Patent Publication Number: US-8121792-B2

Title: Integration of geomechanics and seismic analysis for passive seismic feasibility analysis

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional application 61/072,425 which was filed on Mar. 31, 2008. 
    
    
     TECHNICAL FIELD 
     This description relates generally to the field of assessing the feasibility of conducting a passive seismic survey in a producing field. Specifically, this description relates to one or more systems and methods for assessing the feasibility of conducting a passive seismic survey in a producing field by integrating geomechanics and seismic analysis. 
     BACKGROUND 
     Throughout the life of an oil and gas field, the extraction and injection of fluids results in changes in the in situ stress and physical properties of the reservoir. Microseismicity and surface heave/subsidence are among the common responses to these injection- and production-induced stress perturbations. 
     A passive seismic survey relies upon passive seismology, e.g., seismology that does not rely upon the use of a controlled seismic source of energy for sending sound waves into the earth at predetermined locations, such as dynamite, air guns and/or vibrators. In contrast to active seismic surveys, passive seismic surveys typically rely upon natural or induced teleseismic events and/or microseismic activity that may be recorded at one or more recording location(s). In the case of injection- and production-induced stress perturbations, a successful passive seismic survey requires a determination of when the induced stress change is expected to be large enough to generate microearthquakes. The expected magnitude and recurrence rate of these events should also be determined. The detection capability of a specific seismic array should be determined, and it should be determined if the microseismic signal amplitudes are large enough to be detected in a noisy environment. 
     The present inventors have developed one or more techniques that utilize coupled geomechanical-reservoir simulation that can handle complex 3-D geologic structures and reservoir pressure variations during production to quantify the presence, timing, location, and magnitude of microseismicity in and around the reservoir. The integration of geomechanical modeling with seismic modeling permits an accurate calculation of the probability of detection of seismic events given an acquisition network in the area. 
     SUMMARY 
     In one general aspect, a method for determining time-varying stress and strain fields within a subsurface region includes integrating a seismic model of a reservoir within the subsurface region with a geomechanical model of the subsurface region. An estimate of the time-varying stress and strain fields within the subsurface region during production of the reservoir are determined, wherein the estimate is based on the integration of the seismic model with the geomechanical model. 
     Implementations of this aspect may include one or more of the following features. For example, the seismic model may provide a reservoir simulation of production related fluid-flow within the reservoir. The geomechanical model may provide an estimate of rock and fracture mechanics within the subsurface region. The integration of the seismic model of the reservoir with the geomechanical model may include integrating a reservoir simulation of the reservoir during production, a geologic model of the subsurface region, and/or well log and core test data with the geomechanical model. The integration of the seismic model of the reservoir with the geomechanical model may include creating a three-dimensional finite element model of the subsurface region incorporating parameters associated with structure of the reservoir and overburden of the region, with rock material properties and failure criteria. 
     The seismic model and the geomechanical model may be generated or existing models, wherein the seismic model includes a reservoir flow simulation including pressure and temperature changes within the reservoir and the geomechanical model includes a stress analysis of the subsurface region. Solution histories of pressure and temperature from the seismic model may be mapped to the geomechanical model as boundary conditions. 
     In another general aspect, a method for determining feasibility of a passive seismic survey for a subsurface region includes integrating a seismic model of a reservoir within the subsurface region with a geomechanical model of the subsurface region to form an integrated, three-dimensional model of the subsurface region. An estimate of the time-varying stress and strain fields within the subsurface region during production of the reservoir are determined, wherein the estimate is based on the integrated, three-dimensional model. Earthquake energetics and consistency are analyzed from the integrated, three-dimensional model. Seismic wave propagation is modeled to model and correct for path effects to predict seismic signal amplitudes for a given seismic moment for at least one receiver location. One or more seismic thresholds for a seismic data acquisition network are determined, wherein the at least one receiver is part of the seismic data acquisition network. 
     Implementations of this aspect may include one or more of the following features. For example, the seismic model may provide a reservoir simulation of production related fluid-flow within the reservoir. The geomechanical model may provide an estimate of rock and fracture mechanics within the subsurface region. The seismic model of the reservoir may be integrated with the geomechanical model by integrating a reservoir simulation of the reservoir during production with a geologic model of the subsurface region, and/or well log and core test data with the geomechanical model. The integrated, three-dimensional model may include a three-dimensional finite element model of the subsurface region incorporating parameters associated with structure of the reservoir and overburden of the region with rock material properties and failure criteria. 
     The seismic model and/or the geomechanical model may be generated and/or existing, wherein the seismic model includes a reservoir flow simulation including pressure and temperature changes within the reservoir and the geomechanical model includes a stress analysis of the subsurface region. The solution histories of pressure and temperature from the seismic model may be mapped to the geomechanical model as boundary conditions. Analyzing earthquake energetics and consistency may include predicting changes in total strain energy with time. Total radiated energy available for seismic wave propagation may be calculated from the predicted changes in total strain energy with time. Seismic moment due to induced stress perturbations within the subsurface region may be estimated, wherein the induced stress perturbations may include injection and production-induced stress perturbations, or natural stress perturbations. 
     Analyzing earthquake energetics may include determining average displacement, radiated seismic energy, and total recoverable strain field from the geomechanical model. Seismic wave propagation may be modeled to model and correct for path effects to predict seismic signal amplitudes for a given seismic moment for at least one receiver location. Determining seismic thresholds for the seismic data acquisition network may include determining an estimate of detection capability for the seismic data acquisition network based on seismic signal strength, seismic background noise, seismic recurrence rates, and recording equipment. The estimate may include an estimated magnitude at which more than 90 percent of a plurality of receiver stations can detect an event or seismic source. A detection threshold map may be generated which contains a detection threshold magnitude for each grid of the integrated, three-dimensional model. A passive seismic survey of the reservoir may be conducted during production of hydrocarbons from the reservoir. The passive seismic survey may be used to monitor overpressure and/or seal breach risk in the reservoir based on the results of the passive seismic survey. 
     In another general aspect, a tangible computer-readable storage medium having embodied thereon a computer program configured to, when executed by a processor, generate an integrated, three-dimensional model of a subsurface region based on geomechanical and seismic analysis of the subsurface region, the medium comprising one or more code segments configured to integrate a seismic model of a reservoir within the subsurface region with a geomechanical model of the subsurface region to form the integrated, three-dimensional model of the subsurface region. The seismic model provides a reservoir simulation of production related fluid-flow within the reservoir and the geomechanical model provides an estimate of rock and fracture mechanics within the subsurface region. The one or more code segments are configured to determine an estimate of the time-varying stress and strain fields within the subsurface region during production of the reservoir, wherein the estimate is based on the integrated, three-dimensional model. 
     Implementations of this aspect may include one or more of the following features. For example, the code segments may be configured to analyze earthquake energetics and consistency from the integrated, three-dimensional model, model seismic wave propagation to model and correct for path effects to predict seismic signal amplitudes for a given seismic moment for at least one receiver location, and determine a seismic threshold for a seismic data acquisition network, wherein the at least one receiver is part of the seismic data acquisition network. The code segments may be configured to integrate the seismic model of the reservoir with the geomechanical model by integrating a reservoir simulation of the reservoir during production with a geologic model of the subsurface region, and/or well log and core test data with the geomechanical model. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The patent or application file contains drawings executed in color. Copies of this patent or patent application publication with color drawings will be provided by the Office upon request and payment of the necessary fee. 
         FIG. 1  is a flowchart of an exemplary process for determining feasibility of a passive seismic survey. 
         FIG. 2  is a graphical view of an exemplary energy budget for seismic events based on fault slip-weakening models. 
         FIG. 3A  is a collection of perspective views of a three-dimensional (3-D) geomechanics model for an exemplary field. 
         FIG. 3B  is an exemplary three-dimensional (3-D), finite element modeled geomechanics model for the exemplary field. 
         FIG. 4  is a collection of exemplary screenshots of graphical results from the geomechanics modeling, showing the magnitude of fault slips plotted along the two fault planes from the exemplary field of  FIG. 3B . 
         FIG. 5  is a collection of exemplary screenshots of graphical results showing ray tracing results in a 3-D model (Vp, Qp) utilizing a 3-D seismic modeling system. 
         FIG. 6  is a collection of graphical results obtained from consistency and seismic analyses. 
         FIG. 7  is a flowchart of an exemplary process for integrating results calculated from geomechanical modeling into seismic modeling and analysis. 
     
    
    
     DETAILED DESCRIPTION 
     The techniques presented hereinafter generally relate to passive seismic surveys, e.g., based on injection- and production-induced stress perturbations of a subsurface region, e.g., a subsurface hydrocarbon reservoir and/or regions within or near a subsurface hydrocarbon reservoir. A typical passive seismic project includes three stages: i) feasibility study; ii) pilot acquisition project and data analysis, and iii) long-term or permanent monitoring. 
     Currently microseismic feasibility studies focus mostly on acquisition system design and event location uncertainties, with little or no analysis of the size and occurrence of micro-earthquakes induced by production and injection. Moreover, information on local tectonic seismicity often cannot be obtained from the literature and/or from seismological databases because many oil and gas fields are located in seismically quieter regions where regional seismic network density is sparse, and/or the size of the micro-earthquakes usually falls below the detection threshold. Thus, the potential availability of natural or induced seismic sources for the passive survey is usually not assessed until the completion of a pilot field experiment. However, pilot projects can be expensive, especially if the pilot projects are conducted in an offshore setting. 
     In some studies relating to induced stress perturbations, failure probability is calculated based on the proportion of failed, to non-failed realizations in a model subjected to the Mohr-Coulomb failure criterion and random sampling of input parameters, such as the principal stresses, pore pressure, Biot&#39;s constant, stress orientation, friction angle, and cohesion. However, this approach does not account for relatively complicated geology and/or reservoir pressure changes. Two dimensional (2-D) geomechanical modeling on simple geologic structures to assess the potential of brittle failure in the subsurface is described by Maxwell, S. C., Urbancic T. I., and McLellan, P. in the paper “Assessing the feasibility of reservoir monitoring using induced seismicity,” 65th Conference and Exhibition,  EAGE, expanded abstract  (2003). However, any implied integration of the 2-D geomechanical modeling with seismic modeling is minimal. 
     The present techniques quantify the potential magnitude, timing, and detectability of microseismic events using results from integrated geomechanical and seismic modeling. For example, the present techniques may utilize available geologic and well data, and integrate 3-D forward modeling techniques, such as geomechanical modeling, reservoir simulation, and/or seismic wave propagation. 
       FIG. 1  is a flowchart of an exemplary process  100  for determining feasibility of a passive seismic survey of a subsurface region. Referring to  FIG. 1 , process  100  includes integrating one or more of reservoir simulation  110 , e.g., a seismic model, geologic modeling  120 , and/or well logs and/or core testing  130  with geomechanical modeling. For example, three dimensional (3-D) finite-element geomechanical modeling  140  is coupled to reservoir simulation  110  to estimate the time-varying stress and strain fields in the overburden and reservoir within a target subsurface region. The complex interactions between production related fluid-flow, and rock and fracture mechanics are modeled through this integrated technique. Specifically, the 3-D overburden and reservoir structure is parameterized by adaptive finite element meshes. Rock material properties and failure criteria derived from core testing and well log data  130  are also incorporated in the modeling, e.g., the forward modeling of geomechanical and seismic analysis. 
     In block  150 , earthquake energetics and consistency are analyzed. Specifically, predictions of changes in total strain energy with time, e.g., both recoverable and dissipated energies, are made from the geomechanical modeling  140 . From these energy predictions and reasonable values of seismic efficiencies, the total radiated energy available for seismic wave propagation is calculated. Assuming a reasonable range of static stress drop for the microearthquakes, the size of events, e.g., seismic moment, are estimated due to the injection and production-induced stress perturbations. 
     In block  160 , seismic wave propagation modeling is performed. During the seismic wave propagation modeling  160 , appropriate corrections for path effects are applied to more accurately predict the microseismic signal amplitudes for a given seismic moment at each receiver location, e.g., such as each geophone in a network of geophones. Forward wave propagation modeling is performed to model and correct for path effects due to geometric spreading, reflection/transmission loss, and anelastic attenuation. 
     In block  170 , seismic threshold analysis is performed. The seismic network detection threshold is estimated by incorporating knowledge or assumptions on ambient and cultural noise in the vicinity of the seismic network to obtain the probability of detecting an event by the network. A predicted threshold map for the network can also be constructed. Accordingly, various acquisition designs can be evaluated quantitatively to ensure that signals above the detection threshold can be recorded. 
     Source location uncertainties may also be analyzed as a part of process  100  (but not shown), e.g., after block  170 . Given the velocity structure of the subsurface region, reasonable assumptions on the size of time residuals of P- and S-wave arrivals used in locating the events are used to calculate location error estimates. Further, process  100  may be extended to include conducting a passive seismic survey after a target subsurface region has been identified and process  100  as shown in  FIG. 1  has been implemented. 
     With respect to blocks  110 - 130  and  140 , e.g., the integrated geomechanics-reservoir modeling, the following preferred approach is provided as a detailed example of block  140 . These simulators lack the capability to solve earth stress within and outside the reservoir, because most existing reservoir flow simulators only predict pressure and temperature change within the reservoir. Therefore, the present inventors have determined that a separate geomechanical simulation is beneficial to solve for stress changes. For example, a sequentially coupled technique is adopted to impose the solution histories of pressure and temperature on the geomechanical model for stress analysis. The mesh grids are most likely not coincident because flow and stress analyses are done separately. A 3-D distance weighting, mapped scheme is formulated to map the pressure and temperature to the geomechanical model as boundary conditions. 
     The 3-D weighting scheme is formulated whereby nodal quantities for the geomechanical simulation are mapped from a spatial neighborhood of reservoir simulation gridpoint quantities. The weighting scheme calculates the nodal quantity p(r) in the geomechanical model based on the nodal quantity P i  from the reservoir analysis model through the following equation, 
                     p   ⁡     (   r   )       =         ∑   i     ⁢       w   i     ⁢     P   i             ∑   i     ⁢     w   i                 (   1   )               
in which,
 
                     1     w   i       =           (       r   x     -     ξ   x       )     n       a   n       +         (       r   y     -     ξ   y       )     n       b   n       +         (       r   z     -     ξ   z       )     n       c   n                 (   2   )               
where r is the position vector of the geomechanical node, ξ is the position vector of the reservoir analysis node, respectively, and a, b, c are the semi axes of the three principal directions of a searching ellipsoidal domain, n is the power of weighting and w i  is the distance based weight. The property is mapped based on the property, e.g. pressure or temperature, of surrounding reservoir analysis gridpoints inside the searching ellipsoid.
 
     For example, applicable nomenclature for the 3-D weighting scheme is as follows: 
     p—property at geomechanical node 
     P—property at reservoir gridpoints 
     w i —distance-based weight 
     r—geomechanical nodal position vector 
     r x —x component of geomechanical nodal position vector, m 
     r y —y component of geomechanical nodal position vector, m 
     r z —z component of geomechanical nodal position vector, m 
     ξ—reservoir analysis nodal position vector 
     ξ x —x component of reservoir analysis nodal position vector, m 
     ξ y —y component of reservoir analysis nodal position vector, m 
     ξ z —z component of reservoir analysis nodal position vector, m 
     a—semi axis of searching ellipsoid in x direction, m 
     b—semi axis of searching ellipsoid in y direction, m 
     c—semi axis of searching ellipsoid in z direction, m 
     n—exponent of weighting 
     With respect to block  150 , e.g., analyzing earthquake energetics and consistency analysis, the following exemplary techniques are applicable for the consistency analysis of process  100 . In an elastic material, the work done during loading is stored as recoverable strain energy (E r ) or potential energy in the solid. During the dislocation of a fault, the potential energy stored in earth is released which is the sum of strain energy (E e ) and gravitational energy (E g ).  FIG. 2  is a graphical view of an exemplary energy budget  200  for seismic events based on fault slip-weakening models. Referring to  FIG. 2 , the released energy has three components: radiated energy (E R )  210 , frictional energy (E F )  220 , and fracture energy (E G )  230 . The radiated energy  210 , frictional energy  220 , and fracture energy  230  are show graphically along an x-y axis, wherein the x-axis is the slip (displacement) and the y-axis is the stress. Radiated energy  210  is the proportion of energy required to generate seismic waves. Frictional forces acting on the fault transform kinetic energy into thermal energy, e.g., frictional energy  220 . Fracture energy  230  is associated with the creation of new surfaces during rupture propagation. 
     In summary, the energy budget of an earthquake, such as that described by Kanamori, H., in “Energy budget of earthquakes and seismic efficiency,” in Teisseyre, R., and Majewski, E., eds.,  Earthquake thermodynamics and phase transformations in the Earth&#39;s Interior , New York, Academic Press, pp. 293-305. (2001), is
 
Δ E   r =Δ( E   e   +E   g )= E   R   +E   F   +E   G .  (3)
 
     If the overall vertical movement of the deforming region associated with the earthquake is small, the gravitational energy (E g ) is assumed to be negligible. A range of frictional strength of faults is used in the modeling. Three output parameters are stored from the geomechanics modeling for further analysis: (1) the average displacement (  D ), (2) radiated seismic energy (E R ), and (3) the total recoverable strain field (ΔE r ). The three parameters enable the scientist to obtain three different estimates of seismic moment which should be consistent with each other. In addition, the three parameters can be used to verify that the geomechanical modeling is in agreement with laboratory and field observations of earthquakes. 
     In the first case, e.g., average displacement (  D ), given the fault location and geometry from seismic interpretation, one can estimate the seismic moment, M 0 , using the average fault slip and fault surface area. Both the average fault slip and fault surface area can be obtained readily as the geomechanical modeling output parameters. The seismic moment, M 0 , provides an accurate measure of the size of an earthquake. The seismic moment, M 0 , is equal to the product of the fault surface area (A), the rigidity of the rock (μ), and the average slip on the fault (  D ). 
     In the second case, e.g., radiated seismic energy (E R ), the seismic moment, M 0 , is calculated from the radiated seismic energy, E R . The radiated seismic energy E R  represents the maximum seismic energy release over the time considered. Seismic moment (M 0 ) measures the amount of radiated seismic energy and is proportional to the static stress drop, Δσ, wherein: 
     
       
         
           
             
               
                 
                   
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     Static stress drop Δσ is the difference between the state of stress at a point on the fault before and after rupture, and its value can be obtained from the geomechanical modeling. The scaled energy ratio, E R /M 0 , is commonly known as a measure of the energy density of earthquakes. 
     In the third case, e.g., using the total recoverable strain field (ΔE r ), radiated seismic energy, and the seismic moment, which can each be estimated from the geomechanical modeling, the resulting seismic efficiency, η, is calculated. The seismic efficiency, η, is defined as the ratio of radiated seismic energy to the change of strain energy, specifically: 
                   η   =         E   R         E   R     +     E   F     +     E   G         =         E   R       Δ   ⁢           ⁢     R   r         .               (   5   )               
Studies on laboratory stick-slip friction experiments and shallow earthquakes suggest that the ratio of radiated seismic energy to the change of strain energy, known as seismic efficiency (η), tends to be small and have an upper bound of approximately 0.06. For example, McGarr describes exemplary studies in “Some comparisons between mining-induced and laboratory earthquakes,”  Pure Appl. Geophys.,  142, pp. 467-489 (1994); and “On relating apparent stress to the stress causing earthquake fault slip,”  J. Geophys. Res.,  104, pp. 3003-3011 (1999).
 
 E   R   =ηΔE   r   =ΔE   r −( E   F   +E   G )  (6)
 
The drop of static friction coefficient to its dynamic value according to μ s /μ d ≈1.18 limits the seismic efficiency to this low value. The opposing end-member is a creeping fault. This kind of fault radiates no seismic energy because the available potential energy is completely used to overcome friction and create new surface (i.e., ΔE r =E F +E G ), and E R  equals zero. Thus, we can compare the seismic efficiency from modeling to that of laboratory measurements. According to the conclusions of previous studies, we expect 0≦E R ≦0.06E r . This should cover scenarios ranging from a creeping fault and the case which η≦0.06.
 
     With respect to block  160 , e.g., wave propagation modeling, a preferred seismic wave propagation modeling technique is described hereinafter. Accounting for path effects on small microearthquakes is useful because a large percentage of their energy resides in the strongly attenuating higher frequencies. Therefore, robust estimates of signal amplitude at the receiver is beneficial to acquisition design in passive seismic surveys, e.g., array location, borehole versus surface array, geometry. Radiation patterns and path effects, such as geometric spreading, reflections, mode conversions, and anelastic attenuation, are accounted for in wave propagation  160 . The radiation patterns and path effects are described in further detail in Boore, D. M. and Boatwright, J., “Average body-wave radiation coefficients,”  Bull. Seismol. Soc. Am.,  74, pp. 1615-1621 (1984). In a homogeneous medium, the far-field displacement equation in cylindrical coordinates for P- or S-waves is expressed as follows: 
                     u   ⁡     (   t   )       =         R   ⁡     (     θ   ,   ϕ     )         4   ⁢           ⁢   π   ⁢           ⁢   ρ   ⁢           ⁢     v   3     ⁢   r       ⁢       M   .     ⁡     (       t   -     r   v       ,   θ   ,   ϕ     )       ⁢     ⅇ     -       π   ⁢           ⁢   fr     vQ                   (   7   )               
where R(θ,φ) is the radiation pattern factor, and is expressed as follows:
 
                     R   ⁡     (     θ   ,   ϕ     )       =     {               sin   2     ⁡     (   θ   )       ⁢     sin   ⁡     (     2   ⁢           ⁢   ϕ     )               for   ⁢           ⁢   P   ⁢     -     ⁢   wave                 1   2     ⁢     sin   ⁡     (     2   ⁢           ⁢   θ     )       ⁢     sin   ⁡     (     2   ⁢           ⁢   ϕ     )               for   ⁢           ⁢   SH   ⁢     -     ⁢   wave                 sin   ⁡     (   θ   )       ⁢     cos   ⁡     (     2   ⁢           ⁢   ϕ     )               for   ⁢           ⁢   SV   ⁢     -     ⁢   wave                     (   8   )               
ρ is density, ν is the velocity in the vicinity of the source, r is the distance from seismic source to receiver, Q is the attenuation quality factor, and {dot over (M)}(t,θ,φ) is the moment rate function in the (θ,φ) direction.
 
     The moment rate function of an earthquake is the time-derivative of the dislocation history of a particle on the fault. It controls the amplitude of the body waves and is commonly referred to as the source time function. The fault dimensions for microearthquakes should be small relative to the source-receiver distance and can be treated as a point source for practical purposes. With this point source approximation, the moment rate function becomes independent of station location. Empirically, the moment rate function based on the omega-square source model, e.g., as described by Brune in “Tectonic stress and the spectra from seismic shear waves earthquakes,”  J. Geophys. Res.,  75, pp. 4997-5009 (1970), is found to exhibit f −2  decay of amplitudes beyond a corner frequency, f c : 
                     S   ⁡     (   f   )       =         M   .     ⁡     (   f   )       =       M   0     ⁢         f   c   2         f   2     +     f   c   2         .                 (   9   )               
Taking the point-source approximation and substituting equation (9) into equation (7), the displacement spectrum in frequency domain is expressed as follows:
 
     
       
         
           
             
               
                 
                   
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     The calculation is simplified as amplitude prediction is of interest, while waveform modeling is not of interest. Instead, the magnitude of {dot over (M)}(t,θ,φ), which is the seismic moment divided by the time duration of the earthquake source (M 0 /T), is the real focus of the calculation. Studies were conducted to investigate the empirical scaling relations between the seismic moment and the source duration or dimension, and in general found the relationship M 0 ∝ T p , where p≈3 ( FIG. 2 ). Exemplary studies include Somerville et al., “Comparison of source scaling relations of eastern and western North American earthquakes,”  Bull. Seismol. Soc. Am.,  77, pp. 322-346 (1987); Hiramatsu et al., “Scaling law between corner frequency and seismic moment of microearthquakes: is the breakdown of the cube law a nature of earthquakes?” Geophys. Res. Lett.,  29, pp. 1211, doi:10.1029/2001GL013894. (2002); and Imanishi et al., “Source parameters and rupture velocities of microearthquakes in Western Nagano, Japan, determined using stopping phases,”  Bull. Seismol. Soc. Am.,  94, pp. 1762-1780 (2004). Ray-tracing is employed in a preferred technique to calculate the path effect portion in equation (10) for areas with complex velocity and attenuation structures. 
     The seismic moment, corner frequency, and static stress drop are related through the equation (Brune, 1970), expressed as follows: 
     
       
         
           
             
               
                 
                   
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     With respect to block  170 , a preferred technique for determining seismic threshold is described in greater detail hereinafter. An accurate determination of seismic threshold requires consideration of (1) seismic signal strength; (2) seismic background noise; (3) seismic recurrence rates, and (4) recording equipment, to quantify or evaluate station and network detection capability. For example, the seismic threshold of a seismic acquisition network is the estimated magnitude at which more than 90 percent of the total number of stations can detect the event of that magnitude. 
     An investigation of the expected background noise level of the seismic array should be conducted in a feasibility study, to compare it with the anticipated signal strength of microearthquakes. High noise levels limit the detection capability of a network. A sample of seismic noise records from a similar geographic setting, e.g., onshore, shallow-water, deepwater, borehole, basin; and operating environment are each analyzed. The ideal sampling approach should ensure that background noise is well sampled under different types of meteorological conditions and cultural effects. For example, for each excerpted sample of microseism record, a 1-second moving window is applied to compute the mean and rms values. The rms values of the noise are plotted and studies have shown that the rms values of the noise are usually log-normally distributed, e.g., such as those studies described by von Seggern, in “Seismic background noise and detection threshold in the Southern Great Basin Digital Seismic Network,”  Bull. Seismol. Soc. Am,  94, pp. 2280-2298, (2004). 
     The threshold of a single station is detected as follows. Both signal and noise amplitudes are assumed to follow lognormal distributions. Assuming statistical independence of the observations, the detection probability for any given event such that log A m −μ&gt;0 e.g., a detection is declared whenever the signal exceeds the noise level, generalized to each of the magnitudes, m, is expressed as follows: 
                     P   ⁡     (   m   )       =       Φ   ⁡     [         log   ⁢           ⁢       A   i     ⁡     (   m   )         -     (       μ   i     +     log   ⁢           ⁢   R       )           (       σ   s   2     +     σ   n   2       )       1   /   2         ]       =     Φ   i               (   12   )               
where Φ is the unit normal probability distribution, A i  is the calculated rms amplitude at the station i, μ i  is the average rms logarithmic noise level at station i, σ s   2  and σ n   2  are the variances of logarithmic signal and noise, respectively, and R is the signal to noise (S/N) ratio required for detection. If signals have nearly the same frequency content as the background noise, a relatively high R may be set for calculating detection thresholds. On the other hand, specific processing techniques, e.g., such as commercially available passive seismic emission tomography (PSET™), can be implemented on datasets that have low S/N ratio (R&lt;1).
 
     The detection threshold of a seismic network is determined as follows. The probability of detection is assumed to be statistically independent among stations in the network. As described by von Seggem (2004) and Blandford, in “Seismic threshold determination,”  Bull. Seismol. Soc. Am.,  66, pp. 753-788 (1976), the probability that exactly k out of n stations in the network will detect the event of magnitude m is 
                         P     k   ,   n       ⁡     (   m   )       =       S   k     -       (           k   +   1             k         )     ⁢     S     k   +   1         +       (           k   +   2             k         )     ⁢     S     k   +   2         -     …   ±       (         n           k         )     ⁢     S   n             ⁢     
     ⁢   where           (   13   )                   S   1     =       ∑   i     ⁢     Φ   i         ,       S   2     =       ∑   ij     ⁢       Φ   i     ⁢     Φ   j           ,       S   3     =       ∑   ijk     ⁢       Φ   i     ⁢     Φ   j     ⁢     Φ   k           ,   etc           (   14   )               
The P k,n (m) probabilities in (5) are independent and sum to unity over k=0 to n. The probability that at least k out of n stations will detect the event is
 
     
       
         
           
             
               
                 
                   
                     
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     This is the relevant probability of detection curve for a given network of stations. As mentioned above, an upper limit of 90% of P ≧k,n (m) is defined as the threshold magnitude of the network:
 
 P   ≧k,n ( m )=0.9  (16)
 
     A detection threshold map is generated following an approach similar to von Seggern (2004). The detection threshold magnitude is determined for each regular grid of the 3D model. 
     Referring to  FIGS. 3A-6 , the exemplary field is a deepwater field wherein process  100  has been applied to determine the feasibility of conducting passive seismic survey(s). In the exemplary field, gas injection is planned for both pressure maintenance and gas storage, where the formation pressure is close to the fracture gradient. Accordingly, the feasibility study, e.g., process  100 , is beneficial in evaluating the feasibility of applying passive seismic technology to the exemplary field. For example, passive seismic monitoring is used (after a determination of the feasibility of passive seismic survey with process  100 ) for monitoring reservoir overpressure and/or seal breach risk in the reservoir. 
       FIG. 3A  is a collection of perspective views of a three-dimensional (3-D) geomechanics model for an exemplary field.  FIG. 3B  is an exemplary three-dimensional (3-D), finite element modeled geomechanics model for the exemplary field. Referring to  FIGS. 3A and 3B , a three-dimensional finite-element model ( FIG. 3B ) is constructed based on a geologic model ( FIG. 3A ) and seismic interpretation available. Constitutive models and rock material properties are assigned based on well logs, core, and pressure test results. For example,  FIGS. 3A and 3B  correspond to blocks  110 - 140  of process  100  ( FIG. 1 ). A reservoir simulation model  300 , which includes a full reservoir model  310 , a model of a salt body  320 , a model of the underburden  330 , a model of the salt and reservoir sideburdens  340 , and the reservoir model  350 , based on the planned exploitation strategy serves as the boundary conditions for the geomechanical modeling. In  FIG. 3B , the three-dimensional finite element model  360  includes a salt dome model  325  and a reservoir model  355 , with a pair of faults (Fault A, Fault B) shown along the edges of the reservoir  355 . 
       FIG. 4  is a collection of exemplary screenshots of graphical results from the geomechanics modeling, showing the magnitude of fault slips plotted along the two fault planes from the exemplary field of  FIG. 3B . Referring to  FIG. 4 , the fault slips along the two modeled fault planes are shown together in a first screenshot  410 , and separately with each fault plane&#39;s individual slip (in meters) shown graphically ( 420 ,  430 ) with higher slip occurring in the regions represented with colors on the red end of the spectrum than the blue end of the spectrum, e.g., slip increases from blue regions to red regions in  FIG. 4 . 
       FIG. 5  is a collection of exemplary screenshots of graphical results showing ray tracing results in a 3-D model (Vp, Qp) utilizing a 3-D seismic modeling system. Referring to  FIG. 5 , seismic moment is estimated and the resulting seismic amplitude is compared with the noise statistics. In  FIG. 5 , a mapview  510  having two sectional lines A-A′ and B-B′ includes x-y axes defining an xline and inline coordinate plane. A representative seismogram  520  along the A-A′ sectional line (and plotted along x-axis of xline and y-axis of time) and a pair of individual views taken along A-A′ ( 530 ) and B-B′ ( 540 ) provide ray tracing results with respect to depth (in kilometers). 
       FIG. 6  is a collection of graphical results obtained from consistency and seismic analyses. Referring to  FIG. 6 , a summary of the analyses and the threshold magnitude maps for seismic detection are displayed in  FIG. 6 . At  610 , event location is shown for fault A, along with slip, e.g., similar to  FIG. 4 , and at  620  mean RMS noise amplitude, standard deviation of noise. The fault A event metrics include a critical energy release rate Gc of 43,000 J/m 2 , a predicted seismic moment (Mo) of 2.2×10 13  Nm, and a predicted seismic moment magnitude of 2.8. 
       FIG. 7  is a flowchart of an exemplary process  700  for integrating results calculated from geomechanical modeling into seismic modeling and analysis. Referring to  FIG. 7 , an exemplary process  700  includes details of how geomechanical modeling  710  may be integrated with seismic modeling and analysis  750 . For example, the geomechanical modeling  710  includes defining the geologic structure  715  of the subsurface region. The geologic structure is then used to perform integrated geomechanics modeling, e.g., block  140  in  FIG. 1 , and consistency analysis, e.g., block  150  in  FIG. 1 . The integrated geomechanics modeling and consistency analysis of  FIG. 7  includes iterative determinations of geomechanics finite element analysis mesh and element types  720 , determining and modeling rock material properties and constitutive models  725 , geostatic initialization  730 , geomechanics simulations and field event calibrations  735 , and consistency analysis  740 . The geomechanical modeling  710  is used to determine total strain energy, e.g., recoverable and dissipated energies, with time (and location). The seismic modeling and analysis  750  uses the determinations of total strain energy from the geomechanical modeling  710  to determine feasibility of passive seismic surveys based on passive sources meeting the seismic threshold determinations. Specifically, seismic modeling and analysis  750  includes determining or modeling earthquake efficiency models  755 , total radiated energy  760 , static stress drop models  765 , and seismic moment  770 . A velocity model of the subsurface region is integrated with ray tracing  775  and used for noise estimation  780 , e.g., array location, ambient noise, cultural noise, acquisition design  785 , and ground motion analysis  790 . Determinations of seismic thresholds  795 , e.g., probability of detection by the network and event location uncertainty analysis  796  are used to assess the viability of passive seismic monitoring in the location. For example, the fault A-Event metrics shown in  FIG. 6  are representative of seismic event metrics that may be generated by the process  700 . 
     A number of embodiments have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the invention. For example, with respect to  FIGS. 1-6 , one or more steps of process  100  (and/or screenshots from  FIGS. 3A-6 ) may be performed with the use of commercially available seismic modeling software modules or systems. For example, the NORSAR-3D™ modeling package may be used to implement one or more of the process steps of process  100  shown in connection with  FIGS. 3A-6 . The NORSAR-3D™ modeling package provides 3D model representation techniques, e.g., the Open Ray Model, which permit seismic ray tracing in a preliminary or incomplete depth model, e.g., interfaces may have holes or other undefined areas. 
     Production data may be acquired from at least one well within the subsurface region. The fluid-flow of the reservoir may be simulated in a manner of ways, but may include building multiscenario interpretations that include establishing system fluid exits and paths to fluid exits using seismic data, e.g., 2D, 3D or 4D seismic and production data. Fluid pressure evolution may be evaluated and reconciled with production data and with fluid contacts and pressure evolution, and/or with any changes in chemistry of produced fluids with the multiscenario interpretations of compartments, connections, and/or fluid properties. 
     The aforementioned processes and/or techniques are directed at determining the feasibility of performing effective passive seismic surveys based on one or more passive seismic sources. Specifically, many kinds of natural seismic energy may be recorded and used to evaluate the subsurface region, including, but not limited to earthquakes, fluid flow disturbances, e.g., magnetic or hydrothermal, energy release from power plants, microseismic tremors, ocean wave noise, cultural noise, remote nuclear testing, induced stresses from production related activities, such as drill bit sourced noises, and any other seismic source (natural or artificial) that contributes to acoustic illumination in the subsurface region. The paper entitled “The Untapped Potential of Seismic Imaging,” by Peter B. Edwards, GEOPHYSICS:  The Leading Edge of Exploration , August, 1992, pp. 29-34, describe specific details of exemplary passive seismic surveys that may be conducted once the feasibility of effective passive seismic monitoring has been validated by one or more of the foregoing techniques, e.g., such as process  100 . 
     One or more of the aforementioned processes and/or techniques, e.g., such as the integration of process  100  to include a passive seismic survey, can be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or in any combination thereof. Any of the aforementioned functionality may be implemented as a computer program product, e.g., a computer program tangibly embodied in an information carrier, e.g., in a machine-readable storage device or in a propagated signal, for execution by, or to control the operation of, data processing apparatus, e.g., a programmable processor, a computer, or multiple computers. A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program can be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network. 
     One or more process steps of the invention can be performed by one or more programmable processors executing a computer program to perform functions of the invention by operating on input data and generating output. One or more steps can also be performed by, and an apparatus or system can be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit). In addition, data acquisition and display may be implemented through a dedicated data collection and/or processing system, e.g., containing data acquisition hardware, such as hydrophones and/or geophones, a processor(s), and various user and data input and output interfaces, such as a display component for graphically displaying one or more of the generated connectivity models obtained through any of the aforementioned process steps or processes. 
     Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor receives instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor for executing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. Information carriers suitable for embodying computer program instructions and data include forms of non-volatile memory, including by way of example semiconductor memory devices, e.g., EPROM (erasable programmable read-only memory), EEPROM (electrically erasable programmable read-only memory), and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and CD-ROM (compact disk read-only memory) and DVD-ROM (digital versatile disk read-only memory) disks. The processor and the memory can be supplemented by, or incorporated in special purpose logic circuitry. 
     All such modifications and variations are intended to be within the scope of the foregoing embodiments, as defined in the appended claims. For example, persons skilled in the art will also readily recognize that in preferred embodiments, at least some of the method steps method are performed on a computer, e.g., the method may be computer implemented. In such cases, the resulting model parameters may either be downloaded or saved to computer memory.