AI application for perforation interval prediction under limited well logging data conditions at Te Giac Trang field
Abstract
The development of Te Giac Trang field is facing major challenges due to the lack of well logging data in several potential reservoir intervals such as ULBH/ILBH5.1, which has affected the accuracy of reserve estimation and field development strategy optimization.
To address this issue, the authors applied Artificial Intelligence (AI) through Machine Learning (ML) techniques to predict critical well logging curves, specifically Neutron Porosity (NEU) and Bulk Density (RHOB), based on Gamma Ray (GR) and Resistivity (RD) data. The model training and validation process was rigorously performed using the blind well test method, achieving a reliability of up to 70% at perforated intervals. The study also integrated gas composition data analysis to determine reservoir fluid properties, thereby supporting optimal perforation interval selection.
Field application results at Te Giac Trang field demonstrate that this methodology accurately identified potential oil-bearing zones, contributing to a significant increase in production with optimized costs. This research confirms that AI and machine learning applications can improve hydrocarbon recovery efficiency under limited data conditions, opening a new approach for oil field management and operations in Vietnam and worldwide.
References
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