Gas fields in the Gulf of Thailand (GOT) share some similar operational complexities and experience many common challenges. Such challenges include the huge number of wells and platforms, and the large, complex, interconnected pipeline network. Additionally, each well, of course, exhibits different performance, different enhanced recovery as well as different and diverse flow assurance methods. Fluid streams also vary significantly from well to well; for instance, the differences in condensate to gas ratios (CGR), water to gas ratios (WGR), and the CO2, and H2S levels. Moreover, production performance in the GOT remains very dynamic. The decline in production could be seen early, even though proper reservoir management was achieved because most of the reservoirs were small and compartmentalized. Optimizations aiming to maximize revenue from these fields are very challenging.
State-of-the-art industry solutions to these problems are provided by integrated production modeling, and reservoir simulation. At first consideration, they appear to be reasonable tools that can physically describe the flow of fluid, whether in a reservoir, well or surface facility; however, these tools may not serve well for the complicated compartmentalized characteristics of the gas fields in the Gulf of Thailand. Currently, determining optimum natural gas production rates in the GOT is performed by manually fine-tune the production rate using information from the latest well testing data. This method may simple and convenient but requires large effort and does not guarantee the optimal solution.
This study presents a more efficient production optimization scheme integrating constrained optimization with decline curve analysis to predict future well production performance. The project net present value is translated into the objective function, comprising maximizing condensate production and minimizing waste water production while also honoring daily gas production nomination. Well performance, export specification, and the capacity of pipeline networks are formulated as system constraints. A linear programing optimization algorithm is then used to solve the resulting optimization problem for a single time step. Next, the optimization is integrated with the production decline trend from the decline curve analysis to obtain the forecast of future production performance.
Tested against the production data of a large gas field in the Gulf of Thailand, this method showed a significant increase in the condensate production and a decrease in the water production. This solution not only enhanced production, but also reduced tedious time required for modeling, history matching, or manually configuring well production. Main assumptions, limitations and the conclusion of the proposed method are also included in this study.