Chapter 1 - Foundations of Integrated Production Modelling

01-01 - The Why of Integration (16 min.) Sample Lesson
01-02 - Building Blocks of IPM - Well Modelling (10 min.)
01-03 - From Concept to Model - Reservoir Model (7 min.)
01-04 - The Integrated Production Model (8 min.)
01-05 - Basic Forecasting and Material Balance (9 min.) Quiz: 01-05 - Basic Forecasting and Material Balance

Chapter 2 - Mastering Integrated Production Modelling Challenges

02-01 - Dynamic Interactions (10 min.)
02-02 - Mixed-Level Optimization Problems (5 min.)
02-03 - Troubleshooting and Optimization (9 min.) Quiz: 02-03 - Troubleshooting and Optimization

Chapter 3 - IPM for Effective Field Management

03-01 - Real-Time Optimization (8 min.)
03-02 - Learning From Experience (5 min.)
03-03 - Integrated Production Modelling in Unconventional Assets (4 min.) Quiz: 03-03 - Integrated Production Modelling in Unconventional Assets

Chapter 4 - The Future of Integrated Production Modelling

04-01 - Integrating AI/ML and Digital Twins (11 min.)
04-02 - AI/ML in the Production World (5 min.) Quiz: 04-02 - AI/ML in the Production World

Integrated Modeling and AI in Upstream Oil & Gas / Chapter 1 - Foundations of Integrated Production Modelling

Lesson 01-01 - The Why of Integration

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Transcript

01. Lesson 1.01: The Why of Integration

Welcome. My name is Alan Tominey and I'm here to talk to you about Integrated Production Modeling.

02. Introduction - About Me

I'm a co-founder at Data Insights AI, but I have a background in petroleum engineering. I started off my early days actually as a research chemist but moved very swiftly into the oil and gas industry. And after 2009, I joined a company called Petroleum Experts. Over the years I'veed over 1,000 engineers in integrated production modeling all around the world. And in the later stages of my career with Petroleum Experts, I was in charge of GAP development. So regarding research, implementation and coding of GAP, Prosper, and MBAL, the adjacent applications. I was one of the lead software developers for GAP Transient, which is the transient version of the integrated production modeling tool. And I've been involved in deploying digital twins on almost every continent on the planet and some of the largest assets in the world.
So, I'm here to talk to you today about integrated production modeling and give you an introduction to the concepts, some of the pitfalls, and how everything typically works in an integrated production model.

03. Foundations of Integrated Production Modeling

So, we'll start with an introduction to the foundations of an integrated production modeling.

04. The 'Why' of Integration

We'll talk a little bit about the concepts and the core value for an integrated production model. Why you would want to undertake the activity of building and using an integrated production model. And then all of the activities that come afterwards. Keeping the model working, extracting value, and then communicating that value to all the stakeholders that are involved.
So at its core, almost every production company in the world is there to generate profit from the oil and gas field. And so the very simple equation that fits that model is that profit is basically the revenue you generate minus all the costs. And therefore an optimization problem can basically achieve 1 of 3 objectives: You can increase your revenue; You can reduce your costs; Or you can do both. And that's the goal of an integrated production model.
Petroleum engineers are typically under a lot of pressure to manage quite complex technical scenarios, but also grapple with quite complex business demands as well at the same time. So the commercial aspects of the petroleum engineering world actually leverage or inform how people use an integrated production model.
So the core thesis of my presentations and courses that I teach is that integrated production models are the most efficient way of achieving both the objectives of either increasing revenue or reducing the costs of producing oil and gas.

05. The 'Why' of Integration (2)

So responsibility for modern production assets is usually broken up into lots of different silos. These silos come around because we use lots of different disciplines to try to capture the behavior of an oil and gas system. This is a massive complex value chain comprising of maybe up to 20 - 30 different disciplines all working together to try and achieve the goal of safely extracting hydrocarbons from a reservoir. And it's not unusual to find that many of these different disciplines have what we call competing and contradictory demands or objectives.

06. Simple Case Study: Process vs Production

So these objectives can be related to each other. Because typically, what you'll find is that the point where 2 disciplines meet is the point where these competing and contradictory demands exist. A classic scenario is when we look at a production engineer who's tasked with overseeing the production asset up to the separator. Now, in their worldview, the production is constrained by the delivery pressure to the separator. So their view of the world is that the productivity of the system declines as the increases. Now when we look at a process engineer, they have a different world view, because effectively what they see is that the process system can put more throughput, move more mass through its system as we increase the inlet pressure to their compression system, for example. So we see a competing and contradictory view of the world. Now obviously the optimum for the 2 is where these performance curves meet in the middle, and that's the point where we find the objective of the system, the most optimum part of the production system.

07. Timeline for Integrated Modelling

Capturing all this behavior in models has come a long way since it started. In the early 80s when the oil price began to increase, a lot of money and time and effort was put into modernizing production surveillance technologies and the ability to compute what was going on in an oil and gas system. So the beginning of mainframes and databases and computation in the oil and gas industry started then. By the mid-90s, there were software models that were able to capture individual items like wells, pipelines, reservoirs, but they weren't necessarily deeply tightly integrated. By the 2000s, integrated production models were beginning to be used in anger in most producing oil companies. This was brought on by companies like Petroleum Experts who were developing the integrated production modeling suite of GAP, Prosper, and MBAL. By the mid-2000s, fully integrateds, numerical simulation models coupled with production system models, and process models, began to become more commonplace in the modern production world.

08. The Building Blocks of an Integrated Model

So let's take a look at the building blocks of what an integrated model consists of.

09. Well Modeling

The well is the intersection of a number of different technical disciplines. So it's a natural starting point for us to look at on our introduction to the building blocks of an integrated production model, to look at the well. The building blocks that we'll look at consist of fluid properties, concepts, inflow performance relationships, and thenwhich I'm sure many of you will have heard of. Then we can look at network behavior, which is basically bringing everything together into a single integrated production model. And we can also look at, which can help to supply us with some information about how the reservoir is performing.

10. Well Modeling (2)

To start off with, we have to understand that there's no analytical way to capture and model an entire well. And therefore what we do is we break that into different sections. Now the ability to break that model into different sections is given to us by this concept of nodal analysis, which makes 2 assumptions. The first assumption is that there's pressure continuity. The second assumption is there's mass balance. So the mass balance means that anything that flows into the well from the reservoir has to be produced out of the well. And that if I calculate from any point in the well to another point in the well, I'll always start and arrive at the same pressure, depending on how I calculate or which direction I calculate.

11. Well Modeling - Fluid Properties

In general, there are 3 methods for modeling a fluid in a well. We don't often have as much choice as we think we do over which method we have to use. But it's worth taking a quick overview over the types of fluid modeling options that we have and how they relate to the objective of creating an integrated model. So the 3 types of models are: the models or compositional models; correlations; or fixed tables of properties.

12. Well Modeling - Fluid Properties (2)

To understand the relationship between the 3, we'll take a quick tour through the life cycle of a fluid model. The most common way of getting to a fluid model nowadays is when a well is drilled, a small fluid sample is collected, and that sample is taken to a laboratory. An model is built by the laboratory or by some people in the company who have that expertise. And from that we'll generate a number of different fluid models. And that will be important later because different fluid models can exist for the same fluid, because they'll have different objectives for what that fluid can do.

13. Well Modeling - Fluid Properties (3)

models are the most common models that are used for integrated production models, mostly because they are simple and fast. But we have to understand the pros and cons behind the different oil models. A black oil model is a 2-component model, where a black oil and a fluid are mixed in different ratios. One of the important things to understand about that is that the properties of the black oil, the API, and the dry gas, the gravity of the gas, cannot change. They are fixed parameters. And they're brought about by the conditions that we assume the oil that's in the reservoir will get to the surface. What that means is that we define a path to surface, a number of separation stages that give us a certain oil gravity and gas gravity. Now that's important because when it comes to reality and we come to try and match our model against reality, conditions can change. In Texas, in May it can be 90℉ in the afternoon and it can be sub-zero in the evening because it can very, very cold. So if oil and gas is producing at different conditions, you'll see different amounts of gas leaving the oil as it hits the separation stages. And that needs to be corrected for when we compare live data to a model.

14. Well Modeling - Fluid Properties (4)

In a model, this is also a 2-component model where we have a black oil and a dry gas. Very commonly, any that drops out in the fluid is actually modeled as an equivalent gas volume. So dry gas models are typically 2-phase. They're gas and water if that's present. Any liquid hydrocarbons is kind of added to the gas phase. It's important to understand as well that dry gas is affected quite significantly by the presence of impurities. So if there are lots of impurities in your system, then you have to use this model with care.

15. Well Modeling - Fluid Properties (5)

There are also a number of retrograde and volatile oil models available. They're not terribly common in software modeling packages, but they do exist. And they also try and capture the behavior of more complex fluids like condensates in some simpler type models. It's very important tothat these models should be validated against the composition before you use them.

16. Well Modeling - Fluid Properties (6)

Equations of state are also a correlation of a sort, in theory. They are just slightly more detailed correlations about a closer to first principle property of the fluids. And therefore they can cope with conditions changing, but they cannot be used unmatched. And they can give strange behaviors for densities and viscosities if they are used unmatched.

17. Well Modeling - Fluid Properties (7)

So if we look at the pro-cons for the different types of fluids.s, they're simple, fast, and they're also predictive for things like density and. If you have measured surface properties for a black oil, you will typically be able to get a reasonably good ballpark figure for densities and viscosities from them. The cons: They can't be rigorously mixed, so two black oil models blending don't guarantee you that you get the same properties as the blended fluid in the real world. And the separation conditions cannot change for a black oil model, so the API and theand the relationship between the ratios is fixed. In an, the pros are that they're applicable for large ranges of conditions, sub-zero temperatures for example, where black oil models don't really go. And the separation conditions can change. The con is that they are slower. So for things like reservoir simulations where you require rigorous, massive numbers of number crunching, they can be slow and unyielding. But it is important to understand as well that there are some cases where the fluid model is dictated by the objectives. So for example, in gas-lifted wells it's more traditional to use black oil models, and that's because the that you inject into the well in a black oil model can be kept separate. But in a compositional model it's a little bit more computationally tricky to do that, and therefore it doesn't really get done in most cases. Because if you inject above the bubble point, that gas will immediately become part of the producing fluid.

18. Well Modeling - Fluid Properties (8)

So models overwhelmingly take the form of 4 properties. We calculate the bubble point, the formation volume factor, the-oil ratio, and then separately we calculate the oil viscosity. Black oil models were most commonly derived at the beginning of the 20th century, looking to try and calculate whether there was gas free in the reservoir. The equation you see on the screen is a simple correlation for Standing's black oil model. There are many others. Typically, what you'll find is that the and the dissolved gas ratio are the same equation, just rearranged slightly. The formation volume factor is a slightly more complex calculation. There is a calculation for the saturated and undersaturated black oil formation volume factor. The is slightly more complicated. There are correlations for, live, and the undersaturated oil viscosity as well. But typically we bring these black oil properties together to calculate something that is useful for our purposes of nodal analysis. So for instance the density of the fluid. You can see that from the black oil model, using the gas-oil ratio and the formation volume factor, as well as the gravities that we measure at surface, we can calculate an in-situ density, which is important for us to be able to calculate things like pressure drops in a pipeline or a well.

19. Well Modeling - Fluid Properties (9)

So in general, models are very good at matching the properties that you will see in the field, but they're not necessarily exact. The way that they're derived is by taking large samples of data from different regions of the world and regressing until the equations fit with the coefficients for the various different parameters. These correlations tend to be regional. So, for instance, Standing (the one I've showed in the previous slide) is a North American model. There are models for all around the world in different regions (North Sea, the Middle East), and some models that tend to be more generic and more general.
A common approach to making sure that black oil models can be used properly is to perform some kind of matching on the models. That's typically done in a linear fashion, to match properties like the or the bubble point. So the bubble point is the point at which a bubble of vapor appears in a single-phase mixture of oil. That's an important parameter, because in the modeling of pressure drop in a well the bubble point is one of the most important properties of the fluid to get right, because that's the onset to 2-phase flow. And things become more complicated when we have 2-phase flow. Similarly, it's also an important parameter to know for the reservoir, because the recovery of oil from a reservoir that is below the bubble point and above the bubble point need to be predicted in different ways. We'll see that a little bit later when we look at.

20. Well Modeling - Fluid Properties (10)

models are often thought to be very, very important, and they are because they form the basis of the majority of the parameters, the viscosities and densities that we need to calculate properties like in a well. But there are also other properties that are required whenever we do any calculations, particularly inor network models. We also have to calculate gas properties. Now they're not necessarily part of the traditional black oil models, but even when you are modeling a black oil system, if there's present in the pipelines or in the equipment, you'll need to know what the properties of that free gas are. So there's gas property correlations that are required as well. Not only correlations for the densities and viscosities, but things like, heat capacities, and all that kind of stuff. Water similarly also has to have properties modeled in compressibilities, enthalpies, dissolved gas (if that's an important parameter) as well. And similarly, for oils you don't just need and density, you need enthalpy, entropy,, all that kind of stuff. And for mixtures you also need things likes. So there's a whole host of other correlations that need to be added into a system. So even when you're using an model, you may also be using some other more simple correlations to model certain behaviors in a system.
So that's the fluid properties piece. In the next lesson we'll talk about the multiphase flow aspects.