Pipeline Inner Diameter Optimization Towards Reducing Cost and Minimizing Emissions in Oil and Gas Field Development Projects

Abstract During field development planning, engineers generate numerous development concepts to identify the most viable ones for further analysis and optimization. Concepts include high level surface facility networks represented by a set of nodes (wells, drill centers, gathering centers, etc.) and segments (pipelines). Proper pipeline sizing has a significant impact on costs, emissions, and reservoir performance. An automatic inner diameter (ID) optimization module is introduced to identify the optimal ID for every pipeline in the network based on a defined pressure gradient constraint and an ID search range. Input to the optimization module includes flow rates vs., time at the network boundary conditions (typically resulting from a reservoir simulation model), pressure at the network outlet node and pipeline characteristics. The optimization module addresses, hence, the time-varying inlet boundary conditions, which change with reservoir pressure, fluid characteristics, or operational conditions. For every set of flow rate, a range of acceptable IDs is identified using the given ID search range and the pressure gradient constraint. Then, the optimal ID is selected to be the highest minimum value that applies for the range of flow rates vs. time. We tested the module on two cases. The first network has 10 producers, and 10 injectors connected to four drilling centers, one gathering center, and one processing center through five pipelines. The second network connects 12 horizontal wells where six are producers to two drilling centers, one gathering center, and one processing center through three pipelines. The cases have different fluid compositions that vary throughout a ten-year production period. The networks have outlet pressures of 100 psia and 350 psia respectively. For each case, we identified the optimal ID for each pipeline in the network based on a defined pressure gradient constraint of 100 psi/km and ID search range between 5in and 14in. Then we solved the network using the optimal IDs to predict the variation of flow rates and pressure with time in the entire network including wellhead pressures. Proper sizing of pipelines that is automatically performed in this work is critical for preventing flow assurance issues, optimizing reservoir performance, minimizing capital expenditure and associated emissions, and ultimately enhancing the efficiency of field development plans. This work presents two novel contributions. The first contribution is an automatic method to convert a conceptual network layout into a network model. The second contribution is the automatic identification of the optimal ID for every pipeline in the network while accounting for the variation of inlet boundary conditions (gas, oil, and water flow rates) with time. Keywords—field development planning, flow assurance, inner diameter optimization, network modeling