Phase development is key to any successful field development in complex and compartmentalized reservoirs. The balance of cost, value and risk drives the oil companies to learn from well and reservoir data as they develop the field. Naturally, well productivity will enhance in each phases, by a better well and completion design while the remaining mobile oil, hydrocarbon saturation and reservoir pressure are diminishing. Subsurface target selection and ultimately well design needs to be optimized rigorously in order to meet the expected value of each well in every drilling campaign. This paper gives a new insight in Reservoir Opportunity Index (ROI) or Simulation Opportunity Index (SOI) which combines the key elements of each target (Flow Capacity, Remaining mobile oil and pressure) into a normalized parameter. There are several papers1,2,3 which introduce the application of this method in several case studies. All of those works is guided by existing simulation model which is history matched until present time. The model is then used to generate the SOI map with the MAX (SOI) function will be treated as the optimize location for future well location. We had tested this method in several case studies and have found that more detail analysis in SOI indexes are needed before accepting the results of the MAX (SOI) function. Having several infill drilling campaign in the past gives us an opportunity to evaluate the SOI based on the previous data and production performance. Correction of the mathematical function may be needed to better match the historical data which is not limited to production only but the Productivity Index (PI), pressure and remaining oil map. The method have been applied in several cases including a tight gas reservoir and two vast multiple stacked oil reservoirs with reliable full field model. It will be shown in this paper that this method gives a more reliable targets in subsurface and well design and hence introduced as an integrated part of well optimization workflow. Like other methods, it should be used with care as there are limitations in each case. This paper highlights those limitations that have been observed in our work and advised how to overcome the limitations.

It shall never be over-emphasized that the balance of cost and value is very crucial in determining the commercial feasibility of a field development or redevelopment project. The values are generated by wells that could fetch higher productivity and could effectively drain out larger reservoir hydrocarbon fluid volume. Well drilling and completion costs and their surface production supporting facilities costs have been steadily increasing in recent years. Subsurface engineering studies shall therefore also focus on optimizing the well placement and orientation, the well type and completion selection, the life-cycle control of well inflow and outflow, with the minimum well count to yield higher values. This paper entails various methodologies of selecting drainage and injection points by combining the remaining mobile oil, current productivity, and current pressure depletion maps constructed from history matched reservoir simulation models. Base on predominant drive mechanisms in the reservoirs studied, governing parameters were coupled in 3 property groups and normalized individually. A known heuristic approach was also adapted to construct a Simulated Opportunity Index (SOI) map. A correlation between the SOI and recoverable reserve (EUR) was established by simulation prediction runs for each drainage or injection point selected, sand by sand in the studied reservoirs. The studied reservoir cases including a vast thin oil-rim reservoir, a huge multiple stacked reservoir, a complex compartmentalized reservoir, and a prolific deep-water reservoir. Clustering the selected drainage and injection points in several sands to further maximize the well productivity, optimization of the inflow control for the selected commingled sands, and the design of cost effective completions, shall be addressed later sequentially in separate papers. Introduction The technical challenge is getting difficult as fields are reaching maturity. The complexity and uncertainty of the field require a detail understanding of both reservoir characteristics and facilities performance in order to identify and optimally exploit the field potential. In multi layered reservoirs, substantial reserves is located in minor reservoirs that demand innovative solution for cost effective redevelopment. The wells drilled in later part of the brown field especially require maximizing reservoir contact, higher well productivity for higher recovery to justify the well cost. Various well architecture options with elaborated smart bottom-hole devices is being deployed to control drawdown and sand production. To achieve maximum recovery with suitable well architecture, meticulous selection of optimum drainage and injection point is critical to boosting recovery from a brown field. Drainage point can be selected once confidence over complex remaining oil evaluation is established. Qualitative and quantitative methodologies ranging from surveillance and performance evaluation to 3D models are used to establish drainage and injection points in matured or brown reservoirs.