Crystal Ball 11.1.1 Essentials for the Oil and Gas Industry

On the first day of class, you become familiar with Crystal Ball and Monte Carlo simulation. You examine an oil and gas model, which is used throughout the course. Next, you learn how to determine key drivers in the oil and gas model by creating tornado and spider charts. You then correlate assumptions in the model. Lastly, you define forecasts and run simulations to view the impact on factors such as NPV (Net Present Value).

On the second day of class, you start by creating a variety of reports containing simulation results. Next, you expand your tools set by using Predictor to create time-series forecasts and OptQuest to optimize forecasts. Lastly, you learn to account for uncertainty and imperfect data in a model by applying Baye's theorem. Exercises and case studies help you practice the skills taught.

Learn To:

• Identify factors in models that drive success
• Run simulations with Crystal Ball
• Account for uncertainty and imperfect data in oil and gas models
• Optimize forecasts with OptQuest
• Identify factors in models that drive success
• Run simulations with Crystal Ball

Audience

• Business Analysts
• Technical Consultant

Course Topics Getting Started with Crystal Ball

• About Models
• About Simulations
• About Crystal Ball
• Process for Developing Simulation Models with Crystal Ball
• About Monte Carlo Simulation
• Starting Crystal Ball
• Crystal Ball Menu and Toolbar Options
• Displaying Crystal Ball Comments

Determining Assumptions with Tornado and Spider Charts

• Considering Assumptions
• About Tornado and Spider Charts
• Creating Tornado and Spider Charts
• Examining Tornado Charts
• Examining Spider Charts

Probability Distribution Concepts

• About Probability Distributions
• Basic Distribution Statistics
• Commonly Used Crystal Ball Distributions

Defining Assumptions Based on Expert Opinion

• About Crystal Ball Assumptions
• Defining Assumptions
• Copying, Pasting, and Clearing Crystal Ball Data
• Defining Assumptions by Using Crystal Ball Functions

Defining Assumptions Based on Historical Data

• Fitting Probability Distributions
• Batch Fitting Probability Distributions
• Referencing Distribution Fit Results in Models

Defining Forecasts and Running Simulations

• Defining Forecasts
• Running Simulations
• Analyzing Forecast Results
• Saving Forecast Results

Setting Run Preferences and Configuring Precision Control

• Setting Run Preferences for Simulations
• Configuring Precision Control for Simulations

Correlating Assumptions in Models

• About Correlating Assumptions
• Defining Correlations by Modifying Assumption Definitions
• Defining Correlations by Creating Correlation Matrices

Creating Reports

• About Crystal Ball Reports
• Generating Reports

Creating Time-Series Forecasts with Crystal Ball Predictor

• About Time-Series Forecasts
• About Crystal Ball Predictor
• Starting Predictor
• Specifying Input Data for Predictor
• Specifying Data Attributes in Predictor
• Selecting Time-Series Methods in Predictor
• Configuring Results in Predictor
• Analyzing Predictor Results

Optimizing Forecasts with Crystal Ball OptQuest

• About Optimization
• About Crystal Ball OptQuest
• Configuring and Running Optimizations with OptQuest
• Analyzing Optimization Results

Applying Baye’s Theorem

• About Baye’s Theorem
• Example Use Case 1: Calculating the Probability of Disease
• Example Use Case 2: Deciding to Hire an Oil Consultant

Course Objectives

• Describe Crystal Ball and its benefits
• Determine assumptions by creating tornado and spider charts
• Define and correlate model assumptions based on expert opinion and historical data
• Define forecasts and optimize them with Crystal Ball OptQuest
• Create time-series forecasts with Crystal Ball Predictor
• Run Monte Carlo simulations with Crystal Ball
• Apply Baye’s Theorem to oil and gas models