A Markov-switching dynamic regression analysis of the asymmetries related to the determinants of US crude oil production between 1982 and 2019

Petroleum Science ◽

10.1007/s12182-021-00549-y ◽

2021 ◽

Author(s):

Serge Djoudji Temkeng ◽

Achille Dargaud Fofack

Keyword(s):

Crude Oil ◽

Markov Switching ◽

Oil Production ◽

The Other ◽

Shale Oil ◽

Relative Importance ◽

Crude Oil Production ◽

The Usa ◽

Switching Dynamic ◽

The Relationship

AbstractThe structural changes brought about by shale oil revolution have inspired this paper of which the aim is to analyze the potential asymmetries related to the determinants of crude oil production in the USA. Thus, using a Markov-switching dynamic regression model in which parameters change when oil production moves from one regime to the other, it is found that for both oil production and oil relative importance, the regime that was dominant during the 1980s and the early 1990s when oil production in the USA was substantially high is the same regime that has once again become dominant in the decade corresponding to the shale oil revolution. Furthermore, the study reveals the existence of asymmetries in the relationship between US crude oil production and both manufacturing production and the consumer price index. Asymmetries are also found in the relationship between the relative importance US crude oil and manufacturing production. Finally, it is found that the intercept and the variance parameter also vary from one regime to the other, thus justifying the use of regime-dependent models.

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Application of Machine Learning in Predicting Crude Oil Production Volume

10.2118/207079-ms ◽

2021 ◽

Author(s):

Okechukwu Prince Innocent

Keyword(s):

Machine Learning ◽

Crude Oil ◽

Predictor Variable ◽

Oil Production ◽

Water Volume ◽

Production Volume ◽

Affecting Factors ◽

Factors Affecting ◽

Crude Oil Production ◽

The Relationship

Abstract The production of oil is of great and immense significance as a source of energy worldwide. The major factors affecting the production volume of oil is classified into two groups namely the geological and the human factor. Each group comprises of factors affecting oilfield production volume. The challenge in this project is to find the variable for the crude oil production volume in an oilfield because there are numerous factors affecting the crude oil production volume in an oilfield. The objective of this paper is to provide a more accurate and efficient solution on how to predict the oil production volume. Furthermore, Machine Learning algorithm called Multiple Linear Regression was developed using Python programming Language to predict the production volume of oil in an oilfield. The model was developed and fitted to train and test the factors that affect and influence the oil production volume. After a several studies have been made, the affecting factors were provided from the oilfield which would be trained and tested in order to model the relationship between predictor variable and response variable which are the significant affecting factors and the oil production volume respectively. The predictor variables are the startup number of wells, the recovery percent of previous year, the injected water volume of previous year and the oil moisture content of previous year. The predictor variable is the oil production volume. Moreover, the model was found to possess greater utility in predicting the production volume of oil as it yielded an oil production volume output with an accuracy of 98 percent. The relationship between oil production volume and the affecting factors was observed and drawn to a perfect conclusion. This model can be of immense value in the oil and gas industry if implemented because of its ability to predict oilfield output more accurately. It is an invaluable and very efficient model for the oilfield manager and oil production manager.

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Mediating Effect of the Governance Indicators in the Relationship between Natural Resources Abundance and Economic Growth: Empirical Evidence from the

International Conference on Eurasian Economies 2014 ◽

10.36880/c05.00950 ◽

2014 ◽

Author(s):

Harun Bal ◽

Mehmet Demiral ◽

Emrah Eray Akça

Keyword(s):

Natural Resources ◽

Crude Oil ◽

Oil Production ◽

Gdp Per Capita ◽

Panel Regression ◽

Governance Indicators ◽

Crude Oil Production ◽

Per Capita ◽

The Relationship ◽

Control Of Corruption

This study purposes to identify the relationship between gross domestic product (GDP) and natural resources abundance, focusing on the mediator roles of governance indicators for selected 21 MENA and Caspian countries. Governance indicators used in the study are World Bank’s six global governance indicators. Annual panel data for the period of 1996-2012 are used. In this context, the study estimates the impact of crude oil production per capita (independent variable) on GDP per capita (dependent variable) at first, and then hierarchical panel regression analyses are conducted to determine the mediator variable roles of the governance indicators in this relationship. Sobel test is also applied to confirm whether the mediation effect is significant. Results from the pairwise panel regression analyses reveal that crude oil production per capita is negatively associated with all worldwide governance indicators, mostly with control of corruption, voice and accountability and regulatory quality. The progressive improvements of all dimensions of governance indicators, especially control of corruption, rule of law and government effectiveness, seem to promote GDP per capita. Results from the hierarchical regression analysis demonstrate that governance indicators play an important role as a partial mediator in the relationships crude oil production and GDP per capita. This evidence supports that weak governance indicators tend to hinder natural resources abundance to contribute economic growth. Overall findings highlight the increasing importance of policies intending to reduce corruption and violence, together with stimulating legitimacy, transparency and institutional quality for the countries investigated.

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إنتاج النفط الخام و الصخري و أثر التقنيات الحديثة في زيادة الاحتياطيات النفطية = Crude Oil Production and the Role of Technology in Increasing Reserves and Production of Shale Oil

Alandalus Journal for Applied Sciences ◽

10.12816/0030409 ◽

2016 ◽

Vol 11 (5) ◽

pp. 38-55

Author(s):

ليبيا عبود صالح با حويرث ◽

سالم مبارك صالح بن قديم

Keyword(s):

Crude Oil ◽

Oil Production ◽

Shale Oil ◽

Crude Oil Production

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The Comparative Advantage of Crude Oil in the Top 10 Oil-Producing Countries

International Conference on Eurasian Economies 2019 ◽

10.36880/c11.02310 ◽

2019 ◽

Author(s):

Osama Elsalih ◽

Kamil Sertoğlu ◽

Mustafa Besim ◽

Abdelhakim Embaya

Keyword(s):

Crude Oil ◽

Comparative Advantage ◽

Oil Production ◽

Panel Estimation ◽

Crude Oil Price ◽

Panel Ardl ◽

Long Run ◽

Crude Oil Production ◽

The Usa ◽

Short Run

This paper investigates the comparative advantage of crude oil in the top 10 oil-producing countries through computing the Normalized Revealed Comparative Advantage (NRCA) index and further examines the determinants of this advantage using panel estimation technique. The results of the NRCA index showed that during the study period of 27 years (1990-2016) not all the top10 oil-producing countries have a comparative advantage in crude oil production. Countries like Iran, Iraq, Kuwait, Russia, Saudi, and UAE are found to have a comparative advantage in producing crude oil, while countries like Brazil, China, and the USA have no comparative advantage in producing crude oil. For Canada, its comparative advantage is only revealed just between 2006 and 2016. The result of the Panel ARDL suggested that in the long run, crude oil price (COP) and daily average of crude oil production (DAP) are found to be positive and significantly related to NRCA, whereas proven reserve (PR) and domestic demand for oil (DDO) are negative and significantly related to NRCA. In the short run, COP, ADP, and DDO have the same effect as in the long run and significantly related to NRCA, while PR is statistically insignificant. Finally, a bidirectional Granger-causality is detected between the variables except for the PR and NRCA where a unidirectional causality runs from PR to NRCA.

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