Alternating injection of water and gas (WAG) has been widely applied as an oil recovery strategy since the late 1950s. The expected benefits are improved macroscopic sweep, with the water and gas sweeping lower and upper zones of the reservoir respectively, and improved microscopic sweep due to various effects leading to lowering of the residual oil saturation. Benefits on the microscopic scale are expected especially if the injected gas is miscible with the oil. WAG has been applied to various North Sea assets such as Snorre, Statfjord and Gullfaks.

WAG strategies are typically designed using trial simulations of different scenarios. For fields with many wells it is not generally possible to design an optimal strategy without the use of approaches to systematically explore alternative strategies. Mathematical optimization theory provides such methods. Previously, we have applied such methods to determine optimal drilling sequences for new field developments for a number of Norwegian assets under uncertainty. Here we apply similar concepts to additionally optimize the optimal injection and production strategies for drilled wells.

The development period may take a number of years if many wells are to be drilled, leading to time-varying capacity to (re-)inject gas that is difficult to take into account when the order in which injectors become available is not a priori fixed. Therefore we investigate alternative approaches to characterize WAG strategies during the field development stage, namely switching time controls and injection type controls, also in combination with injection rate controls.

We present a number of examples of numerical experiments for a representative test model. Multiple geological realizations of the model are used to represent the uncertainty. Results indicate that significant improvements in economic returns can be obtained through optimization relative to reasonable base strategies, also if the WAG strategy is optimized for a fixed drilling sequence. We show that not only the expected value can be increased, but that also the value for the worst performing realizations can be improved, thereby reducing risk. Finally, we provide physical interpretations of the optimal strategies in support of decision maturation.


Article metrics loading...

Loading full text...

Full text loading...


  1. Bahagio, D. N. T.
    2013. Ensemble Optimization of CO2 WAG EOR. MS thesis, Delft University of Technology, Delft, Netherlands.
    [Google Scholar]
  2. Chen, B., and Reynolds, A. C.
    , 2016. Ensemble-Based Optimization of the Water-Alternating-Gas-Injection Process. SPE Journal, June 1, doi:10.2118/173217‑PA.
    https://doi.org/10.2118/173217-PA [Google Scholar]
  3. Chen, Y.
    2008. Efficient ensemble based reservoir management. PhD Thesis, University of Oklahoma, USA.
    [Google Scholar]
  4. Chen, Y., Oliver, D.S. and Zhang, D.
    2009. Efficient Ensemble-Based Closed-Loop Production Optimization. SPE Journal14 (4) 634–645. DOI: 10.2118/112873‑PA.
    https://doi.org/10.2118/112873-PA [Google Scholar]
  5. Fonseca, R.M., Stordal, A.S., Leeuwenburgh, O. Van den Hof, P.M.J. and Jansen, J.D.
    2014. Robust ensemble-based multi-objective optimization. Proc. 14th European Conference on the Mathematics of Oil Recovery (ECMOR XIV), Catania, Italy, 8–11 September.
    [Google Scholar]
  6. Fonseca, R.M., Chen, B., Jansen, J.D. and Reynolds, A.C.
    , 2016. A Stochastic Simplex Approximate Gradient (StoSAG) for Optimization under Uncertainty. Accepted for publication in International Journal for Numerical Methods in Engineering.
    [Google Scholar]
  7. Hanea, R. G., Fonseca, R. M., Pettan, C., Iwajomo, M. O., Skjerve, K., Hustoft, L., Chitu, A. G., and Wilschut, F.
    , 2016. Decision maturation using ensemble based robust optimization for field development planning. Proc. 15th European Conference on the Mathematics of Oil Recovery held 29August – 1 September, Amsterdam, Netherlands.
    [Google Scholar]
  8. Hewson, C. W., and Leeuwenburgh, O.
    , 2017. CO2 water-alternating-gas flooding optimization of the Chigwell Viking ‘I’ pool in the Western Canadian Sedimentary Basin. Paper SPE-182597 presented at the SPE Reservoir Simulation Conference held in Montgomery, TX, 20–22 February.
    [Google Scholar]
  9. Leeuwenburgh, O., Chitu, A. G., Nair, R., Egberts, P. J. P., L.Ghazaryan, Feng, T, and Hustoft, L.
    , 2016. Ensemble-based methods for well drilling sequence and time optimization under uncertainty, Proc. 15th European Conference on the Mathematics of Oil Recovery held 29August – 1 September, Amsterdam, Netherlands.
    [Google Scholar]
  10. Meza, J. C., Oliva, R. A., Hough, P. D., and Williams, P. J.
    2007. OPT++: An object-oriented toolkit for nonlinear optimization. ACM Trans. Math. Softw. 33, 2, Article 12 (June 2007), 27 pages. DOI :10.1145/1236463.1236467.
    https://doi.org/10.1145/1236463.1236467 [Google Scholar]
  11. Rodriguez Torrado, R., De Paola, G., Fernandez Perez, A., Rincon Fuenmayor, A., Silva De Azevedo, M., and Embid, S.
    , 2015. Optimize a WAG field development plan, use case of carbonate ultra-deep water reservoir. Paper SPE-174344 presented at the EUROPEC 2015 held in Madrid, Spain, 1–4 June.
    [Google Scholar]

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error