The idea behind this blog is to be a springboard for ideas. Encourage steps in the right direction for the full adoption and correct implementation of intelligent systems used intelligently. In the millenials we all had expected more advances and adoption of information integration, Information for the plant lifecycle within the oil and gas sector. Looking at the automotive and the aviation industries their progress and integration has been stunning. They have completely re-engineered themselves, fully adopting the best that automation has to offer.

Today a stunning array of intelligent systems now exist for design, engineering, procurement, visualisation, construction, commisioning, operations and maintenence. With a variety of EDW’s (Engineering Data Warehouses)  and are used by most of the forward thinking EPC’s.

Not all EPC’s are employing “joined up thinking”…

Perhaps in order to look forward at first we must look at what has gone before…

Paper drawing/document-centric working

In the past many traditional deliverables were produced to propagate and communicate data within giant teams of draughts-men and engineers to allow them to deal with their scope within the overall design.

There were no computers, no email and no internet. Drawings were done by hand; documents were typed with a typewriter.

Visualisation was achieved either with many photographs or by building a plastic model of the intended plan.

2D CAD/electronic document centric working

During the 1980s the first PC and UNIX based CAD (computer aided design/drawing) hardware and software packages were used by engineering companies to produce CAD drawings.

3D CAD centric working & Intelligent discipline tools

Building and annotating 3D models allowed better visualisation and early identification of clashes. Intelligent discipline application tools allowed more efficient working & produces standard deliverables more easily.

Information/Data centric Working

Data collection & data relationships allows intelligent tools to empower users to view understand, interact and enrich the data.

Linking of Intelligent tools data, 3d data, 2d data, Procurement and material data, Electronic document management data, supplier data into an Engineering Data warehouse (ISO 15926). Determining data ownership. Challenging “traditional” deliverables. Intelligent Working, quicker, cheaper and more efficiently, to improve quality lowering risk contractual and commercial and in turn win more work. Deliverables (including 3d models & Drawings) can be generated from reports, where needed. Handing over as data for construction, operations and maintenance. Data Population & progress measurement Data Mining – extra data can be gleaned from data relationships Accurate Data population Progress measurement at regular intervals as the engineering design progresses. Accurate reporting of progress/traceability.

In the early days of paper & CAD drawing centric design, Engineering would produce just enough information to build a plant.

Masses of documentation would be dispatched to site. Construction would build to the designs and where changes were made these changes were marked up as “As-built” drawings.  These “As-built” drawings were then back drafted.  Then masses of “As-built” drawings were delivered to the operations and maintenance teams who might spend years sifting, collecting and collating data from the documentation and drawings in order to operate and maintain the plant. This meant two important things: Plant start-up was delayed which means lost revenue for the client

Collating the data at this phase is “hit and miss”, expensive, holds up production and typically the O&M team would be able to retrieve about 30% of critical tag data e.g. OEM make & model information etc scraping from a typical engineering & design package. Site survey of the kit is a long drawn out exercise expensive in time and resources with no guarantee of finding the critical data.

Data is collected as early as FEED (Front End Engineering Design) for long lead items. Setting a foundation for data collection early on in the project means you are more likely to succeed to get a greater percentage True that Engineering have to deliver more information for the benefit of O&M, but it is much cheaper (within the overall lifecycle costs) and more efficient to do this at the EP phase of the project than at the end of the project. Engineering produce information to build a plant, but also for O&M to maintain and operate the plant. Data collection should not be though of as an add on, more as an alternative approach to better structured reports with full auditability

Masses of documentation may still be dispatched to site, but now databases, electronic document archives and engineering data warehouses are handed over to construction and the O&M team.

By starting data collection earlier and continuing through the lifecycle 75% (or greater) of the critical data can be harvested.