Greenhouse gas emissions from U.S. crude oil pipeline accidents:1968 to 2020

Abstract Crude oil pipelines are considered as the lifelines of energy industry. However, accidents of the pipelines can lead to severe public health and environmental concerns, in which greenhouse gas (GHG) emissions, primarily methane, are frequently overlooked. While previous studies examined fugitive emissions in normal operation of crude oil pipelines, emissions resulting from accidents were typically managed separately and were therefore not included in the emission account of oil systems. To bridge this knowledge gap, we employed a bottom-up approach to conducted the first-ever inventory of GHG emissions resulting from crude oil pipeline accidents in the United States at the state level from 1968 to 2020, and leveraged Monte Carlo simulation to estimate the associated uncertainties. Our results reveal that GHG emissions from accidents in gathering pipelines (~720,000 tCO2e) exceed those from transmission pipelines (~290,000 tCO2e), although significantly more accidents have occurred in transmission pipelines (6883 cases) than gathering pipelines (773 cases). Texas accounted for over 40% of total accident-related GHG emissions nationwide. Our study contributes to enhanced accuracy of the GHG account associated with crude oil transport and implementing the data-driven climate mitigation strategies

An oil pipeline catastrophic failure: Accident scenario modelling and emergency response development

In spite of advanced technologies, inherent safety and safety management system, pipeline loss of containments and large-scale releases of hazardous substances are still common accidents leading to severe consequences for human health, environment and assets, both in Europe and in developing Countries. This paper presents a detailed analysis of the catastrophic failure of a pipeline connecting the port oil terminal with a downstream oil plant, in the North part of Italy, causing a major oil spill into a river and subsequently into the Genoa harbor (Italy). Firstly, the impact of atmospheric dispersion is evaluated then, assuming oil containment failure, the hydrodynamic dispersion of the spill into the sea is studied. By means of numerical methods, we performed a consequence-based assessment incorporating the effects, the hazardous distance and the reaction time scale, related to oil spill. Results are focused on the atmospheric dispersion of the "key" oil volatile fractions and the propagation in the sea of the medium-heavy fractions, both performed by Lagrangian simulations

Precursor Analysis for Offshore Oil and Gas Drilling: From Prescriptive to Risk-Informed Regulation

The Oil Spill Commission’s chartered mission—to “develop options to guard against … any oil spills associated with offshore drilling in the future” (National Commission 2010)—presents a major challenge: how to reduce the risk of low-frequency oil spill events, and especially high-consequence events like the Deepwater Horizon accident, when historical experience contains few oil spills of material scale and none approaching the significance of the Deepwater Horizon. In this paper, we consider precursor analysis as an answer to this challenge, addressing first its development and use in nuclear reactor regulation and then its applicability to offshore oil and gas drilling. We find that the nature of offshore drilling risks, the operating information obtainable by the regulator, and the learning curve provided by 30 years of nuclear experience make precursor analysis a promising option available to the U.S. Bureau of Ocean Energy Management, Regulation and Enforcement (BOEMRE) to bring cost-effective, risk-informed oversight to bear on the threat of catastrophic oil spills.catastrophic oil spills, quantitative risk analysis, risk-informed regulation

Proceedings of the Workshop on Government Oil Spill Modeling

Oil spill model users and modelers were brought together for the purpose of fostering joint communication and increasing understanding of mutual problems. The workshop concentrated on defining user needs, presentations on ongoing modeling programs, and discussions of supporting research for these modeling efforts. Specific user recommendations include the development of an oil spill model user library which identifies and describes available models. The development of models for the long-term fate and effect of spilled oil was examined

High-viscosity biphasic flow characterization in a pipeline: application to flow pattern classification and leak detection

Pipeline systems play an essential role in the oil industry. These systems connect ports, oil fields, refineries, and consumer markets[104]. Pipelines covering long distances require pumping stations, where products are propelled to the next pumping station, refinery, or deposit terminal, thus traveling through most of the country. The product considered in this research work is crude oil. It is usually transported with a combination of crude oil with viscosity reducers (DRA, drag reducer agent) and oil with gas in onshore/offshore pipelines. This mode of transport is efficient for large quantities and large product shipment distances. Problems may arrive when a leak occurs. In major incidents, large scale damage to humans and the environment is possible. Then, this research addresses the problem of how to detect the leak earlier to reduce the impact in the surrounding areas and economic losses, considering five research topics taking into account that the products inside the pipeline are water-glycerol and gas-glycerol mixtures (simulating oil-DRA and oil-gas in the laboratory test apparatus). The first research topic presents a mathematical model to describe the flow of a mixture of water and glycerol in pressurized horizontal pipelines, which emulates the mixture of heavy oil and a viscosity reducer. The model is based on the mass and momentum conservation principles and empirical correlations for the mixture’s density and viscosity. The set of partial differential equations is solved using finite differences. These equations were implemented in a computer platform to be able to simulate a system. This simulation platform is a tool to simulate leak cases for different fractions of water and glycerol to evaluate algorithms for leak detection and localization before their implementation in a laboratory setting.DoctoradoDoctor en Ingeniería Mecánic

Evolving time surfaces and tracking mixing indicators for flow visualization

The complexity of large scale computational fluid dynamic simulations (CFD) demands powerful tools to investigate the numerical results. To analyze and understand these voluminous results, we need to visualize the 3D flow field. We chose to use a visualization technique called Time Surfaces. A time surface is a set of surfaces swept by an initial seed surface for a given number of timesteps. We use a front tracking approach where the points of an in initial surface are advanced in a Lagrangian fashion. To maintain a smooth time surface, our method requires surface refinement operations that either split triangle edges, adjust narrow triangles, or delete small triangles. In the conventional approach of edge splitting, we compute the length of an edge, and split that edge if it has exceeded a certain threshold length. In our new approach, we examine the angle between the two vectors at a given edge. We split the edge if the vectors are diverging from one another. This vector angle criterion enables us to refine an edge before advancing the surface front. Refining a surface prior to advancing it has the effect of minimizing the amount of interpolation error. In addition, unlike the edge length criterion which yields a triangular mesh with even vertex distribution throughout the surface, the vector angle criterion yields a triangular mesh that has fewer vertices where the vector field is flat and more vertices where the vector field is curved. Motivated by the evaluation and the analysis of flow field mixing quantities, this work explores two types of quantitative measurements. First, we look at Ottino\u27s mixing indicators which measure the degree of mixing of a fluid by quantifying the rate at which a sample fluid blob stretches in a flow field over a period of time. Using the geometry of the time surfaces we generated, we are able to easily evaluate otherwise complicated mixing quantities. Second, we compute the curvature and torsion of the velocity field itself. Visualizing the distribution and intensity of the curvature and torsion scalar fields enables us to identify regions of strong and low mixing. To better observe these scalar fields, we designed a multi-scale colormap that emphasizes small, medium, and large values, simultaneously. We test our time surface method and analyze fluid flow mixing quantities on two CFD datasets: a stirred tank simulation and a BP oil spill simulation

Multiple Surface Pipeline Leak Detection Using Real-Time Sensor Data Analysis

Pipelines enable the largest volume of both intra and international transportation of oil and gas and play critical roles in the energy sufficiency of countries. The biggest drawback with the use of pipelines for oil and gas transportation is the problem of oil spills whenever the pipelines lose containment. The severity of the oil spill on the environment is a function of the volume of the spill and this is a function of the time taken to detect the leak and contain the spill from the pipeline. A single leak on the Enbridge pipeline spilled 3.3 million liters into the Kalamazoo river while a pipeline rupture in North Dakota which went undetected for 143 days spilled 29 million gallons into the environment.Several leak detection systems (LDS) have been developed with the capacity for rapid detection and localization of pipeline leaks, but the characteristics of these LDS limit their leak detection capability. Machine learning provides an opportunity to develop faster LDS, but it requires access to pipeline leak datasets that are proprietary in nature and not readily available. Current LDS have difficulty in detecting low-volume/low-pressure spills located far away from the inlet and outlet pressure sensors. Some reasons for this include the following, leak induced pressure variation generated by these leaks is dissipated before it gets to the inlet and outlet pressure sensors, another reason is that the LDS are designed for specific minimum detection levels which is a percentage of the flow volume of the pipeline, so when the leak falls below the LDS minimum detection value, the leak will not be detected. Perturbations generated by small volume leaks are often within the threshold values of the pipeline\u27s normal operational envelop as such the LDS disregards these perturbations. These challenges have been responsible for pipeline leaks going on for weeks only to be detected by third-party persons in the vicinity of the leaks. This research has been able to develop a framework for the generation of pipeline datasets using the PIPESIM software and the RAND function in Python. The topological data of the pipeline right of way, the pipeline network design specification, and the fluid flow properties are the required information for this framework. With this information, leaks can be simulated at any point on the pipeline and the datasets generated. This framework will facilitate the generation of the One-class dataset for the pipeline which can be used for the development of LDS using machine learning. The research also developed a leak detection topology for detecting low-volume leaks. This topology comprises of the installation of a pressure sensor with remote data transmission capacity at the midpoint of the line. The sensor utilizes the exception-based transmission scheme where it only transmits when the new data differs from the existing data value. This will extend the battery life of the sensor. The installation of the sensor at the midpoint of the line was found to increase the sensitivity of the LDS to leak-induced pressure variations which were traditionally dissipated before getting to the Inlet/outlet sensors. The research also proposed the development of a Leak Detection as a Service (LDaaS) platform where the pressure data from the inlet and the midpoint sensors are collated and subjected to a specially developed leak detection algorithm for the detection of pipeline leaks. This leak detection topology will enable operators to detect low-volume/low-pressure leaks that would have been missed by the existing leak detection system and deploy the oil spill response plans quicker thus reducing the volume of oil spilled into the environment. It will also provide a platform for regulators to monitor the leak alerts as they are generated and enable them to evaluate the oil spill response plans of the operators

Characterizing the immiscible transport properties of diesel and water in peat soil

The final publication is available at Elsevier via https://doi.org/10.1016/j.jconhyd.2018.12.005 © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Extensive pipeline and railway corridors crossing Canadian peatlands make them vulnerable to hydrocarbon spills, potentially impairing ecosystem health, so it is important to be able to forecast hydrocarbon fate and transport within and beyond the peatland. The redistribution of hydrocarbon liquids in groundwater systems are controlled by the multiphase flow characteristics of the aquifer material including capillary pressure-saturation-relative permeability (Pc-S-kr) relations. However, these relations have never been characterized for the hydrocarbon-water phases in peat. To address this, the flow and transport of diesel and water in peat soils were examined through a number of one dimensional vertical immiscible displacement tests, in which diesel was percolated into peat pore space displacing peat water, leading to a two-phase flow regime. Inverse modelling simulations using both Brooks and Corey's and power law relative permeability models, matched the data of the immiscible displacement tests well. Irreducible water saturation (Swirr) and the curvature of water relative permeability relation increased with peat bulk density. The residual diesel saturation (SNr) ranged between 0.3% and 17% and increased with bulk density of peat. In a given peat, SNr was a function of saturation history and increased with increasing maximum diesel saturation. The receding contact angles of water in water-air systems and diesel in diesel-air systems, respectively, were 51.7° and 61.2°, showing that the wetting tendency of peat in the air imbibition condition is toward the draining liquid. These experiments advance our knowledge on the behavior of hydrocarbons in peat, and can improve numerical modelling of hydrocarbon transport after a spill

Modeling of pipeline corrosion degradation mechanism with a Lévy Process based on ILI (In-Line) inspections

International audienceIn pipelines, one of the primary testing procedures used to identify the e↵ects and evolution of corrosion over time is through In-Line Inspections (ILI). ILI inspections provide detailed information regarding the inner and outer pipeline condition based on the remaining wall thickness. Based on this information, di↵erent approaches have been proposed to predict the degradation extent of the defects detected. However, these predictions are subject of uncertainties due to the inspection tool and the degradation process that poses some challenges for assessing an entire pipeline within the timespan between two inspections. To address this problem, ILI data was used to formulate a degradation model for steel-pipe degradation based on a Mixed Lévy Process. The model combines a Gamma and Compound Poisson Processes aimed for a better description of the degradation reported by the ILI data. The model seeks to estimate corrosion lifetime distribution and the mean time to failure (MTTF) more accurately. The model was tested on an actual segment of an oil pipeline, and the results have been used to support a preventive maintenance program