Project Management in the Oil and Gas Industry

Cumula
tiv
e net cash 
flow
Figure 2.4 Payout method.

---

50 Project Management in the Oil and Gas Industry
value of the initial investment. But, in general, given a general idea of the 
itinerary of the project economically and, as mentioned in some invest-
ments, may be a time period of recovery of funds that was paid for the 
cost of the project and is the most important factor affecting the decision-
making regardless of the final profit.
In spite of the many advantages of the payout method, alone it is not a 
complete measure of the value of money for the following reasons:
• It does not indicate profit following payout.
• It does not measure total profit.
• Time value of money is not formally included.
• It varies depending on different types and magnitudes of 
investment.
2.3 Economic Risk Assessment
2.3.1 Probability 
Theory 
To enter into the theory of probability, there is some statistical informa-
tion that is important and necessary to understand the probability theory 
and the probability distribution. To illustrate the statistical concepts, we 
will clarify them through a numerical analysis for test results for crushing 
samples of cylinder concrete to measure its strength.
The main statistical parameters will be the following:
• Arithmetic 
average
• Standard 
deviation
• Coefficient of variation
Arithmetic average is the average value of a set of results and is repre-
sented in the following equation:
X
X
X
X
n
n
1
2
,
(2.9)
where n is the number of results and X is read of each test result.
As a practical example of the statistical parameter, assume that we have 
two groups of concrete mixture from different ready mix suppliers. The 
first group has a concrete compressive strength after 28 days for three 
samples which are 310 kg/cm
2
, 300 kg/cm
2
, and 290 kg/cm
2
. When we cal-
culate the arithmetic average using Equation 2.9, the arithmetic mean of 
these readings is 300 kg/cm
2
. 

---

Project Economic Analysis 51
The second group has a test result for cube compressive strength after 
28 days under the same conditions for the first group. The test results are 
400 kg/cm
2
, 300 kg/cm
2
, and 200 kg/cm
2
. When calculating the arithmetic 
mean, we find that it is equal to 300 kg/cm
2
. 
Because the two groups have the same value of the arithmetic mean, does 
that mean that the same mixing has the same quality? Will you accept the 
two mixing? We find that this is unacceptable by engineering standards, but 
when we consider that the mean of the two groups are the same, one should 
choose another criteria by which to compare the results as we cannot accept 
the second group based on our judgment, which will not support us in court.
Standard deviation is a statistical factor that reflects near or far the 
reading results. From the arithmetic mean end, it is represented in the 
following equation:
S
X
X
X
X
X
X
n
n
(
)
(
)
(
)
.
1
2
1
2
2
 
(2.10)
The standard deviation for the first group sample is
S
(
)
(
)
(
)
.
310 300
300 300
290 300
3
2
2
2
Mixing one, S = 8.16 kg/cm
2
.
The standard deviation for the seconding group sample is
S
(
)
(
)
(
)
400 300
300 300
200 300
3
2
2
2
.
Mixing two, S = 81.6 kg/cm
2
One can find that the standard deviation in the second group has a higher 
value than the first group. So the distribution of test data results is far away 
from the arithmetic mean rather than group one. From Equation 2.10, one 
can find that the ideal case is when S equals 0.
We note that the standard deviation has units, as seen in the previous 
example. Therefore, standard deviation can be used to compare between 
the two groups of data as in the previous example where the two groups 
give the value of 300 kg/cm
2 
after 28 days. On the other hand, in the case of 
the comparison between the two different mixes of concrete, for instance, 
there is a resistance of 300 kg/cm
2
in one concrete and 500 kg/cm
2 
in the 
second. In that case, the standard deviation is of no value. Therefore, we 
resort to the coefficient of variation.

---

52 Project Management in the Oil and Gas Industry
The coefficient of variation is the true measure of quality control, as it 
determines the proportion after the readings for the average arithmetic 
profile. This factor has no units and is, therefore, used to determine the 
degree of product quality.