The Role of Success Rate, Discovery, Appraisal Spending, and Transitioning Region on Exploration Drilling of Oil and Gas in Indonesia in 2004–2015

Abstract

Petroleum exploration decision remains a subject of petroleum and economic studies for decades. Most of the studies discuss the investment decision by focusing on either a technical or economic perspective. In reality, economic, geological, and environmental factors are expected to determine the way investors make a decision. This study aims to increase the understanding of best practices in decision-making by scrutinizing integrative perspectives applying panel data of 32 basins in Indonesia in 2004–2015. This study provides several contributions to optimize decisions on wells drilled. First, this study derives an empirical model examining several plausible factors of economy, geology, and environment. Second, the findings demonstrate how to empirically examine which factors significantly determine wells drilled by companies. The last contribution is to empirically support a technical transformation from Western to Eastern exploration due to the natural depletion of oil fields.

Predicting the Oil Investment Decision through Data Mining

“…The exploration and production have been intensified on the western basins of Indonesia, which has a less enormous potential of undiscovered fields, most likely made of numerous small to medium size petroleum. On the contrary, the eastern basins are a relatively high-risk frontier which is generally under-explored and under-exploited with half of the basins being undrilled [9], [11]. The findings cover spending per well by exploration companies in a specific basin.…”

“…In Indonesia, one of the empirical analyses using geological basins conducted by [7] using panel datasets comprising geological and economic variables. The results discussed determinant factors affecting Indonesia's oil exploration in 2004-2013 that open up a way to uncover the role of geological and economic features [7], [9]. Despite that earlier research already offered and empirically tested the model, some economic and institutional variables are overlooked e.g., appraisal spending, national vs. international oil operatorship.…”

“…Over the last 20 years, geological and economic perspectives were applied to study exploration economics comprehensively [5], [6]. Despite the disaggregated studies leading to better insights, most common studies are still developed employing aggregate data for groups of countries, countries, or regions [3], [7], [9].…”

“…The western relatively has a fewer large potential of undiscovered fields which is most likely made of numerous small to medium size petroleum. On the contrary, the eastern basins are relatively high-risk frontier which is generally under-explored and under-exploited with half of the basins being undrilled [9], [11].…”

Predicting Fraudulence Transaction under Data Imbalance using Neural Network (Deep Learning)

“…This has triggered the birth and development of many detection technologies using machine learning in the last decade (Borgne &Bontempi, 2021). On the other hand, the machine can detect quickly and accurately (Asha & Kumar, 2021;Zulfikri et al 2021;Patria, 2021;Patria, 2022).…”