Natural Gas Operation Research Articles

Methane Emissions from Oil and Natural Gas Operations—30 Percent Reduction by 2030 Possible if Domestic and International Actions “Stay the Course”

When sworn into office in January 2021, the Biden–Harris Administration—through the National Climate Task Force—launched an ambitious, whole‐of‐government initiative to significantly double efforts and reduce methane emissions from the oil and natural gas sector, landfills, agriculture, and other lesser sources. These initiatives were documented in the U.S. Methane Emissions Reduction Action Plan, which proposed to use regulations, catalytic financial incentives, transparency and disclosure of actionable data, together with public and private partnerships, to identify and reduce methane emissions. President Biden's Build Back Better agenda was designed to accelerate, through investments, many of these domestic methane emissions reduction efforts.

Optimal resilience-based restoration plan for disrupted interdependent infrastructure networks under uncertainty

The functioning of vital services in a society depends heavily on the proper and continuous operation of critical infrastructure networks, including water, transportation, power, natural gas, and telecommunication. Such networks are essential for maintaining the economy, security, and overall quality of life. However, since these critical networks are interdependent; hence, they are susceptible to various types of disruptions caused by natural disasters, failures, or malicious activities, leading to varying degrees of performance impacts that could directly affect people's daily lives. Thus, a disruption to one network can quickly spread to others, resulting in significant impacts on daily life. Therefore, restoring such disrupted networks in a timely manner is crucial to ensure a prompt recovery and minimize the impact on daily life. In this work, we consider the problem of restoring a set of interdependent infrastructure networks following a disruptive event. We study two essential factors that can accelerate the recovery process: (i) the restoration facilities location, which are considered as dispatching points for restoration crews, and (ii) the routes of the restoration crews within the interdependent networks. Accordingly, we formulate two optimization models using mixed integer programming. The first model, restoration facility location model, finds the optimal location for restoration facilities within interdependent infrastructure networks considering uncertainty in disruptions. The second model, restoration crew routing model, determine the optimal routes for restoration crews within the interdependent infrastructure networks considering: (i) travelled distance, (ii) restoration time required for disrupted network components, and (iii) different importance of network components. The two optimization models are solved through generated interdependent power and water networks to demonstrate their effectiveness.

Optimal planning of integrated electricity-natural gas distribution systems with hydrogen enriched compressed natural gas operation

In future integrated electricity-natural gas distribution systems (IEGDS), hydrogen and methane converted from excessive renewable generation will be mixed with natural gas to form hydrogen enriched compressed natural gas (HCNG) for fewer carbon emissions. Consequently, optimal planning of the HCNG-integrated IEGDS plays an essential role in increasing the economy and technical benefits. However, integrating HCNG operation into the planning of IEGDS still has many challenges, such as unrealistic modeling, nonlinear constraints, and the potential risk of voltage violations. The purpose of this paper is to develop a tractable optimization model for planning and operation of the HCNG-integrated IEGDS, aiming to minimize the investment and operating cost, reduce the voltage violation, and improve the renewable power penetration rate. To achieve this, a bilevel optimization model is developed for optimal planning of wind turbines and soft open points (SOPs) in the IEGDS considering comprehensive HCNG operation, in which both hydrogen and methane injection are considered and optimized for different system operation scenarios. Furthermore, the proposed nonlinear model is reformulated to a tractable bilevel mixed-integer second-order cone model using several reformulation techniques and solved by the reformulation and decomposition algorithm. Case studies, performed using the publicly available datasets, are conducted to demonstrate the economic and technical improvement by implementing the proposed planning model. The annual planning results show that incorporating the coordination between SOPs and methane injection results in a 6.77% reduction in investment cost, a 22.64% reduction in operating cost, a 62.69% decrease in voltage violation, and a 23.58% increase in the wind power penetration rate. Moreover, SOPs are more applicable to the safe operation of the IEGDS under high energy load conditions.

IMPACT OF REAL-LIFE ENVIRONMENTAL EXPOSURES ON REPRODUCTION: Evidence for reproductive health effects following exposure to hydraulic fracturing chemical mixtures.

Unconventional oil and natural gas (UOG) operations, particularly hydraulic fracturing, have revolutionized oil and gas production, using and containing complex mixtures of chemicals that may impact reproductive health. While there is growing evidence for effects on births in hydraulic fracturing/UOG regions and good mechanistic evidence for potential reproductive toxicity, there is much research still needed to make firm conclusions about these practices and reproductive health. Unconventional oil and natural gas (UOG) operations have emerged over the last four decades to transform oil and gas production in the United States and globally by unlocking previously inaccessible hydrocarbon deposits. UOG development utilizes many compounds associated with conventional oil and gas, as well as some specific to UOG extraction, particularly during hydraulic fracturing (HF). While research is increasing on UOG chemicals and their mixtures, this review discusses the current evidence for reproductive toxicity following exposures to UOG/HF mixtures. These complex chemical mixtures have been demonstrated to interact with numerous mechanisms known to influence reproductive health. A growing number of environmental and controlled laboratory testing studies have reported adverse reproductive health effects in animals exposed to various UOG chemical mixtures. An expanding body of epidemiological literature has assessed adverse birth outcomes, although none has directly examined reproductive measures such as time to pregnancy, semen quality, and other direct measures of fertility. The existing literature provides moderate evidence for decreased birth weights, increased risk of small for gestational age and/or preterm birth, increased congenital abnormalities, and increased infant mortality, though importantly, studies are widely variable in methods used. Most studies utilized distance from UOG operations as an exposure proxy and did not measure actual chemical exposures experienced by those living near these operations. As such, while there is growing evidence for effects on births in these regions and good mechanistic evidence for potential reproductive toxicity, there is much research still needed to make firm conclusions about UOG development and reproductive health.

Machine learning prediction of methane, nitrogen, and natural gas mixture viscosities under normal and harsh conditions

The accurate estimation of gas viscosity remains a pivotal concern for petroleum engineers, exerting substantial influence on the modeling efficacy of natural gas operations. Due to their time-consuming and costly nature, experimental measurements of gas viscosity are challenging. Data-based machine learning (ML) techniques afford a resourceful and less exhausting substitution, aiding research and industry at gas modeling that is incredible to reach in the laboratory. Statistical approaches were used to analyze the experimental data before applying machine learning. Seven machine learning techniques specifically Linear Regression, random forest (RF), decision trees, gradient boosting, K-nearest neighbors, Nu support vector regression (NuSVR), and artificial neural network (ANN) were applied for the prediction of methane (CH4), nitrogen (N2), and natural gas mixture viscosities. More than 4304 datasets from real experimental data utilizing pressure, temperature, and gas density were employed for developing ML models. Furthermore, three novel correlations have developed for the viscosity of CH4, N2, and composite gas using ANN. Results revealed that models and anticipated correlations predicted methane, nitrogen, and natural gas mixture viscosities with high precision. Results designated that the ANN, RF, and gradient Boosting models have performed better with a coefficient of determination (R2) of 0.99 for testing data sets of methane, nitrogen, and natural gas mixture viscosities. However, linear regression and NuSVR have performed poorly with a coefficient of determination (R2) of 0.07 and − 0.01 respectively for testing data sets of nitrogen viscosity. Such machine learning models offer the industry and research a cost-effective and fast tool for accurately approximating the viscosities of methane, nitrogen, and gas mixture under normal and harsh conditions.

Methane Quantification Performance of the Quantitative Optical Gas Imaging (QOGI) System Using Single-Blind Controlled Release Assessment.

Quantitative optical gas imaging (QOGI) system can rapidly quantify leaks detected by optical gas imaging (OGI) cameras across the oil and gas supply chain. A comprehensive evaluation of the QOGI system's quantification capability is needed for the successful adoption of the technology. This study conducted single-blind experiments to examine the quantification performance of the FLIR QL320 QOGI system under near-field conditions at a pseudo-realistic, outdoor, controlled testing facility that mimics upstream and midstream natural gas operations. The study completed 357 individual measurements across 26 controlled releases and 71 camera positions for release rates between 0.1 kg Ch4/h and 2.9 kg Ch4/h of compressed natural gas (which accounts for more than 90% of typical component-level leaks in several production facilities). The majority (75%) of measurements were within a quantification factor of 3 (quantification error of -67% to 200%) with individual errors between -90% and 831%, which reduced to -79% to +297% when the mean of estimates of the same controlled release from multiple camera positions was considered. Performance improved with increasing release rate, using clear sky as plume background, and at wind speeds ≤1 mph relative to other measurement conditions.

Influence of Natural Gas Composition and Operating Conditions on the Steady-State Performance of Dry Gas Seals for Pipeline Compressors

Influence of Natural Gas Composition and Operating Conditions on the Steady-State Performance of Dry Gas Seals for Pipeline Compressors

Assessing the Progress of the Performance of Continuous Monitoring Solutions under a Single-Blind Controlled Testing Protocol.

The recent regulatory spotlight on continuous monitoring (CM) solutions and the rapid development of CM solutions have demanded the characterization of solution performance through regular, rigorous testing using consensus test protocols. This study is the second known implementation of such a protocol involving single-blind controlled testing of 9 CM solutions. Controlled releases of rates (6-7100 g) CH4/h over durations (0.4-10.2 h) under a wind speed range of (0.7-9.9 m/s) were conducted for 11 weeks. Results showed that 4 solutions achieved method detection limits (DL90s) within the tested emission rate range, with all 4 solutions having both the lowest DL90s (3.9 [3.0, 5.5] kg CH4/h to 6.2 [3.7, 16.7] kg CH4/h) and false positive rates (6.9-13.2%), indicating efforts at balancing low sensitivity with a low false positive rate. These results are likely best-case scenario estimates since the test center represents a near-ideal upstream field natural gas operation condition. Quantification results showed wide individual estimate uncertainties, with emissions underestimation and overestimation by factors up to >14 and 42, respectively. Three solutions had >80% of their estimates within a quantification factor of 3 for controlled releases in the ranges of [0.1-1] kg CH4/h and > 1 kg CH4/h. Relative to the study by Bell et al., current solutions performance, as a group, generally improved, primarily due to solutions from the study by Bell et al. that were retested. This result highlights the importance of regular quality testing to the advancement of CM solutions for effective emissions mitigation.

Technological innovations in liquefied natural gas operations: Enhancing efficiency and safety

Technological innovations in liquefied natural gas operations: Enhancing efficiency and safety

Modelling spatial & temporal variability of air pollution in an area of unconventional natural gas operations

Despite the growing unconventional natural gas production industry in northeastern British Columbia, Canada, few studies have explored the air quality implications on human health in nearby communities. Researchers who have worked with pregnant women in this area have found higher levels of volatile organic compounds (VOCs) in the indoor air of their homes associated with higher density and closer proximity to gas wells. To inform ongoing exposure assessments, this study develops land use regression (LUR) models to predict ambient air pollution at the homes of pregnant women by using natural gas production activities as predictor variables. Using the existing monitoring network, the models were developed for three temporal scales for 12 air pollutants. The models predicting monthly, bi-annual, and annual mean concentrations explained 23%–94%, 54%–94%, and 73%–91% of the variability in air pollutant concentrations, respectively. These models can be used to investigate associations between prenatal exposure to air pollutants associated with natural gas production and adverse health outcomes in northeastern British Columbia.

Investigation of the technical potential of a hydrogen powered phosphoric acid fuel cell (PAFC) for CHP

Due to the fluctuating feed-in of renewable energies, controllable power plants such as highly efficient CHP plants (combined heat and power) will continue to be required to cover the residual load. Gas engines and turbines currently dominate the natural gas-based CHP market due to their low investment costs and acceptable electrical efficiency. In the event of a future fuel switch due to the energy transition from natural gas to hydrogen, fuel cell systems are becoming increasingly important due to their very high efficiency and improved dynamics in hydrogen operation and can therefore represent an alternative to gas engines and turbines. In addition to a possible fuel switch, good dynamic and full heat utilization represents an additional challenge for CHP systems. Therefore, this study aims to investigate the influence of a fuel switch from natural gas to hydrogen on the operation of a fuel cell (PAFC) in terms of efficiency, heat utilization and dynamics. It was shown that the electrical efficiency of the hydrogen-operated PAFC is significantly higher than in natural gas operation due to the omitted reformer and the associated reformer losses. In hydrogen operation, there is also no limitation of the dynamics by the reformer. Furthermore, in hydrogen operation there is a more favorable ratio of high-temperature to low-temperature heat, which facilitates the use of heat. Detailed and validated simulation models in Aspen Plus are used as the basis for this investigation.

Thermoacoustic Characterization of a Premixed Multi Jet Burner for Hydrogen and Natural Gas Combustion

Abstract In this study, the acoustics and flame dynamics of a prototype multi jet burner with 19 individual mixing tubes for operation with pure hydrogen and pure natural gas are experimentally investigated. The burner transfer matrix (BTM) of the jet burner is determined from experimental data and acoustic network modeling, showing very good agreement. The burner plate and attached mixing tubes are shown to be well approximated with an acoustic model of a perforated plate with bias flow. Accordingly, the burner is found to feature a high level of acoustic damping. A comparison of the flame dynamics of the two fuels considering mass flow and equivalence ratio variation reveals that the flame transfer functions (FTFs) are dominated by a convective mechanism originating from the upstream end of the mixing tubes where the fuel is injected. Consequently, these are most likely fluctuations in the equivalence ratio that feature two characteristic time scales: the convection time in the mixing tubes and along the flame. The overall qualitative shape of the FTFs for hydrogen and natural gas at equal thermal power is found to be similar, with the dynamics of the natural gas flames being more responsive to acoustic excitation, as evident in generally higher gain values. Distinctly less pronounced phase decays are observed for hydrogen compared to natural gas operation. Moreover, the FTFs for H2 are found to change only slightly across the considered range of equivalence ratios. At the same time, we observe only small changes in the corresponding static flame shapes. These observations are consistent with the hypothesis of a dominant convective mechanism. In conclusion, the study provides valuable information on the acoustics and flame dynamics of multi jet burners for flexible fuel operation.

Centrifugal compressor performance prediction and dynamic simulation of natural gas hydrogen blended

It is great significant to explore the changes of compressor performance for hydrogen transportation. We found that a Gray-RBF neural network prediction model improved prediction accuracy. The prediction error of the model is 2.34 % by data verification. It was found that the hydrogen blending ratio was increased from 0 % to 30 %, the pipeline transmission power was reduced by 9.3 %. Simultaneously injecting hydrogen to natural gas causes the performance curve of the compressor to shift downwards. The pressure ratio decreases by 18.9 % and shaft power decreases by 28.6 % when the hydrogen blending ratio increases from 0 % to 30 %. The injection of hydrogen to natural gas causes the surge range of the compressor to increase, so the stable range to narrow. In general, this research may provide reference and guidance for the transportation research of hydrogen blended natural gas and compressor operation about gas industry.

Enhancing Energy Efficiency and Reliability in Floating LNG Operation: A Hydrofluoroolefin-Based SMR Cycle with Thermo-Economic Assessment and Uncertainty Analysis

Natural gas liquefaction is the most energy-intensive technique in floating liquefied natural gas (FLNG) operations. Hence, a hydrofluoroolefin-based single mixed refrigerant (SMR) technology was proposed to achieve energy-efficient and eco-friendly operations. The study was perfomed to enhance the SMR process by substituting expansion valves with hydraulic turbines to improve process reversibility and enhance energy efficiency. The proposed process was optimized using a sine–cosine concept-based algorithm that tuned the design variables which yielded an overall compression energy consumption of 0.2501 (kWh/kg-LNG), resulting in a 21.43% saving as compared to the conventionally used mixed refrigerant-based process. This resulted in a reduction of about 38.81% in overall exergy destruction. The economic analysis results draw curtains that the process saves about 26.02% of total annualized cost as compared to conventional study. Owing to the appropriate mixed refrigerant component mixture ratio and process design improvement. To determine the process reliability under uncertain conditions the optimal compression power and minimum approach temperature were determined. Further, uncertainty analysis reveals that the methane and butane refrigerant flow rates along with evaporation, and condensation pressure, were among the most influential parameters that influence the process most that may affect the whole operation in uncertain process inlet conditions. In summary, our approach presents immense promise for achieving energy efficiency, environmental responsibility, and economic viability in FLNG operations, offering a robust solution for the challenges of offshore plants.

Hydrogen enriched natural gas as fuel for CHP units

This study investigates the potential benefits and drawbacks of adding hydrogen to natural gas grids on stationary cogeneration plants. Fuel blended with up to 30% hydrogen by volume was tested using a commercial six-cylinder spark ignition engine designed for pure natural gas operation without modifications to the engine. In line with normal practice for cogeneration plant engines, the power output, the lower heating value of the air/fuel mixture, the ignition timing and the engine speed were held constant. Results show that increasing hydrogen concentration led to an earlier peak cylinder pressure, indicating significantly accelerated combustion. As a result, peak pressures were up to 39% higher than with natural gas and up to 10% of fuel burned before top dead center. Despite this, thermal efficiency improved up to 6%. Cycle-by-cycle variation decreased by half, indicating reduced misfires on account of hydrogen. However, nitrogen oxide emissions increased exponentially with increasing hydrogen amounts. Our findings suggest that hydrogen-enriched natural gas is a promising fuel for stationary cogeneration plants, but modifications to engine control settings are necessary to ensure optimal performance and compliance with nitrogen oxide emission regulations. These modifications might include adjustments to the mixture control system and ignition timing.

Economic Analysis of Methanating CO2 and Hydrogen-Rich Industrial Waste Gas in Depleted Natural Gas Reservoirs

This study explored underground biomethanation as a means to achieve carbon neutrality and promote carbon circular utilization by methanating CO2 and hydrogen-rich industrial waste gas in depleted natural gas reservoirs (MECHIG). This approach not only aids the development of carbon capture, utilization, and storage (CCUS) technologies, but also effectively processes industrial waste gas, thereby reducing pollutant emissions. In order to verify the feasibility of the MECHIG concept, this study builds upon the analysis of the MECHIG process overview and employs the net present value (NPV) analysis method to investigate its economic viability. Additionally, the study conducts a sensitivity analysis on six factors, namely methanation efficiency, facility site investment, hydrogen content in waste gas, natural gas prices, operation and maintenance (O&M) investment, and CO2 capture and injection prices. The results indicate the following: (1) Under the baseline scenario, the NPV of the MECHIG concept is approximately CNY 5,035,100, which suggests that the concept may be economically viable. (2) The fluctuation in natural gas prices has the most significant impact on NPV, followed by facility site investment and methanation efficiency. In contrast, the variations in hydrogen content in waste gas, O&M investment, and CO2 capture and injection prices have relatively smaller effects on NPV. (3) To ensure the economic feasibility of the concept, the acceptable fluctuation ranges for the factors of methanation efficiency, facility site investment, hydrogen content in waste gas, natural gas prices, O&M investment, and CO2 capture and injection prices are −16.78%, 5.44%, −32.14%, −4.70%, 14.86%, and 18.56%, respectively.

CO2 Reduction Potentials in Diesel, Natural Gas and Hydrogen Operation on a Test Engine

CO2 Reduction Potentials in Diesel, Natural Gas and Hydrogen Operation on a Test Engine

Two Air Quality Regimes in Total Column NO2 Over the Gulf of Mexico in May 2019: Shipboard and Satellite Views

AbstractThe Satellite Coastal and Oceanic Atmospheric Pollution Experiment (SCOAPE) cruise in the Gulf of Mexico was conducted in May 2019 by NASA and the Bureau of Ocean Energy Management to determine the feasibility of using satellite data to measure air quality in a region of concentrated oil and natural gas (ONG) operations. SCOAPE addressed both technological and scientific issues related to measuring NO2 columns over the outer continental shelf. Featured were nitrogen dioxide (NO2) instruments (Pandora, Teledyne API analyzer) at Cocodrie, LA (29.26°, −90.66°), and on the Research Vessel Point Sur operating off the Louisiana coast with measurements of ozone, carbon monoxide, and volatile organic compounds (VOCs). The findings: (a) all NO2 observations revealed two atmospheric regimes over the Gulf, the first influenced by tropical air in 10–14 May, the second influenced by flow from urban areas on 15–17 May; (b) comparisons of OMI v4 and TROPOMI v1.3 TC (total column) NO2 data with shipboard Pandora NO2 column observations averaged 13% agreement with the largest difference during 15–17 May (∼20%). At Cocodrie, the satellite–Pandora agreement was ∼5%. (c) Three new‐model Pandora instruments displayed a TC NO2 precision of 0.01 Dobson Units (∼5%); (d) regions of smaller, older natural gas operations showed high methane readings from leakage; elevated VOCs were also detected. Neither satellite nor spectrometer captured the magnitude of ambient NO2 variability near ONG platforms. Given an absence of regular air quality monitoring over the Gulf of Mexico, SCOAPE data constitute a baseline against which future observations can be compared.

Predicting the monthly consumption and production of natural gas in the USA by using a new hybrid forecasting model based on two-layer decomposition.

As an efficient, economical, and clean energy, natural gas plays an important role in the development of the new energy revolution. Accurate prediction of natural gas consumption and production can adjust energy deployment in advance, which can ensure the stable operation of natural gas. Considering the complex and non-linear characteristics of natural gas production and consumption data, this paper develops a new hybrid forecasting model (WPD-VMD-LSTM) based on the fuzzy entropy, variational mode decomposition (VMD), wavelet packet decomposition (WPD), and Long Short-Term Memory (LSTM). In this model, WPD and VMD undertake the tasks of primary and secondary decompositions, respectively; fuzzy entropy is used for the preprocessing process before the re-decomposition; and LSTM is used to predict the decomposed time series. In particular, the different criteria set by fuzzy entropy lead to the establishment of two prediction models. Then, two models are used to study monthly natural gas consumption and production in the USA. The results demonstrate that the proposed model performs significantly better than other comparable models and the target model has some practical value. Meanwhile, models may cope with different types of energy data, and models can accurately predict energy transformations with strong applicability, which can be applied to future energy forecasting in various fields. Finally, the constructed models are used to forecast the NGC and NGP in the USA in the next 3years and make reasonable policy recommendations based on the forecast results.

Performance Testing and Case Studies of Rural Household Biogas Micro-CHP Systems

Against the backdrop of clean rural heating and dual carbon targets, this paper investigates and develops a household combined heat and power (CHP) unit. The performance of the unit under natural gas operating conditions was tested and evaluated in the laboratory. The test results show that at rated operating conditions the unit has an electrical efficiency of 29% and an overall efficiency of 96%. The micro-CHP was then tested in a civil building in a demonstration site in Xuzhou, Jiangsu province, for trial operation under biogas working conditions. The test results show that when the unit in biogas working condition is heating the room, the unit has a rated working condition power generation of 3.2 KW and still has an average electrical efficiency of about 26.8% and a thermal efficiency of 65.6%, which remains at a high level. And the room temperature data shows that the heat supply meets the heating demand. Finally, this paper compares the performance with micro-CHP units available in the European and American markets. The results show that the performance of this unit is superior to many micro-CHP units available overseas. This paper demonstrates through test results that rural micro-CHP system can meet the daily energy needs of farmers, improve energy use efficiency and reduce carbon emissions.