Method and system for recommending total cost of ownership for a digital product

Abstract

A computer-implemented method includes: processing, by a computing device, a set of answers provided by a customer with respect to storage and processing of data associated with a value chain comprising interconnected assets; generating, based on processing the set of answers, a total cost of ownership associated with the storage and processing of the data; and generating, based on the total cost of ownership, customer pricing information associated with the storage and processing of the data.

Description

METHOD AND SYSTEM FOR RECOMMENDING TOTAL COST OF OWNERSHIP FOR A DIGITAL PRODUCTCROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of an earlier filing date from Italian NonProvisional Application Serial No. 102025000005727 filed March 20, 2025, the entire disclosure of which is incorporated herein by reference.BACKGROUND

[0002] Some approaches for evaluating and estimating the total cost of ownership for a product when pitching the product to a customer are manual, inaccurate, and time consuming for a sales team. Techniques are desired for accurately evaluating and estimating the total cost of ownership before the product is pitched to a potential customer.SUMMARY

[0003] Embodiments of the present disclosure are directed to a computer-implemented method including: processing, by a computing device, a set of answers provided by a customer with respect to storage and processing of data associated with a value chain including interconnected assets; generating, based on processing the set of answers, a total cost of ownership associated with the storage and processing of the data; and generating, based on the total cost of ownership, customer pricing information associated with the storage and processing of the data.

[0004] Embodiments of the present disclosure are also directed to a system 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: processing a set of answers provided by a customer with respect to storage and processing of data associated with a value chain including interconnected assets; generating, based on processing the set of answers, a total cost of ownership associated with the storage and processing of the data; and generating, based on the total cost of ownership, customer pricing information associated with the storage and processing of the data.

[0005] 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: processing a set of answers provided by a customer with respect tostorage and processing of data associated with a value chain including interconnected assets; generating, based on processing the set of answers, a total cost of ownership associated with the storage and processing of the data; and generating, based on the total cost of ownership, customer pricing information associated with the storage and processing of the data.

[0006] 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

[0007] The following descriptions should not be considered limiting in any way. With reference to the accompanying drawings, like elements are numbered alike:

[0008] FIG. 1 illustrates a system supportive of recommending optimized and accurate total cost of ownership for a digital product in accordance with aspects of the present disclosure.

[0009] FIG. 2 illustrates an example flowchart of a method in accordance with one or more embodiments of the present disclosure.

[0010] FIG. 3 depicts a block diagram of a processing system, which can be used for implementing the techniques described herein.

[0011] FIG. 4 illustrates an example flowchart of a method in accordance with one or more embodiments of the present disclosure.DETAILED DESCRIPTION

[0012] Accurately evaluating and estimating the total cost of ownership for a product before pitching the product to a potential customer is important for a sales team. For example, some existing approaches are manual, inaccurate, and time consuming for the sales team. Further, approaches for estimating the total cost of ownership manually in near real time and accurately, for example, may be ineffective due to a lack of expertise by the sales team or available analysis tools. Accordingly, for example, the sales team may be unable to efficiently and accurately calculate a cost and provide a suitable quote that meets customer specifications.

[0013] 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.

[0014] FIG. 1 illustrates a system 100 supportive of recommending optimized and accurate total cost of ownership for a digital product in accordance with aspects of the presentdisclosure. Aspects of the system 100 may be implemented by a computing device implemented using a computing system (e.g., a processing system 300 later described herein).

[0015] The system 100 provides a digital solution which supports effective calculation of total cost of ownership, reducing the complexity and time associated with the calculation process. Embodiments of the present disclosure include applying the system 100 and the digital solution to systems related to resource recovery and fluid sequestration industries, systems related to an end-to-end carbon capture, utilization, and storage (CCUS) value chain, and the like.

[0016] In some aspects, the system 100 provides a complete solution which has a knowledge and understanding of the parameters for effectively calculating the total cost of ownership and may generate a recommendation including an optimized and accurate total cost of ownership 190 for a digital product. In some embodiments, the system 100 provides a digital solution which seamlessly walks a user through a set of predefined questions 106 regarding the customer’s setup and target specifications (e.g., customer requirements). The system 100 may process the customer inputs (e.g., the customer answers 107 to the predefined questions 106) and generate, based on the customer inputs, a set of outputs.

[0017] In an example, the set of outputs may include a cost recommendation 140 and the total cost of ownership 190. The cost recommendation 140 may include recommendations for an operational cost which may be used by a product development team for deploying the solution for the customer. The total cost of ownership 190 may include a cost breakdown, including one time deployment and integration cost, infrastructure usage cost, support and maintenance cost, and the like of the total cost of ownership 190. It is to be understood that the set of outputs provided by the system 100 may include additional, alternative, less, or modified outputs compared to the examples provided herein. Example aspects of generating and providing the cost recommendation 140 and total cost of ownership 190, and the contents thereof, will be further described herein.

[0018] The system 100 may include a software tool 105 and a recommendation engine 135 that support generating a cost recommendation 140 (i.e., a cost model), and accordingly, the total cost of ownership 190 for a digital product in accordance with one or more embodiments of the present disclosure. The software tool 105 may be, for example, a software application provided by the system 100.

[0019] Aspects of the system 100 described herein with respect to generating the total cost of ownership 190 for the digital product may be implemented via the software tool 105and by the recommendation engine 135. In some aspects, the system 100 may generate the total cost of ownership 190 based on data entered by a user 103 with respect to the digital product.

[0020] Non-limiting examples of the digital product include a digital solution for storing and processing of data associated with a CCUS value chain including interconnected assets.

[0021] The system 100 may support operations for generating the cost recommendation 140 and the total cost of ownership 190, example aspects of which will be described herein.

[0022] Components described herein of the system 100 may access data from any other components of the system 100, aggregate the data, forward the data, process the data, and / or provide additional data to any of the components in association with implementing features of the system 100 described herein.

[0023] It is to be understood herein that the example aspects described herein with reference to the functions provided by systems (e.g., system 100, processing system 300 later described herein, and the like) provide techniques for recommending optimized and accurate total cost of ownership for a digital product related to resource recovery and fluid sequestration industries, an end-to-end CCUS value chain, and the like.

[0024] Aspects of the system 100 and the included components in association with recommending optimized and accurate total cost of ownership for a digital product are described herein with reference to FIG. 2.

[0025] FIG. 2 illustrates an example flowchart of a method 200 in accordance with one or more embodiments of the present disclosure. The method 200 is an example computer-implemented method that may be implemented by example aspects of the system 100 (e.g., software tool 105, recommendation engine 135), a system 300 (later described herein), and / or a computing device as described herein. Aspects of the method 200 are described with reference to the system 100 of FIG. 1.

[0026] At block 205, the method 200 may include generating and displaying a set of questions 106 via a user interface (e.g., visualizer dashboard 145, via a computing device). The set of questions 106 may be related to storage and processing of data associated with a value chain including interconnected assets.

[0027] In a non -limiting example, the value chain may be CCUS value chain. Nonlimiting examples of the interconnected assets include a direct air capture plant, a chilled ammonia plant, a compact carbon capture plant, a compressor plant, a utilization plant, a storage site / reservoir, a liquification plant, other equipment supportive of CCUS operations, and the like.

[0028] In some aspects, the questions 106 may be predefined. For example, the system 100 may select the questions 106 based on a particular customer. In some examples, the system 100 may select the set of questions 106 based on customer type or other criteria, using a model 136 trained on historical data 127 (also referred to herein as training data). In some other examples, the system 100 may generate the set of questions 106 by modifying, based on customer type or other criteria, an existing set of predefined questions using the model 136.

[0029] The model 136 may be, for example, an Al model or machine learning model implemented on a computing device implementing the system 100. For example, the computing device may include processing circuitry capable of executing instructions stored on a memory of the computing device in association with performing one or more functions described herein. The processor may utilize data stored in the memory as a neural network for implementing the Al model or the machine learning model. The neural network may include a machine learning architecture. In some other aspects, the neural network may be or include any suitable machine learning network for performing operations described herein. Non-limiting examples of the machine learning network include a deep learning network, a convolutional neural network, a reconstructive neural network, a generative adversarial neural network, or any other neural network capable of accomplishing functions of the computing device described herein. Some elements stored in the memory may be described as or referred to as instructions or instruction sets, and some functions of the computing device may be implemented using machine learning techniques.

[0030] In some examples, the user 103 may be a member of sales personnel, and the user 103 may present the questions 106 to the customer. Additionally, or alternatively, the user 103 may be the customer, and the system 100 may provide the set of questions 106 to the customer via a user interface (e.g., a web interface, a device interface, or the like).

[0031] At block 210, the method 200 may include processing a set of answers 107 provided by the user 103 with respect to the storage and processing of the data associated with the value chain. The set of answers 107 may be input via the visualizer dashboard 145 or another user interface for the software tool 105.

[0032] In a non-limiting example, the set of answers 107 may include a quantity of the interconnected assets included in the value chain.

[0033] In some aspects, the set of answers 107 may include target temporal information and / or trigger criteria associated with the storage and processing of the data. For example, temporal instances when to access and store the data, how often (i.e., frequency) to store the data, and duration for storing the data.

[0034] In some aspects, the set of answers 107 may include customer cloud preferences associated with the storage and processing of the data, (e.g., customer cloud or third party cloud; frequency of collection; target period of time for storage).

[0035] In some aspects, the set of answers 107 include information 170 associated with CCUS operations 165. The information 170 may include an indication of volume of carbon dioxide (CO2) associated with the value chain, target storage specifications for storing the carbon dioxide, and specifications of the value chain.

[0036] At block 215, with respect to processing the set of answers 107, the method 200 may include proceeding to various data collection operations based on which the system 100 may generate a cost recommendation 140. For example, at 215, the method 200 may include proceeding to a data collection pipeline 110 with respect to the project for the customer. The data collection pipeline 110 may include asset onboarding 115, determining data types 120, determining data operations 125, and determining a data storage strategy 130, example aspects of which will be described herein.

[0037] At block 220 of FIG. 2, the method 200 may include performing asset onboarding 115 which is specific to the project for the customer. The asset onboarding 115 may include onboarding of the interconnected assets to the project.

[0038] For example, the asset onboarding 115 may log and record respective types of the interconnected assets (e.g., customer owned, original equipment manufactured by the customer, equipment manufactured by a third party and purchased by the customer), associated segments of the CCUS value chain (e.g., and whether the segments are surface segments or subsurface segments), tags / sensors, locations of the tags / sensors (e.g., which equipment, which site, whether the tags / sensors are installed on the surface or subsurface), cost and maintenance associated with the tags / sensors, and quantity of the interconnected assets. In some aspects, the asset onboarding 115 may log and record the quantity of industrial sites associated with a project, a quantity of industrial plants associated with the project, the assets associated with the industrial sites and industrial plants, the tags / sensors associated with the assets, and the like.

[0039] At block 225, the method 200 may include determining data types 120 of respective data to be acquired from the interconnected assets, and further, temporal information associated with acquiring the respective data. The data may be acquired, for example, from the tags / sensors described herein. Non-limiting examples of the temporal information include frequency (e.g., high, medium, low) for acquiring the data, temporal instances when to acquire the data, patterns for acquiring the data, and the like. In some aspects, at block 225, the method 200 may include identifying the structure, the format, the temporal information, and the volumeof the data to be acquired from the assets. In an example, the temporal information may include a data acquisition frequency of every week, every bi -week, every month, every quarter, every year, or the like.

[0040] At block 230, the method 200 may include determining data operations 125 associated with storing the respective data. Non-limiting examples of determining the data operations 125 include determining the data to be retained, respective data retention periods, respective data archival periods, when to discard data, and the like. The data operations 125 may refer to the amount of data to be transferred from edge to cloud with respect to full value chain assets.

[0041] At block 235, the method 200 may include determining a data storage strategy 130 associated with storing the respective data. For example, the data storage strategy 130 may include storing the data on a cloud storage network maintained by the customer, a cloud storage network maintained by a third party different from the customer, on-site storage (i.e., storage located at a premises where an asset is located), or a combination thereof. The data storage strategy 130 may include temporal information for retaining, archiving, or discarding the data.

[0042] At block 240, the method 200 may include generating, by the recommendation engine 135, a cost recommendation 140 including operational costs associated with the storage and processing of the data. For example, the recommendation engine 135 may generate the cost recommendation 140 based on the information determined from the operations (e.g., asset onboarding 115, determination of the data types 120 and temporal information, determination of the data operations 125, determination of the data storage strategy 130) described herein. Accordingly, for example, the method 200 may include generating the cost recommendation 140 based on processing the set of answers 107.

[0043] In some aspects, the method 200 may include generating the cost recommendation 140 by processing the set of answers 107 using the model 136. For example, the model 136 may process the set of answers 107, and accordingly, the model 136 may implement aspects of asset onboarding 115, determination of the data types 120 and temporal information, determination of the data operations 125, and determination of the data storage strategy 130 as described herein. Further, for example, the model 136 may generate the cost recommendation 140 based on the information determined from the operations (e.g., asset onboarding 115, determination of the data types 120 and temporal information, determination of the data operations 125, determination of the data storage strategy 130). The operations, in general, may refer project operations for CCUS such as, for example, planning, injection, closure, and the like.

[0044] In some aspects, with respect to determining the data storage strategy 130, the model 136 may base the data storage strategy 130 on historical data 127. The historical data 127 may include previous cost recommendations and previous total costs of ownership in combination with previous questions (i.e., previous questions 106), previous answers (i.e., previous answers 107), and information previously determined from operations (e.g., asset onboarding 115, determination of the data types 120 and temporal information, determination of the data operations 125, determination of the data storage strategy 130) performed with respect to the previous questions and previous answers. The historical data 127 may include an indication of customer identities, customer characteristics, customer type, and the like.

[0045] At block 245, the method 200 may include evaluating CCUS operations 165 associated with the project for the customer. For example, the method 200 may include evaluating the information 170 associated with CCUS operations 165, evaluating a gateway 172 associated with the customer, and evaluating a subscription 180 including information related to maintenance 181 desired by the customer.

[0046] In some aspects, the gateway 172 may be a plant-distributed control system (DCS) connection. The method 200 may determine, with respect to the plant-DCS connection, connections between an industrial asset (e.g., plant) associated with the customer and external partners, internal connections, and the like.

[0047] In evaluating the subscription 180 including information related to the maintenance 181, the method 200 may include determining whether the subscription 180 is a basic subscription, a premium subscription, whether the subscription is not required by the customer, and the cost of the subscription 180. For example, the method 200 may include determining whether the cost is a one-time cost or a recurring cost. Non-limiting examples of a one-time cost include a capital expenditure 177 such as, for example, computing equipment, network equipment, storage equipment, data center equipment, and the like. Non-limiting examples of a recurring cost include an operational expenditure 175 such as, for example, utilities (e.g., power), maintenance, staff, licensing from the capital expenditure 177, and the like.

[0048] At 250, the method 200 may include generating, based on processing the set of answers 107, a total cost of ownership 190 associated with the storage and processing of the data. In some aspects, generating the total cost of ownership 190 may be based on or include the cost recommendation 140. In some aspects, generating the total cost of ownership 190 may be based on the information determined from evaluating the CCUS operations 165.

[0049] The total cost of ownership 190 may include, with respect to the storage and the processing of the data, an acquisition cost 191, a usage cost 192, an end of life cost 193, and support and maintenance costs 194. The acquisition cost 191 may include deployment and integration costs. The usage cost 192 may include infrastructure usage costs. The end of life cost 193 may include costs associated with decommissioning, data migration, disposal, and potential replacements. The support and maintenance costs 194 may include, for example, costs associated with a subscription 180 and maintenance 181 for acquiring, storing, and accessing the data. The total cost of ownership 190 may include operational expenditures 175 and capital expenditures 177.

[0050] At 255, the method 200 may include generating, based on the total cost of ownership 190, customer pricing information 195 associated with the storage and processing of the data. The customer pricing information 195 may include the total cost of ownership 190. In some embodiments, the customer pricing information 195 may include data compared to prices of similar solutions in the market, but is not limited thereto.

[0051] In some aspects, the total cost of ownership 190 may be a cost for the product / project in terms of usage from a data engineering and systems engineering perspective. The customer pricing information 195 may be in a format different from the total cost of ownership 190. For example, the system 100 may provide the customer pricing information 195 in a format, with different language, with information added or removed, or the like such that the customer pricing information 195 is understandable by a non-technical user (e.g., sales team). The interface of the software tool 105 may be implemented such that the software tool 105 is relatively easy to use and understandable by a non-technical user.

[0052] Embodiments of the present disclosure support generating and providing, via the visualizer dashboard 145, real-time updates 150 associated with the cost recommendation 140. In some aspects, the system 100 may generate the real-time updates 150 based on multiple information assessment 160.

[0053] For example, through multiple information assessment 160, the system 100 may provide alternative questions to the user 103 and, based on the additional or alternative questions, update or modify the cost recommendation 140 and / or the total cost of ownership 190. For example, (not illustrated), the method 200 may include generating a second total cost of ownership associated with the storage and processing of the data, based on a second answer provided by the customer with respect to the storage and processing of the data. The method 200 may include generating, based on the second total cost of ownership, second customer pricing information associated with the storage and processing of the data.

[0054] In some aspects, the second answer may be in addition to the set of answers 107 initially provided by the customer. Additionally, or alternatively, the second answer may be alternative to one of the set of answers 107.

[0055] In another example, through multiple information assessment 160, the system 100 may update or modify the cost recommendation 140 and / or the total cost of ownership 190 based on different metrics provided by the sales team. Accordingly, for example, the sales team can formulate a strategy 155 (i.e., a sale operations strategy) and / or update the strategy at will. Via the software tool 105, the sales team may view how changes to the strategy 155 affect the cost recommendation 140, the total cost of ownership 190, and the customer pricing information 195.

[0056] Embodiments of the present disclosure support implementations in which a customer wishes to determine the costs associated with collecting data for an existing industrial plant owned by the customer and / or the costs setting up and collecting data for a new industrial plant.

[0057] For example, for the case of an existing industrial plant, the system 100 may provide, in any of the cost recommendation 140, the total cost of ownership 190, and the customer pricing information 195, infrastructure costs associated with bringing data from the existing industrial plant to a given cloud application.

[0058] As has been described herein, aspects of the systems and techniques described herein overcome shortcomings associated with some approaches for recommending a total cost of ownership 190 for a digital product, in which the total cost of ownership 190 has been optimized for a customer and has relatively high accuracy. The systems and techniques described herein provide a software tool 105 which a user 103 (e.g., sales team) may use to accurately evaluate and estimate the total cost of ownership 190 for a product before the product is pitched to a potential customer, and the total cost of ownership 190 may provide meaningful insights as to providing a solution for the customer.

[0059] In accordance with one or more embodiments of the present disclosure, the system and techniques described herein provide technical advantages and improvements such as, for example, accurate calculation of the total cost of ownership 190 based on the customer specifications (e.g., customer requirements).

[0060] The systems and techniques described herein support minimal latency in obtaining a cost recommendation 140 (cost model) and total cost of ownership 190 for the customer specifications, thus facilitating early and accurate quotations. For example, the systems and techniques described herein may obtain an entire end-to-end cost with relativelyminimal latency (e.g., within a few minutes, but not limited thereto) as the recommendation engine 135 (i.e., backend engine) is capable of calculating on the fly based on customized specifications as provided by an end user (e.g., user 103) via the software tool 105. In contrast, manual approaches for deriving a total cost of ownership 190 may take a relatively longer period of time (e.g., hours, days, or weeks, based on amount of information) and may lack the amount of insight and accuracy provided by the systems and techniques described herein. Accordingly, for example, providing the cost recommendation 140 and the total cost of ownership 190 enables effective on-the-fly optimization of operational costs for a customer.

[0061] Aspects of the systems and techniques described herein provide features which overcome shortcomings associated with some other approaches and provide various advantages. For example, via the software tool 105 provided by the system 100, a sales team may effectively and efficiently find, in near real-time, a near accurate total cost of ownership that satisfies customer specifications, and thus the sales team may provide customer pricing information 195 (i.e., quotes) having high accuracy in near real-time. The increased accuracy of the total cost of ownership 190 and the customer pricing information 195 generated by the system 100 may curb potential losses for an organization, as such losses may arise for cases in which a total cost of ownership 190 (and accordingly, customer pricing information 195) is inaccurately calculated. The cost recommendation 140 for optimizing operational cost can help reduce the overall total cost of ownership 190. Further, the software tool 105 is a digital application that can be used easily and quickly by sales and development teams.

[0062] The systems and techniques described herein provide a mechanism to calculate the total cost of ownership 190 for digital products, which proves beneficial in industries such as, for example, CCUS industries. Through the processing of a set of answers 107 provided by a customer and accordingly generating a total cost of ownership 190 and customer pricing information 195, the system 100 enables a more precise and efficient evaluation of costs associated with data storage and processing. The approaches provided herein address the limitations of manual TCO calculations, which may often be time-consuming and prone to inaccuracies due to the lack of technical expertise or knowledge among sales teams.

[0063] The system architecture includes a processor and memory that execute instructions to perform operations such as onboarding interconnected assets, determining data types, and establishing data storage strategies. The integration of components as described herein facilitates a seamless flow of data and decision-making processes, enhancing the ability of the system 100 to provide cost recommendations 140 and calculations of total cost of ownership 190 in near real-time. The use of a trained model 136 to process customer inputs(i.e., answers 107) and historical data 127 further refines the accuracy of cost recommendations 140, leveraging machine learning techniques to adapt to various customer scenarios and target specifications.

[0064] Providing calculations of total cost of ownership 190 in near real-time may significantly reduce the latency associated with traditional methods. The rapid processing capabilities provided by the system with respect to providing a total cost of ownership 190 calculation in near real-time enables sales teams to generate accurate quotes quickly, improving strategic revenue planning and reducing potential financial losses from inaccurate cost estimates. The questions 106 generated by the software tool 105 for a given customer ensures accessibility for non-technical users (e.g., sales team members), broadening the applicability across different organizational roles.

[0065] As the recommendation engine 135 considers data storage strategy 130, the system 100 may take data management practices into account when generating a cost recommendation 140, a total cost of ownership 190, and accordingly, customer pricing information 195. The holistic approach provided by the system 100 and techniques described herein enhance the accuracy of calculations of total cost of ownership 190 and support strategic decision-making in resource-intensive industries. The capability of the system 100 to dynamically adjust to customer inputs and provide real-time updates through the visualizer dashboard 145 is an example of an advantageous practical application in digital environments.

[0066] It is understood that embodiments of the present disclosure are capable of being implemented in conjunction with any suitable type of computing environment now known or later developed.

[0067] For example, FIG. 3 depicts a block diagram of a processing system 300, which can be used for implementing the techniques described herein. For example, aspects described herein of the systems described herein (e.g., system 100) may be implemented by the processing system 300.

[0068] In examples, processing system 300 has one or more central processing units (processors) 321a, 321b, 321c, etc. (collectively or generically referred to as processor(s) 321 and / or as processing device(s)). In aspects of the present disclosure, each processor 321 can include a reduced instruction set computer (RISC) microprocessor. Processors 321 are coupled to system memory (e.g., random access memory (RAM) 324) and various other components via a system bus 333. Read only memory (ROM) 322 is coupled to system bus 333 and can include a basic input / output system (BIOS), which controls certain basic functions of processing system 300.

[0069] Further illustrated are an input / output (I / O) adapter 327 and a communications adapter 326 coupled to system bus 333. I / O adapter 327 can be a small computer system interface (SCSI) adapter that communicates with a hard disk 323 and / or a tape storage drive 325 or any other similar component. I / O adapter 327, hard disk 323, and tape storage drive 325 are collectively referred to herein as mass storage 334. Operating system 340 for execution on processing system 300 can be stored in mass storage 334. A network adapter 326 interconnects system bus 333 with an outside network 336 enabling processing system 300 to communicate with other such systems.

[0070] A display (e.g., a display monitor) 335 is connected to system bus 333 by display adapter 332, which can include a graphics adapter to improve the performance of graphics intensive applications and a video controller. In one aspect of the present disclosure, adapters 326, 327, and / or 332 can be connected to one or more I / O busses that are connected to system bus 333 via an intermediate bus bridge (not shown). Suitable I / O buses for connecting peripheral devices such as hard disk controllers, network adapters, and graphics adapters typically include common protocols, such as the Peripheral Component Interconnect (PCI). Additional input / output devices are shown as connected to system bus 333 via user interface adapter 328 and display adapter 332. A keyboard 329, mouse 330, and speaker 331 can be interconnected to system bus 333 via user interface adapter 328, which can include, for example, a Super I / O chip integrating multiple device adapters into a single integrated circuit.

[0071] In some aspects of the present disclosure, processing system 300 includes a graphics processing unit 337. Graphics processing unit 337 is a specialized electronic circuit designed to manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display. In general, graphics processing unit 337 is very efficient at manipulating computer graphics and image processing and has a highly parallel structure that makes it more effective than general-purpose CPUs for algorithms where processing of large blocks of data is done in parallel.

[0072] Thus, as configured herein, processing system 300 includes processing capability in the form of processors 321, storage capability including system memory (e.g., RAM 324), and mass storage 334, input means such as keyboard 329 and mouse 330, and output capability including speaker 331 and display 335. In some aspects of the present disclosure, a portion of system memory (e.g., RAM 324) and mass storage 334 collectively store an operating system 340 to coordinate the functions of the various components shown in processing system 300.

[0073] Embodiments of the present disclosure support computer implemented methods of real-time monitoring and root cause identification performed by the system 100 and the processing system 300 described herein. In some aspects, the methods may be implemented by an integrated asset model supportive of real time monitoring of a CCUS value chain as described herein.

[0074] FIG. 4 illustrates an example flowchart of a method 400 in accordance with one or more embodiments of the present disclosure. The method 400 is an example computer-implemented method that may be implemented by the example aspects of a system 100, a system 300, and / or a computing device as described herein.

[0075] At 405, the method 400 includes processing, by a computing device, a set of answers provided by a customer with respect to storage and processing of data associated with a value chain including interconnected assets.

[0076] At 410, the method 400 includes generating, based on processing the set of answers, a cost recommendation including operational costs associated with the storage and processing of the data.

[0077] At 415, the method 400 includes generating, based on processing the set of answers, a total cost of ownership associated with the storage and processing of the data. In some aspects, generating the total cost of ownership is based on the cost recommendation.

[0078] At 420, the method 400 includes generating, based on the total cost of ownership, customer pricing information associated with the storage and processing of the data.

[0079] In some aspects, generating the cost recommendation includes: providing the set of answers and historical data to a trained model; and generating, based on processing the set of answers and the historical data by the trained model, the cost recommendation.

[0080] In some aspects, the total cost of ownership includes, with respect to the storage and the processing of the data: an acquisition cost; a usage cost; an end of life cost; and support and maintenance costs.

[0081] In some aspects, the set of answers include: a quantity of the interconnected assets included in the value chain; target temporal information associated with the storage and processing of the data; and customer cloud preference associated with the storage and processing of the data.

[0082] In some aspects, the method 400 may include, based on processing the set of answers: performing onboarding of the interconnected assets; determining data types of respective data to be acquired from the interconnected assets; determining temporal information associated with acquiring the respective data; determining data operationsassociated with storing the respective data; and determining a storage strategy associated with storing the respective data.

[0083] In some aspects, the method 400 may include: generating a second total cost of ownership associated with the storage and processing of the data, based on a second answer provided by the customer with respect to the storage and processing of the data; and generating, based on the second total cost of ownership, second customer pricing information associated with the storage and processing of the data.

[0084] In some aspects, the set of answers include an indication of: volume of carbon dioxide associated with the value chain; target storage specifications for storing the carbon dioxide; and specifications of the value chain.

[0085] In some aspects, the method 400 may include displaying a set of predefined questions based on the customer, where the set of answers respectively correspond to the set of predefined questions.

[0086] 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.

[0087] 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.

[0088] Set forth below are some embodiments of the foregoing disclosure:

[0089] Embodiment 1. A computer-implemented method comprising: processing, by a computing device, a set of answers provided by a customer with respect to storage and processing of data associated with a value chain comprising interconnected assets; generating, based on processing the set of answers, a total cost of ownership associated with the storage and processing of the data; and generating, based on the total cost of ownership, customer pricing information associated with the storage and processing of the data.

[0090] Embodiment 2. The computer-implemented method of embodiment 1, further comprising: generating, based on processing the set of answers, a cost recommendation comprising operational costs associated with the storage and processing of the data, wherein generating the total cost of ownership is based on the cost recommendation.

[0091] Embodiment 3. The computer-implemented method of embodiment 2, wherein generating the cost recommendation comprises: providing the set of answers to a trained model;and generating, based on processing the set of answers by the trained model, the cost recommendation.

[0092] Embodiment 4. The computer-implemented method of embodiment 1, wherein the total cost of ownership comprises, with respect to the storage and the processing of the data: an acquisition cost; a usage cost; an end of life cost; and support and maintenance costs.

[0093] Embodiment 5. The computer-implemented method of embodiment 1, wherein the set of answers comprise: a quantity of the interconnected assets comprised in the value chain; target temporal information associated with the storage and processing of the data; and customer cloud preference associated with the storage and processing of the data.

[0094] Embodiment 6. The computer-implemented method of embodiment 1, further comprising, based on processing the set of answers: performing onboarding of the interconnected assets; determining data types of respective data to be acquired from the interconnected assets; determining temporal information associated with acquiring the respective data; determining data operations associated with storing the respective data; and determining a storage strategy associated with storing the respective data.

[0095] Embodiment 7. The computer-implemented method of embodiment 1, further comprising: generating a second total cost of ownership associated with the storage and processing of the data, based on a second answer provided by the customer with respect to the storage and processing of the data; and generating, based on the second total cost of ownership, second customer pricing information associated with the storage and processing of the data.

[0096] Embodiment 8. The computer-implemented method of embodiment 1, wherein the set of answers comprise an indication of: volume of carbon dioxide associated with the value chain; target storage specifications for storing the carbon dioxide; and specifications of the value chain.

[0097] Embodiment 9. The computer-implemented method of embodiment 1, further comprising: displaying a set of predefined questions based on the customer, wherein the set of answers respectively correspond to the set of predefined questions.

[0098] Embodiment 10. The computer-implemented method of embodiment 1, wherein the value chain comprises a carbon capture, utilization, and sequestration (CCUS) value chain.

[0099] Embodiment 11. A system comprising: a processor and a memory, wherein the memory comprises instructions stored thereon that, when executed by the processor, cause the processor to perform operations comprising: processing a set of answers provided by a customer with respect to storage and processing of data associated with a value chain comprising interconnected assets; generating, based on processing the set of answers, a totalcost of ownership associated with the storage and processing of the data; and generating, based on the total cost of ownership, customer pricing information associated with the storage and processing of the data.

[0100] Embodiment 12. The system of embodiment 11, wherein the instructions, when executed by the processor, further cause the processor to perform operations comprising: generating, based on processing the set of answers, a cost recommendation comprising operational costs associated with the storage and processing of the data, wherein generating the total cost of ownership is based on the cost recommendation.

[0101] Embodiment 13. The system of embodiment 12, wherein generating the cost recommendation comprises: providing the set of answers to a trained model; and generating, based on processing the set of answers by the trained model, the cost recommendation.

[0102] Embodiment 14. The system of embodiment 11, wherein the total cost of ownership comprises, with respect to the storage and the processing of the data: an acquisition cost; a usage cost; an end of life cost; and support and maintenance costs.

[0103] Embodiment 15. The system of embodiment 11, wherein the set of answers comprise: a quantity of the interconnected assets comprised in the value chain; target temporal information associated with the storage and processing of the data; and customer cloud preference associated with the storage and processing of the data.

[0104] Embodiment 16. The system of embodiment 11, wherein the instructions, when executed by the processor, further cause the processor to perform, based on processing the set of answers, operations comprising: performing onboarding of the interconnected assets; determining data types of respective data to be acquired from the interconnected assets; determining temporal information associated with acquiring the respective data; determining data operations associated with storing the respective data; and determining a storage strategy associated with storing the respective data.

[0105] Embodiment 17. The system of embodiment 11, wherein the instructions, when executed by the processor, further cause the processor to perform operations comprising: generating a second total cost of ownership associated with the storage and processing of the data, based on a second answer provided by the customer with respect to the storage and processing of the data; and generating, based on the second total cost of ownership, second customer pricing information associated with the storage and processing of the data.

[0106] Embodiment 18. The system of embodiment 11, wherein the set of answers comprise an indication of: volume of carbon dioxide associated with the value chain;target storage specifications for storing the carbon dioxide; and specifications of the value chain.

[0107] Embodiment 19. The system of embodiment 11, wherein the instructions, when executed by the processor, further cause the processor to perform operations comprising: displaying a set of predefined questions based on the customer, wherein the set of answers respectively correspond to the set of predefined questions.

[0108] Embodiment 20. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform operations comprising: processing a set of answers provided by a customer with respect to storage and processing of data associated with a value chain comprising interconnected assets; generating, based on processing the set of answers, a total cost of ownership associated with the storage and processing of the data; and generating, based on the total cost of ownership, customer pricing information associated with the storage and processing of the data.

[0109] 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.

[0110] 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 / 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.

[0111] 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 variouschanges 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

CLAIMSWhat is claimed is:

1. A computer-implemented method characterized by:processing, by a computing device 300, 321, a set of answers 107 provided by a customer with respect to storage and processing of data associated with a carbon capture, utilization, and sequestration (CCUS) value chain comprising interconnected assets;generating, based on processing the set of answers 107, a total cost of ownership 190 associated with the storage and processing of the data; andgenerating, based on the total cost of ownership 190, customer pricing information 195 associated with the storage and processing of the data.

2. The computer-implemented method of claim 1, further comprising: generating, based on processing the set of answers 107, a cost recommendation 140 comprising operational costs associated with the storage and processing of the data, wherein generating the total cost of ownership 190 is based on the cost recommendation 140 .

3. The computer-implemented method of claim 2, wherein generating the cost recommendation 140 comprises:providing the set of answers 107 to a trained model 136; andgenerating, based on processing the set of answers 107 by the trained model 136, the cost recommendation 140.

4. The computer-implemented method of claim 1 , wherein the total cost of ownership 190 comprises, with respect to the storage and the processing of the data:an acquisition cost 191 comprising deployment and integration costs;an infrastructure usage cost 192 associated with bringing data from the interconnected assets to a cloud application;an end of life cost 193 associated with decommissioning, data migration, disposal, and potential replacements; andsupport and maintenance costs 194.

5. The computer-implemented method of claim 1, wherein the set of answers 107 comprise:a quantity of the interconnected assets comprised in the value chain;target temporal information associated with the storage and processing of the data; and customer cloud preference associated with the storage and processing of the data.

6. The computer-implemented method of claim 1, further comprising, based on processing the set of answers 107:performing onboarding of the interconnected assets;determining data types of respective data to be acquired from the interconnected assets; determining temporal information associated with acquiring the respective data; determining data operations associated with storing the respective data; and determining a storage strategy associated with storing the respective data.

7. The computer-implemented method of claim 1, further comprising: generating a second total cost of ownership 190 associated with the storage and processing of the data, based on a second answer 107 provided by the customer with respect to the storage and processing of the data; andgenerating, based on the second total cost of ownership 190, second customer pricing information 195 associated with the storage and processing of the data.

8. The computer-implemented method of claim 1, wherein the set of answers 107 comprise an indication of:volume of carbon dioxide associated with the value chain;target storage specifications for storing the carbon dioxide; andspecifications of the value chain.

9. The computer-implemented method of claim 1, further comprising: displaying a set of predefined questions 106 based on the customer,wherein the set of answers 107 respectively correspond to the set of predefined questions 106.

10. A system 100, 300 characterized by:a processor and a memory, wherein the memory comprises instructions stored thereon that, when executed by the processor, cause the processor to perform operations comprising:processing a set of answers 107 provided by a customer with respect to storage and processing of data associated with a carbon capture, utilization, and sequestration (CCUS) value chain comprising interconnected assets;generating, based on processing the set of answers 107, a total cost of ownership 190 associated with the storage and processing of the data; andgenerating, based on the total cost of ownership 190, customer pricing information 195 associated with the storage and processing of the data.

11. The system of claim 10, wherein the instructions, when executed by the processor, further cause the processor to perform operations comprising:generating, based on processing the set of answers 107, a cost recommendation 140 comprising operational costs associated with the storage and processing of the data, wherein generating the total cost of ownership 190 is based on the cost recommendation 140.

12. The system of claim 11, wherein generating the cost recommendation comprises: providing the set of answers 107 to a trained model 136; andgenerating, based on processing the set of answers 107 by the trained model 136, the cost recommendation 140.

13. The system of claim 10, wherein the total cost of ownership 190 comprises, with respect to the storage and the processing of the data:an acquisition cost 191 comprising deployment and integration costs;an infrastructure usage cost 192 associated with bringing data from the interconnected assets to a cloud application;an end of life cost 193 associated with decommissioning, data migration, disposal, and potential replacements; andsupport and maintenance costs 194.

14. The system of claim 10, wherein the set of answers 107 comprise:a quantity of the interconnected assets comprised in the value chain;target temporal information associated with the storage and processing of the data; and customer cloud preference associated with the storage and processing of the data.

15. A computer program product comprising 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:processing a set of answers 107 provided by a customer with respect to storage and processing of data associated with a carbon capture, utilization, and sequestration (CCUS) value chain comprising interconnected assets;generating, based on processing the set of answers 107, a total cost of ownership 190 associated with the storage and processing of the data; andgenerating, based on the total cost of ownership 190, customer pricing information 195 associated with the storage and processing of the data.

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