This paper discusses the workflow and method of selecting optimum number of infill and injection wells based on incremental recovery. Normally, for infill wells study, a ‘creaming curve’ method is used to evaluate the optimum number of wells against incremental recovery from the field. However, in the case of determining number of infill wells together with water injection wells, a more comprehensive approach is needed. One needs to evaluate the pressure depletion rate from existing and infill wells together with the dynamic of the producer-injector pairings as well as the sweeping factor.

The paper is based on infill and water injection development plan for a brown field in Sabah basin which located in Malaysia. To maintain operatability of the field in the future, several new infill and water injection wells options are evaluated for optimum field life oil production. Unlike infill or producer-only assessment, the same ‘creaming curve’ approach for combination of infill and water injection wells is less effective as large number of simulation runs are needed to sample the combination of these wells that generate optimum oil recovery. This has proven to be challenging especially when the models are large which is normally the case for brown fields and it requires extensive computational hours.

In the first part, a modified approach bringing some pre-analytic assessment of producer-injector pairing is being used. The pairings are first ranked based on streamlines visualization, drainage tables and their respective contributions towards oil recovery. The ‘creaming curve’ is then built based on the highest contribution as well as the sequencing of the pairings. The second method mentioned in this paper is the numerical approach through multi-objective optimization using assisted history matching and uncertainty tool. With the help of optimizer, the number of simulation runs can be drastically reduced when only best combination of infills and injectors for each total number of wells are considered.

Both alternative methods will be compared with the full computational runs, sampling every single combination of wells. Finally, the optimum number of wells with the combination of infill and water injection wells are analysed based on cumulative oil recovery against the Net Present Value (NPV). This case study therefore demonstrates how alternative methods can be used to resolve the optimum number of infill and water injection wells to avoid lengthy and very large numbers of simulation runs.

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