This month’s Oil & Gas Measurement Podcast episode features Bruce Wallace discussing the future of collecting and validating measurement data using new technology options, such as AI.
In the episode, you will learn about the overshadowing issues seen with data collection, processing and validation, such as validation and synchronization of meter configuration data, and how advancing technologies can help improve the future of data processing and validation.
Measurement Data Processing: Show Notes, Links, and Insider Terms
- Bruce Wallace is the CEO of Peak AI Solutions. Find and connect with Bruce on LinkedIn. Alternatively, email Bruce at Bruce.Wallace@Peak-AI.com.
- Peak AI Solutions is an energy-focused technology company providing real-time data management.
- SCADA (Supervisory Control and Data Acquisition) is a system of software and technology that allows pipeliners to control processes locally or at remote locations. SCADA breaks down into two key functions: supervisory control and data acquisition. Included is managing the field, communication, and control room technology components that send and receive valuable data, allowing users to respond to the data.
- BLM (Bureau of Land Management), part of the U.S Department of the Interior, is responsible for administering, maintaining, and preserving more than 247 million acres of public land across the U.S. This includes the administration of oil and gas production from all Federal and some Indian Tribal Lands.
- FLOWCAL by Quorum Software is an oil and gas measurement software platform that is used by operators for the back-office validation, processing, and reporting of natural gas and hydrocarbon liquids.
- PGAS by Quorum is a legacy liquids/gas measurement software solution for gas and liquid measurement. For more than a decade, under different owners, FLOWCAL and PGAS competed head-to-head for the title of the premier back-office measurement system. With both products now under the Quorum Software banner, they are being consolidated as FLOWCAL by Quorum Software.
- GMAS by Schneider Electric – Gas Measurement and Analysis System is a gas flow measurement software that enables gas pipeline operations to accurately and efficiently collect & validate measurement data.
- GPA 2145 (Table of Physical Properties for Hydrocarbons and Other Compounds of Interest to the Natural Gas and Natural Gas Liquids Industries) is the industry standard for the gas processing industry to capture authoritative numerical values for hydrocarbons and other compounds occurring in natural gas and natural gas liquids.
- Listen to the Oil & Gas Measurement Podcast episode with Michael Thompson on using data analytics to drive measurement validation rules that was mentioned by Weldon.
Measurement Data Processing: Full Episode Transcript
Weldon Wright: Welcome to another episode of the Oil & Gas Measurement Podcast, sponsored by GCI, Gas Certification Institute, providing measurement training, SOPs, and measurement consulting to the oil and gas industry for over 20 years.
Announcer: Welcome to the Oil & Gas Measurement Podcast, where measurement professionals, Bubba geeks, and gurus share their knowledge, experience, and likely a tall tale or two on measurement topics for the oil and gas industry. Now, your host, Weldon Wright.
Weldon: Hello. Welcome to the Oil & Gas Measurement Podcast. I’m here today with Bruce Wallace of Peak AI. A few episodes back, we talked about advanced data validation techniques. There was a lot of interest. I got a lot of feedback on that, and that spurred some more conversation.
What we want to talk about today is, we want to talk about looking forward in measurement data processing and validation. Before we do that, Bruce, tell us a little bit about yourself and what you do over there at Peak AI.
Bruce Wallace: Weldon, thank you very much. First of all, thank you for having me on the program. Then second of all, yes, let me introduce myself.
My name is Bruce Wallace. I’m CEO for Peak AI Solutions, based in Houston, Texas. I’ve been in the biz, the biz being oil and gas with a focus on gas, since 1983. November of 83, I believe. I worked for a little outfit called Mustang Fuel in Oklahoma City, and then stayed with the pipeline when Oklahoma Gas and Electric bought them out, becoming Enogex. I worked my way through various positions, including roustabout, compression, and gas control. In gas control, I got involved with SCADA.
I did a little stint in IT for a while. eventually, I left Enoge and moved to Dallas to work for a natural gas pipeline, Regency Gas Services, where I was Director of Measurement.
Just as a side note, in 1994 I participated in Enogex’s first measurement close using a PGAS client server system instead of using the old mainframe computer. It’s been a while, and technology has changed.
From Regency, I went to work for Quorum Business Solutions where I was a subject matter expert for measurement and PGAS. I was at Quorum for about seven years before receiving an invitation to work for a startup called Panton. Panton gave me and my co-workers the opportunity to build a new measurement system. Web-hosted and device agnostic it is leading edge technology.
Unfortunately, Panton went the way of many startups, but the technology that was developed by me and my colleagues stayed with us and traveled to our new company, Peak AI Solutions. In 2017, I joined Peak. I’ve been there since. I plan to be there for quite a while.
Weldon: Bruce, you rumbled around a little in different areas of the industry there, same path I followed almost. We’ve known each other, as we were talking earlier, going on 27 years now.
You probably have a couple of years on me on the measurement side, but I think I started installing electrical equipment in the oilfield a couple of years prior to that, probably in ’78 or ’79. Either way, we’ve both been bouncing around this industry for quite a while, Bruce. As I mentioned…
Bruce: ’78 and ’79, there would have been a lot of interesting stuff that’s part of electronics happening at that time.
Weldon: I never said electronics. I said electrical.
Bruce: [laughs] OK.
Weldon: I worked for an electrical contractor back in those days setting pump jacks and control panels for pump jacks. There was no electronics involved in that. [laughs]
Bruce: Okey dokey.
Weldon: Back to what we’re all gathered here today for, Bruce, [laughs] as I mentioned earlier, when we were talking with Michael Thompson a couple of months ago, we talked about some really great work that they had done there on detecting meter freezes and some other AI based, software based analysis of validation.
Some of the listeners have written in. We got some comments. I’ve had some good discussions about folks wanting to hear more about validation. That really brings to heart from the back-office world of data validation, I mean data processing rather, validation becomes a very, very big piece of what we’re doing.
Out in the field, that tech only does a drive by once every month, once every three months, maybe less than that in some cases. We rely on the data collection systems, the data processing, SCADA in some cases to tell us something’s wrong and we need to go back out there. The ability to validate that data to detect those problems, that’s key to everyone’s measurement operations.
While things have improved a lot on that, there’s probably still a lot of things to be done out there. What are some of the biggest issues you’ve seen with data validation and data problems in general?
As we go forward, the real issue is that the number of folks in the field to watch this stuff, the number of folks in the field, I mean the back office, to watch it, and the overall expertise is declining as old guys like you and me go out to play golf and play with grandkids.
Bruce: Agree. Totally agree with you, Weldon. The KS&Es, the knowledge, skills, and experience are declining, not only with the age groups that are now in measurement that need to gather that experience and gain the knowledge, but it’s the sheer number of people working, I believe, is probably much less than what it was in past years.
We’ve got a smaller workforce doing a lot more and trying to make things even better in a pretty volatile market. Where we see commodity prices right now, they’re pretty high. A couple of years ago when natural gas prices in West Texas were practically negative, producers were having to pay to get it piped out.
Although the dollars drive most everything, the equipment configurations, the data validations; everything to do with the measurement, should be good through the highs and lows of the market. Cutting corners in low times will hurt you badly in the other times.
I’ve rambled, but what I’m about to talk about, a little bit, is the fact that not everyone knows how to use all of the measurement process inputs for configuring a measurement system and each of the measurement system components; whether it’s the flow computer, the measurement data management system, the SCADA system, or even the accounting system.
All of those components need to have some common way to access the inputs for measurement configuration. Those inputs, in my mind, include contracts and the company’s policies and procedures. Industry standards are critical, and then regulatory standards, which, in some cases, trump everything.
Bruce: Yes…Go ahead.
Weldon: I was going to say, just keying back on what you said a moment ago, there’s so much truth to what you say. Not only are we all doing, trying to do more with less people, the other reality we see is that more and more companies are moving away from in-house measurement operations.
Now we’re more and more seeing companies that rely strictly on contractors, a construction contractor to install the meter tubes, install the equipment, a measurement contractor to go by and do routine calibrations, to collect samples. In many cases, even the back office measurement data processing is being handled by a contractor.
The reality of this contractor world is that that means the same person is not working on those assets all the time, maybe even not month to month. The ability to have one or two key people in your organization that can pull every one of those pieces of information you talked about together and know how a particular meter needs to be configured, that’s a big thing. It’s a big piece being lost.
The knowledge of contracts alone is tough. Even when I was a younger guy in the measurement business, it was hard to get your hands on a physical contract. Those were pretty well guarded.
Bruce: Yeah, access is a tremendous problem.
Weldon: Now when you get removed two or three companies sometimes into the process where the contracts exist, just being able to get that meter configured is tough.
How do you know it’s configured right, I guess is what you’re asking.
Bruce: That’s absolutely true. You don’t know. In many cases you don’t know, especially with asset changes, mergers and acquisitions, multiple mergers and acquisitions. At some point, you just don’t know.
You go do the best you can with what you’ve got, but even sometimes that falls to the wayside. All of this leaves you exposed to negative consequences, even to lawsuits.
Weldon: Absolutely, absolutely.
I know prior to getting started we talked a little bit about your thoughts on this. From the standpoint of data validation, I think what we’re really talking about here is, how do we move data validation, including data validation, configuration validation?
How do we move that from the old school of this one or two key knowledge resources in every company? How do we move that from a people resource into a system that relies more on technology?
Bruce: That’s a good question, Weldon. I believe, and I think you might agree, that most of the parts are there. They’ve already been in place, but used in a less than systematic fashion.
Everything from telecommunications, to the flow computer, to the SCADA system, to the data management system, to the accounting system, and so on and so forth. For configuration and data validation, those are all potential points of failure.
What I see, and I think the technology, if it’s not already available, it’s soon to be available, is the ability to, over time, replace flow computers with smart sensors, have those sensors communicate periodically with a gateway device that shoves the data up to the cloud and makes it available to a cloud-hosted server, so that you’re actually doing all of your, for lack of a better term, historical calculations in a virtual flow computer environment.
At the same time, you should be able to take all of the sources for your other configuration inputs, your contractual terms, policies and procedures, industry standards, regulatory requirements, and digitize those. Put those in a database. Put those terms in a database so that the machine can understand what it’s looking at.
The measurement reporting machine should be able to compare what’s in its system for configuration against all the other systems. Then, at some point in the future when we get to the point where we can actually trust that kind of technology, the machine could make its own changes and just provide reports.
Weldon: There’s a lot to unpack in that last couple of statements there, Bruce. One of which is, one of the things that hit very key there that you said in just a couple of words, is that we’ve lived in a world…let’s go back to when we both got into measurement. Right?
Measurement was very much, “I want to see a mechanical totalizer.” “I want to see a chart.” “I want to see a display locally.” The number that’s reported, the number that is settled on, the number that goes to accounting, the number of people that are paid with better be that number that I saw on that totalizer or what I integrated from that chart.
There was no concept in the days we started, or very little concept, I should say, in the day we started, about how someone might routinely recalculate that data. Simply because what we had was all we knew. It was good enough at the time.
As we made all advances and changes, we’ve come to realize that, hey, applying a gas analysis from a sample that was taken two months ago to the gas volumes for next month is a little bit suboptimal.
Let’s apply that analysis as close as possible. If it’s a composite sample that ran for a month, let’s apply it back to the full month and recalculate those volumes.
We’ve moved, at least on the gas side, into this concept that as an industry we accept the fact that the device in the field is recording our raw data, and it’s giving up some estimates on volume.
It’s recording raw data. It’s putting that in historical records. Then it’s acceptable that we touch it and recalculate that data later.
Now, I don’t know that the crude and the liquids industry is still up to that. There’s a lot of companies in the crude industry where the number on that ticket from the omni or whatever flow computer out there, that’s still the law, but I think slowly crude is getting there also, little by little.
On the gas side, we’ve been in acceptance of that.
I know companies have talked about, from time to time over the last 20 years, moving into this concept of we’re going to calculate our volumes in the office. When you look at it and think about some of the things you mentioned there, the concept of a connected, intelligent set of sensors in the field, pressure transmitters, temperature transmitters, and ultrasonic velocity devices.
Having those sensors in the field connected and then bringing that information into a cloud based environment and doing all of that processing, nobody thinks that that’s a tough thing or a scary thing to the industry.
If you say, “Hey, wait a minute. That’s the only place we’re going to do the calculations.” We’re not going to do the calculations out in the field anymore. The calculations we had weren’t the final volumes. We don’t need them. We’re going to do the calculations back in the cloud. That’s a lot for old measurement guys to swallow.
Bruce: Sure it is, but we’re at the point in technology where we’re doing some amazing things with mobile devices and telecommunications.
With networks becoming so profuse… profuse numbers of networks that are available and will be available in remote areas, there’s no reason that you can’t see the data that you want to see on your mobile device wherever you’re at, whether out in Timbuktu or whether you’re on a plane or you’re in the corporate headquarters.
Weldon: I think that’s right. We’re a long way along that in many areas. As you said, I think we’re not far away from network connectivity anywhere. You hear things coming from companies like Starlink that are talking about, “Hey, we’re going to have cell phone sized device connectivity within…” I don’t know what their latest timeline is, it’s a couple of years, I think.
You having that capability over an entire continent, that’s something that I know I never considered when I first got in the industry and first started to bring those individual flow computers in over those 1,200 Baud modems.
Bruce: That’s right.
Weldon: I think that a lot of what we’re talking about is the technology is available, but when we do we get to the point that we accept that kind of technology being used? When I say that, we all the sudden get into a very much a generational thing. Right?
The guys that were our mentors in the industry, guys like you and I, the concept of saying I’m not going to be able to walk up to that well head in the field and see what the volume was yesterday. I’ve got to look at my phone to get that.
That’s a big step for us. It’s a big step for us just from the way we’ve done business for years. It’s a big step for the standards bodies. It’s a big regulatory step in some places.
Weldon: Our buddies at BLM today, no matter how good or solid or advanced the technology might be, requirements are still there for that information to be available on site.
I agree with you. I don’t think it’s a technological challenge right now. It’s a challenge of getting all the planets in line to allow us to do it.
Bruce: I agree, I agree. It’s going to take that, or it’s going to take some outside environmental factor that will cause pain or show a tremendous savings. The pain factor is anything that could cause a lawsuit or cause safety issues.
The other thing, the money thing, has to do with if you can show great savings without increasing your risk, then I think it will happen. I’m sure there are people out there that are already working to try to make this happen.
Weldon: Right. I think when you’re talking about reducing cost, a lot of that cost is the manpower cost, woman power cost, people cost, whatever we’re saying this week.
A lot of that cost is the boots on the ground. As we mentioned earlier, the expertise of those boots on the ground as a whole is dropping. The dedicated boots on the ground of particular resources are starting to disappear in wide areas throughout the US already.
Why are we putting a complicated device with literally hundreds of individual configurations points out on the field, that must be properly configured for every one of those to get the right answer? Why are we sticking that device out in the field, where it could not be further away from the data needed to do its configuration?
Bruce: That was the best we had at the time when they came around.
Weldon: It absolutely is, but I think we’ve moved beyond the days of a totalized for crude that we write down the number on the first day of the month we go by. I think we’ve moved past chart recorders in a lot of areas, although I understand Peak AI is still doing a lot of work with chart recorders.
You all have actually dragged chart recorders into the modern electronic cage, haven’t you?
Bruce: Absolutely. As a matter of fact, that’s one of the things that we specialize in, is AI using what’s called computer vision to recognize a chart, a chart type, a chart manufacturer, and recognize the process variables that are inked on that chart, and turn that image into volumetric data.
All with the use of anything that can capture the image. Currently one of the things that we’re really proud of is our mobile app that can take the photo of the chart, shoot it through the cloud. Then you’ve got volume results in a minute or so.
Weldon: I can tell you when I started in this industry there was never a concept that you could drive up to the meter, take a picture of it, put a new chart on there and throw the other one away, or throw it in a box for storage, and not see it again. That was never in our wheelhouse.
That’s some amazing changes in technology there. It’s amazing changes in technology for some of the oldest equipment out there in the industry.
Just think what we could do if we really wanted to move the flow measurement devices all the way to the other end of the spectrum, right. There’s a lot that could happen there.
Weldon: We’ve gone a long ways. Don’t get me wrong. I just think there’s more room to do.
The other thing that you mentioned, and I’d like to unpack this some more, that whole concept of where’s the data we need to configure a meter, whether that meter exists in the cloud, whether it exists as a device out in the field, whether it’s a device out in the field plus a configuration in a flow measurement system such as a FLOWCAL, a GMAS, a PGAS, whatever might be there.
Getting all that data together in one place is challenging, and you mentioned earlier the concept of we could digitize all of that. We can all put it to work.
What I hear you say is the concept that we could get to a world where we say, “Hey, I have a new meter. Here’s the address that you go get it from over your communications network. Here’s the meter type,” and have a system that goes in and pulls from your contract records what your contract requirements are, which standards you’re using, it pulls fiscal constants from the right version of [GPA] 2145. Is that the world you envision, and how do we get to that?
Bruce: Absolutely. That is the world we envision. We’re already, as an industry, making headway into that. We have been for some time. There are products out there right now that are database driven that handle contractual terms, handle regulatory requirements, and in some cases handle policies and procedures.
Industry standards, not so much I don’t think, but I don’t see that being something that would be that difficult to overcome.
Weldon: Interesting. I’ve seen some software tools like what you mentioned there where people are going in, and going through, and setting up contractual terms, regulatory requirements. Those are just not being used with measurement right now. There’s probably some opportunity there.
Bruce: That’s right. Those contractual terms actually are pulled from the contract, and there are measurement sections in that contract that go into the contractual terms in the database. It’s just a matter of getting access to it.
Weldon: Interesting thought there, Bruce, really interesting thought.
When we look at this concept of we have only intelligent sensors in the field, if we look at this concept of where we’re talking about we have our contracts, we have our regulations, we have our industry standards, we have all those things tied into a database, we have a system whether, that can validate the way we’re set up, flag if we don’t have problems, or even potentially set up a new meter for us almost automatically.
That was to be some big steps. Where do we go with some of the other validation changes that we, I’m sorry, validation challenges that we see out there?
There’s a million different types of data and types of meters we see. A pipeline custody transfer meter is a pretty sane and pretty stable data stream coming from it compared to a wellhead meter on a well with an intermitter or a gas lift on it.
I know I see a lot of individual people doing a lot of individual work. I see companies doing some stuff. Where do you think we’ll be with that if we look in a crystal ball 10 years down the road? Any thoughts?
Bruce: It’s a lot like mergers and acquisitions as far as companies go. Over time, and the way software’s always worked in the past, over time, this hotbed of activity developing apps for specific applications will continue to be pulled into other companies where they’ll be incorporated into a larger system.
At some point, there will be de facto standards, maybe just a few. Eventually, I see everything coming under maybe one standards body, even for the things that we’re talking about for the measurement data management systems.
Weldon: Wow. One Standards body.
Bruce: Wouldn’t that be nice?
Weldon: I don’t know if my brain can handle that concept.
Bruce: [laughs] Maybe two standards bodies or three. That’s better than what we’ve got.
Weldon: Standards body in the measurement industry, they’re working so much more with each other than they were just a few years ago. That’s undenying.
Bruce: It’s very true.
Weldon: Really, each of those bodies represents a separate set of interests out there. Thinking that someday all of those might get together and become one big happy family of standards, that’s something I think could be unpacked in a whole another episode. Probably one that would need to involve several drinks to talk about.
Bruce: [laughs] I’ll help plant the seed over a couple of drinks. Then I’ll let you pick the appropriate person for that particular contest.
Weldon: There would be a lot of people who would queue up to get in on that one, and everyone would have a very, very different view. [laughs]
Bruce: I think you’re right.
Weldon: Bruce, is there anything else you want to share with us? We probably have to wrap this up in a few minutes.
Bruce: No, I just appreciate the time that you give me, Weldon. This has been enjoyable. I always enjoy talking with you. I’ve certainly enjoyed talking about measurement and measurement data. That’s really it. I’ve already talked about my company a little bit early on. Basically, we do measurement data management already using cloud servers and using mobile applications to feed those cloud servers.
Currently, we are more or less using traditional methods for doing data validation and exception reporting. You can see the way that I’m pointed, and I certainly would like the opportunity for anyone to get with me and discuss going forward with it. With that, I will say thank you.
Weldon: Thanks for being on, Bruce. Your contact information, as well as the transcript of this will all be out on the Pipeline Podcast Network website. People can look you up that way or they can chase you down on LinkedIn, I’m sure also.
Weldon: Again, thanks for sharing some thoughts on where we may go with data validation and meters in general in the future, Bruce.
Weldon: Take care and be talking with you soon.
Bruce: Thank you. Weldon.
Weldon: Thanks again for joining us on the Oil & Gas Measurement podcast. We hope you enjoyed this episode. If you did, please leave us a review on iTunes, Google, or wherever you get your podcast fixes from. If you go to pipelinepodcastnetwork.com, you can find a full transcript of this episode, as well as all of Bruce’s contact information.
If you have suggestions for our podcast, comments, or maybe you want to offer yourself up to the microphone as a guest, then go back to that same website, pipelinepodcastnetwork.com, and fill out the contact form and let us know what you have to say or what your interests are. Thanks again.
Transcription by CastingWords