Patent Description:
Stress, in continuum mechanics, may be considered a measure of the internal forces acting within a volume. Such stress may be defined as a measure of the average force per unit area at a surface within the volume on which internal forces act. The internal forces are oftentimes produced between the particles in the volume as a reaction to external forces applied on the volume.

Understanding the origin and evolution of faults and the tectonic history of faulted regions can be accomplished by relating fault orientation, slip direction, geologic and geodetic data to the state of stress in the Earth's crust. In conventional inverse problems, the directions of the remote principal stresses and a ratio of their magnitudes are constrained by analyzing field data on fault orientations and slip directions as inferred from artifacts such as striations on exposed fault surfaces.

Conventional methods for stress inversion, using measured striations and/or throw on faults, are mainly based on the assumptions that the stress field is uniform within the rock mass embedding the faults (assuming no perturbed stress field), and that the slip on faults have the same direction and sense as the resolved far field stress on the fault plane. However, it has been shown that slip directions are affected by: anisotropy in fault compliance caused by irregular tip-line geometry; anisotropy in fault friction (surface corrugations); heterogeneity in host rock stiffness; and perturbation of the local stress field mainly due to mechanical interactions of adjacent faults. Mechanical interactions due to complex faults geometry in heterogeneous media can be taken into account while doing the stress inversion. By doing so, determining the parameters of such paleostress (and fluid pressure inside fault surfaces) in the presence of multiple interacting faults is determined by running numerous simulations, which takes an enormous amount of computation time to fit the observed data. The conventional parameters space has to be scanned for each possibility, and for each simulation, the model and the post-processes are recomputed.

Motion equations are oftentimes not invoked while using conventional methods, and perturbations of the local stress field by fault slip are ignored. The mechanical role played by the faults in tectonic deformation is not explicitly included in such analyses. Still, a relatively full mechanical treatment is applied for conventional paleostress inversion. However, the results may be greatly improved if multiple types of data could be used to better constrain the inversion.

French patent application <CIT> and UK patent application <CIT> disclose known methods for predicting a stress attribute of a subsurface earth volume wherein use is made of linearly independent far field stress models and discontinuity pressure models. In these documents, a stress attribute in the subterranean volume is predicted based on the superposition of the models, the superposition comprising building a transformation matrix representing the far field stress based on regional stress parameters and the pressure inside discontinuities.

The present invention resides in a method as defined in claim <NUM>, a computer-readable medium as defined in claim <NUM> and a computing system as defined in claim <NUM>.

The following detailed description refers to the accompanying drawings. Wherever convenient, the same reference numbers are used in the drawings and the following description to refer to the same or similar parts. While several embodiments and features of the present disclosure are described herein, modifications, adaptations, and other implementations are possible, the scope of the invention being solely defined by the appended claims.

This disclosure describes stress and fracture modeling using the principle of superposition. Given diverse input data, such as faults geometry, and selectable or optional data sets or data measures, including fault throw, dip-slip or slickline directions, stress measurements, fracture data, secondary fault plane orientations, global positioning system (GPS) data, interferometric synthetic aperture radar (InSAR) data, geodetic data from surface tilt-meters, laser ranging, etc., the systems and methods described herein can quickly generate and/or recover numerous types of results. The systems and methods described herein apply the principle of superposition to fault surfaces with complex geometry in 2D and/or 3D, and the faults are, by nature, of finite dimension and not infinite or semi-infinite. The results are often rendered in real-time and may include, for example, real-time stress, strain, and/or displacement parameters in response to a user query or an updated parameter; remote stress states for multiple tectonic events; prediction of intended future fracturing; differentiation of preexisting fractures from induced fractures; and so forth. The diverse input data can be derived from wellbore data, seismic interpretation, field observation, etc..

The systems and methods described below are applicable to many different reservoir and subsurface operations, including exploration and production operations for natural gas and other hydrocarbons, storage of natural gas, hydraulic fracturing and matrix stimulation to increase reservoir production, water resource management including development and environmental protection of aquifers and other water resources, capture and underground storage of carbon dioxide (CO<NUM>), and so forth.

The system can apply a <NUM>-dimensional (3D) boundary element technique using the principle of superposition that applies to linear elasticity for heterogeneous, isotropic whole- or half-space media. Based on precomputed values, the system can assess a cost function to generate real-time results, such as stress, strain, and displacement parameters for any point in a subsurface volume as the user varies the far field stress value and the fault pressure. In one implementation, the system uses fault geometry and wellbore data, including, e.g., fracture orientation, secondary fault plane data, and/or in-situ stress measurement by hydraulic fracture, to recover one or more tectonic events and fault pressures, or one or more stress tensors represented by a ratio of principal magnitudes and the associated orientation and fault pressures. The system can use many different types of geologic data from seismic interpretation, wellbore readings, and field observation to provide a variety of results, such as predicted fracture propagation based on perturbed stress field.

<FIG> shows an illustrative stress and fracture modeling system <NUM>, according to one or more embodiments disclosed. The faults geometry is often known (and optionally, measured fault throw and imposed inequality constraints such as normal, thrust, etc., may be known). The user may have access to data from wellbores (e.g., fracture orientation, in-situ stress measurements, secondary fault planes), geodetic data (e.g., InSAR, GPS, and tilt-meter), as well as interpreted horizons. A stress and fracture modeling engine <NUM> can recover the remote stress state and tectonic regime for relevant tectonic events and fault pressure, as well as displacement discontinuity on faults, and estimate the displacement and perturbed strain and stress fields anywhere within the system.

Using the principle of superposition, the stress and fracture modeling system <NUM> or engine <NUM> may perform each of three linearly independent simulations of stress tensor models and one fault pressure model in constant time regardless of the complexity of each underlying model. Each model does not have to be recomputed. Then, as introduced above, applications for the system <NUM> may include stress interpolation and fracture modeling, recovery of tectonic events, quality control on interpreted faults, real-time computation of perturbed stress and displacement fields when the user is performing parameters estimation, prediction of fracture propagation, distinguishing preexisting fractures from induced fractures, and numerous other applications.

<FIG> shows the system <NUM> of <FIG> in the context of a computing environment, in which stress and fracture modeling using the principle of superposition can be performed, according to one or more embodiments disclosed.

A computing device <NUM> implements a component, such as the stress and fracture modeling engine <NUM>. The stress and fracture modeling engine <NUM> is illustrated as software, but can be implemented as hardware or as a combination of hardware and software instructions.

The computing device <NUM> may be communicatively coupled via sensory devices (and control devices) with a real-world setting, for example, an actual subsurface earth volume <NUM>, reservoir <NUM>, depositional basin, seabed, etc., and associated wells <NUM> for producing a petroleum resource, for water resource management, or for carbon services, and so forth.

The computing device <NUM> may be a computer, computer network, or other device that has a processor <NUM>, memory <NUM>, data storage <NUM>, and other associated hardware such as a network interface <NUM> and a media drive <NUM> for reading and writing a removable storage medium <NUM>. The removable storage medium <NUM> may be, for example, a compact disk (CD); digital versatile disk/digital video disk (DVD); flash drive, etc. The removable storage medium <NUM> contains instructions, which when executed by the computing device <NUM>, cause the computing device <NUM> to perform one or more methods described herein. Thus, the removable storage medium <NUM> may include instructions for implementing and executing the stress and fracture modeling engine <NUM>. At least some parts of the stress and fracture modeling engine <NUM> can be stored as instructions on a given instance of the removable storage medium <NUM>, removable device, or in local data storage <NUM>, to be loaded into memory <NUM> for execution by the processor <NUM>.

Although the stress and fracture modeling engine <NUM> is depicted as a program residing in memory <NUM>, in other embodiments, the stress and fracture modeling engine <NUM> may be implemented as hardware, such as an application specific integrated circuit (ASIC) or as a combination of hardware and software.

The computing device <NUM> may receive incoming data <NUM>, such as faults geometry and many other kinds of data, from multiple sources, such as wellbore measurements <NUM>, field observation <NUM>, and seismic interpretation <NUM>. The computing device <NUM> can receive many types of data sets <NUM> via the network interface <NUM>, which may also receive data from the Internet <NUM>, such as GPS data and InSAR data.

The computing device <NUM> may compute and compile modeling results, simulator results, and control results, and a display controller <NUM> may output geological model images and simulation images and data to a display <NUM>. The images may be a 2D or 3D simulation <NUM> of stress and fracture results using the principle of superposition. The stress and fracture modeling engine <NUM> may also generate one or more visual user interfaces (UIs) for input and display of data.

The stress and fracture modeling engine <NUM> may also generate and/or produce control signals to be used via control devices, e.g., such as drilling and exploration equipment, or well control injectors and valves, in real-world control of the reservoir <NUM>, transport and delivery network, surface facility, and so forth.

Thus, the system <NUM> may include a computing device <NUM> and an interactive graphics display unit <NUM>. The system <NUM> as a whole may constitute simulators, models, and the example stress and fracture modeling engine <NUM>.

<FIG> shows the stress and fracture modeling engine <NUM> in greater detail, according to one or more embodiments disclosed. The stress and fracture modeling engine <NUM> illustrated in <FIG> includes a buffer for data sets <NUM> or access to the data sets <NUM>, an initialization engine <NUM>, stress model simulators <NUM> or an access to the stress model simulators <NUM>, a optimization parameters selector <NUM>, a cost assessment engine <NUM>, and a buffer or output for results <NUM>. Other components, or other arrangements of the components, may also be used to enable various implementations of the stress and fracture modeling engine <NUM>. The functionality of the stress and fracture modeling engine <NUM> will be described next.

<FIG> shows various methods for recovering paleostress, according to one or more embodiments disclosed. <FIG> shows a technique that uses slip distributions on faults as data input, but provides unstable results. The technique of <FIG> may not augment the slip distributions with other forms of data input, such as GPS data, and so forth. <FIG> shows a (2D) Monte Carlo method, but without the optimization using the principle of superposition. <FIG> shows the method described herein, including techniques that utilize the principle of superposition and drastically reduce the complexity of the model. The method shown in <FIG> may be implemented by the stress and fracture modeling engine <NUM>. For example, in one implementation the initialization engine <NUM>, through the stress model simulators <NUM>, generates three precomputed models of the far field stress associated with a subsurface volume <NUM>. For each of the three models, the initialization engine <NUM> precomputes, for example, displacement, strain, and/or stress values. The optimization parameters selector <NUM> iteratively scales the displacement, strain, and/or stress values for each superpositioned model to minimize a cost at the cost assessment engine <NUM>. The values thus optimized in real-time are used to generate particular results <NUM>.

In one implementation, the three linearly independent far field stress parameters are: (i) orientation toward the North, and (ii) & (iii) the two principal magnitudes. These far field stress parameters are modeled and simulated to generate a set of the variables for each of the three simulated models. Four variables can be used: displacement on a fault, the displacement field at any data point or observation point, a strain tensor at each observation point, and the tectonic stress. The optimization parameters selector <NUM> selects an alpha for each simulation, i.e., a set of "alphas" for the four simulated stress models to act as changeable optimization parameters for iteratively converging on values for these variables in order to minimize one or more cost functions, to be described below. In one implementation, the optimization parameters selector <NUM> selects optimization parameters at random to begin converging the scaled strain, stress, and or displacement parameters to lowest cost. When the scaled (substantially optimized) parameters are assessed to have the lowest cost, the scaled strain, stress, and/or displacement parameters can be applied to predict a result, such as a new tectonic stress.

Because the method of <FIG> uses precomputed values superpositioned from their respective simulations, the stress and fracture modeling engine <NUM> can provide results quickly, even in real-time. As introduced above, the stress and fracture modeling engine <NUM> can quickly recover multiple tectonic events responsible for present conditions of the subsurface volume <NUM>, more quickly discern induced fracturing from preexisting fracturing than conventional techniques, provide real-time parameter estimation as the user varies a stress parameter, and/or rapidly predict fracturing.

While conventional paleostress inversion may apply a full mechanical scenario, the stress and fracture modeling engine <NUM> improves upon conventional techniques by using multiple types of data. Data sets <NUM> to be used are generally of two types: those which provide orientation information (such as fractures, secondary fault planes with internal friction angle, and fault striations, etc.), and those which provide magnitude information (such as fault slip, GPS data, InSAR data, etc.). Conventionally, paleostress inversion has been computed using slip measurements on fault planes.

The technique shown in <FIG> executes an operation that proceeds in two stages: (i) resolving the initial remote stress tensor σR onto the fault elements that have no relative displacement data and solving for the unknown relative displacements (bj in <FIG>); and, (ii) using the computed and known relative displacements to solve for σR (<FIG>). An iterative solver is used that cycles between stages (i) and (ii) until convergence.

The technique shown in <FIG> is based on a Monte-Carlo algorithm. However, this technique proves to be unfeasible since computation time is lengthy, for which the complexity is O(n<NUM>+p), where n and p are a number of triangular elements modeling the faults and the number of data points, respectively. For a given simulation, a random far field stress σR is chosen, and the corresponding displacement discontinuity u on faults is computed. Then, as a post-process at data points, and depending on the type of measurements, cost functions are computed using either the displacement, strain, or stress field. In this scenario, for hundreds of thousands of simulations, the best cost (close to zero) is retained as a solution.

On the other hand, the method diagrammed in <FIG>, which can be executed by the stress and fracture modeling engine <NUM>, extends inversion for numerous kinds of data, and provides a much faster modeling engine <NUM>. For example, a resulting fast and reliable stress inversion is described below. The different types of data can be weighted and combined together. The stress and fracture modeling engine <NUM> can quickly recover the tectonic events and fault pressure as well as displacement discontinuity on faults using diverse data sets and sources, and then obtain an estimate of the displacement and perturbed strain and stress field anywhere within the medium, using data available from seismic interpretation, wellbores, and field observations. Applying the principle of superposition allows a user to execute parameters estimation in a very fast manner.

A numerical technique for performing the methods is described next. Then, a reduced remote tensor used for simulation is described, and then the principle of superposition itself is described. An estimate of the complexity is also described.

In one implementation, a formulation applied by the stress and fracture modeling engine <NUM> can be executed using IBEM3D, a successor of POLY3D. POLY3D is described by <NPL>, and by <NPL>. IBEM3D is a boundary element code based on the analytical solution of an angular dislocation in a homogeneous or inhomogeneous elastic whole- or half-space. An iterative solver can be employed for speed considerations and for parallelization on multi-core architectures. (See, for example, <NPL>. ) However, inequality constraints may not be used as they are nonlinear and the principle of superposition does not apply. In the selected code, faults are represented by triangulated surfaces with discontinuous displacement. The three-dimensional fault surfaces can more closely approximate curvi-planar surfaces and curved tip-lines without introducing overlaps or gaps.

Mixed boundary conditions may be prescribed, and when traction boundary conditions are specified, the initialization engine <NUM> solves for unknown Burgers's components. After the system is solved, it is possible to compute anywhere, within the whole- or half-space, displacement, strain, or stress at observation points, as a post-process. Specifically, the stress field at any observation point is given by the perturbed stress field due to slipping faults plus the contribution of the remote stress. Consequently, obtaining the perturbed stress field due to the slip on faults is not enough. Moreover, the estimation of fault slip from seismic interpretation is given along the dip-direction. Nothing is known along the strike-direction, and a full mechanical scenario is used to recover the unknown components of the slip vector as it will impact the perturbed stress field. Changing the imposed far field stress (orientation and or relative magnitudes) modifies the slip distribution and consequently the perturbed stress field. In general, a code such as IBEM3D is well suited for computing the full displacement vectors on faults, and has been intensively optimized using an H-matrix technique. The unknown for purposes of modeling remains the estimation of the far field stress that has to be imposed as boundary conditions.

In one embodiment, which may be implemented by the stress and fracture modeling engine <NUM>, a model composed of multiple fault surfaces is subjected to a constant far field stress tensor σR defined in the global coordinate system by Equation (<NUM>): <MAT> Assuming a sub-horizontal far field stress (but the present methodology is not restricted to that case), Equation (<NUM>) simplifies into Equation (<NUM>): <MAT> Since the addition of a hydrostatic stress does not change σR, the far field stress tensor σR can be written as in Equation (<NUM>): <MAT>.

Consequently, a definition of a far field stress with three unknowns is obtained, namely {a<NUM>, a<NUM>, a<NUM>}.

The far field stress tensor, as defined in Equation (<NUM>) can be computed using two parameters instead of the three {a<NUM>, a<NUM>, a<NUM>}. Using spectral decomposition of the reduced σR, Equation (<NUM>) may be obtained: <MAT> where, as in Equation (<NUM>): <MAT> the far field stress tensor σR is the matrix of principal values, and in Equation (<NUM>): <MAT> is the rotation matrix around the global z-axis (since a sub-horizontal stress tensor is assumed).

By writing, in Equation (<NUM>): <MAT> Equation (<NUM>) then transforms into Equation (<NUM>): <MAT>.

Omitting the scaling parameter σ<NUM> due to Property <NUM> discussed below (when σ<NUM>=δ in Property <NUM>), σR can be expressed as a functional of two parameters δ and k, as in Equation (<NUM>): <MAT>.

These two parameters are naturally bounded by Equations (<NUM>): <MAT> assuming that uniaxial remote stress starts to occur when k ≥ <NUM>. For k=<NUM>, a hydrostatic stress tensor is found, which has no effect on the model. Also, using a lithostatic far field stress tensor (which is therefore a function of depth z) does not invalidate the presented technique, and Equation (<NUM>) transforms into Equation (<NUM>): <MAT> which is linearly dependent on z. The simplified tensor definition given by Equation (<NUM>) is used in the coming sections to determine θ, k), or equivalently {a<NUM>, a<NUM>, a<NUM>} according to measurements.

Even when <NUM>-dimensional parameter-space is used for the Monte-Carlo simulation using (θ, k, p), where p is the fault pressure, three components are still used for the far field stress, specified by the parameters (α<NUM>, α<NUM>, α<NUM>, α<NUM>). The conversions are given by Expression (<NUM>): <MAT> where, in Equations (<NUM>): <MAT> and (α<NUM>, α<NUM>, α<NUM>) are given by Equation (<NUM>), further below.

The stress and fracture modeling engine <NUM> uses the principle of superposition, a well-known principle in the physics of linear elasticity, to recover the displacement, strain, and stress at any observation point P using the precomputed specific values from linearly independent simulations. The principle of superposition stipulates that a given value f can be determined by a linear combination of specific solutions.

In the stress and fracture modeling engine <NUM>, recovering a far field stress implies recovering the three parameters (α<NUM>, α<NUM>, α<NUM>). Therefore, the number of linearly independent solutions used is three. In other words, in Equation (<NUM>): <MAT> where (α<NUM>, α<NUM>, α<NUM>) are real numbers, and σ(i) (for i=<NUM> to <NUM>) are three linearly independent remote stress tensors. If F is selected to be the strain, stress or displacement Green's functions, then the resulting values ε, σ, and u, at P can be expressed as a combination of three specific solutions, as shown below. Thus, the strain, stress and displacement field for a tectonic loading are a linear combination of the three specific solutions, and are given by Equation (<NUM>): <MAT>.

Similarly, using (α<NUM>, α<NUM>, α<NUM>) allows recovery of the displacement discontinuities on the faults, as in Equation (<NUM>): <MAT> and any far field stress is also given as a combination of the three parameters, as in Equation (<NUM>): <MAT>.

The entire model is oftentimes recomputed to change σR to determine the corresponding unknown displacement discontinuities. Then, at any observation point P, the stress is determined as a superimposition of the far field stress σR and the perturbed stress field due to slipping elements.

For a model made of n triangular discontinuous elements, computing the stress state at point P first involves solving for the unknown displacement discontinuities on triangular elements (for which the complexity is O(n<NUM>), and then performing approximately 350n multiplications using the standard method. By using the principle of superposition, on the other hand, the unknown displacement discontinuities on triangular elements do not have to be recomputed, and many fewer (e.g., <NUM>) multiplications are performed by the stress and fracture modeling engine <NUM>. The complexity is independent of the number of triangular elements within the model, and is constant in time.

Some direct applications of the methods will now be described, such as real-time evaluation of deformation and the perturbed stress field while a user varies a far field stress parameter. Paleostress estimation using different data sets <NUM> is also presented further below, as is a method to recover multiple tectonic phases, and a description of how the example method can be used for quality control during fault interpretation.

Before describing the paleostress inversion method, another method is described first, for real-time computing displacement discontinuity on faults, and the displacement, strain, and stress fields at observation points while the orientation and/or magnitude of the far field stress is varied.

If the tectonic stress σR is given and three independent solutions are known, there exists a unique triple (α<NUM>, α<NUM>, α<NUM>) for which Equation (<NUM>) is honored, and Equations (<NUM>) and (<NUM>) can be applied.

In matrix form, Equation (<NUM>) is written in the format shown in Equation (<NUM>): <MAT> or, in compact form, as in Equation (<NUM>): <MAT>.

Since the three particular solutions σ(i) are linearly independent, the system can be inverted, which gives Equation (<NUM>): <MAT>.

In Equation (<NUM>), Aσ-<NUM> is precomputed by the initialization engine <NUM>. Given a user-selected remote stress, σR, the stress and fracture modeling engine <NUM> recovers the three parameters (α<NUM>, α<NUM>, α<NUM>), then the fault slip and the displacement, strain and stress field are computed in real-time using Equations (<NUM>) and (<NUM>), respectively. To do so, the three particular solutions of the displacement, strain and stress are stored at initialization at each observation point, as well as the displacement discontinuity on the faults. In one implementation, the example stress and fracture modeling engine <NUM> enables the user to vary the orientation and magnitude of σR, and to interactively display the associated deformation and perturbed stress field.

As seen above, the main unknowns while doing forward modeling for the estimation of the slip distribution on faults, and consequently the associated perturbed stress field, are the orientation and relative magnitudes of the far field stress σR.

If field measurements are known at some given observation points (e.g., displacement, strain and/or stress, fractures orientation, secondary fault planes that formed in the vicinity of major faults, etc.), then it is possible to recover the triple (α<NUM>, α<NUM>, α<NUM>), and therefore also recover the tectonic stress σR and the corresponding tectonic regime. The next section describes the method of resolution and the cost functions used to minimize cost for different types of data sets <NUM>.

Applying a Monte Carlo technique allows the parameters (α<NUM>, α<NUM>, α<NUM>) to be found, which minimize the cost functions when given three independent far field stresses (see Equation <NUM>). However, even if (α<NUM>, α<NUM>, α<NUM>) imply a <NUM>-dimensional parameters-space, this space can be reduced to two dimensions (namely, to the parameters θ and k), the conversion being given by Equation (<NUM>) and (θ,k). (σ<NUM>, σ<NUM>, σ<NUM>) → (α<NUM>, α<NUM>, α<NUM>), where, in Equations (<NUM>): <MAT> (see also <FIG> and Algorithm <NUM> for a detailed description). Consequently, the searching method (e.g., the search for optimized parameters) is accelerated by reducing the parameters-space by one dimension.

A simple sampling method can be performed by considering a two-dimensional rectangular domain for which the axes correspond to θ and k. The 2D-domain is sampled randomly with np points, and the associated cost function (to be defined in the coming sections) is used to determine the point of minimum cost. A refinement is then created around the selected point and the procedure is repeated with a smaller domain. Algorithm (<NUM>) depicts a simplified version of the procedure, for which there is no refinement. The example sampling method presented here can be greatly optimized by various techniques.

Other formulations of the Monte-Carlo 2D Domain may be used without departing from the scope of the claims. For example, below is another formulation that may be used.

In the formulation below, θ is defined as a value between <NUM> and π, rather than between π/<NUM> and π/<NUM>. Further, θ = <NUM> corresponds to the north and the angle is defined clockwise.

Equation (<NUM>) above may be changed to the following Equation (<NUM>'): <MAT>.

Equation (<NUM>) above may be changed to the following Equation (<NUM>'), where Rθ is the rotation matrix along the vertical axis (clockwise) with θ∈[<NUM>,π],: <MAT>.

Using the second formulation, the definition of a regional stress has three unknowns, namely (σh σv), (σH σv), and θ. Expressing (<NUM>') using σ<NUM>, σ<NUM> and σ<NUM> for the three Andersonian fault regimes (Anderson, <NUM>), factorizing with (σ<NUM>-σ<NUM>) and introducing the stress ratio, R = (σ<NUM>-σ<NUM>)/(σ<NUM>-σ<NUM>) ∈[<NUM>,<NUM>], the following Equation (<NUM>) results as follows: <MAT>.

By changing R to R' as shown in equation (<NUM>) as follows, a unique stress shape parameter R' is created for the three fault regimes together: <MAT>.

Omitting the scaling factor (σ<NUM> σ<NUM>), the regional stress tensor in (<NUM>) is defined with only two parameters, θ and R'. This definition may be used as shown below to determine (θ, R') according to the data utilized.

Continuing with the second formulation, Equation (<NUM>) above is not used. Further, Equation <NUM> is replaced with the following equation (<NUM>'): <MAT>.

Equation (<NUM>) becomes Equation (<NUM>') as follows: <MAT>.

Further, equations (<NUM>) and (<NUM>) are replaced by: <MAT> <MAT> for normal fault regime
with β being an angle defined as: β = tan-<NUM>(R' <NUM>) for strike slip fault regime <MAT> for thrust fault regime.

Further, Algorithm (<NUM>) may be changed to the following Algorithm (<NUM>'):
<IMG>.

While the above discussion presents a second formulation, other formulations may be used without departing from the scope of the claims.

The particularity of this method lies in a fact that many different kinds of data sets <NUM> can be used to constrain the inversion. Two groups of data are presented in the following sections: the first one includes orientation information and the second includes displacement and/or stress magnitude information.

For opening fractures (e.g., joints, veins, dikes) the orientation of the normal to the fracture plane indicates the direction of the least compressive stress direction in 3D (σ<NUM>). Similarly, the normals to pressure solution seams and stylolites indicate the direction of the most compressive stress (σ<NUM>). Using measurements of the orientations of fractures, pressure solution seams, and stylolites allows the stress and fracture modeling engine <NUM> to recover the tectonic regime which generated such features.

At any observation point P, the local perturbed stress field can be determined from a numerical point of view by using three linearly independent simulations. <FIG> shows fracture and conjugate fault planes, according to one or more embodiments disclosed. <FIG> shows orientation of σ<NUM> relative to an opening fracture (joints, veins, dikes) given by its normal n in 3D. <FIG> shows the same as <FIG>, except for an orientation of σ<NUM> relative to a joint given by its projected normal n (e.g., trace on outcrop). <FIG> show the same as <FIG>, except shown for a stylolite. <FIG> shows orientation of σ<NUM> and σ<NUM> relative to conjugate fault-planes given by one of the normal n in 3D and the internal friction angle θ. The goal is to determine the best fit of the far field stress σR, and therefore parameters (α<NUM>, α<NUM>, α<NUM>), given some orientations of opening fracture planes for which the normals coincide with the directions of the least compressive stress σ<NUM>P at P, or equivalently for which the plane of the fracture contains the most compressive stress (σ<NUM>), as in <FIG>.

By varying (α<NUM>, α<NUM>, α<NUM>), the state of stress at any observation point P can be computed quickly using the three precomputed models. The cost function to minimize is given in Equation (<NUM>): <MAT> where ". " is the dot-product, n is the normal to a fracture plane, and m is the number of observation points. The minimization of a function of the three parameters is expressed by Equation (<NUM>): <MAT>.

Similarly, for pressure solution seams and stylolites, the cost function is defined as in Equation (<NUM>) using the least compressive stress σ<NUM> as in Equation (<NUM>) (see <FIG>): <MAT>.

The orientation of secondary fault planes that develop in the vicinity of larger active faults may be estimated using a Coulomb failure criteria, defined by Equation (<NUM>): <MAT> where θ is the angle of the failure planes to the maximum principal compressive stress σ<NUM> and µ is the coefficient of internal friction. Two conjugate failure planes intersect along σ<NUM> and the fault orientation is influenced by the orientation of the principal stresses and the value of the friction.

The cost function is therefore defined by Equation (<NUM>): <MAT> where σ<NUM> is the direction of the most compressive stress and σ<NUM> is the direction of the intermediate principal stress. The first term of the right hand side in Equation (<NUM>) maintains an orthogonality between the computed σ<NUM> and the normal of the fault plane, whereas the second term ensures that the angle between the computed σ<NUM> and the fault plane is close to θ (see <FIG>).

<FIG> shows a synthetic example using an inclined planar fault as one of two conjugate fault planes selected randomly, according to one or more embodiments disclosed. The inclination of the two conjugate fault planes is presented for a normal fault configuration (<FIG>) and a thrust fault configuration (<FIG>). Dip-azimuth and dip-angle of each conjugate fault plane are used to perform the inversion and the internal friction angle is θ=<NUM>. The main active fault is represented by the inclined rectangular plane <NUM>.

Initially, the model is constrained by a far field stress at some observation points <NUM>, where the two conjugate planes are computed using an internal friction angle of <NUM> degrees. Then, for each observation point <NUM>, one of the conjugate fault planes is chosen randomly and used as input data for the stress inversion.

<FIG> shows the cost function for the synthetic example from <FIG>, according to one or more embodiments disclosed. <FIG> shows the cost function for the normal fault, and <FIG> shows the cost function for the thrust fault. In both cases, the recovered regional stress tensor, displacement on fault, and predicted conjugate fault planes provide a good match with the initial synthetic model.

In the case of fault striations, the cost function is defined as in Equation (<NUM>): <MAT> where <MAT> and <MAT> represent the normalized slip vector from a simulation and the measured slip vector, respectively.

The magnitude of displacements may be used to determine the stress orientation and the magnitude of the remote stress tensor, instead of just the principal stress ratio.

To do so, the procedure is similar to that described previously. However, given Equations (<NUM>) and (<NUM>), it is evident that there exists a parameter δ for which the computed displacement discontinuity on faults and the displacement, strain and stress fields at observation points scale linearly with the imposed far field stress. In other words, as in Equation (<NUM>): <MAT>.

This leads to the following property:
Property <NUM>: Scaling the far field stress by δ ∈ scales the displacement discontinuity on faults as well as the displacement, strain, and stress fields at observation points by δ.

Using this property, measurements at data points are globally normalized before any computation and the scaling factor is noted (the simulations are also normalized, but the scaling factor is irrelevant). After the system is solved, the recovered far field stress, displacement and stress fields are scaled back by a factor of δm-<NUM>.

In the case of a GPS data set, the cost function is defined in Equation (<NUM>): <MAT> where <MAT> is the globally normalized measured elevation changed at point P from the horizon, and <MAT> is the globally normalized computed elevation change for a given set of parameters (α<NUM>, α<NUM>, α<NUM>). The first term on the right hand side in Equation (<NUM>) represents a minimization of the angle between the two displacement vectors, whereas the second term represents a minimization of the difference of the norm.

When using an InSAR data set, there are two possibilities. Either the global displacement vectors of the measures are computed using the displacement u along the direction of the satellite line of sight s, in which case Equation (<NUM>) is used: <MAT> and the same procedure that is used for the GPS data set (above) is applied with the computed <MAT>,, or, the computed displacement vectors are computed along the satellite line of sight, in which case Equation (<NUM>) is used: <MAT> where ". " is the dot product. The cost function is consequently given by Equation (<NUM>): <MAT>.

<FIG>, <FIG>, and <FIG> present a synthetic example using an InSAR data set. More particularly, <FIG> shows a model configuration showing the InSAR data points <NUM> as well as the fault surface <NUM>, <FIG> shows a comparison of the fringes from the original InSAR grid <NUM> and the recovered InSAR grid <NUM>, and <FIG> shows a plot of the cost surface, as a function of θ (x-axis) and k (y-axis), according to one or more embodiments disclosed. On the left is a top view <NUM> of the plot, and on the right is a front perspective view <NUM> of the plot. The minimized cost solution <NUM> in each view is marked by a small white circle (<NUM>).

To utilize an InSAR data set, a forward model is run using one fault plane <NUM> and one observation grid (<FIG>) at the surface of the half-space (see surface holding the data points <NUM>). A satellite direction is selected, and for each observation point <NUM>, the displacement along the satellite line of sight is computed. Then, the stress and fracture modeling method described herein is applied using the second form of the InSAR cost function given in Equation (<NUM>). <FIG> compares the original interferogram <NUM> (left) to the recovered interferogram <NUM> (right). <FIG> shows how complex the cost surface can be, even for a simple synthetic model. In one implementation, the cost surface was sampled with <NUM>,<NUM> data points <NUM> (number of simulations), and took <NUM> seconds on an average laptop computer with a <NUM> processor and with <NUM> GB of RAM running on Linux Ubuntu version <NUM>, <NUM> bits.

Using the mean plane of a given seismic horizon (flattened horizon), the stress and fracture modeling engine <NUM> first computes the change in elevation for each point making the horizon. Then, the GPS cost function can used, for which the uz component is provided, giving Equation (<NUM>): <MAT> If pre- or post-folding of the area is observed, the mean plane can no longer be used as a proxy. Therefore, a smooth and continuous fitting surface has to be constructed which removes the faulting deformations while keeping the folds. Then, the same procedure as for the mean plane can be used to estimate the paleostress. In some circumstances and prior to defining the continuous fitting surface, the input horizon can be filtered from noises possessing high frequencies, such as corrugations and bumps, while preserving natural deformations.

<FIG> shows results when applying a stress and fracture modeling method to a synthetic example using a flattened horizon, according to one or more embodiments disclosed. <FIG> presents a model configuration showing the horizon <NUM> and the fault surface <NUM>. <FIG> shows a comparison of the original dip-slip <NUM> (left) and the recovered dip-slip <NUM> (right). <FIG> shows a comparison of the original strike-slip <NUM> (left) and the recovered strike-slip <NUM> (right). <FIG> shows original vertical displacement <NUM> (left) from the flattened horizon (left) and recovered vertical displacement <NUM> (right) from the flattened horizon.

As shown in <FIG>, a complex shaped fault is initially constrained by a far field stress, and consequently slips to accommodate the remote stress. At each point of an observation plane cross-cutting the fault, the stress and fracture modeling engine <NUM> computes the resulting displacement vector and deforms the grid accordingly. Then, inversion takes place using the fault geometry. After flattening the deformed grid, the change in elevation for each point is used to constrain the inversion and to recover the previously imposed far field stress as well as the fault slip and the displacement field. The comparison of the original and inverted dip-slip (<FIG>) and strike-slip (<FIG>) show that they match well (same scale). A good match is also observed for the displacement field at the observation grid (<FIG>).

When dip-slip data is used, the cost function is defined as in Equation (<NUM>): <MAT> where bem is the measured dip-slip magnitude for a triangular element e, and bec is the computed dip-slip magnitude.

The stress and fracture modeling engine <NUM> can combine the previously described cost functions to better constrain stress inversion using available data (e.g., fault and fracture plane orientation data, GPS data, InSAR data, flattened horizons data, dip-slip measurements from seismic reflection, fault striations, etc.). Furthermore, data can be weighted differently, and each datum can also support a weight for each coordinate.

For multiple tectonic events, it is possible to recover the major ones, e.g., those for which the tectonic regime and/or the orientation and/or magnitude are noticeably different. Algorithm <NUM>, below, presents a way to determine different events from fracture orientation (joints, stylolites, conjugate fault planes) measured along wellbores.

After doing a first simulation, a cost is attached at each observation point which shows the confidence of the recovered tectonic stress relative to the data attached to that observation point. A cost of zero means a good confidence, while a cost of one means a bad confidence. See <FIG> for an example plot of the cost. By selecting data points that are under a given threshold value and running another simulation with these points, it is possible to extract a more precise paleostress value. Then, the remaining data points above the threshold value can be used to run another simulation with the paleostress state to recover another tectonic event. If the graph of the new cost shows disparities, the example method above is repeated until satisfactory results are achieved. During the determination of the tectonic phases, the observation points are classified in their respective tectonic event. However, the chronology of the tectonic phases remains undetermined. <IMG>
<IMG>.

It can be useful to have a method for quality control (QC) for interpreted faults geometries from seismic interpretation. The fracture orientations from wellbores can be used to recover the far field stress and the displacement discontinuities on active faults. Then, the computed displacement field is used to deform the initially flattened horizons. The geometry of the resulting deformed horizons can be compared with the interpreted ones. If some mismatches are clearly identified (e.g., interpreted uplift and computed subsidence), then the fault interpretation is possibly false. For example, an interpreted fault might dip in the wrong direction. An unfolded horizon can be approximated by its mean plane, as described above in relation to flattened horizons.

<FIG> shows a flowchart of a method <NUM> of stress and fracture modeling using the principle of superposition, according to one or more embodiments disclosed. The example method <NUM> may be performed by hardware or combinations of hardware and software, for example, by the example stress and fracture modeling engine.

One or more linearly independent far field stress models and one or more discontinuity pressure models for a subsurface volume can be simulated, as shown at <NUM>.

Stress values, strain values, and/or displacement values for data points in the subsurface volume can be computed, based on a superposition of the one or more linearly independent far field stress models and the one or more discontinuity pressure models, as shown at <NUM>.

A stress attribute of the subsurface volume can be iteratively predicted based on the computed stress, strain, and/or displacement values, as shown at <NUM>.

<FIG> shows a flowchart of a method <NUM> of stress and fracture modeling using the principle of superposition, according to one or more embodiments disclosed. The method <NUM> may be performed by hardware or combinations of hardware and software, for example, by the stress and fracture modeling engine <NUM>.

Fault geometries for a subsurface earth volume are received, as shown at <NUM>.

At least one data set associated with the subsurface earth volume is also received, as shown at <NUM>.

One or more (e.g., three) linearly independent far field stress tensor models and one or more (e.g., one) discontinuity pressure models can be simulated in constant time, as shown at <NUM>. The discontinuity pressure model can be or include a model of a fault, dyke, salt dome, magma chamber, or a combination thereof.

A superposition of the three linearly independent far field stress tensor models and the discontinuity pressure model can be computed to provide computed strain, stress, and/or displacement values, as shown at <NUM>.

A post-process segment of the method <NUM> can commence, which can compute numerous real-time results based on the principle of superposition, as shown at <NUM>. This can involve inversion of the one or more linearly independent far field stress models and/or the one or more discontinuity pressure models.

Optimization parameters for each of the linearly independent far field stress tensor models and fault pressure can be selected, as shown at <NUM>.

The computed stress, strain, and/or displacement values can be scaled by the optimization parameters, as shown by <NUM>.

A cost associated with the scaled computed stress, strain, and/or displacement values can be evaluated, as shown by <NUM>. If the cost is not satisfactory, then the method <NUM> loops back to <NUM> to select new optimization parameters. If the cost is satisfactory, then the method <NUM> continues to <NUM>.

The scaled strain, stress, and/or displacement values can be applied to the subsurface earth volume, e.g., with respect to a query about the subsurface earth volume or in response to an updated parameter about the subsurface earth volume, as shown by <NUM>.

A fault(s) query and/or updated parameter regarding the subsurface earth volume can be received, that seeds or initiates generation of the post-process results in the real-time results section (<NUM>), as shown by <NUM>.

Based on the solution of an angular dislocation in a 3D homogeneous isotropic elastic whole- or half-space, the pressure inside discontinuities (e.g., faults, dykes, salt domes, magma chambers) and the regional stress regime (e.g., orientation of the maximum principal horizontal stress according to North) can be inverted. The stress ratio can be defined as in Equation (<NUM>): <MAT>.

Pressure can be inverted relative to the maximum shear stress (σ<NUM>-σ<NUM>), e.g., the difference between maximum and minimum principal inverted regional stress. Equation (<NUM>) shows the relationship between the real pressure and the normalized inverted pressure: <MAT> where p represents the real pressure and p the normalized inverted pressure.

Three dimensional discontinuities making the cavities, faults, and/or salt domes can be discretized as complex 3D triangular surfaces. To constrain the inversion, at least a portion of the data can be used and combined together such as any fracture orientation (e.g., joints, stylolites) or wellbore ovalization, GPS or InSAR data, microsismicity or focal mechanisms with or without magnitude information, tiltmeters, or the like. Four preliminary simulation results at data points are used to invert for both the pressure and the regional stress. The transformation matrix from the four solutions and the linear coefficient to a given far field stress is given by Equation (<NUM>): <MAT> where <MAT> and <MAT> represent the regional stress parameters for simulation i ∈ [<NUM>,<NUM>] and for which the perturbed stress and displacement fields at data are computed and stored. p<NUM> is a pressure (different from <NUM>) and for which the perturbed stress and displacement fields at data are computed and stored.

Thus, for a given remote stress and fault pressure p, <MAT>, coefficients α = {α<NUM>, α<NUM>, α<NUM>, α<NUM>}T are computed from Equation (<NUM>) using α = A-<NUM>σR. The perturbed stress, strain, and/or displacement at a point P can be given by the linear combination of the stored solutions at p using the same coefficients α. This can be used to find the best α, and therefore to invert for both the regional stress and the pressure using Equation (<NUM>).

<FIG> illustrates a model <NUM> showing dykes orientations from inversion of the pressure inside a vertical plug <NUM>, according to one or more embodiments disclosed. The model <NUM> is in half-space with no rigid boundary. The thin lines <NUM> represent the predicted dykes orientation, and the thick lines <NUM> represent the observed dykes orientation. The inverted stress regime gives R=<NUM> in the normal fault regime with θ=N81E for the orientation. The inverted normalized pressure is <NUM>. In other words, the inverted normalized pressure is <NUM> times greater than (σ<NUM>-σ<NUM>).

<FIG> illustrates a flowchart of a method <NUM> of stress and fracture modeling using the principle of superposition, according to the claimed invention. The method <NUM> includes simulating one or more (e.g., three) linearly independent far field stress models, one or more (e.g., one) discontinuity pressure models, and one or more discontinuity pressure shift models for the subsurface earth volume, as at <NUM>.

The linearly independent far field stress model(s) may include parameters (Sxx, Sxy, Syy, Szz) of one or more far field stresses that are linearly-independent. For instance, one solution may include selecting the first simulation Sxx=<NUM> and the other Sij=<NUM>. For the second simulation, the user may select Sxy=<NUM> and the other Sij=<NUM>. The user may then select Syy=<NUM> and the other Sij=<NUM>. Finally, the user may select Szz=<NUM> and the the other Sij=<NUM>. This may lead to the identity matrix for A. The discontinuity pressure model includes the pressure inside the discontinuity. This may be modelled using a non-null initial boundary value for the third axis of each triangular element, which is parallel to the normal of the triangular element. The gradient far field stress and pressure may have a dependence upon the depth (z). The discontinuity pressure shift model is or includes the value(s) of the pressure at depth z=<NUM> (i.e., at the surface of the Earth). Mathematically, it may be represented as the y-intercept, while the pressure itself may be the slope.

The one or more linearly independent far field stress models, one or more discontinuity pressure models, and one or more discontinuity pressure shift models are generated using data captured by sensors in a wellbore or at the surface, as described in greater detail above. For example, the sensors may be part of a measurement-while-drilling ("MWD") tool or a logging-while-drilling ("LWD") tool in the wellbore, of the sensors may be seismic receivers positioned at the surface.

The one or more linearly independent far field stress models may be or include three linearly independent far field stress models that are based on different data sets, each data set including fault geometry data, fracture orientation data, stylolites orientation data, secondary fault plane data, fault throw data, slickenline data, global positioning system (GPS) data, interferometric synthetic aperture radar (InSAR) data, laser ranging data, tilt-meter data, displacement data for a geologic fault, stress magnitude data for the geologic fault, or a combination thereof. The discontinuity pressure model can be or include a model of a fault, dyke, salt dome, magma chamber, or a combination thereof.

The method <NUM> may also include computing a stress value, a discontinuity pressure shift at (or proximate to) a surface of the Earth (e.g., at z=<NUM>), a strain value, a displacement value, or a combination thereof for data points in the subsurface earth volume based on a superposition of the one or more linearly independent far field stress models, the one or more discontinuity pressure models, and the one or more discontinuity pressure shift models, as at <NUM>. The discontinuity pressure shift may occur at the surface of the Earth, be measured at the surface of the Earth, or both.

The method <NUM> also includes predicting a stress attribute of the subsurface earth volume, based on the computed stress value, the computed discontinuity pressure shift at the surface of the Earth, the computed strain value, the computed displacement value, or the combination thereof, as at <NUM>. The predicted stress attribute may be or include a stress inversion, a stress field, a far field stress value, a stress interpolation in a complex faulted reservoir, a perturbed stress field, a stress ratio and associated orientation, one or more tectonic events, a displacement discontinuity of a fault, a fault slip, an estimated displacement, a perturbed strain, a slip distribution on faults, quality control on interpreted faults, fracture prediction, prediction of fracture propagation according to perturbed stress field, real-time computation of perturbed stress and displacement fields while performing interactive parameters estimation, or discernment of an induced fracture from a preexisting fracture.

The user may use the predicted stress attribute to vary a trajectory of a wellbore and/or to plan the location of a new wellbore (e.g., to avoid a breakout). In addition, the weight on a drill bit drilling the wellbore may be varied in response to the stress attribute. Moreover, the type or flow rate of mud being pumped into the wellbore may be selected or varied in response to the stress attribute.

The method <NUM> is used to invert for both the gradient pressure inside 3D discontinuities (which can represent faults, dykes, magma chamber, salt domes, etc.), the gradient regional stress regime, and the constant shifted pressure value (e.g., salt-pressure-shift: called "Sps"). The method <NUM> extends the methods <NUM> and <NUM> to gradient pressure and far field stress (dependency to depth) and incorporates the inversion of the new parameter: Sps.

Based on the solution of an angular dislocation in a 3D homogeneous isotropic elastic whole-space or half-space, it may be possible to invert for the gradient pressure inside discontinuities (e.g., faults, salt domes, magma chambers, etc.) and the gradient regional stress regime (e.g., orientation of the maximum principal horizontal stress according to the North, and the ratios Rh = σh/σv and RH = σH/σv). The Sps parameter may not depend on depth and instead corresponds to the intersection of the gradient pressure linear function as a function of depth with the Earth's surface.

Three-dimensional discontinuities making the cavities, faults, and/or salt domes may be discretized as complex 3D triangular surfaces. To constrain the inversion, several parameters may be used and combined together such as any fracture orientation (e.g., joints, stylolites, etc.), wellbore ovalization, gps or insar data, microseismicity or focal mechanisms with or without magnitude information, tiltmeters, and the like.

The same principle of superposition that was used above is used here, but inverting for both the pressure and the regional stress includes storing not three but six preliminary simulation results as data points. In the method <NUM>, as opposed to one or more of the other methods disclosed above, discontinuities may now open or close due to pressure boundary conditions, and as a result, the classical stress ratio may not be used in this situation.

The transformation matrix from the six solutions and the linear coefficient to a given far field stress is given by Equation (<NUM>):
<MAT> where I<NUM> represents the identity matrix of dimension six, Sps represents a salt-pressure-shift, Pr represents the pressure inside discontinuities, and α represents the six linear coefficients.

Consequently, α represents the parameters of the far field stress. Then, for a given far field stress <MAT>, the perturbed stress, strain or displacement at a point p may be given by the linear combination of the stored solutions at p using the same coefficients α. Using the same cost functions described above, it is therefore possible to find a more accurate α, and therefore to invert for both the regional stress, pressure and pressure shift using Equation (<NUM>).

In order to take into account the gradient far field stress and gradient pressure for the superposition, a user may compute the six initial solutions using the gradient. The gradient far field stress is defined as: <MAT> <MAT> <MAT> The pressure and pressure shift are defined as: <MAT> <MAT>.

Normalizing by σv, the results may include: <MAT> <MAT> <MAT> <MAT> <MAT>.

Consequently, as use I<NUM> is used, the six initial solutions that may be used for superposition are computed using:.

The pressure inside the discontinuity at depth z is given by Pz = Sps + Pr|z|.

The stress and fracture modeling engine <NUM> applies the property of superposition that is inherent to linear elasticity to execute real-time computation of the perturbed stress and displacement field around a complexly faulted area, as well as the displacement discontinuity on faults. Furthermore, the formulation executed by the stress and fracture modeling engine <NUM> enables rapid paleostress inversion using multiple types of data such as fracture orientation, secondary fault planes, GPS, InSAR, fault throw, and fault slickenlines. In one implementation, using fracture orientation and/or secondary fault planes from wellbores, the stress and fracture modeling engine <NUM> recovers one or more tectonic events, and the recovered stress tensor are given by the orientation and ratio of the principal magnitudes. The stress and fracture modeling engine <NUM> can be applied across a broad range of applications, including stress interpolation in a complexly faulted reservoir, fractures prediction, quality control on interpreted faults, real-time computation of perturbed stress and displacement fields while doing interactive parameter estimation, fracture prediction, discernment of induced fracturing from preexisting fractures, and so forth.

Another application of the stress and fracture modeling engine <NUM> is evaluation of the perturbed stress field (and therefore the tectonic event(s)) for recovering "shale gas. " Since shale has low matrix permeability, fractures are used to provide permeability to produce gas production in commercial quantities. This may be done by hydraulic fracturing to create extensive artificial fractures around wellbores.

In some embodiments, the methods of the present disclosure are executed by a computing system. <FIG> illustrates an example of such a computing system <NUM>, in accordance with some embodiments. The computing system <NUM> may include a computer or computer system 1601A, which may be an individual computer system 1601A or an arrangement of distributed computer systems. The computer system 1601A includes one or more analysis modules <NUM> that are configured to perform various tasks according to some embodiments, such as one or more methods disclosed herein. To perform these various tasks, the analysis module <NUM> executes independently, or in coordination with, one or more processors <NUM>, which is (or are) connected to one or more storage media <NUM>. The processor(s) <NUM> is (or are) also connected to a network interface <NUM> to allow the computer system 1601A to communicate over a data network <NUM> with one or more additional computer systems and/or computing systems, such as 1601B, 1601C, and/or 1601D (note that computer systems 1601B, 1601C and/or 1601D may or may not share the same architecture as computer system 1601A, and may be located in different physical locations, e.g., computer systems 1601A and 1601B may be located in a processing facility, while in communication with one or more computer systems such as 1601C and/or 1601D that are located in one or more data centers, and/or located in varying countries on different continents).

A processor may include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, or another control or computing device.

The storage media <NUM> may be implemented as one or more computer-readable or machine-readable storage media. Note that while in the example embodiment of <FIG> storage media <NUM> is depicted as within computer system 1601A, in some embodiments, storage media <NUM> may be distributed within and/or across multiple internal and/or external enclosures of computing system 1601A and/or additional computing systems. Storage media <NUM> may include one or more different forms of memory including semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable read-only memories (EEPROMs) and flash memories, magnetic disks such as fixed, floppy and removable disks, other magnetic media including tape, optical media such as compact disks (CDs) or digital video disks (DVDs), BLURAY® disks, or other types of optical storage, or other types of storage devices. Note that the instructions discussed above may be provided on one computer-readable or machine-readable storage medium, or alternatively, may be provided on multiple computer-readable or machine-readable storage media distributed in a large system having possibly plural nodes. Such computer-readable or machine-readable storage medium or media is (are) considered to be part of an article (or article of manufacture). An article or article of manufacture may refer to any manufactured single component or multiple components. The storage medium or media may be located either in the machine running the machine-readable instructions, or located at a remote site from which machine-readable instructions may be downloaded over a network for execution.

In some embodiments, the computing system <NUM> contains one or stress attribute prediction module(s) <NUM>. The stress attribute prediction module <NUM> may be used to perform at least a portion of the method <NUM>, <NUM>, and/or <NUM>.

It should be appreciated that computing system <NUM> is only one example of a computing system, and that computing system <NUM> may have more or fewer components than shown, may combine additional components not depicted in the example embodiment of <FIG>, and/or computing system <NUM> may have a different configuration or arrangement of the components depicted in <FIG>. The various components shown in <FIG> may be implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing and/or application specific integrated circuits.

Claim 1:
A computer-implemented method (<NUM>), comprising:
receiving (<NUM>) faults geometry data of a subsurface earth volume;
receiving (<NUM>) at least one data set associated with the subsurface earth volume (<NUM>);
simulating (<NUM>) a plurality of models for a subsurface earth volume based on the received faults geometry data and the received dataset, the plurality of models comprising:
one or more linearly independent far field stress models,
one or more discontinuity pressure models including the pressure inside a 3D discontinuity and
one or more discontinuity pressure shift models including a salt-pressure-shift parameter (Sps) corresponding to an intersection of a gradient pressure linear function as a function of depth from the Earth's surface, wherein pressure inside discontinuity at depth z is given by PZ = Sps + Pr|z|;
using the solutions of six simulation results, to invert for pressure gradient inside 3D discontinuities, the gradient regional stress regime and the salt-pressure-shift parameter, the inversion comprising a transformation matrix: <MAT> where I<NUM> represents the identity matrix of dimension six, Sps represents a salt-pressure-shift, Pr represents the pressure inside discontinuities, and α represents six linear coefficients representing parameters of the far field stress;
predicting the stress attribute of the subsurface earth volume, based on the stress value, the discontinuity pressure shift at the surface of the Earth, the strain value, the displacement value, or the combination thereof computed through inversion of the models; and
generating at least one parameter to configure a control signal based upon the predicted stress attribute, wherein the control signal is configured to cause equipment to perform a physical operation, and wherein the equipment comprises drilling equipment, exploration equipment, a well control injector, a valve, or a combination thereof.