The understanding of downhole and surrounding wellbore formation temperature plays a very significant role during the geothermal drilling operations. Its knowledge is essential in the understanding of the borehole temperature recovery process which affects the operations. In a case of drilling through salt formations, the composition of the annular fluid and rheological properties change and influence the fluid temperature in the annular space in underbalanced drilling (UBD) and overbalanced drilling (OBD) conditions. Thus, it is hereby necessary to establish a simplified model that can predict the accurate temperature distribution in geothermal wells during different drilling conditions and establish the controlled parameters which can be tuned to realize the desired result. The study presents a simplified user-friendly computational system that analyses the influencing parameters for drilling fluid systems performance evaluation and selection optimization in a geothermal well drilling operation using a deterministic quantitative method known as Distance-Based Approach Model and Python® (SALib open library). The effect of salts concentration and compaction effect which affects the porosity values was also incorporated. An overview of existing models for fluid temperature distribution in both OBD and UBD conditions was studied. Furthermore, identifications and screening of parameters that influence fluid temperature distribution with emphasis on geothermal well exploration were examined. The sensitive analysis performance was carried out with Python® (SALib open library) to generate the model inputs. Among some of the established parameters, circulation rate, temperature drop across the bit, influx rate and thermal conductivity of the cement are the main parameters observed during the sensitivity analysis. The selected model was later modified to accommodate the effect of salt concentration in the annular column when drilling through a salt formation. The modified model was used to establish the main governing parameters that influence the estimation of drilling fluid temperature during drilling conditions. The outcome of the modified model is compared with the existing model and the percentage variance was recorded. The accuracy of this developed modified model was later used to predict the PET-2 Field fluid temperature values with an error output of 2% - 3% against 5% - 15% observed for the base model. The modified model was validated with MWD field data to estimate and predict fluid temperature values. The modified computational support system will aid operators or drillers to identify, evaluate, select and control the main parameters that govern the accurate performance prediction of the fluid temperature profile in geothermal drilling conditions.

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