#TITLE_ALTERNATIVE#

• Main Author: DION SALAM (NIM : 12212031), DAMIAN
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/26352
• Institution: Institut Teknologi Bandung

Optimizing production is no longer an option, nowadays, it is a necessity. The wells have to produce longer and better than ever before in order to influence projects attractiveness. However, the process to define variables such as well placement, number and type of wells, well production and injection scheduling, and operational conditions is time-consuming and demands high computational effort. Therefore, the objective of this research is to provide an optimization algorithm resulting good solution efficiently. <br /> <br /> The optimization algorithm used in this work is the hybrid genetic algorithm (HGA) which is the combination of GA with artificial neural networks (ANN) and evolution strategies (ES). This HGA attempts to simplify the complex and diverse parameters governing the production optimization problem. The HGA is coupled with commercial simulator and has been applied in real fields to quantify the benefits of this HGA over a base case with the conventional one. Additionally, it is proposed to minimize the number of combinations of possible solutions that makes the optimization algorithm becomes simply elegant by performing the process efficiently and effectively. <br /> <br /> The HGA proposed in this study is used to optimize the production strategies in realistic reservoir models, defining the number, type, and position of production and injection wells, and also the production or injection flow rates. Therefore, the HGA is proved to be suitable for any kind of reservoir models. Moreover, the number of individuals in a population, the number of generations, and the genetic parameters are varied and adapted by applying ANN and ES to evaluate the efficiency of the algorithm. From this study, an optimization algorithm in production strategy is provided. Results showing the performance of several optimization processes are presented in this work. The analysis and comparison regarding the genetic parameters are presented. Moreover, the proposed HGA is proved to be time-saving and operation-efficient, plus it provides an optimized solution. <br /> <br /> Based on the fact that every well is a new challenge that defines a single-solution approach which results the production optimization strategy becoming more and more complicated, the use of an optimization algorithm to achieve a good solution can be very valuable to the process, yet it can also lead to an exhaustive search, demanding a great number of simulations to test many possibilities. This novel HGA is presented to tackle and handle those issues.