Multiphase flow instability control

Abstract

A method may include using sensor measurements from a multi-phase fluid production system to detect instability of a multi-phase fluid flow in the multi-phase fluid production system; and in response to detecting the instability, increasing a gas injection into the multi-phase fluid production system until a variation metric of the sensor measurements decreases to a level indicative of a stable multi-phase fluid flow in the multi-phase fluid production system.

Description

[0001] Related Applications

[0002] This application claims priority to and the benefit of U.S. Provisional Application Serial No. 63 / 366,640, filed on June 19, 2022, which is incorporated herein by reference in its entirety. Background Art

[0003] The production system may provide for the transport of fluids from well locations to processing facilities, from processing facilities to well locations, etc. Such fluids may be single-phase or multi-phase and include one or more hydrocarbon fluids (e.g., oil, natural gas, etc.), and may include one or more other fluids (e.g., water, etc.). As an example, a system may include a large number of flow lines and production equipment, for example, which are interconnected at connection points to form a network, which may be referred to as a fluid production network. Summary of the invention

[0004] A method may include using sensor measurements from a multiphase fluid production system to detect instability in multiphase fluid flow in the multiphase fluid production system; and in response to detecting instability, increasing gas injection into the multiphase fluid production system until a measure of change in the sensor measurements decreases to a level indicating stable multiphase fluid flow in the multiphase fluid production system. A system may include a processor; a memory accessible by the processor; and processor-executable instructions stored in the memory, wherein the instructions include instructions for commanding the system to perform the following operations: using sensor measurements from a multiphase fluid production system to detect instability in multiphase fluid flow in the multiphase fluid production system; and in response to detecting instability, increasing gas injection into the multiphase fluid production system until a measure of change in the sensor measurements decreases to a level indicating stable multiphase fluid flow in the multiphase fluid production system. One or more computer-readable storage media may include computer-executable instructions that can be executed by a computer, the instructions including the following instructions: using sensor measurements from a multiphase fluid production system to detect instability in a multiphase fluid flow in the multiphase fluid production system; and in response to detecting the instability, increasing gas injection into the multiphase fluid production system until a measure of change in the sensor measurements decreases to a level indicating a stable multiphase fluid flow in the multiphase fluid production system.

[0005] This Summary is provided to introduce a selection of concepts that are further described below in the Detailed Description. This Summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in limiting the scope of the claimed subject matter. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] The features and advantages of the embodiments may be more readily understood by referring to the following description in conjunction with the accompanying drawings.

[0007] Figure 1 An exemplary field system including various components, examples of methods, and examples of apparatus or systems are shown;

[0008] Figure 2 An example of a system and an example of a ternary diagram and an example of a table of related fluid properties are shown;

[0009] Figure 3 An example of a system is shown;

[0010] Figure 4 Examples of networks, examples of systems, and examples of instructions are shown;

[0011] Figure 5 Examples of systems and examples of methods are shown;

[0012] Figure 6 An example of a system is shown;

[0013] Figure 7 An example of a system is shown;

[0014] Figure 8 An example of a graphical user interface is shown;

[0015] Fig. 9 An example of a graphical user interface is shown;

[0016] Fig.10 An example of a graphical user interface is shown;

[0017] Fig.11 An example of a graphical user interface is shown;

[0018] Fig.12 An example of a graphical user interface is shown;

[0019] Fig.13 An example of a graphical user interface is shown;

[0020] Fig.14 An example of a method and an example of a system are illustrated;

[0021] Fig.15 An example of a computing system is shown; and

[0022] Fig.16 Exemplary components of systems and networked systems are shown. DETAILED DESCRIPTION

[0023] The following description includes the best mode currently contemplated for practicing the described embodiments. This description should not be understood in a limiting sense, but is merely intended to describe the general principles of the embodiments. Reference should be made to the published claims to determine the scope of the described embodiments.

[0024] Figure 1 An example of a geological environment 110 including reservoirs 111 - 1 and 111 - 2 that may be fractured by faults 112 - 1 and 112 - 2 , an example of a method 150 , and an example of an apparatus or system 170 are shown. Figure 1 Also shown are some examples of offshore equipment 114 for oil and gas operations associated with reservoir 111 - 2 and onshore equipment 116 for oil and gas operations associated with reservoir 111 - 1 .

[0025] As an example, a model may be made to model the geological environment in conjunction with equipment, wells, etc. For example, the model may be a flow simulation model for use by a simulator to simulate flow in an oil, gas, or oil and gas production system. Such a flow simulation model may include, for example, equations for modeling multiphase flow from a reservoir to a wellhead, from a wellhead to a reservoir, etc. The flow simulation model may also include equations that take into account flow lines and surface facility performance (for example) to perform a comprehensive production system analysis.

[0026] As an example, the flow simulation model may be a network model including various sub-networks specified using nodes, segments, branches, etc. As an example, the flow simulation model may be specified in a manner for modeling branch segments, multi-edge segments, complex completions, intelligent downhole control, etc. As an example, one or more parts of a production network (e.g., optional sub-networks, etc.) or a set of signal components and / or controllers may be modeled as sub-models.

[0027] As an example, a system may be used to transport oil and gas fluids from a well site to a processing facility and may represent a large investment in infrastructure with economic and environmental impacts. The simulation of such a system, which may include hundreds or thousands of flow lines and production equipment interconnected at junctions to form a network, may involve the use of multiphase flow science and engineering and mathematical techniques such as large systems of equations.

[0028] As an example, a flow simulation model may include equations for performing nodal analysis, pressure-volume-temperature (PVT) analysis, gas lift analysis, erosion analysis, corrosion analysis, production analysis, injection analysis, etc. In such examples, one or more analyses may be based in part on flow simulation in the modeled network.

[0029] Regarding node analysis, it can be used to evaluate well performance, make decisions about well completion, etc. Node analysis can be used to understand the behavior of the system and optionally understand the sensitivity of the system (e.g., production, injection, production and injection). For example, system variables can be selected for investigation and sensitivity analysis can be performed. Such analysis can include plotting the inflow and outflow of fluids at one or more nodes in the system, which can indicate where certain opportunities exist (e.g., for injection, for production, etc.).

[0030] The modeling framework may include instructions (e.g., processor executable instructions) to facilitate the generation of flow simulation models. For example, instructions for modeling vertical well completions, horizontal well completions, fractured well completions, etc. may be provided. The modeling framework may include instructions for specific types of equations, such as black oil equations, equations of state (EOS) equations, etc. The modeling framework may include instructions for artificial lift, for example, to model fluid injection, fluid pumping, etc. As an example, consider a set of instructions (e.g., components) that include features for modeling one or more electric submersible pumps (ESPs) (e.g., based in part on pump performance curves, motors, cables, etc.).

[0031] As an example, an analysis using a flow simulation model could be a network analysis to: identify production bottlenecks and constraints; evaluate the benefits of new wells, additional pipelines, compression systems, etc.; calculate the transport capacity of field gathering systems; predict pressure and temperature distribution in a flow path; or plan full field development.

[0032] As an example, a flow simulation model can be used to perform analysis for future times, e.g., to allow optimization of production equipment, injection equipment, etc. As an example, consider an optimal time and condition event based logical representation for daily oil field development operations, which can be used to evaluate the drilling of new development wells, installation of additional treatment facilities over time, throttling of wells to meet production and operating constraints, closing of depleted wells as reservoir conditions decline, etc.

[0033] Regarding equations, a set of conservation equations describing mass, momentum and energy for single-phase, two-phase or three-phase flow (e.g., based on one or more of LEDAFLOW (Kongsberg Oil & Gas Technology, Sandvika, Norway), the OLGATM model (Schlumberger, Houston, Texas), the TUFFP Unified Mechanical Model (Fluid Flow Project, University of Tulsa, Tulsa, Oklahoma), etc.).

[0034] about Figure 1A method 150 is provided for constructing a network model of a production system representing a fluid; a distribution block 154 for distributing equations to subnetworks in the network model (e.g., wherein at least one of the subnetworks is optionally assigned an equation formulated to implicitly and simultaneously solve for pressure and momentum, conserved mass, and energy / temperature); a provision block 156 for providing data; a transmission block 158 for transmitting data to the network model; and a simulation block 160 for simulating physical phenomena associated with the production system using the network model to provide simulation results.

[0035] Method 150 Figure 1 150 as being associated with various computer readable media (CRM) blocks 153, 155, 157, 159, and 161. Such blocks generally include instructions suitable for execution by one or more processors (or processing cores) 172 to command the computing device or system 170 to perform one or more actions. Although various blocks are shown, a single medium may be configured with instructions to, at least in part, enable the execution of the various actions of the method 150. As an example, the computer readable medium (CRM) may be a non-carrier computer readable storage medium, such as a memory device 174 of the computing device or system 170, where the memory device 174 includes a memory.

[0036] A production system may include equipment, for example, where a piece of equipment of the production system may be represented in a subnetwork of a network model (e.g., optionally as one or more submodels, etc.), and, for example, assigned an equation formulated to represent the piece of equipment. As an example, a piece of equipment may include an electric motor operably coupled to a mechanism for moving a fluid (e.g., a pump, a compressor, etc.). As an example, a piece of equipment may include a heater coupled to a power source, a fuel source, etc. (e.g., consider a steam generator). As an example, a piece of equipment may include a conduit for conveying a fluid, where the fluid may be used to convey thermal energy (e.g., consider a steam ejector). As an example, a piece of equipment may include a conduit for conveying a substance (e.g., a chemical, a proppant, etc.).

[0037] As an example, a subnetwork may be assigned equations formulated to represent a fluid at or near a critical point, to represent heavy oil, to represent steam, to represent water, or to represent one or more other fluids (e.g., optionally subject to certain physical phenomena such as pressure, temperature, etc.).

[0038] As an example, a system may include a processor; a memory device having a memory accessible by the processor; and processor-executable instructions stored in the memory of the memory device, the instructions executable to command the system to: construct a network model of a production system representing a fluid; assign equations to subnetworks in the network model; provide data; transfer the data to the network model; and simulate physical phenomena associated with the production system using the network model to provide simulation results.

[0039] As an example, a system may include a subnetwork assigned with equations formulated for steam associated with equipment of an enhanced oil recovery (EOR) process (eg, steam assisted gravity drainage (SAGD) and / or other EOR processes).

[0040] As an example, a system may include a subnetwork that represents a piece of equipment of a production system by assigning to the subnetwork an equation formulated to represent the piece of equipment. In such an example, the piece of equipment may include an electric motor that is operably coupled to a mechanism for moving a fluid (e.g., a compressor, a pump, etc.).

[0041] As an example, one or more computer-readable media may include computer-executable instructions executable by a computer to instruct the computer to: receive simulation results of physical phenomena associated with a production system modeled by a network model; and analyze the simulation results.

[0042] Figure 2 An example of a schematic diagram of a portion of a geological environment 201 including a device and an example of a ternary diagram 250 and an example of a table 260 of associated fluid properties are shown. Figure 2 As shown, environment 201 includes wellsite 202, network 203, and various examples of surface processing equipment such as, for example, separator 244, compressor 254, and pump 256. Wellsite 202 includes wellbore 206 extending into the formation, which is completed and ready for producing fluids from reservoir 211.

[0043] exist Figure 2 In the example of FIG. 2 , a wellbore production device 264 extends from a wellhead 266 of a wellsite 202 to a reservoir 211 to pump fluid to the surface. As shown, the wellsite 202 is operably connected to the network 203 via a delivery line 261 (e.g., a pipeline), which may include a riser 263, which may include an S-shaped portion that may form a fluid trap. As indicated by various arrows, fluids may flow from the reservoir 211 through the wellbore 206 and onto the network 203 and onto a portion 244 thereof. The fluids may then flow from the portion 244 of the network to, for example, one or more fluid processing facilities.

[0044] exist Figure 2 In the example of, the sensor(s) is positioned, for example, to monitor various parameters during operation. The sensor(s) may measure, for example, pressure, temperature, flow rate, composition, and other parameters of a reservoir, wellbore, collection network, processing facility, and / or other portion of an operation. As an example, the sensor(s) may be operably connected to the surface unit 216 (e.g., to command the sensor to acquire data, collect data from the sensor, etc.).

[0045] exist Figure 2 In the example of the ground unit 216, various components such as, for example, a storage device 220, a controller 222, one or more processors 224, one or more interfaces 225, and a display unit 226 (e.g., for managing data, visualizing analysis results, etc.). As an example, data can be collected in the storage device 220 and processed by at least one of the one or more processors 224 (e.g., for analysis, etc.). As an example, data can be collected from a sensor (S) and / or collected through one or more other sources. For example, the data can be supplemented with historical data collected from other operations, user input, etc. As an example, the analyzed data can be used in a decision-making process.

[0046] exist Figure 2 In the example of , a transceiver may be provided to allow communication between the surface unit 216 (e.g., via one or more interfaces 225) and one or more pieces of equipment in the environment 201 (e.g., one or more equipment interfaces, which may be wired and / or wireless). For example, the controller 222 may be used to actuate multiple mechanisms in the environment 201 via the transceiver, optionally based on one or more decisions of the decision-making process. In this way, equipment in the environment 201 may be selectively adjusted based at least in part on the collected data. Such adjustments may be made automatically, for example, based on a computer protocol, manually by an operator, or both. As an example, one or more well plans may be adjusted (e.g., to select optimal operating conditions, avoid problems, etc.). In Figure 2 In an example of , a network can be established, which is a network of devices for transmitting and receiving information (eg, via a network interface).

[0047] To facilitate data analysis, one or more simulators may be implemented (e.g., optionally via surface unit 216 or other units, systems, etc.). As an example, the data fed into the one or more simulators may be historical data, real-time data, or a combination thereof. As an example, simulations via the one or more simulators may be repeated or adjustments may be made based on the received data.

[0048] exist Figure 2In an example, the simulator may include a reservoir simulator 228, a wellbore simulator 230, a surface network simulator 232, a process simulator 234, and an economic simulator 236. As an example, the reservoir simulator 228 may be configured to solve for hydrocarbon flow rates (e.g., and optionally, one or more pressures) through a reservoir and into one or more wellbores. As an example, the wellbore simulator 230 and the surface network simulator 232 may be configured to solve for hydrocarbon flow rates (e.g., and optionally, one or more pressures) through a surface collection network of wellbores and pipelines. With respect to the process simulator 234, it may be configured to model a processing plant in which a fluid containing hydrocarbons is separated into, for example, its constituent components (e.g., methane, ethane, propane, etc.) and prepared for further distribution (e.g., transportation via road, rail, pipeline, etc.) and optionally sold. As an example, the economic simulator 236 may be configured to model costs associated with at least a portion of an operation. For example, consider the MERAK framework (Schlumberger, Houston, Texas), which can provide economic analysis.

[0049] exist Figure 2 , ternary diagram 250 includes vertices representing single-phase gas, oil, and water, while edges represent two-phase mixtures (e.g., gas-oil, oil-water, and gas-water), and points within triangles represent three-phase mixtures. Transition regions indicate where the liquid portion changes from water-in-oil to oil-in-water or from oil-in-water to water-in-oil (e.g., consider emulsions).

[0050] Figure 2 The ternary diagram 250 also indicates some examples of a range of multiphase flow regimes, which can be affected by one or more factors, such as, for example, temperature, pressure, viscosity, density, streamline orientation, etc. Exemplary flow regimes include annular mist, slug flow, and bubbly flow; note that other types of flow regimes (e.g., laminar flow, chaotic flow, dispersed flow, etc.) may occur. Annular mist flow can be characterized by, for example, a layer of liquid on the wall of a tube and droplets (e.g., mist) in an intermediate gas zone. For example, a slug flow can be characterized by a continuous liquid phase and a discontinuous liquid phase that is discontinuous due to gas pocket separation. Bubbly flow can be characterized by, for example, two continuous liquid phases, where at least one of the continuous liquid phases includes bubbles. Figure 2 The illustrative figures of the flow regime correspond to flow in an approximately horizontal conduit; note that the conduit can be arranged at angles other than horizontal, and various factors that may affect the flow can depend on the angle of the conduit. For example, the angle of the conduit relative to gravity may affect the way the fluid flows in the conduit.

[0051] Figure 2Table 260 shows viscosity and density as fluid properties. Regarding one or more other properties, surface tension is considered, for example. As indicated, table 260 may include information for a point specified by ternary diagram 250. As an example, factors such as pressure, volume, and temperature may be considered, such as values ​​regarding fluid properties, phases, flow states, etc.

[0052] As an example, information about a fluid flow can be represented as a flow pattern diagram that identifies flow patterns occurring in various portions of a parameter space defined by component flow rates. For example, consider flow rates such as volume flux, mass flux, momentum flux, or one or more other quantities. Boundaries between various flow patterns in a flow pattern diagram may occur where one flow pattern becomes unstable, and where the growth of this instability leads to a transition to another flow pattern. As with the transition from laminar to turbulent flow in a single-phase flow, multiphase transitions may be quite difficult to predict because they may depend on otherwise trivial features in the flow, such as wall roughness or entrainment and inlet conditions. Therefore, as indicated by the ternary diagram 250, flow pattern boundaries may lack sharpness and exhibit transition zones.

[0053] With respect to properties, in the case where the fluid is a single phase (e.g., water, oil, or gas), a single viscosity value may satisfy a given condition. However, in the case where the fluid is multiphase, two or more simultaneously existing phases may occupy a flow space within a conduit (e.g., a pipe). In such examples, a single viscosity (e.g., or density) value may not correctly characterize the fluid in that flow space. Thus, as an example, one or more values ​​of a mixture viscosity may be used, for example, where the mixture value is a function of the phase fraction of the fluid in the multiphase flow space.

[0054] Regarding surface tension (e.g., σ), it can be defined for gas and liquid, where the liquid can be oil or water, for example. When there is a two-phase liquid-liquid flow (e.g., water and oil), σ can reflect the interfacial tension between the oil and water (e.g., see slug flow and bubble flow).

[0055] Figure 3 An example of a schematic diagram of a production system 300 for performing oilfield production operations is shown. Figure 3As shown in the example of , the production system 300 may include an oilfield network 302, an oilfield production tool 304, one or more data sources 306, one or more oilfield applications 308, and one or more plug-ins 310. As an example, the oilfield network 302 may be an interconnection of pipes (e.g., conduits) connecting a well site (e.g., well site 1 312, well site n 314, etc.) to a processing facility 320. The pipes in the oilfield network 302 may be connected to a processing facility (e.g., processing facility 320), a well site (e.g., well site 1 312, well site n 314, etc.), and / or a connection point where the pipes are connected. As an example, the flow rate of fluids and / or gases entering the pipes may be adjustable; therefore, certain pipes in the oilfield network 302 may be throttled or closed so as not to allow fluids and / or gases to pass through the pipes. When the pipe allows liquids and / or gases to flow, the pipe may be considered open (e.g., optionally throttled). With respect to a throttle valve, a throttle may allow one or more characteristics of a piece of flow equipment (e.g., cross-sectional flow area, etc.) to be adjusted, for example, to fully open flow, to throttle flow, and / or to no flow (e.g., closed).

[0056] As an example, a throttle valve may include an orifice for controlling a fluid flow rate or downstream system pressure. As an example, a throttle valve may be provided in any of a variety of configurations (e.g., for fixed and / or adjustable modes of operation). For example, an adjustable throttle valve may vary fluid flow and pressure parameters to accommodate process or production requirements. As an example, a fixed throttle valve may be configured to resist erosion during prolonged operation or when producing abrasive fluids.

[0057] The oilfield network 302 may be a collection network and / or an injection network. The collection network may be an oilfield network for obtaining hydrocarbons from a well site (e.g., well site 1 312, well site n 314, etc.). In the collection network, hydrocarbons may flow from the well site to the processing facility 320. The injection network may be an oilfield network for injecting injectants (such as water, carbon dioxide, and other chemicals that may be injected into the well site) into the well site. In the injection network, the flow of injectants may flow to the well site (e.g., to well site 1 312, well site n 314, etc.).

[0058] The oilfield network 302 may also include one or more surface units (e.g., surface unit 1 316, surface unit n 318, etc.), such as one surface unit for each well site. Such surface units may include functionality to collect data from sensors (e.g., see Figure 2Surface unit 216). Such sensors may include sensors for measuring flow rate, water content, gas lift rate, pressure, and / or other such variables associated with measuring and monitoring hydrocarbon production. As shown, oilfield network 302 may include one or more transceivers 321, for example, for receiving information, for sending information, for receiving information and sending information, etc. As an example, information may be received and / or sent via wired and / or wireless means. As an example, information may be received and / or sent via a communication network (such as, for example, the Internet, cloud, cellular network, satellite network, etc.).

[0059] As an example, an oilfield production tool 304 can be connected to an oilfield network 302. The oilfield production tool 304 can be a simulator (e.g., a simulation framework) or a plug-in of a simulator (e.g., or other application). The oilfield production tool 304 can include one or more transceivers 322, a report generator 324, an oilfield modeler 326, and an oilfield analyzer 328. As an example, one or more transceivers 322 can be configured to receive information, to send information, to receive information and to send information, etc. As an example, information can be received and / or sent via wired and / or wireless means. As an example, information can be received and / or sent via a communication network (such as, for example, the Internet, a cloud, a cellular network, a satellite network, etc.).

[0060] As an example, one or more of the one or more transceivers 322 may include functionality to collect oilfield data. The oilfield data may be data from sensors, historical data, or any other such data. One or more of the one or more transceivers 322 may also include functionality to interact with a user and display data such as production results.

[0061] As an example, the report generator 324 may include functionality to generate graphical and textual reports. Such reports may display historical oilfield data, production models, production results, sensor data, aggregated oilfield data, or any other such type of data.

[0062] As an example, the data repository 352 may be a storage unit and / or device (e.g., a file system, a database, a collection of tables, or any other storage mechanism) for storing data, such as production results, sensor data, aggregated oilfield data, or any other such type of data. As an example, the data repository 352 may include a plurality of different storage units and / or hardware devices. Such a plurality of different storage units and / or devices may or may not be of the same type, or located in the same physical location. As an example, the data repository 352 or a portion thereof may be protected via one or more security protocols, whether physical, algorithmic, or a combination thereof (e.g., data encryption, secure device access, secure communications, etc.).

[0063] exist Figure 3 In the example of, oilfield modeler 326 may include the functionality of creating wellbore and oilfield network models. As shown, oilfield modeler 326 includes wellbore modeler 360 and network modeler 332. As an example, wellbore modeler 360 may allow a user to create a graphical wellbore model or a single branch model. As an example, a wellbore model may define operating parameters (e.g., actual, theoretical, etc.) of a wellbore (e.g., pressure, flow rate, etc.). As an example, a single branch model may define operating parameters of a single branch in an oilfield network.

[0064] Regarding the network modeler 332, it can allow a user to create a graphical network model that combines a wellbore model and / or a single branch model. As an example, the network modeler 328 and / or the wellbore modeler 360 can model the pipes in the oilfield network 302 as branches of the oilfield network 302 (e.g., optionally modeled as one or more segments, optionally having one or more nodes, etc.). In such examples, each branch can be connected to a well site or a connection point. A connection point can be defined as a group of two or more pipes that intersect at a specific location (e.g., a connection point can be a node or a type of node).

[0065] As an example, the modeled oilfield network can be formed as a combination of subnetworks. In such an example, a subnetwork can be defined as a portion of the oilfield network. For example, a subnetwork can be connected to the oilfield network 302 using at least one branch. A subnetwork can be a set of connected well sites, branches, and connection points. As an example, subnetworks can be disjoint (e.g., a branch and well site in one subnetwork may not exist in another subnetwork).

[0066] As an example, the oilfield analyzer 328 may include functionality to analyze the oilfield network 302 and generate production results for the oilfield network 302. Figure 3 As shown in the example of , the oilfield analyzer 328 may include one or more of the following: a production analyzer 334, a fluid modeler 336, a flow modeler 338, an equipment modeler 340, a single branch solver 342, a network solver 344, a Wegstein solver 348, a Newton solver 350 and an offline tool 346.

[0067] As an example, the production analyzer 334 may include functionality to receive workflow requests and interact with the single branch solver 342 and / or the network solver 344 based on specific aspects of the workflow. For example, a workflow may include nodal analysis to analyze the connection points of a well site or branch, pressure and temperature profiles, model calibration, gas lift design, gas lift optimization, network analysis, and other such workflows.

[0068] As an example, the fluid modeler 336 may include the functionality to calculate fluid properties (e.g., phases present, density, viscosity, etc.) using one or more compositional and / or black oil fluid models. The fluid modeler 336 may include the functionality to model oil, gas, water, hydrates, wax, and asphaltene phases. As an example, the flow modeler 338 may include the functionality to calculate the pressure drop in a pipe (e.g., pipe, tubing, etc.) using industry standard multiphase flow correlations. As an example, the equipment modeler 340 may include the functionality to calculate pressure, temperature, and flow changes in equipment components (e.g., throttle valves, pumps, compressors, etc.). As an example, one or more substances may be introduced via a network for managing asphaltene, wax, etc. As an example, the modeler may include the functionality to model the interaction between one or more substances and a fluid (e.g., including materials present in the fluid).

[0069] As an example, the single branch solver 342 may include functionality to calculate flow rate, pressure drop, and fluid temperature changes in a wellbore or a single flow line branch based on various inputs.

[0070] As an example, the network solver 344 may include functionality to calculate flow rates, pressure drops, and fluid temperature changes throughout the oilfield network 302. The network solver 344 may be configured to connect to an offline tool 346, a Wegstein solver 348, and a Newton solver 350. As an example, one or more other solvers may be provided, alternatively or additionally, e.g., consider a sequential linear programming solver (SLP), a sequential quadratic programming solver (SQP), etc. As an example, the solver may be part of the production tool 304, part of the analyzer 328 of the production tool 304, part of a system to which the production tool 304 may be operatively coupled, and the like.

[0071] As an example, the offline tools 346 may include a well offline tool and a branch offline tool. The well offline tool may include functionality to generate a production model for a well pad or a branch using the single branch solver 342. The branch offline tool may include functionality to generate a production model for a sub-network using a production model of a well pad, a single branch, or a sub-network of a sub-network.

[0072] As an example, a production model may be able to provide a description of a well site for various operating conditions. The production model may include one or more production functions that may be combined, for example, where each production function may be a function of variables associated with the production of hydrocarbons. For example, a production function may be a function of flow rate and / or pressure and / or temperature. In addition, the production function may take into account environmental conditions associated with a sub-network of the oilfield network 302, such as changes in altitude (e.g., gravity head, pressure, etc.), diameter of pipes, combinations of pipes, pressure changes caused by connecting pipes, and changes in pipe or production environment data (such as ambient temperature (ambient fluid velocity, etc.)). The production model may provide an estimate of the flow rate of a well site or a sub-network of the oilfield network.

[0073] As an example, there may be one or more separate production functions that may explain changes in one or more values ​​of operating conditions. Operating conditions may identify properties of hydrocarbons or injected materials. For example, operating conditions may include water cut (WC), reservoir pressure, gas lift rate, etc. Other operating conditions, variables, and environmental conditions may be considered.

[0074] Regarding network solver 344, in Figure 3 In the example of , it is shown as being connected to a Wegstein solver 348 and / or a Newton solver 350. The Wegstein solver 348 and the Newton solver 350 include functionality to combine production models of multiple subnetworks to create production results that can be used to plan an oilfield network, optimize flow rates for well sites in an oilfield network, and / or identify and resolve faulty components within an oilfield network. The Wegstein solver 348 can use an iterative approach with Wegstein acceleration.

[0075] The oil field network can be solved by identifying the pressure drop (e.g., pressure difference), such as by using the momentum equation. As an example, the pressure difference equation can take into account factors such as fluid potential energy (e.g., hydrostatic pressure), friction (e.g., shear stress between the conduit wall and the fluid), and acceleration (e.g., change in fluid velocity). As an example, the equation can be expressed in terms of static reservoir pressure, flowing bottom hole pressure, and flow rate. As an example, the equation can account for the vertical, horizontal, or angular arrangement of equipment. Examples of various equations can be found in dynamic multiphase flow simulators such as the simulator of the OLGA simulation framework (Schlumberger, Houston, Texas), which can be implemented for design and diagnostic analysis of oil and gas production systems. As an example, the simulation framework can include one or more sets of instructions for building models; for fluid and multiphase flow modeling; for reservoir, well, and completion modeling; for field equipment modeling; and for operations (e.g., artificial lift, gas lift, wax prediction, node analysis, network analysis, field planning, multi-well analysis, etc.).

[0076] As an example, the system may implement equations including dynamic conservation equations for momentum, mass, and energy. As an example, pressure and momentum may be solved implicitly and simultaneously, while conservation of mass and energy (eg, temperature) may be solved in a subsequent independent stage.

[0077] As an example, the pressure difference equation can take into account factors such as fluid potential energy (e.g., hydrostatic pressure), friction (e.g., shear stress between the conduit wall and the fluid), and acceleration (e.g., changes in fluid velocity). In addition, as described above, equations can be used to take into account dynamic aspects. For example, equations can take into account the time and forces to accelerate and decelerate fluids (e.g., and objects) inserted into a multiphase flow (e.g., consider a pig, etc.). As an example, a method can consider the time required to save mass and energy (e.g., the amount of time required to empty a system, pipe, or vessel). As an example, a method can consider the ramp-up time of production, such as from one production rate to another, etc. As an example, a method can consider the time required for the first condensate to appear at the outlet of a production network during startup, etc.

[0078] As an example, the equation for the pressure difference (e.g., ΔP) can be rearranged to solve for the flow rate (e.g., Q), where the equation may include the Reynolds number (e.g., Re, the dimensionless ratio of inertial forces to viscous forces), one or more friction coefficients (e.g., which may depend on the flow state), etc.

[0079] By using the equations for the inflow and outflow branches and equating them to zero, a linear matrix for the unknown pressures can be obtained. As an example, a fixed flow branch (ie, a branch where the flow rate does not change) can be solved directly for the nodal pressures.

[0080] As an example, a method may include defining variables and residual equations and branches in an oil field network that may include multiple equipment items. As an example, a branch may be divided into sub-branches, where each sub-branch includes equipment items. As an example, a new node may be used to connect each pair of sub-branches. In this example, the main Newton-Raphson variables may include the flow rate (Q) in each sub-branch in the network. ib ) and the pressure P at each node in the network in In this example, temperature (or enthalpy) and composition can be considered as secondary variables.

[0081] As an example, the residual equations may include branch residuals, internal node residuals, and boundary conditions. In such an example, the branch residuals for a sub-branch relate the branch flow to the pressure at the branch inlet node and the pressure at the outlet node. As an example, the internal node residuals may define that the total flow into a node is equal to the total flow out of the node.

[0082] As an example, an initial solution may be performed using a production model, where for each subsequent iteration, a Jacobian matrix is ​​calculated. The value of the Jacobian matrix may be used to solve the Jacobian equation for Newton-Raphson updates. To solve the Jacobian equation, one or more types of matrix solvers may be used.

[0083] exist Figure 3 In the example of FIG. 3 , one or more data sources 306 include one or more types of data repositories. For example, one or more data sources 306 may be an Internet source, a source from a company that has a connection with the oilfield network 302, or any other location where data may be obtained. The data may include historical data, data collected from other oilfield networks, data collected from the oilfield network being modeled, data describing the environment or operating conditions.

[0084] exist Figure 3 In the example of , one or more oilfield applications 308 may be applications related to hydrocarbon production. One or more oilfield applications 308 may include functionality to evaluate formations, manage drilling operations, evaluate seismic data, evaluate workflows in an oilfield, perform simulations, or perform any other oilfield related functions. Figure 3 In the example, one or more plug-ins 310 can allow integration with packages such as, for example, the TUFPP model, the Infochem Multiflash model (Infochem Computer Services Ltd., London, England), device models, etc. (e.g., consider one or more simulators such as HYSYS (AspenTech, Burlington, Massachusetts), UNISIM (Honeywell, Morristown, New Jersey), etc.).

[0085] although Figure 3 The example of 304 shows the oilfield production tool 304 as a separate component from the oilfield network 302, but the oilfield production tool 304 may alternatively be part of the oilfield network 302. For example, the oilfield production tool 304 may be located at one of the well sites (e.g., well site 1 312, well site n 314, etc.), at the processing facility 320, or at any other location in the oilfield network 302. As another example, the oilfield production tool 304 may exist separately from the oilfield network 302, such as when the oilfield production tool 304 is used to plan the oilfield network.

[0086] Various types of numerical solution schemes can be characterized as explicit or implicit. For example, when the dependent variables can be directly calculated from known quantities, the scheme can be characterized as explicit. However, when the dependent variables are defined by a system of coupled equations and matrix or iterative techniques are implemented to obtain the solution, the scheme can be characterized as implicit.

[0087] As an example, a scheme may be characterized as adaptive implicit ("AIM"). For example, an AIM scheme may adapt one or more variables, attributes, etc. of a model based on one or more gradients. For example, when a model of a subsurface environment includes a region where porosity varies rapidly with respect to one or more physical dimensions (e.g., x, y, or z), an implicit scheme may be used to model the solution for the one or more variables in the region, while the overall solution of the model also includes an explicit scheme (e.g., for one or more other regions for the same one or more variables).

[0088] As an example, a scheme may be implemented as part of the ECLIPSE reservoir simulator and / or the INTERSECT reservoir simulator (Schlumberger, Houston, Texas). As an example, the OLGA simulator mentioned above may include an interface that allows interoperability with the ECLIPSE simulator. The ECLIPSE reservoir simulator may implement a fully implicit scheme, or an implicit-explicit scheme, i.e., pressure implicit, saturation explicit, referred to as IMPES. In the fully implicit scheme, values ​​for both pressure and saturation are provided at the end of each simulation time step; whereas the IMPES scheme uses the saturation value at the beginning of the time step to solve for the pressure value at the end of the time step. In such examples, the reservoir simulator iterates until the pressure values ​​in the grid blocks of the model of the simulated reservoir reach some internally consistent solution. However, if the saturation (which the IMPES scheme assumes is constant over a time step) changes rapidly over that time step (e.g., the percentage of the grid block value of saturation changes greatly), it may be difficult to find a solution. The IMPES scheme can address such issues by reducing the "length" (e.g. duration) of the time steps, but at the expense of adding more time steps (e.g., to obtain a more stable solution).

[0089] The fully implicit scheme mentioned earlier can be a more stable choice, where saturation and pressure are obtained simultaneously, so any differences between their values ​​from one time step to the next will not interfere with the solution process as much as with the IMPES scheme. Therefore, the "length" (e.g., duration) of the time step can be larger in the fully implicit scheme, but this also means that the fully implicit scheme may require additional processing time to obtain a solution (e.g., compared to the IMPES scheme). However, in reservoirs with rapidly changing properties, the fully implicit scheme can provide a solution in less computational time than the IMPES scheme, although the iterations of the fully implicit scheme may take longer to complete than the iterations of the IMPES scheme.

[0090] As described above, production systems can transport oil and gas fluids from well locations along flow lines that are interconnected at connection points, thereby combining fluids from many wells for delivery to processing facilities. Along these flow lines (including at one or more ends of the flow lines), production equipment can be inserted to modify flow characteristics, such as flow rate, pressure, composition, and temperature. As an example, boundary conditions can depend on the type of equipment, the operation of a piece of equipment, etc.

[0091] Figure 4 An example of a relatively small production system network 410 (e.g., optionally a portion of a larger network 401), an example of a system 450, and an example of instructions 470 are shown. As shown, the network 410 forms a tree-like structure in which the flow lines represent branches (e.g., segments) and the connection points represent nodes. Figure 4 As shown, network 410 provides for the transport of oil and gas fluids from well locations along flow lines interconnected at connection points and ultimately to a central processing facility.

[0092] exist Figure 4 In the example of, various portions of network 410 may include conduits. For example, consider a perspective view of geological environment 403 including two conduits, which may be a conduit leading to Man1 and a conduit leading to Man3 in network 410. The conduits may be specified at various points by properties, which may be properties of the environment, properties of the conduits, properties of the fluid in the conduits, etc. For example, consider the elevation of the conduit, which may allow the inclination of the conduit to be determined. As an example, consider the cross-sectional flow area of ​​the conduit, which may be defined by one or more parameters (such as, for example, the diameter of the conduit). As an example, consider a fluid that may flow in a conduit, where the fluid may be characterized at least in part by properties such as, for example, viscosity (see, for example, Figure 2 As an example, thermal conditions may be optionally considered, such as, for example, latent heat, heat transfer, etc. As an example, thermal conditions may depend on the insulation of the equipment, the temperature of the environment, wind, sun, rain, snow, etc. Such factors may be considered when evaluating an existing network, developing a network, expanding a network, etc.

[0093] As an example, given information about the operating conditions at boundary nodes (e.g., where fluids enter and leave the system) and the physical environment between them (e.g., geographic location, altitude, ambient temperature, etc.), production engineers can aim to design a production system that meets commercial and regulatory requirements, subject to the operational limitations of the available equipment.

[0094] As an example, a method may include implementing one or more components to simulate steady-state operation of a production system, such as including a network (e.g., as a subnetwork, etc.), such as Figure 4 Examples of (see also e.g. Figure 1 ). Such methods may include simulating steady state operation over a selected range of operating conditions and configurations (eg, the widest reasonable range that may be selected).

[0095] As explained, production systems can be used to transport oil and gas fluids from well sites to processing facilities and can represent a large investment in infrastructure with economic and environmental impacts. The simulation of such systems, which can include hundreds or thousands of flow lines and production equipment interconnected at connection points to form a network, can be complex and involve multiphase flow science and engineering and mathematical methods to provide solutions (e.g., by solving large nonlinear systems of equations). Factors associated with solid formation, corrosion and erosion, and environmental impacts can add to the complexity and cost.

[0096] like Figure 4 As shown, the exemplary system 450 includes one or more information storage devices 452, one or more computers 454, one or more networks 460, and instructions 470 (e.g., organized as one or more sets of instructions). With respect to the one or more computers 454, each computer may include one or more processors (e.g., or processing cores) 456 and a memory 458 for storing instructions 470 (e.g., one or more sets of instructions), which may be executed, for example, by at least one of the one or more processors. As an example, a computer may include one or more network interfaces (e.g., wired or wireless), one or more graphics cards, a display interface (e.g., wired or wireless), etc. As an example, images, such as ground images (e.g., satellite, geological, geophysical, etc.), may be stored, processed, transmitted, etc. As an example, data may include SAR data, GPS data, etc., and may be stored, for example, in one or more of the storage devices 452. As an example, the information that may be stored in one or more of the storage devices 452 may include information about a device, a location of a device, an orientation of a device, fluid properties, etc.

[0097] As an example, instructions 470 may include instructions (e.g., stored in memory 458) executable by at least one of the one or more processors 456 to instruct system 450 to perform various actions. As an example, system 450 may be configured such that instructions 470 provide a framework for establishing, for example, a network modeling can be performed. As an example, one or more sets of instructions may be used to perform one or more methods, techniques, etc., and these instructions may be, for example, Figure 4 Instruction 470.

[0098] Figure 4An example of instructions 470 is shown, including a graphical user interface (GUI) component 471, a map component 472, a device component 473, a data component 474 (e.g., data for measurements, synthetic data, etc.), a modeling component 475, and one or more other components 476, where a component may be or include a set of instructions.

[0099] As an example, a component may include instructions to command the system to present terrain and device locations to a display (e.g., via GUI component 471, map component 472, device component 473, etc.); receive data of at least a portion of a network (e.g., via data component 474); analyze the data according to a model associated with the terrain and device locations (e.g., via modeling component 475); and present information to a display based at least in part on the analysis (e.g., via GUI component 471, a reporting component, etc.).

[0100] As an example, various features of the system (such as, for example, Figure 4 As an example, one or more sets of instructions may be provided, the one or more sets of instructions comprising instructions executable by one or more processors. As an example, instructions may be provided for executing instructions in parallel, such as taking into account possible differences in geological environment (such as Figure 1 Multiple features of one or more networks associated with the geological environment 110).

[0101] Oil and gas production systems typically include multiple wells that are tied back to a manifold, platform, or shore, etc. (For example, consider a subsea manifold, a wellhead platform, a land-based manifold, etc.). At the manifold or wellhead platform, the outputs of different wells may be mixed together (e.g., merged, mixed, etc.) and fed to one or more multiphase pipelines that can transport the fluids to, for example, a topside or central processing facility. As an example, multiple manifolds and wellhead platforms can feed a topside / central processing facility. As an example, the production fluids from the topside / central processing facility can be fed again to an export pipeline for natural gas and / or oil to feed a market or chemical processing plant.

[0102] For example, the fluid production network can include substantially vertical conduits and substantially horizontal conduits and / or substantially vertical conduits and / or conduits that are neither substantially horizontal nor substantially vertical. For example, the substantially vertical conduits can be oriented at an angle greater than about 50 degrees relative to the horizontal. As an example, the substantially horizontal conduits can be oriented at an angle less than about 40 degrees relative to the horizontal orientation (e.g., between -40 degrees and +40 degrees, depending on whether it is tilted downward or upward relative to a certain direction (which can be the flow direction)). As an example, one or more models can take orientation into account, for example, as one or more parameters of one or more models.

[0103] As an example, the fluid production network may be or include a multiphase fluid production network.As an example, the values ​​output via the model-based framework may include values ​​of fluid flow variables at multiple different times (eg, single phase, multiphase, etc.).

[0104] As an example, the framework may optionally be coupled to one or more data transmission systems, which may include, for example, a supervisory control and data acquisition (SCADA) system. For example, the framework may provide for monitoring a production system using one or more models, wherein one or more notifications (e.g., instructions, commands, alarms, etc.) may be communicated via the one or more data transmission systems in response to model-based results. As an example, a SCADA system may include equipment for monitoring and control, which may operate using coded signals, such as communication channels (e.g., one communication channel per remote station, etc.).

[0105] As described above, slug flow can be associated with gas in a pipeline. The gas can be naturally occurring and / or injected. For example, consider an artificial lift technique that utilizes gas, which can be injected at one or more points in the system, which can include downhole points, subsea points, and / or surface points.

[0106] Figure 5 An example of a system 500 is shown that includes various types of equipment, at least some of which may experience scaling and / or affect scaling mechanisms. For example, gas lift equipment may experience scale formation and may change one or more of pressure, temperature, chemistry, phase dynamics, etc. Figure 5 In the examples, various fluid system components are located at the surface and various fluid system components are located underground; note that the various fluid system components can extend from the underground location to the surface location.

[0107] exist Figure 5 , system 500 illustrates an example of a geological environment 520 including an apparatus and an example of a method 580. System 500 includes a subterranean formation 501 with a well 502. Injection gas is provided to well 502 via a compressor 503 (e.g., a fluid pump, etc.) and a regulator 504 (e.g., a fluid controller). The injected gas can help lift fluids flowing from the subterranean formation 501 to the well 502. The lifted fluids (including the injected gas) can flow to a manifold 505 (e.g., a fluid system component having multiple fluid inlets and / or fluid outlets), where, for example, fluids from multiple wells can be combined. Figure 5As shown in the example of, manifold 505 can be operably connected to separator 506, and the separator can separate the components of fluid. For example, separator 506 can separate oil, water and gas components into substantially separated phases of multiphase fluid. In such examples, oil can be directed to oil storage facilities 508 via oil pipes, and gas can be directed to compressor 503, for example, for re-injection, storage and / or delivery to another location. As an example, water may be directed to drains, water storage facilities, etc. As an example, in various submarine systems, due to the differences between land and sea, separators and related separation processes may not exist.

[0108] like Figure 5 As shown, the geological environment 520 is equipped with a well equipment 530, which includes a wellhead 531 (e.g., a Christmas tree, etc.), an inlet conduit 532 for compressed gas flow, an outlet conduit 534 for production fluid flow, a casing 535, a production conduit 536, and a packer 538 that forms a seal between the casing 535 and the production conduit 536. As shown, the fluid can enter the casing 535 (e.g., through perforations) and then enter the lumen of the production conduit 536, for example, due to a pressure difference between the fluid in the underground geological environment 520 and the lumen of the production conduit 536 at the opening of the production conduit 536. In the case where the inlet conduit 532 for compressed gas flow is used to allow gas to flow to the annular space between the casing 535 and the production conduit 536, the mandrel 540 operably connected to the production conduit 536 includes a recessed cavity 550, which can house a gas lift valve 560, which can regulate the introduction of compressed gas into the lumen of the production conduit 536. In such examples, the introduced compressed gas may encourage the fluid to flow upward to wellhead 531 (eg, against the direction of gravity), where the fluid may be directed away from wellhead 531 via outlet conduit 534 .

[0109] like Figure 5 As shown, method 580 may include: a flow block 582 for causing gas to flow to an annulus (e.g., or more generally, the space outside a production conduit equipped with a gas lift valve); an injection block 584 for injecting gas from the annulus into the production conduit via one or more gas lift valves; and a lift block 286 for lifting fluid in the production conduit due in part to the buoyancy imparted by the injected gas.

[0110] As an example, the production process may optionally utilize one or more fluid pumps, such as, for example, electric submersible pumps (e.g., consider centrifugal pumps, rod pumps, etc.). As an example, the production process may implement one or more so-called "artificial lift" techniques. Artificial lift techniques may operate by adding energy to a fluid, for example, to initiate, enhance, etc., production of the fluid.

[0111] As an example, a method may include gas lift optimization, wherein a supply gas and a plurality of gas injection points are considered to determine an optimal manner of distributing the supply gas to achieve one or more objectives, which may include a production objective, a fluid behavior objective, etc. For example, consider a method that may optimize the distribution of the supply gas in a manner intended to reduce instances of slugging. In such examples, a controller may be utilized to implement feedback to control the delivery of the supply gas to one or more points, which may include downhole, underwater, and / or surface points, as described above. As explained, slugging may occur in a gravity-dependent manner, such as in a riser, which may have an S-shape, as described above. Slugging is an unstable phenomenon that causes the flow to exhibit periodicity or irregularity. Slugging may interfere with sensor readings, particularly when the frequency of sensor readings may be lower than the frequency of slugging stops and surges. For example, if slugging cycles occur faster than measurements obtained from a sensor, such measurements may indicate stops or surges of different flow cycles, etc. To address slugging, the system may include a controller that receives sensor data (e.g., sensor measurements) that may be used as feedback to the controller, where the controller may control delivery of supply gas to one or more points in the system to reduce slugging conditions in the system.

[0112] Figure 6 An example of a gas lift system 600 is shown, which includes a casing 635, a production conduit 636, and a mandrel 640, the mandrel including a cavity 650 for housing a gas lift valve 660. As shown, the mandrel 640 may include a main longitudinal axis (z M ) and relative to the main longitudinal axis (z M ) is offset from the longitudinal axis of the undercut cavity by a certain radial distance (z P ).exist Figure 6 In the example, the axis (z M and z P ) are shown as being substantially parallel such that the hole of the recessed cavity 650 is parallel to the lumen of the core shaft 640. Figure 6 Also shown are two examples of cross-sectional profiles of the mandrel 640, for example, along line AA. As shown, the mandrel may include a circular cross-sectional profile or other shaped profiles, such as, for example, an elliptical profile.

[0113] As an example, the completion may include multiple instances of the mandrel 640, for example, where each cavity of each instance may include a gas lift valve, where, for example, one or more of the gas lift valves may differ from one or more other of the gas lift valves in one or more characteristics (e.g., pressure setting, etc.).

[0114] like Figure 6As shown in the example of , the mandrel 640 may include one or more openings that provide fluid communication with a fluid (e.g., gas and / or other fluid) in an annulus defined by an outer surface of the mandrel 640 and an inner surface of the casing 635 via a gas lift valve 660 disposed in the recessed cavity 650. For example, the gas lift valve 660 may be disposed in the recessed cavity 650, wherein a portion of the gas lift valve 660 is in fluid communication with the annulus (e.g., with casing fluid), and wherein a portion of the gas lift valve 660 is in fluid communication with the lumen (e.g., with tubing fluid). In such examples, fluid may flow from the annulus to the lumen (e.g., a bore) to assist in lifting the fluid in the lumen, or, for example, fluid may flow from the lumen to the annulus. The recessed cavity 650 may include an opening that may be oriented downhole and one or more openings that may be oriented toward the recessed cavity wall, for example, radially toward the lumen space. As an example, the recessed cavity 650 may include a production tubing lumen side opening (e.g., an axial opening) for placement, retrieval, replacement, adjustment, etc. of the gas lift valve. For example, by using the tool, the gas lift valve 660 may be accessed. As an example, when the gas lift valve includes an electrical circuit such as one or more batteries, the tool may optionally provide for charging and / or replacement of the one or more batteries.

[0115] exist Figure 6 , gas is shown entering a gas lift valve 660 from the annulus disposed in a recessed cavity 650. Such gas may be exhausted from a downhole end of the gas lift valve 660, where the gas may help lift fluid in the lumen of the mandrel 640 (e.g., supplied via a bore in a production tubing, etc.).

[0116] As an example, the mandrel may be coupled with a gas lift valve, which may be, for example, a valve that complies with one or more specifications, such as injection pressure operated (IPO) valve specifications. As an example, a positive sealing check valve, such as a valve that complies with API-19 G1 and G2 industry standards and pressure barrier qualifications, may be used. For example, at a test pressure rating of approximately 10,000 psi (e.g., approximately 69,000 kPa), the valve may form a metal-to-metal barrier between the production tubing and the casing annulus, which may help avoid unwanted communication (e.g., or reverse flow) and help mitigate risks associated with gas lift valve check systems.

[0117] Although the gas lift valve is illustrated and described as being suitable for downhole use, one or more other types of gas injection valves may be used at one or more points in the system. As explained, the injected gas may provide benefits, such as facilitating the delivery of fluids (such as fluids containing hydrocarbons) to a processing facility. However, the injected gas and / or other gases may cause undesirable flow behavior, which may include, for example, one or more of slugging, casing-heading, and / or other types of instabilities.

[0118] As an example, casing pressure slugs may occur in gas lift wells where an annulus is built around the well and filled with gas. In such methods, gas can be injected through a check valve at the bottom of the well. Casing pressure instability tends to be a cyclical phenomenon that can include several periods of non-producing pressure buildup and high flow rate periods in the casing. Such oscillations can reduce overall oil production and can damage equipment and facilities.

[0119] As an example, density wave instabilities may occur in gas lift wells. For example, density wave slugging may occur in long risers and wells. During gas lift, when critical flow is achieved by gas injection, the annulus may be separated from the tubing, eliminating the casing pressure fluctuation instability. However, some wells may still produce in a cyclical manner. For example, gas may accumulate at the bottom of the riser (well), causing a change in the mixed density, thereby forming a region of lower density. In such examples, this region can propagate upward as a density wave.

[0120] As an example, a method may include applying feedback control to find a minimum gas injection rate to stabilize slug flow and casing pressure fluctuations in one or more wells, one or more multiphase production networks, and / or one or more risers.

[0121] As explained, pipelines and pipeline networks may be onshore, offshore, and a combination of onshore and offshore. The fluids transported in pipelines and pipeline networks may be two-phase or three-phase.

[0122] The transport of multiphase fluids in production networks and wells is subject to various types of flow instabilities. Figure 2 As explained in , one type of instability is called "slugging." The formation of liquid slugs in multiphase pipes and wells is a problem faced by oil producers. Slugging and casing pressure fluctuations caused by gas lift risers and well topography can cause irregular, intermittent gas and liquid flows, which can lead to poor oil and water separation and separator foaming, which can limit production capacity and cause compressor trips and flaring emissions. To overcome the problems associated with slugging and casing pressure fluctuations, the production network and / or one or more wells can utilize gas lift. However, if the gas is not properly supplied, this gas can exacerbate the problem in a variety of situations.

[0123] The optimal use of lift gas and the amount of lift gas used to stabilize flow in the production network and / or one or more wells may be understood via multiphase flow simulation.For example, the aforementioned OLGA simulator may be utilized.

[0124] As described above, a feedback control scheme can be implemented to find an appropriate gas lift rate that stabilizes unsteady flow in one or more production networks, one or more risers, and / or one or more wells. In various experiments, the exemplary feedback control structure has been demonstrated to be applicable to real-world case studies through simulations using the multiphase flow simulator OLGA.

[0125] As described above, a feedback controller can be implemented to find an appropriate way to supply lift gas (e.g., gas lift rate, etc.) to stabilize an unstable well, riser, and / or production network. By using feedback control, the controller can operate without the use of a model that runs in parallel with the field. However, in various cases, one or more models of the system can be utilized, which is useful for testing control structures and determining appropriate parameters to use in the feedback controller. As an example, a feedback control scheme can utilize one or more measurements, where the control goal can be to adjust the gas lift rate at one or more points in the system so that a suitable statistic (e.g., standard deviation, etc.) in the sensor measurements becomes sufficiently small.

[0126] In statistics, standard deviation is a measure of the amount of variation or dispersion in a set of values. A low standard deviation indicates that values ​​tend to be close to the mean (also called the expected value) of the set, while a high standard deviation indicates that values ​​are distributed over a larger range. Although standard deviation is mentioned, variance and / or one or more other metrics may be utilized. In probability theory and statistics, variance is the expected value of the squared deviations of a random variable from its population mean or sample mean. Variance is a measure of dispersion, meaning it is a measure of how far a set of numbers deviate from its mean. Variance is the square of the standard deviation, the second central moment of the distribution, and the covariance of the random variable with itself. As explained, one or more metrics may be utilized to evaluate measurements in a feedback control scheme.

[0127] Figure 7 An example of a system 700 is shown, which includes various well groups, which may be production wells with and / or without gas injection, shut-in wells, wells for gas injection, etc. As shown, the system 700 or a portion thereof may be represented in a graphical user interface (GUI) 710 of the framework. Figure 7In the example of , the layout of system 700 includes three production manifolds M1, M2 and M3 for directing fluid to a riser, and the height of the riser can be greater than one meter, greater than 10 meters, and greater than 100 meters (e.g., altitude gain). As shown, the riser provides fluid to a surface production facility, which includes one or more separators and an optional compressor that can compress the gas separated by one or more of the one or more separators. In such examples, the separated gas can be used for injection and slug flow control purposes. As an example, a certain gas field may produce excess gas, making the available gas amount unrestricted, or, for example, the gas amount of a certain gas field may be limited. In either case, whether limited or not, gas delivery can be optimized for one or more purposes, which can take into account the stability of the flow.

[0128] As explained, one or more wells may be operated using gas lift.In system 700, slugging and / or other types of instabilities may occur in the wells, production network, and / or risers leading to the production facility.

[0129] exist Figure 7 7, system 700 is shown in a graphical view in a GUI 710, which may be part of a model builder associated with a simulator. In the graphical view, an elevation map is shown indicating how a riser may be S-shaped, where the riser may span a distance of more than 10 meters in height. As explained, various types of instabilities may depend on gravity, where an increase in altitude may affect gas flow due in part to buoyancy. As the gas rises, the pressure may decrease and the volume may increase, which may affect and / or cause one or more types of multiphase fluid flow behavior.

[0130] Figure 8 An example of a GUI 800 is shown that includes pressure graphs at three manifolds M1, M2, and M3 of the system 700. As shown, the pressure at each manifold may cycle and therefore be unstable or become unstable. Figure 8 In the example of , the cycle length is typically less than about 0.4 hours (eg, 24 minutes). As shown, the pressure increases first and then decreases, which is characteristic of slug flow instability.

[0131] Fig. 9 An example of a GUI 900 is shown including a flow diagram at the top (inlet of the separator). Fig. 9The figure in shows the gas volume flow rate under standard conditions, the water volume flow rate under standard conditions and the oil volume flow rate under standard conditions. The problem occurs in a time period of about 0.1 hours (e.g., 6 minutes), wherein the behavior is relatively periodic, occurring about every 0.4 hours (e.g., 24 minutes). As shown, the flow values ​​of multiphase flow and / or single-phase flow over time can be related to the pressure behavior at the manifold. As shown in GUI 900, the flow decreases for about 0.2 hours and then surges for about 0.1 hours. This behavior can be called stagnation and surge, which may be cyclic depending on conditions, etc.

[0132] As an example, a riser flow assurance study using lift gas in the riser base may investigate the relationship between production rate and the supplied gas lift rate. In such an example, the optimal unconstrained gas lift rate may be defined as the rate that maximizes oil production. As an example, the optimal oil production may be equated to minimizing the wellhead pressure (manifold pressure).

[0133] Fig.10 An example of a GUI 1000 is shown, which includes a graph of manifold pressure versus supplied lift gas flow rate, wherein the lift gas flow rate is shown and gradually increases over time at set intervals. Specifically, the GUI 1000 shows the relationship between the wellhead pressure (downstream of the wellhead choke) and the supplied gas lift rate, wherein the first curve represents the pressure at manifold 1 (M1), the second curve represents the pressure at manifold 2 (M2), and the third curve represents the pressure at manifold 3 (M3), and the fourth curve represents the supplied gas lift flow rate at manifold 3 (M3). Fig.10 As shown in the example of , once the gas lift rate reaches about 1, the oscillation of the pressure value is dampened so that its amplitude decreases, and for a period of time, the pressure value stabilizes, noting a decrease with further increases in the gas lift rate and a slight increase with high gas lift rates. However, with each increase, small perturbations in the pressure value can occur, but these diminish as the pressure becomes relatively constant.

[0134] Fig.11 An example of a GUI 1100 is shown in which wellhead pressure (downstream of the wellhead choke) is plotted versus supplied gas lift rate over a 20 hour period, wherein a first curve represents pressure at manifold 1 (M1), a second curve represents pressure at manifold 2 (M2), and a third curve represents pressure at manifold 3, while a fourth curve indicates the supplied gas lift flow rate at manifold 3 (M3).

[0135] From the figure it can be seen that the gas lift rate for stabilizing the instability is about 1.1 kg / s (eg, as mass flow rate), and the optimum gas lift rate (the one that minimizes the wellhead / manifold pressure) is around 3 kg / s.

[0136] As explained, a feedback controller may implement feedback control using feedback control structures together with logic to activate one or more features of the feedback controller.

[0137] As an example, since it can be assumed that the minimum manifold pressure is unknown, the feedback controller may not be set to control the pressure; instead, the feedback controller may be set to minimize the logarithm of the standard deviation (variation) of the pressure over a selected time period. Although standard deviation is mentioned, as explained, one or more other types of metrics may be used to appropriately characterize pressure and / or flow, and the feedback controller may use either or both of them (e.g., see Figure 8 and 9 ); note that temperature may be used additionally or alternatively (e.g., considering PVT methods).

[0138] As an example, based on the values ​​in the various graphs, a slug flow period (e.g., cycle period) can be determined, which can be, for example, approximately 0.35 hours. In such an example, the feedback control scheme can be selected to minimize the logarithm of the standard deviation of one or more measured variables over a period of approximately 0.5 hours.

[0139] As shown in the various figures, the manifold pressure at M3 may be selected as the control variable (e.g., the variable used to minimize the logarithm of the standard deviation); note that one or more other measured variables may be selected, where the selected measured variables show changes due to slugging and become stable as the applied gas lift rate increases. As an example, one or more additive and / or multiplicative combinations of variables may be used. For some exemplary combinations, consider one or more of the following: the sum of the three manifold pressures; the sum of the wellhead pressures (downstream of the wellhead choke); and the sum of the wellhead pressures (upstream of the wellhead choke) multiplied by the wellhead choke opening.

[0140] Expressed mathematically, the measurements can be:

[0141]

[0142] in

[0143] u j (t)≥0, (positive for all t)

[0144]

[0145] As an example, the goal could be:

[0146]

[0147] in

[0148] glr(t) is the supplied gas lift rate

[0149] In various cases, performing numerical minimization can be computationally expensive. As an example, a feedback controller can use feedback control to drive an objective function to a minimum value.

[0150] As an example, using the measured variable m i (t), the feedback controller can drive the objective function to zero. In such examples, the feedback solution can utilize a set point at or near zero (e.g., relative to the amplitude during slugging); note that when slugging occurs, the pressure and / or flow changes may be considerable (e.g., also considering possible temperature changes).

[0151] Fig.12 An example of a GUI 1200 is shown, which includes a graph of the pressure at the manifold 3 (M3) over a period of 0.5h to 30h, the 0.5h standard deviation of the manifold 3 (M3) pressure, the logarithm of the standard deviation, and the supplied gas lift rate. Specifically, the graph shows the pressure at the manifold 3 (M3) over a period of 0.5h to 30h, the 0.5h standard deviation of the manifold 3 (M3) pressure, the logarithm of the 0.5h standard deviation to the base 10, and the supplied gas lift rate.

[0152] exist Fig.12 1200, the standard deviation decreases significantly as the production network becomes more stable. As shown, when the logarithm is less than 1, then the standard deviation is less than 10 kPa and the system can be considered stable. Note that the increase in gas lift rate (step change) at 25 h indicates a disturbance in stability due to the rapid increase in gas lift flow rate. Also note that over the 10 to 20 h time period, as the magnitude of the standard deviation increases, the slugging becomes larger (more severe). However, the frequency becomes lower. Therefore, when the gas lift rate is too low, the consequence is that the slugging flow becomes more severe. Such data can be used to characterize the system, which can, for example, utilize a model. For example, consider a real-time model that can be adjusted to approximate the behavior of the system. In various examples, one or more types of models can be utilized, such as a hierarchical approach, a surrogate approach, etc.

[0153] As an example, the control algorithm may take the logarithm of the 0.5h standard deviation as a measured variable (eg, a metric). In such an example, the controller may manipulate the gas lift rate to drive down the logarithm and standard deviation (eg, drive the metric to a minimum, etc.).

[0154] Fig.13An example of a GUI 1300 is shown, wherein a graph illustrates how a feedback controller may be implemented using a single injection point at manifold 3 (M3) and with the pressure at manifold 3 (M3) as a single measured variable m(t). In the graph, a curve represents the manifold pressure and another curve represents the gas lift rate calculated by the feedback controller. In the graph, there are curves for controller modes, which may be switched appropriately between different modes (e.g., two or more modes). In the graph, one curve represents the 0.5h standard deviation and another curve represents the logarithm of the 0.5h standard deviation. As explained, the graph in GUI 1300 illustrates the relationship between the wellhead pressure (downstream of the wellhead choke) and the supplied gas lift rate, wherein a first curve represents the pressure at manifold 1 (M1), a second curve represents the pressure at manifold 2 (M2), and a third curve represents the pressure at manifold 3 (M3), and a fourth curve represents the supplied gas lift flow rate at manifold 3 (M3).

[0155] As an example, referring to GUI 1300, the feedback controller can be in a specific mode for up to 0.5h. Before 0.5h, the gas injection rate is increased by increasing the set point. In such an example, the mode can be a manual mode, in which the operator manually increases the set point. At 0.5h, the feedback controller can switch modes, such as, for example, switching to a specific automatic mode (e.g., automation level). During the period from 0.5h to 3.1h, the feedback controller can operate and adjust the gas lift rate in the specific automatic mode. As shown in the figure, after a little over 3.1h, the feedback controller can determine that the system is stable enough, and in response, stop updating the gas lift rate. Under this method, the achieved gas lift rate is 1.08kg / s (which is close to the 1.1kg / s achieved when the gas lift rate is gradually increased). As shown in the figure, the corresponding standard deviation in the period before 3.1h is around 10kPa. As time goes by and the effect of gas lift becomes effective, the standard deviation continues to decrease to less than 1. As an example, if a disturbance occurs and instability occurs, the feedback controller may automatically re-enter its particular automatic mode (eg, or another suitable mode) and begin increasing the gas lift rate, as appropriate.

[0156] As an example, if multiple gas injection points are available, one or more of these gas injection points may be used. As an example, individual weights may be determined based on the sensitivity of the measured value m(t) relative to the gas injection rate at point k. For example, the sensitivity may be evaluated by

[0157]

[0158] The weights for different gas injection points can be obtained.

[0159] As an example, a system may utilize multiple gas injection points to improve controllability in a manner that reduces flow instabilities.

[0160] As explained, a metric such as the standard deviation (square root of the sum of squared derivatives) can be used as a positive number that approaches zero when stability is achieved. Therefore, the standard deviations of multiple measurements can be summed (e.g., the sum of positive values). As explained, the standard deviation approaches zero when the system becomes stable. Again, the standard deviation is the square root of the sum of squared deviations. The deviation can be calculated from the corresponding time average. The time average and standard deviation can be calculated within a time period T (e.g., a time window, a time range, etc.). The implication of this approach is adaptability (e.g., it can be implemented in a way that no nominal values ​​are given). As explained, the feedback control scheme can be resistant to model errors and disturbances and is computationally efficient compared to optimization. As shown, a feedback controller can stabilize the flow in the system by controlling the gas lift rate at one or more points in the system, where one or more measured variables (e.g., pressure, flow, temperature, etc.) can be used for feedback.

[0161] As explained, instabilities may occur in a multiphase fluid production system, where such instabilities may lead to control problems in controlling the multiphase fluid production system. As an example, a data-driven approach may be implemented to improve control, where one or more metrics may be derived from field data. In such examples, the controller may operate without the use of a model, because one or more metrics are sufficient to control the multiphase fluid production system to resolve the instability (e.g., by temporarily increasing the gas flow rate, etc.); note that, in addition or alternatively, one or more models may be optionally available. For example, a model may run in the background, which may evaluate field data and control behavior. In such examples, in the case where a data-driven, model-free approach results in undesirable control behavior, as may be detected using a model running in the background, the model, another model, and / or a human may intervene to resolve such control behavior. For example, a model may identify specific aspects of instability and discern the root cause that may be controlled by increasing or not increasing the gas flow rate, and / or a model may provide a deviation metric that may trigger a notification to the device, which prompts personnel to intervene.

[0162] As an example, where the complexity of a multiphase fluid production system increases, one or more models may be implemented that may account for gas availability at one or more wells so that available gas that can be optimally allocated to meet production targets is not adversely affected by increased gas flow rates that are intended to address one or more instability issues at one or more wells that may occur simultaneously, partially overlap, or occur in close proximity to one another. For example, a model may be intended to maintain stable, controllable operation of a multiphase fluid production system in fluid communication with multiple wells utilizing gas lift.

[0163] As an example, the model can be a simulation model that can provide an assessment looking back at the past and / or looking forward to the future. Such a model can be implemented in conjunction with feedback for one or more purposes. As an example, a data-driven approach can provide a degree of foresight. For example, one or more data-derived metrics can indicate a possible impending instability, so that the controller can take one or more control actions to reduce the risk that the possible impending instability actually occurs.

[0164] As an example, a real-time model that receives real-time data can be used, and the real-time data can be in the form of one or more metrics. As an example, the real-time model is adjustable to adapt to a multiphase fluid production system or a portion thereof, wherein the real-time model can make predictions based at least in part on the received field data. In such examples, the real-time model can generate results for variables that may not necessarily be obtained from one or more sensors, wherein such results can be used to determine one or more metrics for control purposes to reduce the risk of instability and / or overcome instability. As an example, the real-time model can be used as a proxy model for one or more variables that may be temporarily or permanently unavailable. For example, consider a sensor failure or a well where a specific sensor is not installed. As explained, the real-time model can be adaptable or adjustable to adapt to a specific multiphase fluid production system or a portion thereof, wherein, for example, adaptation or adjustment can occur before and / or after implementation (e.g., to adapt to changes in conditions such as flow rate, available gas). As an example, variables inferred from the real-time model can be used to calculate one or more metrics, which can be metrics other than one or more metrics derived from data without using a model.

[0165] As an example, a minimum type of data can be specified for a controller. For example, consider a controller that requires data from at least one pressure sensor. In such an example, one or more metrics can be derived from data from a single pressure sensor associated with a well (e.g., or multiple wells), where one or more metrics can be used to control to reduce the risk of instability and / or mitigate instability. As an example, the pressure can be a wellhead pressure, a manifold pressure, a downhole pressure, or other pressure that may be affected by the gas injection rate. Although pressure is mentioned, a single type of measurement can be a flow measurement, a temperature measurement, or another type of measurement that can indicate instability (e.g., current or future). As an example, a pressure, volume and / or temperature (PVT) time method can be used, noting that a mass-based method can be used in addition to or instead of volume. As an example, a method based on a single measurement can utilize one of pressure, flow rate, and temperature.

[0166] As an example, the framework can provide a hierarchical approach. For example, if a method based on pressure measurements does not perform adequately, the model can be implemented as an upgrade layer to provide one or more model-based outputs (e.g., one or more proxy values, etc.) that can be used to achieve adequate performance (e.g., instability detection and / or control). The hierarchical approach can make the framework more robust. As an example, the framework can utilize different time frames, such as, for example, the past (e.g., data), the present (e.g., a real-time model), and the future (e.g., a predictive model). As an example, metrics can be applied to one or more time frames. In such examples, the metrics can be used for current and / or future control. As an example, a hierarchical approach can be implemented in which it can detect and / or respond to one or more sensor problems. For example, if the data received from a sensor becomes unreliable, or a sensor or transmission channel fails, a hierarchical change may occur to solve the problem (e.g., using a proxy model value, etc.).

[0167] As explained, gas lift control may involve set point control, where the set point control may be interrupted to resolve an instability issue (eg, instability control), where control may return to the set point control after the instability issue is resolved.

[0168] With respect to metrics used for control, they can be based on data over a period of time. As explained, a metric can be calculated based on data over a period of time, where if there is stability, the metric tends to a null value (e.g., zero); and if there is a possibility of instability or instability, the metric tends to a non-null value, which may increase as the possibility and / or characteristics of instability increase. As an example, a metric can be formulated as a positive or negative value, and its zero value is zero. As an example, a metric can be formulated so that its value increases significantly in response to data indicating instability, for example, a controller can be intended to drive the metric toward a zero value (e.g., or other appropriate value).

[0169] As an example, a metric may be customized based on the accuracy of one or more sensors. In such examples, the metric may provide one or more indications about sensor performance. For example, the metric may exhibit a certain amount of jitter in response to sensor jitter, which may indicate a sensor problem and / or a data transmission problem. As explained, the framework may respond to detected sensor problems by implementing one or more proxy models that may provide appropriate values ​​to continue controlling instability in the absence of data from the problematic sensor.

[0170] As explained, the control response may include a requirement to ramp up the gas injection rate for one or more wells and then reduce the gas injection rate. With respect to the acceleration, it may occur within a reasonable time frame taking into account the equipment capabilities, flow values ​​and / or characteristics, etc. For example, ramping up too quickly may destabilize the equipment and / or subject the equipment to unnecessary wear; while ramping up too slowly may cause instability to occur, increase in magnitude, propagate, etc.

[0171] With respect to determining an appropriate set point, the framework can ramp up gas injection in response to a detected instability to suppress the instability and then ramp down until another instability condition occurs. In such examples, an upper limit value can be determined to suppress the instability and a lower limit value can be determined to indicate where the instability may occur. Such values ​​can be used as limits, where the set point can be a value that is higher than a lower limit value (e.g., a lower limit) but not higher than an upper limit value (e.g., an upper limit). As an example, the set point value can be determined as the lower limit value plus a fraction of the difference between the upper limit value and the lower limit value. As explained, the framework can operate within a set point control method, where such a framework can adaptively utilize data and / or one or more models to adjust the set point while also suppressing instability or the risk of its occurrence.

[0172] As explained, the goal can be to maintain the pressure in the multiphase fluid production system at the lowest possible level, for example, to reduce gas utilization, etc. In various gas lift operations, the gas supply may be limited and may have to be optimized to assist the production of multiple wells. Gas lift optimization can be a process with multiple constraints. As an example, a framework for solving instability can be part of a gas lift optimization framework, or otherwise operably connected thereto. In such examples, the gas consumed to suppress instability can be budgeted using data, one or more models, etc., optionally. Over time, the budget can be adjusted, for example, from a more conservative value (reserving more gas to solve instability) to a less conservative value, which can provide improved gas utilization and optimization. However, depending on actual performance, the opposite may be shown, that is, more gas is reserved for solving instability problems. A framework that can solve instability problems can temporarily increase gas utilization, but it can reduce overall gas utilization by allowing the use of one or more lower set point values ​​(e.g., values ​​that maintain stability most of the time). For example, the framework can operate to find a suitable set point that can provide sufficient stability in most cases. In such examples, such suitable set points can be used for gas optimization purposes when the framework is operating for multiple wells, for example, as a lower value constraint on the gas injection rate for multiple wells. In the event that the supply of gas may be insufficient to satisfy a certain number of wells, one or more wells can be shut down or otherwise operated so that there is a sufficient amount of gas to operate at a lower value (e.g., a lower limit), while there may also be some additional amount of gas available for control in response to the detection of possible or actual instability. As an example, lower limits can be determined on a well-by-well basis, where these lower limits can be utilized in the gas optimization framework to optimize gas lift for a certain number of wells, which number of wells can be less than the number of all wells, so that the number of wells can be controlled to maintain stability.

[0173] As an example, an instability may be an intermittent instability in a multiphase flow. For example, consider intermittent gas in a liquid flow, where pockets of gas may flow intermittently with the liquid. As explained, the instability may be characterized as a slug flow instability. As an example, an intermittent instability may be a casing pressure fluctuation instability. As explained, the instability may occur at one or more locations within a multiphase fluid production system.

[0174] As explained, the framework can directly utilize the measurement data from the field to calculate the measure of change and take its logarithm to calculate the feedback of the gas injection rate. In such an example, this can be the operating mode of the framework, namely the direct feedback mode, which can operate without using a model by relying solely on feedback from the measurement data.

[0175] As described above, models such as, for example, the aforementioned OLGA model (e.g., an OLGA simulator) can be utilized. As an example, the framework can implement an online method that can use one or more different simulator modes at different time scales. For example, a real-time mode can be intended to simulate a system in real time and provide calculated variables that may or may not be directly measured. The real-time mode can utilize a subset of the measured values, for example, closing the model boundaries and estimating the adjustment parameters so that the selected model variables match or minimize the deviation from the corresponding measured values.

[0176] As an example, the framework can utilize one or more inferred variables from the real-time model as part of the objective (e.g., as measured variables). As an example, the real-time model can periodically dump snapshots, where the snapshots can be loaded into one or more additional simulator modes that use a model corresponding to the system, where such modes can include looking back and looking ahead.

[0177] Regarding the review mode, it can rerun a previous time period from a snapshot saved in the past, and can stream the data recorded in the past to the current time and perform one or more functions. In such an example, a function can be provided to calculate the gas injection rate required to stabilize the multiphase flow in the riser. In such an example, one or more variables calculated by the review model can replace one or more measured variables.

[0178] Regarding the look-ahead mode, it can be run using a dedicated look-ahead model to simulate, for example, from the most recent snapshot to a future time horizon. The look-ahead mode can be used to provide early predictive warnings of possible instabilities, assuming that measurements and inputs remain relatively the same (e.g., as in the most recent snapshot).

[0179] As an example, one or more functions may be implemented that calculate a minimum gas injection rate, for example based on variables predicted by a look-ahead model. In such examples, the method may be an indirect method for calculating one or more gas injection rates.

[0180] As an example, a retrospective method for calculating a gas injection rate may include calculating a revised gas injection rate based on re-running a previous past time horizon (e.g., a past snapshot). As an example, a prospective method for calculating a gas injection rate may include calculating a revised gas injection rate based on a current state and a future time horizon, assuming that the variables remain relatively the same. Thus, as an example, the framework may provide for past, present, and future determinations.

[0181] Regarding the past, it may review the current state based on recalculated variables from the review model. Regarding the present, it may involve optionally using additional inferred variables from current measurements and possibly from the real-time model. As explained, the direct method may utilize current measurements without inferred variables; while the indirect method may additionally or alternatively utilize inferred variables from the real-time model; note that the indirect method for the present may utilize at least one current measurement. Regarding the future, it may involve predicting how the current state evolves into the future (e.g., a future time horizon), which may be based on calculations of gas injection rates.

[0182] As an example, a framework may include features for implementing one or more techniques for determining a minimum gas lift rate (e.g., a gas injection rate). In such an example, consider determining one or more minimum gas lift rates based on: a time range from the past to the current time; current data, which may also include one or more variables inferred from a real-time model (e.g., variables that may or may not be directly measured); and / or future predictions based on current states and predictions for future time ranges.

[0183] Fig.14 An example of a method 1400 is shown, the method including a detection block 1410 for detecting an instability of a multiphase fluid flow in a multiphase fluid production system using sensor measurements from the multiphase fluid production system; and an increase block 1420 for increasing gas injection into the multiphase fluid production system in response to detecting an instability until a measure of change in the sensor measurements decreases to a level indicating a stable multiphase fluid flow in the multiphase fluid production system (e.g., taking into account a threshold value that may be system dependent or otherwise set or determined). As shown, the method 1400 may also include a calculation and adjustment block 1430 for calculating a logarithmic value of the measure of change and adjusting a rate of gas injection based on the calculated logarithmic value; and an adjustment block 1440 for adjusting the rate of gas injection to a lower rate that maintains a stable multiphase fluid flow in the multiphase fluid production system.

[0184] As explained, the feedback controller can operate in various modes, including a mode that responds to detected instabilities and a mode that adjusts gas injection once the instabilities are resolved. For example, consider Fig.131300 , where an operating mode can utilize a logarithmic value of a change metric to adjust gas injection (e.g., one or more gas lift rates) to a value below a previous operating mode setting. As an example, a feedback controller can continuously calculate the logarithmic value and, for example, adjust gas injection and / or switch modes in response to such logarithmic value. As an example, a method can include set point control, for example, considering utilizing a change metric and / or its logarithm in a set point feedback control scheme.

[0185] exist Fig.14 In the example of , a system 1490 is shown, which includes one or more information storage devices 1491, one or more computers 1492, one or more networks 1495, and instructions 1496. With respect to the one or more computers 1492, each computer may include one or more processors (e.g., or processing cores) 1493 and a memory 1494 for storing instructions 1496, which instructions may be executed, for example, by at least one of the one or more processors. As an example, a computer may include one or more network interfaces (e.g., wired or wireless), one or more graphics cards, a display interface (e.g., wired or wireless), etc.

[0186] Method 1400 is shown with various computer readable media blocks 1411, 1424, 1431, and 1441 (e.g., CRM blocks). These blocks can be used to perform one or more actions of method 1400. For example, consider Fig.14 The system 1490 and instructions 1496 may include instructions of one or more of the CRM blocks 1411, 1421, 1431, and 1441. As an example, the system 1490 or a portion thereof may be a controller, a framework operably coupled to a controller, etc. As an example, Fig.14 The method 1400 or a portion thereof may implement and / or use one or more application programming interfaces (APIs). As an example, one or more of a model API, a data API, a data storage API, an actuator API, etc. may be used. As an example, the method 1400 may be performed within a DELFI environment or may be operably coupled to a DELFI environment. As an example, Fig.14 The method 1400 can be implemented on an edge computing device on-site that can be connected to a network, at least for data collection and control.

[0187] As an example, a feedback controller can be implemented as a local controller on site, which can be implemented, for example, using an edge computing device. In such an example, the edge computing device can receive sensor measurements and output a control signal for controlling gas injection. As an example, such a device can be suitable for use in a system having one or more wells. Such a device may include a processor, a memory, one or more interfaces, and instructions that can be stored in the memory and executable by the processor. As an example, the device can be a feedback controller device, the purpose of which is to adjust gas injection to reduce multiphase flow instabilities in a multiphase fluid production system. As an example, the feedback controller scheme can be implemented in conjunction with one or more other types of controllers, which can be centralized, for example, using a suitable computing device, which can include local and / or remote devices (e.g., edge devices, PLC devices, cloud resources, etc.).

[0188] As an example, a method may include using sensor measurements from a multiphase fluid production system to detect an instability in a multiphase fluid flow in the multiphase fluid production system; and in response to detecting the instability, increasing gas injection into the multiphase fluid production system until a measure of change in the sensor measurements decreases to a level indicating a stable multiphase fluid flow in the multiphase fluid production system.

[0189] As an example, the measure of variation can be a standard deviation, variance, or other suitable measure that characterizes variation. As an example, the measure of variation can be a consistently positive value, or a consistently negative value. For example, the standard deviation is a consistently positive value like the variance (e.g., the variance is the square of the standard deviation).

[0190] As an example, the measure of change may be calculated over a period of time. In such an example, the period of time may be greater than the cycle period of the detected instability. As an example, the period of time may be greater than 6 minutes and less than 240 minutes, and may be less than 120 minutes or less than 60 minutes.

[0191] As an example, the instability may be a slug flow instability.As an example, the multiphase fluid may include gas and oil or gas, water and oil.

[0192] For example, the sensor measurements may include pressure measurements, flow measurements, and / or temperature measurements. As an example, the measurements may include upstream measurements upstream of the instability. For example, if slugging occurs in a riser, the measurements may include measurements upstream of the riser (e.g., at a manifold, a wellhead, etc.). With respect to the control system to reduce the instability, gas may be injected upstream of the instability, such as at one or more points upstream of the instability. For example, if instability occurs in a portion of the riser, gas may be injected at an upstream portion of the riser and / or at one or more points upstream of the riser (e.g., a manifold, a well, etc.).

[0193] As an example, the multiphase fluid production system may include at least one manifold in fluid communication with at least one well and / or at least one riser in fluid communication with at least one well. As an example, gas injection may occur at more than one point in the multiphase fluid production system. As an example, gas injection may occur at one or more of the well, the manifold, and the riser. In the foregoing examples, although a manifold is mentioned, the multiphase fluid production system may be a single well, where gas is injected from casing into tubing, where, given that a single well is involved, there may not be a manifold, or, for example, a manifold may be installed where the manifold has a single active conduit (e.g., one or more other conduits may be used for future expansion, etc.).

[0194] As an example, a method can include calculating a logarithmic value of the change measure, and adjusting a rate of gas injection based on the calculated logarithmic value. In such an example, the adjustment can adjust the rate of gas injection to a lower rate that maintains a stable multiphase fluid flow in the multiphase fluid production system. As an example, a logarithmic value of the change measure can be calculated, wherein if the change measure is less than one, the logarithmic value becomes negative. As an example, the logarithmic value can be used in a set point control scheme.

[0195] As an example, a system may include a processor; a memory accessible by the processor; and processor-executable instructions stored in the memory, wherein the instructions include instructions for commanding the system to perform the following operations: using sensor measurements from a multiphase fluid production system to detect instability in a multiphase fluid flow in the multiphase fluid production system; and in response to detecting the instability, increasing gas injection into the multiphase fluid production system until a measure of change in the sensor measurements decreases to a level indicating a stable multiphase fluid flow in the multiphase fluid production system.

[0196] As an example, one or more computer-readable storage media may include computer-executable instructions that can be executed by a computer, the instructions including the following instructions: using sensor measurements from a multiphase fluid production system to detect instability in a multiphase fluid flow in the multiphase fluid production system; and in response to detecting the instability, increasing gas injection into the multiphase fluid production system until a measure of change in the sensor measurements decreases to a level indicating a stable multiphase fluid flow in the multiphase fluid production system.

[0197] In some embodiments, the methods of the present disclosure may be performed by a computing system. Fig.15 An example of such a computing system 1500 according to some embodiments is shown. Computing system 1500 may include a computer or computer system 1501-1, which may be a separate computer system 1501-1 or an arrangement of distributed computer systems, such as systems 1501-2, 1501-3, and 1501-4. According to some embodiments, computer system 1501-1 includes instructions 1502 configured to perform various tasks, such as one or more methods disclosed herein. To perform these various tasks, instructions 1502 may be executed independently or in coordination with one or more processors 1504, which are connected to one or more storage media 1506 and optionally one or more other components 1508. The one or more processors 1504 are also connected to a network interface 1507 to allow the computer system 1501-1 to communicate with one or more additional computer systems and / or computing systems (such as 1501-2, 1501-3 and / or 1501-4) via a data network 1509 (it should be noted that computer systems 1501-2, 1501-3 and / or 1501-4 may or may not share the same architecture as computer system 1501-1 and may be located in different physical locations, for example, computer systems 1501-1 and 1501-2 may be located in a processing facility while communicating with one or more computer systems (such as 1501-3 and / or 1501-4) located in one or more data centers and / or in different countries on different continents).

[0198] A processor may include a microprocessor, a microcontroller, a processor module or subsystem, a programmable integrated circuit, a programmable gate array, or another control or computing device.

[0199] Storage medium 1506 may be implemented as one or more computer-readable or machine-readable storage media. Fig.15In the exemplary embodiment of the present invention, storage medium 1506 is depicted as being within computer system 1501-1, but in some embodiments, storage medium 1506 may be distributed within and / or across multiple internal and / or external enclosures of computing system 1501-1 and / or additional computing systems. Storage medium 1506 may include one or more different forms of memory, including semiconductor memory devices, such as dynamic or static random access memory (DRAM or SRAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and flash memory; magnetic disks, such as fixed disks, floppy disks, and removable disks; other magnetic media, including magnetic tape, optical media (such as compact disks (CDs) or digital video disks (DVDs), disk or other type of optical storage medium); or other type of storage device. It should be noted that the instructions discussed above may be provided on one computer-readable or machine-readable storage medium, or alternatively may be provided on multiple computer-readable or machine-readable storage media distributed in a larger system that may have multiple nodes. Such one or more computer-readable or machine-readable storage media are considered to be part of an article (or product). An article or product may refer to any manufactured single component or multiple components. One or more storage media may be located in the machine running the machine-readable instructions, or at a remote site from which the machine-readable instructions can be downloaded over a network for execution.

[0200] It should be understood that computing system 1500 is an example of a computing system and that computing system 1500 may have more or fewer components than shown, may be combined Fig.15 Additional components not depicted in the exemplary embodiment, and / or computing system 1500 may have Fig.15 Different configurations or arrangements of the depicted components. Fig.15 The various components shown may be implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing circuits and / or application specific integrated circuits.

[0201] In addition, the steps in the processing methods described herein can be implemented by running one or more functional components in an information processing device (such as a general-purpose processor or a dedicated chip (such as an ASIC, FPGA, PLD or other appropriate device). Such components, combinations of these components and / or their combination with general-purpose hardware can be used as part of a system and / or for implementing one or more methods.

[0202] The geological interpretation, model and / or other interpretation aids may be refined in an iterative manner; this concept may be applicable to the methods discussed herein. This may include the use of a feedback loop that is based on an algorithm (such as on a computing device (e.g., computing system 1500, Fig.15 ) and / or by manual control of a user who can make a determination as to whether a given step, action, template, model, or set of curves is sufficient to accurately evaluate the subsurface three-dimensional geological construct under consideration.

[0203] As an example, the system may be a distributed environment, such as a so-called "cloud" environment, in which various devices, components, etc. interact for purposes of data storage, communication, computing, etc. As an example, a device or system may include one or more components for communicating information via one or more of the Internet (e.g., where communication is performed via one or more Internet protocols), a cellular network, a satellite network, etc. As an example, a method may be implemented in a distributed environment (e.g., in whole or in part as a cloud-based service).

[0204] Fig.16 Components of an example of a computing system 1600 and an example of a networking system 1610 are shown. System 1600 includes one or more processors 1602, a memory and / or storage component 1604, one or more input and / or output devices 1606, and a bus 1608. In an exemplary embodiment, instructions may be stored in one or more computer-readable media (e.g., memory / storage component 1604). Such instructions may be read by one or more processors (e.g., processor 1602) via a communication bus (e.g., bus 1608), which may be wired or wireless. One or more processors may execute such instructions to implement (in whole or in part) one or more properties (e.g., as part of a method). A user may view output from a process and interact with the process via an I / O device (e.g., device 1606). In an exemplary embodiment, a computer-readable medium may be a storage component, such as a physical memory storage device, such as a chip, a chip on a package, a memory card, etc. (e.g., a computer-readable storage medium).

[0205] In an exemplary embodiment, the components may be distributed, such as distributed in a network system 1610. The network system 1610 includes components 1622-1, 1622-2, 1622-3, ..., 1622-N. For example, component 1622-1 may include processor 1602, and component 1622-3 may include a memory accessible by processor 1602. In addition, component 1622 may include an I / O device for display and optionally interacting with the method. The network may be or include the Internet, an intranet, a cellular network, a satellite network, etc.

[0206] Although only a few exemplary embodiments have been described in detail above, those skilled in the art will readily appreciate that many modifications may be made in the exemplary embodiments. It is therefore intended that all such modifications be included within the scope of the present disclosure as defined in the appended claims. In the claims, means-plus-function clauses are intended to cover structures described herein as performing the recited function, and not only structural equivalents, but also equivalent structures. Thus, while a nail and a screw may not be structural equivalents because a nail employs a cylindrical surface to fasten wooden parts together, while a screw employs a helical surface, in the context of fastening wooden parts, a nail and a screw may be equivalent structures.

Claims

1. A method comprising: detecting an instability in a multiphase fluid flow in the multiphase fluid production system using sensor measurements from the multiphase fluid production system; as well as In response to detecting an instability, gas injection into the multiphase fluid production system is increased until the measure of change in the sensor measurements decreases to a level indicative of stable multiphase fluid flow in the multiphase fluid production system. The method of claim 1 , wherein the measure of variation is standard deviation. The method of claim 1 , wherein the measure of variation is variance. The method of claim 1 , wherein the measure of change is calculated for a period of time. The method of claim 4 , wherein the time period is greater than a cycle period of the detected instability. The method of claim 4 , wherein the time period is greater than 6 minutes and less than 120 minutes.

7. The method of claim 1, wherein the instability is a slug flow instability.

8. The method of claim 1, wherein the multiphase fluid comprises gas and oil.

9. The method of claim 1, wherein the multiphase fluid comprises gas, water, and oil.

10. The method of claim 1, wherein the sensor measurements include pressure measurements.

11. The method of claim 1 , wherein the sensor measurements include flow measurements.

12. The method of claim 1, wherein the sensor measurements include one or more of pressure measurements, flow measurements, and temperature measurements.

13. The method of claim 1, wherein the multiphase fluid production system comprises at least one manifold in fluid communication with at least one well.

14. The method of claim 1, wherein the multiphase fluid production system comprises at least one riser in fluid communication with at least one well.

15. The method of claim 1, wherein the gas injection occurs at more than one point in the multiphase fluid production system.

16. The method of claim 1, wherein the gas injection occurs at one or more of a well, a manifold, and a riser.

17. The method of claim 1, further comprising calculating a logarithmic value of the variation metric and adjusting a rate of the gas injection based on the calculated logarithmic value.

18. The method of claim 17, wherein the adjusting adjusts the rate of the gas injection to a lower rate that maintains a steady multiphase fluid flow in the multiphase fluid production system.

19. A system comprising: processor; a memory accessible by the processor; as well as Processor-executable instructions stored in the memory, wherein the instructions include instructions for commanding the system to: detecting an instability in a multiphase fluid flow in the multiphase fluid production system using sensor measurements from the multiphase fluid production system; as well as In response to detecting an instability, gas injection into the multiphase fluid production system is increased until the measure of change in the sensor measurements decreases to a level indicative of stable multiphase fluid flow in the multiphase fluid production system.

20. One or more computer-readable storage media comprising computer-executable instructions capable of being executed by a computer, the instructions comprising the following instructions: using sensor measurements from a multiphase fluid production system to detect instabilities in multiphase fluid flow in the multiphase fluid production system; and In response to detecting an instability, gas injection into the multiphase fluid production system is increased until the measure of change in the sensor measurements decreases to a level indicative of stable multiphase fluid flow in the multiphase fluid production system.

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