Forbes Article & Podcast featuring Apache and ThoughtTrace - ThoughtTrace
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Forbes Article & Podcast featuring Apache and ThoughtTrace

Forbes article & AI Today Podcast feature Apache SVP & ThoughtTrace Customer, Tim Custer

In this July 2020 article, Forbes Contributor, Ron Schmelzer writes, “In industries where data is key to gaining competitive advantage, artificial intelligence and machine learning have become necessities.” This article follows the podcast interview with Tim Custer, Apache’s SVP of Land, Business Development & Real Estate, and ThoughtTrace partner discussing how Apache is using AI and ML technologies to extract valuable information from unstructured content.

Tim spearheaded Apache’s adoption of AI + ML technologies by quickly seeing tremendous value in ThoughtTrace software. Managing and operating nearly 60,000 oil and gas leases, Apache now quickly and accurately finds key provisions inside their contracts in just seconds.

Tim found that using ThoughtTrace equates to:

Cost Savings + Increased Data Integrity = Efficiencies that drive down costs

Listen to the Podcast Here:

Read the Podcast Transcript:

Intro (00:01):

The AI today podcast, produced by Cognilytica, cuts through the hype and noise to identify what is really happening now in the world of artificial intelligence. Learn about emerging AI trends, technologies, and use cases from Cognilytica analysts and guest experts.

Kathleen (00:23):

Hello and welcome to the AI today podcast. I’m your host Kathleen Walch.

Ronald (00:26):

And I’m your host, Ronald Schmelzer. Our guest today is Tim Custer, who is the Senior Vice President of Land, Business Development, and Real Estate at Apache. Hi Tim. Thanks for joining us today.

Tim Custer (00:37):

Hi, how are y’all?

Kathleen (00:39):

We’re great. So, welcome Tim and thanks for joining us. We’d like to start by having you introduce yourself to our listeners and tell them a little bit about your background and your current role at Apache.

Tim Custer (00:47):

Sure. As you guys introduced me, my name is Tim Custer and I am the Senior Vice President of Land, Business Development, and Real Estate for Apache. I’ve been in the position for about the last two years and in my role here at Apache. I’m responsible for all of our land records as well as ownership and management of approximately 60,000 oil and gas leases that we currently own across the oil-producing States here in the United States. I’ll give you a little background. I’m a graduate of the University of Texas in Austin. I’ve got a business major. I began my career with a major oil company in the mid-80s – joined Amoco production company and I served in a role as a landman acquiring oil and gas leases from mineral owners at that time and worked for Amoco for about 10 years and in the mid-90s decided to join a smaller aggressive growth company. And Apache was a perfect fit for me. So, nearly 22 years ago I joined Apache and I’ve served in various roles both in the land department and as well as business development where I was responsible for negotiating and conducting transactions with peer companies in the industry and did that for about 10 years. So, really to sum it up, my focus throughout my career has really been on land and business development in the energy industry.

Ronald (02:05):

Well, that sounds great. So many of our listeners have heard about ways in which AI and machine learning is working with unstructured content and data. So can you provide some insights into how AI and machine learning is being used to extract valuable information from this unstructured content?

Tim Custer (02:20):

Sure, and I think for the listeners that may not be familiar with oil and gas business, the way oil and gas companies have the rights to drill for support and produce oil from the mineral estate is through the terms and conditions of an oil and gas lease and we literally have nearly 60,000 oil and gas leases that we manage and operate. And those oil and gas leases vary in length from a fairly simple two or three-page document to a 40 or 50-page document. And within all of those pages contain key provisions, you know, made of sentences of words put together and then paragraphs. And as you might imagine, the larger volume oil and gas leases, you know, every time you have a question about a provision and an oil and gas lease, you’ve got to pull up the digital copy of it, read the oil and gas lease and if it’s 50 pages, you know you’ve got to search through 50 pages of that document to find a particular provision that you’re looking for. And what we’ve done in partnership with ThoughtTrace is we’ve digitized a great number of our oil and gas leases and so through ThoughtTrace’s, software and OCR, optical character recognition. we can search those documents in a matter of seconds or minutes and thousands of documents if you will, looking for keyword searches or key provisions that may be very important for a particular project. And as you might imagine, being able to have those documents search through, you know, artificial intelligence and more importantly, machine learning and grouping provisions with like wording together across a vast number of agreements is an incredible savings of time, which saves us money and creates tremendous efficiency. Not to mention we’ve got a higher accuracy of data integrity, if you will, because we take the human being and the interpretation of those provisions, we sort of remove a little bit of that interpretation because we’re looking for keywords and key phrases in the document.

Kathleen (04:30):

Yeah. You know, Tim, it’s interesting because I think that you guys are in a unique situation where oil and gas leases can be incredibly old from the late 1800s early 1900s and most organizations aren’t dealing with two-page, handwritten leases like you guys are, hopefully that it’s a little bit older than that and at least typewritten and not, you know, written in some sort of old calligraphy. So I can really see use cases for AI. So can you talk to us about some of the tools that you’re using to help speed up some of these processes?

Tim Custer (04:59):

Sure. Kathleen, to your point, we do own and manage leases dating back to the early 1900s. Now, fortunately, I think the majority of the oil and gas leases that we own and manage are typewritten and to a great extent have good clarity of the text because as you might imagine, the OCR capabilities are only as good as the starting text and the original document, if you will. One of the areas that we’re using artificial intelligence and probably more importantly, the machine learning, is through ThoughtTrace’s, Document Insights and I’ll try and explain. As you might imagine, when you have files, you’ve got letters and correspondence and internal memos and then the oil and gas lease itself. And so you might imagine a hard copy file on your desk. And so we’ve taken those old hard copy files and we’ve digitized them. And one of the things that Document Insights enables us to do is organize that data in a much more… It’s largely uncategorized unstructured bulk documents and through Document Insights it can recognize, you know, what a letter is and then what an agreement is or what an assignment is and the accuracy of its recognition of those types of documents and its ability to characterize them and organize them in a fashion, we can take a hard copy file, no longer need to store that in a physical file room, have that digitized, and then have Document Insights organize and manage that file and put it in a very logical order for us. So that when you do pick that digital record up, you can go through it much more quickly than you can flipping through a hard copy file on your desk. So huge efficiencies gained just with that software application itself.

Ronald (06:48):

Yeah, well that definitely sounds incredibly valuable and we know that means a lot, both in terms of doing what you need to do for your business and making your customers happy, but also efficiency, you know. And to that note, you know, we know that the energy industry is heavily regulated, which can sometimes bring some unique challenges to technology adoption to help make these things more efficient. So can you tell us how you see AI and machine learning being applied to the energy industry as a whole and you know, some unique use cases for AI and ML in this regulated environment?

Tim Custer (07:15):

Sure. I don’t want to get too technical on, you know, not knowing the audience of the podcast. But as you might imagine, you know, in our world, in looking for searching for exploring for oil and gas, much of it is geologically and geophysically driven and there have been tremendous strides in technology gains. So let’s say from a seismic imaging and being able to image rock and reservoirs, you know, thousands and thousands of feet below the surface of the earth. We also now, from an industry perspective, we can drill a vertical well into the earth and then turn that well sideways and drill it horizontally really for miles. So much of the disciplines within our industry have had huge technological advances over the years and in the particular function in the industry that I’m in and focus on and have focused most of my career, meaning the land business and managing oil and gas leases, there’s been very little technological advance over the course of time. So when AI and machine learning, and I was not, you know, I was an early adopter, I’d like to think, but I was probably a disbeliever. I wanted to test the concept and tested against human intelligence to make sure that AI and ML weren’t going to replace the H.I. as I referred to it. And that’s the human intelligence. And so really what we’ve done is from a technology standpoint, really embraced both the provisional insight. And I talked a little bit earlier about being able to extract provisions out of documents and do keyword searches and then also file organization and file structure. So those technologies, I think I’ve embraced as a leader here at Apache and I think many of my peers are seeing the same value gains because there’s a tremendous cost savings, not to mention, you know, increased data integrity. And when you add those two together, you’re talking about efficiencies that are really driving costs out of our business through technology.

Kathleen (09:11):

Yeah, these are some great use cases and we always like to hear how different industries are adopting AI because for a lot of use cases they are fairly similar. And then for some they’re very unique. So we always like to hear use cases and I know that, you know, you touched upon efficiency and how it saves with money, but it can also save with time. I know that we had talked about reading documents and how sometimes that can take 30 to 45 minutes per document. So can you talk to us a little bit about some of the time savings that you’re having as well?

Tim Custer (09:41):

Oh, Kathleen, it’s a great point. And you know, if you think about a 50-page document and you are looking for a particular provision, and I’ll digress just for a second. In our industry, many oil and gas leases contain a provision which is a consent to assign, meaning you’ve got to go to the owner of the lease and obtain their consent before you can transfer that ownership. And so it’s a contractual obligation that we have to fulfill if we’re going to enter into a transaction. So you can imagine, you know, an individual sitting at their desk reading a 50-page oil and gas lease searching for the provision, a consent to assign, and that could literally take hours to review. Well, with artificial intelligence and machine learning through OCR as we talked about, it can do that in a matter of seconds. So, if you’re talking a transaction that you’re involving several hundred or several thousand leases, you can do the simple math and see that being able to extract the consent to assign provision in the example that I gave there, I mean, you’re talking hundreds of man-hours of savings.

Kathleen (10:46):

Yeah. You know, Ron and I always say that humans are not good at digesting very large volumes of data, but machines are really good at that. So this is a really great use case for how machines can help with that. So as a final note, what do you believe the future of AI is in general and its application to corporations and beyond?

Tim Custer (11:03):

Oh, that’s a great question. And I’ve talked with some of my peers about it and I would say with respect to AI and ML, we’re in its infancy. There are so many advances that we can use and I see it much beyond our industry, whether it’s the healthcare industry, our government, anywhere we’re managing large volumes of data. And we need to digest that data or synthesize or extract certain provisions or certain keywords for it, you know, the analytics to be able to manage large volumes of data through AI and machine learning is just, I mean it’s incredible. And as I said, I think we’re in our infancy.

Ronald (11:44):

Yeah, definitely. Well we definitely spend a lot of our time talking to folks across all these different industries and learning about their applications of AI and machine learning. And that’s part of the reason for this podcast to give people information and ideas from what other industries are doing and some of the possibilities that people may not have realized like, “Oh this is interesting what’s happening in finance or automotive or in manufacturing or in mining. You know, it’s interesting what’s happening all over the place, with the use of these technologies. So this is part of the reason why we have amazing folks like you joining us on this podcast and contributing your great information and insights. So I really appreciate you sharing what you guys are doing specifically in industry to help give other people great ideas for what they could do as well.

Tim Custer (12:21):

Well, thank y’all for letting me spend a little time with you.

Kathleen (12:23):

Yeah, Tim, thanks for joining us today and listeners, as always, we’ll post any articles and concepts discussed in the show notes. Thanks for listening and we’ll catch you at the next podcast.

Kathleen (12:32):

And that’s a wrap for today. To download this episode, find additional episodes and transcripts, subscribe to our newsletter and more, please visit our website at Join the discussion in between podcasts on the AI today Facebook group and make sure to join the Cognilytica Facebook page for updates on this and future podcasts. Also, subscribe to our podcast in iTunes, Google play, and elsewhere to get notified of future episodes.

Ronald (12:59):

Want to support this podcast and get your message out to our listeners? Then become a sponsor. We offer significant benefits for AI Today, sponsors including promotion in the podcast and landing page and opportunities to be a guest on the AI Today show. For more information on sponsorship, visit the Cognilytica website and click on the podcast link.

Kathleen (13:19):

This sound recording and its contents is copyright 2018 by Cognilytica. All rights reserved. Music by Matsu Gravas. As always, thanks for listening to AI Today and we’ll catch you at the next podcast.

Learn more:

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Podcast: AI Today featuring Apache, ThoughtTrace Partner


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