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Business Intelligence Tools Data Science & Analytics Drilling & Completions Geology & Geoscience Reservoir Engineering

Passion for Change: Bonanza Creek Energy

with Kyle Gorynski, Director Reservoir Characterization and Exploration at Bonanza Creek Energy

If you missed the first part in this series, you can find the start of our conversation here.

Why do you think there’s been so much hesitation around change?

There’s a strong engrained culture in oil and gas with  a generation of people who’ve been doing this for decades – although on completely different rocks, play types and extraction techniques. There’s been pushback on adopting new geology or engineering software because people become so comfortable and familiar with the tools they use. I think a lot of it is simply the unique culture in oil and gas. Shale is still new, and our guesses are still evolving. I think we’re getting closer and closer to what that right answer is.

New organizations will have to adopt new technologies and adapt to new trends, because there’s no other way to make this business work.

As a scientist or engineer or manager in this space, we really have to be cognizant that the goal of a lot of vendors out there is not to help you get the right answer, it’s to make money. The onus is on us to vet everyone and make sure we’re getting the answers we want. 

Machine learning is simply another tool, like a physics-based model, aimed to help us predict an outcome and increase the precision and accuracy of these predictions. People have made pretty fantastic inferences from these kinds of tools. 

You can’t just pay a company to apply machine learning to a project. You need to help them utilize the correct inputs, the relationships, and ensure the predictions and outcomes match the other observations you have from other datasets.

I don’t think any organization should be cutting-edge for the sake of being cutting-edge. The goal is to solve these very specific technical challenges quicker and with more accuracy. Our job is to extract hydrocarbons in the most safe, environmentally friendly, and economic fashion. Technology like machine learning and AI are tools that can help us achieve these goals and needs to be done correctly.

Can you share any successes around data science or machine learning at your company?

The industry has been using these techniques for a long time. In their simplest form, people have been cross-plotting data since the early days of the oil and gas industry, trying to build relationships between things. At the beginning of my career, I remember using neural networks to predict log response.

Now we use predictive algorithms to help us predict log response where we don’t have certain logs. Let’s say we want to predict lithologies—carbonate-clay-quartz-feldspar content in a well— we’ll build relationships between triple-combo logs, and more sophisticated, but scarce elemental capture spectroscopy logs. We don’t have ECS logs everywhere, but we have triple-combo everywhere, so if you can build a relationship between those, then you have a massive dataset you can use to map your asset. That’s a simple way we use this type of technology. 

Like almost every company now, we’re also predicting performance. That’s how we’re able to make live economic decisions. We have a tool where we can put in a bunch of geologic and engineering inputs and it’ll predict production rates through time that we can forecast, add new costs, and run economics live. We’re running Monte Carlo simulations on variable rates, volumes, lateral length, spacing, commodity pricing, and costs that are based in our best estimates to predict tens of thousands of outcomes to try to help us better understand what the best decision could possibly be. I think that’s the most impactful place it’s being used, and I think that trend is being adopted more and more in industry as I talk to my peers. 

Type curve generation is no longer grabbing a set of wells and grouping them together and fitting a curve to it, but it’s trying to predict the infinite amount of outcomes that are between the extremes.

Have you seen any success among your competitors using technology, specifically data science and analytics tools?

There’s some great work out there across the board. I had a lot of fun at Encana (now Ovintiv) seeing a lot of my peers who are exceptionally smart really trying to adopt new technology to solve problems. I’ve seen some amazing work getting people to adopt new ideas, new thoughts, new predictions. I like going to URTeC. I think that’s a fantastic conference. I always find a number of great sets of technical work that has come out. 

I think the industry is doing a great job. There’s a ton of really smart people out there that know how to do this work. I think a lot of young people are really adopting coding and this bigger picture approach to subsurface, where it’s not just you’re an engineer or you’re a geoscientist, you really have to understand the fluid, the pore system, the stresses, what you’re doing to it. There’s no way we can be impactful unless we understand the really big picture, and people are getting much better at that, trying to use tools and develop skillsets that allow them to solve these problems a lot quicker.

How would you describe Petro.ai?

We see you guys as filling a gap we have. It’s the ability to pull data together. It’s the ability to simply apply big data to a dataset we quite frankly don’t have the time or the capability to do in-house. Petro.ai provides us with a very important service that allows us to get to a point that would take us 12-18 months to get to on our own, but in only a couple months. What we really like about it is the fact that we’re developing something that’s unique and new and therefore has our input and involvement, so we’re not just sending you a dataset and asking for an answer, we’re trying to say what we think drives the results, and we also want your feedback. So you’re also a group of experts as well that not only have your own experiences, but you’ve seen people’s assets and plays and how everyone else in industry is looking at it, so it’s nice to have this group of consultants that have the same goal – to address a problem and try to figure it out. We want to get to an answer as quickly as we possibly can and start to apply those learnings as quickly as we possibly can. 

Kyle Gorynski is currently Director of Reservoir Characterization and Exploration at Bonanza Creek Energy.  Kyle previously worked at Ovintiv where he spent 7 years in various technical and leadership roles, most recently as the Manager of Reservoir Characterization for their Eagle Ford and Austin Chalk assets.  Although he is heavily involved on the technical side of subsurface engineering and geoscience, his primarily focus is on their practical applications in resource and business development . Kyle received his B.S. and M.S. in Geology from the University of Kansas in 2008 and 2011, respectively.
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Drilling & Completions Passion for Change Reservoir Engineering

Passion for Change: Bonanza Creek Energy

with Kyle Gorynski, Director Reservoir Characterization and Exploration at Bonanza Creek Energy 

Tell us about your background and what you do now.

I’m from Kansas and got my Bachelor’s and Master’s degrees in Geoscience at University of Kansas, then moved straight out to Denver. Spent the first seven years of my career with Ovintiv, which was previously Encana, and had various roles, starting with mainly geology functions, and eventually working as a manager. I’ve always been interested in the technical side of the industry and novel approaches to petrophysics, geomechanics, and reservoir mapping, and how new data and new analyses can drive decision-making. It’s important that our decisions are driven through science and statistics and less through drillbit and opinion alone. I joined Bonanza Creek about a year and a half ago.

I’m the Director of Reservoir Characterization and Exploration. This role has two primary functions. One is an asset development function and the other is Business Development/Exploration. Asset development is the value optimization of our asset to maximize on key economic metrics by: 

  • understanding the subsurface to determine baseline performance
  • understanding key engineering drivers that impact performance 
  • applying those insights to modify things in real-time like spacing, stacking, completion design, well flowback etc.

At Bonanza Creek, one of the things our CEO Eric Greager likes to say is, “We’re unique because we have the agility of a small company, with the technical sophistication of a larger enterprise.” 

This allows us to respond to things that are changing quite rapidly, from the costs of goods and services to our own evolving understanding of the reservoir. By rapidly adapting, we’re able to maximize value and economic return.

That’s the main piece. The other part of the role is the exploration and business development function. We apply the same principles I just described to other assets inside and outside our basin and work with the greater operations and finance groups to determine an asset’s current value and what its potential future value could be.

How do you incorporate technology into your approach?

Technology is applied everywhere we possibly can. We have powerful technically savvy people who can develop tools and use tools to guide all our decision making. 

That’s where Petro.ai comes in. We need help building additional tools to make real-time decisions. That’s going to help us stay lean and agile but also make sure we have the right information to be making the most informed decisions. It comes down to the right data and the right people.

We need the ability to make decisions at multiple levels within an organization, so decisions can be made quickly without a top-down approach but with a high level of trust. We need to make sure the technical work is vetted and have a culture of best practices built-in for engineering and geoscience evaluation so we can have a lot of trust in our workflows. When that expertise is already built into tools—a lot of the equations, the input, the math—that helps us have trust in the inputs as well as the outputs and allows us to make those quick decisions.

How do you define real-time?

Our ultimate goal on the completions side is to be making real-time changes while we’re pumping – so minute by minute. That’s what motivated the project we have going with Petro.ai, to start turning knobs during the job, making sure the reservoir is sufficiently stimulated and not over capitalized. 

Let’s say we have sixteen wells per section permitted, but all of a sudden commodity prices drop and it’s at forty bucks, so we’ll only drill eight of those wells. Once those eight wells are in the ground, you’re stuck with that decision. Then maybe commodity prices go up, and we get a good price on sand or water, then we rerun the economics and what the type curves look like. Then we’ll make a new decision, for example, on the amount of sand or water or what the size of our stages are. 

We are making really quick decisions in terms of completions on a well-by-well basis. As price fluctuates and we’re teetering on the edge of break-even, we have to be real flexible in terms of trying to maximize the economics. 

Our next step with Petro.ai is using our 3D seismic data, well architecture, geosteering, and drilling data to understand what kind of rock we’re actually treating to make sure we’re putting a specific design for that specific formation, and we’re also reading the rock at the same time. We’re taking that information, which is mainly pressure response during the job, and trying to learn from that live.

Why do you have a passion for change in this industry?

I’m passionate about understanding the subsurface and the whole E&P industry and our evolving understanding of unconventionals. I’ve always been passionate about the big picture, trying to zoom out and understand how everything interconnects.

Change is a reality. It’s unavoidable. The pace of change and the path you take differs between organizations. The industry as a whole has been incredibly slow to adopt change. We’re now on the verge of a large extinction event. The E&P companies that remain will be in a better position to thrive once this is all over.

Unfortunately, something even as simple as generating a return on your investments has eluded many companies. For these companies, it is often the stubborn top-down culture that has resisted change and is their greatest detriment. It’s an exciting and scary time today. However, these events allow the best to survive and force others to adapt and change – for the better. 

Investments on a single well can be aprox. 6 to 10 million dollars for a 2-mile lateral in the U.S.. Investments on learning are 10s to 100s of thousands of dollars for an entire year. There’s a lot of capital destruction that could have been avoided by doing homework, collecting data, doing analysis, and connecting the dots from math to modeling to statistics all the way to a barrel of oil.

Science often ends up in a folder. It takes good leadership, management, and technical work to ensure that you’re making decisions with all your data and information. The point is to make better decisions and to make better wells. 

The conversation continues here.

Kyle Gorynski is currently Director of Reservoir Characterization and Exploration at Bonanza Creek Energy.  Kyle previously worked at Ovintiv where he spent 7 years in various technical and leadership roles, most recently as the Manager of Reservoir Characterization for their Eagle Ford and Austin Chalk assets.  Although he is heavily involved on the technical side of subsurface engineering and geoscience, his primarily focus is on their practical applications in resource and business development . Kyle received his B.S. and M.S. in Geology from the University of Kansas in 2008 and 2011, respectively.
Categories
Data Science & Analytics Reservoir Engineering

Analytics Autonomy using Petrons

The most powerful Petro.ai concept, which instantly captures an engineer’s attention, is the Petron.  Petrons are the most intuitive, efficient, and natural way to interact with oil and gas data.  There is no need to combine spreadsheets, run JOINs in SQL, or create a specialized BI view to form an integrated dataset.  Instead, disparate data sources loaded into a Petron are auto-normalized, classified, and contextualized.   

The result is Analytics Autonomy. 

Do valuable work 

In oil and gas, almost every data source depends on the others: lateral length informs decline curves, completion intensity impacts well spacing, bit aggressiveness impacts failure rates.  Today, when an engineer wants to investigate an interesting question, most of his/her time is spent doing custodial work on the appropriate interconnected datasets.  With Petrons, data is delivered on-demand to users in an analytics-ready format. 

Add new data types 

There are a plethora of new measurement technologies on the market today: DNA tracers, electromagnetics, offset pressure gauges, etc.  The challenge lies in integrating these powerful new datatypes into a model that already exists.  Outside of images in presentations, it is extremely difficult to incorporate these new measurements into the analysis.  Petrons empower engineers to incorporate new measurements into their analysis with just a few clicks. 

Scale insights 

Engineers are perpetually identifying new areas for improvement and performing analysis to support their conclusions.  However, it is difficult to scale a single engineer’s work product in a format that is usable by others.  Petrons are a shared workspace that is automatically populated with each engineer’s latest models and work products.   

Accelerate time to value 

By eliminating analytics friction, Petrons are empowering engineers to 1) solve problems faster and 2) deliver new workflows to their peers.  Organizations can move new ideas from the whiteboard to the field faster than ever before, operationalizing high-value engineering insights in days, not months. 

By eliminating tedious, data normalization work while quickly delivering insights to the business, Petrons transform engineering teams from cost centers to profit centers. 

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Drilling & Completions Reservoir Engineering Transfer

The Impact of Well Orientation on Production in North American Shale Basins

Full house at the January 22nd Calgary Lunch and Learn presented by Kyle LaMotta, VP of Analytics at Petro.ai, on the impact of well orientation in the Montney and Duvernay plays

As North American shale reservoirs reach maturity, the need to optimize development plans has become more demanding and essential. There are many variables that are within our control as we design a well: lateral length, landing zone, completion design, and so on. In situ stress is clearly not something we can directly control, but we can optimize around using an under-appreciated mechanism: well orientation.

The vast scale of available data on monthly production and well orientation provides an opportunity for data science and machine learning to help optimize on this variable.

Our team at Petro.ai has done some clever work, investigating how well orientation with respect to the maximum horizontal stress can be an important variable in well design. This unique approach blends principles of geomechanics with data science techniques to uncover new insights in completions design.

We had the incredible opportunity to visit Calgary on January 22nd, during a thankfully mild week, for a lunch and learn about the effects of well orientation on production in the Montney and Duvernay. We greatly appreciated the warm welcome, fantastic turnout, and keen interest in our presentation by Petro.ai VP of Analytics Kyle LaMotta. He brought his passion and expertise to an awesome and insightful event! 

Because of the amazing response in Calgary, we wanted to bring this lunch and learn event to some upcoming cities. Coming soon to:

For more info on all our upcoming events, please join the Petro.ai community for updates. 

Categories
Reservoir Engineering Transfer

A Whole New Look to Production Forecasting

Petro.ai 4 is here and it’s a big one! Major updates have been added throughout, including an all new web application supporting decline curve analysis as well as machine learning. Users familiar with our previous decline curve tools will recognize some of the intuitive features but now you can batch decline wells and create type curves all in the web. Decline models can be easily moved between ARIES, Spotfire, Excel, and Petro.ai; allowing users to easily compliment existing workflows.

In your browser you can view your wells and filter by any number of parameters to quickly navigate to wells of interest.

Figure 1: Load in public and/or private data, view your wells on an interactive map and easily filter down to your wells of interest.

Type curves are easier than ever! You can dynamically select wells and update oil, gas, and water type curves. These type curves can be saved back to Petro.ai for the same sort of manual tweaking as a single well decline. They are also version controlled and can be recalled and overlaid on new models.

Figure 2: Dynamically generate probabilistic type curves directly from your selection.

Like our previous forecasting tools, you can configure your default decline parameters. Now you can save your defaults or have different set of default parameters for different basins or situations that can be quickly recalled. Flags can be configured to give a quick overview of the quality of fit to enable management by exception.

Figure 3: Configure your decline model and parameters; as well as setup flags for management by exception.

The intuitive user interface puts control at your fingertips – switch to a rate-cum view or toggle on/off individual fluid streams.

Figure 4: Easily switch how you view the declines.

This release of Petro.ai introduces a new social collaboration framework; a first for our industry. You can comment on any data point or model. These comments facilitate collaboration and capture key insights right next to the relevant data. You can also send notifications using @ or create searchable keywords with #.

Figure 5: Comment on any data point, use @notifications and #keywords.

It’s now easier than ever to see how changes to a single decline parameter effect a wells productivity.

Figure 6: Update the auto-forecast and instantly see how the changes effect remaining reserves and EUR.

The production forecasting app is great for asset teams, A&D teams, and even reserves teams. With full audit traceability and a built in approvals workflow, decline models are version controlled and can be rigorously managed.

Figure 7: Decline models are automatically version controlled and tracked for auditability. Petro.ai also supports approval workflows.

Categories
Business Intelligence Tools Geology & Geoscience Reservoir Engineering Transfer

View Well Logs in Spotfire with Petro.ai

https://www.youtube.com/watch?v=NLbAUo38szs

Well logs are a critical input into many engineering and geoscience workflows. However, integrating well logs can be a challenge as many workflows move to tools like TIBCO Spotfire which cannot natively load LAS files or view logs on a vertical plot. This is especially true in unconventionals where engineers typically use a combination of Spotfire and/or Excel rather than more specialized tools like Petrel to design wells.

Petro.ai lets you:

  • Organize LAS files in once place
  • Dynamically load well logs into Spotfire
  • Use Spotfire to view and interact with well logs
  • Access well logs through a REST API
Categories
Drilling & Completions Reservoir Engineering Transfer

Gun Barrel Diagram: Calculate and Visualize Well Spacing Part 1

Introduction

The Petro.ai Gun Barrel workflow allows the user to quickly find the 3D distances between the midpoints of the lateral section of selected nearby horizontal wells. This critically important information was once only possible to calculate using specialized geoscience software or through painstaking and time-consuming manual work. With the Petro.ai integrations, we can now calculate this information directly from Spotfire:


Figure 1: Petro.ai Gun Barrel View