From Theory to Field: Applying Synthetic Logs for Data Acquisition Optimization in Gulf of Thailand Brownfields

In mature fields throughout the Gulf of Thailand, although conventional wireline logging is essential, it increases the financial and rig-time constraints. Using a machine learning (ML) approach, it can be mitigated. The availability of legacy well data facilitates the use of an ML-based synthetic logs methodology. This paper presents Log X-Press, a PTTEP’s customized application, designed to produce Neutron-Density curves from Gamma Ray and Resistivity data, hence, reducing logging frequency while preserving subsurface interpretation and decision-making qualities.

The trial implementation of 15 gas development wells throughout the study area, with synthetic logs compared to actual measured logs to analyze porosity trends, lithological variations, and net gas pay.

The pilot deployment revealed that Log X-Press consistently produced synthetic neutron and density logs that facilitate efficient reservoir assessment. The synthetic logs effectively recorded essential petrophysical indicators and lithological differences in gas zones, well correlating with standard triple combo log data.