In the greenfield development process, one of the key questions that needs to be answered is, "What is the range of EUR for a particular development concept and the associated completion method based on the existing range of subsurface uncertainties?" The key challenge then is how can the team forecast a representative range of EUR efficiently to obtain a range of results that represent a probabilistic outcome. During the reservoir modelling process of this case study, a total of 405 static realizations had been run and then a STOIIP S-curve was generated. In the next step, 20 cases each of "High, Mid and Low" static models were selected based on the S-curve distribution for the next phase of dynamic simulation due to time and resources constraint. In terms of completion, the same development concept and completion method is assumed, where each dynamic case requires 8 horizontal producing wells with 200 metres of completion interval. Wells placement aside, each of the 60 dynamic models should not have the same fixed perforation depths and intervals due to the geological uncertainties with regards to facies distribution and they need to be selected based on the well effective k-h and hydrocarbon saturation along each well trajectory. Manual work could be used to analyse the best intervals for each of the planned wells, or in this case, this laborious process was replaced with an automated selection of the optimum completion interval workflow using Python script. This paper will show the workflow of how a scripted Python code is designed to provide an "automated moving window" to find the best intervals along a well trajectory. This workflow was executed in the pre-processor of the dynamic simulator which has a workflow window with Python-embedded capability. The Python code then generated the simulation keyword COMPDATMD, which contained the best perforation intervals for all the wells as an output. This automated workflow resulted in an optimization of the completion intervals in all the 60 dynamic model cases, while the ultimate recovery for this greenfield development in Peninsula Malaysia increased by 30% compared to EUR from previously "unoptimized runs". This approach is managed to cut down the run preparation time by at least two weeks compared to the manual solution. The improved range of EUR is also considered as a more representative outcome of the field development evaluation. Utilizing emerging technology breakthrough such as ability to customize specific features via a programming language is important towards a successful era of the Fourth Industrial Revolution (4IR). The results of this automated and customized workflow automation demonstrate a successful application of using machine learning for enhanced problem-solving in reservoir simulation.

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.