How the Improved Geomechanical Applications Helped to Enhance the Reservoir Fluid Identification Using Sampling Through Drillpipe in Challenging Reservoirs of Thailand

Abstract Accurate pressure and fluid identification are critical for reservoir characterization and further production. While Oil-Based Mud (OBM) improves wellbore stability (WBS), it masks resistivity logs reading complicating formation fluid evaluation. In laminated sands, fluid gradients are difficult to determine, making reservoir fluid sampling essential. This paper presents the Geomechanical Fluid Prediction (GFP) method, which integrates open hole log data with sampling results to predict fluid types in non-sampled zones of complex multilayer reservoirs. Geomechanics methods follow two approaches: one predicts sand failure and safe drilling windows; the other, through the GFP method, enables post-drill fluid identification. Applying in unstable sandstone reservoirs in Thailand, slim tools are deployed via Through the Drillpipe Logging (TDL) to ensure safe data acquisition. GFP predicts fluid types such as oil, gas, or water by constructing a geomechanical model using compressional wave (DTC) and shear wave slowness (DTS) and resistivity logs to estimate pore pressure via the Eaton equation. The uniqueness of fluid density is critically influent to reliable Pore Pressure Profile (PPP) prediction, as small variation can impact pressure modeling, fluid identification, and wellbore stability. Results are calibrated with formation pressure tests and validated against limited fluid samples, enhancing reservoir understanding in complex multilayer environments. The results of the GFP study were validated using data from seven onshore wells in Thailand. Conventional petrophysics (CP) interpretation showed that the calculated Permeability Index (PI) aligned well with pretest mobility values, supporting the reliability of the interpretation. The generated PPP from GFP demonstrated a strong correlation with identified oil-bearing zones and showed a consistent trend toward gas gradients in the upper portions of subzones across most wells. Mud logs and measurements from Slim Diameter Formation Testing (SDFT) tools, deployed via TDL, confirmed the accuracy of GFP predictions in hydrocarbon -bearing zones. The integration of geomechanical data with pressure and sampling results enabled fluid type identification even in non-sampled zones effectively. Furthermore, the geomechanical insights gained from GFP were used to optimize drilling strategies by identifying safe operating mud weight windows and forecasting wellbore stability. The geomechanics-derived pore pressure model developed through this workflow demonstrates potential for application in future well planning, operational optimization, and improved reservoir characterization in analogous fields.