INTEGRATED PRODUCTION SIMULATOR BASED ON CAPACITANCE-RESISTANCE MODEL

Info

Publication number: 20150337631
Type: Application
Filed: Jan 23, 2015
Publication Date: Nov 26, 2015
Inventors: Sébastien François Matringe (Houston, TX), Kun Liu (Houston, TX)
Application Number: 14/604,330

Abstract

A well-based production simulator is provided, which predicts the quantity of fluids produced per phase, per well and per time as a function of operational field parameters. The invention combines a petroleum reservoir simulator with a petroleum production facility simulator to obtain an integrated model to quickly and accurately forecast production on a well-by-well basis. The efficiency of the petroleum reservoir simulator is derived from its unique formulation, which solves for the production well's flow rate rather than the petroleum reservoir pressure. The simulator properly represents viscous, capillary and gravity forces, as well as complex fluid descriptions, including three-phase black-oil formulations.

Description

Description

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Prov. App. No. 62/002,470, filed May 23, 2014, and entitled “INTEGRATED PRODUCTION SIMULATOR BASED ON CAPACITANCE-RESISTANCE MODEL,” the disclosure of which is incorporated herein in its entirety.

BACKGROUND

Hydrocarbon reservoirs are exploited by drilling wells in a hydrocarbon bearing geologic formation. Both producing wells and injecting wells are typically used. The role of producing wells (producers) is to allow hydrocarbons to flow to the surface. Injecting wells (injectors) are drilled in order to maintain the reservoir pressure by injecting fluids (typically water or gas) to replace the produced fluids.

The key to a successful exploitation operation of a petroleum reservoir is to efficiently design and operate wells. In order to guide and optimize well operations, simulators are often used. The role of reservoir simulators is to forecast the production of wells in order to evaluate the possible outcomes of operational changes.

Reservoir simulators can be created in a variety of ways, but for the purpose of production optimization, it is desirable to take an approach that is both fast and accurate. The accuracy of the simulator is defined as the predictive power of the simulator: its ability to predict future well performance accurately and with a high level of confidence. The simulator's accuracy helps guarantee the economic success of the operational changes implemented. The speed of the simulator is defined as the time it takes to create or update a model and to perform a simulation. A fast simulator is desirable to update the model with new data in order to support daily operational decisions in a timely fashion.

The standard approach followed in the petroleum industry to model reservoirs is to use grid-based reservoir simulators. These simulators often rely on a finite volume discretization of the equations governing the motion of reservoir fluids. Alternate discretization methods, such as finite element methods, are also used from time to time. These methods all have in common that the primary unknowns solved during the computation are the fluid pressures of each fluid phase and the composition of each fluid component.

Classical grid-based reservoir simulation can be very accurate but is usually prohibitively slow. These models are large and require significant computer resources to run them. They are prohibitively slow for use in supporting day-to-day decisions related to production optimization. Grid-based reservoir simulation models are used primarily to support long-term field development decisions, such as the addition of new wells or changes to the exploitation strategy of the field.

In recent years, a new type of reservoir simulation method has been developed in order to offer a faster alternative to classical grid-based reservoir simulators. This new class of methods, coined “Capacitance-Resistance” (“CR”) models in the literature, does not depend on solving the fluid pressures and compositions on a static geometric grid representing the reservoir geology.

The fundamental difference between CR models and classical reservoir simulation models is that CR models rely on a reformulation of the equation governing the flow of fluids in porous media. Where classical reservoir simulation models are designed to find the fluid pressure and compositions within the reservoir, CR models directly solve for the well production rates of each fluid, without having to solve for the fluid pressure and compositions.

Although much faster than classical reservoir simulation models, current CR models are limited in their application as they currently rely on a significant simplification of the reservoir flow equations. Critical limitations include neglecting the effect of fluid flow in the production or injection wellbore and surface facilities. At the reservoir level, these models neglect the effects of fluid compressibility, as well as capillary and gravitational forces. Current formulations also rely on a simplified description of the fluid system, involving only two fluid phases.

BRIEF SUMMARY

Embodiments described herein are directed to modeling a production system and generating a production forecast for individual wells. In one embodiment, a computer system accesses portions of first production system information from a capacitance-resistance model of the production system, where the production system corresponds to a production reservoir. The computer system further accesses portions of second production system information from a well-bore model, a flow line and/or a production facility. The computer system then generates an integrated production simulator using both the first and second accessed production systems information, and implements the integrated production simulator to determine the quantity of fluids produced per phase over time as a function of operational field parameters corresponding to the production system by identifying the flow rate for the production reservoir.

In another embodiment, an integrated well-based production simulator system is provided. The integrated well-based production simulator system includes: a capacitance-resistance (CR) simulator configured to represent the flow of fluids in a production reservoir, a wellbore simulator configured to represent the flow of fluids in a wellbore, a surface facility simulator configured to represent the flow of fluids through at least one of the following surface facilities: pipelines, a production gathering facility, a separation facility, and an injection distribution facility, where the integrated well-based production simulator is configured to provide a system-wide representation of fluid flow through the production reservoir, the wellbore and at least one surface facility.

In yet another embodiment, a computer system generates a production forecast for individual wells. The computer system accesses operational parameters for a well and provides an integrated well-based production simulator by solving a specified system of equations. The integrated well-based production simulator then generates a production forecast for the well using the operational parameters.

This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

Additional features and advantages will be set forth in the description which follows, and in part will be apparent to one of ordinary skill in the art from the description, or may be learned by the practice of the teachings herein. Features and advantages of embodiments described herein may be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims. Features of the embodiments described herein will become more fully apparent from the following description and appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

To further clarify the above and other features of the embodiments described herein, a more particular description will be rendered by reference to the appended drawings. It is appreciated that these drawings depict only examples of the embodiments described herein and are therefore not to be considered limiting of its scope. The embodiments will be described and explained with additional specificity and detail through the use of the accompanying drawings in which:

FIG. 1 illustrates a computer-implemented or computer-controlled architecture that can be used to gather, analyze and/or display data gathered from and about a reservoir.

FIG. 2 illustrates an example schematic of a production and injection system of a petroleum field.

FIG. 3 illustrates a computer architecture in which embodiments described herein may operate including modeling a production system

FIG. 4 illustrates a flowchart of an example method for modeling a production system.

FIG. 5 illustrates a flowchart of an example method for generating a production forecast for individual wells.

FIG. 6 illustrates an embodiment of an integrated well-based production simulator system.

DETAILED DESCRIPTION

Embodiments described herein are directed to modeling a production system and to generating a production forecast for individual wells. In one embodiment, a computer system accesses portions of first production system information from a capacitance-resistance model of the production system, where the production system corresponds to a production reservoir. The computer system further accesses portions of second production system information from a well-bore model, a flow line and/or a production facility. The computer system then generates an integrated production simulator using both the first and second accessed production systems information, and implements the integrated production simulator to determine the quantity of fluids produced per phase over time as a function of operational field parameters corresponding to the production system by identifying the flow rate for the production reservoir.

In another embodiment, an integrated well-based production simulator system is provided. The integrated well-based production simulator system includes: a capacitance-resistance (CR) simulator configured to represent the flow of fluids in a production reservoir, a wellbore simulator configured to represent the flow of fluids in a wellbore, a surface facility simulator configured to represent the flow of fluids through at least one of the following surface facilities: pipelines, a production gathering facility, a separation facility, and an injection distribution facility, where the integrated well-based production simulator is configured to provide a system-wide representation of fluid flow through the production reservoir, the wellbore and at least one surface facility.

In yet another embodiment, a computer system generates a production forecast for individual wells. The computer system accesses operational parameters for a well and provides an integrated well-based production simulator by solving a specified system of equations. The integrated well-based production simulator then generates a production forecast for the well using the operational parameters.

The following discussion now refers to a number of methods and method acts that may be performed. It should be noted that, although the method acts may be discussed in a certain order or illustrated in a flow chart as occurring in a particular order, no particular ordering is necessarily required unless specifically stated, or required because an act is dependent on another act being completed prior to the act being performed.

Embodiments described herein may implement various types of computing systems. These computing systems are now increasingly taking a wide variety of forms. Computing systems may, for example, be handheld devices, appliances, laptop computers, desktop computers, mainframes, distributed computing systems, or even devices that have not conventionally been considered a computing system. In this description and in the claims, the term “computing system” is defined broadly as including any device or system (or combination thereof) that includes at least one physical and tangible processor, and a physical and tangible memory capable of having thereon computer-executable instructions that may be executed by the processor. A computing system may be distributed over a network environment and may include multiple constituent computing systems.

Computing systems (e.g. 102 in FIG. 1) typically include at least one processing unit and memory. The memory may be physical system memory, which may be volatile, non-volatile, or some combination of the two. The term “memory” may also be used herein to refer to non-volatile mass storage such as physical storage media. If the computing system is distributed, the processing, memory and/or storage capability may be distributed as well.

As used herein, the term “executable module” or “executable component” can refer to software objects, routings, or methods that may be executed on the computing system. The different components, modules, engines, and services described herein may be implemented as objects or processes that execute on the computing system (e.g., as separate threads).

In the description that follows, embodiments are described with reference to acts that are performed by one or more computing systems. If such acts are implemented in software, one or more processors of the associated computing system that performs the act direct the operation of the computing system in response to having executed computer-executable instructions. For example, such computer-executable instructions may be embodied on one or more computer-readable media that form a computer program product. An example of such an operation involves the manipulation of data. The computer-executable instructions (and the manipulated data) may be stored in the memory of the computing system 102. Computing systems may also contain communication channels that allow the computing system to communicate with other message processors over a wired or wireless network.

Embodiments described herein may comprise or utilize a special-purpose or general-purpose computer system that includes computer hardware, such as, for example, one or more processors and system memory, as discussed in greater detail below. The system memory may be included within the overall memory. The system memory may also be referred to as “main memory”, and includes memory locations that are addressable by the at least one processing unit over a memory bus in which case the address location is asserted on the memory bus itself. System memory has been traditionally volatile, but the principles described herein also apply in circumstances in which the system memory is partially, or even fully, non-volatile.

Embodiments within the scope of the present invention also include physical and other computer-readable media for carrying or storing computer-executable instructions and/or data structures. Such computer-readable media can be any available media that can be accessed by a general-purpose or special-purpose computer system. Computer-readable media that store computer-executable instructions and/or data structures are computer storage media. Computer-readable media that carry computer-executable instructions and/or data structures are transmission media. Thus, by way of example, and not limitation, embodiments of the invention can comprise at least two distinctly different kinds of computer-readable media: computer storage media and transmission media.

Computer storage media are physical hardware storage media that store computer-executable instructions and/or data structures. Physical hardware storage media include computer hardware, such as RAM, ROM, EEPROM, solid state drives (“SSDs”), flash memory, phase-change memory (“PCM”), optical disk storage, magnetic disk storage or other magnetic storage devices, or any other hardware storage device(s) which can be used to store program code in the form of computer-executable instructions or data structures, which can be accessed and executed by a general-purpose or special-purpose computer system to implement the disclosed functionality of the invention.

Transmission media can include a network and/or data links which can be used to carry program code in the form of computer-executable instructions or data structures, and which can be accessed by a general-purpose or special-purpose computer system. A “network” is defined as one or more data links that enable the transport of electronic data between computer systems and/or modules and/or other electronic devices. When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or a combination of hardwired or wireless) to a computer system, the computer system may view the connection as transmission media. Combinations of the above should also be included within the scope of computer-readable media.

Further, upon reaching various computer system components, program code in the form of computer-executable instructions or data structures can be transferred automatically from transmission media to computer storage media (or vice versa). For example, computer-executable instructions or data structures received over a network or data link can be buffered in RAM within a network interface module (e.g., a “NIC”), and then eventually transferred to computer system RAM and/or to less volatile computer storage media at a computer system. Thus, it should be understood that computer storage media can be included in computer system components that also (or even primarily) utilize transmission media.

Computer-executable instructions comprise, for example, instructions and data which, when executed at one or more processors, cause a general-purpose computer system, special-purpose computer system, or special-purpose processing device to perform a certain function or group of functions. Computer-executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, or even source code.

Those skilled in the art will appreciate that the principles described herein may be practiced in network computing environments with many types of computer system configurations, including, personal computers, desktop computers, laptop computers, message processors, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, mobile telephones, PDAs, tablets, pagers, routers, switches, and the like. The invention may also be practiced in distributed system environments where local and remote computer systems, which are linked (either by hardwired data links, wireless data links, or by a combination of hardwired and wireless data links) through a network, both perform tasks. As such, in a distributed system environment, a computer system may include a plurality of constituent computer systems. In a distributed system environment, program modules may be located in both local and remote memory storage devices.

Those skilled in the art will also appreciate that the invention may be practiced in a cloud computing environment. Cloud computing environments may be distributed, although this is not required. When distributed, cloud computing environments may be distributed internationally within an organization and/or have components possessed across multiple organizations. In this description and the following claims, “cloud computing” is defined as a model for enabling on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services). The definition of “cloud computing” is not limited to any of the other numerous advantages that can be obtained from such a model when properly deployed.

Still further, system architectures described herein can include a plurality of independent components that each contribute to the functionality of the system as a whole. This modularity allows for increased flexibility when approaching issues of platform scalability and, to this end, provides a variety of advantages. System complexity and growth can be managed more easily through the use of smaller-scale parts with limited functional scope. Platform fault tolerance is enhanced through the use of these loosely coupled modules. Individual components can be grown incrementally as business needs dictate. Modular development also translates to decreased time to market for new functionality. New functionality can be added or subtracted without impacting the core system.

FIG. 1 illustrates a computing architecture in which a computer-implemented monitoring system 100 may operate. The computer-implemented monitoring system 100 may be configured to monitor reservoir performance, analyze information regarding reservoir performance, display dashboard metrics, and optionally provide for computer-controlled modifications to maintain optimal oil well performance. Monitoring system 100 may include a main data gathering computer system 102 comprised of one or more computers (potentially located near a reservoir) which are linked to reservoir sensors 104. Each of these computers typically includes at least one processor and system memory. Computer system 102 may comprise a plurality of networked computers (e.g., each of which is designed to analyze a subset of the overall data generated by and received from the sensors 104).

Reservoir sensors 104 are typically positioned at different locations within a producing oil well, and may include both surface and sub-surface sensors. Sensors 104 may also be positioned at water injection wells, observation wells, etc. The data gathered by the sensors 104 can be used to generate performance metrics (e.g., leading and lagging indicators of production and recovery). The computer system 102 may therefore include a data analysis module 106 programmed to generate metrics from the received sensor data. A user interface 108 provides interactivity with a user, including the ability to input data relating to a real displacement efficiency, vertical displacement efficiency, and pore displacement efficiency. Data storage device 110 can be used for long term storage of data and metrics generated from the data.

According to one embodiment, the computer system 102 can provide for at least one of manual or automatic adjustment to production 112 by reservoir production units 114 (e.g., producing oil wells, water injection wells, gas injection wells, heat injectors, and the like, and sub-components thereof). Adjustments might include, for example changes in volume, pressure, temperature, well bore path (e.g., via closing or opening of well bore branches). The user interface 108 permits manual adjustments to production 112. The computer system 102 may, in addition, include alarm levels or triggers that, when certain conditions are met, provide for automatic adjustments to production 112.

Monitoring system 100 may also include one or more remote computers 120 that permit a user, team of users, or multiple parties to access information generated by main computer system 102. For example, each remote computer 120 may include a dashboard display module 122 that renders and displays dashboards, metrics, or other information relating to reservoir production. Each remote computer 120 may also include a user interface 124 that permits a user to make adjustment to production 112 by reservoir production units 114. Each remote computer 120 may also include a data storage device (not shown).

Individual computer systems within monitoring system 100 (e.g., main computer system 102 and remove computers 120) can be connected to a network 130, such as, for example, a local area network (“LAN”), a wide area network (“WAN”), or even the Internet. The various components can receive and send data to each other, as well as other components connected to the network. Networked computer systems (i.e. cloud computing systems) and computers themselves constitute a “computer system” for purposes of this disclosure.

Networks facilitating communication between computer systems and other electronic devices can utilize any of a wide range of (potentially interoperating) protocols including, but not limited to, the IEEE 802 suite of wireless protocols, Radio Frequency Identification (“RFID”) protocols, ultrasound protocols, infrared protocols, cellular protocols, one-way and two-way wireless paging protocols, Global Positioning System (“GPS”) protocols, wired and wireless broadband protocols, ultra-wideband “mesh” protocols, etc. Accordingly, computer systems and other devices can create message related data and exchange message related data (e.g., Internet Protocol (“IP”) datagrams and other higher layer protocols that utilize IP datagrams, such as, Transmission Control Protocol (“TCP”), Remote Desktop Protocol (“RDP”), Hypertext Transfer Protocol (“HTTP”), Simple Mail Transfer Protocol (“SMTP”), Simple Object Access Protocol (“SOAP”), etc.) over the network.

Computer systems and electronic devices may be configured to utilize protocols that are appropriate based on corresponding computer system and electronic device on functionality. Components within the architecture can be configured to convert between various protocols to facilitate compatible communication. Computer systems and electronic devices may be configured with multiple protocols and use different protocols to implement different functionality. For example, a sensor 104 at an oil well might transmit data via wire connection, infrared or other wireless protocol to a receiver (not shown) interfaced with a computer, which can then forward the data via fast Ethernet to main computer system 102 for processing. Similarly, the reservoir production units 114 can be connected to main computer system 102 and/or remote computers 120 by wire connection or wireless protocol.

As indicated above, a capacitance-resistance model (or CR model) may be used to characterize the connectivity between injection and production wells and can determine an injection scheme that maximizes the value of the reservoir asset. CR model parameters are identified using linear and nonlinear regression. The CR model is then used together with a nonlinear optimization algorithm to compute a set of future injection rates which maximize discounted net profit. CR models solve for production rates of each fluid without solving for fluid pressure and compositions (as in grid-based). CR models, however, neglect fluid flow in production facilities and wellbores and neglect the effects of fluid compressibility and capillary and gravitational forces, and are limited to two fluid phases.

Embodiments described herein include a reservoir simulator accurate enough to generate a reliable forecast of individual wells as a function of operational parameters and fast enough to be used in practice to drive the operational decisions required to optimize the exploitation of petroleum reservoirs. The speed of the reservoir simulator is due to multiple factors including a differentiated formulation and solution workflow.

FIG. 2 illustrates a schematic of a production and injection system of a petroleum field. The production wells 203 allow reservoir fluids (from reservoir 208) to flow through their completion 204 and to the surface, where a network of pipelines (e.g. production tubing 205) carry the fluids to production gathering facilities 202, and in turn, to a separator 201. The separator system isolates each fluid phase (typically oil, gas and water). In some cases, the water or gas produced and separated are then sent to an injection distribution system 206. The injection distribution system can also receive injection fluids from exterior sources. The injection wells 207 receive the fluids to be injected from the injection distribution system 206 via a network of pipelines and inject these fluids in the petroleum reservoirs through well completions 204.

In some embodiments, reservoir fluid mixtures may be composed of two or more phases. For example, two phases may be considered in the following derivation, where the oil phase is designated with the subscript “o” and the water phase with the subscript “w”. Embodiments may be extended to more complex fluid compositions including three or more fluid phases. In this example, a producing well is located in a hydrocarbon reservoir. Naming V the drainage volume of the well, the mass balance equation over the water and oil fluid components written over the drainage volume of the well can be expressed as:

$\begin{matrix} \frac{\partial S_{w}}{\partial t} + S_{w} (c_{w} + c_{f}) \frac{\partial p}{\partial t} + \frac{q_{w} - i_{w}}{V} = 0, and & (Eq . 1) \\ \frac{\partial S_{o}}{\partial t} + S_{o} (c_{o} + c_{f}) \frac{\partial p}{\partial t} + \frac{q_{o}}{V} = 0. & (Eq . 2) \end{matrix}$

In Eq. 1 and 2, t designates a time variable. The unknowns of the equations are p, the fluid pressure, as well as S_wand S_o, the water and oil saturations. c_w, c_oand c_fare respectively, the water, oil and rock formation compressibility. q_oand q_ware the oil and water production rates of the well of interest and i_wis the water injection rate received by the drainage volume of the well. To close the system, the fundamental property of the oil and water saturation is used:

S_w+S_o=1. (Eq. 3)

In some cases, reservoir simulators may be configured to directly solve the system formed by Eq. 1 and 2 using a numerical discretization method on a grid describing the reservoir geometry and rock properties. Various approaches may be used including finite difference, finite volume and finite element methods. Simulators typically solve simultaneously the pressure and saturation unknowns, using a scheme referred to as a fully-implicit scheme. Some simulators solve the pressure and saturation unknowns sequentially.

Summing Eq. 1 and 2, and using Eq. 3 to simplify the saturation derivatives, the pressure equation may be obtained, describing the flow problem:

$\begin{matrix} c_{t} V \frac{\partial p}{\partial t} + q_{t} - i_{w} = 0 & (Eq . 4) \end{matrix}$

where the total production rate q_t=q_o+q_wis defined along with the total compressibility c_t=(S_oc_o+S_wc_w+c_f).

Some reservoir simulators may rely on Eq. 4 to solve for the fluid pressure throughout the reservoir, and then solve either Eq. 1 or Eq. 2 in order to obtain the fluid saturations. This solution approach is called the implicit-pressure-explicit-saturation approach and is known to speed up simulation runtime as it allows the numerical discretization algorithms to be tailored to the mathematical character of each equation. The pressure equation of Eq. 4 describes the flow problem, which is near-parabolic in nature, the saturation equations of Eq. 1 and 2 describe the transport problem, which is near-hyperbolic in nature.

Introducing the productivity index J of the producing well, the equation linking the total production rate to the reservoir pressure reads:

q_t=j(p−p_BH) (Eq. 5)

where p_BHdesignates the bottom-hole pressure of the producing well.

By differentiating Eq. 5 with respect to time and replacing the reservoir pressure derivative term in Eq. 4, an equation may be obtained that depends solely on the pressure and rate of the producing well:

$\begin{matrix} τ \frac{\partial q_{t}}{\partial t} + q_{t} = i_{w} - c_{t} V \frac{\partial p_{BH}}{\partial t} & (Eq . 6) \end{matrix}$

where

$τ = \frac{c_{t} V}{J}$

is defined as a time constant.

The CR models solve a system of equation composed of a form of Eq. 6 and either Eq. 1 or Eq. 2. The fundamental difference between CR models and classical reservoir models is that Eq. 6 does not involve the reservoir pressure. Instead, the production rate of the well is determined directly. This difference provides a speed advantage for CR models over traditional methods.

Embodiments described herein are designed to integrate the production system 202 and the injection system 206. CR models are typically used solely as reservoir simulators. The fundamental unknowns of CR models are the production rates of each fluid component. The well bottom-hole pressure is usually seen as a constraint on the producing well. In reality, the bottom-hole pressure is often an unknown just as much as the production rates. The effective well constraint of a well could be located in a variety of upstream locations including, but not limited to, the tubing-head pressure, the flow-line pressure, the manifold pressure or separator pressure. Each of these locations may be constraints on a well's production.

To more completely model the production system, embodiments herein implement CR models in conjunction with models of the wellbore, surface flow-lines and associated production facilities to create an integrated production model. To do so, the bottom-hole pressure of Eq. 6 is viewed as an unknown rather than a constraint and additional equations are introduced to represent the dependence of the bottom-hole pressure on the architecture of the production system. This approach allows the CR model to be constrained by the actual control mechanisms in the field, such as well-head choke size or artificial lift parameters.

Several possible levels of control are possible including the tubing head pressure, p_TH, and the flow-line pressure, p_FL, located at the well-head upstream and downstream of the choke, respectively. Further downstream, pressures at other flow-lines, manifolds or separators could also be used. These are labeled p_DSto designate a general downstream pressure.

Relating the pressures along the production facility system, from the wellbore to the separator is an exercise often performed by Petroleum or Chemical Engineers. A variety of approaches may be used depending on the system components and flow conditions. Any approach will result in a modeling of the system that will link a downstream pressure, p_DSto the bottom-hole pressure, p_BHand flow rates of each fluid component; here, q_oand q_ware considered for completeness, but the approach is not limited to such relatively simplistic systems. The production system may be modeled through a function f_p_c1_{, . . . , p}_cm^facility, such that:

p_BH=f_p_c1_{, . . . , p}_cm^facility(p_DS, q₀, q_w) (Eq. 7)

where p_c1, . . . , p_cmare m independent control parameters on the system.

Combining Eq. 7 with the previous system composed of Eq. 5 and Eq. 1 or Eq. 2 leads to an integrated well-based production simulator (e.g. as shown in FIG. 6).

In different embodiments of the method, f_p_c1_{, . . . , p}_cm^facilitycan take various forms. If the dynamics of the flow in the production system are simple enough, the function can be an analytical formula, explicitly expressing the dependence of the bottom-hole pressure to the downstream pressure, flow rates and operational parameters. In other, more complex cases, a numerical model of the flow equations may be used, so that the function will be embodied as a simulator of wellbore flow dynamics and/or a surface facility simulator. In one embodiment, the function can take the form of a table of pre-computed solutions. Such vertical lift tables may be used to represent the well flow dynamics in reservoir simulation software. These concepts will be explained further below with regard to methods 400 and 500 of FIGS. 4 and 5, respectively.

In view of the systems and architectures described above, methodologies that may be implemented in accordance with the disclosed subject matter will be better appreciated with reference to the flow charts of FIGS. 4 and 5. For purposes of simplicity of explanation, the methodologies are shown and described as a series of blocks. However, it should be understood and appreciated that the claimed subject matter is not limited by the order of the blocks, as some blocks may occur in different orders and/or concurrently with other blocks from what is depicted and described herein. Moreover, not all illustrated blocks may be required to implement the methodologies described hereinafter.

FIG. 4 illustrates a flowchart of a method 400 for modeling a production system. The method 400 will now be described with frequent reference to the components and data elements shown in FIGS. 1, 2 and 3, respectively.

Method 400 includes accessing one or more portions of first production system information from a capacitance-resistance model of the production system, the production system corresponding to at least one production reservoir (410). For example, accessing module 307 of computer system 301 may be configured to access first production system information 315 received from production reservoir 314. The first production system information 315 may be used by the capacitance-resistance (CR) model 308 to model a production system 320. The production system 320 may include, for example, production reservoir 314, well-bore model 317, flow lines 318 and/or a production facility 319. Each of these production system components may cause constraints on the production of fluids such as petroleum.

As such, the CR model 308 takes into account production system information for the production system 320 as a whole and generates production forecasts 313 based on simulations provided by the model. Each of the modules of computer system 301 may interact with or be processed by processor 302 and/or memory 303. Moreover, communications with other computing systems or users may occur using the communications module 304. For example, first and second production system information 315 and 316 may be received from other computing systems. Similarly, user 305 may interact with the computer system 301 using input 306, which is received at the communications module 304.

Method 400 further includes accessing one or more portions of second production system information from at least one of a well-bore model, a flow line and a production facility (420). As indicated above, a production system (e.g. 320) includes multiple different elements, including a production reservoir 314, a well-bore 317, a flow line 318 and a production facility 319, among others. The second production system information 316 may be sent from any of these elements including a well-bore model 317 (or directly from well-bore datasets), from flow line data 318 or from production facility data 319. Each of these parts of the production system 320 may introduce different pressures or forces on the production material (oil, water, gas, etc.). And, as such, each part may introduce constraints on the production system, reducing or increasing efficiency in some manner.

An integrated production simulator 310 may be configured to take both first production system information 315 and second production system information 316 into account when generating a production forecast 313 for a production system. The integrated production simulator may be generated using both the first and second accessed production system information (430). The simulator generating module 309 of computer system 101, for example, may generate integrated production simulator 310, which may be used to determine the quantity of fluids produced per phase over time as a function of operational field parameters corresponding to the production system by identifying the flow rate for the production reservoir (440). Thus, the simulator 310 may not only be used to generate production forecasts, but also to determine the quantity of fluids 311 produced per phase (e.g. separately for oil, gas and water, or other phases). The simulator determines the quantity of fluids produced per phase over time as a function of operational field parameters corresponding to the production system by identifying the flow rate 312 for the production reservoir. The operational field parameters may include well-head choke size, artificial lift parameters or other field parameters. In this manner, the integrated production simulator 310 is constrained by actual control mechanisms used in a production system.

In some embodiments, the determined quantity of fluids produced per phase over time as a function operational field parameters corresponding to the production system 320 may be further analyzed by computer system 301 to determine whether additional operations are to be initiated on the production system. In some cases, the further analysis may indicate that a certain flow line is reducing fluid flow, or a certain well-bore or portion of a production facility is reducing efficiency in producing material. Accordingly, in such cases, additional operations may be undertaken to increase fluid flow in the determined flow line 318. Other operations may be initiated to increase efficiency in the well bore 317 or the determined portion(s) of the production facility 319.

If it is determined that additional operations are to be initiated on one or more components the production system 320, the computer system 301 may analyze the determined quantity of fluids produced per phase over time as a function of the operational field parameters corresponding to the production system to determine the degree to which the additional operations are to be performed. Accordingly, if fluid flow is drastically reduced at a particular component, the computer system 301 may indicate that operations are to be taken immediately in relation to that component to a degree sufficient to counteract the reduction in fluid flow.

The integrated production simulator 310 may thus simulate and show where constraints exist across each piece of the production system 320. The integrated production simulator 310 is configured to show the flow of petroleum and aqueous fluids from the production reservoir through the production facility. In some cases, identifying the flow rate for the production reservoir includes identifying a production rate for one or more fluids in the production reservoir. These fluids may include water, gas, oil or other fluids. The integrated production simulator 310 may be configured to account for fluid flow in both the well-bore and the production facility. The integrated production simulator 310 may further be configured to account for fluid compressibility, capillary forces and/or gravitational forces. As such, the integrated production simulator 310 may provide a more complete and more thorough indication of operating conditions of a particular production system. Determinations of fluid compressibility, capillary forces and gravitational forces act to remove variables in the simulations, and thus provide a more accurate production forecast 313 or indication of the quantity of fluids 311 produced per phase over time.

In some cases, the integrated production simulator 310 is configured to model the bottom-hole's dependence on one or more components of production system architecture (e.g. components 314, 317, 318 or 319). The bottom-hole pressure for the production system 320 may be identified as an unknown element, as opposed to being identified as a constraint. This may further allow the integrated production simulator 310 to provide a more accurate production forecast 313 and/or indication of the quantity of fluids 311 produced per phase over time.

As shown in FIG. 6, the integrated well-based production simulator 601 comprises a system that includes: a capacitance-resistance (CR) simulator 602 configured to represent the flow of fluids in a production reservoir, a well-bore simulator 603 configured to represent the flow of one or more fluids in a wellbore, and a surface facility simulator 604 configured to represent the flow of fluids through surface facilities including pipelines, production gathering facilities, separation facilities and injection distribution facilities. Because the integrated well-based production simulator 601 analyzes data from a CR simulator, a well-bore simulator and a surface facility simulator, the integrated simulator 601 can provide a system-wide representation of fluid flow 605 through the production reservoir, the wellbore and any one or more of the surface facilities.

At least in some embodiments, the fluids in the production reservoir include petroleum and various aqueous fluids. These fluids may flow to a separator (e.g. 201 of FIG. 2) that is configured to isolate each fluid phase. For instance, the separator 201 may isolate the fluids into at least three fluid phases including oil, gas and water. The fluid phases may be analyzed as being compressible when providing a system-wide representation of fluid flow through the production reservoir. Accounting for compressibility allows the integrated well based production simulator 601 to provide a more accurate production system simulation. Along these lines, capillary forces and gravity forces may also be analyzed when providing a system-wide representation of fluid flow through the production reservoir 208.

The integrated well-based production simulator 601 may be designed to include different levels of control including the ability to control tubing head pressure, flow-line pressure located at the well head upstream and downstream of the choke, and downstream pressures of flow-lines, manifolds or separators. In this manner, a user (e.g. 305 of FIG. 3) may use input 306 to control the tubing head pressure, flow-line pressure or downstream pressures. These may be controlled manually or may be adjusted automatically upon a determination by the integrated production simulator that certain production system 320 components are causing flow constraints.

In some cases, the well-based production simulator may be designed to implement a flow function to determine fluid flow for the integrated well-based production simulator system 310. The function may include an analytical formula that expresses dependence on bottom-hole pressure to downstream pressure, dependence on flow rates and/or dependence on operational parameters such as well-head choke size and artificial lift parameters. Still further, the integrated well-based production simulator may be designed to implement a numerical model of a flow function to determine fluid flow for the integrated well-based production simulator system. As such, the flow function includes a surface facility simulator and/or a simulator of wellbore flow dynamics. At least in some cases, the flow function may include a table of pre-computed solutions. Thus, as can be seen, the well-based production simulator may be designed to include multiple different features and functionality components in order to provide highly accurate indications of fluid flow through a production system 320.

Turning now to FIG. 5, a flowchart of a method 500 is illustrated for generating a production forecast for individual wells. The method 500 will now be described with frequent reference to the components and data elements of FIGS. 1, 2 and 3, respectively.

Method 500 includes accessing one or more operational parameters for a well (510). The operational parameters, as indicated above, may be used to determine the quantity of fluids 311 produced per phase over time as a function of these operational field parameters. The simulator generating module 309 of computer system 301 may use the operational parameters to provide integrated well-based production simulator 310 by solving the following system of equations (520):

p_BH=f_p_c1_{, . . . , p}_cm^facility(p_DS, q_o, q_w), (Eq. 7)

where p_c1, . . . , p_cmcomprise m independent operational parameters for the well,

q_t=j(p−p_BH), (Eq. 5)

which links a total production rate to the reservoir pressure, and

$\begin{matrix} \frac{\partial S_{w}}{\partial t} + S_{w} (c_{w} + c_{f}) \frac{\partial p}{\partial t} + \frac{q_{w} - i_{w}}{V} = 0, & (Eq . 1) \end{matrix}$

which provides a mass conservation condition over one or more fluid components written over the drainage volume of the well, where V comprises the drainage volume of the well. Once instantiated, the generated production simulator 310 may create a production forecast for the well using the accessed operational parameters (530).

Within this production simulator 310, the bottom-hole pressure may be treated as an unknown, and as such, may depend on the architecture of the production system 320. This allows the production forecast to take into account multiple different factors, different designs, different architectures, and different physical conditions present in the various components of the production system 320. The production forecast may be generated quickly enough to drive operational decisions used to optimize the exploitation of petroleum reservoirs. Moreover, the production forecast 313 shows increased accuracy as it is based on actual production system information received from actual, working production system components.

Accordingly, methods, systems and computer program products are provided which model a production system, taking into account each of the different components of the production system. Moreover, methods, systems and computer program products are provided which generate a production forecast for individual wells.

The concepts and features described herein may be embodied in other specific forms without departing from their spirit or descriptive characteristics. The described embodiments are to be considered in all respects only as illustrative and not restrictive. The scope of the disclosure is, therefore, indicated by the appended claims rather than by the foregoing description. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope.

Claims

1. At a computer system including at least one processor and a memory, a computer-implemented method for modeling a petroleum production system, the method comprising:

accessing one or more portions of first production system information from a capacitance-resistance model of the production system, the production system corresponding to at least one petroleum production reservoir;

accessing one or more portions of second production system information from at least one of a well-bore model, a flow line and a petroleum production facility;

generating an integrated production simulator using both the first and second accessed production system information; and

implementing the integrated production simulator to determine the quantity of fluids produced per phase over time as a function of one or more operational field parameters corresponding to the petroleum production system by identifying the flow rate for the production reservoir.

2. The method of claim 1, wherein the integrated production simulator shows the flow of petroleum and aqueous fluids from the production reservoir through the petroleum production facility.

3. The method of claim 1, wherein the one or more operational field parameters corresponding to the petroleum production system comprise well-head choke size and artificial lift parameters.

4. The method of claim 1, wherein the determined quantity of fluids produced per phase over time as a function of one or more operational field parameters corresponding to the petroleum production system is further analyzed to determine whether additional operations are to be initiated on the petroleum production system.

5. The method of claim 4, further comprising, upon determining that additional operations are to be initiated on the petroleum production system, analyzing the determined quantity of fluids produced per phase over time as a function of one or more operational field parameters corresponding to the petroleum production system to determine the degree to which the additional operations are to be performed.

6. The method of claim 1, wherein identifying the flow rate for the production reservoir comprises identifying a production rate for at least one of a plurality of fluids in the petroleum production reservoir.

7. The method of claim 1, wherein the integrated production simulator accounts for fluid flow in both the well-bore and the petroleum production facility.

8. The method of claim 7, wherein the integrated production simulator further accounts for fluid compressibility, capillary forces and gravitational forces.

9. The method of claim 1, wherein bottom-hole pressure for the petroleum production system is identified as an unknown element.

10. The method of claim 9, wherein the integrated production simulator is configured to model the bottom-hole's dependence on one or more components of petroleum production system architecture.

11. An integrated well-based production simulator system comprising:

a capacitance-resistance (CR) simulator configured to represent the flow of one or more fluids in a petroleum production reservoir;

a well-bore simulator configured to represent the flow of one or more fluids in a well-bore; and

a surface facility simulator configured to represent the flow of one or more fluids through at least one of the following surface facilities: one or more pipelines, a production gathering facility, a separation facility, and an injection distribution facility;

wherein the integrated well-based production simulator is configured to provide a system-wide representation of fluid flow through the petroleum production reservoir, the wellbore and at least one of the one or more surface facilities.

12. The integrated well-based production simulator system of claim 11, wherein the one or more fluids comprise petroleum and one or more aqueous fluids.

13. The integrated well-based production simulator system of claim 11, wherein the one or more fluids flow to a separator that is configured to isolate each fluid phase.

14. The integrated well-based production simulator system of claim 13, wherein the separator isolates the fluids into at least three fluid phases (oil, gas and water).

15. The integrated well-based production simulator system of claim 13, wherein the fluid phases are analyzed as being compressible when providing a system-wide representation of fluid flow through the petroleum production reservoir.

16. The integrated well-based production simulator system of claim 11, wherein capillary forces and gravity forces are analyzed when providing a system-wide representation of fluid flow through the petroleum production reservoir.

17. The integrated well-based production simulator system of claim 11, wherein the integrated well-based production simulator incorporates one or more control mechanisms of the petroleum production reservoir including at least one of well-head choke size and artificial lift parameters.

18. The integrated well-based production simulator system of claim 11, wherein the integrated well-based production simulator includes different levels of control including one or more of the following: tubing head pressure, flow-line pressure located at the well head upstream and downstream of the choke, and downstream pressures of flow-lines, manifolds or separators.

19. The integrated well-based production simulator system of claim 11, wherein the integrated well-based production simulator implements a flow function to determine fluid flow for the integrated well-based production simulator system, the function comprising an analytical formula that expresses dependence on bottom-hole pressure to downstream pressure, flow rates and one or more operational parameters.

20. The integrated well-based production simulator system of claim 11, wherein the integrated well-based production simulator implements a numerical model of a flow function to determine fluid flow for the integrated well-based production simulator system, such that the flow function comprises at least one of a simulator of wellbore flow dynamics and a surface facility simulator.

21. The integrated well-based production simulator system of claim 11, wherein the integrated well-based production simulator implements a flow function to determine fluid flow for the integrated well-based production simulator system, the flow function comprising a table of pre-computed solutions.

22. The integrated well-based production simulator system of claim 11, wherein the integrated well-based production simulator system includes a computer system.

23. At a computer system including at least one processor and a memory, a computer-implemented method for generating a production forecast for individual production wells of a petroleum reservoir, the method comprising the following:  S w  t + S w  ( c w + c f )   p  t + q w - i w V = 0, ( Eq.  1 ) which provides a mass conservation condition over one or more fluid components written over the drainage volume of the production well, where V comprises the drainage volume of the production well; and

accessing one or more operational parameters for a production well;

providing an integrated well-based production simulator by solving the following system of equations: (Eq. 7) pBH=fpc1,..., pcmfacility(pDS, qo, qw), where pc1,..., pcm comprise m independent operational parameters for the production well; (Eq. 5) qt=j(p−pBH), which links a total production rate to the reservoir pressure; and

the integrated well-based production simulator generating a production forecast for the production well using the accessed operational parameters.

24. The method of claim 23, wherein the fluid components comprise water and oil.

25. The method of claim 23, wherein the production forecast is generated without relying on assumptions about the individual production well.