Water injection induced earthquake high-risk area prediction method

Abstract

The invention relates to the technical field of earthquake research, and discloses a water injection induced earthquake high-risk area prediction method, which comprises the following steps: S1, calculating coulomb failure stress and a porous elastic solution on an interruption level of a water injection induced earthquake dangerous area to obtain a specific fault state, and constructing a prediction model on the basis of the fault state; s2, carrying out three-dimensional network visualization on the fault surface; adopting a stress projection drawing to display coulomb stress at different positions near the injection well; in the polar coordinate graph, the radial direction represents an inclination angle, the polar angle represents an equivalent azimuth angle, the equivalent azimuth angle is defined as ae = theta-az, and theta and az are the direction and the azimuth angle respectively; for a certain specific injection well distance and movement direction, the value of delta S (delta, ae) is calculated, delta is larger than or equal to 0 degree and smaller than or equal to 90 degrees, ae is larger than or equal to 0 degree and smaller than or equal to 360 degrees, and the result is normalized to be [-1, 1]. The invention provides a simple prediction model based on normalized coulomb stress distribution, and potential high-risk areas of injection-induced earthquakes are quickly and efficiently positioned by identifying relative positions and orientations of faults.

Description

Technical Field

[0001] The invention relates to the technical field of earthquake research, and in particular to a method for predicting high-risk areas of water injection-induced earthquakes. Background Art

[0002] Fluid injection is a common industrial activity, such as hydraulic fracturing, geothermal development, and waste fluid reinjection, which plays a significant role in improving energy development efficiency and reducing environmental pollution. However, in recent years, more and more research and practice have shown that such activities may cause earthquakes under certain conditions, especially by inducing fault instability, leading to a series of safety and environmental problems. These induced earthquakes have not only aroused widespread public concern, but also put forward higher risk management requirements for policymakers and industrial operators (Ellsworth, 2013, reference [1]).

[0003] At present, the risk management strategy for fluid injection-induced earthquakes mainly adopts the "traffic light system" (TLS), which adjusts the injection operation by real-time monitoring of seismic activity. However, this method has obvious limitations. The core problem is that it is reactive and often issues warnings only when seismic activity has already occurred and may cause harm (Kao et al., 2018, reference [2]; McGarr et al., 2015, reference [3]). In this process, the activated fault may have released a large amount of strain energy, causing serious impacts on industrial facilities, public safety and environmental protection. In addition, the existing TLS system has limited research on the spatial distribution characteristics of induced earthquakes, making it difficult to provide an accurate high-risk area identification solution (Alghannam & Juanes, 2020, reference [4]).

[0004] Research on the mechanism of fluid injection-induced earthquakes has long focused on the diffusion effect of pore pressure. Previous studies have shown that an increase in pore pressure reduces the effective normal stress of the fault, thereby promoting Coulomb stress changes and inducing fault slip (Chen et al., 2018, reference [5]; Deng et al., 2020, reference [6]). However, the effect of pore pressure is usually limited to a small area near the injection point, and the induced seismic events are relatively small in scale and less destructive (Thomas H. W. Coebel & Brodsky, 2018). In contrast, large destructive earthquakes often occur on faults far from the injection point, and the stress changes in these areas may be determined by more complex poroelastic effects and fault geometry. Existing research in this field has incomplete theoretical models and practical methods (Thomas H. W. Coebel et al., 2017, reference [8]).

[0005] Another challenge is that most current earthquake risk prediction methods rely on complex numerical simulations or large-scale calculations, which not only place high demands on data integrity and computing resources, but also limit their application in actual industrial scenarios (Segall & Lu, 2015, reference [9]). Complex geological conditions and fault network distribution further increase the uncertainty of predictions, making it difficult for existing methods to be widely promoted (Rudnicki, 1986, reference

[10] ).

[0006] In response to these problems, some earthquake risk assessment methods based on Coulomb stress distribution have emerged in recent years. These methods determine whether the fault is close to rupture by calculating the change in shear stress and normal stress on the fault plane. However, these methods usually require the specific geometric parameters of the fault to be known, and have limited intuitive identification capabilities for high-risk areas (McNamara et al., 2015; Baisch et al., 2006, reference

[11] ). In some complex geological conditions, such as areas with drastic changes in fault orientation or dense fault networks, existing methods may not be effectively applied.

[0007] In summary, existing technologies have significant deficiencies in accurately identifying high-risk areas, reducing computational complexity, and improving real-time performance.

[0008] References mentioned or involved in this application:

[0009] [1]Ellsworth,WL(2013).Injection-Induced Earthquakes.Science,341(6142).doi:10.1126 / science.1225942

[0010] [2]Kao,H.,Visser,R.,Smith,B.,&Venables,S.(2018).Performanceassessment of the induced seismicity traffic light protocol for northeasternBritish Columbia and western Alberta.The Leading Edge,37(2),117-126.doi:10.1190 / tle37020117.1

[0011] [3]McGarr,A.,Bekins,B.,Burkardt,N.,Dewey,J.,Earle,P.,Ellsworth,W.,...Sheehan,A.(2015).Coping with earthquakes induced by fluidinjection.Science,347(6224),830-831.doi:10.1126 / science.aaa0494

[0012] [4]Alghannam,M.,&Juanes,R.(2020).Understanding rate effects ininjection-induced earthquakes.Nature Communications,11(1).doi:10.1038 / s41467-020-16860-y

[0013] [5]Chen,X.,Haffener,J.,Goebel,T.H.W.,Meng,X.,Peng,Z.,&Chang,J.C.(2018).Temporal Correlation Between Seismic Moment and Injection Volume foran Induced Earthquake Sequence in Central Oklahoma.Journal of GeophysicalResearch:Solid Earth,123(4),3047-3064.doi:10.1002 / 2017jb014694

[0014] [6]Deng,K.,Liu,Y.,&Chen,X.(2020).Correlation Between PoroelasticStress Perturbation and Multidisposal Wells Induced Earthquake Sequence inCushing,Oklahoma.Geophysical Research Letters,47(20).doi:10.1029 / 2020gl089366

[0015] [7]Goebel,T.H.W.,&Brodsky,E.E.(2018).The spatial footprint ofinjection wells in a global compilation of induced earthquakesequences.Science,361(6405),899-904.doi:10.1126 / science.aat5449

[0016] [8]Goebel,T.H.W.,Weingarten,M.,Chen,X.,Haffener,J.,&Brodsky,E.E.(2017).The 2016Mw5.1 Fairview,Oklahoma earthquakes:Evidence for long-rangeporoelastic triggering at>40km from fluid disposal wells.Earth and PlanetaryScience Letters,472,50-61.doi:10.1016 / j.epsl.2017.05.011

[0017] [9]Segall,P.,&Lu,S.(2015).Injection-induced seismicity:Poroelasticand earthquake nucleation effects.Journal of Geophysical Research:SolidEarth,120(7),5082-5103.doi:10.1002 / 2015jb012060

[0018]

[10] Rudnicki,J.W.J.M.o.m.(1986).Fluid mass sources and point forcesin linear elastic diffusive solids.5(4),383-393.

[0019]

[11] McNamara, DE, Hayes, GP, Benz, HM, Williams, RA, McMahon, ND, Aster, RC,... Earle, P. (2015). Reactivated faulting near Cushing, Oklahoma: Increased potential for a triggered earthquake in an area of ​​United States strategic infrastructure. Geophysical Research Letters,42(20),8328-8332.doi:10.1002 / 2015gl064669

[0020]

[12] Baisch, S., Weidler, R., Voros, R., Wyborn, D., & de Graaf, L. (2006). Induced seismicity during the stimulation of a geothermal HFR reservoir in the Cooper Basin, Australia. Bulletin of the Seismological Society of America,96(6),2242-2256.doi:10.1785 / 0120050255 Summary of the invention

[0021] In order to overcome or alleviate one or more of the above technical problems, including the insufficient positioning accuracy of existing prediction methods for high-risk areas, the poor real-time performance of monitoring and risk assessment, and the fact that risk management methods mainly rely on reactive measures after an earthquake occurs, there is an urgent need for a prediction method that can combine actual geological conditions to quickly and efficiently identify high-risk areas for fluid injection-induced earthquakes.

[0022] The purpose of the present invention is to provide a method for predicting high-risk areas for water injection-induced earthquakes, and to provide a simple prediction model based on normalized Coulomb stress distribution, which can quickly and efficiently locate potential high-risk areas for injection-induced earthquakes by identifying the relative position and orientation of faults. The ultimate goal of this model is to reduce the risk of earthquakes, avoid potential disasters caused by fault activation, and optimize the safety and industrial efficiency of injection operations, while providing theoretical support and technical means for real-time risk management and more accurate earthquake monitoring.

[0023] The present invention is mainly based on the Coulomb stress variation theory and the linear poroelastic model. By analyzing and calculating the stress field distribution caused by fluid injection, the present invention combines the fault geometry characteristics to identify high-risk earthquake areas. The present invention proposes a low-cost, easy-to-operate and widely applicable solution to address this technical problem through a prediction model based on the normalized Coulomb stress distribution.

[0024] The present invention provides the following technical solutions:

[0025] A method for predicting high-risk areas for water injection-induced earthquakes, comprising the following steps:

[0026] S1: Calculate the Coulomb failure stress and poroelastic solution on the fault plane in the water injection-induced earthquake risk area to obtain the specific fault state, and build a prediction model based on this;

[0027] Specifically, when the shear stress between the fault planes exceeds the friction strength (friction strength is proportional to the effective normal stress), the fault will slip. It is usually difficult to directly determine the absolute stress state on the fault, but the effect of external disturbance on fault slip (promoting or inhibiting fault rupture) is usually expressed in the form of changes in Coulomb instability stress:

[0028] ΔS=τ d +μ(τ n +p) (1)

[0029] Among them, τ d and τ n are the changes in shear stress and normal stress decomposed onto the fault plane, respectively; p is the disturbance of pore fluid pressure, μ is the friction coefficient; Td is the shear stress along the slip direction; an increase in shear stress, pore pressure or tensile normal stress will cause ΔS to become positive, bringing the fault closer to rupture.

[0030] For a fault plane characterized by a unit normal vector n and a slip direction d, if the stress tensor acting on the fault plane is σ, the tangential force on the fault plane is:

[0031] T=σ·n2)

[0032] The perturbations of normal and shear stresses are:

[0033] τ n =T·n,τ d =T·d (3)

[0034] The stress tensor and pore pressure due to fluid injection are calculated using linear poroelastic theory, and the governing equations are:

[0035]

[0036]

[0037] where u is the displacement vector, p is the excess pore pressure, λ and μ are the Lame constants, α is the dimensionless coefficient of effective stress, and M -1 is the volume compressibility proposed by Biot (1941), κ is the permeability, η is the fluid dynamic viscosity, and Q is the fluid source density.

[0038] For a point source starting at time t = 0, if the injection rate q(x, t) is a constant, that is, Q(x, t) = qδ(x)H(t), then the solution in uniform infinite space is:

[0039]

[0040] in, represents the distance to the source point, and the functions ξ and g are defined as:

[0041]

[0042] S2: Stereoscopic network visualization of the fault plane;

[0043] The stress projection diagram is used to display the Coulomb stress at different locations near the injection well. In the polar coordinate diagram, the radial angle represents the inclination angle, and the polar angle represents the equivalent azimuth angle, which is defined as a e =θ-az, where θ and az are strike and azimuth respectively; for a specific injection well distance and movement direction, calculate ΔS(δ, a e ), where 0°≤δ≤90° and 0°≤a e ≤360°, and the result is normalized to [-1,1].

[0044] In the above implementation, step S1 accurately solves the stress state of the fault, laying a solid foundation for the construction of the prediction model.

[0045] The stress projection diagram (stereonet) mentioned in step S2 is used to display the Coulomb stress at different positions near the injection well. The introduction of the equivalent azimuth angle is because the point injection problem has rotational symmetry. Figure 1 For example, if two faults with the same motion direction have the same strike-azimuth value, the injection will cause the same disturbance to both faults (τ d , τ n ).

[0046] Attached Figure 3An example of a stress spherical projection plot is shown for a right-lateral strike-slip fault located 1 km from the injection source and at the same depth as the injection source. The normalized Coulomb stress ΔS is represented in color. Red and blue represent positive and negative Coulomb stress values, corresponding to high and low earthquake risk, respectively. For example, at the same depth as the injection source, a right-lateral strike-slip fault will be stabilized if the equivalent azimuth is between 30° and 120° or 210° and 300°, otherwise the fault may be at risk.

[0047] According to some embodiments, in step S2 , the normalized Coulomb stress values ​​are represented by different color codes.

[0048] In the above implementation, the user will quickly and intuitively identify the high-risk seismic areas around the injection point and their corresponding fault geometry.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] Compared with traditional methods, the method for predicting high-risk areas for water injection-induced earthquakes disclosed in the present invention provides a prediction model, which is used to perform three-dimensional network visualization on the prediction model and the fault plane, significantly improving the accuracy and efficiency of earthquake risk prediction. Its prediction model based on Coulomb stress distribution does not need to rely on complex numerical simulation tools, and can directly use existing geological and geophysical data for rapid analysis. This not only reduces the computing cost, but also enables risk assessment to play an important role in real-time monitoring. Through the introduction of three-dimensional network visualization technology, the model provided by the present invention has obvious advantages in the intuitiveness of fault geometric parameters and the ability to identify risk areas. In addition, the versatility and flexibility of the model enable it to adapt to different geological conditions and industrial scenarios, providing important guarantees for the optimization of injection operations and the improvement of public safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A visualization schematic diagram of a three-dimensional network provided by an embodiment of the present invention.

[0052] Figure 2 A schematic diagram of normalized Coulomb stress distribution (clustered earthquake area) provided in an embodiment of the present invention.

[0053] Figure 3 A schematic diagram of normalized Coulomb stress distribution (non-clustered earthquake area) provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] Attached Figure 1-3 The core contents of the technical solution of the present invention are intuitively displayed, covering the visualization technology of Coulomb stress distribution and the identification of high-risk areas for injection-induced earthquakes.

[0055] Attached Figure 1 The visualization scheme of Coulomb stress distribution is presented through projection diagram. In the figure, the polar diameter represents the dip angle of the fault, the polar angle represents the equivalent azimuth angle, and the color coding represents the normalized Coulomb stress value. The red area is the location of high-risk faults, and the blue area is the location of low-risk faults. The accompanying figure provides a concise and intuitive way to identify high-risk faults and their geometric characteristics. The method for predicting high-risk areas of water injection-induced earthquakes provided by the present invention greatly improves the efficiency and ease of use of identifying high-risk areas, and provides important support for real-time monitoring and dynamic adjustment.

[0056] Using data from induced earthquake cases in different regions, including water injection data and information on earthquake locations and focal mechanisms, the spatial hazard results of water injection-induced earthquakes in Cushing, San Ardo, Prague, etc. were obtained based on the above calculations. Figure 2-3 Displayed in.

[0057] Attached Figure 1 , showing the Coulomb stress distribution and earthquake distribution in typical clustered earthquake areas (such as the Cushing area). The color coding represents the normalized Coulomb stress value, the red area represents high Coulomb stress values ​​(high-risk areas), and the blue area represents low Coulomb stress values ​​(low-risk areas). Earthquake events are marked with white dots, which clearly show the spatial consistency between actual earthquake events and high-risk areas predicted by the model. The accompanying drawings show that the prediction model of the present invention can accurately identify high-risk areas and effectively support earthquake risk assessment around injection points.

[0058] Attached Figure 2 , showing the Coulomb stress distribution in non-clustered earthquake areas, such as Prague and St.Gallen. Compared with clustered earthquake areas, high-risk areas for non-clustered earthquakes are usually more dispersed and may occur at locations far away from the injection point. The white dots in the attached figure represent actual earthquake events, and the color gradient shows the stress distribution state. This figure shows the accuracy of the model in predicting high-risk areas for large earthquakes at long distances, further verifying the wide applicability of the model.

[0059] The present invention is described in detail below in conjunction with the embodiments and drawings, but it should be understood that the embodiments and drawings are only used to exemplify the present invention and do not constitute any limitation on the protection scope of the present invention. All reasonable changes and combinations within the scope of the inventive concept of the present invention fall within the protection scope of the present invention.

[0060] In order to verify the accuracy and applicability of the prediction model provided by the present invention, several typical cases of fluid simulation injection-induced earthquakes were selected, including the Cushing region (Oklahoma), San Ardo region (California) and Prague region (Oklahoma) in the United States. These cases cover a variety of geological conditions and fault characteristics, which can fully demonstrate the application effects and technical advantages of the present invention in different scenarios.

[0061] Example 1

[0062] Applications in the Cushing Area

[0063] The Cushing region has become a key area for induced earthquake research due to its rich oil and gas resources and frequent injection activities. The fault distribution in the region is complex, and injection-induced earthquakes are mostly clustered small earthquake events. In this embodiment, the fault geometry parameters, injection point locations, and regional stress field data in the Cushing region are first collected, and then the Coulomb stress distribution is calculated using the prediction model provided by the present invention.

[0064] The calculation results of the prediction model are shown in the attached Figure 2 and 3 As shown in the figure, they together show that the high-risk areas in the Cushing area are mainly distributed on faults within 5 kilometers from the injection point. The fault dips and strikes in these areas are consistent with the main direction of the regional stress field. By comparing historical earthquake data, it is found that the actual small earthquake events are highly consistent with the high-risk areas predicted by the model. This result verifies the effectiveness of the prediction model in identifying cluster earthquake risks.

[0065] In addition, the prediction model also shows the ability to predict areas where no earthquakes have been recorded. Near some injection points in Cushing, although no earthquakes have occurred yet, the high-risk areas predicted by the prediction model provide a scientific basis for targeted strengthening of monitoring. This provides theoretical support for the early deployment of monitoring networks and the reduction of potential risks.

[0066] Example 2

[0067] Applications in the San Ardo area

[0068] The geological conditions in the San Ardo area are rather special, with a complex fault network and injection-induced seismic events showing a high degree of randomness. The application of prediction models in this area focuses on identifying fault slip trends and potential high-risk areas.

[0069] In the San Ardo application, the prediction model revealed two main risk areas around the injection point through Coulomb stress distribution. The specific distribution is shown in the attached figure. Figure 2As shown in the figure. On the one hand, the high-risk areas are concentrated on the faults with smaller dip angles near the injection point, which is related to the local effect of pore pressure diffusion. On the other hand, the model also identified a high-risk area far from the injection point, where the fault dip angle is large and relatively perpendicular to the regional principal stress direction. Actual earthquake data show that although there are fewer earthquake events in these distant high-risk areas, they often have larger magnitudes, showing the model's ability to predict large earthquake risks.

[0070] Another major advantage of the prediction model's prediction results is its ability to exclude low-risk areas. In some fault areas where no earthquakes occurred, the ΔS calculated by the model showed a negative value, which provided an important basis for judging the safe operation of the injection point, thereby reducing unnecessary operating costs.

[0071] Example 3

[0072] Applications in Prague

[0073] The Prague region is known for its large earthquakes in history and is an important case study for the study of isolated large earthquakes induced by fluid injection. The application of the proposed model in this region aims to assess the risk distribution of distant faults near the injection point.

[0074] By calculating the Coulomb stress distribution, the prediction model identified a high-risk fault about 10 kilometers away from the injection point. The strike of this fault is parallel to the direction of the regional stress field. Figure 3 Actual earthquake events show that the fault has experienced many isolated large earthquakes, which is highly consistent with the model predictions. In addition, the model reveals a potential medium-risk area where the fault geometry shows a trend that does not fully match the principal stress direction, but there is still the possibility of slip instability. This identification of medium-risk areas provides direction for further earthquake risk research.

[0075] In the above three cases, the prediction model of the present invention has shown the ability to accurately predict high-risk areas. By calculating the normalized Coulomb stress distribution, the prediction model can not only accurately identify high-risk areas for clustered earthquakes, but also effectively predict potential faults for isolated large earthquakes. This adaptability to different earthquake types makes the model highly versatile.

[0076] At the same time, the prediction model's ability to exclude low-risk areas has also been verified in multiple cases. For example, in some fault areas of Cushing and San Ardo, the ΔS predicted by the prediction model showed negative values, but in fact no earthquakes occurred in these areas. This result further shows that the prediction results of the prediction model are credible and have scientific guiding significance.

[0077] In addition, the fast calculation and visualization capabilities of this model give it significant advantages in practical industrial applications. Users can quickly obtain the distribution map of high-risk areas by inputting simple geological parameters without relying on complex numerical simulations or expensive computing resources. This ease of operation provides a convenient tool for industrial operators and also provides effective support for researchers to further explore the mechanism of injection-induced earthquakes.

[0078] The above embodiments are only preferred implementations of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A method for predicting high-risk areas for water injection-induced earthquakes, characterized in that: It includes the following steps: S1: Calculate the Coulomb failure stress and poroelastic solution on the fault plane in the water injection-induced earthquake risk area, obtain the specific fault state, and build a prediction model based on the fault state; Specifically, the effect of external disturbance on the promotion or inhibition of fault slip is expressed in the form of changes in Coulomb instability stress: ΔS=τ d +μ(τ n +p) (1) Among them, τ d and τ n are the changes of shear stress and normal stress decomposed on the fault plane; p is the disturbance of pore fluid pressure, μ is the friction coefficient; τ d is the shear stress along the slip direction; For a fault plane characterized by a unit normal vector n and a slip direction d, if the stress tensor acting on the fault plane is σ, the tangential force on the fault plane is: T=σ·n (2) The perturbations of normal and shear stresses are: t n =T·n,τ d =T d (3) The stress tensor and pore pressure due to fluid injection are calculated using linear poroelastic theory, and the governing equations are: where u is the displacement vector, p is the excess pore pressure, λ and μ are the Lame constants, α is the dimensionless coefficient of effective stress, and M -1 is the volume compressibility, κ is the permeability, η is the fluid dynamic viscosity, and Q is the fluid source density; S2: Stereoscopic network visualization of the fault plane; The stress projection diagram is used to display the Coulomb stress at different locations near the injection well. In the polar coordinate diagram, the radial angle represents the inclination angle, and the polar angle represents the equivalent azimuth angle, which is defined as a e =θ-az, where θ and az are strike and azimuth respectively; for a specific injection well distance and movement direction, calculate ΔS(δ, a e ), where 0°≤δ≤90° and 0°≤a e ≤360°, and the result is normalized to [-1,1].

2. The method for predicting high-risk areas for water injection-induced earthquakes according to claim 1, characterized in that: In step S1, for a point source starting at time t = 0, if the injection rate q(x, t) is a constant, that is, Q(x, t) = qδ(x)H(t), then the solution in uniform infinite space is: in, represents the distance to the source point, and the functions ξ and g are defined as:

3. The method for predicting high-risk areas for water injection-induced earthquakes according to claim 1, characterized in that: In step S2, the normalized Coulomb stress values ​​are represented by different color codes.

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