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Modeling and Predicting the Cost of Oil-Onshore Facilities Based on Oil Prices: A Case Study in Indonesia

Proceedings Title : Proc. Indon. Petrol. Assoc., 48th Ann. Conv., 2024

Russia's invasion of Ukraine, which started in February 2022, had a significant impact on the global energy market and world energy security. The long-term effects will be felt worldwide, resulting in price volatility, supply shortages, and economic uncertainty.

As of December 2021, the Indonesian government has approved approximately 559 projects, with more than 200 of them being onshore oil projects. These projects are expected to contribute to the goal of producing 1 million barrels of oil per day by 2030.

The objective of this paper is to estimate the cost of oil-onshore facilities based on oil prices by utilizing statistical data from 244 onshore oil projects and oil prices from 2003–2022. Additionally, it aims to assist contractors in minimizing the risk associated with oil and gas investments and accurately calculating and analyzing project economics.

The steps undertaken in this paper include collecting the cost data of oil-onshore facilities from 2003 to 2022, gathering historical oil price data from 2003 to 2022, calculating the deviation between the cost of oil-onshore facilities (“cost of surface facilities”) and oil prices, and developing a formula to estimate the unit cost of facilities at specific oil prices. These steps are based on statistical data obtained from 244 onshore oil projects in Indonesia.

Based on the evaluation and statistical analysis of 244 onshore oil projects in Indonesia, the formula (equation) that demonstrates the correlation between the unit cost of surface facilities (US$/bbl.) and oil prices (US$/bbl.) is 𝒛 = 0.0073𝒙 + 2.0961. In this equation, z represents the unit cost of surface facilities and x represents the oil price. By using this equation and assuming the oil price is 100 US$/bbl., the estimated unit cost of surface facilities is 2.83 US$/bbl.

This paper could potentially serve as a reference for minimizing risk and creating a portfolio for project economics in the oil and gas industry.

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