Changes

As previously stated, the purpose of this model is to estimate the volume of oil charge to individual traps during the last 15 m.y.

As previously stated, the purpose of this model is to estimate the volume of oil charge to individual traps during the last 15 m.y.

 

===Step 2: develop a base-case scenario===

===Step 2: develop a base-case scenario===

The next step is to develop and calibrate a base-case scenario. Values for the selected parameters used in this example are listed in the "Most Likely" column of Table 2. In this hypothetical model, only one calibration point is present, so a match to the data is relatively straightforward, but is also nonunique ([[:file:H4CH12FG5.JPG|Figure 5]]). The uncertainty around the single temperature measurement (±15°C) is indicated by the error bars.

The next step is to develop and calibrate a base-case scenario. Values for the selected parameters used in this example are listed in the "Most Likely" column of Table 2. In this hypothetical model, only one calibration point is present, so a match to the data is relatively straightforward, but is also nonunique ([[:file:H4CH12FG5.JPG|Figure 5]]). The uncertainty around the single temperature measurement (±15°C) is indicated by the error bars.

''¹The base case used the "Most Likely" values. The "Minimum" and "Maximum" values were used in the screening step and as bounds on the Monte Carlo distributions.''

''¹The base case used the "Most Likely" values. The "Minimum" and "Maximum" values were used in the screening step and as bounds on the Monte Carlo distributions.''

''²oHI = original [[hydrogen]] index of the [[source rock]]; oTOC = original [[total organic carbon (TOC)|total organic carbon]] content of the source rock; OMT = organic matter type of the source rock.

''²oHI = original [[hydrogen]] index of the [[source rock]]; oTOC = original [[total organic carbon (TOC)|total organic carbon]] content of the source rock; OMT = organic matter type of the source rock.

===Step 3: identify and estimate uncertainty in input parameters===

===Step 3: identify and estimate uncertainty in input parameters===

In addition to the uncertainties in source type, kinetics, and thickness, it is hypothesized that uncertainties in the depths, amount of missing (eroded) section, lithology (rock properties), shale grain conductivity, radiogenic heat contribution, surface temperature, and basal heat flow (magnitude and timing of extension) could significantly affect the outcome of the model. The estimations of uncertainty in these input parameters are listed in the “Minimum” and “Maximum” columns in Table 2.

In addition to the uncertainties in source type, kinetics, and thickness, it is hypothesized that uncertainties in the depths, amount of missing (eroded) section, lithology (rock properties), shale grain conductivity, radiogenic heat contribution, surface temperature, and basal heat flow (magnitude and timing of extension) could significantly affect the outcome of the model. The estimations of uncertainty in these input parameters are listed in the “Minimum” and “Maximum” columns in Table 2.

[[file:H4CH12FG7.JPG|thumb|300px|{{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.]]

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

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.

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.

The parameters in a tornado plot are sorted by decreasing the range of the net yield resulting from the range given to each input parameter. By constructing the plot in this way, the input parameter uncertainties producing the greatest range in model results are at the top. Twenty-six different parameters were varied in this run; only the 16 parameters with the widest range are shown in [[:file:H4CH12FG6.JPG|Figure 6]]. The uncertainties associated with the bottom three parameters, and the 10 not shown, are not significant enough to justify the additional effort. Even the uncertainty in some of those “above the line” might not warrant further work. This is because sorting by the widest range does not necessarily equate to sorting by the most important impact on the decisions made based on the results. In this example, the concern is oil yield, so a better sorting might be by the minimum oil yield. In this case, the depth of the Miocene source rock, radiogenic component of the shale, the original total organic carbon in the Miocene source, and the lithology of the Miocene–Pliocene section could be considered the most important, especially if the minimum value of oil yield required for success was on the order of 100 million stock tank barrels (MSTB). The other parameters may not warrant further work or resources.

The parameters in a tornado plot are sorted by decreasing the range of the net yield resulting from the range given to each input parameter. By constructing the plot in this way, the input parameter uncertainties producing the greatest range in model results are at the top. Twenty-six different parameters were varied in this run; only the 16 parameters with the widest range are shown in [[:file:H4CH12FG6.JPG|Figure 6]]. The uncertainties associated with the bottom three parameters, and the 10 not shown, are not significant enough to justify the additional effort. Even the uncertainty in some of those “above the line” might not warrant further work. This is because sorting by the widest range does not necessarily equate to sorting by the most important impact on the decisions made based on the results. In this example, the concern is oil yield, so a better sorting might be by the minimum oil yield. In this case, the depth of the Miocene source rock, radiogenic component of the shale, the original total organic carbon in the Miocene source, and the lithology of the Miocene–Pliocene section could be considered the most important, especially if the minimum value of oil yield required for success was on the order of 100 million stock tank barrels (MSTB). The other parameters may not warrant further work or resources.

====Nonlinear behavior====

====Nonlinear behavior====

Commonly, when looking at yields or charge, the behavior is nonlinear and at first glance may not be intuitive. In this example, the magnitude of the extension and the amount of lower Miocene missing section do not, as might be expected, bracket the base case. That is, the post-15 Ma yields are higher than the base case for both the minimum and maximum input values. Consider the straightforward nonlinear relationship between hydrocarbon yield after trap formation and the basal heat flow illustrated in [[:file:H4CH12FG8.JPG|Figure 8]]. At a low heat flow, the source rock is immature and too little hydrocarbons are generated, and at a high heat flow, the source rock is depleted before the trap forms. This behavior is clearly nonlinear because both high– and low–heat flow scenarios can generate less yield than the base case. However, in the example case presented, the relationship between yield and basal heat flow is the opposite, both high– and low–heat flow cases generate more yield than the base case. This seemingly nonintuitive behavior is a consequence of the inclusion of multiple source rocks in the model and becomes clear when we examine the yield for the three extension cases in detail ([[:file:H4CH12FG9.JPG|Figure 9]]).

Commonly, when looking at yields or charge, the behavior is nonlinear and at first glance may not be intuitive. In this example, the magnitude of the extension and the amount of lower Miocene missing section do not, as might be expected, bracket the base case. That is, the post-15 Ma yields are higher than the base case for both the minimum and maximum input values. Consider the straightforward nonlinear relationship between hydrocarbon yield after trap formation and the basal heat flow illustrated in [[:file:H4CH12FG8.JPG|Figure 8]]. At a low heat flow, the source rock is immature and too little hydrocarbons are generated, and at a high heat flow, the source rock is depleted before the trap forms. This behavior is clearly nonlinear because both high– and low–heat flow scenarios can generate less yield than the base case. However, in the example case presented, the relationship between yield and basal heat flow is the opposite, both high– and low–heat flow cases generate more yield than the base case. This seemingly nonintuitive behavior is a consequence of the inclusion of multiple source rocks in the model and becomes clear when we examine the yield for the three extension cases in detail ([[:file:H4CH12FG9.JPG|Figure 9]]).

In the base case, the Cretaceous and Jurassic source rocks are depleted by about 25 Ma, and the Miocene source rock barely starts generating during the last 2 m.y. In the minimal extension case, the yield from the Cretaceous and Jurassic source rocks is delayed (relative to the base case) so a significant amount of generation from the Cretaceous and Jurassic sources occurs during the last 15 m.y. In the large extension case, the Cretaceous and Jurassic source rocks are depleted by about 35 Ma, before trap formation, and Miocene source rocks begin generating at 12 Ma instead of at 2 Ma. The net result is that in the base case, little post–trap formation net yield occurs. Volumes of post–trap formation yield are greater than the base case yield in both the minimal and large extension cases. Calculating post–15 Ma hydrocarbon yield as a function of extension allows this effect to be illustrated clearly ([[:file:H4CH12FG10.JPG|Figure 10]]).

In the base case, the Cretaceous and Jurassic source rocks are depleted by about 25 Ma, and the Miocene source rock barely starts generating during the last 2 m.y. In the minimal extension case, the yield from the Cretaceous and Jurassic source rocks is delayed (relative to the base case) so a significant amount of generation from the Cretaceous and Jurassic sources occurs during the last 15 m.y. In the large extension case, the Cretaceous and Jurassic source rocks are depleted by about 35 Ma, before trap formation, and Miocene source rocks begin generating at 12 Ma instead of at 2 Ma. The net result is that in the base case, little post–trap formation net yield occurs. Volumes of post–trap formation yield are greater than the base case yield in both the minimal and large extension cases. Calculating post–15 Ma hydrocarbon yield as a function of extension allows this effect to be illustrated clearly ([[:file:H4CH12FG10.JPG|Figure 10]]).