Sensitivity Analysis in the Energy Sector

Cash flow analysis of an upstream project in the energy sector requires inputs from various sources. Some of the data come from technical departments such as reservoir engineering, G&G, and the planning team. These data are mostly related to field costs and production profiles. Additional inputs are gathered from legal and commercial teams, including fiscal contracts, partner's equity and working interests, and carry structures. The corporate finance team provides other critical inputs such as price, inflation, exchange rate, and discount rate.

It's important to note that these technical, fiscal, and economic data are not completely certain. There is always a possibility that some of them may differ at the time of actual project implementation. For example, the price could be significantly different than the one used for estimating the NPV at a given point, or costs may have changed due to overruns or unexpected escalations. The reserve may not perform as initially expected, or the well's productivity may exceed initial estimates.

Sensitivity analysis is a process used to test the impact of variations in the input on the final output by altering various inputs one at a time. This method helps determine the robustness of the project and provides insight into which inputs the project economics are most sensitive. It also allows for comparing different variables that can be traded off.

For instance, questions such as "How much incremental investment is required to enhance production by X barrels?" or "Is it worth spending more in Capex if we can cut Opex by X %?" can be effectively answered. Additionally, it considers scenarios like "What happens to the project NPV if costs rise by X % or X $?"

By varying a single input variable (such as price, Opex, Capex, etc.) at a time, we can gain a clearer understanding of its effect on selected economic indicators like NPV, IRR, and NCF. The impact of higher or lower Capex, price fluctuations, production changes, and tax rates can be assessed.

Although varying just one input variable at a time may not fully reflect real-life scenarios where several inputs interact and seldom vary in isolation—such as a crash in oil prices affecting both Capex and Opex—this method allows for easier isolation of their effects for further scrutiny. For example, if a project's NPV is highly sensitive to a delay in first oil, methods to accelerate the project and associated costs and resources should be examined.

Typical parameters varied in sensitivity analysis include: 

- Capex 

- Opex (fixed and/or variable) 

- Production 

- Project delay leading to delayed first production 

- Tax rates 

- Profit Oil splits and Cost Oil limit 

- Discount rate 

- Crude price 

- Inflation and escalation

Negotiable fiscal terms can also be analyzed, with cases run to assess their impact on specific indicators of interest such as NPV and IRR, typically performed during bidding or when negotiating new licenses.

In sensitivity analysis, changes in the target value relative to the original or base value are examined. First, a case is run with standard inputs termed as the base case. Then, a specific variable is modified, and the change in the target variable's value (economic indicators such as NPV, IRR, etc.) is noted. The results of the sensitivity analysis can be presented both in grid form and as charts and graphs.

One of the commonly used graphs in the energy sector is called the Spider Diagram. This graphical representation is essential for cash flow analysis as it plots the change in NPV against the percentage change in a variable parameter. The chart's name, Spider Diagram, is derived from its shape.  

On the Y axis of the Spider Diagram, NPV is usually plotted. The X axis represents variations in inputs. The origin indicates the base NPV with the base inputs. In the figure above, for example, an increase in price or production results in an increase in NPV (the red and blue lines show a positive slope, indicating that NPV is positively related to changes in price and production).  

The Spider Diagram is particularly useful for optimizing a project within the energy sector. By drawing a horizontal line on the Spider Diagram, it is possible to determine the equivalence between different percentage changes of various inputs. For instance, a 10% increase in Capex may result in the same decrease in NPV as a 10% increase in Opex.

The Spider plot offers a visual representation of the sensitivity of the Net Present Value (NPV) to various input variables, which is crucial in cash flow analysis within the energy sector. The variable with the steepest slope on the diagram indicates the highest sensitivity of NPV. Consequently, we can rank the input variables based on their relative impact on the project's indicator or value.

Additionally, the Spider diagram facilitates an understanding of trade-offs between variables. For instance, it may be observed that an X% increase in Capex or a Y% increase in fixed Opex results in an equivalent reduction in NPV compared to the baseline. From this, we can infer that Capex could be increased by X% if it leads to a Y% reduction in fixed Opex. Alternatively, the plot might suggest that an X% increase in Capex is justifiable if it corresponds to a Y% increase in reserves.

However, caution must be exercised when drawing conclusions from sensitivity analysis results, as such analyses often overlook the interdependencies among input variables and the output variable.

In addition to the Spider Diagram, there is another chart frequently used by economists and planners in cash flow analysis, particularly in the energy sector. This chart is known as a Tornado chart. A sample Tornado Chart is shown below. Unlike a Spider chart, a Tornado chart displays the variance in the output indicator for a specific percentage change in the input values, rather than for all percentage changes.

For instance, the Tornado chart below illustrates the change in NPV for a +/- 10% change in various input categories. The dark red bar indicates the change in NPV if the variable increases by 10%. The blue bar represents the change in NPV if the input variable decreases by 10%. The red line signifies the base NPV.

Unlike sensitivity analysis, cash flow analysis through Scenario Analysis creates multiple cases by varying several variables simultaneously based on different production, cost, price, and fiscal assumptions in the energy sector. 

For example, we may have a “Mid” case with optimistic production and cost profiles, a “Low” case with lower production and higher costs, and a “High” case with higher production and lower costs. Each scenario combines various sensitive factors into one case.

These analyses ensure that the company manages its assets strategically in the energy sector, remaining resilient in unfavorable scenarios and benefiting from favorable ones.

In some cases, only the key variables that drive the value of a project in the energy sector are analyzed to determine their impact on project economics. Generally, there are two primary drivers of value: oil price and reserve size. To identify the minimum oil price and minimum field size required for a given fiscal contract, a cash flow analysis is conducted. This analysis determines the lowest oil price at which the project remains profitable. Similarly, identifying the threshold field size reveals the minimum reserve size necessary for the project to proceed to development.