The Midstream Center supports Lamar University faculty by funding applied research grants important to the midstream industry. In early 2020, a listing of faculty strengths and interests was developed and forced ranked in importance to the midstream industry by the Center’s eight member Industry Advisory Board. This assessment led to a focus by the Midstream Center on four primary areas:
In its first full calendar year of operations, the Center funded 16 research projects relevant to midstream companies.
Lamar Faculty Midstream Projects:
LNG
Title:
Dynamic Modeling and Simulation for LNG Loading, BOG Generation, and BOG Recovery at LNG Exporting Terminals
Investigator:
Description:
Liquefied natural gas (LNG) is a prominent clean energy source available in abundance. LNG has high calorific value, while lower price and emissions. Vapors generated from LNG due to heat leak and operating-condition-changes are called boil-off gas (BOG). Because of the very dynamic in nature, the rate of BOG generation during LNG loading (jetty BOG, or JBOG) changes significantly with the loading time, which needs to be well studied. In this project, the LNG vessel loading process is dynamically simulated to obtain JBOG generation profiles. The effects of various parameters including holding-mode heat leak, initial-temperature of LNG ship-tank, JBOG compressor capacity, and maximum cooling-rate for ship-tank, on JBOG profile have been comprehensively studied. Meanwhile, possible JBOG reuse/recovery strategies have also been investigated in this work. Understanding JBOG generation would help in designing and retrofitting BOG recovery facilities in an efficient way. The study would also help proper handling of BOG problems in terms of minimizing flaring at LNG exporting terminals, and thus reducing waste, saving energy, and protecting surrounding environments.
Publications:
Kurle, Y. M., Xu, Q.*, “Dynamic Simulation Study for Boil-off Gas Minimization at Liquefied Natural Gas Exporting Terminals”, Industrial & Engineering Chemistry Research, 57 (17), 5903–5913, 2018.
Study on FLNG Sloshing Impacts
Title:
A Comprehensive Study on Sloshing Impacts for FLNG Gas Pre-treatment Systems via the Integration of Multi-scale Simulation, Experimental Validation, and AI Technologies
Investigator:
Dr. Qiang Xu
Description:
Liquid sloshing on vessels subjected to forced ocean-wave acceleration could cause
tremendous operating or safety issues for the FLNG (floating liquefied natural gas) process, such as the hydraulic jump and liquid entrainment in vapor phase of various separators. Hitherto, studies are still lacking in this important area. In this project, a comprehensive study will be performed to quantitatively investigate the sloshing impact on FLNG gas pre-treatment systems, including the maximum hydraulic jump, the maximum forces on equipment wall, and the decrease of separation efficiencies under different ocean-wave conditions. The study will firstly develop a multi-scale simulation scheme coupling computational fluid dynamics (CFD) and process modeling to simulate and characterize dynamic sloshing behaviors for FLNG gas pre-treatment units. Next, the experimental unit supported by Schlumberger Inc. will be employed to validate and refine the developed simulation models. Finally, artificial intelligence (AI) technologies such as the artificial neural network (ANN) method will be utilized to learn and predict the sophisticated sloshing behaviors effectively and efficiently based on abundant simulation results. The study will help characterizing and predicting sloshing impacts on FLNG gas pre-treatment systems. It could also help for the future design, control, and operation for floating production, storage, and offloading equipment.
Surge Analysis for LNG Loading Arms
Surge Analysis for LNG Loading Arms Using Fluid Dynamic Simulation for Suppression of Hydraulic Hammer and Optimization of Piping System
Investigators:
Dr. Xianchang Li
Dr. Xinyu Liu
Dr. Jenny Zhou
Description:
In a pipeline that transports liquids such as liquefied natural gas (LNG), the opening/closing of a valve during an emergency can cause a sudden change in velocity, which results in a hydraulic hammer. The high surge pressure may damage the pipeline, its supports and equipment such as pumps and sensors, hence considered a serious concern. The loading arm of an LNG plant is a critical equipment susceptible to hydraulic hammers. This objective of this project is to identify the issues and create effective solutions for the loading arm operations through fluid dynamic simulation of the entire system under various operation scenarios. By simulating the transient LNG flow in the pipeline, we can evaluate how valves, pumps, and other components dynamically interact with each other during a surge event. We will quantitatively determine the forces generated by transient pressure to help optimize the pipeline design and validate the surge suppression equipment to mitigate potentially catastrophic effect of hydraulic hammer/surge and other undesirable system transients like check valve and relief valve chatter. This project will empower Lamar’s professors to collaborate with local midstream industries. The results can serve as the pilot research for seeking larger external funding.
BLE-Based Cognitive IoT System for Pipeline Networks
A BLE-based Cognitive IoT system for Pipeline Sensor Networks
Investigator:
Dr. Xingya Liu
Description:
While many technologies (e.g., drone, RF, WiFi, Satellite, etc.) have been developed for monitoring systems, their prohibitively high installation complexity, short coverage range, low security-awareness, and/or high energy consumption make them unsuitable for large-scale sensors in long-distance monitoring systems, such as Pipeline. Billions of dollars are spent every year in this country on pipeline maintenance and repair due to the damage cost by natural and human factors, not to mention the indirect economic losses. On the other hand, the Bluetooth Low Energy (BLE) has been rapidly developed these years as a candidate for the backbone technique to fulfill the promise of the Internet of Things (IoT). It has been applied to many areas given its large-scale self-organized feature.
In this project, we plan to design a BLE-based sensor network for Midstream pipeline monitoring. BLE sensors are deployed as IoT tags to form a beacon-advertising system, which can uniquely identify each sensor in the system simultaneously and keep tracking their quantity. Other benefits derived from the
proposed system include the high quality of service, high security, low cost, and low energy consumption. The system can adapt to any onsite pipeline (sensor-side) and off-the-shelf smartphone/PC (receiver-side) without modification on their existing infrastructures.
Anti-corrosive Superhydrophobic Top Coating with High Mechanical Durability for Pipeline Infrastructures
Title:
Develop Anti-corrosive Superhydrophobic Top Coating with High Mechanical Durability for Pipeline Infrastructures
Researchers:
Description:
Corrosion, a significant problem of pipeline infrastructures, generates enormous costs to the oil and gas industries. The cost of corrosion to these industries surpasses $1.3 billion each year, according to NACE International’s estimates. Therefore, corrosion protection coatings with more extended performance are desired. Dr. Yao and his research group have been working on novel coatings specifically for metallic materials to enhance significant material properties such as anticorrosion. Results showed that the superhydrophobic top coating offer effective protection. However, mechanical durability remains a primary concern of these coatings; therefore, there is a need to develop and test durable superhydrophobic top coatings with enhanced surface resistance to corrosion. The main objectives of the proposed project are to develop and characterize novel top coatings for enhanced corrosion resistance and mechanical durability. Also, this project will study the mechanism that governs mechanical durability on the superhydrophobic top coatings.
AI Algorithms for Corrosion Prediction
Development of AI algorithms for Corrosion Prediction in Midstream Industry
Investigator:
Dr. Sidney Lin
Description:
Corrosion has been causing the mid-stream industry billions of dollars per year. Even though ultrasonic sensors and smart pigging technologies are available for corrosion measurement, these reactive approaches are always too late. There are rigorous first principles (electrochemistry, reaction kinetics, thermodynamics, etc.) based models for corrosion prediction, however, due to inherent model complexity, they are very difficult to be implemented for online corrosion prediction.
This project aims at developing accurate AI algorithms based on simulation data generated from sound first-principles based models. The hydrocarbon composition and physical property data will represent real-world data from different wells and tanks. If successful, the research results will help bridge the gap from “post-corrosion” measurement to proactive corrosion management, revolutionize the practice of corrosion management, enhance the reliability of mid-stream assets, improve operation efficiency and reduce potential environmental risks
Drone and AI Solutions for Oil and Gas Pipeline Resilience
DARTS – Drone and Artificial Intelligence Reconsolidated Technological Solution for Increasing Oil and Gas Pipeline Resilience
Investigator:
Dr. Seokyon Hwang
Description:
Pipelines are the critical asset of the oil and gas midstream industry for transporting the essential energy resources. Meanwhile, the leakage of pipelines can impose significant impacts on public safety, the economy, and the ecosystem. The need for reliable pipeline operation and effective preventive maintenance grows as the demand for oil and gas increases. Among various problems causing pipeline failures, this research focuses on the root causes that can contribute to developing damages, such as cracks or fractures, on pipes and pipe joints, which, in turn, result in oil and gas leakage. The root causes to be tackled in this study include pipeline misalignment, deterioration of supporting structure, and soil movement near the pipeline. The ultimate goal of this research endeavor is to develop DARTS – Drone and Artificial Intelligence Reconsolidated Technological Solution. The specific purposes of DARTS are to detect the targeted problems and predict the progress of the detected problems by using periodically collected image data, aiming to support decision-making for predictive maintenance. Within the scope of the proposed project, the research team will conduct experiments to examine the feasibility of DARTS. To this end, the team will develop an early-stage model for collecting data—still images and video streams—and algorithms for identifying the targeted problems. The model and algorithms will be tested at the lab and field on a small-scale in collaboration with a midstream industry expert. DARTS is envisioned to help the midstream industry with enhancing pipeline maintenance, increasing system resiliency, and promoting environmental and public safety.
Design, Surface Modification of Corrosion Resistant High-Entropy Alloys
Design and Surface Modification of Corrosion Resistant High-Entropy Alloys for Oil and Gas Pipelines
Investigator:
Dr. Zhe Fan
Description:
Corrosion of oil and gas pipelines results in significant economic costs and substantial damages in midstream industry. The pipelines in service are generally made of cast iron and steels which may not be corrosion resistant. Operating pipelines in a safe, environmentally friendly, and economically efficient manner, demands adequate replacements of the existing pipeline materials with advanced alloys or time-efficient maintenance and repair of these materials. In this project, we aim to i) design corrosion resistant high-entropy alloys* as potential replacements for the existing pipeline materials, and ii) explore advanced surface modification techniques to improve the corrosion resistance of alloys. A direct comparison will be drawn between pipeline steels and high-entropy alloys with/without surface modification. This project will uncover the corrosion responses of these alloys along with the associated microstructural and chemical changes. The research results can reveal the underlying mechanisms for corrosion resistant high-entropy alloys and provide novel surface modification methods to improve the corrosion resistance of pipeline materials, offering alternative solutions to alleviate corrosion cost and damage in pipeline operation.
*High entropy-alloys generally contain 4 or more principal elements in near equiatomic composition. The name, high-entropy alloys, comes from the high mixing entropy due to multiple principal elements.
Deep Learning-Based Defect Classification and Prediction for Compressors
Deep Learning-based Defect-Classification and Prediction for Midstream Compressors
Investigator:
Dr. Maryam Hamidi
Description:
In the previous phase of this project, compressors’ microphone data, provided by Well Checked Systems, was successfully analyzed and a deep learning-based auditory model for compressor anomaly was developed. Currently at Well Checked, when an anomaly is detected, a maintenance technician is assigned to manually inspect and diagnose the type of anomaly. Next, the technician reports information such as the time stamp of inspection and the type of failure diagnosed. In this proposed project, Lamar University will continue collaborating with Well Checked to automate the process, by predicting the time and type of anomaly and issuing maintenance orders to repair the anomaly. Here,
first a database will be designed for technician reports, real time and historical time-series data, including the raw audio data and the 2-dimensional representations of before and after an anomaly. Next, a model will be developed and trained to predict the time and type of anomaly (classification), and the prediction model will be tested with real-time monitoring of Well Checked compressors. A failure prediction will be considered successful once confirmed by a technician, and finally, the training dataset will be updated using a feedback loop when a technician finds a new failure.
Assessment of Corrosion Development
Assessment of Corrosion Development
Investigator:
Dr. Rafael Tadmor
Description:
We developed a surface property (interfacial modulus, Gs), that quantifies the resilience of a solid to interact with a given liquid. With the help of Gs, we can characterize the outer walls of a unit (e.g. the outer wall of a pipe) in terms of its resilience to interact with liquids of choice. Such an outer wall will also be influenced by corrosion that develops at the inner side of that wall. This is due to the corrosion being a voltaic phenomenon and metals being a conductor: an electric potential at one point is conducted throughout the metal. A paint coating at the outer surface can only reduce the corrosion on that side, but not eliminate it. The paint itself is also influenced by the corrosion since this is the medium in which the metal is oxidized. This proposal aims to use our lab expertise in characterizing surfaces, especially utilizing the interfacial modulus, to assess the amount of corrosion in a wall. This includes a cleaning protocol after which we perform the tests that will allow us to characterize Gs, and from it we will develop a calibration curve, that would allow us to estimate the amount of corrosion.
Hybrid Model to Detect Crude Oil Leaks in a Pipeline System
Title:
Develop a Hybrid Model to Detect Crude Oil Leak in Midstream Pipeline System
Investigators:
Dr. Yueqing Li
Dr. Xinyu Liu
Description:
Pipelines are considered as one of the most practical transportation means in midstream for petroleum products, such as crude and product oil. Therefore, the safe operation of pipeline is extremely important. Pipeline failures such as blockage and leakage may result in environmental pollution, economic losses, and even serious threats to human safety and property. Consequently, monitoring
pipelines is an important task. Detection of exact fault quantity and its location is necessary for smooth operation of factories and industries and environmental safety. It is essential to develop a timely and reliable method to detect and locate pipeline leakage. This project aims to develop a hybrid model to detect crude oil leak in midstream pipeline system. With the self-developed pipeline system, hardware-based methods and software-based methods will be tested. Then, a hybrid model will be developed regarding the detection performance (accuracy, time) and cost. The proposed model will be tested in different scenarios based on the real environment and the most practical operating parameters will be finalized. The study is expected to improve the performance of oil leak detection in the pipeline system in midstream.
Novel Approach to Evaluating Pitting Corrosion
Title:
A Novel Approach for Evaluating Pitting Corrosion
Investigator:
Dr. Chun-wei Yao
Description:
Corrosion could degrade the mechanical properties of equipment, lowering materials’ performances. Corrosion damage could lead to rupture and raise safety issues. The localized corrosion phenomena involving pitting corrosion, have been a hot topic for advanced corrosion studies. However, the prediction of localized corrosion is a problem for various reasons. Out of all limiting factors, the most significant one being the fact that all the reactions and other thermodynamic events occur at a microscopic scale, and the passive film is generally of nanometer thickness, and the location of initiation is even smaller. Therefore, there is a need to study the process of localized corrosion occurring on coatings at a micrometer/nanometer resolution. This project will study the mechanism that governs pit initiation and propagation on coatings. Furthermore, the PI will evaluate the mechanical performance of coatings with pit growth.
H2S Removal
Title:
H2S Removal from Oil and Gas Streams
Researchers:
Description:
The aim of this research is to develop and test a series of absorbents (known as scavengers) for the removal of hydrogen sulfide (H2S) from crude liquid oils. Crude oils that have sulfur concentrations more than 0.5 wt% are considered sour crudes, since they are characterized by a foul, odorous smell. Sour crudes are of lower quality and present serious health and environmental concerns. Therefore, sustainable measures to lower the sulfur content (i.e. crude oil sweetening) are of significant importance, financially and environmentally. Hydrogen sulfide (H2S), however, is normally removed using amine based absorbing materials, known as scavengers. Removing of H2S at the wellhead before transporting via pipeline or railcar increases the value of crude oil and in some cases is necessary to conform to legal transport laws. Phase 1 of this work explored the solubility and liquid phase activity coefficients for triazine-type scavengers. Phase 2 explores replacing trianzine compounds with ionic liquids. Triazine have a tendency to produce volatile organic compounds (VOCs) during the regeneration phase in the absorption/stripping process. Ionic liquids have relatively no boiling point, making them more environmentally attractive as an absorbent. Laboratory-based experiments will yield solubility parameters needed for equipment design and sizing.
Carbon Dioxide Transportation and Storage
Title:
Carbon Dioxide Transportation and Storage project (GoMCarb)
Researchers:
University of Texas
Description:
This is a collaboration with UT-Austin (lead institute). Through a $16.5 million grant from the Department of Entergy, the project - Offshore Gulf of Mexico Partnership for Carbon Storage - Resources and Technology Development (GoMCarb) – brings the two universities together with other carbon capture storage (CCS) stakeholders in government, academic and industry.
Lamar University areas are:
- Estimating CO2 leakage rates and dispersion from an injection site
- Optimizing CO2 gathering and compressing from industrial facilities for delivery to injection sites
- Raising public awareness of potential subsea geologic CO2 storage
Infrastructure Anomaly Detection
Title:
Deep Learning-based Anomaly Detection for Midstream Infrastructures-Phase II
Researcher:
Funding Source:
Internal Grant
Pipeline Leak Mitigation
Title:
Modeling and Experimental Study on the Transport of Natural Gas in Southeast Texas Soil from the Underground Pipeline Leak
Researcher:
Funding Source:
Internal Grant