Journal articles on the topic 'Oil spills Decision making Mathematical models'
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Temitope Yekeen, Shamsudeen, and Abdul-Lateef Balogun. "Advances in Remote Sensing Technology, Machine Learning and Deep Learning for Marine Oil Spill Detection, Prediction and Vulnerability Assessment." Remote Sensing 12, no. 20 (October 18, 2020): 3416. http://dx.doi.org/10.3390/rs12203416.
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Although advancements in remote sensing technology have facilitated quick capture and identification of the source and location of oil spills in water bodies, the presence of other biogenic elements (lookalikes) with similar visual attributes hinder rapid detection and prompt decision making for emergency response. To date, different methods have been applied to distinguish oil spills from lookalikes with limited success. In addition, accurately modeling the trajectory of oil spills remains a challenge. Thus, we aim to provide further insights on the multi-faceted problem by undertaking a holistic review of past and current approaches to marine oil spill disaster reduction as well as explore the potentials of emerging digital trends in minimizing oil spill hazards. The scope of previous reviews is extended by covering the inter-related dimensions of detection, discrimination, and trajectory prediction of oil spills for vulnerability assessment. Findings show that both optical and microwave airborne and satellite remote sensors are used for oil spill monitoring with microwave sensors being more widely used due to their ability to operate under any weather condition. However, the accuracy of both sensors is affected by the presence of biogenic elements, leading to false positive depiction of oil spills. Statistical image segmentation has been widely used to discriminate lookalikes from oil spills with varying levels of accuracy but the emergence of digitalization technologies in the fourth industrial revolution (IR 4.0) is enabling the use of Machine learning (ML) and deep learning (DL) models, which are more promising than the statistical methods. The Support Vector Machine (SVM) and Artificial Neural Network (ANN) are the most used machine learning algorithms for oil spill detection, although the restriction of ML models to feed forward image classification without support for the end-to-end trainable framework limits its accuracy. On the other hand, deep learning models’ strong feature extraction and autonomous learning capability enhance their detection accuracy. Also, mathematical models based on lagrangian method have improved oil spill trajectory prediction with higher real time accuracy than the conventional worst case, average and survey-based approaches. However, these newer models are unable to quantify oil droplets and uncertainty in vulnerability prediction. Considering that there is yet no single best remote sensing technique for unambiguous detection and discrimination of oil spills and lookalikes, it is imperative to advance research in the field in order to improve existing technology and develop specialized sensors for accurate oil spill detection and enhanced classification, leveraging emerging geospatial computer vision initiatives.
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Ishiki, Shane, and Dexter Chan. "U.S. COAST GUARD SPILL PLANNING, EXERCISE, AND RESPONSE SYSTEM (SPEARS)." International Oil Spill Conference Proceedings 1995, no. 1 (February 1, 1995): 1039–40. http://dx.doi.org/10.7901/2169-3358-1995-1-1039.
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ABSTRACT The confluence of managing risk, achieving preparedness, partnership, and effective pollution response actions is a “sine qua non” for minimizing water pollution and damage to the environment and natural resources. To succeed, the U.S. Coast Guard is implementing a new computer-based tool called SPEARS for use by Coast Guard on scene coordinators for incidents involving hazardous chemical or oil pollution. Highly capable, versatile, and user friendly, SPEARS uses state of the art technology, databases, mathematical models, and digital maps to manage information and support decision making for risk management, planning, exercises, and pollution response.
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Semanov, G. N., A. N. Gutnik, S. N. Zatsepa, A. A. Ivchenko, V. V. Solbakov, V. V. Stanovoy, and A. A. Shivaev. "Net environmental benefit analysis — a tool of decision-making at oil spill response." Arctic: Ecology and Economy, no. 1(25) (March 2017): 47–58. http://dx.doi.org/10.25283/2223-4594-2017-1-47-58.
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Development of oilfields started in Arctic requires adequate response preparedness to potential oil spills. Mechanical recovery due to specific conditions of Arctic has a lot of limitation in application and cannot prevent pollution of Special protected areas (SPA). It is necessary to consider application of dispersants and in situ burning (ISB). Oil spill dispersants are mixtures of nontoxic surface active agents in organic solvent, specifically formulated to enhance the natural dispersion of oil into the sea water column thus enhancing the biodegradation processes. Dispersed oil is practically non adhesive to feather of birds and hair of mammals. The treatment of oil with dispersants requires a cautious strategy in making decisions. It can be achieved by usage of special tool –Net Environmental Benefit Analysis (NEBA) procedures. The decision of dispersants application should be based on the following comparison: “What would be the impact of the pollution when treated with dispersant and when non treated with dispersant?” The NEBA should consider the behaviour of the treated non-treated oil, assess consequently the different resources which will be concerned either by the treated oil or by the surface film oil, assess the sensitivity of the different resources at concern towards the dispersed oil and toward the floating oil film. These analyses assist decision makers when considering whether or not the use of dispersants is appropriate to minimize the environmental/economic damage. This article describes the experience of NEBA application to substantiate decisions how to respond to potential oil spills at the sites on Aniva bay of Sakhalin-2 project at different oil spills scenarios. It was used incremental approach to choose them. Based on sensitivity maps, information about level of impact dispersed and floating oil on bioresources and results of mathematical modelling efficacy of different response methods application: monitoring (no actions to recover spilt oil), mechanical recovery and mechanical recovery together with dispersants application it was shown that SPA can be protected from pollution in most scenarios only in case of dispersants application. Amount of oil stranded on shore in case of application of response method was used as criteria of efficacy of method application level of damage.
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Hu, Zhi Hua. "Framework and Key Modules for Emergency Resource Decision Support System to Response Oil Spill Disasters." Advanced Materials Research 113-116 (June 2010): 1509–13. http://dx.doi.org/10.4028/www.scientific.net/amr.113-116.1509.
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Oil spills represent one of the most destructive environmental disasters. The frameworks of decision support system (DSS) for peace time and emergency situation are proposed. The monitoring network acquires the foundational data and information for decision from sensor network, information system and social network. The peace time DSS models the monitoring network and the general monitoring, prediction, simulation and management modules for contingent events and emergency resources. The emergency DSS is modeled as a layered architecture. Form the information acquisition to the decision layer, the information flow and real-time decision-making modules are revealed. Finally, the key models and algorithm for resource deployment and scheduling are studied.
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Yudhbir, Lalit, and Eleftherios Iakovou. "A Maritime Oil Spill Risk Assessment Model." International Oil Spill Conference Proceedings 2001, no. 1 (March 1, 2001): 235–40. http://dx.doi.org/10.7901/2169-3358-2001-1-235.
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ABSTRACT Mantime oil transportation decision-making models that integrate with oil spill risk assessment methodologies are scarce. Recently, first time quantitative efforts have been developed for the maritime transportation of petroleum products. However, there still exists a serious gap in the literature concerning risk assessment models that provide a rather significant input to any maritime oil transportation model, namely the estimation and assignment of risk costs to the links of such a network. The authors first present a critical review of oil spill risk assessment efforts found in the literature and then the development of a novel oil spill risk assessment model. The goal of this risk assessment methodology is twofold: first, to determine and assign risk costs to the links of a maritime transportation network, and second, to provide insights into contributors that lead to spills. Such insights may further lead to guidelines for the prevention of future incidents leading to spills. A federal regulatory agency (such as the U.S. Coast Guard) and/or a commercial shipper may use the identification of the dominant contributors to oil spills to evaluate the merits of alternative regulatory and shipping policies that could lead to improved safety performance of the marine system. The authors finally exhibit the usage of the proposed methodology on a real case scenario.
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Elizariev, Alex, Timur Yusupov, and Elena Elizarieva. "Oil spills forecasting in rail accidents." Bulletin of scientific research results, no. 3-4 (January 19, 2017): 28–35. http://dx.doi.org/10.20295/2223-9987-2016-3-4-28-35.
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Objective: To scientifically substantiate and develop forecasting basis of emergency situations consequences on railway transport. Methods: Theoretical generalization and analysis of the current knowledge and understanding of oil spills forecasting, a geographic information system. Results: In accordance with the analysis of statistical data, the emergency situation during the transportation of oil and oil products by rail are associated more with mechanical damage to special tanks and release of petroleum products into the environment with subsequent ignition, or by contamination of land or water areas. One of the key safety components on rail transport of petroleum products is the prediction of possible emergency situations, modelling of development processes of the strait of petroleum products and risk assessment. Based on the analysis of existing methods of calculation of the consequences strait of petroleum products, as well as features of the simulation of the expiry with use of modern software such as Autodesk Inventor, ArcGIS, Surfer, the proposed methodological framework for prediction of consequences of emergency situations on objects of railway transport. The paper shows the opportunity on the basis of threedimensional models of the terrain in the zone of emergency, by means of geographic information modeling to determine the shape of the spill of petroleum product of a multifactorial consideration of the different parameters determining the quantitative and qualitative sides of the processes of the strait of oil products will allow to improve the accuracy of predictive assessments, and the use of modern IT-technologies to provide efficiency calculations. Practical importance: Applicationof the proposed approach will determine the quality of any system of support of decision-making, especially when planning rescue operations, including in the justification of the choice of those or other technologies of their conducting and use of various rescue equipment.
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Kimrey, LT Christopher M. "METACOGNITIVE DECISION MAKING IN OIL SPILL RESPONSE-BEHAVIORAL BIAS IN RELATION TO PERCEIVED RISK." International Oil Spill Conference Proceedings 2017, no. 1 (May 1, 2017): 1453–70. http://dx.doi.org/10.7901/2169-3358-2017.1.1453.
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ABSTRACT 2017-205 Catastrophic events like Deepwater Horizon, Exxon Valdez, major hurricanes, and other such anomalies have a tendency to overwhelm the initial crisis management leadership due to the chaotic nature of the event. The inability to quickly and accurately make critical assessments about the magnitude and complexity of the emerging catastrophe can spell disaster for crisis managers long before the response ever truly takes shape. This paper argues for the application of metacognitive models for sense and decision-making. Rather than providing tools and checklists as a recipe for success, this paper endeavors to provide awareness of the cognitive processes and heuristics that tend to emerge in crises including major oil spills, making emergency managers aware of their existence and potential impacts. Awareness, we argue, leads to recognition and self-awareness of key behavioral patterns and biases. The skill of metacognition—thinking about thinking—is what we endeavor to build through this work. Using a literature review and cogent application to oil spill response, this paper reviews contemporary theories on metacognition and sense-making, as well as concepts of behavioral bias and risk perception in catastrophic environments. When catastrophe occurs—and history has proven they will—the incident itself and the external pressures of its perceived management arguably emerge simultaneously, but not necessarily in tandem with one another. Previous spills have demonstrated how a mismanaged incident can result in an unwieldy and caustic confluence of external forces. This paper provides an awareness of biases that lead to mismanagement and apply for the first time a summary of concepts of sense-making and metacognition to major oil spill response. The views and ideas expressed in this paper are those of the author and do not necessarily reflect the views of the U.S. Coast Guard or Department of Homeland Security.
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Shavranskii, M. V., V. I. Sheketa, and V. M. Shavranskii. "An intellectual system for supporting decision making in the control of the borring process." METHODS AND DEVICES OF QUALITY CONTROL, no. 1(44) (June 28, 2020): 119–37. http://dx.doi.org/10.31471/1993-9981-2020-1(44)-119-137.
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The problem of development of the method of identification of complications arising in the process of drilling of oil and gas wells, which operates under the conditions of a priori and current uncertainty under the influence of various perturbations based on methods of fuzzy set theory and fuzzy logic, is considered. A methodological approach to the estimation of the level of complications in the drilling of oil and gas wells, based on the principles of linguistic parameters of the drilling process, linguistic and hierarchical knowledge about the complications in the drilling of wells is proposed. Mathematical models of a controlled object have been developed that, unlike deterministic mathematical models, allow to describe in natural language the cause and effect relationships between the parameters of the drilling process and the possible complication. These models reflect the logic of the operator's reasoning with the involvement of non-numerical and fuzzy information from an expert to formalize Fuzzy Logic decision-making procedures using the parameters and indicators of the oil and gas drilling process. The structure of the decision support system for controlling the drilling of wells in the conditions of complications is proposed. The results of simulation modeling of the developed methods of modeling of complications based on the methods of fuzzy set theory and fuzzy logic are presented. Their advantages over the well-known in accuracy of the tasks of identification of an estimation and control in the conditions of uncertainty concerning structure and parameters of object are shown. The real complications have been identified, the elimination of which will increase the level of safety of the drilling process. It is shown that the developed methods and models can find application for modeling and identification of a wide class of complications on drilling rigs operating under the conditions of a priori and current uncertainty regarding their structure, parameters and geographic environment.
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Neralla, V. R., and S. Venkatesh. "REAL TIME APPLICATION OF AN OIL SPILL MOTION PREDICTION SYSTEM." International Oil Spill Conference Proceedings 1985, no. 1 (February 1, 1985): 235–42. http://dx.doi.org/10.7901/2169-3358-1985-1-235.
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ABSTRACT This paper deals with the prediction in real time of the motion of experimental oil slicks. These slicks were the subject of an oil spill experiment organized by the joint Government/Industry Canadian Aerial Applications Task Force. These experiments offshore were conducted during September 1983 near Halifax on the east coast of Canada, at 44°30′ N, 63°00′ W. The primary objective of the experiments was to determine the suitability of oil spill dispersants as countermeasures. A secondary objective was the testing and verification of oil spill trajectory models and systems. The Atmospheric Environment Service (AES) participated in the experiments to test the capability of its oil spill motion prediction system in providing real time trajectory forecasts. The AES system resident on computer facilities at the Canadian Meteorological Centre in Montreal was accessed through standard telephone lines, with appropriate output products available on a computer terminal near the experiment site. The experiment consisted of three sets of spills. Each set had a control slick and a test slick. Sixteen barrels of crude oil were used in each spill. The test slicks were used to test the effectiveness of various dispersants, the control slicks were used to verify trajectory forecasts. The spill trajectories and oil weathering information obtained from the system during the experiments demonstrated the relative ease with which the system could handle the required input and provide timely forecasts. The accuracy of these forecast trajectories was confirmed by observations, and their utility was demonstrated by their application in the operational decision making process.
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Gao, Xinran, Junwei Wang, and Liping Yang. "An Explainable Machine Learning Framework for Forecasting Crude Oil Price during the COVID-19 Pandemic." Axioms 11, no. 8 (July 29, 2022): 374. http://dx.doi.org/10.3390/axioms11080374.
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Financial institutions, investors, central banks and relevant corporations need an efficient and reliable forecasting approach for determining the future of crude oil price in an effort to reach optimal decisions under market volatility. This paper presents an innovative research framework for precisely predicting crude oil price movements and interpreting the predictions. First, it compares six advanced machine learning (ML) models, including two state-of-the-art methods: extreme gradient boosting (XGB) and the light gradient boosting machine (LGBM). Second, it selects novel data, including user search big data, digital currencies and data on the COVID-19 epidemic. The empirical results suggest that LGBM outperforms other alternative ML models. Finally, it proposes an interpretable framework for facilitating decision making to interpret the prediction results of complex ML models and for verifying the importance of various features affecting crude oil price. The results of this paper provide practical guidance for participants in the crude oil market.
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Kuzmin, Anton. "Mathematical Exchange Rates Modeling: Equilibrium and Nonequilibrium Dynamics." Mathematics 10, no. 24 (December 9, 2022): 4672. http://dx.doi.org/10.3390/math10244672.
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The development of the author’s concept of the International Flows Equilibrium Exchange Rate (IFEER) is the basis for the mathematical exchange rate modeling of two interconnected equal economies. IFEER-concept allows modeling the exchange rate dynamics of relatively medium-term equilibrium and short- and long-term disequilibrium. Discrete and integral versions of the concept are the basis for further modeling. New structural models of medium-, short- and long-term dynamics and new final structural dependencies of the exchange rate on the system of fundamental factors are the main results. The models include mathematically formalized export-import and capital flows and international competitive advantages indicators. The modeling allowed the revealing of the structural pricing mechanism of the exchange rate dynamics from new positions. We verify the US dollar to the Russian ruble exchange rate modeling during periods of financial and economic crises in recent Russian history, based on a systematic analysis of the exchange rate policy. Because of the analysis, the fall in export prices of oil and other energy carriers in international markets, the rise in consumer prices within the country, and the fall in aggregate output are the main reasons for the fall of the Russian ruble. The conducted modeling allows for the evaluation of the short-term contribution to the crisis depreciation dynamics. The mathematical tools allow for the development of the decision-making process on the exchange rate regulation.
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Kulterbayev, Kh P. "Determination of Eigenvalues in Problems on the Buckling of Compressed Rods (Part I)." IOP Conference Series: Earth and Environmental Science 988, no. 5 (February 1, 2022): 052061. http://dx.doi.org/10.1088/1755-1315/988/5/052061.
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Abstract In many technical academic courses and in the calculations of mechanical design engineers, buckling is a difficult problem. The applied methods of its solution are of a private nature and are unsuitable for modern, more complex structures in such sectors of the economy as mechanical engineering, construction, instrument making, oil and gas facilities, the nuclear industry, power networks, etc. complex problems of the stability of a compressed rod and at the same time achieve greater clarity and versatility with the proposed methods. Using specific examples, the application of the traditional analytical and the proposed analytical-graphic methods is shown. The proposed mathematical models and decision algorithms are verified using computational experiments. Based on the results obtained, practical conclusions are drawn.
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Farmer, C. L. "Uncertainty quantification and optimal decisions." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 473, no. 2200 (April 2017): 20170115. http://dx.doi.org/10.1098/rspa.2017.0115.
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A mathematical model can be analysed to construct policies for action that are close to optimal for the model. If the model is accurate, such policies will be close to optimal when implemented in the real world. In this paper, the different aspects of an ideal workflow are reviewed: modelling, forecasting, evaluating forecasts, data assimilation and constructing control policies for decision-making. The example of the oil industry is used to motivate the discussion, and other examples, such as weather forecasting and precision agriculture, are used to argue that the same mathematical ideas apply in different contexts. Particular emphasis is placed on (i) uncertainty quantification in forecasting and (ii) how decisions are optimized and made robust to uncertainty in models and judgements. This necessitates full use of the relevant data and by balancing costs and benefits into the long term may suggest policies quite different from those relevant to the short term.
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Glukhikh, I. N., T. G. Shevelev, R. А. Panov, A. M. Izotov, M. O. Pisarev, D. A. Liss, V. S. Bykov, А. V. Abramov, and K. Z. Nonieva. "Automatic configuration of the gas treatment system based on ontological models." Ontology of designing 12, no. 4 (December 1, 2022): 518–31. http://dx.doi.org/10.18287/2223-9537-2022-12-4-518-531.
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The article presents a description of an intelligent system for supporting the conceptual design of fields, which includes an ontological knowledge base and a software prototype for automatically configuring a gas treatment system. The ontological knowledge base acts as a tool for preservation and reproduction of infrastructure solutions, which have shown effectiveness in the implementation of oil and gas projects. Model-layers of the ontology are described, including the following ontologies: partonomy, taxonomy, attributive ontology, ontology of processes, ontology of functions, ontology of requirements, ontology of computational models, ontology of equipment. Each ontology layer contains the necessary knowledge for configuring the gas treatment system, formalized using a variety of concepts and relationships be- tween them. Relying on the ontological approach and the function-oriented ontology, a software prototype has been created to solve the following tasks: configuration to requirements, checking the fulfillment of requirements and identifying inconsistencies when changing configurations. The integration of knowledge engineering methods and rigorous mathematical algorithms in decision-making processes makes it possible to use both objective physical laws of oil and gas processes and less formalized information about objects and relations between them. Automatic generation of technological options based on multiple requirements is intended for use in the early stages of design and aims to speed up commissioning and reduce changes.
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Lima, Relvas, Barbosa-Póvoa, and Morales. "Adjustable Robust Optimization for Planning Logistics Operations in Downstream Oil Networks." Processes 7, no. 8 (August 2, 2019): 507. http://dx.doi.org/10.3390/pr7080507.
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The oil industry operates in a very uncertain marketplace, where uncertain conditions can engender oil production fluctuations, order cancellation, transportation delays, etc. Uncertainty may arise from several sources and inexorably affect its management by interfering in the associated decision-making, increasing costs and decreasing margins. In this context, companies often must make fast and precise decisions based on inaccurate information about their operations. The development of mathematical programming techniques in order to manage oil networks under uncertainty is thus a very relevant and timely issue. This paper proposes an adjustable robust optimization approach for the optimization of the refined products distribution in a downstream oil network under uncertainty in market demands. Alternative optimization techniques are studied and employed to tackle this planning problem under uncertainty, which is also cast as a non-adjustable robust optimization problem and a stochastic programing problem. The proposed models are then employed to solve a real case study based on the Portuguese oil industry. The results show minor discrepancies in terms of network profitability and material flows between the three approaches, while the major differences are related to problem sizes and computational effort. Also, the adjustable model shows to be the most adequate one to handle the uncertain distribution problem, because it balances more satisfactorily solution quality, feasibility and computational performance.
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Germanova, Svetlana E., Tatiana V. Dremova, Nataliya B. Sambros, and Polina A. Petrovskaya. "Impact of the oil production complex on land pollution in Russia." Revista Amazonia Investiga 9, no. 27 (March 21, 2020): 294–300. http://dx.doi.org/10.34069/ai/2020.27.03.32.
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The assessment of the impact of the economic activities of the Russian oil-producing complex on land pollution contributes to the adoption of evolutionary management decisions. It also helps to take into account the opinion of society, the Ministry of Emergency Situations. In the oil complex, industrial pollution negatively affects flora and fauna. It's important to identify the level of exposure, the degree of its danger, the location of the contamination. The work deals with the methodology, information-logical and mathematical model of solving the above problem. The main result of the work is a system and procedure (technique) for analyzing the results of monitoring and forecasting of land cover take into consideration the sanitary and hygienic consequences of residual content of petroleum products. As a result of the system analysis, an approach to the construction of alternative solutions has been proposed, taking into account not only permissible pollution standards, but also environmental, sanitary and epidemiological norms and assessment methods. The emergence of soil systems, their categories, is taken into account. In particular, (in importance) risks for soil cover-morphological, bio-physical-chemical, ecological-health, toxic influence and irreversible processes and bifurcations, including taking into account regional peculiarities and restoration potential of soil, are considered. Proposed algorithm of simulation and system analysis is based on situational modeling. Evolutionary modeling allows you to adapt the prediction and assessment procedure (methodology) to the risk factors of the environment. It increases accuracy (formalization and evidence) and completeness of conclusions, efficiency of situation analysis, which affects manageability of risk both for oil complex and for individual enterprise of the industry. The results of the work may be used for the development of software tools, in particular expert and predictive systems. Situational models are needed when oil companies are addressing multi-criteria and multi-factor decision-making challenges.
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Hrechanyi, O., T. Vasilchenko, A. Vlasov, S. Fedorenko, D. Syniavskyi, and Y. Tsehelnyi. "Using the "minimum risk" method in the technical diagnosis of metallurgical equipment." System technologies 3, no. 140 (April 8, 2022): 24–34. http://dx.doi.org/10.34185/1562-9945-3-140-2022-03.
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The difficult operating conditions of metallurgical equipment due to dynamic loads require special attention when designing components in the field of reliability and fail-free operation. In order to increase the reliability and durability of the spindle drive unit of the rolling stand of the hot rolling mill "1680", it is proposed to switch from "oil mist" type lubrication systems to "oil-air" type systems for bronze liners and bearings of the balancing mechanism. The oil-air lubrication principle has undeniable advantages in terms of component lubrication, flow distribution, and provides a volumetric flow of oil by injecting air into each bearing of the equipment, guaranteeing an accurate volume at each lubrication point, regardless of bearing back pressure, atmospheric pressure, temperature and oil viscosity . In order to optimize decision-making when designing new components and parts of metallurgical equipment, the vector of making reliable design decisions is increasingly shifting towards mathematical modeling of production processes and situations that arise during the performance of technological operations. It has been established that in order to determine the permissible value of the content of wear products in the form of metal shavings, one can use the general theory of recognition, which is an important section of technical cybernetics and deals with the recognition of images of any nature, namely, the "minimal risk" method. Recognition algorithms are partly based on diagnostic models that establish a connection between the state of a technical system and diagnostic signals coming from these systems. The performed calculations make it possible to accurately establish the limiting values of iron-containing impurities in the working fluid of the "oil-air" lubrication system and indicate that if the limit value x0 = 11 is exceeded, that is, if the content of iron-containing impurities in the working fluid is more than 11 g per 100 cm3, the object should be stopped for inspection and the working fluid should be cleaned by filtration. The possibility of determining the permissible value of the content of wear products in the form of metal shavings in "oil-air" lubrication systems using the general theory of recognition, namely the "minimal risks" method, which simplifies the process of setting the date of its cleaning by filtration, without burdening it with especially cumbersome formulas and calculations.
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Levin, V. M., and N. P. Guzhov. "Predictive risks assessment of power supply interruption to oil production consumers, taking into account changes in significant factors." Power engineering: research, equipment, technology 24, no. 5 (December 9, 2022): 84–96. http://dx.doi.org/10.30724/1998-9903-2022-24-5-84-96.
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THE GOAL. To develop a decision-making procedure for the commissioning of equipment in the power supply system of oil production facilities in accordance with the minimum risk criterion. To substantiate the effectiveness of its practical application in the tasks of equipment repair management with the "technical condition" strategy based on a risk-oriented approach.METHODS. When solving the tasks, the following methods were used: the flowchart method for calculating the structural reliability indicators of the distribution electrical network with changes in its operational composition and technical condition of elements (equipment), the method for predicting the risks of power supply disruption in the circuit of each technological consumer, taking into account the possibility of reserving and imposing emergency recovery of the main element on the planned repair of the backup, scenario approach for determining scenarios for changing the risk of power supply failures when the operating state of the electrical network circuit changes.RESULTS. As a result of solving the problems, the values of the structural reliability indicators of the studied distribution electrical network are calculated under the most likely scenarios of changes in its operational composition and technical condition of equipment, the features of the integrated assessment of the technical condition of objects of voltage class 6 kV in their operating conditions are considered. The functions of forecasting the probabilities of power supply failures of oil production facilities are obtained depending on the integral assessment of their technical condition, which, along with the severity of the consequences of failures, make it possible to predict risks.CONCLUSION. A computational procedure has been developed that includes mathematical models and an algorithm for prioritizing equipment with a strategy "according to technical condition" when putting it into repair based on a forecast of the risks of power supply disruption to consumers. On a concrete example, the verification of calculation models and algorithm was carried out, the effectiveness of the developed computational procedure and its applicability in solving practical problems of managing repairs of electrical equipment of oil production facilities based on a risk-oriented approach was shown.
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Borodin, Alex, Manuela Tvaronavičienė, Irina Vygodchikova, Andrey Kulikov, Marina Skuratova, and Natalia Shchegolevatykh. "Improving the Development Technology of an Oil and Gas Company Using the Minimax Optimality Criterion." Energies 14, no. 11 (May 28, 2021): 3177. http://dx.doi.org/10.3390/en14113177.
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The article deals with the problem of adaptation of the Russian oil and gas company (Novatek, Russia) to the rapidly changing external environment, the avalanche of data from competitors, and the need to filter important information for business development and the prosperity of the industry as a whole. The approach is based on the system of integrated software monitoring of key business processes at the enterprise developed by the authors—from the formation of the idea of a new product to its implementation to paying customers. The scientific novelty lies in the use of an optimization model that allows for minimizing the maximum losses of the investor at all levels of decision-making, from the distribution of capital between companies, to the optimization of internal reserves to increase the competitiveness of the company. The toolkit is a minimax model that allows you to redistribute the shares of investor influence at the portfolio level, and then within the business processes of each company selected by investors, in order to achieve the optimal solution in accordance with the selected estimated indicators. Application of the well-known portfolio investment models of Markowitz, Tobin, Sharp, etc. is not possible due to the lack of necessary data on the basis of which the probabilistic parameters involved in the model are estimated. Even if we get them, it is necessary to take into account the level of correlation influence of the technological process in the composition of each subsystem, which is unacceptable for the data used, as it leads to a strong increase in errors. Using minimax and a systematic approach allows you to minimize such errors by choosing a balanced concentration of distributed assets for both the investor and the buyer. To this end, a three-way analysis of the company’s development was carried out and a technology for comprehensive improvement of the company’s activities was developed in the following areas: the company’s rating in the industry, financial condition, and interaction with counterparties using merchandising technologies. Tools for optimal image zoning at the Novatek site using the minimax approximation criterion have been developed. The technology provides a procedure for creating a comfortable mode of image perception based on high-tech visualization of merchandising, zoning of the screen area, and a mathematical approach that allows you to develop a calculation algorithm.
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Osborne, Orla Eileen, Megan M. C. Willie, and Patrick D. O’Hara. "THE EFFECTS OF OIL SPILL DISPERSANT USE ON MARINE BIRDS: A REVIEW OF SCIENTIFIC LITERATURE AND IDENTIFICATION OF INFORMATION GAPS." Environmental Reviews, December 5, 2022. http://dx.doi.org/10.1139/er-2022-0072.
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Dispersants, a class of chemical spill-treating agents used to treat oil spills, are commonly used globally as an alternative response measure. Applying dispersants to an oil slick, shortly after the spill has occurred, can protect shoreline environments and sea surface-dwelling animals, such as some marine bird species, limiting individuals or local populations from the consequences of coming into contact with large quantities of oil. However, this benefit comes with the cost of increasing oil exposure risk to marine biota that spend time in the water column. It is generally believed that the benefits of dispersant use outweigh the costs under most circumstances. However, it is rarely acknowledged that the use of dispersants may have negative impacts on marine biota at the individual or local population level, including marine birds.
In Canada, Corexit EC9500A, a regulated dispersant, is being proposed for expanded use beyond treating spills from an offshore oil and gas facility. To understand what the potential impacts from dispersant use are to marine birds, we conducted a literature review to identify the direct and indirect effects of their use. We also provide oil spill responders with a Pathway of Effects conceptual model, a tool for understanding the interactions between dispersants, marine birds, and their environment in order to support a holistic consideration as part of the oil spill response decision-making process. Fundamental uncertainties remain, however, and if left unaccounted for in the decision-making process, they may compromise the appropriateness of spill response approaches and outcomes.
We recommend that oil spill responders incorporate the known benefits and costs of dispersant use on marine birds into a decision-making framework such as a Net Environmental Benefits Analyses (NEBA) and with consideration of the Pathway of Effects concept models provided. These recommendations are particularly relevant where a decision-making framework such as NEBA is becoming a more standardized component of the response process. Additionally, greater investment in lab and field-based research, and field observations through monitoring, is required to address existing decision-making uncertainties and provide information gap closure.
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Bowles, Joe, and Carmine Dulisse. "Building an Elite Force of Spill Responders in a World with Few Spill Response Opportunities." International Oil Spill Conference Proceedings 2021, no. 1 (May 1, 2021). http://dx.doi.org/10.7901/2169-3358-2021.1.711543.
Full textAbstract:
ABSTRACT As the performance of Industry improves and spills decrease, SMTs, OSROs, Regulators, and Oil and Gas Operators are all facing a lack of direct experience and knowledge when it comes to spill response. The recruiting and grooming of elite Responders for a large response organization is further challenged by a tight labor market that is increasingly occupied by a generation that demands accelerated advancement and growth. The Marine Spill Response Corporation (MSRC) is taking a new approach to identify, develop and retain Responder competencies and proficiencies, and to offer a career/development path in the absence of actual incidents. The first element of this program provides a clear path for professional growth to satisfy the growing desire for advancement by replacing a time-based promotion system with one that is focused on performance. The second element requires a consistent methodology and framework of evaluation to ensure employees in a nationwide organization are measured and evaluated using the same standards. Replacing the focus on hard skills with soft skills during talent acquisition “fit factor” when hiring new Responders sets the tone for growth. The hard skills are easier to teach and develop, while soft skills like learning curiosity, collaboration, effective communication, problem solving, and decision making are the differentiators that shape an elite Responder. Removing the emphasis on spill experience and replacing it with well-defined competency models that define abilities which can be demonstrated outside of spill incidents is essential to fostering professional growth in a Responder. These competencies include the technical skills that are required by each position and emphasize leadership abilities, teamwork, and commitment. Metrics and expectations must be defined at the right level of detail to provide Responders with the opportunity during steady state operations to demonstrate abilities in a variety of scenarios that mirror those needed in spill response.