22 May If Watson spoke Engineer, what would she say?
I was part of APPEA’s official social media team at their recent annual conference in Perth and had a chance to take in a presentation from Woodside’s data science team. Woodside’s executive is investing in some very innovative practical digital solutions for oil and gas.
Some of Australia’s west coast off shore gas facilities have been in production for decades, and want to be in production for decades more. The same ambition, a long and productive industrial life, is true for all of Canada’s oil sands mining assets, and I suspect for assets in other mature basins, like the North Sea.
Not only do these assets handily outlast their designers, but they’re now outlasting their maintenance engineering staff, operations, logistics managers. In short, the complete original workforce.
But the oil and gas industry has long relied on the memory of its people to retrieve critical information about its assets, information beyond the kinds of data easily found in modern systems. Answers to questions like “why did we design it this way”, and “have we encountered this problem before” depend on the memories of workers.
One of my clients once told me that the human workforce that manages complex assets involuntarily commit the assets to memory over time. It just happens. Wetware memory has worked reliably for decades, although it breaks down when in times of high turnover – one of the oil sands companies has told me that when turnover breaches 7% annually, corporate memory falters.
One might ask why an industrial enterprise would design its business with important information about its operations able to just walk away. If it ain’t broke, don’t fix it.
Well, several trends are at play that are forcing exploration of new ways to approach this situation.
Changing workforce composition
The teams of people who look after these plants are changing composition. There are more contractors, and outsourced services providers. The average age of the oil industry, prior to the price drop in 2015, was 55 or greater, and much of this grey gold has cashed in and left the industry to retire, taking their memories with them, and leaving behind a much younger and less experienced team. Not only has turnover breached 7%, but it’s the wrong 7%.
Fortunately, the new workforce has been exposed to how technology has impacted other sectors like banking, retail, entertainment, and are more open to experiment with modern tools.
Large and growing accumulated intellectual property
Oil and gas facilities accumulate lots of studies over the years. Reports, diagrams, emails, meeting notes, investigations, spreadsheets, analysis – it’s a pretty large pile and it’s growing all the time as business matures and as facilities owners give frequentthought to debottlenecking, capacity expansions, growth, cost reductions, quality improvements, and so on. Keeping tabs on it all is a challenge.
Expanding document complexity
As time marches on, the content changes too. It’s becoming more comprehensive, more complex, lengthier, richer. Better tools, techniques and technologies mean that studies can cover more ground, and can be richer in terms of actual content. Instead of just one computation carried out by slide ruler in 1960, modern computers can run millions of simulations, under different assumption sets, all captured in the studies.
Technology obsolescence that strands content
Even the technology used to create content can be a hindrance. Tools obsolesce and are abandoned as better ones come along, potentially stranding the analysis and work products over time.
This situation creates a number of risks, that, in times of high oil prices and light regulation, facilities owners comfortably address by retaining large numbers of high salaried employees. Those days are certainly behind us now.
How much time might be spent by time stressed valuable engineers just locating old but valuable studies?
The presenters in Perth estimated that some 80% of engineering time was typically occupied with finding documents, and reading them to discover what, if anything, could be useful. That’s a lot of valuable engineering cycles that could otherwise be devoted to more valuable activities, like, say engineering.
Aside from low productivity, and the high cost of search services by using engineers, this approach cannot easily speed up or scale up. People can’t simply read faster, and sometimes it’s not feasible to throw more engineers into the search task.
I can imagine that in certain times, as when something unexpected happens, the pressure to find the right prior analysis becomes super critical (thinking Deep Water Horizon here).
High operational risk
A process that is highly dependent on people’s memory is risky when the outcome is based on finding the right document or collection of documents, and avoiding the wrong documents. Those people might not be there, and memory can be faulty. When that documentation is important for in-the-moment operations, then operational risks will be higher.
What happens when some piece of analysis can’t be found? Oil companies often find themselves purchasing the same analysis or data over and over simply because they couldn’t find it the first time and presumed it was lost.
If there’s one thing that computers are already very good at, it’s sifting through mountains of documentation to quickly find things that match a set of criteria. Just look at how good Google is at finding web pages and documents based on a few key words.
If there’s something that computers are getting good at, it’s interpreting spoken language and figuring out what we mean.
What if we combine the very best in content capture, search and language processing?
Imagine being able to ask (not type) a complex engineering question, with all its jargon, to a system that contained all company prior content, and the system could quickly, within a couple of seconds, return every page of every document that matched the question, ranked by best fit? Imagine asking a more precise question with fewer possible answers, and it returns the most likely answer along with the evidence to justify its reasoning. Imagine being able to teach the system over time so that it gets smarter at interpreting questions and identifying answer sources that are more reliable.
This is like having the memories of every former and current engineer, every former and current contractor, every former and current specialist and all of their accumulated expertise in one super quick engineer who can do 80% of the job practically instantly, gets smarter with every question, never sleeps, never takes leave. What’s that worth? Squillions.
The combination of Adlib (which normalises all the content, including the really old documents, to a common searchable format) and IBM Watson(who processes language queries and searches the content), creates the virtual engineer of the future.
Where does this apply?
The use case for accessing engineering documentation works perfectly well in many instances – oil sands plants, mines, refineries, petrochemical plants. Here’s several more.
Aspects of reservoir analysis would be good candidates for a dose of cognitive computing and artificial intelligence. Finding and sifting through well logs, drilling records, land documents and reservoir studies surely takes up considerable time by petroleum engineers, who would probably prefer to spend time in analysis.
Frequently in mergers, companies rationalize workforces to take advantage of scale economies, but sacrifice corporate memory. Applying Adlib and Watson to a merger is like keeping a portion of all prior employees as part of the new organisation, and not just the engineers, but in those parts of the business most impacted in mergers (IT, supply chain, HR, Finance, Corporate).
Oil and gas businesses strangle themselves with contracts and keeping tabs on them is painful. Contract managers would likely value having a rich conversation with their contracts database, where they could ask which contract is most current, what terms are in force, which contracts are going to expire and when.
There are lots of other random questions in every day life at an oil and gas facility. “What time does the next vessel arrive”, and “when was that valve last repacked”, and “is there anyone on crew with a certificate in high voltage”, and “how many valves by this manufacturer are on site, including in inventory”. These are the kinds of questions that IBM Watson, and soon, his avatar siblings are now able to answer.
I’m grateful for the kind assistance of Chris Keeling of Adlib Software who helped with some of the use cases.