I present a systematic method to primary develop existing black oil fields. This method uses integrated reservoir development workflow (IRDW) that relies on integrated asset model (IAM). Developing any existing field means providing a plan that generally serves the development goal(s) specified by management. However, serving the development goal(s) by itself does not guarantee an optimal development plan. Plans that do not rely on an IAM are less accurate. Some plans do not include economics in their evaluation. Such plans are technically accepted but usually impractical or unprofitable. Plans that only evaluate the field based on current, or short-term, conditions are potential candidates for bottlenecks, thus costly reevaluations. In addition, plans that do not consider all suitable options are misleading and have no room for optimization. Finally, some plans are based on “rules of thumb,” ease of operations, or operators’ preference, not on technical evaluation. These plans mostly lower long-term profitability and cause further production problems. To overcome these problems, project management must form a multidisciplinary team that uses the IRDW. The IRDW guides the team through its phases, stages, and steps to selecting the optimal development plan. The IAM consists of geological, reservoir, wellbore, facility, and economic models. The IRDW dictates building an IAM for the base (do nothing) case and for each development plan. The team must evaluate each scenario over the lifetime of the field, or over the timeframe the management specifies. Net present value (NPV) and Present value ratio (PVR) for all options are compared to the base case and against each other. The optimum development plan is the one that have the highest NPV and highest PVR. The results of the research showed that forming a multidisciplinary team and using a LDFC saves time and it guarantees selecting the optimal development plan if all applicable development options are considered.

[pt] Esta dissertação investiga um sistema baseado em algoritmos genéticos e algoritmos culturais, aplicado ao processo de desenvolvimento de um campo de petróleo. O desenvolvimento de um campo de petróleo consiste, neste caso, da disposição de poços num reservatório petrolífero, já conhecido e delimitado, que permita maximizar o Valor Presente Líquido. Uma disposição de poços define a quantidade e posição de poços produtores e injetores e do tipo de poço (horizontalou vertical) a serem empregados no processo de exploração. O objetivo do trabalho é avaliar o desempenho de Algoritmos Genéticos e Algoritmos Culturais como métodos de apoio à decisão na otimização de alternativas de produção em reservatórios petrolíferos. Determinar a localização de novos poços de petróleo em um reservatório é um problema complexo que depende de propriedades do reservatório e critérios econômicos, entre outros fatores. Para que um processo de otimização possa ser aplicado nesse problema, é necessário definir uma função objetivo a ser minimizada ou maximizada pelo processo. No problema em questão, a função objetivo a ser maximizada é o Valor Presente Líquido (VPL). Para se estabelecer o VPL, subtrai-se os gastos com a exploração do valor correspondente ao volume de petróleo estimado da reserva. Devido à complexidade do perfil de produção de petróleo, exige-se a utilização de simuladores de reservatório para esta estimativa. Deste modo, um simulador de reservatórios é parte integrante da função de avaliação. O trabalho de pesquisa foi desenvolvido em quatro etapas: um estudo sobre a área de exploração de petróleo; um estudo dos modelos da inteligência computacional empregados nesta área; a definição e implementação de um modelo genético e cultural para o desenvolvimento de campo petrolífero e o estudo de caso. O estudo sobre a área de exploração de campo de petróleo envolveu a teoria necessária para a construção da função objetivo. No estudo sobre as técnicas de inteligência computacional definiu-se os conceitos principais sobre Algoritmo Genético e Algoritmo Cultural empregados nesta dissertação. A modelagem de um Algoritmo Genético e Cultural constitui no emprego dos mesmos, para que dado um reservatório petrolífero, o sistema tenha condições de reconhecê-lo e desenvolvê-lo, ou seja, encontrar a configuração (quantidade, localização e tipo de poços) que atinja um maior Valor Presente Líquido. Os resultados obtidos neste trabalho indicam a viabilidade da utilização de Algoritmos Genéticos e Algoritmos Culturais no desenvolvimento de campos de petróleo. / [en] This dissertation investigates a system based in genetic algorithms and cultural algorithms, applied to the development process of a petroleum field. The development of a petroleum field consists in the placement of wells in an already known and delimited petroleum reservoir, which allows maximizing the Net Present Value. A placement of wells defines the quantity and position of the producing wells, the injecting wells, and the wells type (horizontal or vertical) to be used in the exploration process. The objective of this work is to evaluate the performance of Genetic Algorithms and Cultural Algorithms as decision support methods on the optimization of production alternatives in petroleum reservoirs. Determining the new petroleum wells location in a reservoir is a complex problem that depends on the properties of the reservoir and on economic criteria, among other factors. In order to an optimization process to be applied to this problem, it s necessary to define a target function to be minimized or maximized by the process. In the given problem, the target function to be maximized is the Net Present Value (NPV). In order to establish the NPV, the exploration cost correspondent to the estimated reservoir petroleum volume is deducted. The complexity of the petroleum s production profile implies on the use of reservoirs simulators for this estimation. In this way, a reservoir simulator is an integrant part of the evaluation function. The research work was developed in four phases: a study about the petroleum exploration field; a study about the applied computational intelligence models in this area; the definition and implementation of a genetic and cultural model for the development of petroliferous fields and the case study. The study about the petroleum exploration field involved all the necessary theory for the building of the target function. In the study about the computational intelligence techniques, the main concepts about the Genetic Algorithms and Cultural Algorithms applied in this dissertation were defined. The modeling of Genetic and Cultural Algorithms consisted in applying them so that, given a petroleum reservoir, the system is capable of evolve and find configurations (quantity, location and wells type) that achieve greater Net Present Values. The results obtained in this work, indicate that the use of Genetic Algorithms and Cultural Algorithms in the development of petroleum fields is a promising alternative.

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.