Predicting the Oil Investment Decision through Data Mining Empirical Evidence in Indonesia Oil Exploration Sector
DOI:
https://doi.org/10.32734/jocai.v6.i1-7539Keywords:
oil, gas, exploration, machine learning, decision, investment, data miningAbstract
Petroleum investment decision remains subject to economic and financial research for decades. Due to capital intensive and higher risk on oil exploration, the investment decision has become more important than ever before. This study aims to shed some light on this issue by conducting four machine learning algorithms to predict the decision applying the dataset from 2007-2019. This study includes the Decision Tree, Random Forest, Naïve Bayes, and Support Vector Machine. A comparative performance analysis is the illustrated using confusion matrix, Cohen’s Kappa value, and the accuracy of each model and Area under the ROC Curve. In this study, a machine learning approach was carried out on the oil exploration data. The findings demonstrate that Naïve Bayes has the most accurate performance for the classification (94.5%), followed by Decision Tree (92.9%), Random Forest (90.9%), and Support Vector Machine (89.6%). In practice, the selected Naïve Bayes model was applied to assess the decision using a new data test. The findings can diminish the subjective blindness and confirmation bias in the investment decision and bring about a reasonable and orderly exploration and development of petroleum reserves.
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Boyce, J. R., & Nøstbakken, L. (2011). Exploration and development of U.S. oil and gas fields, 1955-2002. Journal of Economic Dynamics and Control, 35(6), 891–908. https://doi.org/10.1016/j.jedc.2010.12.010
Greiner, A., Semmler, W., & Mette, T. (1989). An Economic Model of Oil Exploration and Extraction. Computational Economics, 40(4), 387–399. https://doi.org/10.1007/s10614-011-9272-0
Mohn, K., & Misund, B. (2009). Investment and uncertainty in the international oil and gas industry. In Energy Economics (Vol. 31). https://doi.org/10.1016/j.eneco.2008.10.001
Mohn, K., & Osmundsen, P. (2008). Exploration economics in a regulated petroleum province: The case of the Norwegian Continental Shelf. Energy Economics, 30(2), 303–320. https://doi.org/10.1016/j.eneco.2006.10.011
Patria, H., & Adrison, V. (2015). Oil Exploration Economics: Empirical Evidence from Indonesian Geological Basins. Economics and Finance in Indonesia, 61(3), 196. https://doi.org/10.7454/efi.v61i3.514
Satyana, A. H. (2018). Future Petroleum Play Types of Indonesia: Regional Overview. Proceedings, Indonesian Petroleum Association, (May 2017). https://doi.org/10.29118/ipa.50.17.554.g
Yuhua, Z., & Dongkun, L. (2009). Investment optimization in oil and gas plays. Petroleum Exploration and Development, 36(4), 535–540. https://doi.org/10.1016/S1876-3804(09)60145-2
Woodmac Research and SKK Migas, Oil Field Exploration Report, 2009, unpublished.
Woodmac Research and SKK Migas, Oil Field Exploration Report, 2010, unpublished.
Woodmac Research and SKK Migas, Oil Field Exploration Report, 2011, unpublished.
Woodmac Research and SKK Migas, Oil Field Exploration Report, 2012, unpublished.
Woodmac Research and SKK Migas, Oil Field Exploration Report, 2013, unpublished.
Woodmac Research and SKK Migas, Oil Field Exploration Report, 2014, unpublished.
Woodmac Research and SKK Migas, Oil Field Exploration Report, 2015, unpublished.
Woodmac Research and SKK Migas, Oil Field Exploration Report, 2016, unpublished.
Woodmac Research and SKK Migas, Oil Field Exploration Report, 2017, unpublished.
Woodmac Research and SKK Migas, Oil Field Exploration Report, 2018, unpublished.
Woodmac Research and SKK Migas, Oil Field Exploration Report, 2019, unpublished.
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