Reservoir Fluid Data Acquisition Using Advanced Mud-Logging Gas in Shale Reservoirs

Oil production from shale reservoirs has increased dramatically in recent years. To identify drilling targets and optimize well completions, it is important to get early access to reservoir fluid properties. However, due to the low permeability of shale reservoirs, fluid samples often become available only after the most important development decisions have been made. Therefore, it has been an abiding challenge in the industry how to acquire fluid properties data earlier in shale reservoirs. Mud-logging gas data acquired while drilling provide the earliest hydrocarbon response from the reservoir. In an earlier study, we demonstrated that advanced mud gas data have a large potential to predict reservoir fluid properties. In general, fluid properties are strongly correlated with the thermal maturity of the source rock. In shale reservoirs, reservoir fluids are still in the source rock, as low permeability limits the migration and convection of the reservoir fluids. As a result, the reservoir fluid systems in shale reservoirs are relatively undisturbed and have a high degree of consistency. This provides the possibility to correlate advanced mud-logging gas data and reservoir fluid properties. Based on a reservoir fluid database with more than 60 samples from different shale reservoirs, we developed a machine-learning algorithm to predict fluid properties from advanced mud-logging gas data. The accuracy of the new method is significantly improved compared with the previous model, which used an explicit correlation based on wetness. In addition, the new approach is more general and does not depend on a specific shale reservoir. We applied the new model to 11 wells with advanced mud-logging gas data. The predicted gas-oil ratios (GOR) are close to the measurement from early production data when advanced mud-logging gas data are of good quality. This publication demonstrates that advanced mud-logging gas data can be used to predict reservoir fluid properties in shale reservoirs. Such an approach provides a novel and cost-efficient solution for the sampling challenges in the early phase. In addition, the method provides continuous fluid data along the entire well, as opposed to a single fluid sample taken at a specific location. Hence, the results provide insight into the fluid distribution in shale reservoirs. The method can be widely used for sweet spot identification and optimizing fracking strategy in shale reservoirs.