#TITLE_ALTERNATIVE#

• Main Author: FARID GHOZALI (NIM: 22215023) , L.
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/28587
• Institution: Institut Teknologi Bandung

Hydraulic fracturing has proven as the most feasible stimulation method to develop <br /> <br /> TLS reservoir in ALEA field. More than a hundred wells have been treated with <br /> <br /> hydraulic fracturing technique. As the wells candidate become challenging as it is <br /> <br /> identified to be less potential, a new technology has been introduced which is called <br /> <br /> flow channel hydraulic fracturing. Flow channel hydraulic fracturing created higher <br /> <br /> fracture conductivity which will benefit to increased production. <br /> <br /> One of success criteria of hydraulic fracturing program is production rate post <br /> <br /> fracturing treatment. The well should be able to produce hydrocarbon in economical <br /> <br /> rate. A prediction of production rate post fracturing treatment plays important role <br /> <br /> prior to proposing well candidate to be stimulated with flow channel fracturing. <br /> <br /> Well simulation software such as Wellflo can be used to predict production <br /> <br /> performance post fracturing treatment. Artificial intelligence approach also offers <br /> <br /> an alternative solution that can be used for this objective and it will be discussed in <br /> <br /> this study. <br /> <br /> Adaptive neuro-fuzzy inference system (ANFIS) is used in this study to predict the <br /> <br /> production performance of well in TLS reservoir post flow channel fracturing. In <br /> <br /> this modeling, the input were reservoir properties of TLS and hydraulic fracture <br /> <br /> parameters while the output was the production rate.