DEVELOPMENT OF GENETIC ALGORITHM WITH OBJECTIVE FUNCTIONS BASED ON RESERVOIR INPUT DATA FOR DETERMINATION OF MULTI-VERTICAL WELL IN MULTI-LAYER RESERVOIR ON AN COMMINGLED AND SELECTED

The process of determining the well location for both new and mature field is one important factor that determines the amount of oil recovery. The process has been conducted by the conventional trial and error method that is time consuming and requires a lot of work, especially for large oil or gas...

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Main Author: HARYADI (NIM : 22210015), FEBI
Format: Theses
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/20099
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Institution: Institut Teknologi Bandung
Language: Indonesia
id id-itb.:20099
spelling id-itb.:200992017-09-27T15:07:44ZDEVELOPMENT OF GENETIC ALGORITHM WITH OBJECTIVE FUNCTIONS BASED ON RESERVOIR INPUT DATA FOR DETERMINATION OF MULTI-VERTICAL WELL IN MULTI-LAYER RESERVOIR ON AN COMMINGLED AND SELECTED HARYADI (NIM : 22210015), FEBI Indonesia Theses INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/20099 The process of determining the well location for both new and mature field is one important factor that determines the amount of oil recovery. The process has been conducted by the conventional trial and error method that is time consuming and requires a lot of work, especially for large oil or gas field. Hence, a new robust method is proposed by modify Genetic Algorithm (GA) to solve this optimization problem. <br /> <br /> <br /> The Genetic Algorithm will be applied to optimize well location candidates by evaluating a proposed fitness function (objective function) to maximize the production of wells penetrating multilayer reservoir. By employing basic reservoir properties of each layer obtained from a reservoir simulation model, i.e., permeability, porosity, oil saturation, pressure of reservoir, and grid thickness as the GA's objective function parameters, the optimum coordinates of new wells are determined. <br /> <br /> <br /> A model of oil field has been applied to validate the proposed method. The result of GA are the locations of wells in two-dimensional coordinates (x,y). The wells are vertical wells which is perforated in commingled scenario. There are two perforation scenarios, i.e. perforation in all productive layers and perforation in the layer that meet certain constraints only. Furthemore, by using a reservoir simulator, the results of GA will be compared with the result of conventional method. By using the tested field model, GA gives better results than conventional method. GA gave recovery factor 3% greater than conventional method. text
institution Institut Teknologi Bandung
building Institut Teknologi Bandung Library
continent Asia
country Indonesia
Indonesia
content_provider Institut Teknologi Bandung
collection Digital ITB
language Indonesia
description The process of determining the well location for both new and mature field is one important factor that determines the amount of oil recovery. The process has been conducted by the conventional trial and error method that is time consuming and requires a lot of work, especially for large oil or gas field. Hence, a new robust method is proposed by modify Genetic Algorithm (GA) to solve this optimization problem. <br /> <br /> <br /> The Genetic Algorithm will be applied to optimize well location candidates by evaluating a proposed fitness function (objective function) to maximize the production of wells penetrating multilayer reservoir. By employing basic reservoir properties of each layer obtained from a reservoir simulation model, i.e., permeability, porosity, oil saturation, pressure of reservoir, and grid thickness as the GA's objective function parameters, the optimum coordinates of new wells are determined. <br /> <br /> <br /> A model of oil field has been applied to validate the proposed method. The result of GA are the locations of wells in two-dimensional coordinates (x,y). The wells are vertical wells which is perforated in commingled scenario. There are two perforation scenarios, i.e. perforation in all productive layers and perforation in the layer that meet certain constraints only. Furthemore, by using a reservoir simulator, the results of GA will be compared with the result of conventional method. By using the tested field model, GA gives better results than conventional method. GA gave recovery factor 3% greater than conventional method.
format Theses
author HARYADI (NIM : 22210015), FEBI
spellingShingle HARYADI (NIM : 22210015), FEBI
DEVELOPMENT OF GENETIC ALGORITHM WITH OBJECTIVE FUNCTIONS BASED ON RESERVOIR INPUT DATA FOR DETERMINATION OF MULTI-VERTICAL WELL IN MULTI-LAYER RESERVOIR ON AN COMMINGLED AND SELECTED
author_facet HARYADI (NIM : 22210015), FEBI
author_sort HARYADI (NIM : 22210015), FEBI
title DEVELOPMENT OF GENETIC ALGORITHM WITH OBJECTIVE FUNCTIONS BASED ON RESERVOIR INPUT DATA FOR DETERMINATION OF MULTI-VERTICAL WELL IN MULTI-LAYER RESERVOIR ON AN COMMINGLED AND SELECTED
title_short DEVELOPMENT OF GENETIC ALGORITHM WITH OBJECTIVE FUNCTIONS BASED ON RESERVOIR INPUT DATA FOR DETERMINATION OF MULTI-VERTICAL WELL IN MULTI-LAYER RESERVOIR ON AN COMMINGLED AND SELECTED
title_full DEVELOPMENT OF GENETIC ALGORITHM WITH OBJECTIVE FUNCTIONS BASED ON RESERVOIR INPUT DATA FOR DETERMINATION OF MULTI-VERTICAL WELL IN MULTI-LAYER RESERVOIR ON AN COMMINGLED AND SELECTED
title_fullStr DEVELOPMENT OF GENETIC ALGORITHM WITH OBJECTIVE FUNCTIONS BASED ON RESERVOIR INPUT DATA FOR DETERMINATION OF MULTI-VERTICAL WELL IN MULTI-LAYER RESERVOIR ON AN COMMINGLED AND SELECTED
title_full_unstemmed DEVELOPMENT OF GENETIC ALGORITHM WITH OBJECTIVE FUNCTIONS BASED ON RESERVOIR INPUT DATA FOR DETERMINATION OF MULTI-VERTICAL WELL IN MULTI-LAYER RESERVOIR ON AN COMMINGLED AND SELECTED
title_sort development of genetic algorithm with objective functions based on reservoir input data for determination of multi-vertical well in multi-layer reservoir on an commingled and selected
url https://digilib.itb.ac.id/gdl/view/20099
_version_ 1821120047961079808