Patent Description:
The present disclosure relates to simulation of hydrocarbon reservoirs.

The oil and gas industry has developed numerous enhanced oil recovery (EOR) methods that include chemical, thermal, and gas based processes. Water injection, also known as waterflooding, is a commonly used EOR method. In this method, water is injected to an oil-bearing reservoir to increase pressure in the reservoir, thereby displacing residual oil and increasing oil production. The injected water can be seawater, aquifer water, or any surface water.

<NPL>) describes experiments regarding multiscale water ion interactions at interfaces for smartwater flooding in carbonates.

<NPL>) describes experiments regarding linking pore scale mechanisms with macroscopic to core scale effects in controlled ionic composition low salinity waterflooding processes.

<CIT> describes a method for selecting an additive for enhanced recovery from a subterranean hydrocarbon reservoir.

<CIT> describes processes for evaluating enhanced oil recovery mechanisms in carbonate based reservoirs.

Recently, it has been found that an effect of the water chemistry of the injected water has an impact on oil recovery processes, particularly in carbonate formations. Based on this finding, some waterflooding methods have altered the ionic composition of the injected water to improve hydrocarbon recovery from carbonate reservoirs without injecting additional chemicals or fluids with the injected water. In order to maintain and improve oil production from subterranean formations, it is useful to understand enhanced oil recovery (EOR) processes. One way to understand EOR processes is to simulate performance of the processes. However, existing models do not accurately simulate performance of the processes, particularly more complex processes (for example, the waterflooding mechanism that alters the ionic composition of the injected water).

This disclosure describes a workflow for modeling EOR processes, such as waterflooding, in order to improve oil production from subterranean formations. The disclosed workflow more accurately models waterflooding processes than existing models, and therefore, facilitates determining parameters for waterflooding processes that improve the performance of the processes over what is achievable in practice.

Aspects of the subject matter described in this specification may be embodied in methods that include the actions of: performing, using a nanoscale model, a simulation of fluid-fluid and fluid-rock interactions in the subterranean formation; upscaling first results of the simulation of fluid-fluid and fluid-rock interactions to a microscale level; performing, using a microscale model and the upscaled first results, a simulation of fluid flow inside rocks of the subterranean formation; upscaling second results of the simulation of fluid flow inside rocks to a macroscale level; and performing, using a core-scale model and the upscaled second results, a simulation of fluid flow across the subterranean formation.

The previously-described implementation is implementable using a computer-implemented method; a non-transitory, computer-readable medium storing computer-readable instructions to perform the computer-implemented method; and a computer system including a computer memory interoperably coupled with a hardware processor configured to perform the computer-implemented method or the instructions stored on the non-transitory, computer-readable medium. These and other embodiments may each optionally include one or more of the following features.

In a first aspect, the methods further comprising determining waterflooding parameters that increase oil recovery from the subterranean formation.

In a second aspect, where the first results comprise a set of parameters that characterize brine/crude-oil/calcite interfaces at the nanoscale, and where the set of parameters comprises zeta-potential for surface charges, intermolecular forces, contact-angle, adhesive, and cohesive forces.

In a third aspect, where the second results comprise a set of parameters that characterize the fluids inside the rock pore space, and where the parameters comprise brine and crude-oil spatial distribution, residual oil, pressure distribution, velocity distribution, porosity, and permeability.

In a fourth aspect, where third results of the simulation of fluid flow across the subterranean formation comprise a set of parameters including pressure drop across the subterranean formation, injection rate, oil recovery rate, and connate brine composition.

In a fifth aspect, where the nanoscale model is based on one or more of surface complexation models (SCM), molecular dynamics (MD), and density functional theory (DFT).

In a sixth aspect, where the microscale model is based on one or more of computational fluid dynamics, Lattice-Boltzmann methods, pore-network modeling, and percolation theory.

In a seventh aspect, wherein the macroscale model is based on one or more of multiphase Darcy's law, dual porosity theory, Darcy□Brinkman's law, and Darcy-Forchheimer's law.

The subject matter described in this specification can be implemented in particular implementations, so as to realize one or more of the following advantages. The disclosed workflow provides an improved insight on the physiochemical interactions associated with EOR processes. Additionally, the disclosed workflow improves waterflooding processes and increases the recovery of crude oil and relevant hydrocarbons from subsurface formations.

The details of one or more implementations of the subject matter of this specification are set forth in the Detailed Description, the accompanying drawings, and the claims. Other features, aspects, and advantages of the subject matter will become apparent from the Detailed Description, the claims, and the accompanying drawings.

As previously described, understanding the mechanisms related to oil recovery is useful for maintaining and improving oil production from subterranean formations. For example, numerical models of processes are used in the oil and gas industry to understand petroleum exploration and production activities. These numerical models are used to identify and screen new prospects, to optimize recovery mechanisms, and to design optimal surface facilities. In the context of enhanced oil recovery (EOR) processes, macroscale models (for example, reservoir scale models) have been developed to mimic and understand performance of the processes inside a subterranean formation. However, these macroscale models do not provide thorough details of the physicochemical reservoir interactions, such as those occurring at fluid-fluid and fluid-rock interfaces.

Disclosed is a workflow for accurately modeling and simulating EOR processes. In particular, the disclosed workflow provides insight on the physiochemical interactions associated with EOR processes. Understanding the interfacial physiochemical interactions occurring at the microscopic scale and the nanoscale is useful for improving (for example, optimizing) EOR processes. For example, in waterflooding methods, there is a complex interaction of a myriad of forces, such as viscous, capillary, gravity, reactive, and electrokinetic forces. Such forces, which occur at small scales (for example, atomic-scales or pore-scales), dictate the oil recovery at the reservoir scale (for example, a mile or kilometer scale). The disclosed workflow facilitates understanding of these forces, which in turn, facilitates determining parameters for improved waterflooding processes, thereby increasing the recovery of crude oil and other relevant hydrocarbons.

<FIG> illustrates a modeling workflow <NUM>, according to some implementations. The modeling workflow <NUM> is a workflow for accurately modeling and simulating an EOR process in a subterranean formation (for example, an oil-bearing reservoir). As shown in <FIG>, the modeling framework is a multi-scale workflow that includes nanoscale modeling <NUM>, pore-scale modeling <NUM>, and macroscale modeling <NUM>. As also shown in <FIG>, the modeling workflow <NUM> is an interdependent workflow in which an output of a modeling process can be used as an input for one or more of the other modeling processes.

In the nanoscale modeling <NUM>, a simulation is performed using a nanoscale model to analyze fluid-fluid and fluid-rock interactions at a nanoscale level of the subterranean formation. The nanoscale model may be based on one or more of surface complexation models (SCM), molecular dynamics (MD), and density functional theory (DFT). The output of the simulation is a set of parameters that characterize the brine/crude-oil/calcite interfaces at an atomic scale. Example nanoscale output parameters include zeta-potential for surface charges, intermolecular forces (for example, Van der Waals, Coulombic, and structural components), adhesive forces, and cohesive forces (wettability and interfacial tension).

In the pore-scale modeling <NUM>, a simulation is performed using a pore-scale model to analyze fluid dynamics inside rock pores of the subterranean formation at a microscale level. In an implementation, the input parameters for the pore-scale simulations include upscaled nanoscale parameters. The microscale model may be based on one or more of computational fluid dynamics, Lattice-Boltzmann methods, pore-network modeling, and percolation theory. The output of the simulation is a set of parameters that characterize the fluids inside the rock pore space. Example output pore-scale parameters include brine and crude-oil spatial distribution, residual oil, pressure distribution, velocity distribution, porosity, and permeability.

In the macroscale modeling <NUM>, macroscale simulations provide macroscopic displacement behavior for a rock sample or for various production and injection wells at the subterranean reservoir scale. In an implementation, the input parameters for the core-scale simulations include upscaled microscale parameters. The macroscale model may use one or more of multiphase Darcy's law, dual porosity theory, Darcy-Brinkman's law, and Darcy-Forchheimer's law. The output of the simulation is a set of parameters that characterize the fluids inside the rock pore space. Example parameters include pressure drop across the rock sample, injection rate, oil recovery rate, and connate brine.

<FIG> illustrates a block diagram of a method <NUM> for modeling an enhanced oil recovery (EOR) process, according to some implementations. For clarity of presentation, the description that follows generally describes method <NUM> in the context of the other figures in this description. For example, the method <NUM> can be performed by the computing system <NUM> shown in <FIG>. However, it will be understood that the method <NUM> may be performed, for example, by any suitable system, environment, software, and hardware, or a combination of systems, environments, software, and hardware, as appropriate. In some implementations, various steps of the method <NUM> can be run in parallel, in combination, in loops, or in any order. The following description of method <NUM> is consistent with the general workflow described in <FIG>.

Method <NUM> starts at step <NUM>, where the method involves performing, using a nanoscale model of a subterranean formation, a simulation of fluid-fluid and fluid-rock interactions in the subterranean formation. In an implementation, the nanoscale model characterizes fluid-fluid and fluid-rock interactions at a nanoscale level. The nanoscale model may be based on one or more of surface complexation models (SCM), molecular dynamics (MD), and density functional theory (DFT). The outcome of the simulation is a set of parameters that characterize brine/crude-oil/calcite interfaces at the atomic scale or nanoscale. Example parameters include zeta-potential for surface charges, intermolecular forces (for example, Van der Waals, Coulombic, and structural components), contact-angle, adhesive forces, and cohesive forces (wettability and interfacial tension).

At step <NUM>, method <NUM> involves upscaling first results of the simulation of fluid-fluid and fluid-rock interactions to a microscale level. Upscaling involves scaling up the results, and any derived properties from the results, from the nanoscale to the microscale. As an example, molecular dynamics (MD) modeling is performed to calculate interfacial tension of a water and oil interface. The difference between the average tangential and normal stresses (computed based on statistically averaged molecular interactions) at the fluid interface corresponds to the interfacial tension, which is an upscaled parameter based on nanoscale molecular dynamics.

At step <NUM>, method <NUM> involves performing, using a microscale model and the upscaled first results, a simulation of fluid flow inside rocks of the subterranean formation at a microscale level. The microscale model characterizes fluid dynamics inside the rock pores at the microscale level. In an implementation, the input parameters for the pore-scale simulations include the computed nanoscale parameters. The microscale model may use one or more of computational fluid dynamics, Lattice-Boltzmann methods, pore-network modeling, and percolation theory. The output of the simulation is a set of parameters that characterize the fluids inside the rock pore space. Example parameters include brine and crude-oil spatial distribution, residual oil, pressure distribution, velocity distribution, porosity, and permeability.

At step <NUM>, method <NUM> involves upscaling second results of the simulation of the fluid flow at the microscale to a macroscale level (for example, a core-scale level). Upscaling involves scaling up the results, and any derived properties from the results, from the microscale to the macroscale. As an example, upscaling can be performed by averaging a simulated pore-scale velocity field. Based on the applied pressure gradient, fluid viscosity, sample size, and computed average pore-scale velocity, a macroscopic permeability is calculated using a Darcy model.

At step <NUM>, method <NUM> involves performing, using a macroscale model and the upscaled second results, a simulation of fluid flow inside rocks of the subterranean formation at a microscale level. The macroscale model characterizes macroscopic displacement behavior of the waterflooding process for a subterranean rock sample or for various production and injection wells at the subterranean reservoir scale. In an implementation, the input parameters for the core-scale simulations include the upscaled microscale parameters. The macroscale model may use one or more of multiphase Darcy's law, dual porosity theory, Darcy-Brinkman's law, and Darcy-Forchheimer's law. The outcome of the simulation is a set of parameters that characterize the fluids inside the rock pore space. Example parameters include pressure drop across the studied sample, injection rate, oil recovery rate, and connate brine.

More specifically, the output of the macroscale simulation is indicative of the oil recovery from a subterranean rock sample at the core-scale. In an implementation, the multiscale modeling method <NUM> is used to determine waterflooding parameters. For example, optimal waterflooding parameters can be determined, which leads to maximizing oil recovery from a subterranean rock sample. For instance, the ions in the brine used in waterflooding have a strong impact on the zeta-potential electrokinetic parameter, which is an input in the microscale model. The brine ions can be tuned to find the zeta-potentials that lead to minimum oil trapping at the microscale. Therefore, the multiscale method <NUM> can help in determining an optimal set of ions in the waterflooding that leads to maximum recovery of oil, which is the output of the macroscale simulation. Within examples, one or more input values into the model may be tuned, automatically or manually, in order to determine a desired output. For instance, the desired output may be a threshold oil recovery. Accordingly, the method <NUM> facilitates conducting a sensitivity analysis for injected brine properties that can further enhance and increase oil recovery in a systematic approach.

The example method <NUM> shown in <FIG> can be modified or reconfigured to include additional, fewer, or different steps (not shown in <FIG>), which can be performed in the order shown or in a different order. As an example, after step <NUM>, the method <NUM> can include determining waterflooding parameters. As another example, the method <NUM> can include performing a waterflooding process using the determined waterflooding parameters.

<FIG> illustrates a workflow <NUM> for validating the modeling workflow <NUM> of <FIG>, according to some implementations. As shown in <FIG>, the workflow <NUM> includes validating nanoscale modeling <NUM>, validating microscale modeling <NUM>, and validating macroscale modeling <NUM>. In an implementation, the workflow <NUM> generates experimental data that is used to validate the simulated data from the modeling workflow <NUM>.

For the nanoscale models, experimental data, such as surface/intermolecular forces, adhesion forces, interfacial tension, contact angle, thin film thickness, disjoining pressure, nano-CT images, and zeta-potentials can be used to validate the simulation results, such as interfacial tension and contact angle. Devices and tools that can be used to measure the experimental data include atomic force microscopy, interfacial tensiometer, and zeta/streaming potential analyzer. For microscale models, devices used to measure the experimental data include microfluidic devices, micro-CT scanners, SEM imaging, and 3D printed rock-replica models, each of which provide data that can be compared with pore-scale simulation results for validation purposes. For macroscale models, validating experimental measurements based on unsteady state coreflood, steady-state coreflood, centrifuge experiments, and spontaneous imbibition provide experimental data that is utilized to validate the macroscale models that are used to predict oil recovery.

<FIG>, <FIG>, <FIG>, <FIG>, <FIG>, and <FIG> illustrate example results from performing the workflow <NUM>, according to some implementations.

<FIG> illustrates an example nanoscale model <NUM> that characterizes fluid-fluid and fluid-rock interactions at a nanoscale level. The nanoscale model <NUM> includes a nano-water thin-film layer <NUM> sandwiched between a calcite surface <NUM> and a crude-oil surface <NUM>. The water thin-film <NUM> includes dissolved ions corresponding to a specific water-chemistry composition. Table <NUM> provides brine samples with example ionic compositions, and Table <NUM> includes properties of the crude-oil surface <NUM>. In an implementation, a surface complexation model (SCM) is used as the nanoscale model, which describes the equilibrium state of ion adsorption based on specified surface reactions. For the calcite/brine/crude-oil model <NUM>, the adsorption of ions on crude-oil/brine and calcite/brine interfaces determines the surface charges and the corresponding zeta-potentials, which provides information indicative of the electrokinetic and wettability properties for various water chemistries. The affinity of different ion types (listed in Table <NUM>) is determined through surface chemistry reactions that describe the calcite and crude-oil surfaces. A list of equilibrium constants describing such surface chemistry reactions is shown in Tables <NUM> and <NUM>.

<FIG> and <FIG> illustrate a comparison between experimental and simulated results of nanoscale modeling. Specifically, <FIG> illustrates a comparison between experimental and SCM zeta-potential values at the calcite/brine interface, and <FIG> illustrates a comparison between experimental and SCM zeta-potential values at the oil/brine interface. As shown in <FIG> and <FIG>, experimental generated results, for example, using a zeta-potential equipment, validate the simulation results. As such, the experimental data supports the nanoscale model's characterization of the brine/crude-oil and brine/calcite interfacial interactions at the nanoscale.

<FIG> illustrate two example microscale models for simulating fluid dynamics at the pore-scale. The first example uses a micro-computed tomography (micro-CT) image scan of a rock sample, and models the fluid flow inside a void space of the rock sample. <FIG> illustrates the three-dimensional (3D) pressure distribution inside the rock pores. Permeability and porosity are computed based on the simulation results, which are listed in Table <NUM>. These parameters are upscaled quantities that are used in the macroscale modeling. In the second example, a pore-scale multiphase flow simulation is conducted inside a three-dimensional (2D) transparent micromodel to visualize the fluid flow dynamics and crude-oil/brine distributions. Table <NUM> lists the relevant parameters for crude-oil and brine fluids. <FIG> illustrates that the pore-scale model predicts the remaining oil (black color) inside the micromodel void space. Such microscale models can be utilized to find the injected water parameters (for example, optimal parameters) leading to reduced (for example, a minimal) residual oil.

<FIG> illustrates a comparison between experimental and simulated results of macroscale modeling. In macroscale modeling, a macroscale model is used to predict the oil recovery for a subterranean rock sample. <FIG> illustrates oil recovery vs. injected pore-volumes (PV) for a subterranean carbonate rock sample. The red stars are indicative of the experimental results and the solid curve is indicative of the simulation results, which are generated, for example, using multiphase Darcy. In <FIG>, the oil recovery is shown as a function of brine pore-volume injected. As also shown in <FIG>, the simulation results match the core flooding experimental data. The two bumps at <NUM> and <NUM> PV are indicative of an increase in oil recovery due to a change in the water ionic composition. As described previously, the change in injected water chemistry alters the crude-oil/brine and brine/calcite interfacial properties. This alteration in interfacial properties leads to a decrease in the residual oil parameter, which results in an overall oil recovery increase.

<FIG> is a block diagram of an example computer system <NUM> used to provide computational functionalities associated with described algorithms, methods, functions, processes, flows, and procedures described in the present disclosure, according to some implementations of the present disclosure. The illustrated computer <NUM> is intended to encompass any computing device such as a server, a desktop computer, a laptop/notebook computer, a wireless data port, a smart phone, a personal data assistant (PDA), a tablet computing device, or one or more processors within these devices, including physical instances, virtual instances, or both. The computer <NUM> can include input devices such as keypads, keyboards, and touch screens that can accept user information. Also, the computer <NUM> can include output devices that can convey information associated with the operation of the computer <NUM>. The information can include digital data, visual data, audio information, or a combination of information. The information can be presented in a graphical user interface (UI) (or GUI).

At a high level, the computer <NUM> is an electronic computing device operable to receive, transmit, process, store, and manage data and information associated with the described subject matter. According to some implementations, the computer <NUM> can also include, or be communicably coupled with, an application server, an email server, a web server, a caching server, a streaming data server, or a combination of servers.

The computer <NUM> can receive requests over network <NUM> from a client application (for example, executing on another computer <NUM>). The computer <NUM> can respond to the received requests by processing the received requests using software applications. Requests can also be sent to the computer <NUM> from internal users (for example, from a command console), external (or third) parties, automated applications, entities, individuals, systems, and computers.

Each of the components of the computer <NUM> can communicate using a system bus <NUM>. In some implementations, any or all of the components of the computer <NUM>, including hardware or software components, can interface with each other or the interface <NUM> (or a combination of both), over the system bus <NUM>. Interfaces can use an application programming interface (API) <NUM>, a service layer <NUM>, or a combination of the API <NUM> and service layer <NUM>. The API <NUM> can include specifications for routines, data structures, and object classes. The API <NUM> can be either computer-language independent or dependent. The API <NUM> can refer to a complete interface, a single function, or a set of APIs.

The service layer <NUM> can provide software services to the computer <NUM> and other components (whether illustrated or not) that are communicably coupled to the computer <NUM>. The functionality of the computer <NUM> can be accessible for all service consumers using this service layer. Software services, such as those provided by the service layer <NUM>, can provide reusable, defined functionalities through a defined interface. For example, the interface can be software written in JAVA, C++, or a language providing data in extensible markup language (XML) format. While illustrated as an integrated component of the computer <NUM>, in alternative implementations, the API <NUM> or the service layer <NUM> can be stand-alone components in relation to other components of the computer <NUM> and other components communicably coupled to the computer <NUM>. Moreover, any or all parts of the API <NUM> or the service layer <NUM> can be implemented as child or sub-modules of another software module, enterprise application, or hardware module without departing from the scope of the present disclosure.

The computer <NUM> includes an interface <NUM>. Although illustrated as a single interface <NUM> in <FIG>, two or more interfaces <NUM> can be used according to particular needs, desires, or particular implementations of the computer <NUM> and the described functionality. The interface <NUM> can be used by the computer <NUM> for communicating with other systems that are connected to the network <NUM> (whether illustrated or not) in a distributed environment. Generally, the interface <NUM> can include, or be implemented using, logic encoded in software or hardware (or a combination of software and hardware) operable to communicate with the network <NUM>. More specifically, the interface <NUM> can include software supporting one or more communication protocols associated with communications. As such, the network <NUM> or the interface's hardware can be operable to communicate physical signals within and outside of the illustrated computer <NUM>.

The computer <NUM> also includes a database <NUM> that can hold data for the computer <NUM> and other components connected to the network <NUM> (whether illustrated or not). For example, database <NUM> can be an in-memory, conventional, or a database storing data consistent with the present disclosure. In some implementations, database <NUM> can be a combination of two or more different database types (for example, hybrid in-memory and conventional databases) according to particular needs, desires, or particular implementations of the computer <NUM> and the described functionality. Although illustrated as a single database <NUM> in <FIG>, two or more databases (of the same, different, or combination of types) can be used according to particular needs, desires, or particular implementations of the computer <NUM> and the described functionality. While database <NUM> is illustrated as an internal component of the computer <NUM>, in alternative implementations, database <NUM> can be external to the computer <NUM>.

The computer <NUM> also includes a memory <NUM> that can hold data for the computer <NUM> or a combination of components connected to the network <NUM> (whether illustrated or not). Memory <NUM> can store any data consistent with the present disclosure. In some implementations, memory <NUM> can be a combination of two or more different types of memory (for example, a combination of semiconductor and magnetic storage) according to particular needs, desires, or particular implementations of the computer <NUM> and the described functionality. Although illustrated as a single memory <NUM> in <FIG>, two or more memories <NUM> (of the same, different, or combination of types) can be used according to particular needs, desires, or particular implementations of the computer <NUM> and the described functionality. While memory <NUM> is illustrated as an internal component of the computer <NUM>, in alternative implementations, memory <NUM> can be external to the computer <NUM>.

There can be any number of computers <NUM> associated with, or external to, a computer system containing computer <NUM>, with each computer <NUM> communicating over network <NUM>. Further, the terms "client," "user," and other appropriate terminology can be used interchangeably, as appropriate, without departing from the scope of the present disclosure. Moreover, the present disclosure contemplates that many users can use one computer <NUM> and one user can use multiple computers <NUM>.

Implementations of the subject matter and the functional operations described in this specification can be implemented in digital electronic circuitry, in tangibly embodied computer software or firmware, in computer hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Software implementations of the described subject matter can be implemented as one or more computer programs. Each computer program can include one or more modules of computer program instructions encoded on a tangible, non-transitory, computer-readable computer-storage medium for execution by, or to control the operation of, data processing apparatus. Alternatively, or additionally, the program instructions can be encoded in/on an artificially generated propagated signal. For example, the signal can be a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to a suitable receiver apparatus for execution by a data processing apparatus. The computer-storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of computer-storage mediums.

The terms "data processing apparatus," "computer," and "electronic computer device" (or equivalent as understood by one of ordinary skill in the art) refer to data processing hardware. For example, a data processing apparatus can encompass all kinds of apparatuses, devices, and machines for processing data, including by way of example, a programmable processor, a computer, or multiple processors or computers. The apparatus can also include special purpose logic circuitry including, for example, a central processing unit (CPU), a field-programmable gate array (FPGA), or an application-specific integrated circuit (ASIC). In some implementations, the data processing apparatus or special purpose logic circuitry (or a combination of the data processing apparatus or special purpose logic circuitry) can be hardware- or software-based (or a combination of both hardware- and software-based). The apparatus can optionally include code that creates an execution environment for computer programs, for example, code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of execution environments. The present disclosure contemplates the use of data processing apparatuses with or without conventional operating systems, such as LINUX, UNIX, WINDOWS, MAC OS, ANDROID, or IOS.

A computer program, which can also be referred to or described as a program, software, a software application, a module, a software module, a script, or code, can be written in any form of programming language. Programming languages can include, for example, compiled languages, interpreted languages, declarative languages, or procedural languages. Programs can be deployed in any form, including as stand-alone programs, modules, components, subroutines, or units for use in a computing environment. A program can be stored in a portion of a file that holds other programs or data, for example, one or more scripts stored in a markup language document, in a single file dedicated to the program in question, or in multiple coordinated files storing one or more modules, sub-programs, or portions of code. A computer program can be deployed for execution on one computer or on multiple computers that are located, for example, at one site or distributed across multiple sites that are interconnected by a communication network. While portions of the programs illustrated in the various figures may be shown as individual modules that implement the various features and functionality through various objects, methods, or processes, the programs can instead include a number of sub-modules, third-party services, components, and libraries. Conversely, the features and functionality of various components can be combined into single components as appropriate. Thresholds used to make computational determinations can be statically, dynamically, or both statically and dynamically determined.

Computers suitable for the execution of a computer program can be based on one or more of general and special purpose microprocessors and other kinds of CPUs. The elements of a computer are a CPU for performing or executing instructions and one or more memory devices for storing instructions and data. Generally, a CPU can receive instructions and data from (and write data to) a memory. A computer can also include, or be operatively coupled to, one or more mass storage devices for storing data. In some implementations, a computer can receive data from, and transfer data to, the mass storage devices including, for example, magnetic, magneto-optical disks, or optical disks. Moreover, a computer can be embedded in another device, for example, a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive.

Computer-readable media (transitory or non-transitory, as appropriate) suitable for storing computer program instructions and data can include all forms of permanent/non-permanent and volatile/non-volatile memory, media, and memory devices. Computer-readable media can include, for example, semiconductor memory devices such as random access memory (RAM), read-only memory (ROM), phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and flash memory devices. Computer-readable media can also include, for example, magnetic devices such as tape, cartridges, cassettes, and intemal/removable disks. Computer-readable media can also include magneto-optical disks and optical memory devices and technologies including, for example, digital video disc (DVD), CD-ROM, DVD+/-R, DVD-RAM, DVD-ROM, HD-DVD, and BLU-RAY. The memory can store various objects or data, including caches, classes, frameworks, applications, modules, backup data, jobs, web pages, web page templates, data structures, database tables, repositories, and dynamic information. Types of objects and data stored in memory can include parameters, variables, algorithms, instructions, rules, constraints, and references. Additionally, the memory can include logs, policies, security or access data, and reporting files. The processor and the memory can be supplemented by, or incorporated into, special purpose logic circuitry.

Implementations of the subject matter described in the present disclosure can be implemented on a computer having a display device for providing interaction with a user, including displaying information to (and receiving input from) the user. Types of display devices can include, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), a light-emitting diode (LED), and a plasma monitor. Display devices can include a keyboard and pointing devices including, for example, a mouse, a trackball, or a trackpad. User input can also be provided to the computer through the use of a touchscreen, such as a tablet computer surface with pressure sensitivity or a multi-touch screen using capacitive or electric sensing. Other kinds of devices can be used to provide for interaction with a user, including to receive user feedback including, for example, sensory feedback including visual feedback, auditory feedback, or tactile feedback. Input from the user can be received in the form of acoustic, speech, or tactile input. In addition, a computer can interact with a user by sending documents to, and receiving documents from, a device that the user uses. For example, the computer can send web pages to a web browser on a user's client device in response to requests received from the web browser.

The term "graphical user interface," or "GUI," can be used in the singular or the plural to describe one or more graphical user interfaces and each of the displays of a particular graphical user interface. Therefore, a GUI can represent any graphical user interface, including, but not limited to, a web browser, a touch-screen, or a command line interface (CLI) that processes information and efficiently presents the information results to the user. In general, a GUI can include a plurality of user interface (UI) elements, some or all associated with a web browser, such as interactive fields, pull-down lists, and buttons. These and other UI elements can be related to or represent the functions of the web browser.

Implementations of the subject matter described in this specification can be implemented in a computing system that includes a back-end component, for example, as a data server, or that includes a middleware component, for example, an application server. Moreover, the computing system can include a front-end component, for example, a client computer having one or both of a graphical user interface or a Web browser through which a user can interact with the computer. The components of the system can be interconnected by any form or medium of wireline or wireless digital data communication (or a combination of data communication) in a communication network. Examples of communication networks include a local area network (LAN), a radio access network (RAN), a metropolitan area network (MAN), a wide area network (WAN), Worldwide Interoperability for Microwave Access (WIMAX), a wireless local area network (WLAN) (for example, using <NUM> a/b/g/n or <NUM> or a combination of protocols), all or a portion of the Internet, or any other communication system or systems at one or more locations (or a combination of communication networks). The network can communicate with, for example, Internet Protocol (IP) packets, frame relay frames, asynchronous transfer mode (ATM) cells, voice, video, data, or a combination of communication types between network addresses.

Cluster file systems can be any file system type accessible from multiple servers for read and update. Locking or consistency tracking may not be necessary since the locking of exchange file system can be done at application layer. Furthermore, Unicode data files can be different from non-Unicode data files.

While this specification contains many specific implementation details, these should not be construed as limitations on the scope of what may be claimed, but rather as descriptions of features that may be specific to particular implementations. Certain features that are described in this specification in the context of separate implementations can also be implemented, in combination, in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations, separately, or in any suitable sub-combination. Moreover, although previously described features may be described as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can, in some cases, be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.

Moreover, the separation or integration of various system modules and components in the previously described implementations should not be understood as requiring such separation or integration in all implementations. It should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

Accordingly, the previously described example implementations do not define or constrain the present disclosure. Other changes, substitutions, and alterations are also possible without departing from the scope of the present disclosure.

Claim 1:
A computer-implemented simulation method (<NUM>) for multi-scale simulation of fluid flow in a subterranean formation, the method comprising:
performing (<NUM>), using a nanoscale model of the subterranean formation, a first simulation that simulates fluid-fluid and fluid-rock interactions in the subterranean formation;
upscaling (<NUM>) first results of the first simulation to a microscale level, wherein upscaling the first results comprises calculating, based on the first results, an interfacial tension of a water and oil interface;
performing (<NUM>), using a microscale model of the subterranean formation and the upscaled first results as input to the microscale model, a second simulation that simulates fluid flow inside rocks of the subterranean formation;
upscaling (<NUM>) second results of the second simulation to a macroscale level, wherein upscaling the second results comprises calculating, based on the second results, a macroscopic permeability; and
performing (<NUM>), using a core-scale model of the subterranean formation and the upscaled second results as input to the core-scale model, a third simulation that simulates fluid flow across the subterranean formation.