Method for optimizing autonomous chemical injections for equipment at a field site

Abstract

A method for optimizing autonomous chemical injections for equipment at a field site. The method includes receiving data from the equipment disposed at the field site and then identifying a real-time value of the received data. The method includes automatically generating an insight in response to the identified real-time value, wherein the insight includes an actionable task related to manual operation of the equipment, an advisory of equipment that is at-risk, or a recommendation to optimize chemical injection within the equipment. The recommendation to optimize chemical injection may include calculating a risk index based on the received data and determining an optimal injection rate and an optimal chemical concentration based the calculated risk index. The method may also include automatically injecting chemicals within the equipment based on the generated insight. The method further includes performing a field site action in response to the generated insight.

Description

Attorney Docket No.: IS24. 1151-WO-PCTMETHOD FOR OPTIMIZING AUTONOMOUS CHEMICAL INJECTIONS FOR EQUIPMENT AT A FIELD SITECross-Reference to Related Applications

[0001] This patent application claims priority to U.S. Provisional Patent Application No. 63 / 694,244, filed on September 13, 2024, which is incorporated by reference.Background

[0002] Production systems and equipment at a field site may be dynamic by nature since they may operate under constantly varying conditions. Chemical injection systems therefore often involve repeated adjustment to ensure continuous protection of assets and optimize operating expenses.

[0003] What is needed is a system and method which prevents common flow assurance and asset integrity challenges such as corrosion and scale by harmoniously adjusting chemical injection in accordance with the changes in production rates and other dynamic system conditions. The method should protect field site equipment against corrosion, scale, paraffins, asphaltenes, hydrates, and extend the run life of equipment, such as pumps, valves, completions, or sandscreens, via real-time condition monitoring, analysis, and autonomous chemical injection optimization.Summary

[0004] A method for optimizing autonomous chemical injections for equipment at a field site. The method may include receiving data related to an injection procedure occurring at the field site by an optimization system, calculating a risk index based on the received data, and automatically generating a recommendation in response to the calculated risk index. The method may also include transmitting a command signal corresponding to the recommendation to the field site, automatically injecting an additive to the equipment disposed at the field site according to the command signal, and performing a field site action in response to the generated recommendation.

[0005] Also provided is a computing system which includes one or more processors and a memory system having one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations. The operations may include receiving data related to an injection procedureAttorney Docket No.: IS24. 1151-WO-PCT occurring at the field site by an optimization system, calculating a risk index based on the received data, and automatically generating a recommendation in response to the calculated risk index. The operations may also include transmitting a command signal corresponding to the recommendation to the field site, automatically injecting an additive to the equipment disposed at the field site according to the command signal, and performing a field site action in response to the generated recommendation.

[0006] Also provided is a non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations. The operations may include receiving data related to an injection procedure occurring at the field site by an optimization system, calculating a risk index based on the received data, and automatically generating a recommendation in response to the calculated risk index. The operations may also include transmitting a command signal corresponding to the recommendation to the field site, automatically injecting an additive to the equipment disposed at the field site according to the command signal, and performing a field site action in response to the generated recommendation.

[0007] It will be appreciated that this summary is intended merely to introduce some aspects of the present methods, systems, and media, which are more fully described and / or claimed below. Accordingly, this summary is not intended to be limiting.Brief Description of the Drawings

[0008] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present teachings and together with the description, serve to explain the principles of the present teachings. In the figures:

[0009] Figure 1 illustrates an example of a system that includes various management components to manage various aspects of a geologic environment, according to an embodiment.

[0010] Figure 2 illustrates a flow chart representing a flow of data between field site equipment where data is collected, according to an embodiment.

[0011] Figure 3 illustrates an optimization workflow based on a calculated risk index, according to an embodiment.

[0012] Figured 4 illustrates a distributed architecture for implementing the current method, according to an embodiment.Attorney Docket No.: IS24. 1151-WO-PCT

[0013] Figure 5 illustrates a flowchart of a method for optimizing autonomous chemical injections for equipment at a field site, according to an embodiment.

[0014] Figure 6 illustrates a schematic view of a computing system for performing at least a portion of the method(s) described herein, according to an embodiment.

[0015] Figure 7 illustrates a table outlining the differences of various inhibitors, according to an embodiment.Detailed Description

[0016] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings and figures. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to one of ordinary skill in the art that the invention may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

[0017] It will also be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first object or step could be termed a second object or step, and, similarly, a second object or step could be termed a first object or step, without departing from the scope of the present disclosure. The first object or step, and the second object or step, are both, objects or steps, respectively, but they are not to be considered the same object or step.

[0018] The terminology used in the description herein is for the purpose of describing particular embodiments and is not intended to be limiting. As used in this description and the appended claims, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and / or” as used herein refers to and encompasses any possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises” and / or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / orAttorney Docket No.: IS24. 1151-WO-PCT groups thereof. Further, as used herein, the term “if’ may be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context.

[0019] Attention is now directed to processing procedures, methods, techniques, and workflows that are in accordance with some embodiments. Some operations in the processing procedures, methods, techniques, and workflows disclosed herein may be combined and / or the order of some operations may be changed.System Overview

[0020] Figure 1 illustrates an example of a system 100 that includes various management components 110 to manage various aspects of a geologic environment 150 (e.g., an environment that includes a sedimentary basin, a reservoir 151, one or more faults 153-1, one or more geobodies 153-2, etc ). For example, the management components 110 may allow for direct or indirect management of sensing, drilling, injecting, extracting, etc., with respect to the geologic environment 150. In turn, further information about the geologic environment 150 may become available as feedback 160 (e.g., optionally as input to one or more of the management components 110).

[0021] In the example of Figure 1, the management components 110 include a seismic data component 112, an additional information component 114 (e.g., well / logging data), a processing component 116, a simulation component 120, an attribute component 130, an analysis / visualization component 142 and a workflow component 144. In operation, seismic data and other information provided per the components 112 and 114 may be input to the simulation component 120.

[0022] In an example embodiment, the simulation component 120 may rely on entities 122. Entities 122 may include earth entities or geological objects such as wells, surfaces, bodies, reservoirs, etc. In the system 100, the entities 122 can include virtual representations of actual physical entities that are reconstructed for purposes of simulation. The entities 122 may include entities based on data acquired via sensing, observation, etc. (e.g., the seismic data 112 and other information 114). An entity may be characterized by one or more properties (e.g., a geometrical pillar grid entity of an earth model may be characterized by a porosity property). Such properties may represent one or more measurements (e.g., acquired data), calculations, etc.Attorney Docket No.: IS24. 1151-WO-PCT

[0023] In an example embodiment, the simulation component 120 may operate in conjunction with a software framework such as an object-based framework. In such a framework, entities may include entities based on pre-defined classes to facilitate modeling and simulation. A commercially available example of an object-based framework is the MICROSOFT® .NET® framework (Redmond, Washington), which provides a set of extensible object classes. In the .NET® framework, an object class encapsulates a module of reusable code and associated data structures. Object classes can be used to instantiate object instances for use in by a program, script, etc. For example, borehole classes may define objects for representing boreholes based on well data.

[0024] In the example of Figure 1, the simulation component 120 may process information to conform to one or more attributes specified by the attribute component 130, which may include a library of attributes. Such processing may occur prior to input to the simulation component 120 (e.g., consider the processing component 116). As an example, the simulation component 120 may perform operations on input information based on one or more attributes specified by the attribute component 130. In an example embodiment, the simulation component 120 may construct one or more models of the geologic environment 150, which may be relied on to simulate behavior of the geologic environment 150 (e.g., responsive to one or more acts, whether natural or artificial). In the example of Figure 1, the analysis / visualization component 142 may allow for interaction with a model or model-based results (e.g., simulation results, etc.). As an example, output from the simulation component 120 may be input to one or more other workflows, as indicated by a workflow component 144.

[0025] As an example, the simulation component 120 may include one or more features of a simulator such as the ECLIPSE1Mreservoir simulator (SLB, Houston Texas), the INTERSECT™ reservoir simulator (SLB, Houston Texas), etc. As an example, a simulation component, a simulator, etc. may include features to implement one or more meshless techniques (e.g., to solve one or more equations, etc.). As an example, a reservoir or reservoirs may be simulated with respect to one or more enhanced recovery techniques (e.g., consider a thermal process such as SAGD, etc ).

[0026] As an example, the simulation component 120 may include one or more features of a simulator such as SYMMETRY™ software (SLB, Houston, Texas). More particularly, SYMMETRY™ may process workflows in a single integrated environment with accurateAttorney Docket No.: IS24. 1151-WO-PCT thermodynamic fluid representation and consistent modeling across multiple disciplines including process, production, and HSE. The simulator integrates steady-state and transient (e.g., dynamic) analyses that can be tailored for each domain. This approach enables users to optimize processes in upstream, midstream, and downstream sectors while maximizing profits and minimizing capital expenditures. It may also help reduce emissions, energy consumption, and waste.

[0027] As an example, the simulation component 120 may include one or more features of a simulator such as PIPESIM™ (SLB, Houston, Texas). More particularly, PIPESIM™ is steadystate multiphase flow simulator that incorporates the three areas of flow modeling: multiphase flow, heat transfer and fluid behavior.

[0028] As an example, the simulation component 120 may include one or more features of a simulator such as OLGA™ (SLB, Houston, Texas). More particularly, OLGA™ is a dynamic multiphase flow simulator that models transient flow (e.g., time-dependent behaviors) to maximize production potential. Transient modeling is a component for feasibility studies and field development design. Dynamic simulation is useful in deep water and is used in both offshore and onshore developments to investigate transient behavior in pipelines and wellbores. Transient simulation with the OLGA™ simulator provides an added dimension to steady-state analysis by predicting system dynamics, such as time-varying changes in flow rates, fluid compositions, temperature, solids deposition, and operational changes.

[0029] In an example embodiment, the management components 110 may include features of a commercially available framework such as the PETREL® seismic to simulation software framework (SLB, Houston, Texas). The PETREL® framework provides components that allow for optimization of exploration and development operations. The PETREL® framework includes seismic to simulation software components that can output information for use in increasing reservoir performance, for example, by improving asset team productivity. Through use of such a framework, various professionals (e.g., geophysicists, geologists, and reservoir engineers) can develop collaborative workflows and integrate operations to streamline processes. Such a framework may be considered an application and may be considered a data-driven application (e.g., where data is input for purposes of modeling, simulating, etc.).

[0030] In an example embodiment, various aspects of the management components 110 may include add-ons or plug-ins that operate according to specifications of a framework environment. For example, a commercially available framework environment marketed as the OCEAN®Attorney Docket No.: IS24. 1151-WO-PCT framework environment (SLB, Houston, Texas) allows for integration of add-ons (or plug-ins) into a PETREL® framework workflow. The OCEAN® framework environment leverages .NET® tools (Microsoft Corporation, Redmond, Washington) and offers stable, user-friendly interfaces for efficient development. In an example embodiment, various components may be implemented as add-ons (or plug-ins) that conform to and operate according to specifications of a framework environment (e.g., according to application programming interface (API) specifications, etc.).

[0031] Figure 1 also shows an example of a framework 170 that includes a model simulation layer 180 along with a framework services layer 190, a framework core layer 195 and a modules layer 175. The framework 170 may include the commercially available OCEAN1®" framework where the model simulation layer 180 is the commercially available PETREL® model-centric software package that hosts OCEAN® framework applications. In an example embodiment, the PETREL® software may be considered a data-driven application. The PETREL® software can include a framework for model building and visualization.

[0032] As an example, a framework may include features for implementing one or more mesh generation techniques. For example, a framework may include an input component for receipt of information from interpretation of seismic data, one or more attributes based at least in part on seismic data, log data, image data, etc. Such a framework may include a mesh generation component that processes input information, optionally in conjunction with other information, to generate a mesh.

[0033] In the example of Figure 1, the model simulation layer 180 may provide domain objects 182, act as a data source 184, provide for rendering 186 and provide for various user interfaces 188. Rendering 186 may provide a graphical environment in which applications can display their data while the user interfaces 188 may provide a common look and feel for application user interface components.

[0034] As an example, the domain objects 182 can include entity objects, property objects and optionally other objects. Entity objects may be used to geometrically represent wells, surfaces, bodies, reservoirs, etc., while property objects may be used to provide property values as well as data versions and display parameters. For example, an entity object may represent a well where a property object provides log information as well as version information and display information (e.g., to display the well as part of a model).Attorney Docket No.: IS24. 1151-WO-PCT

[0035] In the example of Figure 1, data may be stored in one or more data sources (or data stores, generally physical data storage devices), which may be at the same or different physical sites and accessible via one or more networks. The model simulation layer 180 may be configured to model projects. As such, a particular project may be stored where stored project information may include inputs, models, results and cases. Thus, upon completion of a modeling session, a user may store a project. At a later time, the project can be accessed and restored using the model simulation layer 180, which can recreate instances of the relevant domain objects.

[0036] In the example of Figure 1, the geologic environment 150 may include layers (e.g., stratification) that include a reservoir 151 and one or more other features such as the fault 153-1, the geobody 153-2, etc. As an example, the geologic environment 150 may be outfitted with any of a variety of sensors, detectors, actuators, etc. For example, equipment 152 may include communication circuitry to receive and to transmit information with respect to one or more networks 155. Such information may include information associated with downhole equipment 154, which may be equipment to acquire information, to assist with resource recovery, etc. Other equipment 156 may be located remote from a well site and include sensing, detecting, emitting or other circuitry. Such equipment may include storage and communication circuitry to store and to communicate data, instructions, etc. As an example, one or more satellites may be provided for purposes of communications, data acquisition, etc. For example, Figure 1 shows a satellite in communication with the network 155 that may be configured for communications, noting that the satellite may additionally or instead include circuitry for imagery (e.g., spatial, spectral, temporal, radiometric, etc.).

[0037] Figure 1 also shows the geologic environment 150 as optionally including equipment 157 and 158 associated with a well that includes a substantially horizontal portion that may intersect with one or more fractures 159. For example, consider a well in a shale formation that may include natural fractures, artificial fractures (e.g., hydraulic fractures) or a combination of natural and artificial fractures. As an example, a well may be drilled for a reservoir that is laterally extensive. In such an example, lateral variations in properties, stresses, etc. may exist where an assessment of such variations may assist with planning, operations, etc. to develop a laterally extensive reservoir (e.g., via fracturing, injecting, extracting, etc.). As an example, the equipment 157 and / or 158 may include components, a system, systems, etc. for fracturing, seismic sensing, analysis of seismic data, assessment of one or more fractures, etc.Attorney Docket No.: IS24. 1151-WO-PCT

[0038] As mentioned, the system 100 may be used to perform one or more workflows. A workflow may be a process that includes a number of worksteps. A workstep may operate on data, for example, to create new data, to update existing data, etc. As an example, a may operate on one or more inputs and create one or more results, for example, based on one or more algorithms. As an example, a system may include a workflow editor for creation, editing, executing, etc. of a workflow. In such an example, the workflow editor may provide for selection of one or more predefined worksteps, one or more customized worksteps, etc. As an example, a workflow may be a workflow implementable in the PETREL® software, for example, that operates on seismic data, seismic attribute(s), etc. As an example, a workflow may be a process implementable in the OCEAN® framework. As an example, a workflow may include one or more worksteps that access a module such as a plug-in (e.g., external executable code, etc.).Method for Optimizing Autonomous Chemical Injections for Equipment at a Field Site

[0039] Asphaltene deposition in the wellbore and in wellhead and processing equipment may lead to production losses, equipment failure, and unplanned shutdowns. The current approach of manually collecting data, running analysis, and executing changes over periods of days to months is not effective and issues may occur despite the continuous injection of inhibitor. The continuously changing environment of production systems may benefit from a near real time solution that may manage asphaltenes through evergreen risk monitoring and autonomous chemical injection. A method is proposed below which combines field and laboratory data, physics-based models, and a near real time software platform to autonomously control asphaltene inhibitor injection to ensure appropriate dosing is achieved.

[0040] According to some embodiments, the method may use an extract, transform, and load (ETL) process to combine multiple data sources into a fully contextualized data platform. Sensor data, laboratory data, fluid properties, flow measurements, and wellbore data may be brought together to get a complete picture of the current conditions in the well. The contextualized data may provide a basis to execute data driven physics-based models, machine learning models, Al models, or hybrid models in near real-time to better understand asphaltene risk and manage inhibitor injection.

[0041] According to certain embodiments, the method leverages an equation of state based thermodynamic equilibrium model that captures fluid properties from downhole to wellhead. TheAttorney Docket No.: IS24. 1151-WO-PCT thermodynamic equilibrium model may predict precipitation of asphaltene by treating the precipitate as a second dense liquid phase. These models may be calibrated using SARA analysis, AOP, and WAT to ensure that they accurately reflect phase behavior in the real system. The method may use asphaltene precipitation predictions along with other field data to determine the optimal amount of inhibitor to inject, according to certain embodiments.

[0042] With contextualized near real time data and physics-based modeling, the method may continuously monitor changes in well conditions and their impact on asphaltene risk. By leveraging digital capabilities, what were previously manual chemical treatment processes requiring frequent field visits may be transformed into autonomous chemical injection systems. These insights may be coupled with flowrate data and pump automation to determine the appropriate amount of chemical to inject based on in situ conditions in the well. According to certain embodiments, the method may reduce the time from insight to action by acquiring and processing data in near real time to provide continuous optimization recommendations and autonomous action. The method may eliminate over- and undertreatment and their deleterious effects on production.

[0043] Figure 2 illustrates a flow chart representing a flow of data between connected data sources 202 such as field site equipment where data is collected, for example an electric submersible pump 204, a surface temperature and / pressure sensor 206, a production data system 208, an injection pump 210, and / or a corrosion probe 212, and an on-site processor, edge intelligence or computing component 214 running a number of control functions or analysis models including but not limited to a virtual flow model 216, a scale prediction 218, a corrosion prediction 220, a target rate 222, and / or a chemical pump control 224. According to certain embodiments, the field site may be a wellsite, a refinery, a pipeline, or any other appropriate location or facility. The on-site processor or computing component 214 in turn may be connected to a cloud database, processor, or other intelligence 226 via a network. The cloud intelligence 226 may generate recommendations or insights 228, maintain an activity log 230, and / or present a user a visualization 232 of the method through a graphical interface. According to certain embodiments, existing field site equipment is connected for real-time data acquisition to inform inline use of physics-based models such as a virtual flowmeter, edge analytics, and cloud computing. From this contextual basis, the method and system autonomously may generate actionable insights and adjust chemical injection. The continuous optimization of injection extends both equipment uptime andAttorney Docket No.: IS24. 1151-WO-PCT the life of production equipment. The number of field visits may be significantly reduced, along with their corresponding carbon emissions.

[0044] According to certain embodiments, the method for optimized and autonomous chemical injection of corrosion, scale, asphaltene, paraffin, and hydrate inhibitors may include the identification of real-time corrosion, scale, paraffin, asphaltene, and hydrate risk. Having up-to- the-minute information means no more waiting months for usable data from traditional manual monitoring. According to certain embodiments, the method may also include the automated generation of actionable insights for manual mode operation. An automated advisory system may identify at-risk wells and generate corresponding insights in order to optimize chemical injection to not only avoid possible damage to the equipment disposed at the site, but to also prevent unwanted declines in production at the site. According to certain embodiments, the method may operate in an autonomous mode operation. Chemical pump rates may be adjusted in real time in response to the automated insights so as to ensure effective chemical injection and treatment.

[0045] The manual performance of monitoring production equipment may delay event detection. The weak link in chemical treatment programs is that the manual technique that is traditionally employed to monitor risks and chemical performance introduces delays in taking preventive action and undermines alignment of the chemical treatment with the ever-changing production conditions. According to certain embodiments, the current method may significantly reduce the time from insight to action by acquiring and processing data in real time to provide continuous optimization recommendations and autonomous action. The reduction in time may be done by connecting the field site equipment 202 to for example a local edge gateway or intelligence 214 which may provide computing power at the field site and then transmits the data collected from the field site 202 equipment to the cloud 226.

[0046] The timing of samples and well test data often may not line up with manual adjustments that are performed to control issues found during monitoring. This may lead to ineffective chemical treatment or chronic overtreatment. Ineffective treatment may lead to equipment failure due to issues with corrosion, scale, asphaltene, paraffin, and hydrates. On the other hand, overtreatment of equipment may increase costs among other issues. According to certain embodiments, the current method may leverage edge intelligence 214 and execute physics-based models, machine learning models, Al models, or hybrid models using real-time data from the field site equipment 202 to continuously monitor changes in production conditions and their impact on corrosion, scale,Attorney Docket No.: IS24. 1151-WO-PCT hydrate, paraffin, and asphaltene risk in order to ultimately mitigate the occurrence of equipment failures. Physics based risk models may be used to determine how severe the issues are based on the pressure and temperature conditions and other physical properties of the system. According to certain embodiments, a calculated risk may be used to determine optimal injection rates that are based on a determined risk index and current flowrates.

[0047] Wells are not static systems and manually managed chemical treatments may remain unchanged for weeks due to the difficulty in physically getting to the injection location. This lack of access may result in chronic overtreatment or undertreatment, neither of which mitigates corrosion, scale, asphaltene, paraffin, or hydrate risk effectively and may lead to production shutdowns and increased costs. According to certain embodiments, the current method enables real-time autonomous control of the chemical injection pumps 210. An optimal injection rate or schedule may be adjusted autonomously to ensure that chemical injection is continuously on target, thereby eliminating over- and undertreatment and their deleterious effects on production. According to certain embodiments, the optimal injection rate may be determined by the edge gateway 214 performing optimization calculations on site. The system may then make injection control actions on site. The gateway may also transmit data to the cloud and allow for pump control from the cloud so as to manage the chemical injection equipment remotely through a digital controller and to change the operation of the chemical injection equipment between a manual and an autonomous mode.

[0048] Figure 3 illustrates an optimization workflow 300 based on a calculated risk index. According to certain embodiments, parameter data 302 (for example, data received from a customer or client) may be combined with field measurements 304 from the field site equipment 306, for example a chemical injection pump, and stored in an internal or edge-based database 310 to calculate the risk index using a risk index evaluation 308. The parameter data 302 may include but is not limited to water chemistry data 312, well test data 314, fluid analysis data 316, corrosion coupon data 318, and / or well geometry and casing material data 320. In certain embodiments, the parameter data 302 may include historical or prior data collected from the field site equipment 306, or it may include data that is result of other ongoing procedures or processes occurring at the field site or which may have been received from a lab or other testing facility. For example, while testing for scaling or corrosion in a fluid flow at the field site, a corrosion coupon may be inserted somewhere within the fluid flow. After a sufficient time period has elapsed, the corrosion couponAttorney Docket No.: IS24. 1151-WO-PCT may be removed from the fluid flow and then sent to a testing facility where various appropriate tests may be conducted to determine the level of scaling or corrosion of the corrosion coupon and thus the level of scaling or corrosion within the fluid flow at the field site. Such data may be denoted as corrosion coupon data 318 which in turn may be included within parameter data 302 and ultimately stored in the internal database 310. In certain embodiments, the parameter data 302 may be first stored within a client database 322 that is remotely connected to the internal database 310.

[0049] In certain embodiments, the field measurements 304 may be received at the internal database 310 via a connection over a Cloud network 322 that may be in turn receive the data directly from the field site equipment 306 and an edge gateway 324.

[0050] In certain embodiments, risk control evaluations 308 and risk control calculations 326 may be performed at different points around the field site including around or in a well or pipeline to assess overall risk. A risk which may be determined to be the most likely or most detrimental may be used to determine the optimal chemical injection rate. According to certain embodiments, physics based models, Al models, machine learning models, hybrid models, and equations of state based models may be used in the development of the risk index 308 in order to, for example, determine optimal injection rate calculations.

[0051] According to certain embodiments, the optimal chemical concentration may be defined according to equation 1 :C = K * max( / ? - Rt, 0) + Cmin, (1)

[0052] where C is a desired concentration, K is a constant for risk control, R is the risk index, Rt is a risk index threshold, Cmin is a minimum chemical concentration. According to certain embodiments, the optimal chemical concentration to maintain the risk index at a predetermined threshold may be defined according to equation 2:C = min ( / ? — / ?t), (2) where C is a desired concentration, R is the risk index, and Rt is a risk index threshold. According to certain embodiments, a risk index calculated from EOS may be defined by equation 3: (3)

[0053] According to certain embodiments, a water production rate based on a total liquid rate and well test-water cut provided by a steady-state multiphase flow simulator may be defined by equation 4:Attorney Docket No.: IS24. 1151-WO-PCT

[0054] According to certain embodiments, an optimal chemical injection rate based on the calculated optimal chemical concentration (equation (l))and water production rate (equation (4)) may be defined by equation 5:where C is a desired concentration, and Fw is a calculated water production rate.

[0055] According to certain embodiments, the parameter data, the received field measurements, the risk index, the risk model, and / or the risk control algorithm may vary or be customized depending on the type of inhibitor that is used at the injection site. Figure 7 outlines the differences of the various inhibitors.

[0056] Returning to Figure 3, the risk index evaluation 308 may be used in conjunction with one or more risk control algorithms 326 to determine an optimal chemical concentration and / or a chemical injection rate given the level of risk. In certain embodiments, the results of the risk index evaluation 308 and the risk control algorithm 326 may be stored in the internal database 310. In certain embodiments, the determined optimal injection rate may be sent or transmitted to a chemical pump control service 328 when in an autonomous mode. Alternatively according to certain embodiments, when in the manual mode, the optimal injection rate may only be recommended to the user via an injection decision graphical interface 330, for example a graphical interface displayed on a screen. In certain embodiments, a record of the users input at the injection decision graphical interface 330 may be stored in the internal database 310. When in the manual mode, the user may then apply the recommended injection rate by sending a corresponding signal to the edge gateway 324 via the Cloud network 322 or directly to the chemical injection equipment or field site equipment 306.

[0057] Figure 4 illustrates a distributed architecture 400 for implementing the current method, according to an embodiment. The distributed architecture 400 may include a portion disposed or located at a field site 402 and another portion which may disposed or accesses over a Cloud network 404. Risk models and optimization calculations may be performed locally at the field site 402 using an edge gateway 406, while the Cloud network 404 may support risk model and optimization calculations as well as data collection from a client database 408, and a client user interface 410 which itself may include an administrative configuration and user notifications.Attorney Docket No.: IS24. 1151-WO-PCT

[0058] According to certain embodiments, the edge gateway 406 may be used to compute fast loop control calculations using real-time field measurements received from a number of sensors disposed within the field site 402 including but not limited to a flowmeter 414, a corrosion probe 416, a temperature sensor 418, a pressure sensor 420, data received from an electric submersible pump (ESP) 422, and at least one chemical pump 424.

[0059] The cloud architecture 404, for example the client database 408 and an application database 412 in turn may be used for sharing of configuration data, configuration management, and allowing the user to change operating modes and pump settings remotely. For example, the application database 412 may receive filed measurements, event notifications, and edge application data from the edge gateway 406 and additional related data such as well properties or the results of previous water analysis from the client database 408. After calculating a risk index at the edge gateway 406 and generating a recommendation based on the risk index, the recommendation may be displayed to the user at the interface 410. When in a manual mode, the user may interact with the interface 410 to implement the recommendation, for example, choosing to operate the chemical pump 424 to provide a recommended concentration of a specific additive at a recommended injection rate. The signal corresponding to the user’s input may be sent directly to the chemical pump 424 disposed at the field site 402 via the edge gateway 406. When in an autonomous mode, the recommendation generated from the risk models at the edge gateway 406 including an optimized injection rate and / or an optimized additive concentration may be automatically implemented by the chemical pump 424. In certain embodiments, the user may use the interface 410 to change any parameters and / or configuration data related to operation of the chemical pump 424 while in the autonomous mode. For example, the user may adjust the time of the implementation of the optimization recommendation or the related thresholds of the risk index which determine when an optimization recommendation may be necessary. In certain embodiments, the user may switch the operations of the distribution architecture 400 from the manual mode to the autonomous mode, and vice versa, using the interface 410.

[0060] According to certain embodiments, the edge gateway 406 may be used to monitor pump changes made locally to the physical device or equipment 424, while actions which are initiated at the physical device or equipment 424 may be used to override any autonomous actions provided that the equipment is the autonomous mode. For example, if a certain injection rate has been recommended by the risk models while in the autonomous mode, a user at the field site 402 mayAttorney Docket No.: IS24. 1151-WO-PCT override or cancel the implementation of the recommended injection rate by interacting with the chemical pump 424 itself, for example by interacting with an interface of the chemical pump 424.

[0061] According to certain embodiments, the architecture 400 may be operated in autonomous and manual modes as discussed above. In the manual mode, changes may be recommended to the user through the graphical interface 410 but are not automatically implemented. In the autonomous mode, optimal changes may be automatically applied to the chemical pump 424, according to certain embodiments. The ability to manually override the chemical injection equipment between an on or off state and / or the injection rate from the field site equipment that is initially in the autonomous mode may be provided. According to certain embodiments, the architecture 400 may also provide the ability to take back or resume control of the chemical injection equipment while it is in the autonomous mode as this allows field users to perform work as needed at the field site without setting the chemical injection equipment to manual before arriving at site. In certain embodiments, notifications may be provided in the user interface 410 if any part of the field site equipment 414-424 is disabled from the field site 402 while in the autonomous mode.Exemplary Method

[0062] Figure 5 illustrates a flowchart of a method 500 method for optimizing autonomous chemical injections for equipment at a field site. According to certain embodiments, the method includes receiving data related to an injection procedure occurring at the field site by an optimization system, as at 502. The equipment may include pumps, field instrumentation, or an internal system associated with a user. In certain embodiments the data may be related to corrosion, scale, paraffins, asphaltenes, or hydrates disposed in or on the equipment. In certain embodiments, receiving data related to the injection procedure at the field site by the optimization system may include receiving real-time field measurements from the equipment at the field site. Receiving data related to the injection procedure at the field site by the optimization system may also include receiving parameter data stored on a client database. In certain embodiments, parameter data may include water chemistry data, well test data, fluid analysis data, corrosion coupon data, well geometry and casing material data, or a combination thereof. In certain embodiments, receiving data related to the injection procedure occurring at the field site by the optimization system includes receiving data from a pressure sensor, a temperature sensor, a densitometer, a viscometer, a corrosion probe, a multiphase flowmeter, or a combination thereof.Attorney Docket No.: IS24. 1151-WO-PCT

[0063] The method may also include calculating a risk index based on the received data, as at 504. Calculating the risk index based on the received data may include calculating a probability of damage occurring to the equipment, a probability of a production value falling below a predetermined threshold, or a combination thereof.

[0064] According to certain embodiments, the method 500 may also include automatically generating a recommendation in response to the calculated risk index, as at 506. According to certain embodiments, the recommendation may include an actionable task related to manual operation of the equipment, an advisory of equipment that is at-risk, or a suggestion to optimize an injection process of the additive within the equipment. In certain embodiments, the recommendation may be generated locally at the field site. In certain embodiments, automatically generating the recommendation in response to the calculated risk index may include determining an optimal injection rate of the additive and an optimal chemical concentration of the additive based on the calculated risk index. In certain embodiments, generating the recommendation includes determining an optimal chemical concentration of the additive based on the calculated risk index. Determining the optimal chemical concentration of the additive may be defined by:C = K * max(R - Rt, 0) + Cmin, where C is a desired concentration, K is a constant for risk control, R is the risk index, Rt is a risk index threshold, Cmin is a minimum chemical concentration. In certain embodiments, determining the optimal chemical concentration of the additive further may include determining an optimal chemical concentration of the additive to maintain the risk index at a predetermined threshold. The optimal chemical concentration of the additive to maintain the risk index at a predetermined threshold may be defined by:C = min ( / ? — Rt), where C is a desired concentration, R is the risk index, and Rt is a risk index threshold.

[0065] According to certain embodiments, the method 500 may include displaying the recommendation on a graphical interface displayed to a user, as at 508. Displaying the generated recommendation may include displaying a status of the injected additive within a graphical interface on a screen that is remotely connected to the field site.

[0066] According to certain embodiments, the method 500 may include transmitting a command signal corresponding to the recommendation to the field site, as at 510. Transmitting the command signal may include transmitting an optimal injection rate to the equipment when in an autonomousAttorney Docket No.: IS24. 1151-WO-PCT mode, and transmitting the optimal injection rate to a user when in a manual mode. In certain embodiments, transmitting the command signal corresponding to the recommendation to the field site may include the user transmitting the command signal to the field site via the graphical interface when the optimization system is in the manual mode.

[0067] According to certain embodiments, the method 500 may include automatically injecting an additive to equipment disposed at the field site according to the command signal, as at 512. Automatically injecting the additive to equipment disposed at the field site according to the command signal may include adjusting the rate of injection at a chemical pump in real time.

[0068] According to certain embodiments, the method 500 may include performing a field site action in response to the generated recommendation, as at 514. Performing the field site action may include generating or transmitting a signal that instructs or causes an action to occur. The action may include a physical action. The physical action may include selectively activating the equipment based on the received optimal injection rate when in the manual mode via the graphical interface, selectively changing operation of the equipment between an autonomous mode and a manual mode, selectively adjusting equipment settings, selectively overriding an autonomous action made by the equipment when in the autonomous mode, and providing a notification to the user interface if equipment is disabled manually at the field site while in an autonomous mode.Exemplary Computing System

[0069] In some embodiments, the methods of the present disclosure may be executed by a computing system. Figure 6 illustrates an example of such a computing system 600, in accordance with some embodiments. The computing system 600 may include a computer or computer system 601A, which may be an individual computer system 601A or an arrangement of distributed computer systems. The computer system 601A includes one or more analysis modules 602 that are configured to perform various tasks according to some embodiments, such as one or more methods disclosed herein. To perform these various tasks, the analysis module 602 executes independently, or in coordination with, one or more processors 604, which is (or are) connected to one or more storage media 606. The processor(s) 604 is (or are) also connected to a network interface 607 to allow the computer system 601 A to communicate over a data network 609 with one or more additional computer systems and / or computing systems, such as 60 IB, 601C, and / or 601D (note that computer systems 601B, 601C and / or 601D may or may not share the sameAttorney Docket No.: IS24. 1151-WO-PCT architecture as computer system 601 A, and may be located in different physical locations, e.g., computer systems 601 A and 601B may be located in a processing facility, while in communication with one or more computer systems such as 601 C and / or 60 ID that are located in one or more data centers, and / or located in varying countries on different continents).

[0070] A processor may include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, or another control or computing device.

[0071] The storage media 606 may be implemented as one or more computer-readable or machine-readable storage media. Note that while in the example embodiment of Figure 6 storage media 606 is depicted as within computer system 601A, in some embodiments, storage media 606 may be distributed within and / or across multiple internal and / or external enclosures of computing system 601A and / or additional computing systems. Storage media 606 may include one or more different forms of memory including semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable read-only memories (EEPROMs) and flash memories, magnetic disks such as fixed, floppy and removable disks, other magnetic media including tape, optical media such as compact disks (CDs) or digital video disks (DVDs), BLURAY® disks, or other types of optical storage, or other types of storage devices. Note that the instructions discussed above may be provided on one computer-readable or machine-readable storage medium, or may be provided on multiple computer-readable or machine-readable storage media distributed in a large system having possibly plural nodes. Such computer-readable or machine-readable storage medium or media is (are) considered to be part of an article (or article of manufacture). An article or article of manufacture may refer to any manufactured single component or multiple components. The storage medium or media may be located either in the machine running the machine-readable instructions, or located at a remote site from which machine-readable instructions may be downloaded over a network for execution.

[0072] In some embodiments, computing system 600 contains one or more artificial intelligence module(s) 608. In the example of computing system 600, computer system 601 A includes the Al module 608. In some embodiments, a single Al module may be used to perform some aspects of one or more embodiments of the methods disclosed herein. In other embodiments, a plurality of Al modules may be used to perform some aspects of methods herein.Attorney Docket No.: IS24. 1151-WO-PCT

[0073] It should be appreciated that computing system 600 is merely one example of a computing system, and that computing system 600 may have more or fewer components than shown, may combine additional components not depicted in the example embodiment of Figure 6, and / or computing system 600 may have a different configuration or arrangement of the components depicted in Figure 6. The various components shown in Figure 6 may be implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing and / or application specific integrated circuits.

[0074] Further, the steps in the processing methods described herein may be implemented by running one or more functional modules in information processing apparatus such as general purpose processors or application specific chips, such as ASICs, FPGAs, PLDs, or other appropriate devices. These modules, combinations of these modules, and / or their combination with general hardware are included within the scope of the present disclosure.

[0075] Computational interpretations, models, and / or other interpretation aids may be refined in an iterative fashion; this concept is applicable to the methods discussed herein. This may include use of feedback loops executed on an algorithmic basis, such as at a computing device (e.g., computing system 600, Figure 6), and / or through manual control by a user who may make determinations regarding whether a given step, action, template, model, or set of curves has become sufficiently accurate for the evaluation of the risk index.

[0076] The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or limiting to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. Moreover, the order in which the elements of the methods described herein are illustrated and described may be re-arranged, and / or two or more elements may occur simultaneously. The embodiments were chosen and described in order to best explain the principles of the disclosure and its practical applications, to thereby enable others skilled in the art to best utilize the disclosed embodiments and various embodiments with various modifications as are suited to the particular use contemplated.

Claims

Attorney Docket No.: IS24. 1151-WO-PCTCLAIMSWhat is claimed is:

1. A method for optimizing autonomous chemical injections for equipment at a field site, the method comprising: receiving data related to an injection procedure occurring at the field site by an optimization system; calculating a risk index based on the received data; automatically generating a recommendation in response to the calculated risk index; transmitting a command signal corresponding to the recommendation to the field site; and automatically injecting an additive to the equipment disposed at the field site according to the command signal.

2. The method of claim 1, wherein receiving data related to the injection procedure at the field site by the optimization system comprises receiving real-time field measurements from the equipment at the field site.

3. The method of claim 1, wherein receiving data related to the injection procedure at the field site by the optimization system comprises receiving parameter data stored on a client database.

4. The method of claim 3, wherein the parameter data comprises water chemistry data, well test data, fluid analysis data, corrosion coupon data, well geometry and casing material data, or a combination thereof.

5. The method of claim 1, wherein calculating the risk index based on the received data comprises calculating a probability of damage occurring to the equipment, a probability of a production value falling below a predetermined threshold, or a combination thereof.

6. The method of claim 1, wherein the recommendation comprises an actionable task related to manual operation of the equipment, an advisory that the equipment is at-risk, a suggestion to optimize an injection process of the additive within the equipment, or a combination thereof.Attorney Docket No.: IS24. 1151-WO-PCT7. The method of claim 1, wherein automatically generating the recommendation in response to the calculated risk index comprises determining an optimal injection rate of the additive and an optimal chemical concentration of the additive based on the calculated risk index.

8. The method of claim 1, further comprising displaying the recommendation on a graphical interface displayed to a user.

9. The method of claim 8, wherein transmitting the command signal corresponding to the recommendation to the field site comprises the user transmitting the command signal to the field site via the graphical interface when the optimization system is in a manual mode.

10. The method of claim 1, wherein transmitting the command signal corresponding to the recommendation to the field site comprises automatically transmitting the command signal to the field site over a network connection when the optimization system is in an autonomous mode.

11. A computing system, comprising: one or more processors; and a memory system comprising one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations, the operations comprising: receiving data related to an injection procedure occurring at a field site by an optimization system; calculating a risk index based on the received data; automatically generating a recommendation in response to the calculated risk index; transmitting a command signal corresponding to the recommendation to the field site; and automatically injecting an additive to equipment disposed at the field site according to the command signal.Attorney Docket No.: IS24. 1151-WO-PCT12. The computing system of claim 11, wherein receiving data related to the injection procedure occurring at the field site by the optimization system comprises receiving data from a pressure sensor, a temperature sensor, a densitometer, a viscometer, a corrosion probe, a multiphase flowmeter, or a combination thereof.

13. The computing system of claim 11, wherein the data related to the injection procedure occurring at the field site comprises data related to corrosion, scaling, paraffins, asphaltenes, or hydrates disposed in or on the equipment.

14. The computing system of claim 11, wherein generating the recommendation comprises determining an optimal chemical concentration of the additive based on the calculated risk index, wherein determining the optimal chemical concentration of the additive is based upon the risk index, a risk index threshold, a minimum chemical concentration, and a constant for risk control.

15. The computing system of claim 14, wherein determining the optimal chemical concentration of the additive further comprises determining an optimal chemical concentration of the additive to maintain the risk index at a predetermined threshold, wherein of the optimal chemical concentration of the additive to maintain the risk index at a predetermined threshold is based upon the risk index and a risk index threshold.

16. The computing system of claim 11, wherein generating the recommendation comprises determining an optimal injection rate of the additive based on the calculated risk index.

17. The computing system of claim 11, wherein automatically injecting the additive to equipment disposed at the field site according to the command signal comprises adjusting a rate of injection at a chemical pump in real time.

18. The computing system of claim 11, the operations further comprising displaying the generated recommendation and a status of the injected additive within a graphical interface on a screen remotely connected to the field site.Attorney Docket No.: IS24. 1151-WO-PCT19. The computing system of claim 11, the operations further comprising performing a field site action in response to the generated recommendation, wherein performing the field site action comprises generating or transmitting a signal that instructs or causes an action to occur, wherein the action comprises a physical action, and wherein the physical action comprises selectively activating the equipment based on the received optimal injection rate when in the manual mode via the graphical interface, selectively changing operation of the equipment between an autonomous mode and a manual mode, selectively adjusting equipment settings, and selectively overriding an autonomous action made by the equipment when in the autonomous mode, wherein a notification is provided to the user interface if equipment is disabled manually at the field site while in an autonomous mode.

20. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations, the operations comprising: receiving data related to an injection procedure occurring at a field site by an optimization system; calculating a risk index based on the received data; automatically generating a recommendation in response to the calculated risk index; transmitting a command signal corresponding to the recommendation to the field site; and automatically injecting an additive to the equipment disposed at the field site according to the command signal.

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