This paper presents a new approach to GOR prediction from advanced mud gas data for gas flooded reservoirs. This new approach predicts the GOR of the reservoir oil and/or gas while drilling in gas flooded reservoirs and makes real-time well decisions based on identifying the reservoir fluids. The method is currently under extensive verification in real field operations. The new method's potential is significant for accurately mapping resources for in-fill wells, boosting profitability, and lowering carbon footprint in gas flooded fields.

This study widens our previous machine learning model from advanced mud gas (Yang et al., 2019) developed for predicting in-situ reservoir fluid properties by extending the methodology to include gas flooded reservoirs. The methodology is verified by compositional modeling of a large field undergoing pressure depletion, water, and gas injection over 30 years. The compositional reservoir model is used to generate advanced mud data (light components C1 to C5) to predict fluid phase properties by machine learning. The predicted fluid properties are then compared to the actual model data. The data points represent different well locations and different times of the field development, i.e., initial state, water injection, gas injection, and pressure depletion.

To accurately predict the fluid properties using a machine learning model, the "training database" must contain a wide range of fluids that covers the "compositional window" in the gas injection process. To achieve this, we have introduced synthetic fluid samples into the machine learning fluid database. The synthetic data are generated through slim tube simulation by developing miscibility between original reservoir fluid composition and representative injection gases. We have verified the machine learning model by comparing the GOR from the reservoir simulation model against the predicted GOR at different depths during the different production stages of the field. The GOR prediction shows good agreement with the simulation results.

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