Oil Supply Chain Research Articles

Analysis of Supply Chain Risk Control in Aceh Nut Work Using the House Of Risk Method Approach (Case Study: Nutmeg Oil Industry in South Aceh Regency)

South Aceh nutmeg oil plays an important role in the trade of essential oils from Indonesia. 70-80% of Indonesia's nutmeg oil trade comes from South Aceh. However, on the other hand, there is a disruption in price stability in the production of South Aceh nutmeg oil which is influenced by the quality standards of nutmeg oil. The purpose of this study is to identify potential risks that arise in the supply chain, find risk agents as the cause of the risk and control the risk by designing risk mitigation and its formulation in the structure of the nutmeg oil supply chain in South Aceh. The technique used in this study is the House Of Risk (HOR) method. The study has obtained 23 risk events and 17 priority risk agents covering Source, Make, Distribution and Costumer (Return) activities. Then from the risk agents obtained, 11 priority mitigation actions were also obtained as a control of the performance of the nutmeg oil supply chain that can be applied in an effort to reduce the occurrence of supply chain risks so that the industry is able to produce optimally and is able to improve the quality of nutmeg oil which of course has a positive impact on the selling value.

Sustainability Service Chain Capabilities in the Oil and Gas Industry: A Fuzzy Hybrid Approach SWARA-MABAC

This study aims to redefine the capabilities of the service supply chain in the Iranian oil and gas industry, where the concept of service chains has remained underdeveloped due to a traditional focus on conventional service chains. It seeks to compare the capabilities of the oil and gas service supply chain to those in other sectors and explore whether applied research is necessary to improve service chain performance. The study was conducted in three stages, involving ten petroleum and natural gas experts. The first stage focused on identifying key capabilities, yielding seven significant capabilities and 28 critical sub-capabilities. In the second stage, the SWARA fuzzy hybrid approach was employed to weigh and prioritize these capabilities, while the fuzzy MABAC methodology was used for strategic decision-making. Finally, the model’s sensitivity was assessed using fuzzy methods to validate the findings. The results highlight the highest priority capability for service providers: the ability to utilize information for updating information processing capacity in making decisions within the service supply chain. The selected location, G1, emerged as a key area of focus. This approach presents a novel method for optimizing service supply chain locations within the oil and gas sector. This paper introduces a unique approach to selecting optimal service supply chain locations in the oil and gas industry, addressing critical gaps in previous research. Employing advanced fuzzy hybrid methodologies provides valuable insights into improving service chain capabilities, offering a competitive advantage for companies operating in this sector.

Hydrocarbon trade deflection and supply‑chain trade restructuring under sanctions imposed during theRussia–Ukraine conflict

In response to international sanctions, Russia has restructured its hydrocarbon commerce through trade deflection, redirecting exports from sanctioning to non-sanctioning countries. Theresearch aimed to analyze hydrocarbon trade deflection under theRussia–Ukraine conflict within thecontext of theChina–Russia Strategic Partnership and BRICS, and to assess therestructuring of Russia’s crude oil and natural gas supply chains from 2020 to 2023. Two questions were addressed: Whether trade deflection shifted towards non-sanctioning countries, including China and other BRICS countries; whether Russia’s oil and gas supply chains were restructured towards these regions? Design incorporates themethod developed by theauthor which generates and utilizes an integrated database of Bill of Lading and export data to analyze supply-chain trade restructuring, identifying specific shifts in trade flows by product, country, and enterprises. Findings reflected that after sanctions were imposed in early 2022, Russian hydrocarbon exports to sanctioning countries declined sharply, while exports to non-sanctioning countries, particularly China and India, increased significantly. Findings demonstrate theeffectiveness of trade deflection as a strategic response to sanctions. This strategy has mitigated theadverse impacts on Russian oil and gas industry, with significant increases in exports to China and India. TheComprehensive Strategic Partnership with China has helped secure a substantial market for Russian crude oil and increased China’s energy security. This study enhances understanding of trade deflection mechanisms and provides a framework for analyzing theinterplay between international trade, geopolitical strategies, and economic resilience. Geopolitical alliances and trade partnerships have ensured ­resilience and continuity in global trade. This shift indicates strategic diversification towards Asian markets and increased Central Asian involvement. BRICS engagement has provided Russia with a platform to advocate for energy security and challenge Western dominance in global energy governance. Future research should explore other supply-chain components and analyze trade within theBRICS+ group and Russia–China bilateral trade.

Corporate farming model for sustainable supply chain crude palm oil of independent smallholder farmers

Independent oil palm smallholders in Indonesia contributed significantly to the sustainable of Crude Palm Oil (CPO) supply chain. On the other hand, sustainability in the process of implementing the supply chain system for independent smallholders is an issue that is extremely important for the success of the supply chain system for the CPO company. This study’s objective is to develop an alternative business model with the goal of strengthening the interaction that already exists between independent farmers and cooperatives in ensuring the existence of a supply chain for raw materials and having access to financial resources, knowledge, and technology. The selection of the research location considered several factors, including the size of the area’s oil palm plantations, the number of oil palm farmers who are self-sufficient, and the possibility of locating CPO plant. Study findings that, the new model by business canvas analysis as the upgraded model of existing model business recommended that it should be built based on the cooperative palm oil family which provides farmers with access to financial, replanting, intensification, and extensification and infrastructure by APKASINDO, BPDPKS, Bank, and other stakeholders to sustain the FFB supply chain in cooperation through a partnership program in a long-term scheme. This whole system was developed by corporate farming system based on the cooperative as the off taker of FFB farmers and develop mini plant CPO to produce cooking oil and biofuels and by product as the waste management system as the value added for farmers.

Developing an integrated and collaborated evaluation index system for crude oil supply chains: A case study from China

The crude oil supply chain involves production, transportation, refining, marketing, storage and trade with numerous interconnected links and complex interrelationships. Enhancing the overall value of crude oil supply chains depends on the coordinated development of their upper, middle and lower reaches. However, the absence of a scientific and objective evaluation index system for collaborative development within the crude oil industry precludes identification of weak links that require improvement, thereby impeding supply chain development decision-making. This study aims to construct an integrated and coordinated development evaluation index system for crude oil supply chains, providing a crucial reference framework for scientific evaluation, guidance, and assessment of crude oil supply chains. Taking China's crude oil supply chain as a case study, this research analyzes the current state of crude oil supply chain research and employs the 5 dimensions of SCOR as the primary evaluation index to examine production, transportation, refining, marketing, storage, and trading. An evaluation index system for integrated and coordinated development of the crude oil supply chain is presented, followed by the development of an evaluation model combining the combination weighting method with the TOPSIS approach. The index system and model established in this study are applied to PetroChina. The findings indicate that PetroChina achieved the highest integration and coordination level in 2021, with a score of 0.578, attributed to optimal supply chain efficiency; conversely, the integration coordination degree in 2017 was subpar, with a score of only 0.468, primarily due to the supply chain's lack of responsiveness. This evaluation index system and model offer a comprehensive assessment of the development in each link and aspect within the supply chain, which can intuitively reflect the weak points of the supply chain and provide essential theoretical support for integrated and coordinated development of PetroChina.

Bilevel Optimization for Capacity Contingency Debottlenecking Considering Trade-Offs within the Waste Oil Supply Chain

Bilevel Optimization for Capacity Contingency Debottlenecking Considering Trade-Offs within the Waste Oil Supply Chain

Integrated risk management and maintenance planning in Oil and Gas Supply Chain operations under market uncertainty

The Oil and Gas Supply Chain (OGSC) is a multifaceted network comprising diverse activities and echelons. Instability or interruptions can cause economic fluctuations, impacting industries, markets, and consumers. Maintenance activities, which pause production but extend facilities' life, are recommended during non-peak demand periods to avoid production losses and meet customer demand. To mitigate these effects, decisions on operations planning, maintenance scheduling, and maintenance team assignments should be optimized in a risk management framework. The proposed model adopts a mixed-integer linear programming (MILP) framework and is solved via a sequential approach that incorporates the relax-and-fix (RF) heuristic in order to find a solution that is close to optimal. Subsequently, the solution serves as an initial solution for the CPLEX solver, which employs a branch-and-cut algorithm to attain the exact optimal solution. The practicality of this model has been showcased through its application to the supply chain in Saudi Arabia. The model efficiently schedules maintenance activities evenly and consistently across the OGSC plants over the planning period to reduce lost sales by keeping plants operational during high-demand periods. Furthermore, a sensitivity analysis was conducted to investigate the influence of the decision-maker's risk attitude on the outcomes that were obtained.

Constructing a measurement-based spatially explicit inventory of US oil and gas methane emissions (2021)

Abstract. Accurate and comprehensive quantification of oil and gas methane emissions is pivotal in informing effective methane mitigation policies while also supporting the assessment and tracking of progress towards emissions reduction targets set by governments and industry. While national bottom-up source-level inventories are useful for understanding the sources of methane emissions, they are often unrepresentative across spatial scales, and their reliance on generic emission factors produces underestimations when compared with measurement-based inventories. Here, we compile and analyze previously reported ground-based facility-level methane emissions measurements (n=1540) in the major US oil- and gas-producing basins and develop representative methane emission profiles for key facility categories in the US oil and gas supply chain, including well sites, natural-gas compressor stations, processing plants, crude-oil refineries, and pipelines. We then integrate these emissions data with comprehensive spatial data on national oil and gas activity to estimate each facility's mean total methane emissions and uncertainties for the year 2021, from which we develop a mean estimate of annual national methane emissions resolved at 0.1° × 0.1° spatial scales (∼ 10 km × 10 km). From this measurement-based methane emissions inventory (EI-ME), we estimate total US national oil and gas methane emissions of approximately 16 Tg (95 % confidence interval of 14–18 Tg) in 2021, which is ∼ 2 times greater than the EPA Greenhouse Gas Inventory. Our estimate represents a mean gas-production-normalized methane loss rate of 2.6 %, consistent with recent satellite-based estimates. We find significant variability in both the magnitude and spatial distribution of basin-level methane emissions, ranging from production-normalized methane loss rates of < 1 % in the gas-dominant Appalachian and Haynesville regions to > 3 %–6 % in oil-dominant basins, including the Permian, Bakken, and the Uinta. Additionally, we present and compare novel comprehensive wide-area airborne remote-sensing data and results for total area methane emissions and the relative contributions of diffuse and concentrated methane point sources as quantified using MethaneAIR in 2021. The MethaneAIR assessment showed reasonable agreement with independent regional methane quantification results in sub-regions of the Permian and Uinta basins and indicated that diffuse area sources accounted for the majority of the total oil and gas emissions in these two regions. Our assessment offers key insights into plausible underlying drivers of basin-to-basin variabilities in oil and gas methane emissions, emphasizing the importance of integrating measurement-based data when developing high-resolution spatially explicit methane inventories in support of accurate methane assessment, attribution, and mitigation. The high-resolution spatially explicit EI-ME inventory is publicly available at https://doi.org/10.5281/zenodo.10734299 (Omara, 2024).

Challenges and sustainable economic strategies in global vegetable oil trade: A qualitative analysis of cadets in nautical and technical programs

Abstract The global trade of vegetable oils is a critical component of the global supply chain, with significant economic, environmental, and policy implications. This research focuses on cadets in nautical and technical programs, who are prospective seafarers responsible for the distribution of vegetable oils. Incorporating a qualitative research approach and a comprehensive literature review, this study aims to elucidate the cadets’ understanding of and perspectives on the challenges and sustainable economic strategies associated with vegetable oils. The methodology includes semi-structured interviews with cadets, followed by thematic data analysis. The findings are expected to provide insights for policy change, curriculum development, and industry strategies. Understanding the cadets’ viewpoints is crucial for crafting effective solutions in an industry marked by price fluctuations, environmental concerns, and geopolitical influences. This research emphasizes the importance of sustainable economic strategies in ensuring the long-term viability and environmental responsibility of the vegetable oil supply chain. By enhancing the awareness and preparedness of future seafarers, this study contributes to the ongoing dialogue on sustainable and economically viable vegetable oil trade.

A dynamic two-phased approach for planning downstream oil supply chain network under uncertainty

This paper introduces an innovative approach for designing a downstream oil supply chain network under uncertain conditions, aiming to bridge existing gaps in tactical decision-making and day-to-day operational management. In the initial phase, the focus lies on tactical and operational decisions related to network planning, while the second phase emphasizes real-time transshipment decisions for daily operations. This two-phased strategy proves to be adaptable and proficient in effectively handling uncertainties. Applying the mixed possibilistic-flexible programming and a rolling horizon approach, we enhance the overall performance of the oil supply chain network. To validate the efficacy of our proposed method, we conduct a comprehensive case study utilizing a database sourced from governmental agencies, specifically tailored to the Iranian oil supply chain. Our results demonstrate the efficiency of the proposed approach in addressing uncertainties, including demand fluctuations and network costs. Furthermore, the methodology exhibits potential for broader applications in other supply chain systems and industries grappling with similar challenges.

The conceptual model of Indonesian palm oil supply chain based on blockchain

Abstract Indonesia is the world’s largest producer and consumer of palm oil. However, the palm oil supply chain faces numerous challenges related to actors, traceability, transparency, and sustainability. To address these issues, a conceptual model for a blockchain-based palm oil supply chain in Indonesia is proposed. The model aims to leverage the decentralized and immutable nature of blockchain technology. It incorporates components such as smart contracts, distributed ledgers, and data sharing mechanisms to enhance traceability, transparency, and sustainability in the palm oil supply chain. The research utilized the SSM (Soft System Methodology) and CATWOE (Customer, Actor, Transformation, Worldview, Owner, Environment) methodologies. The actors involved in the palm oil supply chain include smallholders, plantation companies, third-party suppliers, middlemen, traders, palm oil mills, refineries, distributors, retailers, and consumers. The blockchain conceptual model is structured based on a sustainable supply chain model that considers economic, social, and environmental aspects. Blockchain transactions regulate the process of supply chain actors, while stakeholders such as the government, financial institutions, palm oil research organizations, NGOs, and consumers are involved. The blockchain-based supply chain model is expected to address the key issues of the palm oil supply chain, aiming for a sustainable supply chain system. By implementing this model, it is anticipated that the palm oil industry can achieve greater accountability, transparency, and environmental sustainability.

Linear Programming Model for Sunflower Oil Supply Chain Network

Food supply networks are vital to the global economy, producing and consuming products worldwide. Supply chain management (SCM) has emerged as a crucial technique to optimize the integrated network of suppliers, factories, warehouses, customers, and distribution channels. SCM aims to ensure efficient raw material sources, transformation, transportation, and timely customer delivery. Edible oil, a crucial ingredient for cooking and food preparation, is utilized by people from all social classes. Vegetable oils, particularly sunflower, soybean, peanut, and others, are the main source of edible oils, which have less unsaturated fatty acids compared to animal oils. The production and consumption of edible oils have substantial environmental impacts, necessitating increased research to address these implications. Sunflower, a valuable oilseed crop, is grown worldwide due to its short growing season and adaptability to various climates and soil types. Sunflower seeds have high oil and protein content, making them popular for their health benefits. This article uses a linear optimization model for the sunflower oil supply chain from the farmer to the end user. The objective is to minimize the overall transportation cost subject to capacity, demand fulfillment, and waste management.

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.

2G-lactic acid from olive oil supply chain waste: olive leaves upcycling via Lactobacillus casei fermentation

The transition towards a sustainable model, particularly the circular economy, emphasizes the importance of redefining waste as a valuable resource, paving the way for innovative upcycling strategies. The olive oil industry, with its significant output of agricultural waste, offers a promising avenue for high-value biomass conversion into useful products through microbial processes. This study focuses on exploring new, high-value applications for olive leaves waste, utilizing a biotechnological approach with Lactobacillus casei for the production of second-generation lactic acid. Contrary to initial expectations, the inherent high polyphenol content and low fermentable glucose levels in olive leaves posed challenges for fermentation. Addressing this, an enzymatic hydrolysis step, following a preliminary extraction process, was implemented to increase glucose availability. Subsequent small-scale fermentation tests were conducted with and without nutrient supplements, identifying the medium that yielded the highest lactic acid production for scale-up. The scaled-up batch fermentation process achieved an enhanced conversion rate (83.58%) and specific productivity (0.26 g/L·h). This research confirms the feasibility of repurposing olive waste leaves for the production of lactic acid, contributing to the advancement of a greener economy through the valorization of agricultural waste.Key points• Olive leaves slurry as it did not allow L. casei to ferment.• High concentrations of polyphenols inhibit fermentation of L. casei.• Enzymatic hydrolysis combined to organosolv extraction is the best pretreatment for lactic acid production starting from leaves and olive pruning waste.Graphical

DEA-driven risk management framework for oil supply chains

This paper develops a comprehensive risk management framework to optimise the Nigerian oil supply chain risk mitigation strategies. Given the complexities of the oil supply chain, this framework aids researchers and decision-makers in navigating innovative risk management approaches and considering the implementation of associated challenges. Data Envelopment Analysis (DEA) is initially employed to prioritise risk factors, and based on obtained results, optimal response strategies are determined. The study's findings reveal that criminality and terrorist attacks are the paramount Nigerian oil supply chain risk factors. Additionally, the research identifies practical mitigation strategies, such as risk transfer, safety planning, alternative energy carriers, improved energy efficiency, emergency rescue plans, expected shortages, and diplomatic relations. This research contributes valuable insights to both academia and industry, fostering a deeper understanding of risk dynamics in the Nigerian oil sector. The framework presented offers a powerful approach to risk management, providing a foundation for informed decision-making and strategic planning in the ever-evolving landscape of supply chain dynamics.

Information Technology, Dynamic Capabilities, and Strategic Planning in the Oil and Gas Supply Chain

Information Technology, Dynamic Capabilities, and Strategic Planning in the Oil and Gas Supply Chain

Lean transportation tools towards waste reduction and digital transformation in the offshore industry: an action-research

Purpose To address the absence of Lean in transportation logistics in the digital era, this study aims to investigate the application of Lean transportation (LT) tools to reduce waste and facilitate the digital transformation of dedicated road transportation in the offshore industry. Design/methodology/approach The study adopts action research with a multimethod approach, including a scoping review, focus groups (FG) and participant observation. The research is conducted within the offshore supply chain of a major oil and gas company. Findings Implementing LT’s continuous improvement tools, particularly value stream mapping (VSM), reduces offshore transportation waste and provides empirical evidence about the intersection of Lean and digital technologies. Applying techniques drawn from organisational learning theory (OLT), stakeholders involved in VSM mapping and FGs engage in problem-solving and develop action plans, driving digital transformation. Waste reduction in loading and unloading stages leads to control actions, automation and process improvements, significantly reducing downtime. This results in an annual monetary gain of US$1.3m. The study also identifies waste related to human effort and underutilised digital resources. Originality/value This study contributes to theory and practice by using action research and LT techniques in a real intervention case. From the lens of OLT, it highlights the potential of LT tools for digital transformation and demonstrates the convergence of waste reduction through Lean and Industry 4.0 technologies in the offshore supply chain. Practical outputs, including a benchmarking questionnaire and a plan-do-check-act cycle, are provided for other companies in the same industry segment.

Enhancing supply chain resilience facing partial and complete disruptions: The application in the cooking oil industry

Nowadays, food supply chains are more vulnerable to disruptions due to competition, uncertain environments, and global catastrophes such as the recent pandemic. Due to the importance of addressing disruption risks, this study develops a hybrid mechanism to enhance resilience in a multi-product supply chain. The proposed mechanism improves supplier resilience in a Cooking Oil Supply Chain (COSC) network design. First, we identify elements that affect supplier resilience and present a mechanism based on the Analytical Network Process (ANP) to evaluate resilience. In addition, several strategies are determined to enhance network resilience. Moreover, a stochastic mathematical formulation is proposed to utilize suppliers’ scores and a combination of identified strategies to strengthen supply chain resilience. The developed stochastic model encompasses three objectives and considers possible partial and complete disruptions of the supply chain network. Furthermore, we develop a robust counterpart formulation to handle the disruption uncertainty of the COSC and utilize the Normalized Normal Constraint (NNC) method to generate Pareto optimal solutions. Lastly, we put the implemented mechanism into practice in a real-world case to demonstrate its effectiveness in enhancing supply chain resilience. Likewise, we unveil that the implementation of suitable resilience strategies within the supply chain can lead to substantial improvements in performance metrics. For example, the results obtained illustrate that by embracing resilience strategies, the proposed mechanism has the potential to reduce the overall cost of the supply chain by as much as 56%.

Forecasting retail fuel demand in Chinese gasoline stations: a structural (double) damped trend approach

Forecasting retail fuel demand represents a crucial task for petrol companies. Indeed, the accurate prediction of demand allows improving the management of gasoline stations as well as the whole oil supply chain process. This paper provides a time series forecasting model, defined as double damped trend model, that allows efficiently forecasting the daily retail fuel demand. We propose a flexible state-space approach that encompasses several exponential smoothing alternatives. This model was first considered for the case when a single source of error drives the dynamics of the process. However, little attention was given to the case where multiple sources of errors drive its dynamics, the so-called structural approach. In this paper we focus on this case by providing closed-form results that allows simplify its likelihood estimation as well as the construction of prediction intervals. Moreover, using data for more than 400 gasoline stations in China, we show that our approach outperforms standard benchmarks in predicting both the amount of customer’s demand and prediction intervals, for different types of fuels. These results make our approach appealing for petrol companies.

First validation of high-resolution satellite-derived methane emissions from an active gas leak in the UK

Abstract. Atmospheric methane (CH4) is the second-most-important anthropogenic greenhouse gas and has a 20-year global warming potential 82 times greater than carbon dioxide (CO2). Anthropogenic sources account for ∼ 60 % of global CH4 emissions, of which 20 % come from oil and gas exploration, production and distribution. High-resolution satellite-based imaging spectrometers are becoming important tools for detecting and monitoring CH4 point source emissions, aiding mitigation. However, validation of these satellite measurements, such as those from the commercial GHGSat satellite constellation, has so far not been documented for active leaks. Here we present the monitoring and quantification, by GHGSat's satellites, of the CH4 emissions from an active gas leak from a downstream natural gas distribution pipeline near Cheltenham, UK, in the spring and summer of 2023 and provide the first validation of the satellite-derived emission estimates using surface-based mobile greenhouse gas surveys. We also use a Lagrangian transport model, the UK Met Office's Numerical Atmospheric-dispersion Modelling Environment (NAME), to estimate the flux from both satellite- and ground-based observation methods and assess the leak's contribution to observed concentrations at a local tall tower site (30 km away). We find GHGSat's emission estimates to be in broad agreement with those made from the in situ measurements. During the study period (March–June 2023) GHGSat's emission estimates are 236–1357 kg CH4 h−1, whereas the mobile surface measurements are 634–846 kg CH4 h−1. The large variability is likely down to variations in flow through the pipe and engineering works across the 11-week period. Modelled flux estimates in NAME are 181–1243 kg CH4 h−1, which are lower than the satellite- and mobile-survey-derived fluxes but are within the uncertainty. After detecting the leak in March 2023, the local utility company was contacted, and the leak was fixed by mid-June 2023. Our results demonstrate that GHGSat's observations can produce flux estimates that broadly agree with surface-based mobile measurements. Validating the accuracy of the information provided by targeted, high-resolution satellite monitoring shows how it can play an important role in identifying emission sources, including unplanned fugitive releases that are inherently challenging to identify, track, and estimate their impact and duration. Rapid, widespread access to such data to inform local action to address fugitive emission sources across the oil and gas supply chain could play a significant role in reducing anthropogenic contributions to climate change.