Sensitivity analysis

Definition

Sensitivity analysis is a technique used to determine how different values of an input variable impact a particular output variable under a given set of assumptions. This method helps identify which variables have the most influence on outcomes, aiding in decision-making and improving understanding of the factors driving performance. By assessing the impact of changes in inputs, it allows for better risk assessment, resource management, and strategic planning.

5 Must Know Facts For Your Next Test

1. Sensitivity analysis helps to prioritize which variables should be monitored closely due to their significant impact on the results.
2. In resource estimation, sensitivity analysis can reveal how changes in input assumptions affect the predicted resource potential.
3. Numerical modeling techniques frequently employ sensitivity analysis to evaluate how various parameters influence model outputs.
4. In economic feasibility studies, sensitivity analysis is vital for understanding how fluctuations in costs or energy prices affect project viability.
5. This technique is integral in history matching as it assesses the influence of different model parameters on the accuracy of model predictions compared to actual historical data.

Review Questions

• How does sensitivity analysis enhance the understanding of input variables in resource estimation techniques?
  • Sensitivity analysis enhances understanding by quantifying how variations in input assumptions, such as temperature or pressure conditions, affect the estimates of geothermal resources. By identifying which inputs have the most significant influence on output estimates, stakeholders can focus their efforts on collecting more accurate data for those critical variables. This leads to more reliable resource assessments and improved project planning.
• Discuss the role of sensitivity analysis in improving the accuracy of numerical modeling techniques used in geothermal systems engineering.
  • Sensitivity analysis plays a crucial role in numerical modeling by allowing engineers to evaluate how changes in model parameters impact simulation results. By systematically varying parameters and observing the corresponding changes in outputs, engineers can identify which factors are most influential and ensure that their models are calibrated accurately. This process aids in refining models to better reflect real-world conditions, ultimately enhancing predictive capabilities and decision-making.
• Evaluate the importance of sensitivity analysis in economic feasibility studies for geothermal projects and how it influences decision-making processes.
  • Sensitivity analysis is essential in economic feasibility studies as it provides insights into how variations in key economic parameters—such as capital costs, operational expenses, and energy prices—impact project viability. By assessing these relationships, stakeholders can identify risks associated with financial projections and make informed decisions regarding investments and project design. This comprehensive understanding helps optimize resource allocation and minimizes potential losses due to unexpected changes in market conditions.

Related terms

Monte Carlo Simulation: A computational algorithm that relies on repeated random sampling to obtain numerical results, often used in risk analysis to understand uncertainty.

Parameter Estimation: The process of using data to determine the values of parameters that define a model, crucial for ensuring accurate modeling results.

Scenario Analysis: A process of analyzing possible future events by considering alternative outcomes or scenarios, often used in conjunction with sensitivity analysis.