Friday, March 5, 2021

Data Manager Bucket List

10 things you should do before becoming a Certified Petroleum Data Manager:

*  See real-time data from the field being used to update a 3D geomodel 
*  Be present for a wireline logging run 
*  Watch a seismic survey being acquired in the field
*  Compare the histograms for SEGY data loaded in IEEE and IBM format  
*  Visualize the scale of a seismic trace on an outcrop scale reservoir analog 
*  Visit a fully automated core storage facility
*  Read the hand-written notes on the margins of an original scout ticket  
*  Observe the beginning or end of a well test 
*  Hold a sample of a reservoir quality oil shale 
*  Look at the header block of a paper seismic section 

The Core Shed

As part of a data transition project, I had the opportunity to speak with a group of high-level business consultants from a leading global firm, onsite to audit the change management plan between two organizations transferring a major producing oil field. This is the kind of conversation that makes it worth being a professional petroleum data manager. These gentlemen (and yes in this case they were all gentlemen) wanted to know all about the data types that would be important to operating the field after the handover. Remember these guys are used to doing audits on banks and internet companies, with no oilfield experience. We talked about digital geoscience data, real-time operational data from the production platforms, QSHE, maintenance and logistics records, asset registers from SAP, and personnel on board logs. They hung in pretty well until the end of the conversation, which went something like this: CG (Consultant Guy) So what other data types are there? DG: (Data Guy, that’s me) We have physical data too. CG: You mean like documents in offsite storage? DG: Yes, and some data we have stored in a core laboratory. CG: A core laboratory, what is it? DG: It’s a big building with an automated forklift, but that’s not important right now. DG: No seriously, what is it? DG: It’s a warehouse. CG: What’s in it? DG: Rocks. CG: Excuse me? DG: Rocks. We’re geologists, we study rocks, and we need someplace to keep them. CG: So the national oil company is paying to keep a building full of rocks? Surely you can’t be serious. DG: I am serious, and stop calling me Shirley. Actually, the rocks are quite valuable. CG: Like how much? DG: Well, when you consider the field acquisition and climate controlled storage ... CG: Wait, the rocks get climate controlled storage? DG: Yeah, otherwise boxes deteriorate, labels with critical metadata fall off, evaporative loss changes the water saturation, minerals can decay, … (noticing CG’s eyes glaze over) …and oil samples can degrade. CG: You keep your oil there too? DG: Yes, but only samples of fresh oil from the rocks … and then there is the CAT scan machine. CG: A CAT scan machine? For rocks? DG: Yeah, they run a scan on a rock sample, like they do on you in a hospital. CG: Like in a hospital? What is it? DG: It’s a big building where they keep sick people, but that’s not important right now. CG: No seriously, they do CAT scans on rocks? DG: Yeah, it lets you see internal structures like porosity, nodules, bedding and fractures … CG: They X-Ray rocks for fractures? Like they might have a broken bone in there? DG: Not quite, see there can be fractures in the rocks, and in a carbonate or shale reservoir the orientation of the fractures can help you … (seeing CG’s eyes glaze over again) … it can help support a decision about how to stimulate the reservoir. CG: You stimulate the rocks? DG: Yeah, sometimes we inject acid. CG: I totally believe that. Ok, so how much are these rocks worth? DG: Well like I was saying, if you count the value of the decisions being made, maybe around twenty million dollars. CG: Twenty million dollars of rocks? I gotta see this place. Where is it? DG: It’s about 90 kilometers from here CG: Can we go? DG: (Grabbing a cooler of beer) … ROAD TRIP!
Author’s note: If you have never seen a fully automated, research enabled core and sample library, it’s on the top 10 bucket list for Certified Petroleum Data Analysts

Thursday, March 4, 2021

AI and the Energy Transition

In late February I attended the Petroleum Club of Western Australia Industry Dinner on “Digitalisation, AI and Machine Learning in the Energy Sector”, billed as a discussion of how these technologies can contribute to productivity and the energy transition. The discussion got off to an early start as a participant in the Club’s Next Generation School Program described her experience in teaching students about technology using the example of a young boy given the job of tending a de-watering engine in an early English coal mine, who automated the process so that he could learn the game of marbles with his time instead 1. Her point was that automation does not threaten jobs, it gives workers the opportunity to learn new skills. The panel discussion included Anthony Brockman, General Manager of Software Integrated Solutions for Schlumberger Australasia and Far East Asia and located in Perth. His recurring theme was that Australia is a natural hub for digital leadership in the energy industry, with a robust university talent pool, access to all major energy resources (hydrocarbon, mineral and renewable), a relatively supportive government regime (for now), and a history of innovation and success (he noted that during WWII, an Australian P.O.W. led one of the most successful mass escapes from a German prison camp 2. He gave the example of a team from OMV’s Digital Excellence team in Austria who visited Perth to “see how digitalization works”, including an extended visit with Woodside, and he lamented that perhaps one of the few remaining barriers to the adoption of Artificial Intelligence is failing to see it as progress. In discussing Schlumberger’s partnership with innovative companies at their technology research lab in Palo Alto in California’s Silicon Valley, Brockman revealed that one insight obtained was that the level of collaboration was one of the only reliable predictors for success of digital initiatives, and that they would have to tackle the fear of people losing their jobs to machines in order to fully realize the potential of digital platforms like DELFI. Other panellists included Tom Georke, Innovation Centre Lead for Cisco in Perth, who observed that he considered the oil and gas industry to be only “nascent” in the adoption of machine assisted or data led decisions. He had his comparisons to the financial technology sector roundly challenged as perhaps not being the best example of using technology to maintain a “license to operate”. One of the more interesting observations of the evening came from Miranda Taylor, CEO of National Energy Resources Australia (NERA), who asserted that while many energy industries are very good at innovating internally, “you can’t scale innovation with a supply chain that operates in a silo”. This aligns well with NERA’s mandate to foster collaboration and innovation and help the energy resources sector respond to workforce trends 3. As a follow up to the discussion from Cisco, Richard Jones, VP of Asia Pacific for Dataiku, based in Singapore, polled the audience to see how many regularly used machine assisted recommendations. Only about 15% of the audience responded affirmatively, but I doubt many of them thought about letting their car GPS or public transport app tell them the best way to get to the hotel for the meeting, or odering something online that “others who ordered this also liked”. So maybe the best success criteria for artificial intelligence is that people shouldn’t realize they are using it 4. 1) 2) 3) 4)