Abstract
This study shows the application of real-time advanced mud gas (AMG) to characterize reservoir fluid and a comparison to production results on the Snorre Field. The AMG is corrected for the efficiency of extraction, and its results generate a comparison to pressure-volume-temperature (PVT) samples from the field. Furthermore, machine learning is demonstrated to contribute to real-time petrophysical and operational decisions. The field of surface data logging has used AMG in real time for over a decade. For this study, a constant volume, constant temperature (heated) mud gas extractor was used. To account for the unique efficiency of gas extraction for each species of interest, an extraction efficiency correction (EEC) method was applied. The EEC method provides in real time the quantitative composition of formation fluid through analysis of the methane through pentane components. These results are comparable to downhole PVT samples and have been used to optimize wireline tool runs and fluid sampling programs over the years. The consistent dynamic EEC data provided from AMG are demonstrated to successfully distinguish the types of fluids as compared to PVT samples in the Snorre Field. These data are presented as continuous logs, which allow for the evaluation of the thickness of reservoir zones. This information is available while drilling, and with modern real-time data services, operators can access it from almost anywhere. The gas-oil ratio (GOR) prediction results are compared to GOR production, showing acceptable accuracy for data collected while drilling. The promising results generate confidence in the application of quality AMG data to development wells for real-time petrophysical and operational decisions. The field case demonstrates a new and broad application area for AMG in production wells using EEC results to compare to PVT and GOR prediction of the Snorre field with later production analysis.
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