Machine Learning in Real-Time Sampling Advisory to Predict OBM Contamination Levels

Reservoir fluid samples obtained from testing or production wells need to undergo PVT analysis to determine its properties under varying pressure, volume and temperature conditions. However, samples need to be tested for contamination, to ensure that the sample obtained is clean and representative of the downhole formation fluid, so that the PVT analysis gives reliable results.

During sampling, formation fluid is pumped through the wireline to the borehole. Initially, the fluid that flows will primarily consist of mud filtrate, and as pumping continues, the sample will gradually transition to a mixture of mud filtrate and formation fluid. Ideally, it is best for the sample to achieve the lowest possible contamination level, but it is impractical to pump indefinitely due to the cost of the rig time. The proposed solution, Live Integrated Sampling Advisory (LISA) leverages predictive modeling to perform real-time composition analysis of C1-C6+ hydrocarbon components to assess the contamination level of reservoir fluid. This could be implemented real-time, to aid reservoir engineers in determining the optimal time to sample reservoir fluid with contamination level within acceptance range.