Early detection of gas conformance in desired reservoir zones as well as its breakthrough from producing wells is critical for successful implementation of gas EOR projects. The installation of temperature and pressure sensors in deepwater wells are underway as they provide valuable data useful for flow analysis and quantification. Modeling using the sensor data is valuable since interventions in deepwater wells are either not possible or prohibitively expensive. In this study, temperature and pressure sensor data is used to model and monitor the conformance of injected gas. The presented model is further used for two applications 1) conformance of injected gas in an injection well and 2) identification of gas breakthrough from a production well.

In this study, the production from a well producing from multiple reservoirs is modeled as a series of producing (vertical flow along with radial flow from a producing reservoir section) and non-producing (only vertical flow through the production tubing) zones. The energy balance and the momentum balance equations are coupled to quantify flow rates from individual producing reservoir sections. In addition to the sand face temperature measurements, surface temperature, total surface flow rate as well as pressure and temperature measurements in the producing tubing are used as input to the model. A difference between the temperature and pressure data obtained from the sensors and that obtained from the model is used to detect and quantify gas conformance in injection well as well as gas breakthrough in production well.

The temperature data can be used to identify locations of producing and non-producing zones in the reservoir and quantify production from individual producing zones. This model will help determine the number of temperature and pressure sensors required for conformance of gas injected as well as effective detection of gas breakthrough in producing wells. The model can also be adapted to detect water breakthrough during secondary injection. The sensors along with the model have the potential to become an integral part of production monitoring for reservoir management.