WO2025262201A1 - Real-time risk assessment and advisory with alert - Google Patents

Abstract

A computer-implemented method includes performing a root cause analysis associated with one or more alerts generated in association with an asset. Performing the root cause analysis includes identifying a target risk associated with the asset based on the one or more alerts and generating recommendation data associated with mitigating or preventing the target risk. The computer-implemented method includes providing one or more notifications including the one or more alerts, the target risk, and the recommendation data.

Description

REAL-TIME RISK ASSESSMENT AND ADVISORY WITH ALERT

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of an earlier filing date from Italian Non Provisional Application No. 102024000014254, filed June 20, 2024, the entire disclosure of which is incorporated herein by reference.

BACKGROUND

[0002] Various embodiments supported by aspects of the present disclosure relate to automated risk management with alert detection in association with resource recovery and fluid sequestration industries, and more particularly, to carbon capture, utilization and storage (CCUS) industries.

[0003] In the resource recovery and fluid sequestration industries, some systems are configured for providing alerts related to hazards or conditions. However, the systems lack automated techniques for analyzing and assessing risks associated with an alert in the systems. In some cases, risk analysis and risk assessment are manually performed by a field operator, which may be both time consuming and inaccurate, thus increasing risks and hazards associated with the systems.

SUMMARY

[0004] Embodiments of the present disclosure are directed to a computer- implemented method including: performing a root cause analysis associated with one or more alerts generated in association with an asset, wherein performing the root cause analysis includes: identifying a target risk associated with the asset based on the one or more alerts; and generating recommendation data associated with mitigating or preventing the target risk; and providing one or more notifications including the one or more alerts, the target risk, and the recommendation data.

[0005] Embodiments of the present disclosure are also directed to a system including: analysis equipment including a processor and a memory, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to perform operations including: performing a root cause analysis associated with one or more alerts generated in association with an asset, wherein performing the root cause analysis includes: identifying a target risk associated with the asset based on the one or more alerts; and generating recommendation data associated with mitigating or preventing the target risk; and providing one or more notifications including the one or more alerts, the target risk, and the recommendation data.

[0006] Embodiments of the present disclosure are also directed to a computer program product including a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform operations including: performing a root cause analysis associated with one or more alerts generated in association with an asset, wherein performing the root cause analysis includes: identifying a target risk associated with the asset based on the one or more alerts; and generating recommendation data associated with mitigating or preventing the target risk; and providing one or more notifications including the one or more alerts, the target risk, and the recommendation data.

[0007] Further aspects supported by the present disclosure and features of example embodiments are illustrated in the accompanying drawings and/or described in the following description.

BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The following descriptions should not be considered limiting in any way. With reference to the accompanying drawings, like elements are numbered alike:

[0009] FIG. 1 is a diagram illustrating an example embodiment of a system for realtime risk assessment and advisory alerts associated with energy industry operations in accordance with aspects of the present disclosure.

[0010] FIG. 2 illustrates an example workflow in accordance with one or more embodiments of the present disclosure.

[0011] FIG. 3 illustrates an example flowchart of a method in accordance with one or more embodiments of the present disclosure.

DETAILED DESCRIPTION

[0012] A detailed description of one or more embodiments of the disclosed apparatus and method are presented herein by way of exemplification and not limitation with reference to the Figures.

[0013] FIG. 1 is a diagram illustrating an example embodiment of a system 100 for automated risk management with alert detection in association with energy industry operations in accordance with aspects of the present disclosure. [0014] The system 100 is configured to perform any suitable energy industry operation, such as, for example, a drilling operation, a stimulation operation, a measurement operation and/or a production operation. In some embodiments, the energy industry operations may include CCUS operations. However, example aspects of the techniques for automated risk management with alert detection as supported by the system 100 as described herein are not limited to energy industry operations and an associated downhole environment.

[0015] According to one or more embodiments of the present disclosure, the systems and techniques described herein support digital solutions for automated risk management with alert detection in CCUS operations. The systems and techniques described herein support real-time risk detection with increased accuracy compared to some other approaches.

[0016] The system 100 includes a borehole 135 in a subsurface formation 130. A borehole string 140 (also referred to herein as a drill string) is disposed in the borehole 135 that penetrates the formation 130. The borehole 135 may be an open hole, a cased hole or a partially cased hole. In one embodiment, the borehole string 140 is a stimulation or injection string that includes a tubular, such as a coiled tubing, pipe (e.g., multiple pipe segments) or wired pipe, that extends from a wellhead at a surface location (e.g., at a drill site or offshore stimulation vessel).

[0017] As described herein, a “string” refers to any structure or carrier suitable for lowering a tool or other component through a borehole or connecting a drill bit to the surface, and is not limited to the structure and configuration described herein. The term "carrier" as used herein means any device, device component, combination of devices, media and/or member that may be used to convey, house, support or otherwise facilitate the use of another device, device component, combination of devices, media and/or member. Example nonlimiting carriers include casing pipes, wirelines, wireline sondes, slickline sondes, drop shots, downhole subs, BHAs and drill strings.

[0018] In one embodiment, the system 100 is configured as a hydraulic stimulation system. As described herein, “hydraulic stimulation” includes any injection of a fluid into a formation. A fluid may be any flowable substance such as a liquid or a gas, and/or a flow able solid such as sand.

[0019] In this embodiment, the borehole string 140 includes a stimulation assembly that includes one or more tools 150 or components to facilitate stimulation of the formation 130. Non-limiting examples of the tools 150 included in the borehole string 140 include a fracturing assembly (e.g., a fracture or “frac” sleeve device), a perforation assembly (e.g., shaped charges, torches, projectiles and other devices for perforating the borehole wall and/or casing), and isolation or packer subs.

[0020] The tools 150 may support various processes including formation drilling, geosteering, and formation evaluation (FE) for measuring versus depth and/or time one or more physical quantities in or around a borehole 135. The tools 150 may be included in or embodied as a BHA, drillstring component, or other suitable carrier. A “carrier” as described herein means any device, device component, combination of devices, media and/or member that may be used to convey, house, support or otherwise facilitate the use of another device, device component, combination of devices, media and/or member. Example non-limiting carriers include drill strings of the coiled tubing type, of the jointed pipe type and any combination or portion thereof. Other carriers include, but are not limited to, casing pipes, wirelines, wireline sondes, slickline sondes, drop shots, downhole subs, bottom-hole assemblies, and drill strings.

[0021] One or more of the tools 150 may include suitable electronics or processors configured to communicate with a surface processing unit (e.g., a computing device 105) and/or control the respective tool 150 or assembly.

[0022] The system 100 includes surface equipment 110 for performing various energy industry operations. For example, the surface equipment 110 is configured for injection of fluids into the borehole 135 in order to, e.g., fracture the formation 130. In one or more embodiments, the surface equipment 110 includes an injection device such as a high pressure pump 115 in fluid communication with a fluid tank 120, mixing unit or other fluid source or combination of fluid sources. The pump 115 injects fluid into the borehole string 140 or the borehole 135 to introduce fluid into the formation 130, for example, to stimulate and/or fracture the formation 130. The pump 115 may be located downhole or at a surface location.

[0023] One or more flow rate and/or pressure sensors 125 may be disposed in fluid communication with the pump 115 and the borehole string 140 for measurement of fluid characteristics. The sensors 125 may be positioned at any suitable location, such as proximate to (e.g., at the discharge output) or within the pump 115, at or near the wellhead, or at any other location along the borehole string 140 or the borehole 135. The sensors described herein are exemplary, as various types of sensors may be used to measure various parameters.

[0024] A computing device 105 (e.g., computing device 105-a) may be disposed in operable communication with components such as sensors 125 located above the surface, the pump 115, and/or downhole components. For example, the computing device 105 may be in operable communication with sensors (e.g., pressure sensors, temperature sensors, vibration sensors, gas sensors, and the like) located below the surface and/or in the borehole string 140. In some examples, the computing device 105-a may be in operable communication with a tool 150 (or multiple tools).

[0025] The system 100 supports communication between the computing device 105 and other devices of the system 100 via wired communication protocols, wireless communication protocols (e.g., electromagnetic (EM) signals, WiFi, Bluetooth™, ZigBee™, Ubiquiti™, 3G, 4G, LTE, and the like), and/or combinations including one or more of the foregoing.

[0026] The system 100 supports telemetry techniques capable of transmitting data from components located downhole to the surface and/or surface equipment 110. Nonlimiting examples of the telemetry techniques include acoustic telemetry or mud pulse (MP) telemetry supportive of transmitting information by generating vibrations in fluid in the borehole 135, electromagnetic (EM) telemetry supportive of transmitting information by way of signals that propagate at least in part through the earth (e.g., through formations 130). Other non-limiting examples of telemetry techniques supported by aspects of the present disclosure include the use of hardwired drill pipe, fibre optic cable, or drill collar acoustic telemetry to carry data to the surface and/or surface equipment 110.

[0027] The system 100 may include one or more access nodes 170 supportive of communicating data along the borehole string 140 (e.g., up or down the borehole string 140). In one or more embodiments, the access nodes 170 may be implemented in the borehole 135 or a communication borehole (not illustrated) separate from the borehole 135. In some examples, the one or more access nodes 170 may provide functionality as wireless access nodes for relaying data from a tool 150 to the surface (e.g., to a computing device 105).

[0028] In one or more embodiments, the system 100 may include a chain of access nodes 170 spaced apart along the borehole string 140, and the chain of access nodes 170 may support repeating of data in a unidirectional (e.g. downhole to surface or surface to downhole) or bidirectional manner. For example, an access node 170 (or chain of access nodes 170) may support the communication of data between a computing device 105, a tool 150, and the like.

[0029] Accordingly, for example, the communication protocols and telemetry techniques supported by the system 100 enable communication between computing devices 105 (e.g., computing device 105-a, computing device 105-b, and the like) and downhole components. [0030] The computing device 105 is configured to receive, store and/or transmit data generated from components (e.g., pump 115, fluid tank 120, sensors 122, sensors 125, sensors 155, sensors 160, and the like) included in the surface equipment 110 and/or downhole components (e.g., a tool 150, downhole sensors 155, and the like). The computing device 105 includes processing components configured to analyze received data (e.g., data received from the pump 115, fluid tank 120, sensors 125, a tool 150, and the like). The computing device 105 includes processing components configured to provide data (and/or control signals to other components of the system 100. The computing device 105 includes any number of suitable components, such as processors, memory, communication devices and power sources.

[0031] The sensors 155 may be configured to measure various parameters of the formation 130 and/or borehole 135. For example, the sensors 155 may include formation evaluation sensors (e.g., resistivity, dielectric constant, water saturation, porosity, density and permeability), sensors for measuring borehole parameters (e.g., borehole size, borehole inclination and azimuth, and borehole roughness), sensors for measuring geophysical parameters (e.g., acoustic velocity, acoustic travel time, electrical resistivity), sensors for measuring borehole fluid parameters (e.g., viscosity, density, clarity, rheology, pH level, and gas, oil and water contents), boundary condition sensors, and sensors for measuring physical and chemical properties of the borehole fluid.

[0032] The system 100 includes sensors 160 for measuring force, operational and/or environmental parameters related to bending or other static and/or dynamic deformation of one or more downhole components. The sensors 160 are described collectively herein as “deformation sensors” and encompass any sensors, located at the surface and/or downhole, that provide measurements relating to bending or other deformation, static or dynamic, of a downhole component. Examples of deformation include deflection, rotation, strain, torsion and bending. Such sensors 160 provide data that is related to forces on the component (e.g., strain sensors, WOB sensors, TOB sensors) and are used to measure deformation or bending that could result in a change in position, alignment and/or orientation of one or more other sensors 160 or one or more sensors 155. In some non-limiting embodiments, the sensors 160 may include one or more of: (i) a strain gauge, (ii) a transmitter oriented at a non-X, non-Z angle, (iii) a receiver oriented at a non-X, non-Z angle, (iv) a differential magnetometer, (v) a differential accelerometer, (vi) an optical sensor, and (vii) an optical fiber sensor.

[0033] For example, the system 100 may include a distributed sensor system (DSS) disposed at the borehole string 140 and tool 150 (e.g., a BHA) and including a plurality of sensors 160. The sensors 160 may perform measurements associated with forces on the borehole string 140 that may result in deformation, and can thereby result in misalignment of one or more sensors 155. Non-limiting example of measurements performed by the sensors 160 include accelerations, velocities, distances, angles, forces, moments, and pressures. Sensors 160 may also be configured to measure environmental parameters such as temperature and pressure. In a non- limiting example, the sensors 160 may be distributed throughout the borehole string 140 and/or at a tool 150 (e.g., a drill bit) at the distal end of borehole string 140. In other embodiments, the sensors 160 may be configured to measure directional characteristics at various locations along the borehole 135. Examples of such directional characteristics include inclination and azimuth, curvature, strain, and bending moment.

[0034] In some examples, the system 100 may include sensors 160 coupled to a downhole component, such as, for example, a drill pipe section or a tool 150 (e.g., a BHA). In some examples, the sensors 160 may be deformation sensors (e.g., strain gauges configured to measure strain).

[0035] The system 101 may include machine learning model(s) 107 which may be trained and/or updated based on training data provided or accessed by any devices or systems described herein. The machine learning model(s) 107 may be built and updated by a computing device 105 (e.g., computing device 105-a, computing device 105-b, or the like) based on the training data (also referred to herein as training data and feedback).

[0036] The machine learning model(s) 107 may be provided in any number of formats or forms. In one or more embodiments, the operations described herein may implement machine learning and/or rule-based systems to generate a mixing instruction. In one or more embodiments, the system 101 may include (e.g., for theoretical and empirical processes) rule-based systems using predefined rules to make decisions or perform tasks, which may operate based on if-then statements.

[0037] In one or more embodiments, the system 101 may include natural language processing (NLP) techniques supportive of the interaction between computers and human language, enabling machines to understand, interpret, and generate human language (e.g., risk descriptions, risk remediation plans, risk mitigation plans, alerts, and the like).

[0038] In one or more embodiments, the system 101 may include computer vision techniques supportive of image processing, object recognition, and image segmentation. In an example, the computer vision techniques may support the integration of sensor data (e.g., from sensors 155, sensors 160, or the like). [0039] In one or more embodiments, the system 101 may include data mining techniques supportive of discovering patterns and relationships in large datasets (e.g., from database 180, data sources 203 later described herein with reference to FIG. 2, other data later described herein with reference to FIG. 2, and the like) and extract information. For example, the data mining techniques may support the discovery of interdependencies described herein such as, for example, interdependencies between alerts 157, risks 137, plans previously implemented for preventing or mitigating the risks 137, and the like.

[0040] In one or more embodiments, the system 101 may include or implement genetic algorithms supportive of determining approximate solutions to optimization and search problems, expert systems (computer systems) configured to emulate the decisionmaking ability of a human expert in a specific domain, fuzzy logic techniques supportive of modeling uncertainty and imprecision in data, simulation and modeling, regression analysis, clustering techniques, and dimensionality reduction (e.g., principal component analysis (PCA) or t-SNE).

[0041] Example aspects of the machine learning model(s) 107, such as generating (e.g., building, training) and applying the machine learning model(s) 107, are described with reference to the figure descriptions herein.

[0042] Aspects of the system 101 described herein support reducing operator workload and enhancing the efficiency of mapping of particular risks 137 based on generated alerts 157, and in some aspects, mapping of the risks 137 and/or alerts 157 to recommendation data 187 including risk prevention or risk mitigation plans. Example aspects of methods, procedures, and processes supported by the system 101 are described herein. The automation supported by system 101 entails a comprehensive software application design that enables an automated workflow, fostering increased user confidence.

[0043] The system 101 provides a user interface (e.g., at a computing device 105) that enables operators (e.g., field engineers) with the ability to review and receive alerts 157 (e.g., rule-based alerts) and alerts 190 (e.g., as generated by the risk evaluation engine 175). Via the user interface, the system 101 supports user review, confirmation, implementation, or modification of risk prevention or risk mitigation plans provided in recommendation data 187. In an example, once the recommendation data 187 has been reviewed and confirmed (or modified) by an operator, the computing device 105 may dispatch the parameters associated with the recommendation data 187 to an automation solution for execution.

[0044] In one or more embodiments, the system 101 integrates a machine learning algorithm (implemented using one or more trained machine learning models 107) that, based on risks 137 provided in a risk registry (e.g., risk register 235 later described herein with reference to FIG. 2) and generated alerts 157, is capable of generating and providing recommendation data 187 to operators for prevention and/or mitigation of risks 137 identified by the system 101.

[0045] In one or more embodiments, aspects of the automation solution may be implemented at computing device 105 (e.g., computing device 105-a), another computing device 105 (e.g., computing device 105-b) in electronic communication with the computing device 105 via a wired and/or wireless communication protocol, surface equipment 110, and/or processing circuitry included in the surface equipment 110. In the example of FIG. 1, computing device 105-a may be located at the drill site, and computing device 105-b may be located at the drill site or at a remote site. In some cases, computing device 105-a and/or computing device 105-b may be a server.

[0046] According to one or more embodiments of the present disclosure described herein, aspects of the system 100 provide real-time risk assessment and advisory. The system

100 supports automated association of risks to alerts generated by an industrial system. The terms “alerts” and “alarms” may be used interchangeably herein.

[0047] As will be described herein, the system 101 provides a hardware and software platform supportive of end-to-end monitoring of the system 100. For example, the system

101 supports end-to-end monitoring of conditions associated with surface equipment 110, formation 130, borehole 135, borehole string 140, tools 150, and the like).

[0048] For example, the system 101 is capable of generating one or more alerts 157 (e.g., rule-based alerts) in response to a trigger condition associated with the system 100. In an example, the trigger condition may be the exceeding of a threshold condition associated with the borehole 135 or the borehole string 140. The threshold condition may be, for example, a threshold force on the borehole string 140, a threshold environmental parameter (e.g., temperature, pressure, or the like), or the like which may have an adverse effect on the system 100 and energy industry operations performed by the system 100.

[0049] In another example, the system 101 may generate an alert 157 (or multiple alerts 157) in response to other trigger conditions (e.g., threshold temperature exceeded, threshold pressure exceeded, or the like). In a non-limiting example, the system 101 may generate an alert 157 in response to determining that a pressure at the borehole 135 (e.g., as measured by a sensor 125, a sensor 155, or the like) exceeds a threshold pressure value.

[0050] Using risk evaluation engine 175, the system 101 may autonomously identify a risk 137 (e.g., a potential hazard or adverse effect, an existing hazard) associated with the system 100 based on the alert 157. In some aspects, the system 101 may identify the risk 137 from a set of registered risks 137, example aspects of which are later described herein. Nonlimiting examples of the risks 137 include a leak, a construction issue, a problem with surface equipment 110, a problem with the integrity of the borehole 135 or borehole string 140, or the like. Another non-limiting example of the risks 137 includes cement bond log integrity. Another non- limiting example of the risks 137 includes loss of well integrity due to incompatible cement and formation fluids. Another non-limiting example of the risks 137 includes CO2 escaping vertically through the primary seal due to well integrity failure but accumulating below the secondary seal, which may cause cap rock integrity failure.

[0051] The risk evaluation engine 175 may map the alert 157 with the risk 137 on a real-time basis through a root cause analysis performed by the risk evaluation engine 175. Accordingly, for example, the system 101 may identify a risk 137 (or multiple risks 137) associated with an asset of the system 100 based on the alert 157. In some aspects, the system 101 may identify a risk 137 (or multiple risks 137) associated with an asset based on multiple instances of the same alert 157 and/or multiple different alerts 157.

[0052] The risk evaluation engine 175 may further autonomously generate recommendation data 187 associated with mitigating or preventing the risk 137. In some aspects, the risk evaluation engine 175 may generate an alert 190 including the recommendation data 187 and/or an indication of the risk 137 as determined by the risk evaluation engine 175. In an example, the system 101 may provide or display the alert 190 (and recommendation data 187 and/or risk 137) to a user, for example, via a computing device 105 (e.g., computing device 105-a, computing device 105-b, a data file (not illustrated), or the like).

[0053] Example aspects of the risk 137, risk evaluation engine 175 (and root cause analysis implemented by the risk evaluation engine 175), recommendation data 187, and alert 190 will further be described herein with reference to FIG. 2.

[0054] Accordingly, for example, the system 101 supports detecting problems or challenges associated with energy industry operations (e.g., downhole) much earlier compared to some other approaches. For example, some other approaches may involve an operator periodically examining a set of alerts which have been generated by an alarm system over time, for example, through a manual review of the alerts. In some cases, the manual examination of the alerts by some other approaches, even if performed periodically, may result in delayed identification of a root cause (e.g., a problem with surface equipment 110, a problem with the integrity of the borehole 135 or borehole string 140, or the like) associated with the alerts, inaccurate identification of the root cause, and/or delayed mitigation or remediation of the root cause. In some cases, the examination as implemented by some other approaches may result in a delay in identifying (or a failure to identify) adverse effects that the root cause has on the system 100 and energy industry operations performed by the system 100.

[0055] The providing of additional alerts 190 by the system 101 support improving the response time for reacting to an alarm situation and mitigating a problem associated with energy industry operations performed by the system 100. Through providing the alerts 190 as an immediate notification in real-time, the system 101 may ensure that corrective actions may be implemented at the system 100 before conditions deteriorate. The root cause analysis techniques and alert generation supported by the system 101 support real-time analysis of alerts (e.g., alerts 157) in response to the generation of each alert.

[0056] It is to be understood that the example aspects described herein of the system 100, system 101, and risk evaluation engine 175 may be implemented in accordance with energy industry operations such as, for example, CCUS applications and carbon storage, but are not limited thereto. Aspects of the system 101 and risk evaluation engine 175 support improved maintenance of wellbore integrity, reservoir integrity, and the like. Aspects of the system 101 and risk evaluation engine 175 support improved prevention of contamination situations (e.g., potential contamination of an aquifer).

[0057] Embodiments of the present disclosure support technical advantages and improvements to the technology. For example, the system 100 supports automated generation of mitigation and remediation recommendations. The system 100 supports implementing (e.g., by risk evaluation engine 175) a workflow supportive of autonomously recommending data for analyzing a risk and autonomously recommending operations for mitigating and remediating the risk, based on an artificial intelligence (Al) approach which evaluates historical data including previously applied mitigations and remediations. In some embodiments, the system 100 supports fully autonomous (e.g., without manual intervention) and/or semi-autonomous generation of mitigation and remediation recommendations.

[0058] The system 100 supports implementing (e.g., at risk evaluation engine 175) workflows supportive of root cause analysis, issue resolution, and reduction of risk impact. In some aspects, the techniques described herein provide improved accuracy for risk detection. In some examples, the techniques described herein include generating and providing a risk score associated with an alert (or a risk determined by the system 100 as corresponding to the alert) based on a quantity of occurrences of the alert. For example, the techniques described herein include Al based analysis of historical occurrences of an alert (e.g., number of alert occurrences).

[0059] As will be described herein, the Al based analysis may include calculating the probability of a risk and an impact of the risk. By providing such probability information (e.g., risk probability, probability of impact), the techniques described herein support improved effectiveness (e.g., 99.9 % reduction in time for risk management and risk assessment) compared to some manually implemented risk management techniques.

[0060] Aspects of the techniques described herein provide a software digital platform which is cloud agnostic. For example, the risk evaluation engine 175 may be implemented on premise at a surface location (e.g., at a drill site or offshore stimulation vessel). For example, the computing device 105 -a on which the risk evaluation engine 175 is implemented may be an on-site computing device. Additionally, or alternatively, the risk evaluation engine 175 may be implemented at an on-site server or an off-site server (a remote server) (e.g., implemented at computing device 105-b).

[0061] That is, for example, features of the risk evaluation engine 175 and associated operations (e.g., root cause analysis 280 later described with reference to FIG. 2) may be implemented via relatively lightweight software services (expressed another way, built on software services) capable of executing the operations in a single step or multiple steps.

[0062] The software digital platform described herein provides user assistance for understanding risks identified by the platform as being associated with a generated alert. The platform may autonomously analyze historical mitigations and remediations previously applied in association with a given asset (e.g., surface equipment 110, borehole 135, tool 150, or the like). The term “asset” used herein may refer to a physical entity (e.g., surface equipment 110, borehole 135, tool 150, or the like) and/or a logical representation of the physical entity.

[0063] The platform may provide risk scores associated with an alert, based on historical occurrence of the alert. Accordingly, for example, the system 100 may provide identified risks and corresponding risk scores based on which the system may autonomously apply actions for risk mitigation and remediation. Additionally, or alternatively, the system 100 may provide a user with data including identified risks, corresponding risk scores, and recommended actions for risk mitigation and remediation, in a format supportive of easy understanding of risks by a user. For example, based on the data, the user may select one or more recommended actions for risk mitigation and remediation. [0064] Example aspects of features provided by the risk evaluation engine 175 are later described herein with reference to FIG. 2.

[0065] FIG. 2 illustrates an example workflow 200 in accordance with one or more embodiments of the present disclosure. The workflow 200 may be implemented by system 100 and system 101 of FIG. 1.

[0066] For example, the workflow 200 may be implemented by a computing device 105, a server, surface equipment 110 (e.g., processing circuitry included in the surface equipment 110,), and/or subsurface equipment described herein. Repeated descriptions of like elements are omitted for brevity.

[0067] According to one or more embodiments of the present disclosure described with reference to FIGS. 1 and 2, techniques are described that support real-time risk assessment and advisory alerts associated with energy industry operations. The techniques support automated association of risks to alerts generated by an industrial system (e.g., an automated association of one or more risks 237 to one or more alerts 257 generated by system 100 of FIG. 1).

[0068] The risk evaluation engine 275 may include aspects of risk evaluation engine 175 described with reference to FIG. 1. Sensors 240 may include aspects of sensors (e.g., sensors 125, sensors 155, sensors 160, and the like) described with reference to FIG. 1. Data sources 203 may be implemented at a database 180 described with reference to FIG. 1. For example, data sources 203 may be external to the computing device 105 described with reference to FIG. 1. In some examples, data sources 203 may be associated with product lines associated with energy industry operations (e.g., resource recovery and fluid sequestration, CCUS, and the like). Repeated descriptions of like elements are omitted for brevity.

[0069] Example aspects of the workflow 200 as supported by aspects of the present disclosure are described herein with reference to FIG. 2.

[0070] At S201, the workflow 200 may include risk catalogue readiness operations. For example, at S201, the workflow 200 may include receiving a risk catalogue uploaded by a user (e.g., a field operator). The risk catalogue may include a set of candidate risks associated with the system 100.

[0071] At S202, the workflow 200 may include configuring a risk register 235. The risk register 235 may be a register of risks that are currently active or applicable to the system 100. In some examples, the workflow 200 may include configuring the risk register 235 based on asset details 205 extracted from data sources 203 and based on the risk catalogue described with reference to S201. In some cases, the risk register 235 may be a list of risks uploaded by the user.

[0072] At S203, the workflow 200 may include alert rule configuration operations. For example, at S203, the workflow 200 may include receiving an alert rule set associated with the system 100. In some aspects, at S203, the workflow 200 may include configuring rules 212 into the system 100 based on which the system 100 may process data (e.g., realtime data) provided by sensors 240. The workflow 200 may include storing the configuration as an alert rule configuration 210.

[0073] At S204, the workflow 200 may include processing data 242 (e.g., real-time data, data provided based on a defined interval, or the like) provided by sensors 240 against rules 212 described herein and/or against analytics information 217 included in data 215. In some aspects, data 215 may be historic/sample data corresponding to prior occurrences of an alert (e.g., number of alert occurrences), plans previously applied in association with mitigating or preventing a risk associated with a given asset or alert, and the like. For example, at S204, the workflow 200 may include processing the data (e.g., data 242, rules 212, analytics information 217) by a rule- analytics execution engine 250.

[0074] At S205, the workflow 200 may include alert generation 255 by the ruleanalytics execution engine 250. For example, the analytics execution engine 250 may generate one or more alerts 257 by applying rules 212 and/or analytics information 217 to the data 242. Accordingly, for example, the workflow 200 may include producing actual alerts (e.g., alerts 257) in the system 100 based on whether a measured value satisfies a threshold condition (e.g., pressure at a given sensor 240 is greater than or equal to a threshold value, or the like). The alerts 257 may be referred to herein as rule-based alerts.

[0075] The risk evaluation engine 275 may perform a root cause analysis (at 280) associated with one or more alerts 257 generated in association with an asset (e.g., surface equipment 110, borehole 135, a tool 150, or the like).

[0076] For example, performing the root cause analysis (at 280) may include identifying a target risk associated with the asset based on the one or more alerts 257. In some aspects, the root cause analysis (at 280) may include identifying the target risk from among a set of risks 237 associated with the asset. The set of risks 237 may include risks described herein (e.g., catalogued risks) as audited at 236 from the risk register 235.

[0077] In some aspects, performing the root cause analysis (at 280) may include identifying risk elements 285 associated with the target risk based on the one or more alerts 257 and the set of catalogued risks 237. Non-limiting examples of the risk elements 285 include location information (risk location) associated with one or more candidate risks, category information (risk category) associated with the one or more candidate risks, hazard information (hazards) associated with the one or more candidate risks, and control settings (controls) associated with the one or more candidate risks.

[0078] In some examples, the risk evaluation engine 275 may generate (through root cause analysis (at 280)) recommendation data 287 associated with mitigating or preventing the target risk. In some aspects, the risk evaluation engine 275 may generate the recommendation data 287 based on the risk elements 285.

[0079] Non-limiting examples of the recommendation data 287 include remediation recommendations associated with the target risk, mitigation recommendations associated with the target risk, insight data associated with the target risk, and a risk score associated with the target risk. That is, for example, the recommendation data 287 may include one or more plans associated with mitigating or preventing the target risk and/or insight data associated with one or more previous plans associated with mitigating or preventing the target risk.

[0080] The risk evaluation engine 275 may support dynamic risk scoring. For example, the risk evaluation engine 275 may generate a risk score associated with an identified risk 237 (target risk) and include the risk score in the recommendation data 287. In some aspects, the risk evaluation engine 275 may adjust the risk score associated with an identified risk 237 based on one or more criteria. In an example, the risk evaluation engine 275 may adjust the risk score associated with an identified risk 237 based on whether a quantity of times the risk 237 is identified by the risk evaluation engine 275 satisfies a threshold value.

[0081] In another example, the risk evaluation engine 275 may adjust the risk score associated with an identified risk 237 based on whether a frequency in which the risk 237 is identified by the risk evaluation engine 275 satisfies a threshold frequency value. In another example, the risk evaluation engine 275 may adjust the risk score associated with an identified risk 237 based on a temporal threshold. For example, the risk evaluation engine 275 may adjust the risk score associated with an identified risk 237 for cases in which a time period since the risk 237 was last identified by the risk evaluation engine 275 is less than a threshold time period.

[0082] In some other aspects, the risk evaluation engine 275 may generate, set, or adjust the risk score associated with an identified risk 237 (target risk) based on a quantity of occurrences of one or more alerts 257 (rule-based alerts) with reference to one or more criteria (e.g., threshold quantity, threshold frequency of occurrence, threshold temporal duration between occurrences, or the like).

[0083] In some aspects, the risk score may include a probability of a given risk occurring again in the future. In some other aspects, the risk score may include a weight or impact level of the risk to the system 100.

[0084] In some examples, the remediation recommendations provided in recommendation data 287 may include recommended plans or actions associated with removing or completely preventing the risk. In some examples, the mitigation recommendations may include recommended plans or actions associated with reducing an impact of the risk. In some examples, the insight data may include insights associated with one or more previous remediation recommendations and/or mitigation recommendations, insights associated with one or more remediation recommendations and/or mitigation recommendations determined by the root cause analysis (of 280), insights associated with causes of the target risk, and the like.

[0085] In some embodiments, the risk evaluation engine 275 may generate the risk score based on a quantity of occurrences of one or more alerts 257 with reference to one or more criteria. For example, the risk evaluation engine 275 may set or adjust (e.g., increase, decrease) the risk score for a target risk based on a quantity of occurrences of an alert 257 with respect to a threshold quantity. In some aspects, the risk evaluation engine 275 may set or adjust the risk score for the target risk based on a quantity of occurrences of the alert 257 with respect to a temporal duration (e.g., quantity of occurrences over a time period).

[0086] At S207, the workflow 200 may include providing one or more notifications 290 including the target risk and the recommendation data 287. In some embodiments, the one or more notifications 290 may include the one or more alerts 257, the target risk, and the recommendation data 287. In some aspects, the workflow 200 may include providing a portion or all of the recommendation data 287 in combination with a corresponding notification 290. In some embodiments, the notification 290 may be the same as the one or more alerts 257, further updated with the risk information and recommendation data 287 before the notification 290 is provided to the user.

[0087] At S208, the workflow 200 may include providing a notification 291 (e.g., via a user interface described herein). In some examples, the notification 291 may be a visual notification, an audible notification, a haptic notification, or the like. In some embodiments, the notification 291 may include a notification 290 and data associated with the notification 290. For example, the notification 291 may include an indication of the target risk and/or the recommendation data 287. In some embodiments, alternative or additional to providing the notification 291, the workflow 200 may include directly providing the notification 290 of S207.

[0088] As described herein, aspects of the present disclosure support performing features of the workflow 200 in real-time. For example, the risk evaluation engine 275 may perform the root cause analysis (at 280) and provide one or more notifications 290 in realtime.

[0089] The workflow 200 supports feeding back data 267 to the risk evaluation engine 275. In some embodiments, the risk evaluation engine 275 may perform a root cause analysis (at 280), generate recommendation data 287, and provide one or more notifications 290 as described herein, without processing the data 267. In some alternative and/or additional embodiments, the risk evaluation engine 275 may perform a root cause analysis (at 280), generate recommendation data 287, and provide one or more notifications 290 as described herein, based on the data 267. Example aspects of the data 267 are later described herein.

[0090] The workflow 200 may include evaluating (at S209) a plan or action as included in recommendation data 287 and/or notification 291 as described herein. The workflow 200 may include implementing (at S210) the plan. In some cases, the workflow 200 may include updating (at S211) the plan. In accordance with one or more embodiments of the present disclosure, the system 100 supports autonomously (e.g., without user input) performing the features described with reference to S209 through S211and semi- autonomously (e.g., with some user input) performing the features described with reference to S209 through S211.

[0091] At S212, the workflow 200 may include adding the plan (as implemented or updated with reference to S210 and/or S211) to an audit history 260. In an example, the audit history 260 may be a data repository of plans recommended by the risk evaluation engine 275. In some aspects, from among the plans recommended by the risk evaluation engine 275, the data repository may include plans currently implemented in association with the system 100, plans previously implemented in association with the system 100, and/or plans not previously implemented in association with the system 100. The audit history 260 may include plans which are implemented, previously implemented, and/or recommended in association with mitigating or preventing a target risk for an asset of the system 100.

[0092] Accordingly, for example, plans stored to the audit history 260 may be referred to herein as reference operational plans associated with mitigating or preventing a target risk for an asset (e.g., surface equipment 110, borehole 135, tool 150, or the like). In some aspects, the plans may be referred to as reference mitigation plans or reference remediation plans.

[0093] At S213, the workflow 200 may include may include determining an asset association 265 between assets of the system 100 and one or more reference plans. For example, the workflow 200 may include providing data 267 indicating the asset association 265, to the risk evaluation engine 275.

[0094] In some embodiments, the risk evaluation engine 275 may be implemented by a machine learning model (e.g., machine learning model(s) 107) as described herein. For example, the workflow 200 may include providing input data (e.g., one or more alerts 257, risks 237, and/or data 267) to a machine learning model implemented at the risk evaluation engine 275. The risk evaluation engine 275 may output, in response to the machine learning model processing the input data, the target risk and the recommendation data 287 described herein.

[0095] As described herein, the workflow 200 supports (e.g., at risk evaluation engine 275) mapping of one or more alerts 257 to one or more risks 237. That is, for example, the workflow 200 includes identifying what risks 237 are associated with an alert 257, once a sensor value (e.g., pressure) corresponding to the alert 257 satisfies a threshold condition (e.g., is above a threshold value, is below a threshold value, is equal to a threshold value, or the like).

[0096] The risk evaluation engine 275 may extract data about the risks 237 from the risk register 235. For example, the risk evaluation engine 275 may extract risk elements 285 such as, for example, risk location, risk category, hazards, and control parameters. The risk elements 285 may be referred to as categories associated with the risks 237.

[0097] The risk evaluation engine 275 may perform a root cause analysis 280 and chum over an extensive list of risks 237 (and risk elements 285) through alerts described herein. The risk evaluation engine 275 may generate a risk score described herein based on a frequency or a quantity of a given risk 237 and/or a frequency or a quantity of a given alert 257 (associated with the risk 237).

[0098] Aspects of the workflow 200 provide advantages of increased accuracy and reduced turnaround time for identifying, eliminating, and mitigating risks compared to approaches in which a user (e.g., a field operator) manually reviews a list of potential risks in an attempt to identify a risk based on an alert 257. For example, in such manual approaches, the user may be unable to effectively identify a risk, let alone determine a remediation plan for effectively preventing or mitigating the risk.

[0099] In contrast, for example, the systems and techniques described herein provide a real-time (or near real-time) root cause analysis (at 280) supportive of identifying one or more risks 237 from one or more generated alerts 257, generating recommendation data 287 for preventing or mitigating the identified one or more risks 237, and providing the recommendation data 287 to a user for evaluation and implementation as described herein. In some embodiments, (not illustrated), the system 100 may further include control circuitry capable of autonomously controlling any suitable assets (e.g., surface equipment 110, tools 150, sensors 155, sensors 160, or the like) associated with preventing or mitigating the identified one or more risks 237, based on the recommendation data 287.

[00100] According to one or more embodiments of the present disclosure, the system 100 supports maintaining a complete audit of the system 100 at a granular level, by one or multiple users. For example, the system 100 may track (e.g., using aspects described with reference to workflow 200) every step that a user is taking or has taken to address alerts 257 as generated by the system 100. In some examples, the system 100 may identify that a given risk 237 is has repeatedly occurred, but for different alerts 257. The system 100 supports capturing/monitoring of every prevention step or mitigation step which a user (or multiple users) are taking or have taken for each of the different alerts 257. In some examples, the system 100 may track actions (e.g., prevention steps, mitigation steps) as implemented by hundreds of users or more, providing automatic maintenance of audits. For example, the system 100 may autonomously audit risks 237 (e.g., at 236) and/or autonomously audit plans or actions (e.g., at S212).

[00101] The system 100 provides a technical solution for autonomous and realtime mapping of particular risks 237 based on generated alerts 257, and in some embodiments, autonomous control of suitable assets (e.g., surface equipment 110, tools 150, sensors 155, sensors 160, or the like) in association with preventing or mitigating the particular risks 237. The autonomous and real-time mapping of particular risks 237 (e.g., through root cause analysis techniques described herein), generation of recommendation data 287, and generation of notifications 290 support earlier identification, remediation, and/or mitigation of hazards or conditions associated with resource recovery and fluid sequestration processes, thereby providing improved safety, increased production efficiency, and reduced operating overhead. [00102] Aspects of the system 100 described herein provide advantages over approaches in which risk assessment is performed only during initial phase of project development, prior to any project execution. That is, for example, aspects of the system 100 (and workflow 200) described herein may be implemented during the operational phase of the system 100, concurrent to an energy industry operation. Accordingly, for example, aspects of the system 100 bridge the gap between sensor data (e.g., as provided by sensors 125, sensors 155, sensors 160, and the like) and how the sensor data can be used to provide insights to risks. The system 100 is capable of leveraging risk management data (e.g., provided by field operators or other users) and sensor data in association with identifying risks, root causes of the risks, and recommendations (e.g., recommendation data 287) for preventing or mitigating the risks.

[00103] FIG. 3 illustrates an example flowchart of a method 300 in accordance with one or more embodiments of the present disclosure. The method 300 is an example computer-implemented method that may be implemented by the example aspects of a system 101 and/or a computing device 105 as described herein.

[00104] The method 300 may be implemented, for example, by a computing device 105 in response to a processor (not illustrated) of the computing device 105 executing program instructions stored on a computer readable storage medium which is included in or coupled to the computing device 105.

[00105] At 305, the method 300 includes performing a root cause analysis associated with one or more alerts generated in association with an asset.

[00106] In some aspects, performing the root cause analysis may include identifying (at 310) a target risk associated with the asset based on the one or more alerts.

[00107] In some aspects, identifying the target risk (at 310) is further based at least one of: a reference plan associated with mitigating or preventing the target risk for the asset; an audit history associated with the asset; and asset association data indicating an association between the target risk and the asset.

[00108] In some aspects, performing the root cause analysis may include identifying (at 315) a set of risk elements associated with the target risk based on the one or more alerts and a set of catalogued risks associated with the asset.

[00109] In some aspects, the set of risk elements may include at least one of: one or more locations associated with one or more candidate risks; one or more categories associated with the one or more candidate risks; one or more hazards associated with the one or more candidate risks; and one or more control settings associated with the one or more candidate risks. In some aspects, the one or more candidate risks include at least the target risk.

[00110] In some aspects, performing the root cause analysis may include generating (at 320) recommendation data associated with mitigating or preventing the target risk.

[00111] In some aspects, generating the recommendation data is based on the set of risk elements (identified at 315).

[00112] In some aspects, the recommendation data may include: one or more plans associated with mitigating or preventing the target risk; and insight data associated with one or more previous plans associated with mitigating or preventing the target risk.

[00113] In some aspects, the recommendation data may include a risk score associated with the target risk. In some aspects, the method 300 may include generating the risk score associated with the target risk based on a quantity of occurrences of the one or more alerts with reference to one or more criteria (e.g., a threshold quantity of occurrences the risk is identified, a threshold frequency at which the risk is identified, a temporal threshold between instances in which the risk is identified).

[00114] At 325, the method 300 includes providing one or more notifications comprising the one or more alerts, the target risk, and the recommendation data.

[00115] In some aspects, performing the root cause analysis (at 305) and providing the one or more notifications (at 325) may be in real-time.

[00116] In some aspects, the method 300 may include providing the one or more alerts to a machine learning model. In some aspects, the one or more alerts may include one or more rule-based alerts. In some aspects, the machine learning model may output at least one of: the target risk; and the recommendation data.

[00117] In the descriptions of the flowcharts herein, the operations may be performed in a different order than the order shown, or the operations may be performed in different orders or at different times. Certain operations may also be left out of the flowcharts, one or more operations may be repeated, or other operations may be added to the flowcharts.

[00118] In the descriptions of the flowcharts herein, the operations may be performed in a different order than the order shown, or the operations may be performed in different orders or at different times. Certain operations may also be left out of the flowcharts, one or more operations may be repeated, or other operations may be added to the flowcharts. [00119] Set forth below are some embodiments of the foregoing disclosure:

[00120] Embodiment 1. A computer-implemented method characterized by: performing a root cause analysis associated with one or more alerts generated in association with an asset, wherein performing the root cause analysis is characterized by: identifying a target risk associated with the asset based on the one or more alerts; and generating recommendation data associated with mitigating or preventing the target risk; and providing one or more notifications characterized by the one or more alerts, the target risk, and the recommendation data.

[00121] Embodiment 2. A computer- implemented method as in any prior embodiment, wherein: performing the root cause analysis is characterized by identifying a set of risk elements associated with the target risk based on the one or more alerts and a set of catalogued risks associated with the asset; and generating the recommendation data is based on the set of risk elements.

[00122] Embodiment 3. A computer- implemented method as in any prior embodiment, wherein identifying the target risk is further based at least one of: a reference plan associated with mitigating or preventing the target risk for the asset; an audit history associated with the asset; and asset association data indicating an association between the target risk and the asset.

[00123] Embodiment 4. A computer- implemented method as in any prior embodiment, wherein the set of risk elements is characterized by at least one of: one or more locations associated with one or more candidate risks; one or more categories associated with the one or more candidate risks; one or more hazards associated with the one or more candidate risks; and one or more control settings associated with the one or more candidate risks, wherein the one or more candidate risks include at least the target risk.

[00124] Embodiment 5. A computer- implemented method as in any prior embodiment, wherein the recommendation data is characterized by: one or more plans associated with mitigating or preventing the target risk; and insight data associated with one or more previous plans associated with mitigating or preventing the target risk.

[00125] Embodiment 6. A computer- implemented method as in any prior embodiment, wherein the recommendation data is characterized by a risk score associated with the target risk.

[00126] Embodiment 7. A computer-implemented method as in any prior embodiment, further characterized by: generating a risk score associated with the target risk based on a quantity of occurrences of the one or more alerts with reference to one or more criteria.

[00127] Embodiment 8. A computer-implemented method as in any prior embodiment, further characterized by: providing the one or more alerts to a machine learning model, wherein the one or more alerts are characterized by one or more rule-based alerts, wherein the machine learning model outputs at least one of: the target risk; and the recommendation data.

[00128] Embodiment 9. A computer- implemented method as in any prior embodiment, wherein performing the root cause analysis and providing the one or more notifications are in real-time.

[00129] Embodiment 10. A system characterized by: analysis equipment characterized by a processor and a memory, wherein the memory is characterized by instructions stored thereon that, when executed by the processor, cause the processor to perform operations characterized by: performing a root cause analysis associated with one or more alerts generated in association with an asset, wherein performing the root cause analysis is characterized by: identifying a target risk associated with the asset based on the one or more alerts; and generating recommendation data associated with mitigating or preventing the target risk; and providing one or more notifications characterized by the one or more alerts, the target risk, and the recommendation data.

[00130] Embodiment 11. A system as in any prior embodiment, wherein: performing the root cause analysis is characterized by identifying a set of risk elements associated with the target risk based on the one or more alerts and a set of catalogued risks associated with the asset; and generating the recommendation data is based on the set of risk elements.

[00131] Embodiment 12. A system as in any prior embodiment, wherein identifying the target risk is further based at least one of: a reference plan associated with mitigating or preventing the target risk for the asset; an audit history associated with the asset; and asset association data indicating an association between the target risk and the asset.

[00132] Embodiment 13. A system as in any prior embodiment, wherein the set of risk elements is characterized by at least one of: one or more locations associated with one or more candidate risks; one or more categories associated with the one or more candidate risks; one or more hazards associated with the one or more candidate risks; and one or more control settings associated with the one or more candidate risks, wherein the one or more candidate risks include at least the target risk.

[00133] Embodiment 14. A system as in any prior embodiment, wherein the recommendation data is characterized by: one or more plans associated with mitigating or preventing the target risk; and insight data associated with one or more previous plans associated with mitigating or preventing the target risk.

[00134] Embodiment 15. A system as in any prior embodiment, wherein the recommendation data is characterized by a risk score associated with the target risk.

[00135] Embodiment 16. A system as in any prior embodiment, wherein the instructions, when executed by the processor, further cause the processor to perform operations characterized by: generating a risk score associated with the target risk based on a quantity of occurrences of the one or more alerts with reference to one or more criteria.

[00136] Embodiment 17. A system as in any prior embodiment, wherein the instructions, when executed by the processor, further cause the processor to perform operations characterized by: providing the one or more alerts to a machine learning model, wherein the one or more alerts are characterized by one or more rule-based alerts, wherein the machine learning model outputs at least one of: the target risk; and the recommendation data.

[00137] Embodiment 18. A system as in any prior embodiment, wherein performing the root cause analysis and providing the one or more notifications are in realtime.

[00138] Embodiment 19. A computer program product characterized by a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform operations characterized by: performing a root cause analysis associated with one or more alerts generated in association with an asset, wherein performing the root cause analysis is characterized by: identifying a target risk associated with the asset based on the one or more alerts; and generating recommendation data associated with mitigating or preventing the target risk; and providing one or more notifications characterized by the one or more alerts, the target risk, and the recommendation data.

[00139] Embodiment 20. A computer program product as in any prior embodiment, wherein: performing the root cause analysis is characterized by identifying a set of risk elements associated with the target risk based on the one or more alerts and a set of catalogued risks associated with the asset; and generating the recommendation data is based on the set of risk elements. [00140] The use of the terms “a” and “an” and “the” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. Further, it should be noted that the terms “first,” “second,” and the like herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The terms “about”, “substantially” and “generally” are intended to include the degree of error associated with measurement of the particular quantity based upon the equipment available at the time of filing the application. For example, “about” and/or “substantially” and/or “generally” can include a range of ± 8% of a given value.

[00141] The teachings of the present disclosure may be used in a variety of well operations. These operations may involve using one or more treatment agents to treat a formation, the fluids resident in a formation, a borehole, and I or equipment in the borehole, such as production tubing. The treatment agents may be in the form of liquids, gases, solids, semi-solids, and mixtures thereof. Illustrative treatment agents include, but are not limited to, fracturing fluids, acids, steam, water, brine, anti-corrosion agents, cement, permeability modifiers, drilling muds, emulsifiers, demulsifiers, tracers, flow improvers etc. Illustrative well operations include, but are not limited to, hydraulic fracturing, stimulation, tracer injection, cleaning, acidizing, steam injection, water flooding, cementing, etc.

[00142] While the invention has been described with reference to an exemplary embodiment or embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed as the best mode contemplated for carrying out this invention, but that the invention will include all embodiments falling within the scope of the claims. Also, in the drawings and the description, there have been disclosed exemplary embodiments of the invention and, although specific terms may have been employed, they are unless otherwise stated used in a generic and descriptive sense only and not for purposes of limitation, the scope of the invention therefore not being so limited.

Claims

1. A computer- implemented method characterized by: receiving a risk catalogue which is provided by a user and characterized by a set of candidate risks (237) associated with an asset; performing a root cause analysis (280) associated with one or more alerts (257) generated in association with the asset, wherein performing the root cause analysis (280) is characterized by: identifying a target risk (237) associated with the asset based on the one or more alerts (257) and the set of candidate risks (237) in the risk catalogue; and generating recommendation data (287) associated with mitigating or preventing the target risk (237); and providing one or more notifications (290) characterized by the one or more alerts (257), the target risk (237), and the recommendation data (287).

2. The computer-implemented method of claim 1 , further characterized by configuring a risk register (235) based on the risk catalogue, wherein: performing the root cause analysis (280) characterized by extracting, from the risk register and based on the one or more alerts (257), a set of risk elements (285) associated with the target risk (237); and generating the recommendation data (287) is based on the set of risk elements (285).

3. The computer-implemented method of claim 2, wherein identifying the target risk (237) is further based on at least one of: a reference plan associated with mitigating or preventing the target risk (237) for the asset; an audit history (260) associated with the asset; and asset association data indicating an association between the target risk (237) and the asset.

4. The computer-implemented method of claim 2, wherein the set of risk elements (285) comprises at least one of: one or more locations associated with one or more candidate risks; one or more categories associated with the one or more candidate risks; one or more hazards associated with the one or more candidate risks; and one or more control settings associated with the one or more candidate risks, wherein the one or more candidate risks include at least the target risk (237).

5. The computer-implemented method of claim 1 , wherein the recommendation data (287) comprises: one or more plans associated with mitigating or preventing the target risk (237); and insight data associated with one or more previous plans associated with mitigating or preventing the target risk (237).

6. The computer-implemented method of claim 1 , wherein the recommendation data (287) comprises a risk score associated with the target risk (237).

7. The computer-implemented method of claim 1, further comprising: generating a risk score associated with the target risk (237) based on a quantity of occurrences of the one or more alerts (257) with reference to one or more criteria.

8. The computer-implemented method of claim 1, further comprising: providing the one or more alerts (257) to a machine learning model (107), wherein the one or more alerts (257) comprise one or more rule-based alerts (257), wherein the machine learning model (107) outputs at least one of: the target risk (237); and the recommendation data (287).

9. The computer-implemented method of claim 1 , wherein performing the root cause analysis (280) and providing the one or more notifications (290) are in real-time.

10. A system (100) characterized by: analysis equipment (105) comprising a processor and a memory, wherein the memory is characterized by instructions stored thereon that, when executed by the processor, cause the processor to perform operations characterized by: receiving a risk catalogue which is provided by a user and is characterized by a set of candidate risks (237) associated with an asset; performing a root cause analysis (280) associated with one or more alerts (257) generated in association with the asset, wherein performing the root cause analysis (280) is characterized by: identifying a target risk (237) associated with the asset based on the one or more alerts (257) and the set of candidate risks (237) in the risk catalogue; and generating recommendation data (287) associated with mitigating or preventing the target risk (237); and providing one or more notifications (290) characterized by the one or more alerts (257), the target risk (237), and the recommendation data (287).

11. The system (100) of claim 10, wherein the instructions, when executed by the processor, further cause the processor to perform operations comprising configuring a risk register (235) based on the risk catalogue, wherein: performing the root cause analysis (280) is characterized by extracting, from the risk register and based on the one or more alerts (257), a set of risk elements (285) associated with the target risk (237); and generating the recommendation data (287) is based on the set of risk elements (285) (285).

12. The system (100) of claim 11, wherein identifying the target risk (237) is further based on at least one of: a reference plan associated with mitigating or preventing the target risk (237) for the asset; an audit history (260) associated with the asset; and asset association data indicating an association between the target risk (237) and the asset.

13. The system (100) of claim 11, wherein the set of risk elements (285) comprises at least one of: one or more locations associated with one or more candidate risks; one or more categories associated with the one or more candidate risks; one or more hazards associated with the one or more candidate risks; and one or more control settings associated with the one or more candidate risks, wherein the one or more candidate risks include at least the target risk (237).

14. A computer program product characterized by a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform operations characterized by: receiving a risk catalogue which is provided by a user and comprises a set of candidate risks (237) associated with an asset; performing a root cause analysis (280) associated with one or more alerts (257) generated in association with the asset, wherein performing the root cause analysis (280) is characterized by: identifying a target risk (237) associated with the asset based on the one or more alerts (257) and the set of candidate risks (237) in the risk catalogue; and generating recommendation data (287) associated with mitigating or preventing the target risk (237); and providing one or more notifications (290) characterized by the one or more alerts (257), the target risk (237), and the recommendation data (287).

15. The computer program product of claim 14, wherein the instructions, when executed by the processor, further cause the processor to perform operations comprising configuring a risk register (235) based on the risk catalogue, wherein: performing the root cause analysis (280) comprises extracting, from the risk register and based on the one or more alerts (257), a set of risk elements (285) associated with the target risk (237); and generating the recommendation data (287) is based on the set of risk elements (285)

(285).