ACIDIZING CANDIDATES SELECTION IN STEAM FLOOD ENVIRONMENT: A GENETIC MODEL STATISTICAL APPROACH

• Main Author: Alamsyah, Yunus
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/5188
• Institution: Institut Teknologi Bandung

<b>Abstract:<p align=\"justify\"> <br /> In oil field operation, there are many unique problems faced by engineers to. manage their at. One of the most popular problems often happening in oil field is formation damage. And one of the most popular types of formation damage is scaling problem due to CaCO3-generally overcome by acidizing job.<p align=\"justify\"> <br /> The most important thing impacting to the success of the job is candidate selection process To opt for the candidates engineers usually do simple production trend analysis only by looking at qualitative decline trend. Other ways are to apply water analysis or to search if the well has scale history in the its pulling job record.<p align=\"justify\"> <br /> The fact from case study in the steam flood project, SF Project, concluded-that chance of success by applying the above practices is 62% with average oil gain was varies from 20 to 30 BOPD. Therefore, there is a high enough risk performing the job while the cost is not low.<p align=\"justify\"> <br /> A statistical analysis to genetic model of well consisting 16 genes has been done to generate new approach on candidate selection process in attempt to reduce failure. Gradient of cumulative SLR and different in maturity hold critical effect to success of acid job. Using this technique there is an improvement of probability to increase NPV by 170%.