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Title: Numerical simulation of water-alternating-gas (WAG) injection : best practices
Author: Alzayer, Hassan Ali
ISNI:       0000 0004 9356 2984
Awarding Body: Heriot-Watt University
Current Institution: Heriot-Watt University
Date of Award: 2019
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To reliably predict the water-alternating-gas (WAG) injection performance, a transformational shift in the modelling of the WAG process is needed. Therefore, this thesis focused on identifying the shortcomings of the current reservoir simulators and suggesting a new methodology to improve the simulation prediction of WAG injection performance. To achieve this, several core-scale WAG injection experiments were analysed to identify trends and behaviours. Furthermore, these experiments were simulated using ECLIPSE-100 to identify the limitations of the current commercial simulators. Based on these exercises, a new methodology to improve the modelling process of WAG injection cycles using the current simulation capabilities were suggested. The results of five unsteady-state water-alternating-gas injection experiments performed by various researchers, from Heriot-Watt University, at different conditions were used in this simulation study. These WAG injection core-flood experiments were analysed and simulated using the new approach. The simulation of the five different WAG injection experiments confirmed the positive impact of updating the WAG-hysteresis parameters in the later WAG injection cycles. This change significantly improved the match between simulation and WAG experimental results. Therefore, a systematic workflow to acquire the relevant data and analyse them to generate the input parameters required for WAG injection simulation has been suggested. In addition, a logical procedure was suggested to update the simulation model after the third injection cycle as a workaround to overcome the limitation in the current commercial simulators. This guideline can be incorporated in the numerical simulators to help the industry in improving the accuracy of WAG injection simulation using the current simulation capabilities.
Supervisor: Sohrabi, Mehran Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available