Unlocking Data Potential: Unleashing the Power of Advanced Mud Gas for Real-Time Reservoir Insights

Recent advancements in mud gas logging have significantly enhanced its contribution to the analysis of reservoir fluids during drilling operations. The utilization of advanced mud gas technology has significantly enhanced the accuracy of gas data analysis, enabling better predictions of hydrocarbon presence and fluid properties, and presented as continuous logs along the well while drilling. The development of a machine learning method called Real-Time Fluid Identification (RFID), leverages a vast reservoir fluid database to predict fluid properties. This method has led to a marked increase in the use of advanced mud gas technology, particularly in development wells. Emphasizing the necessity of thorough data quality assessments and a strong quality control (QC) framework is crucial for achieving reliable predictions from machine learning models. Furthermore, the use of visualization techniques, such as Radar plots, crossplots and histograms, are presented as a means to effectively analyze and compare gas component ratios. Overall, the integration of competence to carry out a robust data quality review, machine learning with real-time logging data emerges as a crucial requirement for accurate reservoir fluid predictions, showcasing the transformative impact of these technologies on the mud gas logging industry.