New Approaches to Make Plant and Project Information Rapidly Available

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Maria Carpenter
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1 W H I T E P A P E R New Approaches to Make Plant and Project Information Rapidly Available Recent Integration of New Information Management Technologies Unleashes the Information Potential for Brownfield Assets

2 Contents 1. Introduction Technologies to Organize Unstructured Information Simple File-Folder Hierarchies Use of Metadata and Object/Relationship Models Optical Character Recognition Conversion of 2D Drawings Full-text Retrieval Object Aliasing Thumbnails Crawlers Workflow Control Web Portals Organizing and Using Unstructured Information Role-based Information Navigation High-definition Surveying The Path Forward i

1. Introduction Brownfield assets and even new greenfield construction projects suffer from handling large volumes of unstructured information.

3 1. Introduction Brownfield assets and even new greenfield construction projects suffer from handling large volumes of unstructured information. They often do not have 3D models, data warehouses, or integrated operation systems with high-quality structured information. Even if they do, unstructured information in the form of documents and drawings regularly arrives from suppliers, vendors, and engineering partners. Unstructured information is difficult to control. With limited engineering, administrative, and IT personnel on a plant site, organizing and keeping track of this information is a significant challenge, particularly when the plant is subject to continual updates, revamps, turnarounds, and maintenance changes. Recent breakthroughs in information management (IM) and high-definition surveying (HDS) technologies offer hope for a change to this state of affairs, leading to higher productivity, safety, and operational excellence. This white paper will highlight the technologies that are available to help owner operators and engineering, procurement, and construction (EPC) companies to extract value from unstructured data and documents in a simple and cost-effective manner. 1

2. Technologies to Organize Unstructured Information Unstructured information comes in such a wide variety of forms and file formats that there is not just one technology that can solve all of the

4 2. Technologies to Organize Unstructured Information Unstructured information comes in such a wide variety of forms and file formats that there is not just one technology that can solve all of the issues associated with its identification, classification, organization, storage, and visualization. This section summarizes many of these technologies, which when brought together, would form the basis of a comprehensive information management system. 2.1 Simple File-Folder Hierarchies Nearly everyone is familiar with the use of simple file-folder structures, such as Microsoft Windows Explorer. In Figure 1, you can see that there is a file folder system which has been set up to represent structures or relationships that a user understands. In this example, the user has structures that include supplier and vendor data. Under the Vendor Data folder are various vendor names, and under these the user has created folders that represent purchase orders, against which the equipment was acquired. Figure 1: This is an example of a document hierarchy in Microsoft Windows Explorer. This is great for the person that set up the hierarchy in the first place, but how would another user be able to traverse this hierarchy and locate the documents they need? For example, would a safety engineer know to look for an accident report in a folder named to represent a purchase order? Would a project engineer know that there were documents related to System 77 inside this, or any other folder? As subsequent technologies are discussed, you will see how you can turn this hierarchical folder/file/metadata paradigm into a very powerful engine for organizing and making unstructured information available. 2

5 2.2 Use of Metadata and Object/Relationship Models In response to the limitations of simple file-folder systems, many organizations have implemented more comprehensive document management systems (DMS) at the departmental level, corporate level, or perhaps even in the public cloud. These have been successful where there is structure in the content and the rigor to ensure they stay under control. They rely heavily on metadata to characterize the content with an index that is used to search for the information. However, there are drawbacks with relying on metadata alone, especially when dealing with complex engineering documents. Too much metadata can be as bad as too little. Forcing a user to enter screens full of data can be counter-productive. Users may not know all of the keywords that need to be entered to make the document searchable. For example, a user would have to know all of the tags on a drawing to enter them in some form of list if this were a primary key. If users are allowed to take a shortcut and not enter metadata, some will certainly do so, rending the document un-findable, or WOLF write once, lost forever. Some who implement DMS solutions employ picklists to limit metadata choices. Picklists can be helpful, but would anyone be able to know all of the possible values to design upfront in system implementation? If you leave it to the user community to develop such lists during production use, you will end up with more variation than is possible to understand. The same applies to dependent picklists where the first choice drives the second, and so on. What if you only knew a value in the last picklist in the chain how would you know the sequence to pick to get to the end result? Bulk loading of metadata is an option. However, this relies on having a structured index or load file with high-quality metadata before you start, which again is somewhat counter-intuitive to the desired end result: the continuous capture of unstructured information. Instead of using metadata alone to provide structure, classification of the content, and context for the information, many users are now supplementing metadata-based systems with an object/relationships representation of the content and its structure. There are many good reasons to combine these approaches: It is more natural to users as they understand the real world of physical objects and how they relate to each other. Then can use these relationships to navigate the information structure and discover information that they would not normally know was available to them. The relationships between objects provide the context or meaning. Filtering out a particular relationship type is a powerful way to provide role-based views of the information structure. Hierarchical views, such as the traditional cabinet>folder>file paradigm of DMS, lead the user to suspect that documents will be in the lowest level of the hierarchy. However, in an object/relational model, they can reside anywhere in the structure. This is a very useful aid to change impact analysis if this is changed, what else will be impacted? 2.3 Optical Character Recognition Optical character recognition (OCR) has been available for a number of years. Primarily associated with the image/fax/document scanning industry, it has been designed to recognize text from raster (pixel-based) images and turn it into computer-recognizable text. Of course, OCR has become more sophisticated over recent years. Not only has its recognition accuracy increased to upwards of 95 percent or higher, but many vendors provide utilities to search for patterns and structure inside the raster image. For example, search in the bottom right-hand corner of a drawing and extract title-block information, or search for a block that contains a phrase such as contract number, and then look for a pattern in the proximity of it, which could be the actual contract number to extract. 3

6 2.4 Conversion of 2D Drawings Intergraph SmartPlant Foundation SmartConverter is to AutoCAD and MicroStation vector files as OCR is to image/raster files. With schematic drawings, such as P&IDs in particular, locating text within drawings that matches patterns is important. However, as shown in Figure 2, text may not be a single contiguous readable string to recognize the tag pattern. It often lies within and around symbology that represent objects to be identified and captured. It has to be concatenated and parsed to determine the true tag number pattern, which means there needs to be a rule base that is configurable to match these patterns. Figure 2: This illustration shows that text is not always laid out in a single contiguous readable string for tag pattern recognition. 2.5 Full-text Retrieval Besides all of the patterns used by OCR and SmartConverter in identifying unstructured information, there are also many useful words and phrases in the documents to be used in a search. Most people are familiar with this Google -like searching. For example, you could find all the documents with the text phrase operating procedure inside them. However, as with the other techniques described here, this on its own is not the whole answer to searching. When you perform such a search and it returns hundreds of pages of results, does anyone look at every page of results? This is usually not the case, because most people will refine the search more accurately, adding more or different search terms. As a result, full-text retrieval (FTR), in conjunction with the other classification technologies outlined earlier, is useful where the user does not know the exact search pattern or word. 2.6 Object Aliasing It would be great if everyone followed your naming and numbering system. However, this is an unrealistic expectation. You may have implemented a very thorough tag naming system, such that you may be expecting tag numbers with a pattern of AA-NNNN, where A equals Alpha character, such as an equipment type; the dash equals a delimiter; and N equals a sequential number. Instead, you receive documents from vendors and suppliers with all forms of variations. For example, you may have required PA-4401 but instead found such variations as PA 4401, PA/4401, or 77-PA It gets even more complex when there are primary and standby equipment with prefixes or suffixes added to the tag name, such as PA-4401-A or PA-4401-A/B/C. The same applies to drawings. The owner operator may have its own numbering system. The EPC would also have its own numbering system, as would a supplier or vendor. These different numbers may have all been referring to the exact same document. 4

Clearly with these situations, the ability to support all naming and numbering patterns and provide aliases, such that each individual can locate the same information by their coding system, would be

7 Clearly with these situations, the ability to support all naming and numbering patterns and provide aliases, such that each individual can locate the same information by their coding system, would be a great advantage. 2.7 Thumbnails Human vision can spot patterns, shapes, and definition at an astonishing rate. An engineer is often able to identify a drawing from a small-scale pictorial representation more quickly than trying to determine the correct drawing from a list of metadata. Modern printing and visualization software is able to provide accurate and efficiently rendered representations of the original drawing/document/image for quick display. 2.8 Crawlers The Internet would not be the Internet without crawlers continuously reading, indexing, and re-reading every updated page on the web. There is no way you could keep up with the task and volume of information if you were to manually update and index your websites. The same applies to your unstructured information stores. Of course, you could manually check-in each document, or bulk-load a new batch of documents every once in a while. It is better to take a leaf out of the Internet s page and continuously crawl the content, searching for new or updated unstructured information. 2.9 Workflow Control Your business runs on processes and procedures. Workflow control systems can enforce and automate the process of delivery, execution, and notification. You do not have to look for new things to do as they are delivered to you. Plus, all of the capabilities that have been outlined earlier can be orchestrated to rapidly gather new unstructured information and process it accordingly Web Portals Web portals have become so ubiquitous that their value is often forgotten. They can create windows into a number of different information systems/sources from a single web page, and have each of these windows updated and be triggered by actions from other windows on the same page. Users can also read and review content from these applications without necessarily having the originating application on their computer systems, and they can browse the information remotely over the internet. These are all beneficial traits in the context of unstructured information. 5

8 3. Organizing and Using Unstructured Information Compared to the file-folder hierarchical example in Section 2.1, you will see how much more efficient your ability to search for information can be once the unstructured information is organized using the technologies outlined earlier. Figure 3 shows how a crawler could read the folder structure to create objects and relationships. The 11 documents are individual objects, as are the purchase orders and the supplier. You will see from the example in Section 2.1 how these match the original names. The arrows represent relationships between the objects that can be used for inter-object navigation. Figure 3: This is an example of how a crawler could read the folder structure to create objects and the relationships between them. In this example, the user could search for a document and then navigate to the related purchase order. The user can then navigate to the supplier and, from there, see how many purchase orders were issued to that supplier, as well as the various documents related to those purchase order objects, and so on. In Figure 4, the crawler has also read the filename and has decoded the constituent parts using a pre-defined template. It has then created other objects and relationships based on this structure. This codification is typical for document/drawing naming and numbering systems. You can see that 77 represents the Sulphur Recovery Unit as a plant hierarchical structure item, A represents the discipline, DA represents the document type, and so on. 6

As indicated earlier, the appropriate content reader is determined by the type of file, such as OCR for image, SmartConverter for drawing, and so on.

9 Figure 4: This is an example of how a crawler can also read filename structure to create additional objects and relationships. In Figure 5, the document has been read to extract data-matching patterns in this case, tag numbers. As indicated earlier, the appropriate content reader is determined by the type of file, such as OCR for image, SmartConverter for drawing, and so on. Here you can see tag numbers 77-PA-4352, 77-PA-4533, and 77-PA-4534 were extracted from the document M-77-DA-A-4533, Accident report Data, and relationships were established between the document and the tags. Figure 5: Objects and relationships are created from data-matching patterns that have been extracted from document contents. This document-to-tag linkage is recognized as important by owner operators and EPCs alike. Often, this is requested in the form of a tag-to-document register at handover time. You can also see that the tag 77-PA-4534 also appears on four documents. This reverse relationship is important for managing the impact of change. Here you can clearly see that if the data regarding this one tag is changed, it will potentially impact four documents. Such information is tremendously valuable once the entire document set is captured. So how can these objects and relationships be utilized for search and navigation? 7

4. Role-based Information Navigation An important part of the find what you need by what you know concept means that everyone in different roles within the organization knows something different

In Figure 6, you can see that an engineering manager originally queried for the PBS item 77, the Sulphur Recovery Unit.

10 4. Role-based Information Navigation An important part of the find what you need by what you know concept means that everyone in different roles within the organization knows something different about the plant and their tasks. They will start from a different location in their information discovery and navigate to what is important for the task they have at hand. In Figure 6, you can see that an engineering manager originally queried for the PBS item 77, the Sulphur Recovery Unit. From a single place, he is then able to see all of the documents that describe this unit. He is able to do this with a single query, rather than having to trawl through multiple file systems. He can then view and print these documents to determine their status, or narrow the search with additional criteria. Figure 6: A user is able to quickly find applicable documents that describe a specific plant item, such as a unit. This ability to find information by what you know would also be valuable to a maintenance planner who has to work on a specific piece of equipment. He would be able to search for the specific tag name (for example, 77-PA-4534 as shown in Figure 7), and determine in which documents this tag occurs. This is particularly useful if the tag has registered an alarm, and the user needs to find the information quickly for an urgent resolution. Figure 7: A maintenance planner can find documents associated with a specific tag. What if the piece of equipment from the previous example has now failed completely? The purchasing admin would need to find the original manufacturer to order a direct replacement. As shown in Figure 8, the purchasing admin can simply traverse the relationship between the tag, to the document, to its purchase order, and onto the supplier. Clearly this sort of information may already be in a CMMS and/or 8

11 ERP system, in which case this navigation may traverse a link to these systems and the associated information. Figure 8: A purchasing admin can search for equipment supplier information starting from a tag name. What if an accident occurred during the installation of that same piece of equipment? The safety planner needs to prevent such accidents from occurring again. As shown in Figure 9, to find the related information quickly, users may start from an accident report object and navigate around the crossreferences between these organizational items, using what they know as reference points. Figure 9: A safety planner can quickly find the relevant information, starting from an accident report. The examples covered in this section have illustrated how quickly and continuously organized unstructured information can be used as an invaluable asset. However, one area of navigation is still missing from this picture. What about the plant itself? Its fidelity and resolution are undeniable. What if the information assets covered in the previous examples could be quickly linked to visually identifiable representations of the physical plant? 9

5. High-definition Surveying When it is not cost-effective to use 3D CAD to model an existing plant (reverse engineering) for information navigation and review purposes, modern HDS systems can be

12 5. High-definition Surveying When it is not cost-effective to use 3D CAD to model an existing plant (reverse engineering) for information navigation and review purposes, modern HDS systems can be used to rapidly integrate 3D point clouds and high-resolution 360 images into a single interactive display. For example, the Leica Geosystems ScanStation C10 provides full 360º x 270º field-of-view, high accuracy, long range reflectivity), and high scan speed (50k pts/sec). It can capture a 3D, full-scale representation of the plant in minutes rather than hours for manual measurement. The TruView of the plant generated from the Leica Geosystems software (shown in Figure 10) is a blended view of the point cloud data and the stitched photograph that can be rotated around. The user can quickly orient/rotate within the plant from this realistic rendering, and can traverse from one scan location to another. This gives the impression of walking through the plant from point to point. With this full 3D point cloud available, the remote user can study the actual plant and take measurements useful for plant re-engineering studies, without the cost and hazard of being at the plant. Figure 10: This is an example of a TruView laser scan image, hot-spotted, and navigable within an information portal. When linked via hotspots to the unstructured information resource, this can provide a photo-realistic comparison between the as-built condition of the plant and as-exists condition of the design basis. This is useful in design studies, safety and emergency planning, and other situations where crossreferenced information would be invaluable. 10

13 6. The Path Forward The dirty secret about unstructured information is that this issue has existed as long as there have been computers. Arguably, technology has exacerbated a problem that has been around a lot longer in the paper world. The answer may not be more rigor in your existing document management system, or an extension of your ERP or CMMS. Many companies already use such systems, and still the issue persists. The real solution is to recognize that legacy information, which is often uncontrolled and unstructured, will always exist in organizations. The ability to react to business situations like those outlined in this white paper and others will be constrained by how well you deal with it. This white paper is the second in a three-part series. To find out about the latest approaches and technologies that are being applied to the management of unstructured information, please also read: Understanding Information Challenges for Brownfield Assets and Greenfield Projects Unstructured information continues to expose operations and engineering companies to unnecessary cost, risk, delay, and hazard. This white paper highlights the challenges in collating, organizing, and accessing unstructured information, particularly in brownfield plants, offshore facilities, and greenfield construction projects. SmartPlant Fusion: Technology and Capabilities This white paper discusses the technology platform being developed by Intergraph to solve the unstructured information challenges of the process, power, and marine industries. 11

14 For more information about Intergraph, visit our Web site at Intergraph Corporation. All rights reserved. Intergraph is part of Hexagon. Intergraph, the Intergraph logo, and SmartPlant are registered trademarks of Intergraph Corporation or its subsidiaries in the United States and in other countries. Other brands and product names are trademarks of their respective owners. Intergraph believes that the information in this publication is accurate as of its publication date. Such information is subject to change without notice. Intergraph is not responsible for inadvertent errors. 5/14 PPM-US-0181B-ENG

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