Academic literature on the topic 'Oil spills Decision making Mathematical models'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Contents
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Oil spills Decision making Mathematical models.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Oil spills Decision making Mathematical models"
1
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.
Full textAbstract:
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.
2
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.
Full textAbstract:
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.
3
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.
Full textAbstract:
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.
4
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.
Full textAbstract:
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.
5
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.
Full textAbstract:
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.
6
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.
Full textAbstract:
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.
7
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.
Full textAbstract:
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.
8
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.
Full textAbstract:
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.
9
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.
Full textAbstract:
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.
10
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.
Full textAbstract:
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.
Books on the topic "Oil spills Decision making Mathematical models"
1
J, Hilterman Fred, Society of Exploration Geophysicists, and European Association of Geoscientists and Engineers, eds. Seismic amplitude interpretation: 2001 Distinguished Instructor Short Course. [Tulsa, Okla.]: SEG, 2001.
Find full text
2
Office, General Accounting. Air pollution: Status of dispute over Alaska oil pipeline air quality controls : report to the chairman, Subcommittee on Energy Regulation and Conservation, Committee on Energy and Natural Resources, U.S. Senate. Washington, D.C: The Office, 1988.
Find full textConference papers on the topic "Oil spills Decision making Mathematical models"
1
Li, Jizhou, Yufen Shao, Yuzixuan Zhu, and Kevin Furman. "Decision-Driven Subsurface Surrogate Model for Development Optimization Under Uncertainties." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211808-ms.
Full textAbstract:
Abstract With ever-increasing complexity in Upstream project planning, to ensure decision quality, the dynamics of subsurface resources need to be embedded into concept screening to maintain consistency between the production forecast and development plan. We developed a decision-driven subsurface surrogate model that encapsulates key reservoir dynamics into the machine augmented mathematical technologies for holistic decision recommendation in concept selection and development planning under uncertainties. The surrogate model replicates the essential subsurface dynamics by using a hybrid-approach that takes into accounts both reservoir simulation data and physical first principle. In addition to standalone usage on production forecast for rapid profile screening under resource uncertainties, the subsurface surrogate model is incorporated into mathematical optimization models that simultaneously consider surface network, commercial obligation and project economics etc. to provide alternative concepts under various uncertainties. Our subsurface surrogate model has been applied for decision making on gas gathering system design, field development optimization, field-management timing and sequencing, and field tie-back study etc. Results not only show the capability of surrogate models to enable large scale rapid decision screening, but also bear a close resemblance between the predicted production profiles and the reservoir simulation results when fed with the field operating strategy recommended by our decision models with surrogate dynamics. The study demonstrates the reliability of our surrogate modeling technology on ensuring decision quality and helps build business’ confidence on technology adoption. By further incorporating subsurface uncertainties into surrogate models, the decision makers are provided with: probabilistic analysis of the outcomes, value of information analysis, cost of optionality and flexibility, and holistic project outlook etc. Our decision-driven surrogate modeling technology incorporated in mathematical decision models is the first of its kind for holistic decision support in concept selection and development planning in oil and gas industry, and has full potentials in a variety of asset lines with reliable subsurface performance prediction under uncertainties.
2
Shao, Yufen, Jizhou Li, Ming–Jung Seow, Yuzixuan Zhu, Yuanyuan Guo, Daman Pradhan, Deepak Malpani, and Kevin Furman. "Integrated Concept Analytics and Development Optimization Under Uncertainties." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211442-ms.
Full textAbstract:
Abstract Decision-making complexity in the oil and gas industry has risen dramatically in recent years, especially in consideration of uncertainties related to geopolitics, policies, marketing, subsurface resources etc. To enable decision making with the best quality opportunities and projects, we are developing an integrated suite of machine augmented mathematical technologies to recommend holistic decisions for concept selection and development planning under uncertainties. Our ongoing technology development is progressing a set of prototypes and use cases including: 1) AI-based uncertainty handling technologies aiming to detect uncertainties, quantify impacts, and translate to influence factors for decision-making (e.g., IRR, cost); 2) Decision-driven surrogate reservoir models approximating subsurface dynamics to enable rapid concept screening; 3) a set of mathematical optimization-based decision models in the form of mixed-integer linear programs (MILP) to provide solution alternatives to address different business challenges under uncertainties. We demonstrate that the use of systematic technical applications combined with human interaction can improve the decision quality significantly by considering all influence factors, searching through the entire decision space, and recommending a range of alternatives for business users to consider with minimal bias. These technologies have been designed to plug into existing processes and platforms to accelerate technology adoption and usage.
3
Jiang, Xiaoli, Haiyang Yu, and Miroslaw Lech Kaminski. "Assessment of Residual Ultimate Hull Girder Strength of Damaged Ships." In ASME 2014 33rd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/omae2014-23153.
Full textAbstract:
The risk of ship collision and grounding has increased significantly in recent years as a result of the growing size and number of ships at sea. The potentially costly consequences of collision and grounding in the form of fatalities, property, and cargo, as well as environmental pollution in the form of oil spills, etc., are the main motivations for research on collision and grounding. From a structural evaluation standpoint, there is a great deal of uncertainty related to the residual strength of damaged ships considering various influential parameters, such as damage size, geometry and location, internal structural arrangement, material property, loading case, and sea weather. Therefore, it is important to clarify the residual hull girder strength of damaged ships by collision or grounding in order to ensure their safety. The present study undertook a deliberate finite element analysis to investigate the residual ultimate strength of damaged ship hull, where two damage models were assumed and compared. One model simulated actual damage resulting from an accident in the form of hole with adjacent plastic deformation, while the other applied simplified damage, considering unavailable measurement of the damage by removing the damaged part from the original ship hull. The comparison showed that the assessment of residual ultimate strength of a damaged ship based on the simplified damage model could produce a sufficiently accurate result and stay slightly safer, provided that a reasonable criterion of simplification was defined first. The studies showed that it is possible to accurately estimate the residual ultimate strength of a damaged ship without detailed measurement of the damage, and consequently facilitate decision-making regarding the ship salvage under emergency.
4
Dulkarnaev, Marat Rafailevich, Evgeny Alexandrovich Malyavko, Ekaterina Evgenievna Semyonova, Oksana Alexandrovna Gorbokonenko, Yuri Alexeyevich Kotenev, Shamil Khanifovich Sultanov, Alexander Viacheslavovich Chibisov, and Daria Yuryevna Chudinova. "The Use of Quantum Dot Inflow Tracers in Multi-Well Reservoir Production Surveillance and Inter-Well Diagnostics." In SPE Symposium: Petrophysics XXI. Core, Well Logging, and Well Testing. SPE, 2021. http://dx.doi.org/10.2118/208430-ms.
Full textAbstract:
Abstract Reservoir pressure maintenance is an extremely important factor in field development. In enhanced oil recovery water flooding projects, it is essential to optimize the flooding efficiency in a timely manner and reduce uncertainties in inter-well hydrodynamic modelling. Usually, the inter-well space parameters are assessed using interference tests or tracer- based surveillance. These methods offer such advantages as reliable information on the flow communication in the target area and the reservoir connectivity in different zones of the field. However, the duration and cost of the described surveillance technologies pose a significant drawback, and therefore alternative physical and mathematical methods with simplified forecast models are widely spread. This paper describes a method for integrating the results of dynamic marker-based inflow production surveillance in horizontal wells and the Spearman's rank-order correlation method. This approach is applied to provide better interventions for reservoir pressure maintenance, optimization of in-fill drilling, update existing hydro-dynamic models and reduce the level of uncertainty in decision making.
5
Nejadi, Siavash, and Stephen M. Hubbard. "Measuring Connectivity in Complex Reservoirs: Implications for Oil Sands Development." In SPE Canadian Energy Technology Conference. SPE, 2022. http://dx.doi.org/10.2118/208927-ms.
Full textAbstract:
Abstract The Lower Cretaceous McMurray Formation in the Athabasca Oil Sands consists of channel belt deposits formed from meandering river systems. Large-scale fluvial point bars and other components of meander-belts compose this heterogeneous formation and are the source of complex sedimentary facies relationships. Recognition and correct interpretation of the spatial facies distribution, hence connectivity of the reservoir system, is essential to optimal field development and project economics. It is, therefore, crucial to understand river depositional processes, link associated facies to connectivity metrics, and implement them in flow modelling for hydrocarbon exploration. In the geological modelling phase, we analyzed data collected through high-density drilling, extensive coring, and three-dimensional (3D) seismic to map the internal stratigraphic architecture for different reservoir levels. The model captures the 3D representation of different depositional elements, including point bars, counter point bars, side bars, and abandoned channel fills. The deterministic interpretations constrain the stochastic simulation of the reservoir parameters, and distinct morphology, facies associations, and reservoir potential characterize the zones. Our workflow improves the geological realism of subsurface models and allows quantitative analysis of the spatial uncertainty. Including depositional bedding geometries in the modelling helps reduce uncertainties in net continuous bitumen estimations. It improves the knowledge of reservoir connectivity and compartmentalization. The ultra-defined model provides the framework for detailed analysis and optimal field development. This paper presents a new computationally efficient measure for connectivity based on detailed geological interpretations and mapping inclined heterolithic strata (IHS) in point bar deposits. In the calculations, we account for: facies distributions, porosity, permeability along the principal flow axis, and oil saturation,pressure and elevation (potential energy gradients),well locations, andtortuosity of the fluid flow streamlines. To evaluate the effect of sedimentary heterogeneities on key reservoir performance indicators, we formulate the reservoir connectivity as a mathematical optimization problem and estimate the flux in the connected porosity. Applying the methodology on a point-bar deposit shows that the connectivity factor strongly correlates with the ensuing recovery responses. This novel, computationally inexpensive approach captures the uncertainty in reservoir rock distributions and provides a quick and practical measurement for decision-making in reservoir management problems. Its features enable evaluating multiple reservoir parameters and using Monte Carlo techniques to quantify uncertainty and risk propagation in the presence of geological uncertainty to rank field portfolios. In the SAGD examples, the method estimates steam chamber development and conformance with high confidence, supporting optimal well placement for new development wells and infill drilling, optimizing the well spacing and orientation.
6
Ijomanta, Henry, Lukman Lawal, Onyekachi Ike, Raymond Olugbade, Fanen Gbuku, and Charles Akenobo. "Digital Oil Field; The NPDC Experience." In SPE Nigeria Annual International Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/207169-ms.
Full textAbstract:
Abstract This paper presents an overview of the implementation of a Digital Oilfield (DOF) system for the real-time management of the Oredo field in OML 111. The Oredo field is predominantly a retrograde condensate field with a few relatively small oil reservoirs. The field operating philosophy involves the dual objective of maximizing condensate production and meeting the daily contractual gas quantities which requires wells to be controlled and routed such that the dual objectives are met. An Integrated Asset Model (IAM) (or an Integrated Production System Model) was built with the objective of providing a mathematical basis for meeting the field's objective. The IAM, combined with a Model Management and version control tool, a workflow orchestration and automation engine, A robust data-management module, an advanced visualization and collaboration environment and an analytics library and engine created the Oredo Digital Oil Field (DOF). The Digital Oilfield is a real-time digital representation of a field on a computer which replicates the behavior of the field. This virtual field gives the engineer all the information required to make quick, sound and rational field management decisions with models, workflows, and intelligently filtered data within a multi-disciplinary organization of diverse capabilities and engineering skill sets. The creation of the DOF involved 4 major steps; DATA GATHERING considered as the most critical in such engineering projects as it helps to set the limits of what the model can achieve and cut expectations. ENGINEERING MODEL REVIEW, UPDATE AND BENCHMARKING; Majorly involved engineering models review and update, real-time data historian deployment etc. SYSTEM PRECONFIGURATION AND DEPLOYMENT; Developed the DOF system architecture and the engineering workflow setup. POST DEPLOYMENT REVIEW AND UPDATE; Currently ongoing till date, this involves after action reviews, updates and resolution of challenges of the DOF, capability development by the operator and optimizing the system for improved performance. The DOF system in the Oredo field has made it possible to integrate, automate and streamline the execution of field management tasks and has significantly reduced the decision-making turnaround time. Operational and field management decisions can now be made within minutes rather than weeks or months. The gains and benefits cuts across the entire production value chain from improved operational safety to operational efficiency and cost savings, real-time production surveillance, optimized production, early problem detection, improved Safety, Organizational/Cross-discipline collaboration, data Centralization and Efficiency. The DOF system did not come without its peculiar challenges observed both at the planning, execution and post evaluation stages which includes selection of an appropriate Data Gathering & acquisition system, Parts interchangeability and device integration with existing field devices, high data latency due to bandwidth, signal strength etc., damage of sensors and transmitters on wellheads during operations such as slickline & WHM activities, short battery life, maintenance, and replacement frequency etc. The challenges impacted on the project schedule and cost but created great lessons learnt and improved the DOF learning curve for the company. The Oredo Digital Oil Field represents a future of the oil and gas industry in tandem with the industry 4.0 attributes of using digital technology to drive efficiency, reduce operating expenses and apply surveillance best practices which is required for the survival of the Oil and Gas industry. The advent of the 5G technology with its attendant influence on data transmission, latency and bandwidth has the potential to drive down the cost of automated data transmission and improve the performance of data gathering further increasing the efficiency of the DOF system. Improvements in digital integration technologies, computing power, cloud computing and sensing technologies will further strengthen the future of the DOF. There is need for synergy between the engineering team, IT, and instrumentation engineers to fully manage the system to avoid failures that may arise from interface management issues. Battery life status should always be monitored to ensure continuous streaming of real field data. New set of competencies which revolves around a marriage of traditional Petro-technical skills with data analytic skills is required to further maximize benefit from the DOF system. NPDC needs to groom and encourage staff to venture into these data analytic skill pools to develop knowledge-intelligence required to maximize benefit for the Oredo Digital Oil Field and transfer this knowledge to other NPDC Asset.
7
John, Carolyn J., Consuelo E. Guzman-Leong, Thomas C. Esselman, and Sam L. Harvey. "Methods to Define Failure Probability for Power Plant Heat Exchangers." In ASME 2017 Power Conference Joint With ICOPE-17 collocated with the ASME 2017 11th International Conference on Energy Sustainability, the ASME 2017 15th International Conference on Fuel Cell Science, Engineering and Technology, and the ASME 2017 Nuclear Forum. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/power-icope2017-3367.
Full textAbstract:
In response to the technical challenges faced by aging plant systems and components at nuclear power plants (NPP), the Electric Power Research Institute (EPRI) has a product entitled Integrated Life Cycle Management (ILCM). The ILCM software is a quantitative tool that supports capital asset and component replacement decision-making at NPPs. ILCM is comprised of models that predict the probability of failure (PoF) over time for various high-value components such as steam generators, turbines, generators, etc. The PoF models allow the user to schedule replacements at the optimum time, thereby reducing unplanned equipment shutdowns and costs. This paper describes a mathematical model that was developed for critical heat exchangers in a power plant. The heat exchanger model calculates the probability of the tubes, shell, or internals failing individually, and then accumulates the failures across the heat exchanger sub-components. The dominant degradation mechanisms addressed by the model include stress corrosion cracking, wear, microbiologically influenced corrosion, flow accelerated corrosion, and particle-induced erosion. The heat exchanger model combines physics-based algorithms and operating experience distributions to predict the cumulative PoF over time. The model is applicable to shell and tube heat exchangers and air-to-water heat exchangers. Many different types of fluids including open cycle fresh water, closed cycle fresh water, sea water, brackish water, air, closed cooling water, steam, oil, primary water, and condensate are included. Examples of PoF over time plots are also provided for different fluid types and operating conditions.
8
Mogollon, Jose Luis, Edwin Tillero, Carlos Calad, and Larry Lake. "Comparative Analysis of Data-Driven, Physics-Based and Hybrid Reservoir Modeling Approaches in Waterflooding." In SPE Annual Technical Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/210373-ms.
Full textAbstract:
Abstract Hydrocarbon production optimization is essential in pursuing the best scenarios for economic outcomes. But because of complex and multi-dimensional nature of production processes, thousands of scenarios are possible. Extensive data collection may allow uncovering patterns still unidentified. With on-site computing power increasing, cloud availability, and artificial intelligence evolution, mathematical optimization methods are becoming powerful and accessible. Data type-tailored models are implemented for history matching and prediction of operational efficiency of the asset. This paper presents a comprehensive analysis and comparison of three data type-tailored reservoir modeling methods and their optimization process for waterflooding field cases. The mathematical techniques used were Data-Driven Capacitance Resistance Model (CRM), Numerical Simulators (Data-Physics) coupled to Smart Algorithms Optimizers, and Hybrid Model (Machine Learning Physics-Based). They were compared to 1-identify the benefits of mathematical optimization techniques, 2-illustrate the methods developed to sort out time and computing capacity restrictions, and 3-validate the techniques by comparing the forecast with actual results. The six study cases of different reservoir types in Argentina, Venezuela, and the USA, had different types data availability. Four had no static model. In two cases, field results were available to confirm the accuracy of the forecasted injection and production. The forecasted increase in Net Present Value (NPV) and cumulative oil production (Np) ranged to 30%, and optimized water injection rates decreased by 50%. Traditional modeling techniques yielding unreliable result in one field with hundreds of producing layers and unknown lateral and vertical continuity were solved using a machine learning technique. In some cases, they pointed toward non-intuitive infill drilling sequence and injection water redistribution. Also, they pointed to options that reduce economic risk. The methods yielded many better economic scenarios and increased the flexibility of operationalizing plans. In one field requiring excessive computing power, using time horizons reduction and successive year-by-year optimization yielded 4 times the NPV of the base case. This approach solves objections related to long computing time and system instability. With the three mathematical techniques, the asset value could be continually maximized by a novel implementation of a heuristic decision-making approach that continuously challenge the current scenario. It makes a systematic formulation of conceivable new scenarios, competing through an objective function determining the probity of compared scenarios. The optimization also resulted in an up to 50% decrease in water injection requirements and the same percentual CO2 emissions reduction.
9
Zhainakov, Timur, and Yin Chao Chong. "Application of Linear Programing in Optimisation of Gas Blending Operations." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211208-ms.
Full textAbstract:
Abstract Different fields and wells produce natural gas of different composition, which directly influences the value of the gas. This gas is then sent to customers depending on the individual specification. Gases from different origins are sometimes blended to make up for the minimum rate requirement. This study presents a mathematical approach, linear programming, to process big data and generate an optimized route that solves a rate allocation problem keeping in place various operational constraints. A base model was created based on linear programming algorithm for the proof of concept. As in industry, multiple sets of constraints were applied to the model. These constraints include, for instance, maximum allowable carbon dioxide concentration of the total blend, minimum amount of gas, or target ethane concentration for various purposes. Another feature that was included in the model was Blend Specification Requirements. This feature navigates the solver to which result is more favourable and would provide higher profit. The final goal of the solver is to provide a scenario that complies with all the constraints providing the best revenue. The results of the multiple test optimizations, generally, showed close agreement with predictions made prior the tests. Once the model was validated, more complex scenarios were evaluated. Here, the model-generated results showed completely different from the expected. This error in predictions is due to the nature of the problem i.e., rate allocation planning, where the number of considered variables directly influences the complexity of the decision, making it impossible for basic experience-based predictions. The soft-constraint Blend Specification Requirement principles proved to function and direct the solver towards the higher profitable arrangements, while complying to the hard-constraints. Soft and hard terms here relate to the level of the obligement that must be performed by the solver. Overall, the model succeeded in optimizing the relationship between the economic values of different natural gas compositions and the operational and blend constraints, identifying the most profitable gas distribution plan. It is no longer practical to rely solely on the experience of individuals when dealing with complex rate allocation operations. This study presents a simple, reliable and elegant method to build computer-based optimisation models. Connected to an online stream of Big Data, the models can potentially contribute to oil and gas pipeline rate allocation operations, making decisions fit-for-purpose, cost effective and reliable.