Innovative Water Salinity Management Through Integrated Asset Model Applied to Mexico Area-1
- Stefano Maiorano (eni S.p.A.) | Pietro Selvaggio (eni S.p.A.) | Ripalta Eleonora Distaso (eni S.p.A.) | Roberto Rossi (eni S.p.A.) | Elena Stano (eni S.p.A.)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Characterisation and Simulation Conference and Exhibition, 17-19 September, Abu Dhabi, UAE
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- Water Salinity Management, Integrated Asset Model, Mexico Area 1, Water Injection, Souring Risk
- 4 in the last 30 days
- 65 since 2007
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The objective of this work is the prediction of water salinity evolution trend for Mexico Area-1 development that foresees the injection of a mixture of seawater and produced water from the six different reservoirs connected to the same FPSO.
Prediction of salinity trend evolution is crucial for forecasting possible biogenic hydrogen sulphide (H2S) formation and foreseeing the relating impacts over completion and facility material selection and on health, safety and environment (HSE) management.
Traditional numerical simulations through stand-alone models do not consider the effects of the reciprocal interaction among the fields on production profiles and are not able to simulate salinity evolution of produced and injected water mixture, variable over time. To overcome this limit, a new tool was developed. It consists in a python script that, introduced into the Area-1 Integrated Asset Model, allowed to generate forecasts of the water salinity along the project lifetime. These simulations were essential for souring risk assessment, providing the following results:
water salinity trend evolution at each injector well;
water salinity trend evolution at each producer well;
injection water breakthrough timing at the producer wells.
Moreover, it gave the opportunity to assess the injection strategy efficiency and to quantify the impact of changing salinity on water viscosity and on the field recovery.
In conclusion, the innovative methodology applied in the Area-1 IAM (Integrated Asset Model) permits to predict the salinity of injected water and to foresee salinity evolution of produced water generating several valuable information, providing a flexible tool that allows to investigate simultaneously several uncertainties related to the project and to evaluate promptly solutions and mitigation.
Moreover, when the reservoirs will be on production, the numerical models integrated with the developed script will reproduce the historical salinity data allowing to identify preferential flow path established by fluids virtually acting as a reservoir tracer technology.
|File Size||1 MB||Number of Pages||11|
De Siqueira, A. G., Araujo, C. H. V., Reksidler, R., & Pereira, M. de C. (2009, January 1). Uncertainty Analysis Applied to Biogenic Reservoir Souring Simulation. Society of Petroleum Engineers. doi:10.2118/121175-MS
Maxwell, S., & Spark, I. (2005, January 1). Souring of Reservoirs by Bacterial Activity During Seawater Waterflooding. Society of Petroleum Engineers. doi:10.2118/93231-MS
Selvaggio, P., Freni, F., Rossi, R., Di Giorgio, D. C., & Colombo, I. (2018, November 12). New Generation Integrated Asset Modelling: High-Resolution Reservoir Multi Models Coupled with an External Steady-State Network Solver. Society of Petroleum Engineers. doi:10.2118/192603-MS
Sunde, E., Thorstenson, T., Torsvik, T., Vaag, J. E., & Espedal, M. S. (1993, January 1). Field-Related Mathematical Model To Predict and Reduce Reservoir Souring. Society of Petroleum Engineers. doi:10.2118/25197-MS