Patent Description:
There are many different techniques for modeling a subterranean volume and simulating flow through faulted media. For example, models may be formed by grid elements (cells) representing discrete areas of the model. A variety of different types of grids may be applied to the models, producing cells that represent discrete areas of the formation.

Further, the subterranean formation being modeled may have faults. The representation of the faults, e.g., in reservoir-scale models, may be implicit, such that the faults are defined at the interface between grid cells, but not separately represented in the model. This representation is convenient for reservoir simulation, and the influence of the fault on the flow in the reservoir is controlled by a fault transmissibility multiplier (TM). This dimensionless multiplier simply defines the ratio of the transmissibility through the fault and the adjacent grid cells in comparison to the transmissibility through those grid cells with no fault present. However, such a simplification can lead to inaccuracy in the model. The publication "<NPL>, describes structural modelling and workflow to limit modeling compromises. <CIT> discloses systems and methods for building axes, co-axes and paleo-geographic coordinates related to a stratified geological volume.

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

For example, a first object or step could be termed a second object or step, and, similarly, a second object or step could be termed a first object or step, without departing from the scope of the present disclosure. The first object or step, and the second object or step, are both, objects or steps, respectively, but they are not to be considered the same object or step.

The terminology used in the description herein is for the purpose of describing particular embodiments and is not intended to be limiting. As used in this description and the appended claims, the singular forms "a," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term "and/or" as used herein refers to and encompasses any possible combinations of one or more of the associated listed items. It will be further understood that the terms "includes," "including," "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Further, as used herein, the term "if" may be construed to mean "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context.

Attention is now directed to processing procedures, methods, techniques, and workflows that are in accordance with some embodiments. Some operations in the processing procedures, methods, techniques, and workflows disclosed herein may be combined and/or the order of some operations may be changed.

<FIG> illustrates an example of a system <NUM> that includes various management components <NUM> to manage various aspects of a geologic environment <NUM> (e.g., an environment that includes a sedimentary basin, a reservoir <NUM>, one or more faults <NUM>-<NUM>, one or more geobodies <NUM>-<NUM>, etc.). For example, the management components <NUM> may allow for direct or indirect management of sensing, drilling, injecting, extracting, etc., with respect to the geologic environment <NUM>. In turn, further information about the geologic environment <NUM> may become available as feedback <NUM> (e.g., optionally as input to one or more of the management components <NUM>).

In the example of <FIG>, the management components <NUM> include a seismic data component <NUM>, an additional information component <NUM> (e.g., well/logging data), a processing component <NUM>, a simulation component <NUM>, an attribute component <NUM>, an analysis/visualization component <NUM> and a workflow component <NUM>. In operation, seismic data and other information provided per the components <NUM> and <NUM> may be input to the simulation component <NUM>.

In an example embodiment, the simulation component <NUM> may rely on entities <NUM>. Entities <NUM> may include earth entities or geological objects such as wells, surfaces, bodies, reservoirs, etc. In the system <NUM>, the entities <NUM> can include virtual representations of actual physical entities that are reconstructed for purposes of simulation. The entities <NUM> may include entities based on data acquired via sensing, observation, etc. (e.g., the seismic data <NUM> and other information <NUM>). An entity may be characterized by one or more properties (e.g., a geometrical pillar grid entity of an earth model may be characterized by a porosity property). Such properties may represent one or more measurements (e.g., acquired data), calculations, etc..

In an example embodiment, the simulation component <NUM> may operate in conjunction with a software framework such as an object-based framework. In such a framework, entities may include entities based on pre-defined classes to facilitate modeling and simulation. A commercially available example of an object-based framework is the MICROSOFT®. NET® framework (Redmond, Washington), which provides a set of extensible object classes. NET® framework, an object class encapsulates a module of reusable code and associated data structures. Object classes can be used to instantiate object instances for use in by a program, script, etc. For example, borehole classes may define objects for representing boreholes based on well data.

In the example of <FIG>, the simulation component <NUM> may process information to conform to one or more attributes specified by the attribute component <NUM>, which may include a library of attributes. Such processing may occur prior to input to the simulation component <NUM> (e.g., consider the processing component <NUM>). As an example, the simulation component <NUM> may perform operations on input information based on one or more attributes specified by the attribute component <NUM>. In an example embodiment, the simulation component <NUM> may construct one or more models of the geologic environment <NUM>, which may be relied on to simulate behavior of the geologic environment <NUM> (e.g., responsive to one or more acts, whether natural or artificial). In the example of <FIG>, the analysis/visualization component <NUM> may allow for interaction with a model or model-based results (e.g., simulation results, etc.). As an example, output from the simulation component <NUM> may be input to one or more other workflows, as indicated by a workflow component <NUM>.

As an example, the simulation component <NUM> may include one or more features of a simulator such as the ECLIPSE™ reservoir simulator (Schlumberger Limited, Houston Texas), the INTERSECT™ reservoir simulator (Schlumberger Limited, Houston Texas), etc. As an example, a simulation component, a simulator, etc. may include features to implement one or more meshless techniques (e.g., to solve one or more equations, etc.). As an example, a reservoir or reservoirs may be simulated with respect to one or more enhanced recovery techniques (e.g., consider a thermal process such as SAGD, etc.).

In an example embodiment, the management components <NUM> may include features of a commercially available framework such as the PETREL® seismic to simulation software framework (Schlumberger Limited, Houston, Texas). The PETREL® framework provides components that allow for optimization of exploration and development operations. The PETREL® framework includes seismic to simulation software components that can output information for use in increasing reservoir performance, for example, by improving asset team productivity. Through use of such a framework, various professionals (e.g., geophysicists, geologists, and reservoir engineers) can develop collaborative workflows and integrate operations to streamline processes. Such a framework may be considered an application and may be considered a data-driven application (e.g., where data is input for purposes of modeling, simulating, etc.).

In an example embodiment, various aspects of the management components <NUM> may include add-ons or plug-ins that operate according to specifications of a framework environment. For example, a commercially available framework environment marketed as the OCEAN® framework environment (Schlumberger Limited, Houston, Texas) allows for integration of add-ons (or plug-ins) into a PETREL® framework workflow. The OCEAN® framework environment leverages. NET® tools (Microsoft Corporation, Redmond, Washington) and offers stable, user-friendly interfaces for efficient development. In an example embodiment, various components may be implemented as add-ons (or plug-ins) that conform to and operate according to specifications of a framework environment (e.g., according to application programming interface (API) specifications, etc.).

<FIG> also shows an example of a framework <NUM> that includes a model simulation layer <NUM> along with a framework services layer <NUM>, a framework core layer <NUM> and a modules layer <NUM>. The framework <NUM> may include the commercially available OCEAN® framework where the model simulation layer <NUM> is the commercially available PETREL® model-centric software package that hosts OCEAN® framework applications. In an example embodiment, the PETREL® software may be considered a data-driven application. The PETREL® software can include a framework for model building and visualization.

As an example, a framework may include features for implementing one or more mesh generation techniques. For example, a framework may include an input component for receipt of information from interpretation of seismic data, one or more attributes based at least in part on seismic data, log data, image data, etc. Such a framework may include a mesh generation component that processes input information, optionally in conjunction with other information, to generate a mesh.

In the example of <FIG>, the model simulation layer <NUM> may provide domain objects <NUM>, act as a data source <NUM>, provide for rendering <NUM> and provide for various user interfaces <NUM>. Rendering <NUM> may provide a graphical environment in which applications can display their data while the user interfaces <NUM> may provide a common look and feel for application user interface components.

As an example, the domain objects <NUM> can include entity objects, property objects and optionally other objects. Entity objects may be used to geometrically represent wells, surfaces, bodies, reservoirs, etc., while property objects may be used to provide property values as well as data versions and display parameters. For example, an entity object may represent a well where a property object provides log information as well as version information and display information (e.g., to display the well as part of a model).

In the example of <FIG>, data may be stored in one or more data sources (or data stores, generally physical data storage devices), which may be at the same or different physical sites and accessible via one or more networks. The model simulation layer <NUM> may be configured to model projects. As such, a particular project may be stored where stored project information may include inputs, models, results and cases. Thus, upon completion of a modeling session, a user may store a project. At a later time, the project can be accessed and restored using the model simulation layer <NUM>, which can recreate instances of the relevant domain objects.

In the example of <FIG>, the geologic environment <NUM> may include layers (e.g., stratification) that include a reservoir <NUM> and one or more other features such as the fault <NUM>-<NUM>, the geobody <NUM>-<NUM>, etc. As an example, the geologic environment <NUM> may be outfitted with any of a variety of sensors, detectors, actuators, etc. For example, equipment <NUM> may include communication circuitry to receive and to transmit information with respect to one or more networks <NUM>. Such information may include information associated with downhole equipment <NUM>, which may be equipment to acquire information, to assist with resource recovery, etc. Other equipment <NUM> may be located remote from a well site and include sensing, detecting, emitting or other circuitry. Such equipment may include storage and communication circuitry to store and to communicate data, instructions, etc. As an example, one or more satellites may be provided for purposes of communications, data acquisition, etc. For example, <FIG> shows a satellite in communication with the network <NUM> that may be configured for communications, noting that the satellite may additionally or instead include circuitry for imagery (e.g., spatial, spectral, temporal, radiometric, etc.).

<FIG> also shows the geologic environment <NUM> as optionally including equipment <NUM> and <NUM> associated with a well that includes a substantially horizontal portion that may intersect with one or more fractures <NUM>. For example, consider a well in a shale formation that may include natural fractures, artificial fractures (e.g., hydraulic fractures) or a combination of natural and artificial fractures. As an example, a well may be drilled for a reservoir that is laterally extensive. In such an example, lateral variations in properties, stresses, etc. may exist where an assessment of such variations may assist with planning, operations, etc. to develop a laterally extensive reservoir (e.g., via fracturing, injecting, extracting, etc.). As an example, the equipment <NUM> and/or <NUM> may include components, a system, systems, etc. for fracturing, seismic sensing, analysis of seismic data, assessment of one or more fractures, etc..

As mentioned, the system <NUM> may be used to perform one or more workflows. A workflow may be a process that includes a number of worksteps. A workstep may operate on data, for example, to create new data, to update existing data, etc. As an example, a workflow may operate on one or more inputs and create one or more results, for example, based on one or more algorithms. As an example, a system may include a workflow editor for creation, editing, executing, etc. of a workflow. In such an example, the workflow editor may provide for selection of one or more pre-defined worksteps, one or more customized worksteps, etc. As an example, a workflow may be a workflow implementable in the PETREL® software, for example, that operates on seismic data, seismic attribute(s), etc. As an example, a workflow may be a process implementable in the OCEAN® framework. As an example, a workflow may include one or more worksteps that access a module such as a plug-in (e.g., external executable code, etc.).

<FIG> illustrates a flowchart of a method <NUM> for modeling and simulating a subterranean volume, according to an example. The method <NUM> may include obtaining data (e.g., core samples, seismic data, well logs, and/or any other data) representing properties of a subterranean volume, as at <NUM>. From these properties, an initial model of the subterranean volume may be generated, as at <NUM>.

The initial model may be gridded, i.e., partitioned into cells, which may be structured or unstructured, as at <NUM>. Transmissibility and/or other fault properties may be determined for the subterranean volume, e.g., on a cell-by-cell basis, as at <NUM>. Once the model is populated with the cell and fault properties, fluid flow may be simulated, as at <NUM>.

<FIG> illustrates a flowchart of a method <NUM> for determining fault properties in the model of the subterranean volume, according to an embodiment. The method <NUM> may provide at least a part of blocks <NUM>-<NUM> in <FIG>, in addition to one or more other aspects thereof; however, in other embodiments, the method <NUM> may be executed separately from the method <NUM>. Further, it will be appreciated that the individual worksteps described herein for the method <NUM> may be conducted in a different order than shown, combined with other worksteps, conducted in parallel with other worksteps, or separated into two or more worksteps, without departing from the scope of the present disclosure.

The method <NUM> includes obtaining geological data representing a subterranean volume, as at <NUM>. The geological data may be gathered from any source, such as publicly available or proprietary databases, well logs, seismic logs, core sample analyses, etc. The geological data may represent a variety of formation properties, such as permeability, porosity, pore pressure, structure, rock type/composition, and/or any other relevant formation property at a variety of locations in the subterranean volume, fault property data and/or relationships for predicting fault properties.

The method <NUM> is executed by generating at least two versions of a geological model, which both represent the subterranean volume and its properties. In order to do so, the method <NUM> begins by generating a structural model, in geological space, of the subterranean volume using the geological data, as at <NUM>. Geological space is defined in general terms as the physical shape of the structure of the subterranean formation in space, i.e., based on the geological data, how it would appear at a given time in three-dimensional space. An example of such a structural model is shown in <FIG>, represented by reference number <NUM>. As illustrated, the model <NUM> represents the geology of the volume, e.g., showing several layers or geological "zones" <NUM>. The geology also includes faults <NUM>, the presence of which may render the zones <NUM> discontinuous and offset across the faults <NUM>. As such, in the geological space, the layers <NUM> may be relatively complex, having varying thickness, may occupy a variety of depths, may be discontinuous, etc..

Accordingly, the method <NUM> proceeds to transforming the model <NUM> into the depositional space, as at <NUM>, as shown in a model <NUM> that represents the same volume as the model <NUM>. In the depositional space model <NUM>, the layers <NUM> are represented in a vertical direction by a time parameter w, rendering the layers <NUM> flat (when not eroded or including discontinuities), despite the presence of faults <NUM>. In turn, the faults <NUM> are represented as surfaces that extend in the model <NUM>.

As indicated at <NUM> of <FIG>, the model <NUM> in the depositional space then has a grid defined therein, so as to delineate cells, generating the gridded, depositional-space model <NUM>. The grid cells are uniformly defined, for example, in the horizontal directions (u, v) and in the vertical direction w, e.g., generally resulting in a grid of uniform shaped cells in the vertical w direction in depositional space, per zone <NUM>.

As indicated at <NUM>, the gridded model <NUM> in the depositional space isn be transformed, along with the grid, back to the structural space, thereby generating the model <NUM> in <FIG>. As with the initial model <NUM>, the model <NUM> reflects the structure of the subterranean volume consistent with physical parameters of horizontal position and depth. The model <NUM> is also referred to as a "depogrid" <NUM>. This transformation to depositional space defines a one-to-one mapping (except at the faults <NUM>) between points in the structural model <NUM> in geological space and points in the corresponding model <NUM> in depositional space. A point located exactly on a fault surface of the model <NUM> in depositional space corresponds to two (or more at fault-fault intersections) points in geological space (e.g., in model <NUM>), as the point may be separately considered to lie on each of the two sides of that fault. Each point on the fault surface in geological space (e.g., in the depogrid <NUM>) is therefore represented by several co-located points that have unique depospace locations. This correspondence of points in the depositional and geological spaces will be used later to define the fault seal properties of the depogrid.

The faults <NUM> and zones <NUM> of the depogrid <NUM> thus accurately match those of the original structural model <NUM>, while the additional grid layers between the seismic horizons (i.e., the boundaries of the original layers <NUM>) honor the stratigraphic nature of the depositional mapping. When viewed in the geological space, the w coordinate lines retain their orthogonal relationship with the local seismic horizons that was enforced in depositional space.

Referring again to <FIG>, with the structural models in the depositional space and the geological space, with grid, prepared, the method <NUM> focuses on the use of inverse depositional mapping to infer fault seal properties of the subterranean volume (represented by the depogrid <NUM>). Accordingly, the method <NUM> includes selecting first and second cells that are intersected by a fault, which are juxtaposed (e.g., directly adjacent) in the depogrid (e.g., the gridded model in geological space), as at <NUM>. For example, the first and second cells may meet at a fault over multiple planar fault face polygons, with the first cell A being on the upthrown side and the second cell B being on the downthrown side. The method <NUM> then includes selecting, as at <NUM>, a point on one of these fault faces, e.g., point P (xp, yp, zp) in geological space, which may be at the center of one of the fault faces. As indicated at <NUM>, the point P residing on the fault face represents a single point in geological space, but two points PA and PB on opposite sides of the fault in depositional space, as will be described in greater detail below.

Proceeding now to <FIG>, there is shown a portion (e.g., "zone") of a model <NUM> of the subterranean formation in geological space, focusing on a single fault <NUM>. In this example, the fault offset is sufficient to produce some areas of self-juxtaposition of the zone across the fault <NUM>. <FIG> shows the same zone, but in the depositional space. In the depositional space view, the fault offsets have been removed and the faults (e.g., the fault <NUM>) may be seen as zero-offset traces on the zone. The fault <NUM> surface represents a discontinuity in geological space; any point on the fault surface in geological space (<FIG>) may be considered to lie in either the upthrown or downthrown side of the fault <NUM>. Such a point has the same (x, y, z) location in geological space but different depositional space (u, v, w) coordinates according to the assumed side of fault. <FIG> illustrates the depogrid version of this zone. As shown in <FIG>, a specific region <NUM> of the depogrid and fault <NUM> is used to illustrate an example of analysis of the fault offsets and fault seal properties.

Proceeding now to <FIG>, there is shown an enlarged view of the specific region <NUM> from <FIG>, specifically illustrating an upthrown side <NUM> and a downthrown side <NUM> of the fault <NUM>. Referring again additionally to block <NUM> of <FIG>, the first cell is labeled as cell A and the second cell is labeled as cell B. As can be seen, cell A is generally on the upthrown side <NUM>, in geological space, and the second cell (cell B) is generally on the downthrown side <NUM>, in geological space. As can be seen in <FIG>, the fault <NUM> has many fault faces <NUM>, the geometry of which relates to the combined overlaps of grid cells and the faces of the tetrahedral mesh of the structural model. The fault face geometry thereby honors the original structural model, as explained above.

The point P (selected at <NUM>) is thus first considered as a part of cell A, specifically, part of the upthrown side <NUM> of the fault in geological space, and therefore part of the faces of cell A that lie on the fault surface. The depositional space coordinates of the point P for cell A are denoted by PA(uA, vA, wA). In depositional space, cell A is one part of a single hexahedral cell that is cut by the faults of the model. Sub-parts of a given hexahedral cell are referred to as the "sibling" cells. Accordingly, the method <NUM> includes identifying one or more sibling cells for the first cell (cell A), as at <NUM>. In this example, cell A, which is still referred to as the "first cell" although it is actually representing a portion of the first cell as defined in depositional space, has a single sibling cell A' on the downthrown side <NUM> of the fault, as is visible in <FIG>.

Next, the method <NUM> includes, at <NUM>, identifying a point in the sibling cell A' that has the same depositional space coordinates as the point PA. In this example, a point Q(xQ, yQ, zQ) in geological space is defined as the point on the faces of the sibling cell A' that has the same depositional space coordinates as PA, namely Q(uA, vA, wA). In depositional space, PA for cell A is co-located with Q at (uA, vA, wA), but in the geological space, the points PA and Q are offset across the fault <NUM>.

Next, the point P is considered part of the downthrown side <NUM> of the fault in geological space, and therefore part of the faces of the second cell (cell B) that lie on the fault surface. The depositional space coordinates of the point P for cell B are denoted as PB(uB, vB, wB). In depositional space, cell B is one part of a single hexahedral cell that is cut by the fault <NUM>. As with the first cell, the method <NUM> proceeds to identifying one or more sibling cells for the second cell (cell B), as at <NUM>. In this case, cell B (which is representing a portion of the second cell denoted in the depositional space) has a sibling cell B' on the upthrown side <NUM> of the fault <NUM>, as shown in <FIG>.

The method <NUM> then, at <NUM>, includes defining a point R(xR, yR, zR) that is on a face of the sibling cell B' that has the same depositional space coordinates as PB, namely R(uB, vB, wB). That is, in depositional space, PB for cell B is co-located with R at coordinates (uB, vB, wB), but the two points PB and R are offset in the geological space by the presence of the fault <NUM>.

The inverse transformation of the depogrid cells from depositional space to geological space allows consideration of one point P in the geological space to be two points PA, PB, one on each side of the fault <NUM>, i.e., P = PA(xP, yP, zP) = PB(xP, yP, zP). Each of these points PA, PB has a depositional space neighbor: PA(uA, vA, wA) = Q and PB(uB, vB, wB) = R; that is, in the depositional space, PA is co-located with Q and PB is co-located with R.

The method <NUM> then proceeds to determining slip curves between the point P (i.e., the points PA and PB) and the sibling points Q and R offset across the fault <NUM> therefrom in geological space, as at <NUM>. The inverse transformation (from depositional space to geological space) associates PA with Q and PB with R. Points PA and PB are co-located with points Q and R, respectively, in depositional space and separated along-side-dependent slip curves <NUM>, <NUM> in <FIG> in geological space. The curves <NUM>, <NUM> are defined directly from the depositional transformation or inferred from the geological space locations of P(xP, yP, zP), Q(xQ, yQ, zQ) and R(xR, yR, zR).

The fault offset at P is defined by considering the separate offsets between PA and Q (downthrown side offset) and PB and R (upthrown side offset) along the indicated slip direction curves <NUM>, <NUM>. The total fault offsets may be the average of the side-dependent results.

The fault properties are then determined based on side-dependent slip curves, as at <NUM>. For example, the calculation of the fault displacement is consistent with the definition of the side-dependent SGR (shale gouge ratio) properties at P. The fault rock properties at P for each side of the fault are defined by the grid clay volume (VClay) properties encountered along the side-dependent slip direction curves <NUM>, <NUM>. Further, the SGR for the upthrown side <NUM> of the fault is the average clay content of the upthrown side <NUM> grid VClay property along the curve <NUM> from PB to R (<FIG>). The SGR for the downthrown side <NUM> is the average clay content of the downthrown side <NUM> grid VClay property along the curve <NUM> from PA to Q (Figure 3D). The SGR at P for the fault <NUM> is then the weighted average of the side-dependent SGR values, weighted by the side-dependent displacement values.

Other fault clay content estimates such as the ESGR (effective SGR) and clay smearing properties follow directly from the same extraction of the host VClay properties along the side-dependent slip direction curves <NUM>, <NUM>. These fault clay content predictions are based on properties from the modeled fault surface but define properties specific to the areas on the fault where both upthrown and downthrown cells occur.

The subsequent fault seal properties such as fault threshold pressure, sealing capacity, permeability and thickness for exploration and development fault seal workflows follow using standard calculations based on the above definitions of the fault clay content and displacement estimates. In particular, the fault thickness, threshold pressure and permeability are input properties for the fluid flow simulation of the depogrid. The property calculation is then extended to as many points P and/or cells of the grid as appropriate, e.g., along each of the faults.

The depogrid generated by the method <NUM> thus represents an advancement by providing more accurate information in the model, specifically of fault properties. Further, the method <NUM> may include visualizing the newly generated depogrid, e.g., to illustrate the formation on a computer screen. This depogrid is then used to assist in simulations of fluid flow in the subterranean volume, e.g., to assist in the identification of likely hydrocarbon reservoirs.

Embodiments of the disclosure also provides an integrated modeling-to-simulation method for a more accurate fault transmissibility calculation, which facilitates accurate history matching. In general, the method may include providing a fault transmissibility calculation algorithm inside a simulator based on geological fault properties defined on a reservoir model. The geological information and properties are specified in the modeling environment; these properties together with the fault permeability and fault thickness information are passed to the simulator. The simulator calculates the cell-to-cell transmissibility for the individual cells including the cells adjacent to the faults. This transmissibility calculation can use either two-point or multi-point flux approximations within the numerical description of cell-to-cell transmissibility. This approach may avoid the use of a fault transmissibility multiplier (TM) property.

The geological realizations (modified grid permeability, modified fault permeability, modified fault thickness) may be provided to the simulator, which uses this geological data to calculate modified grid cell transmissibilities and to tune the simulation model. Embodiments of the present disclosure may host the geological definition of the fault properties within the tight simulator history-matching workflow. This may facilitate consideration of uncertainty in the fault displacement distributions, the host grid clay content from which the fault clay content is derived, the fault clay content algorithms used to predict the fault permeability, and the geological history of the faults that controls the fault clay to permeability transform.

The static modeling environment and the reservoir simulator may both access a fault property calculation service. The service will receive grid geometrical and property data and provide updated fault property calculations, based on a geological scenario containing key algorithmic choices and empirical data.

Turning to the specific, illustrated embodiment, <FIG> illustrates a flowchart of a method <NUM> for modeling a subterranean volume and simulating fluid flow therein, according to an embodiment. The method <NUM> may provide at least a portion of blocks <NUM> and <NUM> of <FIG> and/or others, but in other embodiments, may be executed separately therefrom. Further, it will be appreciated that the individual worksteps described herein for the method <NUM> may be conducted in a different order than shown, combined with other worksteps, conducted in parallel with other worksteps, or separated into two or more worksteps, without departing from the scope of the present disclosure.

The method <NUM> may be executed in two stages <NUM>, <NUM>, in which stage <NUM> uses a static model environment and stage <NUM> uses a simulator. The operation of the stages <NUM>, <NUM> may be iterative, with changes made in one stage affecting the operation of the other stage.

The stage <NUM> begins with obtaining geological data representing the subterranean volume, as at <NUM>. Next, the method <NUM> includes generating a static model of the subterranean volume, as at <NUM>. The model includes a grid of cells, similar to (or the same as) the grid of cells discussed above. The cells represent discrete regions of the subterranean volumes. Faults that are present in the subterranean model are represented implicitly, i.e., the faults affect cell boundary properties (e.g., transmissibility), but are not separately represented by cells.

The method <NUM> includes identifying faults and cells that have one or more faces that lie on one or more of the faults, as at <NUM>. The faults in the grid may be represented implicitly, e.g., as faces of the cells, but instead of calculating a multiplier for transmissibility across an entire fault, the present method <NUM> may calculate fault properties, such as fault thickness and/or fault permeability, for the individual fault faces of the identified cells, as at <NUM>.

Fault permeability and fault thickness may be spatially varying and act at individual cell-to-cell connections across the fault. These two fault-face properties together with the description of the fault faces, adjacent grid cell geometry and adjacent grid cell permeability may allow the reservoir simulator (next phase <NUM>) to both calculate and geologically modify the fault transmissibility within the various cell-to-cell transmissibility calculation approximations available. This may enable consistent adjustment of the fault flow behavior via a modifier to the geological fault properties.

The method <NUM> may also include populating the model with the fault-face properties, as at <NUM>, and then passing the model <NUM> to the simulator, thereby beginning the second stage <NUM>. The simulator may then calculate transmissibility using the fault face properties, as at <NUM>. For example, the simulator may represent the faults as volumes extending from the cell boundaries between adjacent cells, as at <NUM>.

The method <NUM> may then, at <NUM>, include simulating the subterranean volume on a production timescale using the model, e.g., including the transmissibility calculated at <NUM>. In an extended workflow, the full geological definition of the fault properties may be integrated fully into this history matching workflow. The full uncertainty and optimization approach may then refer to the input static geological properties, the grid geometry (specifically the fault displacement as a structural uncertainty), and the fault property algorithms and relationships.

Referring additionally to <FIG>, there is illustrated a conceptual view of this representation of the faults, according to an embodiment. As noted above, the faults are implicit in the model, formed on cell boundaries. In <FIG>, two adjacent "host" grid blocks (cells) <NUM> and <NUM> are shown, with a boundary <NUM> therebetween. The cells <NUM>, <NUM> meet at a fault, and thus the boundary <NUM> implicitly represents the fault. As noted above, the fault properties calculated on a cellular level include fault thickness and permeability. Thus, the simulator may, in effect, consider the boundary <NUM> to have a thickness T and a permeability corresponding to the fault thickness and permeability. The thickness T may extend into both of the cells <NUM>, <NUM>. Accordingly, the fault has separate thicknesses on the two sides of the boundary <NUM> and the thickness T is the total of these two separate parts. The amount of thickness T that extends into the respective grid cells <NUM>, <NUM> is a function of (e.g., based on, depends on) the side-dependent thickness. The simulator may thus consider the cells <NUM>, <NUM> to be partially formed from the fault, and may thus calculate cell-to-cell transmissibility taking the thickness and the permeability of the fault in each cell <NUM>, <NUM> into consideration.

Thus, the fault permeability and thickness properties are calculated separately at the two sides of the fault. Although the fault is represented implicitly in the grid, the effect on the cell-to-cell transmissibility is modeled by "replacing" the adjacent host grid volume with the corresponding fault volume (separately on the two sides of the fault) and calculating the combined transmissibility. This may be applied to any of the cell-to-cell transmissibility approximations used in reservoir simulation and may enable the use of a multi-point flux approximation (MPFA) for a more accurate transmissibility calculation for all of the grid cells during simulation.

The internal transmissibility algorithms may be executed within a simulator to calculate and apply the fault transmissibility for the cells that are adjacent to the fault. Thus, the fault property data may become part of the normal cell-to-cell transmissibility calculation for the grid in the simulator. The details of this calculation are represented in <FIG> for simple cell-to-cell connection. In general, the approach for cells that partially overlap at faults may be more complex.

The method <NUM> may also include conducting a history-matching analysis using the fault-face properties, as at <NUM>. In this process, the user will want to 'tune' their model to match known historical data. The method <NUM> may tune the simulation based on the history-matching analysis by adjusting the transmissibility calculation, as at <NUM>. The adjustment may be a simple manipulation of the previously calculated fault thickness and permeability or it could be a more complex re-calculation of any input to the original calculation. For example, the method <NUM> may iterate back directly to simulating the fluid flow again or may return to block <NUM> and recalculate the fault face properties in total. In some embodiments, adjusting at <NUM> may be accomplished by changing a multiplier for one or more of the fault -face properties, and thus such changing of a multiplier of a fault-face properties is within the scope of the phrase "adjusting one or more of the fault-face properties" of the model.

The process involves applying changes to the geological grid permeability and the transmissibility of the entire fault. The reservoir simulator may thus allow the user to manipulate the provided fault-face properties directly and/or supply multipliers to them as history-matching parameters. That is, the multipliers are associated with the fault-face properties (e.g., fault thickness and permeability) and not directly to the transmissibility. Hence, the user may run uncertainty analyses on the geological parameters in order to understand the sensitivity of their model to geologically sensible modifications of the fault transmissibility calculation. This may increase accuracy and facilitate arriving at a history match under an improved geological representation of the reservoir, thus providing better forward predictions of reservoir performance.

The method <NUM> thus generates a model of the subterranean domain that incorporates the implicit modeling of the faults, while more accurately modeling the transmissibility by considering fault thickness and permeability on the cellular level during simulation. In addition, the model may be visualized on a computer screen to assist with, for example, the identification of probable hydrocarbon reservoirs, production amounts in wells, etc. The computer-generated model may thus more accurately represent the subterranean volume than other models.

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> includes a computer or computer system 901A, which may be an individual computer system 901A or an arrangement of distributed computer systems. The computer system 901A 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 901A to communicate over a data network <NUM> with one or more additional computer systems and/or computing systems, such as 901B, 901C, and/or 901D (note that computer systems 901B, 901C and/or 901D may or may not share the same architecture as computer system 901A, and may be located in different physical locations, e.g., computer systems 901A and 901B may be located in a processing facility, while in communication with one or more computer systems such as 901C and/or 901D 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> are 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 901A, in some embodiments, storage media <NUM> may be distributed within and/or across multiple internal and/or external enclosures of computing system 901A and/or additional computing systems. Storage media <NUM> 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 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, computing system <NUM> contains one or more modeling and simulation module(s) <NUM>. In the example of computing system <NUM>, computer system 901A includes the modeling and simulation module <NUM>. In some embodiments, a single modeling and simulation module may be used to perform some aspects of one or more embodiments of the methods disclosed herein. In other embodiments, a plurality of modeling and simulation modules may be used to perform some aspects of methods herein.

It should be appreciated that computing system <NUM> is merely 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.

Further, the steps in the processing methods described herein may be implemented by running one or more functional modules in information processing apparatus such as general-purpose processors or application specific chips, such as ASICs, FPGAs, PLDs, or other appropriate devices. These modules, combinations of these modules, and/or their combination with general hardware are included within the scope of the present disclosure.

Computational interpretations, models, and/or other interpretation aids may be refined in an iterative fashion; this concept is applicable to the methods discussed herein. This may include use of feedback loops executed on an algorithmic basis, such as at a computing device (e.g., computing system <NUM>, <FIG>), and/or through manual control by a user who may make determinations regarding whether a given step, action, template, model, or set of curves has become sufficiently accurate for the evaluation of the subsurface three-dimensional geologic formation under consideration.

Claim 1:
A computer-implemented method, comprising:
obtaining (<NUM>) geological data representing a subterranean volume;
generating (<NUM>) a structural model of the subterranean volume in depositional space and geological space,
wherein the structural model comprises a grid of cells, wherein generating (<NUM>) the structural model comprises:
generating (<NUM>) the structural model, in geological space, of the subterranean volume using the geological data;
transforming (<NUM>) the structural model from the geological space to depositional space;
defining (<NUM>) the grid of cells for the structural model in depositional space; and
transforming (<NUM>) the structural model, including the grid, to the geological space to define a depogrid;
selecting (<NUM>) a first cell and a second cell of the grid of cells, the first and second cells being juxtaposed in the depogrid and defining a fault face where the first and second cells are intersected by a fault;
identifying (<NUM>) a first point in the depogrid that is on the fault face of the first and second cells, wherein the first point is a single point in the geological space and is represented as a plurality of first points in the depositional space;
the method characterized by:
transforming the structural model from the depositional space to the geological space to identify a plurality of second points in the geological space based on a plurality of second points in the depositional space, the plurality of second points being co-located with the plurality of first points in the depositional space;
calculating (<NUM>) a plurality of slip curves, wherein respective slip curves originate at the first point in the depogrid and extend across the fault in the geological space to a respective second point of a plurality of second points in the depogrid;
calculating (<NUM>) one or more fault rock properties at the first point based on grid clay volume (VClay) properties encountered along the slip curves;
adjusting the depogrid to include the one or more fault rock properties; and
outputting the adjusted depogrid for use in identifying and drilling hydrocarbon reservoir locations.