98 research outputs found

    Optimal scheduling of field activities using constraint satisfaction problem theory

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    The challenge of identifying problematic wells and planning their workover operations is common in oil and gas fields. On top of this, the well intervention resources are seldom easily accessible so it is crucial to target the right set of wells at the right time. Oil and gas reservoirs are complex dynamic systems the production and injection patterns of which can significantly affect the reservoir and well response. This represents a complex mathematical optimisation problem where the overall life performance of the field strongly depends on the workover planning decisions. This work presents a reliable and effective tool that is able to screen and explore the large search space of the potential work-overs that adds value to the reservoir management process. The proposed solution considers the overall performance of the field throughout a specified period while respecting all operational limitations as well as considering the risks and costs of the interventions. The proposed workflow combines the commercial optimiser techniques with constraint satisfaction problem optimiser to identify the optimal workover scheduling. The schedule found is guaranteed to satisfy all predefined field constraints. The presented results showed better performance achieved by the proposed hybrid optimiser compared to classical gradient-free optimisation techniques such as Genetic Algorithm in maximising the defined objective function. The suggested workflow can greatly enhance the decisions related to field development and asset management involved with large number of wells and with limited intervention resources

    DRILLING RIG SCHEDULE OPTIMIZATION

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    DRILLING RIG SCHEDULE OPTIMIZATION

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    Best Practices and Guidelines for Scheduling Oil Drill Rig Resources for Projects on Alaska's North Slope

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    Presented to the Faculty of the University of Alaska Anchorage In Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCEThe recent increase in the number of the projects and activities on the North Slope of Alaska has become challenging, leading to numerous scheduling conflicts for equipment and resources. This project explains steps that can be taken to improve resource allocation and guidelines for scheduling oil drill rig work activities for oil and gas projects on Alaska’s North Slope. The project includes insights from two years of research to improve the oil drill rig scheduling process, a survey of subject matter experts involved in the oil drill rig scheduling process, research of similar Arctic environment projects, and the researchers professional experience identifying and mitigating risks and schedule conflicts in the mid-term planning phase of oil and gas projects. Implementing the proposed guidelines has improved the oil drill rig scheduling process, roles and responsibilities are more clearly defined, communication among groups has been improved and support groups have adequate time to complete their work. Results include reduction of oil drill rig move downtime and a reduction in the time to produce oil after the oil drill rig leaves the well site.Disclaimer / Abstract / Project Research Key Words / List of Exhibits / List of Appendix / Project Research Approach / Organizational Research / Academic Research / Conclusions / Recommendations / Future Research / References / Glossary / Appendixe

    A multiperiod optimization model to schedule large-scale petroleum development projects

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    This dissertation solves an optimization problem in the area of scheduling large-scale petroleum development projects under several resources constraints. The dissertation focuses on the application of a metaheuristic search Genetic Algorithm (GA) in solving the problem. The GA is a global search method inspired by natural evolution. The method is widely applied to solve complex and sizable problems that are difficult to solve using exact optimization methods. A classical resource allocation problem in operations research known under Knapsack Problems (KP) is considered for the formulation of the problem. Motivation of the present work was initiated by certain petroleum development scheduling problem in which large-scale investment projects are to be selected subject to a number of resources constraints in several periods. The constraints may occur from limitations in various resources such as capital budgets, operating budgets, and drilling rigs. The model also accounts for a number of assumptions and business rules encountered in the application that motivated this work. The model uses an economic performance objective to maximize the sum of Net Present Value (NPV) of selected projects over a planning horizon subject to constraints involving discrete time dependent variables. Computational experiments of 30 projects illustrate the performance of the model. The application example is only illustrative of the model and does not reveal real data. A Greedy algorithm was first utilized to construct an initial estimate of the objective function. GA was implemented to improve the solution and investigate resources constraints and their effect on the assets value. The timing and order of investment decisions under constraints have the prominent effect on the economic performance of the assets. The application of an integrated optimization model provides means to maximize the financial value of the assets, efficiently allocate limited resources and to analyze more scheduling alternatives in less time

    Proposta de Modelo Matemático para o Problema de Roteamento de Sondas de Intervenção a Poços de Petróleo Terrestres Revelados Dinamicamente com Período de Atendimento Viável

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    Durante a fase de produção em poços de petróleo terrestres uma das atividades mais importantes e caras é o uso de sondas de intervenção. O funcionamento de cada um desses poços ocorre, na maioria das vezes, em condições difíceis com perfil de produção com grande variação em função da sua geologia, localização e a maneira como o poço é desenvolvido. Estas características levam ao longo da vida produtiva dos poços à necessidade de intervenções de manutenção nomeadas como workover, que são fundamentais para manter a produção ou mesmo melhorar a produtividade ao corrigir falhar que tipicamente ocorrem nos equipamentos dos poços. Como as sondas de intervenção são equipamentos caros e, por isto, em menores quantidades comparadas a quantidade de poços terrestres que demandam intervenções de manutenção, ocorre a geração de filas de poços aguardando atendimento. Isto leva a necessidade de geração de rotas de atendimento aos diferentes poços com as escassas sondas existentes, desafio conhecido como Problema de Roteamento de Sondas de Intervenção (PRSI). Na literatura, verificam-se modelos e métodos de solução para o PRSI estático, ou seja, que busca minimizar a perda total de produção, não considerando a possibilidade de novas informações relevantes para o roteamento serem reveladas ao longo do horizonte de planejamento. Sendo assim, busca-se neste trabalho estudar e propor um modelo matemático com abordagem dinâmica para o PRSI que minimize a perda total de produção dos poços revelados ao longo de um horizonte de planejamento. O PRSI Dinâmico foi resolvido ao ser criado algoritmo estrutural executado ao longo de um horizonte de planejamento, contendo o modelo matemático proposto, linearizado para execução no solver CPLEX, utilizando técnicas de Programação Linear Inteira Mista. Os resultados computacionais foram obtidos considerando instâncias geradas artificialmente, e a conclusão dessa pesquisa mostra que o modelo proposto aproxima o PRSI Dinâmico do contexto operacional do problema, o que impacta no processo de definição das rotas e agendamento dos atendimentos
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