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==Introduction==

==Introduction==

Basin modeling is an increasingly important element of exploration, development, and production workflows. Problems addressed with basin models typically include questions regarding burial history, source maturation, hydrocarbon yields (timing and volume), hydrocarbon migration, hydrocarbon type and quality, reservoir quality, and reservoir pressure and temperature prediction for pre–drill analysis. As computing power and software capabilities increase, the size and complexity of basin models also increase. These larger, more complex models address multiple scales (well to basin) and problems of variable intricacy, making it more important than ever to understand how the uncertainties in input parameters affect model results.

Basin modeling is an increasingly important element of exploration, development, and production workflows. Problems addressed with basin models typically include questions regarding burial history, source [[maturation]], hydrocarbon yields (timing and volume), [[hydrocarbon migration]], hydrocarbon type and quality, reservoir quality, and reservoir pressure and temperature prediction for pre–drill analysis. As computing power and software capabilities increase, the size and complexity of basin models also increase. These larger, more complex models address multiple scales (well to basin) and problems of variable intricacy, making it more important than ever to understand how the uncertainties in input parameters affect model results.

Increasingly complex basin models require an ever-increasing number of input parameters with values that are likely to vary both spatially and temporally. Some of the input parameters that are commonly used in basin models and their potential effect on model results are listed in Table 1. For a basin model to be successful, the modeler must not only determine the most appropriate estimate for the value for each input parameter, but must also understand the range of uncertainty associated with these estimates and the uncertainties related to the assumptions, approximations, and mathematical limitations of the software. This second type of uncertainty may involve fundamental physics that are not adequately modeled by the software and/or the numerical schemes used to solve the underlying partial differential equations. Although these issues are not addressed in this article or by the proposed workflow, basin modelers should be aware of these issues and consider them in any final recommendations or conclusions.

Increasingly complex basin models require an ever-increasing number of input parameters with values that are likely to vary both spatially and temporally. Some of the input parameters that are commonly used in basin models and their potential effect on model results are listed in Table 1. For a basin model to be successful, the modeler must not only determine the most appropriate estimate for the value for each input parameter, but must also understand the range of uncertainty associated with these estimates and the uncertainties related to the assumptions, approximations, and mathematical limitations of the software. This second type of uncertainty may involve fundamental physics that are not adequately modeled by the software and/or the numerical schemes used to solve the underlying partial differential equations. Although these issues are not addressed in this article or by the proposed workflow, basin modelers should be aware of these issues and consider them in any final recommendations or conclusions.

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| Source properties

| Source properties

* Thickness, original [[total organic carbon (TOC)|total organic carbon]] and [[hydrogen]] index

* Thickness, original [[total organic carbon (TOC)|total organic carbon]] and [[hydrogen index]]

* Kinetics, retention/expulsion model

* [[Kinetics]], retention/expulsion model

|| Source properties control the timing, rate, and fluid type for hydrocarbon generation and expulsion from the [[source rock]]s.

|| Source properties control the timing, rate, and fluid type for hydrocarbon generation and expulsion from the [[source rock]]s.

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A hypothetical example is presented to illustrate the approach described. Although the geology is synthetic, it was constructed with realistic basin modeling issues in mind. In this example, the traps of interest formed about 15 Ma. The primary question addressed by the model is, “What is the volume of oil charge to each of the traps during the last 15 m.y.?”

A hypothetical example is presented to illustrate the approach described. Although the geology is synthetic, it was constructed with realistic basin modeling issues in mind. In this example, the traps of interest formed about 15 Ma. The primary question addressed by the model is, “What is the volume of oil charge to each of the traps during the last 15 m.y.?”

For the purposes of this illustration, the migration analysis has been simplified, and it has been assumed that a present-day map-based drainage analysis is sufficient. A map view of the key surface for the map-based drainage analysis is shown in [[:file:H4CH12FG2.JPG|Figure 2]], and a cross section through the model is shown in [[:file:H4CH12FG3.JPG|Figure 3]]. A burial history curve at location X in [[:file:H4CH12FG2.JPG|Figure 2]] is shown in [[:file:H4CH12FG4.JPG|Figure 4]]. Also shown in [[:file:H4CH12FG4.JPG|Figure 4]] are three potential hydrocarbon source rocks, Upper [[Jurassic]], Lower [[Cretaceous]], and lower [[Miocene]]. The sources are modeled as uniformly distributed [[marine]] [[source rock]]s with some terrigenous input.

For the purposes of this illustration, the migration analysis has been simplified, and it has been assumed that a present-day map-based drainage analysis is sufficient. A map view of the key surface for the map-based drainage analysis is shown in [[:file:H4CH12FG2.JPG|Figure 2]], and a [[cross section]] through the model is shown in [[:file:H4CH12FG3.JPG|Figure 3]]. A burial history curve at location X in [[:file:H4CH12FG2.JPG|Figure 2]] is shown in [[:file:H4CH12FG4.JPG|Figure 4]]. Also shown in [[:file:H4CH12FG4.JPG|Figure 4]] are three potential hydrocarbon source rocks, Upper [[Jurassic]], Lower [[Cretaceous]], and lower [[Miocene]]. The sources are modeled as uniformly distributed [[marine]] [[source rock]]s with some terrigenous input.

===Step 1: identify the purpose of the model===

===Step 1: identify the purpose of the model===

===Step 4: perform screening simulations to identify key input parameters===

===Step 4: perform screening simulations to identify key input parameters===

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H4CH12FG6.JPG|{{figure number|6}}Tornado chart for total net oil yields in million stock tank barrels (MSTB) in a selected drainage polygon during the last 15 m.y. The parameters are sorted by the range of net yields (on a linear scale) for each parameter. Uncertainties in net yields caused by uncertainty in the parameters shown below the horizontal dashed line are too small to be important. Uncertainties in net yields caused by the uncertainty in the parameters for some of the parameters shown above the horizontal line may also be unimportant, particularly for ranges with high low sides. oTOC = original total organic carbon; oHI = original hydrogen index.

H4CH12FG6.JPG|{{figure number|6}}Tornado chart for total net oil yields in million stock tank barrels (MSTB) in a selected drainage polygon during the last 15 m.y. The parameters are sorted by the range of net yields (on a linear scale) for each parameter. Uncertainties in net yields caused by uncertainty in the parameters shown below the horizontal dashed line are too small to be important. Uncertainties in net yields caused by the uncertainty in the parameters for some of the parameters shown above the horizontal line may also be unimportant, particularly for ranges with high low sides. oTOC = original total organic carbon; oHI = original hydrogen index.

H4CH12FG7.JPG|{{figure number|7}}Tornado chart for total yield (million stock tank barrels [MSTB]). In this example, uncertainties in the properties of the Upper Jurassic and Lower Cretaceous source rocks have the most effect on the total oil yield. oTOC and oHI are the original source rock total organic carbon and hydrogen index, respectively. oTOC = original total organic carbon; oHI = original hydrogen index.

H4CH12FG7.JPG|{{figure number|7}}Tornado chart for total yield (million stock tank barrels [MSTB]). In this example, uncertainties in the properties of the Upper Jurassic and Lower Cretaceous source rocks have the most effect on the total oil yield. oTOC and oHI are the original source rock total organic carbon and hydrogen index, respectively. oTOC = original total organic carbon; oHI = original hydrogen index.

Evaluating the sensitivity of results to individual parameters involves exploring the solution space by running a series of basin model simulations in which each parameter is set equal to the maximum value and then to the minimum value while all of the other parameters are held at their base-case value. This process results in 2N + 1 realizations, where N is the number of parameters for which ranges have been defined. In this example, uncertainties were defined for the surface temperature, the magnitude, age, and duration of the rifting event, the background heat flow, the shale conductivity and radiogenic heat generation, the lithology of the upper Miocene and Pliocene [[isopach]]s, the depths, the missing section, and the generative characteristics of all three source rocks. These uncertainties are summarized in the "Minimum" and "Maximum" columns of Table 2.

Evaluating the sensitivity of results to individual parameters involves exploring the solution space by running a series of basin model simulations in which each parameter is set equal to the maximum value and then to the minimum value while all of the other parameters are held at their base-case value. This process results in 2N + 1 realizations, where N is the number of parameters for which ranges have been defined. In this example, uncertainties were defined for the surface temperature, the magnitude, age, and duration of the rifting event, the background heat flow, the shale conductivity and radiogenic heat generation, the lithology of the upper Miocene and Pliocene [[isopach]]s, the depths, the missing section, and the generative characteristics of all three source rocks. These uncertainties are summarized in the "Minimum" and "Maximum" columns of Table 2.

The simulation results are summarized in a tornado chart ([[:file:H4CH12FG6.JPG|Figure 6]]). The yields are plotted on a log scale to more clearly examine the low-yield (high-risk) cases. Analysis of this plot provides a good opportunity to think about the problem. What properties are important? How important are they? Are there any surprises? The basin modeler should spend some time evaluating the behavior of each parameter to make sure it is understood and makes geologic sense. A limitation of this process is that it does not account for dependencies between input parameters. Thus, the modeler should also give potential dependencies some thought. Examples include a positive correlation between the source rock total organic carbon and hydrogen index, between the mudline temperature and paleo–water depth, between the ages and thickness of isopachs and the timing and magnitude of extension, and between the stratigraphy and paleo–water depths.

The simulation results are summarized in a tornado chart ([[:file:H4CH12FG6.JPG|Figure 6]]). The yields are plotted on a log scale to more clearly examine the low-yield (high-risk) cases. Analysis of this plot provides a good opportunity to think about the problem. What properties are important? How important are they? Are there any surprises? The basin modeler should spend some time evaluating the behavior of each parameter to make sure it is understood and makes geologic sense. A limitation of this process is that it does not account for dependencies between input parameters. Thus, the modeler should also give potential dependencies some thought. Examples include a positive correlation between the source rock total organic carbon and [[hydrogen index]], between the mudline temperature and paleo–water depth, between the ages and thickness of isopachs and the timing and magnitude of extension, and between the stratigraphy and paleo–water depths.

Modelers should realize that although it is possible in this sort of analysis for some of these scenarios to be inconsistent with the calibration data, a mismatch on its own is not sufficient reason to narrow the range of values for one of these variables. A particular value of one parameter can cause a mismatch with the data because the value of another parameter is incorrect. If both values were set appropriately, then the model results might be consistent with the calibration data. These interdependency issues will be discussed in more detail later.

Modelers should realize that although it is possible in this sort of analysis for some of these scenarios to be inconsistent with the calibration data, a mismatch on its own is not sufficient reason to narrow the range of values for one of these variables. A particular value of one parameter can cause a mismatch with the data because the value of another parameter is incorrect. If both values were set appropriately, then the model results might be consistent with the calibration data. These interdependency issues will be discussed in more detail later.