Unified User Interface for Optimizing Subsea Production and Drilling Systems

Abstract

The present invention relates to a computer, server or web application that provides a unified software platform and user interface for the means of optimizing subsea oil and gas production and drilling operations. In more detail, the present invention relates to a unified platform that performs condition monitoring for both real time and historical time series data as well as integration and access to unstructured data that may be related to but difficult to corelate automatically without false positives.

Claims

1. A software tool and system comprising: First a digital subsea asset model comprising multiple software objects that represent equipment arranged to match the makeup of a subsea production or drilling system. Each object is made up of parameters that include but are not limited to part numbers, serial numbers, descriptions, relationship linkages to parent and child objects in the system as well as methods to automatically detect normal or anomalous behavior of data flow through the relational definitions and operational limits embodied within the object(s) whereby the asset model provides means to accept and route data in an organized way to be processed to yield actionable information to users in real time or on historical data frames; Second a function or process in which new data object described as an ALO in this disclosure is automatically or manually generated upon the detection of an anomaly which serves the purpose of creating a structured book mark that embodies relevant parameters to subsea production and drilling equipment and systems such as but not limited to tags and tag list, start and end time stamps, durations, system descriptions, cause descriptions, references to other data or events whereby allowing users to link unstructured data or operational condition relevant to the new ALO instances allowing efficient processing and comparison of historical ALO instances to current the current ALO instance within large data sets; Third a method of automatically assigning new ALO described above a match score based upon the relevance and occurrence frequency of other historical ALO so that the disclosed invention can recommend cause and action to users upon detection of the new anomaly and become more accurate as additional user action is taken and lessons can be retained and automatically recalled for user by this disclosed invention.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The detailed description of some embodiments of the invention is made below with reference to the accompanying figures, in which:

[0039] FIG. 1 is a typical block diagram that shows where the disclosed invention would obtain the raw data from the subsea production or drilling system. The disclosed invention would interface with one or many database(s) or historian(s) that are provided or managed by others which is considered outside of the scope of the disclosed invention. Referring to FIG. 1 the location of the disclosed invention would reside in the client tools section.

[0040] FIG. 2 is a block diagram view of a typical asset model showing a conceptual block representation for each equipment object and how the parent/child relationship would be linked in accordance with the teachings of the present invention so that someone skilled in the art of subsea production systems would be able to understand and create. As disclosed each equipment object contains reference linkages of how it relates or connects to its parent/child objects within the rest of the system in the asset model. Each object will also have defined parameters and relationships between tagged time series data that relates how the data should behave normally relative to other data within the defined parameters of the asset model. Each object may have methods that employ mathematical filtering and correlation algorithms that can be individually called upon to furnish relevant information or results to the system which can be used in conjunction with results and information from other objects to provide data insights to the user as well as trigger the generation of new ALO objects.

[0041] FIG. 3 is a workflow diagram that teaches those skilled in the art how data is processed and integrated within the disclosed invention. As shown in FIG. 1 the invention obtains the raw time series data from available historians or databases as a client tool. The call out box 100 within FIG. 3 is where new data from the subsea system is accepted by this disclosed invention. The data is manipulated to fit into a structured format and stripped of bad data if required. Within call out box 200 of FIG. 3, The data is then collected into a buffer and then run through the asset model objects as defined within the relationship linkages of the asset model. The unified interface then requests roll up calculations to be made on each equipment object from child to parent level to detect anomalies against the defined parameters and behaviors in the asset model to form a list of anomalies linkage objects (ALO) as shown in block 300. Within block 400 the system compares the new ALO object to historic ALO within a database. The system then looks for matches and similarities within the ALO database and calculates a match score that is using methods and weighting that is tuned in a manner that someone skilled in the art of subsea would be able to derive to reduce false positives. The new ALO are then assigned a match score based on parameters and occurrence of the ALO against ALO in the database. If the match score of the historic ALO is above a defined threshold, the ALO will be linked to the new ALO and the defined cause and risk associated with the older ALO will be suggested to the user as shown in block 600 of FIG. 3. If within block 400 of FIG. 3 the new ALO does not have a match score greater than the defined threshold, the new ALO will be flagged to the user as a new unique ALO as shown in block 500 of FIG. 3.

[0042] The disclosed invention then iterates around from either block 600 or 500 to block 100 and starts the automated process comprising of all the blocks outlined in red over again. When the expert user interacts with the disclosed invention, they do so through the provided unified interface to perform the action blocks outlined in black within FIG. 3.

[0043] Block 700 is where the disclosed invention presents new ALO to the user and provides linkages to past ALO and anomaly case files which include forms of unstructured free form data and information. The user can than use the data tools and access to other non-structured data and operational notes to validate the findings and linkages made by the invention in the ALO as well as update the fields and additional linkages and contextual information to the ALO.

[0044] Block 800 is where the disclosed invention presents the newly generated ALO to the user as a new unidentified ALO. The user can then use the data tools and access to other non-structured data and operational notes to validate and update the finding as a new risk or expected behavior. The user can then and link previous related ALO to the new ALO as well as update the fields and additional linkages and contextual information for the new ALO as shown in block 900, 1000 and 1100.

[0045] The red blocks combined with the user interaction in the black blocks formulates the basis for the learning and retention benefits of the disclosed invention. This method and system is novel and non-obvious to practitioners in the art of subsea production and drilling systems, and provides means for learning retention within a system for unique subsea production and drilling assets.

[0046] FIG. 4 is a depiction of an Anomaly Linkage Object (ALO) and the key parameters within the ALO object that are relatable to subsea production and drilling systems. Reference 1 within FIG. 4 shows an auto generated ALO object that is pre-populated by the system using information in the asset model (depicted in red font). Reference 2 within FIG. 4 shows the same ALO object updated after intervention by a user (depicted in green font). The purpose of the ALO is to provide a means to pinpoint the start and end time of an anomaly for quicker recall on large data sets as well as describe the nature of the anomaly for match scores. It links one or more related data tags involved in the system anomaly together as well as provide means for linking unstructured data in a structured way for future recall aided by computers. The ALO consists of key parameters such as tag names, time frames, descriptions and linkage list but are not limited to these parameters. Those familiar in the art will be able to identify what parameters are needed or useful. Table 1 below describes the basic parameters that make up an anomaly linkage object, one skilled in the art of subsea systems will be able to use the teachings and concepts within this invention disclosure to develop additional parameters that provide a structured linkage convention to leverage the power of computer algorithms and large datasets comprised of structured and unstructured data.

[0047]

TABLE-US-00001 TABLE 1 Anomaly Linkage Object (ALO) Data structure Description Parameter Name Description ALO ID Assigned Unique ID of the ALO object EQUIPMENT_TAG Equipment identification tag taken from the asset model TAG_NAME_LIST Tag name for the related data sources associated with the anomaly ALO_START_TIME The start timestamp of the identified anomaly ALO_END_TIME The end timestamp of the identified anomaly ALO_DURATION The time duration of the anomaly ALO_TYPE A key word associated with the type of anomaly which is used to sort and corelate between anomaly types. The key words used are defined in a convention appropriate for the systems being monitored. DESCRIPTION Description of the anomaly, this is a free field for user inputs VALIDATED This is a Boolean flag for if the ALO has been validated by a user RISK RANK This is a field to hold the risk ranking of the ALO, this field may be split into multiple fields to which represent severity and likelihood. FINACIAL_RISK This is a Boolean flag which is set TRUE if the ALO is identified to introduce a financial risk ENVIROMENT_RISK This is a Boolean flag which is set TRUE if the ALO is identified to introduce a environmental risk REPUTATION_RISK This is a Boolean flag which is set TRUE if the ALO is identified to introduce a reputational risk RISK BEARING This is a Boolean flag which is set TRUE if the ALO is identified to introduce measurable risk to operations CAUSE A key word associated with the cause of an anomaly which is used to sort and corelate between ALO objects. The key words used are defined in a convention appropriate for the systems being monitored. The cause key word is used as an input to the match score weighting system CAUSE DESCRIPTION This is free form cause of the description that will be used to suggest cause to users if future matching ALO are found. SYSTEM The system defined tag within the asset model SYSTEM DESCRIPTION The user-friendly description of the system pulled from the asset model EFFECTED SYSTEMS List of affected subs systems which form child relationships to the SYSTEM being affected. This is defined within the asset model. ALO LINK LIST This is a list variable that contains references to other ALO objects that have a match score greater than a defined threshold ALO LINK MATCH SCORE Tliis is a field which displays the highest match score to existing ALO ANOMOLY CASE FILE LINK This is a list of linkages in the form of URL or identifications of anomaly case files which contain ongoing structured and unstructured information related to findings and investigations of previous related anomalies.

DETAILED DESCRIPTION OF THE INVENTION

[0048] Referring now to the figures, a first embodiment of the of the present invention is indicated generally as a PC, Laptop or smart Device under the client tool section in FIG. 1. However, the disclosed invention can take the form of a server or web application, cloud service or a combination of all. In the embodiment shown, the present invention comprises of a software tool that includes a graphical user interface, computational capability, database(s), linkages to external unstructured data as well as access to one or more datastores that contain time series data from subsea production or drilling system assets.

[0049] Turning to these components in more detail, the disclosed invention is a software tool and system that integrates subsea system generated time series data and unstructured data into actionable information by means of processing the time series data through an asset model which is comprised of objects that digitally represent equipment hardware that is linked together through references within the objects which match the makeup for the subsea system. Each digital hardware object is embodied with operational parameters and operating limits and include mathematical methods for detecting unusual behavior. The methods may include filtering bad information, filling in data points for compressed data, imposing relational rules between multiple tags which those experienced in the art of subsea hardware and systems can derive and create based on these instructions. The asset model embodies methods for performing iterative calculations that may include Fourier transformations, differential peak searching and curve fitting to historical events for quick book marking in large historical data sets through the aide of the ALO data linkages.

[0050] The disclosed invention provides means for auto generating Anomaly Linkage Objects (ALO) which describe the nature of the anomaly and provides means of linking unstructured data to the anomaly instance by users. The ALO object creates a means to link anomaly instances with operational activity, cause, description and risk in a structured format to allow quick comparisons and book marking of previous events in large datasets. The ALO parameters include but are not limited to equipment TAG_NAME, SYSTEM, DESCRIPTION, VALIDATED, DOWNTIME, FINACIAL RISK, ENVIRONMENTAL RISK, REPUTATIONAL RISK, CAUSE, CAUSE DESCRIPTION, RELATED AOI, CASE FILE REFERENCE.

[0051] The disclosed invention provides means to automate notification through email or text message in real time upon detection of anomaly to users to prevent the need for users to continuously interact with the disclosed invention.

[0052] Those skilled in the art who have the benefit of this disclosure will also recognize that changes can be made to the component parts of the present invention without changing the manner in which those component parts function and/or interact to achieve their intended result. All such changes, and others that will be clear to those skilled in the art from this description of the preferred embodiment(s) of the invention, are intended to fall within the scope of the following, non-limiting claims.