digilib@itb.ac.id +62 812 2508 8800

ABSTRAK Indira Frida Gabriella
PUBLIC Suharsiyah

The decline in oil and gas production poses a significant challenge for the industry in Indonesia, with oil production experiencing a decrease of 4.23% and gas production declining by 3.53% between 2016 and 2021. To address this issue and prevent production opportunity losses, it is crucial to improve existing methods of enhancing production. Well stimulation has emerged as one of the most common techniques employed for this purpose. However, the short-lived effects and a considerable number of failed stimulation jobs have highlighted the need for more efficient approaches. These challenges can be attributed to errors in manual evaluation and the lack of integration in databases, necessitating the urgent digitalization of the oil and gas industry. In order to tackle the challenges, this developed tool, ForTECH, is designed to optimize the duration of well stimulation effect using machine-learning algorithm. ForTECH utilizes Decline Curve Analysis (DCA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA) methods to forecast the duration of stimulation effects based on well properties and stimulation plans. With these two methods, a wide range of data can be analyzed and produce reliable results which the accuracy is validated to be at least 89%. Also, the user-friendly interface and short evaluation time of ForTECH provide the users a practical and effective solution to optimize stimulation strategies. As a pilot project, this study focuses on mature offshore fields in Southeast Sumatra with sandstone and carbonate formations where acid stimulation jobs were conducted between 2018 and 2022. The initial study conducted with ForTECH serves as the foundation for continuous improvement by expanding the database to encompass a wider range of stimulation methods. The tool is designed to be adaptable to forecast the effect of operations in many other fields. Furthermore, the applicability of ForTECH extends beyond well stimulation and can be modified for other operations such as drilling and workover. By adopting a data-driven approach, ForTECH has the potential to become a comprehensive solution for validating stimulation plans, thereby driving a major improvement in the future of oil and gas industry.