Basin modeling: identifying and quantifying significant uncertainties

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Basin Modeling: New Horizons in Research and Applications
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Series	Hedberg
Chapter	Identifying and Quantifying Significant Uncertainties in Basin Modeling
Author	P. J. Hicks Jr., C. M. Fraticelli, J. D. Shosa, M. J. Hardy, and M. B. Townsley
Link	Web page
Store	AAPG Store

Identifying and quantifying significant uncertainties in basin modeling, 2012, Hicks, P. J. Jr., C. M. Fraticelli, J. D. Shosa, M. J. Hardy, and M. B. Townsley, in Peters, Kenneth E., David J. Curry, and Marek Kacewicz, eds., Basin modeling: New horizons in research and applications: AAPG Hedberg Series no. 4, p. 207-219.</ref>

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.

Table 1 Some of the input parameters commonly used in basin models and their potential effects on model results.

Property	Effect
Depths (or isopachs)	Thickness of each unit controls the relative amount of each rock type (see bulk rock properties) and controls the depth and, therefore, temperature and maturity of each stratigraphic unit.
Ages	Ages of each surface and event control timing and thereby control the transient behavior in the model.
Bulk rock properties Stratigraphy/lithology Compaction curves Thermal conductivity Density, heat capacity Radiogenic contribution	The bulk rock properties control the thermal and fluid transport properties (thermal conductivity, density, heat capacity, radiogenic heat, and permeability) that control thermal and pressure evolution in the model.
Missing section (erosion)	The amount of missing section controls the burial history of sediments below the associated unconformity. The burial history controls bulk rock properties (primarily thorugh the compaction history) and the temperature and pressure through time.
Fluid properties	Fluid properties control bulk rock properties in the porous rocks, migration rates, and affect seal capacity.
Source properties Thickness, original total organic carbon and hydrogen index Kinetics, retention/expulsion model	Source properties control the timing, rate, and fluid type for hydrocarbon generation and expulsion from the source rocks.
Bottom boundary condition Heat flow (or temperature)	The bottom boundary condition directly affects the temperatures throughout the model. The effects are roughly proportional to depth.
Top boundary Temperature, and water depth	Changes in the top temperature boundary condition affect the steady-state temperature through the model ~1:1. Changes in water depth can affect the top temperature boundary condition and the pressure at the mudline.
Calibration data Temperature, maturity, observed hydrocarbons including known accumulations Lithology, and rock properties	The quantity of good calibration data has a direct impact on the quality of the model and the reliability of predictions based on model outputs.

Considering uncertainty

Uncertainty is present in most, if not all, model inputs and calibration data. These uncertainties generate uncertainties in the model outputs. Sometimes, the resultant uncertainties are not significant enough to impact decisions based on the model results. Other times, these uncertainties can make the model results virtually useless in the decision-making process. Of course, a wide range of cases exist between these extremes, and this is where basin modelers commonly work. In these cases, the model results can be useful, but the uncertainties surrounding the model predictions can be difficult to fully grasp and communicate. Successful decisions based on models in which significant uncertainties exist require that the modeler (1) identify and quantify uncertainties in key input parameters, (2) adequately propagate these uncertainties from input through to output, particularly for three-dimensional models, and (3) clearly communicate this information to decision makers.

Several potential pitfalls can be avoided. Some of the most common are:

• not keeping the goal of the model in mind as it is built to direct and focus the appropriate levels of effort during the model construction
• not clearly identifying which uncertainties in input parameters will have significant impacts on the results and subsequent business decisions
• ignoring the uncertainties because not enough time or a lack of knowledge on how to adequately handle them exists
• working hard on issues that we are comfortable of working on, instead of on those that are truly important
• not recognizing feasible alternative scenarios

Figure 1 Illustration of the importance of considering uncertainty in an analysis. The "High Most Likely" case (green) has a most likely charge greater than the minimum and the "Low Most Likely" case (red) has a most likely charge less than the minimum. However, a consideration of the probability distributions (triangular distributions in this example) can alter our perception of what is "low risk" and what is "high risk."

Why consider uncertainty? Is not a single deterministic case sufficient for analysis? Consider the simple case of estimating charge volume to a trap. The necessary minimum charge volume required for success (i.e., low charge risk) and a range associated with this minimum charge have been defined and are illustrated by the vertical black solid and dashed lines, respectively, in Figure 1. If a model predicts a charge volume greater than the minimum, then it might be said that little or no charge risk exists. Similarly, if a model predicts a charge volume less than the minimum, then we might say that a significant charge risk exists. These cases are illustrated in Figure 1 and are labeled “low risk” and “high risk,” respectively. However, the perception of what is low risk and what is high risk can change greatly when the probability of an outcome is considered. In this example, the difference between low risk and high risk becomes less definitive, as indicated in Figure 1. Although this is a simplistic illustration, all of the key input parameters in a basin model have the potential to cause this degree of ambiguity in the final results. For that reason, estimates of the range of possible outcomes are as important to the final analysis as estimates of the most likely outcome.

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