Cross-Reference to Related Applications

[0001] This application claims priority to U.S. Provisional Patent Application Serial No 62/248,800, filed on October 30, 2015, which is incorporated herein by reference in its entirety.

Background

[0002] Production system design and operations employ modeling and simulation of multiphase flow to gain insights into flow behavior, including the physics describing flow through the entire production systems, from reservoir pore to process facility. Some simulators, such as the OLGA ^® (commercially-available from SCHLUMBERGER) dynamic multiphase flow simulator, model time-dependent behaviors, or transient flow, to maximize production potential. Transient modeling is used in feasibility studies and field development design. Dynamic simulation is used in both offshore and onshore developments to investigate transient behavior in pipelines and wellbores.

[0003] Transient simulation provides an added dimension to steady-state analyses by predicting system dynamics such as time-varying changes in flow rates, fluid compositions, temperature, solids deposition and operational changes. From wellbore dynamics for well completions to pipeline systems with all types of process equipment, the simulator may provide an accurate prediction of key operational conditions involving transient flow.

[0004] However, the sheer size of the fluid flow networks being modeled and the complexity of modeling multiphase flow in such networks may make such modeling computationally-intensive. Dedicated infrastructures are sometimes employed to handle such computational intensity, which may be costly.

Summary

[0005] Embodiments of the disclosure may provide a method for simulating fluid flow in a production system. The method includes provisioning one or more computing resources for simulating fluid flow in a production model, receiving the production model, which is built by a customer using a graphical user interface, and simulating fluid flow in the production model using the one or more provisioned computing resources. Simulating generates a result. The method also includes transmitting the result to the user via the graphical user interface.

[0006] In an embodiment, the graphical user interface is executing on a customer system, the one or more provisioned computing resources being non-local to the customer system.

[0007] In an embodiment, the method also includes transmitting usage information related to a use of the one or more provisioned computing resources to the customer through a portal.

[0008] In an embodiment, the method further includes receiving a service inquiry from the customer via the portal, and provisioning the one or more resources based on the service inquiry.

[0009] In an embodiment, the method also includes directing administrative functions using the portal. Further, in an example of such an embodiment, simulating includes executing a flow simulator using the one or more provisioned computing resources, and the one or more provisioned computing resources interact with the customer through the graphical user interface without user direction.

[0010] In an embodiment, provisioning the one or more computing resources includes selecting the one or more provisioned computing resources based on requirements of the flow simulator.

[0011] In an embodiment, provisioning includes creating a secure container for data received from the customer. In an example of such an embodiment, the method includes securely destroying the secure container after simulating the fluid flow.

[0012] Embodiments of the disclosure may provide a computing system including one or more processors, and a memory system including one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations. The operations include provisioning one or more computing resources for simulating fluid flow in a production model, receiving the production model, which is built by a customer using a graphical user interface, and simulating fluid flow in the production model using the one or more provisioned computing resources. Simulating generates a result. The operations also include transmitting the result to the user via the graphical user interface.

[0013] Embodiments of the disclosure may further provide a non-transitory computer-readable media storing instructions that, when executed one or more processors of a computing system, cause the computing system to perform operations. The operations include provisioning one or more computing resources for simulating fluid flow in a production model, receiving the production model, which is built by a customer using a graphical user interface, and simulating fluid flow in the production model using the one or more provisioned computing resources. Simulating generates a result. The operations also include transmitting the result to the user via the graphical user interface.

[0014] It will be appreciated that this summary is intended merely to introduce some aspects of the present methods, systems, and media, which are more fully described and/or claimed below. Accordingly, this summary is not intended to be limiting.

Brief Description of the Drawings

[0015] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present teachings and together with the description, serve to explain the principles of the present teachings. In the figures:

[0016] Figure 1 illustrates an example of a system that includes various components to manage aspects of a pipeline environment, according to an embodiment.

[0017] Figure 2 illustrates a flowchart of a method for simulating fluid flow in a pipe network, according to an embodiment.

[0018] Figure 3 illustrates a schematic view of a computing system, according to an embodiment.

Detailed Description

[0019] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings and figures. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to one of ordinary skill in the art that the invention may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

[0020] It will also be understood that, although the terms first, second, etc., may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. 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 invention. 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.

[0021] The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the description of the invention 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 and all 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.

[0022] 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.

[0023] Figure 1 illustrates an example of a system 100 that includes various management components 1 10 to manage various aspects of a pipeline environment 150 (e.g., an environment that includes wells, transportation lines, risers, chokes, valves, separators, etc.). For example, the management components 110 may allow for direct or indirect management of design, operations, control, optimization, etc., with respect to the pipeline environment 150. In turn, further information about the pipeline environment 150 may become available as feedback 160 (e.g., optionally as input to one or more of the management components 110).

[0024] In the example of Figure 1, the management components 110 include a pipeline configuration component 1 12, an additional information component 114 (e.g., fluid measurement data), a processing component 116, a simulation component 120, an attribute component 130, an analysis/visualization component 142 and a workflow component 144. In operation, pipeline configuration data and other information provided per the components 1 12 and 114 may be input to the simulation component 120.

[0025] In an example embodiment, the simulation component 120 may rely on pipeline components or "entities" 122. The pipeline components 122 may include pipe structures and/or equipment. In the system 100, the components 122 can include virtual representations of actual physical components that are reconstructed for purposes of simulation. The components 122 may include components based on data acquired via sensing, observation, etc. (e.g., the pipeline configuration 112 and other information 1 14). An entity may be characterized by one or more properties (e.g., a pipeline model may be characterized by changes in pressure, heat transfer, pipe inclination and geometry, etc.). Such properties may represent one or more measurements (e.g., acquired data), calculations, etc.

[0026] In an example embodiment, the simulation component 120 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. In the .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 by a program, script, etc. For example, borehole classes may define objects for representing boreholes based on well data.

[0027] In the example of Figure 1, the simulation component 120 may process information to conform to one or more attributes specified by the attribute component 130, which may include a library of attributes. Such processing may occur prior to input to the simulation component 120 (e.g., consider the processing component 116). As an example, the simulation component 120 may perform operations on input information based on one or more attributes specified by the attribute component 130. In an example embodiment, the simulation component 120 may construct one or more models of the pipeline environment 150, which may be relied on to simulate behavior of the pipeline environment 150 (e.g., responsive to one or more acts, whether natural or artificial). In the example of Figure 1, the analysis/visualization component 142 may allow for interaction with a model or model-based results (e.g., simulation results, etc.). As an example, output from the simulation component 120 may be input to one or more other workflows, as indicated by a workflow component 144.

[0028] As an example, the simulation component 120 may include one or more features of a simulator such as a simulator provided in OLGA ^® (Schlumberger Limited, Houston Texas. Further, in an example embodiment, the management components 110 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, pipeline engineers, 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.).

[0029] In an example embodiment, various aspects of the management components 110 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 addons (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.).

[0030] Figure 1 also shows an example of a framework 170 that includes a model simulation layer 180 along with a framework services layer 190, a framework core layer 195 and a modules layer 175. The framework 170 may include the commercially-available OCEAN ^® framework where the model simulation layer 180 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.

[0031] 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 pipeline configuration, one or more attributes based at least in part on pipeline configuration, 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.

[0032] In the example of Figure 1, the model simulation layer 180 may provide domain objects 182, act as a data source 184, provide for rendering 186 and provide for various user interfaces 188. Rendering 186 may provide a graphical environment in which applications can display their data while the user interfaces 188 may provide a common look and feel for application user interface components.

[0033] As an example, the domain objects 182 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).

[0034] In the example of Figure 1 , 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 180 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 180, which can recreate instances of the relevant domain objects.

[0035] In the example of Figure 1, the pipeline environment 150 may be outfitted with any of a variety of sensors, detectors, actuators, etc. For example, equipment 152 may include communication circuitry to receive and to transmit information with respect to one or more networks 155. Such information may include information associated with downhole equipment 154, which may be equipment to acquire information, to assist with resource recovery, etc. 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, Figure 1 shows a satellite in communication with the network 155 that may be configured for communications, noting that the satellite may additionally or alternatively include circuitry for imagery (e.g., spatial, spectral, temporal, radiometric, etc.).

[0036] Figure 1 also shows the geologic environment 150 as optionally including equipment 157 and 158 associated with a well. As an example, the equipment 157 and/or 158 may include components, a system, systems, etc. for pipeline condition monitoring, sensing, valve modulation, pump control, analysis of pipeline data, assessment of one or more pipelines 156, etc. The pipelines 156 may include at least a portion of the well, and may form part of, or be representative of, a network of pipes which may transport a production fluid (e.g., hydrocarbon) from one location to another.

[0037] As mentioned, the system 100 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 workstep 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 pipeline configuration, 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.).

[0038] Figure 2 illustrates a flowchart of a method 200 for simulating fluid flow in a model, such as a production network, e.g., as part of an oilfield, according to an embodiment. The method 200 may include providing a portal for interaction with the customer, as at 201. The portal may be accessed over the internet (or another network) by the customer. Further, the portal may provide administrative information related to provisioned resources. The provisioned resources may be computing nodes and/or heads, providing high-performance computing (FTPC) clusters for performing simulation jobs or workflows. The provisioning of such resources will be described below. The portal may provide visualizations that track usage of the provisioned resources by the customer. In some embodiments, the portal may also track jobs presently being executed by the provisioned resources, such as, for example, the completeness of a simulation job currently being run.

[0039] The portal may provide for a customer to register as a valid customer or otherwise provide for customer access of a customer database (CDB) that is part of a provider business system. Further, the portal may provide the ability for registered customers to subscribe to "cloud" services; that is, computing services that are provided as a service using provisioned, remote computing resources. Further, the portal may provide a menu of available services, which may simplify the way in which a customer signs up for services.

[0040] The portal may also provide for one or more administrative tasks. Such administrative tasks may include allowing customers to add, cancel, or adjust active subscriptions, redirect new customers seeking to register, send automatic email messages to the account managers once a user signs up, send an automatic message to the cloud administrator once the subscription service is approved by the account manager for provisioning, send automated messages to the customer when each part of the service provisioned is completed, display the customer active subscriptions and remaining time once the services were provisioned on the FIPC cloud, generate usage reports based on the customer usage of the services provisioned from the cloud provider, track customer (and individual users within a customer entity) activities, including sign-up, access to services, applications, etc., and/or provide reporting capabilities for resource usage and automated billing. Further, the portal may allow for privileged customers to add, remove or otherwise edit the users within their own cloud subscription service.

[0041] The method 200 may also include receiving an inquiry for simulation services from a customer, as at 202. The inquiry may be received via the portal. The inquiry may begin a negotiation process between customer and the software provider(s). For example, the inquiry may be received, and from the inquiry, a quote request may be generated. The quote request may stipulate certain terms, e.g., capabilities of the software to be licensed. A quote may be generated based on the request, and may be transmitted to the customer, e.g., after garnering the appropriate approvals on the provider end (e.g., by an account manager). The quote may include terms and conditions, which, once agreed to by the customer, may form the basis for a contractual relationship between the customer and provider, in which the customer acquires the right to use certain software, as well as resources for executing at least a portion of that software, as will be discussed below.

[0042] Once the contractual relationship is established, the method 200 may include provisioning one or more computing resources based on the terms of service, as at 204. This may include non-exclusive or exclusive provisioning. In non-exclusive provisioning, other parties may share the same resources (e.g., computers, cores, bandwidth, etc.). In exclusive provisioning, the resources are dedicated to the customer. Arrangements in which there is a mix of non-exclusive and exclusive resource provisioning are also contemplated.

[0043] In an embodiment, cloud administrators may provision an environment for the customer as per the approved request from the account manager. This process may be handled manually without interaction of automated cloud provisioning engines; however, in other embodiments, the process may be handled at least partially automatically via such cloud provisioning engines. Provisioned cloud head and compute nodes may have performance capabilities selected based on simulation speed performance. Further, file transfer mechanisms may be selected such that they are capable of handling file transfers of large simulation and/or fluid flow (pipe) network models across high latency and congested communication/network links from the customers. In some embodiments, the overall performance of the data upload and retrieval from the provisioned resources may be similar to that of a local area network (LAN) environment.

[0044] Nodes to be provisioned on the cloud infrastructure may have sufficient connectivity to be compliant with the simulator operating specifications. Connectivity from the cloud infrastructure to the internet may be sufficient on each of the datacenters to support multiple users uploading larger OLGA simulation at a given time. The provisioning infrastructure may be capable of supporting provisioning of independent storage containers able to store the large datasets while the simulations are executed on the compute nodes. The infrastructure may further support multiple instances or nodes for multiple realization (MR) workflows, as will be described in greater detail below. The infrastructure may also support multi -tenancy of HPC nodes for the customers or an equivalent model to support independent environments for the customer provisioned environments.

[0045] The provider may have the ability to auto-provision simulator head and compute nodes within a certain time of subscription, e.g., about 24 hours. Support for provisioning of instances based on an image/template with the simulator software provided by the software provider, so as to deploy the software to the head and compute nodes. Further, once the instances or nodes are deleted from the infrastructure, the data processed and/or stored thereby may be securely deleted. The instances may be dismantled upon decommissioning of services that had been provisioned

[0046] The method 200 may include providing a graphical user interface (GUI) for a production flow simulator for interaction with the customer, as at 206. In some embodiments, the simulator may be OLGA ^® , commercially available from SCHLUMBERGER, but in other embodiments, may be any other pipe flow simulator. In an embodiment, the GUI may be accessible to the customer via a web-portal, e.g., accessible through a web-browser over the internet. In another embodiment, the GUI may be executing on the customer's computer. In the latter case, the GUI may be communicable with the provisioned computing resources in the background, such that the customer may employ the GUI, which may handoff compute-intensive simulations to the provisioned resources, e.g., through upload over a network (e.g., the internet).

[0047] The GUI may allow a user to build a representation (model) of a pipe network. The pipe network may include pipes, risers, elbows, valves, orifices, flow control devices, pumps, and/or any other components that may affect fluid flow in a pipe network. The GUI may access a database, which may populate values for the various components, or may provide for input of characteristics of the components for storage in the database. Once the model is built, it may be stored and uploaded to the provisioned computing resources for use in simulation, as will be described below.

[0048] Thus, the method 200 may include interfacing with a graphical pre- and/or postprocessor. This may provide the ability for a user/customer to import production system description data and to create a simulation model in the GUI on the customer's system, with the data/simulation model then being uploaded to the provisioned resources.

[0049] The method 200 may also include receiving a simulation job from the customer via the GUI, as at 210. The simulation job may be received over a secure connection (e.g., secure socket layer or SSL), including any suitable level of encryption. The simulation job may contain reference to a model to be simulated, or may include the model itself for simulation. The simulation job may be received seamlessly and in context of the operation of the model-building GUI, from the perspective of the user. For example, the simulation job may be automatically transmitted to the provisioned computing resources when the customer selects a button or otherwise indicates a desire for a simulation of a model. In other embodiments, the GUI may be executed by the provisioned resources, and thus the model may be present in memory of the computing resources, and available on-demand for simulation.

[0050] Accordingly, the simulation job submission may be from the same graphical pre- and/or post-processor (e.g., the system executing the GUI) to the remote (non-local) high-performance computing cluster (e.g., the provisioned resources). This may provide for a customer to submit, monitor, and control fluid flow simulations on a non-local cluster. Further, the input data may be encrypted in secure storage, e.g., using a "blob store" as provided by AZURE ^® or another cloud computing host. Communication with the storage service provide may enable the creation of a "container" in the provisioned resource, which may contain the input case data for simulation that may be downloaded location for simulation results.

[0051] The method 200 may include processing the simulation job using the provisioned computing resources, as at 212. The simulation job may be processed using any amount or type of provisioned resources. Processing the simulation job may include conducting one or more chronological (e.g., time-step) simulations of fluid flow in a pipe network built using the GUI or otherwise uploaded to the provisioned resources. The fluid flow simulations may be single phase or multi-phase, and may include any parameters suitable.

[0052] The simulation workflows executed in the provisioned resource as a single simulation workflow. The customer may be able to configure the cloud access to the provisioned resources via the GUI. The user may provide credentials (e.g., username and password) through the GUI, and select a simulation case that should be submitted. The job may then be executed (simulated) using the provisioned computing resources executing a simulation engine. In another embodiment, multiple realization (MR) workflows may be employed.

[0053] One type of MR workflow that may be executed is a Risk Management and Optimisation (RMO) workflow. RMO workflows may allow the customer to perform sensitivity analysis and uncertainty analysis, and then fine tune their models. For example, RMO workflows may offer a systematic approach for uncertainty analyses used to identify risks in flow assurance simulation. RMO workflows may help to derive uncertainty bands for the operational envelope for any probability range, for example, P10, P50, and/or P90. In addition, tuning capabilities may be provided with global and local methods.

[0054] As mentioned above, the portal may track usage of the provisioned resources, and thus, e.g., before, during, or after processing the simulation job, the method 200 may include transmitting monitoring data to the customer via the portal, as at 214. Such job-status reporting during simulation progressing on the provisioned resources may provide the customer the ability to interface with the provisioned computing resources to determine usage, status, availability, etc. Further, the administrative status/progress of the provisioned computing resources may be accessed by any type of computing device connectable to a portal, e.g., a desktop, laptop, or mobile computer (e.g., tablet). The portal may also provide a job -scheduling utility for queuing job submissions for processing on the provisioned resources.

[0055] Once the simulation (e.g., a certain time-step or the like) is completed, the result of the simulation may be transmitted to the customer via the graphical user interface, as at 216. Again, this may be a substantially seamless process, from the perspective of the user, as the simulation results may be populated to the GUI without prompting from the customer, whether the GUI is executing on the customer's system or in the provisioned resources and merely displaying (e.g., through a web-portal) on the customer's system. Once the results are retrieved, redundant containers in the "blob store" may be deleted, and simulation results may be visualized on the GUI. Further, during the results-providing process, the results may be retrieved and decrypted from the appropriate container.

[0056] In some embodiments, the methods of the present disclosure may be executed by a computing system. Figure 3 illustrates an example of such a computing system 300, in accordance with some embodiments. The computing system 300 may include a computer or computer system 301 A, which may be an individual computer system 301 A or an arrangement of distributed computer systems. The computer system 301A includes one or more analysis modules 302 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 602 executes independently, or in coordination with, one or more processors 304, which is (or are) connected to one or more storage media 306. The processor(s) 304 is (or are) also connected to a network interface 307 to allow the computer system 301 A to communicate over a data network 309 with one or more additional computer systems and/or computing systems, such as 301B, 301C, and/or 301D (note that computer systems 301B, 301C and/or 301D may or may not share the same architecture as computer system 301A, and may be located in different physical locations, e.g., computer systems 301 A and 301B may be located in a processing facility, while in communication with one or more computer systems such as 301C and/or 301D that are located in one or more data centers, and/or located in varying countries on different continents).

[0057] A processor may include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, or another control or computing device.

[0058] The storage media 306 may be implemented as one or more computer-readable or machine-readable storage media. Note that while in the example embodiment of Figure 3 storage media 306 is depicted as within computer system 301 A, in some embodiments, storage media 306 may be distributed within and/or across multiple internal and/or external enclosures of computing system 301A and/or additional computing systems. Storage media 306 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 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.

[0059] In some embodiments, computing system 300 contains one or more flow simulation module(s) 308. In the example of computing system 300, computer system 301A includes the flow simulation module 308. In some embodiments, a single flow 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 flow simulation modules may be used to perform some aspects of methods herein. [0060] It should be appreciated that computing system 300 is merely one example of a computing system, and that computing system 300 may have more or fewer components than shown, may combine additional components not depicted in the example embodiment of Figure 3, and/or computing system 300 may have a different configuration or arrangement of the components depicted in Figure 3. The various components shown in Figure 3 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.

[0061] 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.

[0062] Geologic 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 300, Figure 3), 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.

[0063] The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or limiting to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. Moreover, the order in which the elements of the methods described herein are illustrate and described may be re-arranged, and/or two or more elements may occur simultaneously. The embodiments were chosen and described in order to best explain the principals of the disclosure and its practical applications, to thereby enable others skilled in the art to best utilize the disclosed embodiments and various embodiments with various modifications as are suited to the particular use contemplated.