Abstract
Oil prices see large fluctuations peculiarly over the last eight years due to natural disasters, political instability, and Covid-19 pandemic shock. These prompt to anxiety towards expenditure in planning and forecasting of a field development plan (FDP). Economic optimization of a reservoir under water drive can be extremely tedious and time consuming especially for complex field. Traditionally, upon completion of forecast optimization on fluid production, reservoir engineer willhand over the reservoir models to petroleum economist for economical evaluation. If the chosen development strategy is not economically viable, the model strategies will have to be updated, and continue the repetition of financial evaluation all over again. Hence, this paper established an automated workflow that diminished the dilemma on iterations obligation between simulation runs and financial reviews in searching for most efficient waterflooding strategy.
The automated workflow is accomplished by bridging three tools together seamlessly utilizing python scripting. These include the cash flow economic spreadsheet model, the dynamic simulator, and an assisted uncertainty analysis tool. The process first started with defining the economic parameters such as OPEX, CAPEX, oil price, taxes, discounted rates, and other financial parameters on an annual basis in spreadsheet. The uncertainty parameters: water injection rate, maximum water cut, and injection duration will be evaluated during forecast optimization to produce project efficiency indexes: Net Present Value (NPV) and Benefit-Cost Ratio (BCR). This integration was achieved by python script that automatically creates a coding path which exchanges simulation production and economic spreadsheet data at every simulation time step and each development strategy, that require no manual intervention.
The integrated economic-dynamic model workflow has successfully applied on West Malaysian field and Olympus model, a development strategy that maximize oil recovery without neglecting cost of water disposal, storage for total water produced from the reservoir. This paper successfully identified the most efficient waterflooding strategy and production constraints for each well using BCR as objective function for optimization. The optimum development scenario does have a BCR which is more than 2 which show that investment on that particular development strategy is profitable. The results also demonstrated a crucial impression that the highest oil cumulative production may not results in high BCR due to cost involvement in resolving water production and field maintenance services.
This paper outlined the methodology, python scripting codes, and how integration automation works that successfully optimized an injection strategy in a development project using economic model from third-party application. The results of this automated workflow demonstrate a successful utilization of new technologies and simple customize programming knowledge that promote cross-discipline integration for enhanced work-time efficiencies in problem solving that is suitable for all reservoir model type to determine its success rate and economic viability during FDP.
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