A model that uses the Time Series Batch Fit feature to project fuel prices in a simulation of the next year's fuel costs.
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A model that uses the Time Series Batch Fit feature to project fuel prices in a simulation of the next year's fuel costs.
It is important in the oil industry to estimate a schedule of capital expenditures and production, and to see how this schedule depends on the anticipated volumes of oil recovery. Here you will find two examples models to do this:
1. A deterministic model to get started.
2. A basic @RISK model with uncertainty about the size of the oil field.
Click here to see a video of this example.
The oil and gas industry needs to make forecasts of production, revenues, and expenses for a multi-year period in the face of much uncertainty. Find below three examples to deal with this type of problem using @RISK:
1. A deterministic model to get started.
2. A basic @RISK model with a number of uncertainties.
3. A version using the RiskCollect function to help with a sensitivity analysis of the basic @RISK model.
A model that illustrates the Time Series Fit feature can be used to generate future forecasts.
A set of examples to illustrate a volumetric reserves calculation in the oil industry:
1. A deterministic model to get started.
2. A basic @RISK model where the output, a product of three uncertain quantities, is shown to be lognormally distributed.
3. A version that uses several potential distributions of uncertain inputs to compare their effects on the output distribution.
4. A version where the amount of methane is the product of five uncertain quantities.
5. This model is similar to other volumetric analyses in this series, but the context now is the Austin Chalk horizontal well, as reported in a Journal of Petroleum Technology article. There are now five uncertain inputs that are multiplied or divided to obtain the Reserves output.
In many circumstances one wishes to calculate the aggregate impact of many possible yes/no type events. For example, it is often important to answer questions such as "What is the loss amount that will not be exceeded in 95% of cases?" Simulation is usually required to answer such questions. In this model, the "yes/no" events are modeled using Binomial distributions. The results profile shows a multi-peaked distribution, which is typical when there are discrete-type inputs.
A set of basic @RISK models for modeling events:
1. Any (or all) events could occur.
2. A version illustrating the RiskMakeInput function for use in sensitivity analysis.
3. A version where dependent events can occur (or not occur) sequentially in time.
A model which illustrates how the bullwhip effect can occur with a single retailer and supplier and a second model that illustrates the bullwhip effect when there are multiple tiers of suppliers.
This model of a company's multiple projects over a 12-month horizon. Each project is planned to start in a given month, and from that month on, it has anticipated costs, some of which are known (or 0) and some of which are uncertain. There are possible random delays (or, for a few projects, possible earlier starting months), which shift the cost schedule to the right (or the left). In addition, each project has a 5% chance of failing in any month after its actual starting month. If it fails in a given month, that month's costs plus any remaining months' costs are not incurred._x000D_
A model for determining whether the total of uncertain costs meets a budget
The World Cup features 32 international soccer teams that are allocated into eight groups of four. Within each group, all four teams play against each other. The top two teams from each group advance to a group of 16 teams. A win at this level accounts for 3 points, a defeat for 0 points, and a draw accounts for only one point to both teams. An astounding number of results are possible at this stage. Once the group is winnowed down to 16 teams, a bracket-style tournament ensues. This simulation details how teams of high, intermediate, and low ranking will fare in in the 2014 World Cup tournament using @RISK and PrecisionTree. Read more about this model here.
The RiskSimtable feature can be used to run multiple simulations to test the sensitivity of the model, for example to changes in the parameters of a distribution. This model shows how the RiskSimtable feature is used to test the sensitivity of the distribution of profit to changes in the standard deviation of the revenues.
