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This paper presents an innovative application of the Integrated Asset Model (IAM) approach for simulating a surface network collecting many fields on production and multiple constraints, using a last-generation High-Resolution Reservoir Simulator (HRRS) applied to low-permeability reservoirs and complex wells models. The reservoir models are directly coupled by a Field Manager (FM) process to an external network simulator. The presented approach is flexible and highly efficient, using the logic of a modern High-Resolution Reservoir Simulator integrated within a unique IAM model, where the network simulator acts as a cyclic constraints updater for the independent reservoirs in order to continuously account for flow assurance effects. The proposed method relies on HRRS modularity, characterized by the possibility of integrating a Field Manager process with multiple reservoirs simulation processes: for each time step, the former provides updated pressure constraints at Tubing Head and well allocations according to the defined strategy, the latter solves the reservoir equations for each model. The FM acts as an orchestrator for a variety of reservoirs and network simulation instances, allowing to change reservoir and network simulator type without modifying the development strategy. The network simulator computes pressure drops and temperature along the pipelines by appropriate multiphase correlations, tuned against the available measured data. The proposed flexible IAM approach was preliminary tested on a single reservoir model to optimize the computational efficiency with respect to the needed process details in terms of memory usage and simulation run-time. Then, the methodology was implemented on the full asset: three low-permeability reservoirs with horizontal multi-fractured wells interconnected to a complex surface network, constrained by limited gas market demand and zero flaring policy. The IAM approach provided a flexible method to analyze different development options and wells/pipelines routing configurations to maximize oil production, improving asset gas management. As a result, the three dynamic models were successfully coupled, honoring overall asset and facilities constraints. The comparison between the resulting production profiles with the standalone model simulations, constrained by fixed minimum Tubing Head Pressure (THP), clearly shows the effectiveness of the proposed IAM approach: being the THP calculated in IAM according to the actual flow conditions, the proposed methodology resulted in a strong improvement especially during tail-end production phase that impacts ultimate recovery and reserves estimation. With the proposed approach, the asset performance could be properly evaluated by correctly taking into account the backpressure of the multiple interdependent platforms. Moreover, the application of HRRS enables to run the reservoir simulations in an efficient way on a High Performance Computing (HPC) cluster to speed up the overall process.
This paper presents an innovative application of the Integrated Asset Model (IAM) approach for simulating a surface network collecting many fields on production and multiple constraints, using a last-generation High-Resolution Reservoir Simulator (HRRS) applied to low-permeability reservoirs and complex wells models. The reservoir models are directly coupled by a Field Manager (FM) process to an external network simulator. The presented approach is flexible and highly efficient, using the logic of a modern High-Resolution Reservoir Simulator integrated within a unique IAM model, where the network simulator acts as a cyclic constraints updater for the independent reservoirs in order to continuously account for flow assurance effects. The proposed method relies on HRRS modularity, characterized by the possibility of integrating a Field Manager process with multiple reservoirs simulation processes: for each time step, the former provides updated pressure constraints at Tubing Head and well allocations according to the defined strategy, the latter solves the reservoir equations for each model. The FM acts as an orchestrator for a variety of reservoirs and network simulation instances, allowing to change reservoir and network simulator type without modifying the development strategy. The network simulator computes pressure drops and temperature along the pipelines by appropriate multiphase correlations, tuned against the available measured data. The proposed flexible IAM approach was preliminary tested on a single reservoir model to optimize the computational efficiency with respect to the needed process details in terms of memory usage and simulation run-time. Then, the methodology was implemented on the full asset: three low-permeability reservoirs with horizontal multi-fractured wells interconnected to a complex surface network, constrained by limited gas market demand and zero flaring policy. The IAM approach provided a flexible method to analyze different development options and wells/pipelines routing configurations to maximize oil production, improving asset gas management. As a result, the three dynamic models were successfully coupled, honoring overall asset and facilities constraints. The comparison between the resulting production profiles with the standalone model simulations, constrained by fixed minimum Tubing Head Pressure (THP), clearly shows the effectiveness of the proposed IAM approach: being the THP calculated in IAM according to the actual flow conditions, the proposed methodology resulted in a strong improvement especially during tail-end production phase that impacts ultimate recovery and reserves estimation. With the proposed approach, the asset performance could be properly evaluated by correctly taking into account the backpressure of the multiple interdependent platforms. Moreover, the application of HRRS enables to run the reservoir simulations in an efficient way on a High Performance Computing (HPC) cluster to speed up the overall process.
The derivation of an optimised field development strategy is one of the most important tasks in the oil and gas industry. Modern workflows apply complex integrated asset models (IAM) where reservoir and production network simulators are coupled to provide a single model of the entire asset. In this paper we present the solutions adopted to build an IAM for an asset in the current producing scenario, but also modelling future incremental surface facilities’ upgrades, tasks found to be beyond the capabilities of current commercial IAM products. The asset consists of a number of oil and gas fields producing into a common network. A variable gas offtake through existing and future facilities is expected in the field development. Commercial IAM solutions cannot model this or update the constraint on the total production based on the changing offtake volumes. These limitations have been solved by a set of custom scripts embedded in the simulators. The scripts manage the inline separator efficiency both in the current scenario and also when a volume of the produced gas will be sent to the LNG plant planned in the asset future development. The two configurations, i.e., the current producing scenario and the future development one, are based on the same set of reservoir models with different network schemes and facilities’ constraints. The integration of the reservoir and the network enables the determination of the impact of the facilities’ upgrades, namely the installation of a process unit close to the oil production platform and the routing of a fraction of the gas to feed an LNG plant under construction. The total production is increased because of the reduction of the backpressure due to the new location of the process unit and the reduction of the gas flowing to the current treatment terminal. The average gain both in the oil and gas production rates is around 20% for 5 years. Moreover, due to the unique flexibility of the IAM solution, additional projects (such as further developments to the existing producing reservoirs or tie-in of other fields already discovered) can be easily added to the model to simulate the future asset production. For the particular asset being studied, a solution to the challenging reservoir-network coupling issue was developed employing a set of simulation scripts (including Python ones) that enable the modelling of the network configuration in a rigorous way. This approach overcame the current commercial application limitations and expanded the functionalities of each simulator included in the IAM.
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