Project Management in the Oil and Gas Industry

2.5 Monte-Carlo Simulation Technique
Simulation is the process of replicating the real world based on a set of 
assumptions and conceived models of reality.
The Monte-Carlo simulation is required for problems involving random 
variables with known (assumed) probability distributions.
This method of simulation was started as an idea by Enrico Fermi in the 
1930s. Stanisław Ulam, in 1946, first had the idea and later contacted John 
von Neumann to work on it and he started to use this simulation in a secret 
project. After World War II, this simulation was published in many papers 
as a simulation technique.
The Monte-Carlo simulation technique is frequently used to verify results 
of analytical methods. Rushedi (1984) used the Monte-Carlo simulation 
approach to obtain the first two statistical moments (mean, value, and stan-
dard deviation) of the failure mode expression of brittle and ductile frames 
and, consequently, a system safety index. Ayyub and Halder (1985) sug-
gested advanced simulation methods for the estimation of system reliability.
Fellow et al. (1993) used the Monte-Carlo simulation program (M-Star) 
to understand the load and resistance factor design (LRFD). Nikolaos (1995) 
used the Monte-Carlo simulation to study the reliability of reinforced con-
crete members strengthened with carbon-fiber-reinforced plastic. 
This method depends on simulating the case of study by its parameters 
and each parameter will be represented by its probabilistic distribution, 
mean, and standard deviation.
The simulation will have two parameters: a variable and uncertainty. For 
example, the length of the men in a country is a variable as it represents 
a normal distribution. But managing a project by time and cost is usu-
ally uncertain and is represented by a triangle distribution by knowing the 
minimum, maximum, and most likely. 
So, the risk assessment for the cost estimate and the risk assessment for 
the project time through the PERT method also uses Monte-Carlo simu-
lation. If you want to predict the cost of a large project, you should break 
it into parts, define the cost of each part, and add them together. As time 
management is discussed in Chapter 4, the project time schedule plan is 
broken into small activities and, based on the PERT method, each activity 
has a three values as we showed before.
Each random variable is described by its statistical parameters: mean, 
standard deviation, and type of distribution. The distribution type of the 
random variable is chosen among the different probability distributions 
provided by the program.

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76 Project Management in the Oil and Gas Industry
Figure 2.23 presents an overview of the Monte-Carlo simulation technique 
as the input data for the variables will be a probabilistic distribution and, after 
simulation, will obtain the outputs by the graphs and statistical data.
The simulation model contains all the input data of the deterministic 
parameters, the random variables, and the equations. The model will run 
for at least 10,000 trials, as in the following flowchart. The Monte-Carlo 
simulation technique is simple and is presented in Figure 2.24. 
Inputs
Outputs
Fixed parameter
Statistical data
for analysis
Simulation variables
Simulation
model