relationship between price and rate of production of crude

International Journal of Scientific & Engineering Research Volume 11, Issue 3, March-2020 322 ISSN 2229-5518

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Relationship Between Price and Rate of Production of Crude Oil in Nigeria.

Ogunyemi Ephraim Oluwole, Ajibade Bright. Abstract — It is expected that significant relationship should exist between price and production of crude oil. However, production may not

necessarily determine price or production may not have significant relationship with price due to some factors. Irrespective of the relationship

between the key factors, production can be studied independently as well as fluctuation in price of crude oil to determine the future occurrence. In

this study, the effect of production was investigated on price of crude oil using scientific approach. Time series data were collected on two

variables from 1999 to 2019. Charts were used to aid visual impression and numerical analyses were carried out for decision making. Correlation,

Regression and Trend analysis were used in the study to model the possible relationship. It was discovered that the two variables; price and

production rate are independent, therefore trend analysis was used to determine the future value. It was observed that the price of crude oil may

be lower than the present value, all things been equal. The rate of production of crude oil is quite promising as future values are predicted to be

higher than the present value.

Keywords— Correlation, Mean Square Error, Nonlinear Model, Price, Production, P-value, Regression, Trend.

—————————— ——————————

1.1 Introduction

According to Organization for Economic Co-operation and

Development (OECD) [4], Crude oil production is defined

as the quantities of oil extracted from the reservoir after the

removal of inert matter or impurities [3] (Nwanze, 2007). It

includes crude oil, natural gas liquids (NGLs) and

additives. This indicator is measured in thousand ton of oil

equivalent. Crude oil is a complex mixture of naturally

occurring hydrocarbon with impurities, of colour ranging

from yellow to black and of variable density and viscosity.

NGLs are the liquid or liquefied hydrocarbons produced in

the process of purification and stabilization of natural gas.

With oil on high demand as global commodity, comes the

possibility that major fluctuations in price can have a

significant economic impact. The two primary factors that

impact the price of oil are: supply and demand, market

sentiment.The concept of supply and demand is fairly

straightforward. As demand increases the price should go

up. As demand decreases the price should go down.Not

quite. The price of oil is actually set in the oil future market.

An oil future contract is a binding agreement that gives one

the right to purchase oil by the barrel at a predefined price

on a predefined date in the future. Under a futures contract,

both the buyer and the seller are obligated to fulfill their

side of the transaction on the specified date.

Basic supply and demand theory states that the more of a

product is produced, the more cheaply it should sell, all

things being equal. The reason more was produced in the

first place is because it became more economically efficient

to do so.

Despite Nigeria’s huge oil wealth, Nigeria has remained

one of the poorest in the world. In addition, the insurgency

in the North, Niger-Delta Avengers in the South,

kidnappings for ransomed and the rampaging Fulani

herdsmen have all compounded Nigeria’s problem in no

small measure. The problems with Nigerian economy have

been traced to failure of successive governments to use oil

revenue and excess crude oil income effectively in the

development of other sectors of the economy[10] (Alley et

al, 2014).

The economy has been bedeviled by sustained

underdevelopment evidenced by poor human

developmental and economic indices including poor

income distribution, militancy and oil violence in the Niger

Delta, endemic corruption, unemployment, relative poverty

[11] (Nwezeaku, 2010). Nigeria’s extreme reliance on the

crude oil market has triggered structural difficulties for the

economy, as earnings from crude oil fluctuate along with

market trends [1] (Aigbedion and Iyayi, 2007). Crude oil

became the dominant resource in the mid-1970s.

On-shore oil exploration accounts for about 65% of total

production, which is located mainly in the swampy areas of

the Niger Delta, while the remaining 35% represents

offshore production and involves drilling for oil in the deep

waters of the continental shelf. The massive increase in oil

revenue as an aftermath of the Middle - East war of 1973

created unprecedented, unexpected and unplanned wealth

for Nigeria, and then began the dramatic shift of policies

from a holistic approach to benchmarking them against the

State of the oil sector [6] (Oladipo and Fabayo, 2012).

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In 2000, oil and gas exports accounted for more than 98% of

export earnings and about 83% of federal government

revenue. Nigeria's proven oil reserves are estimated to be

35 billion barrels; natural gas reserves are well over 100

trillion cuft [2] (Gbadebo, 2008). Nigeria is a member of the

Organization of Petroleum Exporting Countries (OPEC),

and in mid 2001, its crude oil production was averaging

around 2.2 million barrels per day [2] (Gbadebo, 2008).

Due to the contribution of crude oil to the GDP of the

country, there is need for proper study of production rate

as well as price of the product in the past, present and

possibly (predict the) future for proper planning.

2.1 Methodology

The following techniques are used in the study; histogram,

scatter plot, correlation analysis, regression analysis, trend

analysis (linear and quadratic models).

Diagrams generally aid visual expression and communicate

easily with the readers especially those with weak

numerical knowledge. Histogram can be used to determine

the fit of the data. It can also be used to determine the

characteristics of the variables of interest.

Histogram with highest bar at the centre suggested

normally distributed data. It can also show positively

skewed or negatively skewed data.

Scatter plot can be used to determine nature of relationship

between variables and can be used to determine

appropriate model for variables. Scatter plot can show

positive, negative or no relationship between variables. It

has the strength to show linearity between variables. It this

study, it was used to determine the best model for trend

analysis.

Correlation analysis can be parametric or non-parametric,

depending on the type of variables. For two independent or

two dependent variables, the correlation approach is non-

parametric which can be referred to as Spearman Rank

Correlation. For independent and dependent variables, the

correlation approach is parametric correlation which is

product moment correlation. The value of correlation lies

between negative and positive one. Correlation value of

zero implies spurious correlation between variables. The

higher the correlation value, the stronger the bond between

the variables of interest and the lower the correlation value,

the weaker the bond between the variables. Positive

correlation implies direct relationship between the

variables and negative correlation implies inverse

relationship between the variables.

Regression analysis can be used to determine the

mathematical relationship between or among variables.

Regression analysis that involves two variables;

independent and dependent variable is referred to as

simple regression analysis and regression analysis with

more than one independent variable is referred to as

multiple regressions. In this study, simple regression was

used.

Regression analysis can also be linear or nonlinear which

can be shown using scatter plot. In this study, non-linear

and linear regression was used.

Trend analysis is commonly used in time series analysis.

Time series analysis involves time dependent data;

collection of data over a period of time. The data used for

the study are time dependent as the data were collected

over a specified period of time. Trend analysis can be used

to determine the future occurrence of a variable using time

as independent variable. In this study, both price and

production rate were predicted using trend analysis.

3.1 Data Analysis

Based on the aim of the study, the following charts are

presented;

2400200016001200800400

90

80

70

60

50

40

30

20

10

0

PRODUCTION RATE (Thousands)

Fre

qu

en

cy

Histogram of PRODUCTION RATE (Thousands)

Figure 1: Histogram of production rate of crude oil

From the chart, it can be observed that production of the

crude oil increases as time increases the highest was

recorded at the end of the series. This implies production of

the product experiences gradual increase.

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12010080604020

50

40

30

20

10

0

PRICE($)

Fre

qu

en

cy

Histogram of PRICE($)

Figure 2: Histogram of price of crude oil

The chart shows significant fluctuation in the price of the

product. The pattern can be best explained using quadratic

function due to the shape of the diagram. The fluctuation in

price of the product does not correspond with the increase

in production. Therefore, insignificant relationship between

production and price of crude oil is suspected.

Descriptive Statistics: PRODUCTION RATE

(Thousands), PRICE($)

Variable Mean SE Mean StDevMinimum

Maximum SkewnessKurtosis

PRODUCTION RATE 2277.7 16.2 250.9 200.0 2695.0 -

2.40 18.25

PRICE($) 62.66 1.99 30.93 16.80 132.72 0.42 -

0.95

The descriptive statistics of the variables is as shown above.

The average production of crude oil is 2277.7 with standard

error of 16.2. Highest recorded production is 2695unit. For

the period under consideration, average price of the

product is 62.66unit. The lowest recorded price of the

product is 16.8 and the highest recorded price is 132.72unit.

140120100806040200

3000

2500

2000

1500

1000

500

0

PRICE($)

PR

OD

UC

TIO

N R

ATE (

Th

ou

sa

nd

s)

Scatterplot of PRODUCTION RATE (Thousands) vs PRICE($)

Figure 3: Scatter diagram of the variables

The chart shows irregular pattern which can be interpreted

as insignificant relationship between the variables.

Although, the direction of the diagram indicates positive

relationship but weak based on the cluster of the points.

To confirm the assertion, there is need for further test such

as correlation analysis and regression analysis.

Correlations: PRODUCTION RATE (Thousands),

PRICE($)

Pearson correlation of PRODUCTION RATE (Thousands)

and PRICE($) = 0.287

P-Value = 0.000

The correlation value of 0.287 can be interpreted as weak

positive relationship between the variables. This implies

production of the product has insignificant positive effect

on the price. Since the P-value is less than 0.05, there is need

for regression analysis to ascertain the level of the

relationship using coefficient of determination.

Regression Analysis: PRODUCTION RATE (Thousands)

versus PRICE($)

The regression equation is

PRODUCTION RATE (Thousands) = 2132 + 2.33 PRICE($)

Predictor Coef SECoef T P

Constant 2131.67 35.10 60.73 0.000

PRICE($) 2.3298 0.5025 4.64 0.000

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R-Sq = 8.3% R-Sq(adj) = 7.9%

Analysis of Variance

Source DF SS MS F P

Regression 1 1246441 1246441 21.49 0.000

Residual Error 239 13859188 57988

Total 240 15105629

Regression analysis shows (confirms) positive relationship

between the variables. The rate of change of price with

respect to production is 2.33unit. This implies a unit

increase in price of the product will lead to 2.33unit

increase in production.

Coefficient of determination of the model is 8.3% which

implies price is responsible for only 8.3% of the fluctuation

in the production of crude oil.

Using T-test, the parameters in the model are significant

since the P-value of the parameters are less than 0.05. With

coefficient of determination less than 50%, it is not

advisable to predict production of crude oil using price or

vice versal. Therefore, trend analysis is necessary.

Trend Analysis for PRODUCTION RATE (Thousands)

Data PRODUCTION RATE (Thousands)

Length 241

NMissing 0

Fitted Trend Equation

Yt = 2030.4 + 7.502*t - 0.03390*t**2

Accuracy Measures

MAPE 9.4

MAD 138.8

MSD 38751.0

Trend Analysis Plot for PRODUCTION RATE

(Thousands)

24019214496481

3000

2500

2000

1500

1000

500

0

24019214496481

140

120

100

80

60

40

20

0

PRODUCTION RATE (Thousands)

Index

PRICE($)

Time Series Plot of PRODUCTION RATE (Thousands), PRICE($)

Figure 4: Time Series Plot of the variables

240216192168144120967248241

3000

2500

2000

1500

1000

500

0

Index

PR

OD

UC

TIO

N R

ATE (

Th

ou

sa

nd

s)

MAPE 9.4

MAD 138.8

MSD 38751.0

Accuracy Measures

Actual

Fits

Forecasts

Variable

Trend Analysis Plot for PRODUCTION RATE (Thousands)Quadratic Trend Model

Yt = 2030.4 + 7.502*t - 0.03390*t**2

Figure 5: Trend Analysis of Production of Crude Oil

Trend Analysis for PRODUCTION RATE (Thousands)

Data PRODUCTION RATE (Thousands)

Length 241

NMissing 0


Yt = 2362.7 - 0.702599*t

Accuracy Measures

MAPE 12.3

MAD 192.3

MSD 60289.7

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Forecasts

Period Forecast

242 2192.64

243 2191.93

Trend Analysis Plot for PRODUCTION RATE

(Thousands)

240216192168144120967248241

3000

2500

2000

1500

1000

500

0

Index

PR

OD

UC

TIO

N R

ATE (

Th

ou

sa

nd

s)

MAPE 12.3

MAD 192.3

MSD 60289.7

Accuracy Measures

Actual

Fits

Forecasts

Variable

Trend Analysis Plot for PRODUCTION RATE (Thousands)Linear Trend Model

Yt = 2362.7 - 0.702599*t

Figure 6: Trend Analysis of Production of Crude Oil Using

Linear model

Summary of the trend analysis models for production

Accuracy Measures Quadratic

Linear

MAPE 9.4 12.3

MAD 138.8 192.3

MSD 38751.0 60289.7

Considering the values, it can be observed that accuracy

measures for quadratic model are better than that of linear

model. Therefore, the model that best explain the trend of

production of crude oil is quadratic model. Using the

model, the future values are 1860.36 and 1851.42.

Using the same approach for price of the product, the

output is shown below;

Trend Analysis for PRICE($)

Data PRICE($)

Length 241

NMissing 0


Yt = -3.83 + 1.1675*t - 0.003839*t**2

Accuracy Measures

MAPE 34.149

MAD 17.207

MSD 401.333

Forecasts

Period Forecast

242 53.8867

243 53.1923

244 52.4902

245 51.7805

246 51.0630

247 50.3379

248 49.6051

249 48.8646

250 48.1165

251 47.3606

252 46.5971

253 45.8259

Trend Analysis Plot for PRICE($)

2502252001751501251007550251

140

120

100

80

60

40

20

0

Index

PR

ICE($

)

MAPE 34.149

MAD 17.207

MSD 401.333

Accuracy Measures

Actual

Fits

Forecasts

Variable

Trend Analysis Plot for PRICE($)Quadratic Trend Model

Yt = -3.83 + 1.1675*t - 0.003839*t**2

Figure 7: Trend Analysis of Price of Crude Oil Using

Quadratic model

The prediction of future price of crude oil shows the

possibility having prices lower than the present price. It

shows that the price of the product can be as low as 45unit

considering the present situation and the available data.

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Based on the shape of the diagram, the best model is

quadratic model to capture the fluctuation of the variable.

4.1 Summary and Conclusion

In the location considered, price of crude oil is not a

determinant factor for the volume of crude produced in the

region. The production of crude has experienced significant

increase based on the available data and there is possibility

of more increase. Price of the product highly fluctuates

throughout the period. It was as low as 16unit but later

increased up to 133unit.

In modeling, scatter plot is necessary to determine the best

model that can best explain the variables of interest. Based

on this fact, scatter plot was constructed to determine the

best model for trend analysis of the variables. It was found

that quadratic model best explain the variables compare

with linear model.

Correlation analysis was used to determine the strength

and nature of relationship between the variables which was

found to be positively weak as the value was below 0.5.

Regression analysis also confirms the weakness of the

relationship between price of crude oil and the rate of

production as the value was 8.3%. The results of both

correlation and regression analysis led to further analysis;

trend analysis, to determine the future values of the

variables.

A warning signal was discovered as the prediction of price

of crude oil resulted to a very low figure; 40unit. For a

nation that sole depend on crude oil for its survival, drastic

measures must be taken a boost the GDP of such nation.

Diversification of economy may be needed to strengthen

the economy of the country.

Future value of production of crude oil in the region shows

promising value as the future values are higher than the

present value. This implies more production of crude oil is

expected but this depends on government policies and the

policies of the regulatory bodies.

References

[1] Aigbedion and Iyayi (2007).Diversifying Nigeria‟s

Petroleum Industry. International Journal of Physical

Sciences, 2(10), 263-270

[2] Gbadebo, O. O. (2008). Crude Oil and the Nigerian

Economic Performance.

http://www.ogbus.com/article/crude-oil-and-the-nigerian-

economic-performance

[3] Nwanze, K. O. (2007). The Nigerian Petroleum

Downstream Sector and Product Pricing: Issues and the

Way Forward, Nigerian Economic Summit Group (NESG).

[4] OECD (2008). Organization for Economic Co-Operation

and Development Guidelines for Multinational

Enterprises.www.oecd.org/publishing/corrigenda

[5] OECD (2011).The Effects of Oil Price Hikes on Economic

Activity and Inflation. OECD Economics Department

Policy Notes, 4

[6] Oladipo and Fabayo, (2012).Global Recession, Oil Sector

and Economic Growth in Nigeria. Asian Transactions on

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[7] Tang, W., Wu, L. & Zhang, Z. (2010).Oil price shocks

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[8] Umanhonlen, O. F. & Lawani, I. R. (2015). Effect of

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[9] World Bank. (2011). The Changing Wealth of Nations:

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[10] Alley Ibrahim, Asekomeh Ayodele,Mobolaji Hakeem

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[11] Nwezeaku N.C (2010)The Impact of Public Sector

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