Optimal scheduling of field activities using constraint satisfaction problem theory

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

Improving Crude Oil Supply Assignment Process Using Simulated Annealing Algorithm: Case Study in Indonesia

Oil industry is one of the most profitable industry in the world, with considerably high level of income and margin profit, which imply that any improvement no matter how small could have a bigger impact than expected. For this research a case study is reviewed which focuses on a supply chain department of an oil refinery in East Indonesia. One of the responsibility of said department is to create a crude oil tank assignment plan. The current condition of the job is quite wasteful as the job is still done manually, thus taking very long time to complete. This research is aimed to improve the method of creating crude oil tanks assignment by utilizing an algorithm that will considerably reduce the computation time needed, as well as improving the crude composition target achievement. Simulated annealing algorithm is the chosen method of this research because of its capability to reliably solve a problem with large dataset in reasonable amount of time. From 3 experiments with differing datasets, the results shows that the algorithm is capable of improving the target achievement of crude classification composition up to below 1 % deviation from the target as well as significantly reducing the time needed to do the assignment from several hours into under 1 minutes

DECISION SUPPORT FOR OPTIMAL WELL PLACEMENT, INFRASTRUCTURE INSTALLATION AND PRODUCTION PLANNING IN OIL FIELDS

Ph.DDOCTOR OF PHILOSOPH

Um modelo matemático multi terminais e uma metaheurística Adaptive Large Neighborhood Search para o problema de roteamento de navios aliviadores visando o escoamento de petróleo offshore

The oil and natural gas production chain presents a large complexity and involves a set of steps to obtain such derivative. Particularly noteworthy are the logistics support services to production that must provide quick responses to the production system chain, minimizing costs. The importance of Floating Production Storage Offloading (FPSO), generally large vessels capable of producing, processing and storing the oil that is transferred to the land by shuttle tanks or by oil pipelines, is emphasized. In this context, the flow of oil by shuttle tanks, together with the effective establishment of the routes, is very relevant. Therefore, this work presents a mult-terminals mathematical model for the problem of shuttle tanks routing and an Adaptive Large Neighborhood Search meta - heuristic (ALNS) that is able to define shuttle tanks routes at a minimum cost. The computational tests showed that the mathematical model performs better than another proposed model in the literature and that the ALNS meta-heuristic is able to find good solutions in a reduced time.A cadeia produtiva de petróleo e gás natural apresenta grande complexidade e envolve um conjunto de etapas para se obter tal derivado. Pode-se destacar especialmente os serviços de apoio logístico à produção que devem prover respostas rápidas ao sistema da cadeia de produção, minimizando ao máximo os custos, desonerando a cadeia produtiva. Ressalta-se a importância dos FPSO (Floating Production Storage Offloading), navios, em geral de grande porte com capacidade de produzir, processar e armazenar o petróleo que é transferido para a terra por meio de navios aliviadores ou oleodutos. Neste contexto, o escoamento do petróleo por meio de navios aliviadores, aliado ao estabelecimento eficaz das rotas, mostra-se bastante relevante. Portanto, este trabalho apresenta um modelo matemático multi terminais para o problema de roteamento de navios aliviadores e uma meta-heurística Adaptive Large Neighborhood Search (ALNS) que é capaz de definir as rotas dos navios aliviadores ao menor custo possível. Os testes computacionais mostraram que o modelo matemático possui um desempenho melhor que um outro proposto na literatura e que a meta-heurística ALNS é capaz de encontrar boas soluções em um reduzido tempo computacional

Meta-heurísticas para resolução de alguns problemas de planejamento e controle da produção

Este estudo aborda a resolução de três diferentes problemas, amplamente encontrados no real contexto de planejamento e controle da produção. Inicialmente, é proposta uma meta-heurística GRASP para solucionar um problema de balanceamento de linhas de montagem (SALBP-2). O método proposto apresentou resultados competitivos em relação à literatura, também focando numa simplicidade de operação para ser aplicada em casos reais. Na sequência, utilizou-se o mesmo método para solucionar o problema Job Shop Scheduling (JSP). O GRASP desenvolvido para o JSP também apresentou bons resultados, com baixo desvio relativo médio em relação às melhores soluções conhecidas da literatura. Em seguida, abordou-se uma extensão do JSP, o problema Job Shop Scheduling Flexível (FJSP). O JSP limita-se ao sequenciamento de operações em máquinas fixas, enquanto que no FJSP a atribuição de uma operação não é pré-fixada e pode assim ser processada num conjunto de máquinas alternativas. Portanto, o FJSP não se restringe ao sequenciamento, estendendo-se na atribuição de operações para as máquinas adequadas (roteamento). O FJSP é, portanto, mais complexo do que o JSP, pois considera a determinação da atribuição da máquina para cada operação. Para solucionar o FJSP, propôs-se quatro meta-heurísticas: GRASP, Simulated Annealing (SA), Iterated Local Search (ILS) e Clustering Search (CS). O SA apresentou resultados inferiores, porém, ao incorporá-lo numa versão híbrida do ILS, que o utiliza como busca local, os resultados melhoraram, principalmente em instâncias mais complexas. Considerando a característica híbrida do CS, utilizou-se também o SA, nesse caso como meta-heurística geradora de soluções. Essa abordagem também apresentou resultados superiores ao SA. Tanto o ILS quanto o CS geraram resultados com valores iguais ou próximos àqueles das melhores soluções conhecidas para um extenso conjunto de instâncias para o FJSP, assim como também proveram alguns novos melhores valores conhecidos

Multiphase flow modelling for enhanced oil and gas drilling and production

From the exploration to the abandonment of an oil and gas discovery, operators and engineers are constantly faced with the challenge of achieving the best commercial potential of oil fields. Although the petroleum engineering community has significantly contributed towards maximising the potential of discovered prospects, the approach adopted so far has been compartmentalised with little (heuristics-based) or no quality integration. The highly interconnected nature of the decision factors affecting the management of any field requires increased implementation of Computer-Aided Process Engineering (CAPE) methods, thus presenting a task for which chemical engineers have the background to make useful contributions. Drilling and production are the two primary challenging operations of oilfield activities, which span through different time horizons with both fast and slow-paced dynamics. These attributes of these systems make the application of modelling, simulation, and optimisation tasks difficult. This PhD project aims to improve field planning and development decisions from a Process Systems Engineering (PSE) perspective via numerical (fluid dynamics) simulations and modelbased deterministic optimisation of drilling and production operations, respectively. Also demonstrated in this work is the importance of deterministic optimisation as a reliable alternative to classical heuristic methods. From a drilling operation perspective, this project focuses on the application of Computational Fluid Dynamics (CFD) as a tool to understand the intricacies of cuttings transport (during wellbore cleaning) with drilling fluids of non-Newtonian rheology. Simulations of two-phase solid-liquid flows in an annular domain are carried out, with a detailed analysis on the impact of several drilling parameters (drill pipe eccentricity, inclination angle, drill pipe rotation, bit penetration rate, fluid rheology, and particle properties) on the cuttings concentration, pressure drop profiles, axial fluid, and solid velocities. The influence of the flow regime (laminar and turbulent) on cuttings transport efficiency is also examined using the Eulerian-Eulerian and Lagrangian-Eulerian modelling methods. With experimentally validated simulations, this aspect of the PhD project provides new understanding on the interdependence of these parameters; thus facilitating industrial wellbore cleaning operations. The second part of this project applies mathematical optimisation techniques via reduced-order modelling strategies for the enhancement of petroleum recovery under complex constraints that characterise production operations. The motivation for this aspect of the project stems from the observation that previous PSE-based contributions aimed at enhancing field profitability, often apply over-simplifications of the actual process or neglect some key performance indices due to problem complexity. However, this project focuses on a more detailed computational integration and optimisation of the models describing the whole field development process from the reservoir to the surface facilities to ensure optimal field operations. Nonlinear Programs (NLPs), Mixed-Integer Linear Programs (MILPs), and Mixed-Integer Nonlinear Programs (MINLPs) are formulated for this purpose and solved using high-fidelity simulators and algorithms in open-source and commercial solvers. Compared to previous studies, more flow physics are incorporated and rapid computations obtained, thus enabling real-time decision support for enhanced production in the oil and gas industry