Evaluation of Real Options Valuation of Petroleum using Deep Learning Algorithms based on Crude Oil Futures Information with Dual Information

The valuation of petroleum reserves tends to be undervalued in an uncertain market. The most common method used in the valuation of natural resources is Discounted Cash Flow (DCF), also known as Net Present Value (NPV). However, this method does not consider several important aspects such as flexibility management and uncertainty value. The purpose of this study is to conduct a valuation of petroleum reserves using methods in which the aspects of flexibility management and uncertainty value are considered. The Real Option Valuation (ROV) method is calculated by analogizing it to a Financial Option. There are three types of data used in the valuation, namely: the oil price obtained from futures contracts listed by New York Mercantile Exchange (NYMEX) and Intercontinental Exchange (ICE Futures Exchange), the long-term risk-free interest rate, and the number of oil production and reserves. Artificial Intelligence Algorithm was then used to evaluate the characteristics of oil price movements based on the information retrieved from NYMEX. The ROV method was found to be suitable for the valuation of petroleum reserves. The method could accommodate flexibility management and uncertainty value present in the petroleum reserve market. A deep learning machine was also shown to be an effective tool for predicting futures contracts.