (1900) the type of the microseismic event and the corresponding moment tensor in the study volume using a maximum likelihood method; calculating (2000) a moment magnitude of the event; and comparing (2100, 6100) of microseismic events at different points in time to obtain a temporal resolution of the change in seismic activity.

Description

[0001] SYSTEM AND METHOD FOR INTEGRATED PASSIVE AND MICROSEISMIC MONITORING OF RESERVOIR INTEGRITY AND VELOCITY CHANGES DURING ONSHORE CO2 INJECTION

[0002] 1. Field of the invention

[0003] This invention relates to a system and a method for acquiring seismic data, in particular passive microseismic data in an onshore environment.

[0004] 2. Background

[0005] Microseismic monitoring allows for continuous assessment of the medium state and for control over physical processes generating elastic waves, in particular the process of hydraulic fracturing, commonly known as fracking, where a well is drilled into the rock formation and a mixture of water, sand, and chemicals is injected under high pressure to crack or fracture the rock and release the trapped hydrocarbons. Passive low frequency seismic (LFS) techniques are used in the exploration for natural resources, the study of Earth's structure and composition, and the monitoring of environmental and man-made changes in the Earth's surface.

[0006] Low frequencies (< 20 Hz) are delicate in nature and can be easily contaminated by man-made noise. Serious misinterpretation issues can occur when the acquired low frequency seismic data is not processed accurately. As anthropogenic noise can produce similar signals as the targeted direct hydrocarbon indicator (DHI) in the same frequency domain. This means that acquisition of these passive low frequencies requires sensitive recording equipment with broadband seismometers. Processing of the acquired signal data also requires specialized techniques. Compressional body waves from noise are more challenging to retrieve than surface waves because their reflection amplitudes decay more rapidly with distance and the demands on the distribution of the ambient-noise sources are more severe. In general, surface wave noise drowns out the subtle body-wave noise required for imaging subsurface structures with high resolution. In practice, at frequencies lower than o.i Hz, white spatial distribution of random noise is lacking, and events and energy of Rayleigh waves dominate the energy of body waves.

[0007] There is a need for improved systems and methods for accurately identifying and filtering out correlated noise, estimating the properties of search objects and effectively monitoring changes in seismic activity over time. Summary

[0008] The above objects are at least partially achieved by the subject matter of independent claim i. Preferred embodiments are the subject of the dependent claims, and the skilled person will find clues to other suitable aspects of the present invention in the overall disclosure of the present application.

[0009] An aspect of the invention relates to a method of acquiring seismic data, the method comprising: positioning a plurality of sensors to record microseismic signals in a predetermined observation area comprising a study volume with a plurality of study points; recording microseismic signals in the predetermined area, the microseismic signals comprising waves with a vertical component and a horizontal component; processing the recorded microseismic signals; a first modeling step for calculating expected vertical components in the predetermined area; generating a predicted subsurface Earth structure by comparing the recorded and processed microseismic signals to the calculated expected vertical components in the predetermined area; a second modeling step for modeling seismic-mechanical properties for the predetermined observation area based on prior data; simulating expected microseismic responses on sensors from the study points of the study volume; locating a microseismic event; determining the type of the microseismic event and the corresponding moment tensor in the study volume using a maximum likelihood method; calculating a moment magnitude of the event; and comparing of microseismic events at different points in time to obtain a temporal resolution of the change in seismic activity. Such a method can be used in the field of oil and gas exploration to monitor subsurface activities and structures. By deploying sensors and recording passive microseismic signals, users like geophysicists can create accurate models of the Earth's subsurface. This helps in locating resources, identifying drilling hazards, and understanding the impact of extraction processes. The detailed analysis, including the calculation of moment magnitudes and event types, allows for precise monitoring of subsurface conditions and changes over time. This temporal resolution of seismic activity can be important for effective resource management and ensuring the safety of extraction or injection operations. Additionally, this method can be used in monitoring hydraulic fracturing activities, providing real-time data for better operational control and decision-making. Seismic data can be microseismic data, in particular passive microseismic data.

[0010] The method can be improved when the step of processing comprises retrieving the vertically propagating waves from the microseismic ambient background in the recorded microseismic signals.

[0011] Such an additional processing step can help accurately identifying and isolating important seismic data from ambient noise. This enhancement can improve the clarity and reliability of the recorded microseismic signals, leading to more precise modeling of subsurface structures. By retrieving vertically propagating waves from the microseismic ambient background, users like geophysicists can better interpret the data, which is necessary for tasks such as locating oil and gas reservoirs, monitoring drilling or injection activities, and assessing the impact of hydraulic fracturing. This precise data extraction can contribute to more informed decision-making and enhances the overall effectiveness of seismic data analysis.

[0012] This embodiment can be further improved when the step of processing comprises filtering noise in the recorded microseismic signals associated with a correlated horizontal wave component from the vertical wave component.

[0013] Such a filtering step can further refine the quality of seismic data by effectively reducing noise. By filtering out the noise associated with the horizontal wave component, the vertical wave component can be isolated more accurately. This enhanced signal clarity allows for more precise modeling and analysis of subsurface structures. In the context of oil and gas exploration, this improved data processing step helps users like geophysicists to better locate resources, monitor drilling or injection activities, and understand the geological environment. The ability to distinguish between vertical and horizontal wave components leads to more reliable interpretations and supports safer and more efficient extraction or injection operations. This method is also beneficial in hydraulic fracturing monitoring, providing clearer real-time data for optimizing fracturing processes and mitigating potential risks.

[0014] This embodiment can be further improved when the step of processing comprises filtering a narrowband harmonic component of the recorded microseismic signals.

[0015] Such a further filtering step can enhance the accuracy and clarity of seismic data by targeting and removing narrowband harmonic noise. This specific filtering process can help eliminate repetitive, predictable noise that can obscure the true seismic signals. By isolating the meaningful microseismic data, geophysicists can create more accurate models of subsurface structures, which can be important in oil and gas exploration. This improved data processing step aids in precisely locating resources, monitoring extraction activities, and understanding the geological environment. The ability to filter out narrowband harmonic components ensures that the data used for analysis is of the highest quality, leading to better decision-making and safer operations. Additionally, this method supports the monitoring of hydraulic fracturing processes by providing clearer real-time data, enabling more effective control and optimization of fracturing activities.

[0016] Further improvement can be achieved when the step of retrieving in the step of processing comprises excluding broadband interference from the microseismic ambient background wave field.

[0017] Such a processing step in the step of retrieving can enhance seismic data accuracy by excluding broadband interference, which can contaminate the microseismic signals. By removing this type of noise, users like geophysicists can more effectively isolate the meaningful vertical wave components. This refined data processing can lead to more precise modeling of subsurface structures. The exclusion of broadband interference can result in clearer, more reliable seismic data, facilitating for instance better resource location, drilling activity monitoring, and geological analysis. This method can also improve the monitoring of hydraulic fracturing operations, providing higher-quality real-time data for optimizing processes and ensuring safety. By focusing on excluding broadband interference, the method ensures that the seismic signals used for analysis are of superior quality, supporting more accurate interpretations and informed decision-making.

[0018] Further improvement can be achieved when the step of retrieving in the step of processing comprises accumulation of the tensor of crosscorrelation functions.

[0019] Such a step of retrieving can improve the precision of seismic data analysis by accumulating the tensor of cross-correlation functions. This step can enhance the ability to detect and analyze the relationships between different seismic signals, leading to more accurate identification of vertically propagating waves. This detailed data processing can help users like geophysicists create more precise models of subsurface structures, for instance aiding in resource location and monitoring drilling or injection activities. The accumulation of the tensor of cross-correlation functions ensures a comprehensive understanding of the seismic data, facilitating better decision-making and safer extraction or injection operations. Additionally, this method can support e.g., hydraulic fracturing monitoring by providing a robust analysis of real-time data, allowing for more effective control and optimization of fracturing processes. By focusing on cross-correlation functions, the method ensures high-quality seismic signal retrieval, leading to more reliable geological interpretations.

[0020] This method can be further improved when the step of retrieving in the step of processing comprises suppressing the scattered component of the Rayleigh surface wave using the tensor of the cross-correlation functions. Such a method can further refine seismic data accuracy by suppressing the scattered component of the Rayleigh surface wave. Utilizing the tensor of cross-correlation functions, this step helps isolate the meaningful vertical wave components by reducing interference from scattered surface waves. This enhanced data processing technique can enable users like geophysicists to create more accurate models of subsurface structures, aiding in resource detection and monitoring of drilling or injection activities. By suppressing unwanted surface wave components, the method provides clearer and more reliable seismic data, supporting better decision-making and safer extraction or injection operations. This approach can also be beneficial in monitoring hydraulic fracturing processes, as it can ensure high-quality real-time data, allowing for more effective control and optimization of fracturing activities. By focusing on the suppression of scattered Rayleigh wave components, the method can ensure superior seismic signal retrieval, leading to more precise geological interpretations.

[0021] The method can be further improved when the step of retrieving in the step of processing comprises performing final filtering on the collection of cross-correlation functions to exclude an inclined component in the gathering of cross-correlation functions.

[0022] Such a method can further enhance seismic data accuracy by performing a final filtering step on the cross-correlation functions to remove undesired components. This additional filtering can ensure that only the most relevant vertical wave components are retained, providing a clearer and more accurate representation of the subsurface structures. In oil and gas exploration, this refined data processing step allows geophysicists to create highly precise models, aiding in the effective location of resources and the monitoring of drilling or injection activities. The exclusion of undesired components leads to superior quality seismic data, supporting safer and more efficient extraction or injection operations. This method can also improve the monitoring of hydraulic fracturing processes by ensuring high-quality real-time data, which facilitates more effective control and optimization. By focusing on final filtering of cross-correlation functions, the method can lead to more reliable geological interpretations and informed decision-making. The method can be further improved when the first modeling step comprises filtering of data to extract the expected vertical component in the predetermined area by way of a seismic simulation that enables the propagation of seismic waves in a multiphase medium, wherein the modeling is based on prior data for the predetermined area, including vertical seismic profile, depth and time maps, and / or elevation data.

[0023] Such a method can leverage advanced seismic simulations to enhance the accuracy of the first modeling step. By incorporating prior data, such as vertical seismic profiles, depth and time maps, and / or elevation data, the simulation can accurately model the propagation of seismic waves in a multiphase medium. This results in a more precise prediction of the vertical component in the observation area. For instance, in oil and gas exploration, this improved modeling capability can help users like geophysicists better understand subsurface structures, leading to more accurate resource location and drilling activity monitoring. The use of detailed prior data ensures that the seismic models are highly accurate, supporting safer and more efficient extraction or injection operations. Additionally, this method can enhance the monitoring of hydraulic fracturing processes by providing accurate, high-quality data that facilitates effective control and optimization of theses processes. By focusing on advanced seismic simulations and comprehensive prior data, the method can achieve superior modeling of seismic wave propagation, resulting in more reliable geological interpretations and informed decision-making.

[0024] Further improvement can be achieved when the second modeling step comprises simulating of wave responses based on seismic mechanical properties for the predetermined observation area based on prior data, wherein the prior data comprises a vertical seismic profile, maps of active seismic depth and / or time.

[0025] Such a method can employ a comprehensive approach for the second modeling step by using prior data to accurately model seismic- mechanical properties. This data includes vertical seismic profiles and active seismic depth and / or time. Such detailed prior information can ensure that the seismic-mechanical properties of the observation area are modeled with high precision. The detailed modeling based on prior data provides important insights into the seismic properties of the area, leading to improved extraction or injection efficiency and reduced risks. Additionally, this method enhances the monitoring of hydraulic fracturing processes by delivering precise seismic-mechanical property data, enabling effective control and optimization. By focusing on the use of comprehensive prior data for seismic-mechanical modeling, the method ensures high-quality seismic analysis, supporting more reliable geological interpretations and informed decision-making.

[0026] Further improvement can be achieved when the method comprises calibrating the first and second models using inverse filters.

[0027] Such a calibration step can enhance the accuracy and reliability of seismic data analysis by incorporating a calibration step using inverse filters for both the first and second models. This calibration process adjusts the models to better match the observed data, reducing discrepancies and improving the overall precision of the seismic simulations. This enhanced calibration can ensure that the models of subsurface structures and seismic-mechanical properties are highly accurate, leading to more effective resource location, drilling activity monitoring, and safer extraction or injection operations. The use of inverse filters for calibration provides a refined and validated set of models, resulting in clearer and more reliable seismic data. This method also benefits the monitoring of hydraulic fracturing processes by delivering calibrated, high-quality real-time data, enabling more effective control and optimization of these processes. By focusing on the calibration of seismic models using inverse filters, the method can ensure optimized seismic analysis and supports more accurate geological interpretations and informed decision-making.

[0028] The method can be further improved when the second modeling step comprises modeling seismic-mechanical properties for the predetermined observation area based on prior data, wherein the prior data comprises a vertical seismic profile, maps of active seismic depth and / or time can be further improved when the prior data further comprises one or more of the following: aerial surveillance data; coordinates of wells; coordinates of the observation area; injection parameters of injection wells; inclination of injection wells; true vertical depth of a study interval; layout of industrial communications and noise producing facilities in or near the observation area; well logs comprising measurement data of physical quantities in or around a well; lithological and / or stratigraphic column; well completion report; acoustic measurement data; formation velocity data; structural maps and maps of the P-wave travel time from the surface to stratigraphic tops; seismic cube data; seismic velocities; injection and / or production parameter in wells in or near the observation area; and / or velocity model calibration data.

[0029] Such a method can leverage an extensive range of prior data to model seismic-mechanical properties with high precision. The inclusion of comprehensive data sets such as aerial surveillance, well coordinates, injection parameters, well inclination, and true vertical depth can ensure a detailed and accurate representation of the observation area. Additional data like well logs, lithological and stratigraphic columns, well completion reports, acoustic measurements, formation velocities, structural maps, P-wave travel times, seismic cube data, and seismic velocities further enhance the model's accuracy. The detailed modeling of seismic-mechanical properties can aid in identifying potential hazards and optimizing extraction and injection processes. The method can also improve hydraulic fracturing monitoring, as it allows for precise control and optimization based on high-quality data. By integrating a wide range of prior data, this method can ensure optimized seismic analysis and supports reliable geological interpretations and informed decision-making.

[0030] The method can be further improved when the method comprises a step of filtering quasi-harmonic interference in the collection of recorded microseismic signals by channel, preferably after the step of simulating expected microseismic responses.

[0031] Such a method can further refine seismic data by filtering out quasi- harmonic interference on a per-channel basis after simulating expected microseismic responses. This step can enhance the clarity and accuracy of the recorded signals by removing repetitive, predictable noise that can obscure important seismic data. This improved filtering process allows users like geophysicists to obtain cleaner and more reliable seismic data, leading to more precise modeling of subsurface structures and better decision-making for resource location and drilling or injection activities. The ability to filter quasi-harmonic interference ensures high-quality data for monitoring hydraulic fracturing processes, providing real-time insights that facilitate effective control and optimization. By focusing on channel-specific filtering, the method can improve seismic signal quality, supporting accurate geological interpretations and safer, more efficient extraction or injection operations.

[0032] Even further improvement is achieved when the comparing comprises measurement and / or modeling of data at a point in time before and a point in time after an injection.

[0033] Such a method can enhance the analysis of seismic data by comparing measurements and models before and after an injection event. This comparison allows geophysicists to detect changes in subsurface conditions induced by the injection, providing valuable insights into the impact of such activities. In the context of oil and gas exploration, this approach helps in monitoring the effectiveness and safety of injection operations, such as hydraulic fracturing or water flooding. By evaluating the differences in seismic responses before and after injection, geophysicists can better understand reservoir behavior, optimize extraction processes, and mitigate potential risks. This method ensures more accurate and reliable geological interpretations, supporting informed decision-making and enhancing the overall efficiency and safety of subsurface operations.

[0034] This embodiment can be improved when the injection comprises injecting carbon dioxide.

[0035] Such a method can be particularly applied to the injection of carbon dioxide (CO2). By comparing seismic data before and after CO2 injection, users like geophysicists can monitor changes in subsurface conditions, which is important for applications such as carbon capture and storage (CCS) projects. This comparison helps in understanding how C02 injection affects the geological formations, ensuring that the injected CO2 remains securely stored and does not cause unintended consequences. For instance, in oil and gas exploration, CO2 injection can also be used for enhanced oil recovery (EOR), and this method aids in optimizing the injection process and monitoring its effectiveness. By analyzing the seismic responses before and after CO2 injection, the method can provide valuable insights into the behavior of the subsurface, supporting safer and more efficient injection operations, better reservoir management, and more effective strategies for mitigating environmental impact.

[0036] This method is further improved when the injection is carried out into a hydrocarbon-saturated reservoir.

[0037] Such a method can be applied to injecting substances, such as carbon dioxide, into a hydrocarbon-saturated reservoir. By comparing seismic data before and after the injection, users like geophysicists can monitor and analyze the changes within the reservoir. This can be useful for enhanced oil recovery (EOR) techniques, where CO2 injection is used to increase the extraction of hydrocarbons. The method helps in assessing the effectiveness of the injection, optimizing the process, and ensuring that the CO2 or other substances are behaving as expected within the reservoir. This approach supports more efficient resource extraction, better reservoir management, and safer operational practices. It also provides valuable data for understanding the dynamics of the injected substances, ensuring that environmental and operational risks are minimized. By focusing on hydrocarbon-saturated reservoirs, the method ensures that the seismic analysis is tailored to the specific conditions and challenges of these environments, leading to more accurate and reliable geological interpretations and informed decisionmaking.

[0038] Another aspect of the invention is a system for acquiring seismic data, preferably passive microseismic data, the system comprising a plurality of sensors to record microseismic signals; and a computing device, configured and dedicated to executing a method according to the invention.

[0039] Such a system is designed for high-precision seismic data acquisition and analysis, useful in fields such as oil and gas exploration and environmental monitoring. The system's sensors can be strategically positioned to capture detailed microseismic signals from a predetermined observation area. These signals contain both vertical and horizontal wave components, providing comprehensive data on subsurface activities. The computing device is integral to the system, executing algorithms and processing steps as outlined in the previous paragraphs. This includes filtering noise, modeling seismic properties, simulating responses, and comparing seismic data before and after events such as injections. By leveraging these advanced methods, the system can deliver accurate, high-resolution seismic data that supports important decision-making processes. For example, in oil and gas exploration, this system can help geophysicists locate resources, monitor drilling or injection activities, and assess the impact of extraction or injection processes. The ability to compare data over time allows for detailed analysis of subsurface changes, enhancing the safety and efficiency of operations. In carbon capture and storage (CCS), the system ensures that C02 injections are effectively monitored, providing insights that help maintain the integrity of storage sites. Brief description of the figures

[0040] In the following, preferred embodiments of the disclosure are disclosed by reference to the accompanying figures.

[0041] Fig. 1: illustrates a high-level flowchart for implementing a method according to the invention.

[0042] Fig. 2: shows a detailed flowchart of a processing step in a method according to the invention.

[0043] Fig. 3: depicts a detailed flowchart of a retrieving step in a processing step in a method according to the invention. Fig. 4: illustrates the detailed first modeling step in a preferred embodiment.

[0044] Fig. 5: shows the detailed second modeling step in a preferred embodiment.

[0045] Fig. 6: illustrates the comparing step in a preferred embodiment. Detailed description of the figures

[0046] The subsequent sections provide a detailed description of the invention, referencing the accompanying illustrations for clarity. The descriptions represent examples only and are not intended to limit the invention's scope. Identical reference numerals across the figures and text denote the same components. The illustrations may not reflect actual size or scale; their dimensions, proportions, and depictions of elements might be enhanced for better understanding and visual convenience.

[0047] Figure 1 illustrates a high-level flowchart for implementing a method according to the invention. The method of acquiring seismic data, preferably passive microseismic data starts with a step of positioning 1100 a plurality of sensors to record microseismic signals in a predetermined observation area comprising a study volume with a plurality of study points. Afterwards, a step of recording 1200 microseismic signals in the predetermined area follows, wherein the microseismic signals comprise waves with a vertical component and a horizontal component. Then, a processing 1300 step of processing the recorded microseismic signals follows. Subsequently, a first modeling step 1400 for calculating expected vertical components in the predetermined area is executed. A step of generating 1500 a predicted subsurface Earth structure by comparing the recorded and processed microseismic signals to the calculated expected vertical components in the predetermined area follows. Next, a second modeling step 1600 for modeling seismic-mechanical properties for the predetermined observation area based on prior data follows. Then, a step of simulating 1700 expected microseismic responses on sensors from the study points of the study volume is executed. Afterwards, locating 1800 a microseismic event follows. Subsequently, determining 1900 the type of the microseismic event and the corresponding moment tensor in the study volume using a maximum likelihood method follows. The next step is calculating 2000 a moment magnitude of the event. Consequently, a step of comparing 2100 of microseismic events at different points in time to obtain a temporal resolution of the change in seismic activity is executed.

[0048] The process begins with positioning sensors to capture microseismic signals in a defined area. By recording and processing these signals, the user can model expected vertical components and generate a predicted subsurface structure. The method includes detailed steps to simulate responses, locate events, and determine their types and magnitudes. For example, after positioning sensors and recording signals, the data is processed to isolate vertical components. Using prior data such as seismic profiles and depth maps, the method models the seismic- mechanical properties of the area. By simulating expected responses and locating microseismic events, the method provides insights into subsurface conditions. Determining the type and magnitude of these events, and comparing them over time, helps track changes in seismic activity. This comprehensive approach supports resource location, drilling and injection activity monitoring, and the optimization of hydraulic fracturing processes, ensuring safe and efficient extraction or injection operations.

[0049] Figure 2 shows a detailed flowchart of a processing step in a method according to the invention. The processing 2300 comprises the step of filtering noise 2320 in the recorded microseismic signals associated with a correlated horizontal wave component from the vertical wave component. Afterwards, a step of filtering a narrowband harmonic component 2330 of the recorded microseismic signals follows. Next, a step of retrieving 2310 the vertically propagating waves from the microseismic ambient background in the recorded microseismic signals is executed. These processing steps can for instance allow users to obtain high- quality seismic data that accurately represents subsurface conditions. By focusing on vertically propagating waves and eliminating various types of noise, the method ensures that the resulting data is reliable and precise. This improved data quality supports better modeling of subsurface structures, aids in the detection and analysis of seismic events, and enhances the overall efficiency and safety of resource extraction, injection and monitoring processes.

[0050] Figure 3 depicts a detailed flowchart of a retrieving step in a processing step in a method according to the invention. The step of processing the recorded microseismic signals 3300 comprises a step of retrieving 3310 the vertically propagating waves from the microseismic ambient background in the recorded microseismic signals. The step of retrieving 3310 comprises a step of excluding broadband interference 3312 from the microseismic ambient background wave field. Afterwards follows a step of accumulation of the tensor 3314 of cross-correlation functions. Next is a step of suppressing 3316 the scattered component of the Rayleigh surface wave using the tensor of the cross-correlation functions. Consequently, a step of performing final filtering 3318 is executed on the collection of cross-correlation functions to exclude undesired components in the gathering of cross-correlation functions.

[0051] This retrieving process can enhance the quality and accuracy of the seismic data analysis. The method begins by extracting vertically propagating waves, important for understanding subsurface structures. By excluding broadband interference, the method ensures that only relevant signals are considered, enhancing the clarity of the data. The accumulation of the tensor of cross-correlation functions allows for a comprehensive analysis of the seismic signals, providing deeper insights into the subsurface dynamics. Suppressing the scattered component of the Rayleigh surface wave further refines the data, eliminating noise that could distort the interpretation of seismic events. The final filtering step, which excludes inclined components, ensures that the remaining data is highly accurate and relevant for further analysis. By focusing on vertically propagating waves and filtering out various types of noise and interference, the method supports the creation of accurate subsurface models. This leads to more effective resource location, improved monitoring of drilling or injection activities, and enhanced safety and efficiency in extraction or injection operations. The ability to accurately retrieve and process seismic data is also important for monitoring hydraulic fracturing processes, ensuring optimal control and minimizing environmental impact.

[0052] Figure 4 illustrates the detailed first modeling step in a preferred embodiment. The first modeling step 4400 comprises filtering to extract the expected vertical component 4410 in the predetermined area by way of a seismic simulation that enables the propagation of seismic waves in a multiphase medium, wherein the modeling is based on prior data for the predetermined area, including vertical seismic profile, depth maps and time, and / or elevation data.

[0053] This method enhances the accuracy of seismic data analysis by utilizing advanced simulations for the first modeling step. By incorporating prior data such as vertical seismic profiles, depth maps and time, and elevation data, the method ensures that the simulation accurately represents the subsurface conditions. This detailed modeling of the expected vertical component is important for understanding how seismic waves propagate through different layers and materials in a multiphase medium. Accurate modeling based on comprehensive prior data allows for better prediction of the vertical component, leading to more effective resource location and monitoring of drilling or injection activities. The ability to simulate seismic wave propagation in a multiphase medium provides deeper insights into the geological environment, supporting safer and more efficient extraction or injection operations. By focusing on detailed seismic simulations and leveraging extensive prior data, this method ensures high-quality seismic analysis, resulting in reliable geological interpretations and informed decisionmaking. This approach is also beneficial for monitoring hydraulic fracturing processes, as it provides accurate real-time data that facilitates optimal control and minimization of environmental impact.

[0054] Figure 5 shows the detailed second modeling step in an embodiment. The second modeling step 5600 comprises simulating wave responses based on seismic-mechanical properties 5610 for the predetermined observation area based on prior data, wherein the prior data comprises a vertical seismic profile, maps of active seismic depth and / or time.

[0055] This method enhances the accuracy of seismic data analysis by focusing on the seismic-mechanical properties of the observation area. By utilizing detailed prior data, such as vertical seismic profiles and maps of active seismic depth and / or time, the method ensures precise modeling of how the subsurface materials respond to seismic activity. For example, in oil and gas exploration, this modeling step is important for understanding the mechanical behavior of the subsurface, which influences how resources are located and extracted and gases or fluids might be injected. Accurate seismic-mechanical models help in predicting how different layers of rock and other materials will react to drilling and other activities like injecting, leading to safer and more efficient operations. By incorporating comprehensive prior data, this method supports users like geophysicists in creating highly reliable models of the subsurface, aiding in resource management, drilling or injection activity monitoring, and risk mitigation. This approach is also beneficial for monitoring hydraulic fracturing processes, providing detailed insights into the mechanical properties of the subsurface, which is necessary for optimizing fracturing techniques and minimizing environmental impact.

[0056] Figure 6 illustrates the comparing step in a preferred embodiment. The step of comparing 6100 comprises measurement and / or modeling 6110 of data at a point in time before and a point in time after an injection.

[0057] This method improves the monitoring and analysis of subsurface changes by comparing seismic data before and after an injection event. This step involves taking measurements or modeling data at specific points in time to observe the impact of the injection. For example, in oil and gas exploration, comparing data before and after injecting substances such as carbon dioxide or water helps geophysicists understand how the injection affects the reservoir. This comparison can reveal changes in pressure, fluid distribution, and mechanical properties, which are important for optimizing extraction processes and ensuring the stability of the reservoir. In carbon capture and storage (CCS) projects, this method aids in monitoring the effectiveness of CO2 injection and storage, ensuring that the CO2 remains securely stored and does not migrate in unintended ways. By analyzing the changes in seismic responses over time, the method provides valuable insights into the dynamic behavior of the subsurface, supporting more informed decision-making and enhancing the safety and efficiency of injection operations. This approach is also useful for hydraulic fracturing, where monitoring the effects of fluid injection on the subsurface helps optimize fracturing techniques and minimize environmental impact. By focusing on temporal comparisons of seismic data, the method ensures accurate tracking of subsurface changes, leading to better resource management and operational control.

[0058] Reference list:

[0059] 1000: method of acquiring seismic data noo: positioning

[0060] 1200: recording

[0061] 1300, 2300, 3300: processing

[0062] 1400, 4400: first modeling step

[0063] 1500: generating

[0064] 1600, 5600: second modeling step

[0065] 1700: simulating

[0066] 1800: locating

[0067] 1900: determining

[0068] 2000: calculating

[0069] 2100, 6100: comparing

[0070] 2310, 3310: retrieving

[0071] 2320: filtering noise

[0072] 2330: filtering narrowband harmonic component

[0073] 3312: excluding broadband interference

[0074] 3314: tensor accumulation

[0075] 3316: suppressing scattered components of the Rayleigh surface wave

[0076] 3318: final filtering

[0077] 4410: filtering to extract expected vertical components

[0078] 5610: simulating wave responses based on seismic-mechanical properties

[0079] 6110: measurement and / or modeling

Claims

CLAIMS1. A method of acquiring seismic data, the method comprising: positioning a plurality of sensors to record microseismic signals in a predetermined observation area comprising a study volume with a plurality of study points; recording microseismic signals in the predetermined area, the microseismic signals comprising waves with a vertical component and a horizontal component; processing the recorded microseismic signals; a first modeling step for calculating expected vertical components in the predetermined area; generating a predicted subsurface earth structure by comparing the recorded and processed microseismic signals to the calculated expected vertical components in the predetermined area; a second modeling step for modeling seismic-mechanical properties for the predetermined observation area based on prior data; simulating expected microseismic responses on sensors from the study points of the study volume; locating a microseismic event; determining the type of the microseismic event and the corresponding moment tensor in the study volume using a maximum likelihood method; calculating a moment magnitude of the event; and comparing of microseismic events at different points in time to obtain a temporal resolution of the change in seismic activity.

2. A method according to claim 1, wherein the step of processing comprises retrieving the vertically propagating waves from the microseismic ambient background in the recorded microseismic signals.

3. A method according to claim 2, wherein the step of processing comprises filtering noise in the recorded microseismic signals associated with a correlated horizontal wave component from the vertical wave component.

4. A method according to claim 2 or claim 3, wherein the step of processing comprises filtering a narrowband harmonic component of the recorded microseismic signals.

5. A method according to any of the preceding claims 2 to 4, wherein the step of retrieving in the step of processing comprises excluding broadband interference from the microseismic ambient background wave field.

6. A method according to any of the preceding claims 2 to 5, wherein the step of retrieving in the step of processing comprises accumulation of the tensor of cross-correlation functions.

7. A method according to any of the preceding claims 2 to 6, wherein the step of retrieving in the step of processing comprises suppressing the scattered component of the Rayleigh surface wave using the tensor of the crosscorrelation functions.

8. A method according to any of the preceding claims 2 to 7, wherein the step of retrieving in the step of processing comprises performing final filtering on the collection of cross-correlation functions to exclude an inclined component in the gathering of cross-correlation functions.

9. A method according to any of the preceding claims, wherein the first modeling step comprises filtering of data to extract the expected vertical component in the predetermined area by way of a seismic simulation that enables the propagation of seismic waves in a multiphase medium, wherein the modeling is based on prior data for the predetermined area, including vertical seismic profile, depth and time maps, and / or elevation data.

10. A method according to any of the preceding claims, wherein the second modeling step comprises simulating of wave responses based on seismic mechanical properties for the predetermined observation area based on priordata, wherein the prior data comprises a vertical seismic profile, maps of active seismic depth and / or time. n. A method according to any of the preceding claims, wherein the method comprises calibrating the first and second models using inverse filters.

12. A method according to claim io, wherein the prior data further comprises one or more of the following: aerial surveillance data; coordinates of wells; coordinates of the observation area; injection parameters of injection wells; inclination of injection wells; true vertical depth of a study interval; layout of industrial communications and noise producing facilities in or near the observation area; well logs comprising measurement data of physical quantities in or around a well; lithological and / or stratigraphic column; well completion report; acoustic measurement data; formation velocity data; structural maps and maps of the P-wave travel time from the surface to stratigraphic tops; seismic cube data; seismic velocities; injection and / or production parameter in wells in or near the observation area; and / or velocity model calibration data.

13. A method according to any of the preceding claims, wherein the method comprises a step of filtering quasi-harmonic interference in the collection of recorded microseismic signals by channel, preferably after the step of simulating expected microseismic responses.14- A method according to any of the preceding claims, wherein the comparing comprises measurement and / or modeling of data at a point in time before and a point in time after an injection.

15. A method according to claim 14, wherein the injection comprises injecting carbon dioxide.

16. A method according to claim 14 or 15, wherein the injection is carried out into a hydrocarbon-saturated reservoir.

17. A system for acquiring seismic data, preferably passive microseismic data, the system comprising: a plurality of sensors to record microseismic signals; and a computing device, configured and dedicated to executing a method according to one of the preceding claims 1 to 16.

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