The multiobjective genetic algorithm can be used to optimize two conflicting objectives, oil production and polymer utility factor in polymer flood design. This approach provides a set of optimal solutions which can be considered as trade-off curve (Pareto front) to maximize oil production while preserving polymer performance. Then an optimal polymer flood design can be considered from post-optimization analysis. A 2D synthetic example, and a 3D field-scale application, accounting for geologic uncertainty, showed that beyond the optimal design, a relatively minor increase in oil production requires much more polymer injection and the polymer utility factor increases substantially. iii DEDICATION To my parents for their love, care and support iv ACKNOWLEDGEMENTS I would like to thank my academic advisor, Dr. Akhil Datta-Gupta for his valuable guidance throughout the course of this research. Also I want to thank my committee members, Dr. King and Dr. Mohanty for his valuable feedback and questions that have shaped the work in this research.I would like to thanks Fulbright for financial support and this valuable experience to be part of the family, PTTEP for their support and working experience which is very useful for my study.Special thanks to my colleagues at MCERI group; Satyajit for his inspiring ideas,

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Production optimization can play a major role in increasing recovery and decreasing operation cost. In many oilfields, the geology, production operations, and their related constraints are very complex. These complexities can complicate the formulation and solution of the pertinent optimization problems and increase the computational cost of finding a solution. Although full reservoir simulation provides detailed analysis and prediction of reservoir performance, the significant uncertainty and complexity of reservoir models can make the simulation results and their interpretations questionable. Moreover, in some cases, a reservoir model may not even be available to perform full simulation for performance optimization. The cost and complexity of developing full-scale simulation models, together with the considerable computational overhead associated with production optimization (especially under geologic uncertainty), call for development of fast proxy models for production optimization. To this end, various reduced-order and surrogate models have been designed to approximate the production behavior of a reservoir at a fraction of the computation required for full simulation. We present an efficient production optimization scheme by integrating constrained optimization with fast decline curve analysis for predicting well production performance. The proposed production optimization approach is formulated as a constrained optimization problem by defining a desired objective function and a set of existing field/facility constraints. An efficient gradient-based optimization algorithm is then adopted to solve the resulting optimization problem for a single timestep. The optimization is then coupled with the decline curve analysis to predict future production rates. The optimization process is performed recursively in time for a specified duration. The predictions with the decline curve analysis are reasonable so long as the operating conditions remain unchanged. Using field data, we demonstrate that the proposed formulation can provide fast solutions to large-scale production optimization problems. The results in this paper suggest that the developed technique can be applied to improve production performance and operation efficiency with a minimal computational cost when compared to production optimization with full-scale reservoir simulation. It also offers the flexibility to adjust the problem formulation under various field conditions and is particularly useful when a full-scale reservoir model does not exist simulate the reservoir response for production optimization.

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Gas fields in the Gulf of Thailand (GOT) share some similar operational complexities and experience many common challenges. Such challenges include the huge number of wells and platforms, and the large, complex, interconnected pipeline network. Additionally, each well, of course, exhibits different performance, different enhanced recovery as well as different and diverse flow assurance methods. Fluid streams also vary significantly from well to well; for instance, the differences in condensate to gas ratios (CGR), water to gas ratios (WGR), and the CO 2 , and H 2 S levels. Moreover, production performance in the GOT remains very dynamic. The decline in production could be seen early, even though proper reservoir management was achieved because most of the reservoirs were small and compartmentalized. Optimizations aiming to maximize revenue from these fields are very challenging.State-of-the-art industry solutions to these problems are provided by integrated production modeling, and reservoir simulation. At first consideration, they appear to be reasonable tools that can physically describe the flow of fluid, whether in a reservoir, well or surface facility; however, these tools may not serve well for the complicated compartmentalized characteristics of the gas fields in the Gulf of Thailand. Currently, determining optimum natural gas production rates in the GOT is performed by manually fine-tune the production rate using information from the latest well testing data. This method may simple and convenient but requires large effort and does not guarantee the optimal solution.This study presents a more efficient production optimization scheme integrating constrained optimization with decline curve analysis to predict future well production performance. The project net present value is translated into the objective function, comprising maximizing condensate production and minimizing waste water production while also honoring daily gas production nomination. Well performance, export specification, and the capacity of pipeline networks are formulated as system constraints. A linear programing optimization algorithm is then used to solve the resulting optimization problem for a single time step. Next, the optimization is integrated with the production decline trend from the decline curve analysis to obtain the forecast of future production performance.Tested against the production data of a large gas field in the Gulf of Thailand, this method showed a significant increase in the condensate production and a decrease in the water production. This solution not only enhanced production, but also reduced tedious time required for modeling, history matching, or manually configuring well production. Main assumptions, limitations and the conclusion of the proposed method are also included in this study.