Predicting Oil Production Sites for Planning Road Infrastructure: Trip Generation Using SIR Epidemic Model

Eunsu Lee,Debananda Chakraborty,Melanie Mcdonald

doi:10.3390/infrastructures6020015

Eunsu Lee, Debananda Chakraborty + Show 1 more

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https://doi.org/10.3390/infrastructures6020015

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Journal: Infrastructures	Publication Date: Jan 21, 2021
Citations: 1	License type: CC BY 4.0

Affiliation: New Jersey City University

Abstract

Drilling activity produces a significant amount of road traffic through unpaved and paved local roads. Because oil production is an important contributor to the local economy in the state of North Dakota, the state and local transportation agencies make efforts to support local energy logistics through the expansion and good repair and maintenance of transportation infrastructure. As part of this effort, it is important to build new roads and bridges, maintain existing road pavement and non-marked road surface conditions, and improve bridge and other transportation infrastructure. Therefore, the purpose of this study is to review previous oil location prediction models and propose a novel geospatial model to predict drilling locations which have a significant impact on local roads, to verify and provide a better prediction model. Then, this study proposes a SIR (susceptible–infected–recovered) epidemic model to predict oil drilling locations which are traffic generators. The simulation has been done on the historical data from 1980 to 2015. The study found that the best fit parameters of β (contact rate) and μ (recovery rate) were estimated by using a dataset of historical oil wells. The study found that the SIR epidemic model can be applied to predict the locations of oil wells. The proposed model can be used to predict other drilling locations and can assist with traffic, road conditions, and other related issues, which is a much needed predictive model that is key in transportation planning and pavement design and maintenance.

Highlights

IntroductionPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
Received: December 2020Accepted: January 2021Published: 21 January 2021Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.Due to the increase in oil prices and rapid advancements in drilling technologies known as horizontal drilling and hydraulic fracturing, the number of oil wells has increased since the mid-2000s [1,2,3]
To our knowledge, based on an intensive literature review, we found that there is a need for a knowledge, based on an intensive literature review, we found that there is a need for a novel spatial model for predicting horizontal drilling locations over a long-term period to novel spatial model for predicting horizontal drilling locations over a long-term period to support transportation planning and pavement design

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Due to the increase in oil prices and rapid advancements in drilling technologies known as horizontal drilling and hydraulic fracturing (i.e., fracking), the number of oil wells has increased since the mid-2000s [1,2,3]. The most productive oil and natural gas production regions are Bakken, Niobrara, Permian, Eagle Ford, Haynesville, Utica, and Marcellus shale formations across the United States [4]. The technologies require a significant amount of water, fracking sand, and other additives [1]. The median duration of drilling time for horizontal drilling has significantly increased, from approximately one month in 1977 up to three to four months in 2013, from permit approval to well completion, in Texas [1]

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