Gas Supply Chain Research Articles

Methane emissions along biomethane and biogas supply chains are underestimated

Methane emissions along biomethane and biogas supply chains are underestimated

Classical vs. reactive distillation technologies for biodiesel production: An environmental comparison using LCA methodology

Sustainable fuels and technologies are expected to significantly contribute towards climate change mitigation considering that the transportation sector provides a 23% share out of the total CO2 emissions. Biodiesel is a green fuel produced by transesterification of triglycerides with an alcohol in the presence of a catalyst. The current study entails modelling and simulation of biodiesel production, through an acid transesterification process, using both classic and intensified methods, coupled with an environmental impact analysis performed following the Life Cycle Assessment methodology. The traditional biodiesel production consists in the synthesis and separation sections, while the intensified method is based on reactive distillation. ChemCAD software was used to simulate and evaluate the processes from a technical point of view. Methanol is generated using CO2 and H2, this method being considered as a new approach for CO2 utilization. Water electrolysis is employed for H2 generation, with either biomass or natural gas as power source. A cradle-to-gate environmental analysis is performed within the current research by means of GaBi software, considering the following system boundaries: i) upstream processes: catalyst supply chain, sunflower oil supply chain, natural gas supply chain, limestone extraction and decomposition, ii) main-processes: methanol and biodiesel production, iii) downstream processes: disposal of wastes. ReCiPe method was chosen as the impact assessment method. High purities for the main product and by-product are obtained (i.e., purities higher than 99%). The outcome of the process simulation points to the conclusion that the intensified path gives better performances from technical point of view. The environmental results show that the classic approach performs better when CO2 and H2 are used as raw materials, while reactive distillation displays a higher efficiency when natural gas is used as feedstock.

Applying small-scale liquefied natural gas supply chain by fluvial transport in the isolated systems: The case study of Amazonas, Brazil

There are currently several studies about the necessity of increasing access to sustainable electricity for isolated communities or in remote areas using alternative energy sources. There are about 212 energy grid isolated systems in Brazil, mainly concentrated in the North of the country, especially in the state of Amazonas, largely supplied by diesel power plants. The isolated systems in Amazonas present significant logistical challenges due primarily to the dependence on fluvial transport. The small-scale liquefied natural gas by fluvial transport can be an alternative to natural gas supply to remote areas and isolated systems and the non-dependence or construction of new pipelines. Based on this context, the work aims to evaluate the small-scale liquefied natural gas economic costs by fluvial transport to replace diesel oil with natural gas in power plants in the state of Amazonas. It then also analyses whether this substitution can significantly mitigate greenhouse gas emissions of the electricity sector at the local level. As a result, the use of natural gas in just a few scenarios elaborated from the case studies can provide energy security, decrease local emissions of CO2eq, and reduce the electricity cost to the final consumer.

Strategic interactions between liquefied natural gas and domestic gas markets: A bilevel model

This paper presents a bilevel programming model to aid decision-making for two players who interact strategically in the liquefied natural gas (LNG) supply chain, possibly with conflicting interests. In the proposed model, an LNG operator is the leader and a natural gas (NG) producer is the follower. The LNG operator attempts to optimally locate LNG export terminals, purchase gas from the NG producer, and export it as LNG. The NG producer aims to optimize production, pipeline investments, and sales to domestic consumers and the LNG operator. To solve the bilevel problem, we first reformulate it as a single-level problem by exploiting the convexity of the lower-level problem. Then, we use disjunctive reformulations of complementarity constraints and piecewise linear approximations of objective function terms to convert the problem into a convex quadratic mixed-integer program (MIQP). Computational experiments confirm that the MIQP is tractable and can be solved efficiently. We apply our bilevel framework to a case study of the Gulf-Southwest region of the United States and evaluate several decision-making scenarios. The scenario results emphasize the importance of using the bilevel methodology to anticipate the effects of new LNG export facilities on NG prices across the domestic gas network. Adding these large gas-consuming facilities at specific locations puts upward pressure on domestic gas prices, although this is somewhat mitigated by the NG producer’s optimal production response to the increased demand.

Understanding AI Application Dynamics in Oil and Gas Supply Chain Management and Development: A Location Perspective

The purpose of this paper is to gain a better understanding of Artificial Intelligence (AI) application dynamics in the oil and gas supply chain. A location perspective is used to explore the opportunities and challenges of specific AI technologies from upstream to downstream of the oil and gas supply chain. A literature review approach is adopted to capture representative research along these locations. This was followed by descriptive and comparative analysis for the reviewed literature. Results from the conducted analysis revealed important insights about AI implementation dynamics in the oil and gas industry. Furthermore, various recommendations for technology managers, policymakers, practitioners, and industry leaders in the oil and gas industry to ensure successful AI implementation were outlined. Doi: 10.28991/HIJ-SP2022-03-01 Full Text: PDF

Auxiliary cognition system‐based management strategy optimization of supply chain of new energy in oil–gas enterprises

AbstractThe work focuses on the optimization of management strategies of energy supply chain in oil–gas enterprises to find out the optimal management strategy and overcome the fragility of the traditional energy supply system. Back propagation neural network (BPNN) is applied to energy supply predicting and energy security early warning in oil–gas enterprises, and the security early warning system is designed based on the auxiliary cognitive system. The security early warning system is used to predict the peak value of oil and gas supply, and then the detection performances and the data security transmission performances of different models are compared and analysed. Finally, a comprehensive evaluation model for security management of oil–gas supply chain based on the neural network is established. It is found that compared with convolutional neural network (CNNs), the improved BPNN model algorithm proposed shows increased accuracy by 5.4%, reduced model training time and test time, and improved accuracy and recall rate. The accuracy, recall rate, and F1 value of the proposed model are 88.48%, 75.88%, and 96.21%, respectively, which were obviously higher than those of the CNN. It suggests that the improved security management model of oil–gas supply chain shows better recognition and prediction accuracy. Based on the quantitative and qualitative analysis results, the optimization suggestions for management strategies in oil–gas enterprises are put forward, which is of strategic significance for improving the security of oil–gas supply.

Measuring the natural gas supply security performance of China's natural gas suppliers: A comprehensive framework using FAHP-Entropy-PROOTHEE method

The increasing dependence on foreign natural gas has brought severe challenges to China's natural gas supply security. Hence, choosing secure and reliable natural gas suppliers is crucial. This paper first proposes a comprehensive evaluation framework for the natural gas supply security of natural gas suppliers from two aspects: suppliers' own factors and the interaction factors between importers and suppliers based on the risk factors of the natural gas supply chain. On this basis, the PROOTHEE model based on the combination weight method is introduced to measure the supply security performance of China's 15 natural gas suppliers from 2015 to 2019. The results show that the supply security performance of China's natural gas suppliers varies greatly and the gap widened. Australia, Russia and the United States are the top three countries in terms of the overall supply security. Qatar, Norway, Turkmenistan, Kazakhstan, Malaysia and Uzbekistan show moderate supply security. Oman, Papua New Guinea, Trinidad and Tobago and Nigeria demonstrate poor supply security. Most suppliers have medium and low supply security and China's natural gas imports from suppliers do not match their supply security levels to some extent, which results in China's natural gas imports facing a relatively high supply risk. Considering risk controllability, China should focus more on interaction factors, especially to strengthen energy diplomacy.

Electrical and Hydrogen Microgrid - Energy Control of a Self-Sufficient Supply System Based on a Combined Electrical and Hydrogen Distribution Grid

The main goal of assembling a self-sufficient microgrid is to integrate all technical equipment into an autonomous energy supply system as a virtual power plant (VPP). The system integration focuses on the power electronic devices and the combination of gas and electrical supply chains. The developed microgrid structure is fed from renewable energy systems (green hydrogen), the electrical grid and the H2 gas grid with a liquid or gaseous energy source. In comparison to an island grid, the microgrid can be operated in parallel mode with the common public grid. The associated challenges of transient energy flows and the holistic view of a regulated microgrid based on an electrical grid and an H2 gas network are part of this article.

Blockchain in oil and gas industry: Applications, challenges, and future trends

Today's systems, approaches, and technologies leveraged for managing oil and gas supply chain operations fall short of providing operational transparency, traceability, audit, security, and trusted data provenance features. Also, a large portion of the existing systems are centralized, manual, and highly disintegrated, which make them vulnerable to manipulation and the single point of failure problem. In this survey, we explore the potential opportunities and applications of blockchain technology in managing the exploration, production, and supply chain and logistics operations in the oil and gas industry as it can offer traceability, immutability, transparency, and audit features in a decentralized, trusted, and secure manner. We discuss state-of-the-art blockchain-based schemes, research projects, business initiatives, and case studies to highlight the practicability of blockchain in the oil and gas industry. We present the potential opportunities brought about by blockchain technology in various use cases and application scenarios. We present several systems that use blockchain-based smart contracts to automate critical services such as petroleum product tracking and tracing, international trade document protection, and coordination of purchasing and bidding activities for granting oil exploration rights to petroleum exploration and development companies. Finally, we present open challenges acting as future research directions.

The Effect of Supply Chain Management Practices on Corporate Performance in Libya Oil and Gas Industry

In this article, supply chain management practices (SCMPs) are examined to see whether they affect the performance of the Libyan oil and gas sector. The research is quantitative and is based on a survey using simple random sampling and data collection via a standard questionnaire. The surveys were sent to 479 individuals employed in the Libyan oil and gas sector, with an average service experience of 16 to 20 years. There were 86.6 percent of men and 13.4 percent of women in the sample. Descriptive statistics and structural modelling systems are used in conjunction with Smart PLS software to evaluate hypotheses about the relationship between variables. The findings of this research show that SCMPs are greatly and positively linked to the performance of Libyan oil companies. Financial performance (FP) was significantly and positively affected by three SCMPs: Strategic Supplier Partnering (SSP) 1 , Logistics Management (LM) 2 , and Information Sharing and Quality (IS & Q) 3 . As well as non-financial performance (NFP), of Libya's petroleum industry was significantly and positively affected by two SCMPs: SSP 1 and LM 2 . The findings of the study can be used to improve the oil and gas supply chain performance, and they show that more SCMPs can lead to better company performance. Furthermore, supply chain directors will learn about the implications of efficient and high-quality supply chain implementation practices in their companies' supply chains, such as: strategic supplier partnering, logistics management, information sharing and quality, and materials procurement and inventory management. This study complements the literature by looking at the association between supply chain management techniques and firm performance in the oil and gas industry.

Multi-objective optimization of natural gas supply chain in shortage period

ABSTRACT Natural gas consumption usually has the characteristics of seasonal fluctuations. Some countries have experienced gas shortages in recent years due to the imbalance between supply and demand, especially in winter or peak gas consumption periods. Under the conditions of a gas shortage, it is necessary to ensure the benefits of marketers and improve their satisfaction from users’ perspectives, so it is difficult to find the optimal purchase and sales strategy. Considering that different pipeline transportation pricing mechanisms and different gas distribution schemes have a great impact on the benefits of marketers, this paper proposes a multi-objective optimization model that maximizes the marketer’s benefits and minimizes the hypoxia index for different pipeline transportation mechanisms because of the supply-side gas shortage in the upstream gas source. The object-weighted method is used to process the multi-objective problem. The CPLEX solver is adopted to solve the optimization problem. Finally, the model is applied to a long-distance natural gas supply chain system to prove its applicability. After multi-objective optimization, under the two pipeline transport mechanisms, profits increased by 17.5% and 6.9%, respectively, and the hypoxia index decreased by 1.11 and 1.93, respectively, which greatly improved economic benefits and user satisfaction. Overall, the optimized scheme can maintain the stable operation of the natural gas supply chain and make up for the losses caused by the gas shortage.

Evaluating natural gas supply security in China: An exhaustible resource market equilibrium model

The continuous growth of natural gas demand in China has brought about supply security from the insufficient supply of domestic resources and ever-increasing dependency on natural gas imports. A multi-agent game framework for supply security is proposed to investigate availability, accessibility, and affordability along natural gas industry and supply chain. In this framework, an exhaustible resource market equilibrium model based on mixed complementary programming (MCP) is developed, taking into account the dynamic depletion of the remaining recoverable reserves, to evaluate the gas supply security in China. The results indicate the supply security of China's natural gas is far from optimistic from 2020 to 2049, and the newly-increased recoverable reserves of 200 bcm/a are required to meet the expanding scale of the natural gas market. However, it will face supply risks with lower Reserve-Production ratios after 2030 and higher dependencies after 2040. Therefore, the equilibrium model is used to simulate for seeking feasible solutions to ensure the natural gas supply security from three aspects: market structure, discount rate, and resources tax. Several combinational strategies suitable for China's natural gas supply security are suggested according to the market structure, natural gas import, discount rate, and resources tax for building a more diversification system of natural gas supply, establishing an efficient allocation system of the natural gas market and setting up an effective strategic stockpile system of natural gas resources.

A decision support system for sustainability prioritization of air pollution control technologies in energy and carbon management: Oil & gas industry of Iran

The environmental, economic, social, and technical challenges associated with implementing the air pollution control technologies (APCTs) in oil, gas, and petrochemical industries (OGPIs) necessitate a sustainability prioritization analysis. Environmental issues, economic parameters, social concerns, and technical performances all must assessed and weighted. Accomplishing such a crucial decision with rigorous potential environmental, economic, social, and technical effects needs a robust decision support system (DSS) for guiding the sustainability prioritization process of the technologies. This study purposes in developing a hybrid DSS by integrating the analytical network process (ANP), decision-making trial and evaluation laboratory (DEMATEL), and multi-objective optimization on the basis of ration analysis (MULTIMOORA) methods based on the fuzzy set theory for sustainability prioritization of implementing APCTs. Three cases of the petrochemical plant, gas refinery, and oil refinery in Iran are selected to assess and prioritize the most commonly used of APCTs including four alternatives of Electrostatic precipitators, Fabric filters, and Wet scrubbers, and Cyclones by the proposed DSS. The sustainability ranking of implementing the four technologies sequentially for three cases is obtained. The suggested DSS is feasible for group decision-making based on experts’ opinions and uncertain data. It can assist the stakeholders/decision-makers to attain the energy and carbon management (ECM) policies in three cases and apply to advance the oil & gas supply chains towards the targets of sustainability, green growth, and low-carbon economy. Finally, sensitivity analysis indicates the robustness of the proposed approach for the prioritization order.

Multi-echelon inventory optimization in closed-loop supply chain

In this paper, we are interested in the stock control in the closed-loop supply chain and its application to the cylinder packaged gas supply chains of Air Liquide company. Air Liquide is a multinational company producing and distributing industrial and medical gases. Firstly, we introduce a new classification of demand profile and perform a comprehensive study of the goodness of fit of demands sizes and time between successive demands based on real data. As well known in the literature (Boylan and Syntetos (2021)), we find that negative binomial distribution fits better demand sizes and intervals. Secondly, we explain how to use a single echelon model that is easy to implement on the field, to control inventory and compute the stock target in the observable part of the closed-loop supply chain. It is modeled as a multi-echelon serial inventory system. Finally, we perform numerical analysis and comparisons of methods to compute the stock target of the observable part of the closed-loop supply chain with real data. Those methods are derivated from the up-to-level periodic inventory review policy where we vary the statistical distribution of the demand and the measure of the service level. Using the Item Fill Rate (IFR) as a service level measure in the stock target calculation, reduce the stock target by more than 10% compared to the stock target obtained using the Cycle Service Level (CSL). We also investigate methods using compound Poisson distribution for demand models.

Optimisation of natural gas supply chain considering pipeline transportation cost reformation in China

Optimisation of natural gas supply chain considering pipeline transportation cost reformation in China

Subsidized and unsubsidized price competition in a multi-echelon natural gas supply chain with governmental and private members

Determining the optimal prices among supply chain members, including both governmental and private natural gas producers, processing plants (PPs), and distribution centers (DCs) is an important and challenging task. This paper analyses the competitions between the members of a multi-echelon natural gas supply chain for determining their prices, which specify the demand for their products. This study proposes a novel game theory model to investigate cooperative and non-cooperative pricing scenarios. While, in Scenario 1, all supply chain members make decisions cooperatively as a centralized model, the members compete simultaneously to obtain the Nash equilibrium in Scenario 2. The Karush-Kuhn-Tucker (KKT) conditions are defined for the non-cooperative game and the Nash equilibrium is obtained using two different methods. The proposed model is applied to a realistic case study based on a data set derived from the natural gas industry to analyze the optimal pricing decisions under the two scenarios and verify the practicality of the proposed approach. Moreover, the variability in the profits of the members with respect to the changes in the base demands and self-price sensitivity parameters are studied. The results show that the government’s total profit in the cooperative scenario is more than the non-cooperative scenario, while the profit of the private DC in the Nash equilibrium of the non-cooperative scenario is more than that of the cooperative scenario. Moreover, increasing the base demand for the private DC leads to a decrease and increase in its profit in Scenarios 1 and 2, respectively. These findings illustrate the potential sources of conflicts between governmental and private members of the natural gas supply chains when seeking collaboration to improve their profits and highlight the need for future research on incentive mechanism designs facilitating such collaboration.

Simulation model of natural gas supply chain in a function of costs optimization: the case of Croatia

The purpose of this study is to develop a natural gas simulation model for costs optimization. The main focus of the model is on the transmission system since its imbalance leads to the penalties which must be paid by the suppliers. The total nominated amount of natural gas (the gas injected into the transmission system) must be consumed (withdrawn from the system) in order for the system to be in balance. In practice, this is not the case since it is very hard to accurately predict the future hourly consumption (in order to make a nomination) and certain deviations appear which leads to the imbalance. However, this problem could be solved by introducing a special electromotor valve which would be placed at the beginning of the distribution system and control the accumulation of the system. To test this solution, a simulation model was created using Arena Simulation tool. Data for the simulation model are collected by the natural gas distributor and consist of natural gas consumption and nomination values for one measuring-reduction station on the hourly basis. Thus, the final dataset includes 8.754 records. The separate As-Is and To-Be models for seven (summer) months were made and the results were compared. The simulation experiment shows that the positive rebalancing energy would be reduced by 32%, and the negative one by 34%. The created model can serve as a good initial step for the analysis of the justification of investment in the implementation of a technical solution that would manage the accumulation of the distribution system.

Optimizing profit and reliability using a bi-objective mathematical model for oil and gas supply chain under disruption risks

Today, oil and natural gas are the most important sources of human energy. These two essential sources have strategic importance in economic and political equations of the world and can play a significant role in the process of international relations. To this end, this research design a multi-echelon network for the oil and gas supply chain includes extraction, purification, storage and shipping to the target market. Furthermore, a bi-objective mathematical model is formulated which attempts to maximize total profit from the sale of fossil fuels and to maximize the reliability of processing plants to meet the applicants’ demand. Besides, there may be disturbances in the extraction phase due to the failure of the extractors. To this end, several disruption scenarios are provided to deal with possible disorders. Moreover, a real-world case study in the Iranian oil and gas industry is applied to verify the proposed model and it is solved using augmented ε-constraint and goal programming methods. Also, the sensitivity analysis is performed to provide some useful managerial insights. The results show that the ε-constraint method was selected as the best approach in terms of CPU time and objective functions’ values. Moreover, an increment in demand led to the use of more gathering centers, which increased the inventories and economic factors such as costs. Likewise, shipping costs of products to gathering centers are also very effective in selecting them. Therefore, locating gathering centers is a very important and precise task that is affected by geographical conditions, transport equipment, and proximity to key applicants.

A multi-objective model for an integrated oil and natural gas supply chain under uncertainty

The oil and gas networks are overlapped because of the inclusion of associated gas in crude oil. This necessitates the integration and planning of oil and gas supply chain together. In recent years, hydrocarbon market has experienced high fluctuation in demands and prices which leads to considerable economic disruptions. Therefore, planning of oil and gas supply chain, considering market uncertainty is a significant area of research. In this regard, this study develops a multi-objective stochastic optimization model for tactical planning of downstream segment of oil and natural gas supply chain under uncertainty of price and demand of petroleum products. The proposed model was formulated based on a two-stage stochastic programming approach with a finite number of realizations. The proposed model helps to assess various trade-offs among the selected goals and guides decision maker(s) to effectively manage oil and natural gas supply chain. The applicability and the utility of the proposed model has been demonstrated using the case of Saudi Arabia oil and gas supply chain. The model is solved using the improved augmented ε-constraint algorithm. The impact of uncertainty of price and demand of petroleum products on the obtained results was investigated. The Value of Stochastic Solution (VSS) for total cost, total revenue, and service level reached a maximum of 12.6%, 0.4%, and 6.2% of wait-and see solutions, respectively. Therefore, the Value of the Stochastic Solution proved the importance of using stochastic programming approach over deterministic approach. In addition, the obtained results indicate that uncertainty in demand has higher impact on the oil and gas supply chain performance than the price.

Multi-Objective optimization of the Hydrocarbon supply chain under price and demand uncertainty

ABSTRACT The energy sector is known for its enormous investments, despite the erratic behaviour of the oil market (e.g. changes in crude oil prices). Therefore, strategic and tactical planning of the Hydrocarbon supply chain (HCSC), considering market uncertainty, is a significant area of research. HCSC construction involves the integration of crude oil and natural gas supply chains (SCs). In this study, a stochastic multi-objective optimization model is developed for the tactical planning of HCSC. The model considers price and demand uncertainty by formulating a two-stage stochastic programming model. Financial objectives are considered in terms of cost minimization and revenue maximization, while a non-financial objective is considered in terms of depletion rate minimization (i.e. reserves sustainability maximization). The model assists the decision-maker in quantifying the amount of production required to meet demand under different scenarios. Furthermore, the proposed model assists in evaluating the trade-offs among alternatives. A real-world HCSC is used to elucidate the practicability of the model, and some managerial insights are derived by conducting a sensitivity analysis. For instance, production can be reduced during high demand periods to maintain enough reserves, and the excess demand can be satisfied from the outside market based on medium-term contracts.