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Effective asset-level decision-making relies on a sound understanding of the complex sub-components of the hydrocarbon production system, their interactions, along with an overarching evaluation of the asset's economic performance under different operational strategies. This is especially true for the LNG upstream production system, from the reservoir to the LNG export facility, due to the complex constraints imposed by the gas processing and liquefaction plant. The evolution of the production characteristics over the asset lifetime poses a challenge to the continued and efficient operation of the LNG facility. To ensure a competitive landed LNG cost for the customer, the economics of the production system must be optimized, particularly the liquefaction costs which form the bulk of the operating expenditure of the LNG supply chain. Forecasting and optimizing the production of natural gas liquids helps improve the asset economics. The risks due to demand uncertainty must also be assessed when comparing development alternatives. This paper describes the application of a comprehensive field management framework that can create an integrated virtual asset by coupling reservoir, wells, network, facilities, and economics models and provides an advisory system for efficient asset management. In continuation of previously published work (Khan, Ali, Elfeel, Biniwale, & Dandekar, 2020), this paper focuses on the integration of a steady-state process simulation model that provides high-fidelity thermo-physical property prediction to represent the gas treatment and LNG plant operation. This is accomplished through the Python-enabled extensibility and generic capability of the field management system. This is demonstrated on a complex LNG asset that is fed by sour gas of varying compositions from multiple reservoirs. An asset wide economics model is also incorporated in the integrated model to assess the economic performance and viability of competing strategies. The impact of changes to the wells and production network system on LNG plant operation is analyzed along with the long-term evolution of the inlet stream specifications. The end-to-end integration enables component tracking throughout the flowing system over time which is useful for contractual and environmental compliance. Integrated economics captures costs at all levels and enables the comparison of development alternatives. Flexible integration of the dedicated domain models reveals interactions that can be otherwise overlooked. The ability of the integrated field management system to allow the modeling of the sub-systems at the ‘right’ level of fidelity makes the solution versatile and adaptable. In addition, the integration of economics enables the maximization of total asset value by improving decision making.
Effective asset-level decision-making relies on a sound understanding of the complex sub-components of the hydrocarbon production system, their interactions, along with an overarching evaluation of the asset's economic performance under different operational strategies. This is especially true for the LNG upstream production system, from the reservoir to the LNG export facility, due to the complex constraints imposed by the gas processing and liquefaction plant. The evolution of the production characteristics over the asset lifetime poses a challenge to the continued and efficient operation of the LNG facility. To ensure a competitive landed LNG cost for the customer, the economics of the production system must be optimized, particularly the liquefaction costs which form the bulk of the operating expenditure of the LNG supply chain. Forecasting and optimizing the production of natural gas liquids helps improve the asset economics. The risks due to demand uncertainty must also be assessed when comparing development alternatives. This paper describes the application of a comprehensive field management framework that can create an integrated virtual asset by coupling reservoir, wells, network, facilities, and economics models and provides an advisory system for efficient asset management. In continuation of previously published work (Khan, Ali, Elfeel, Biniwale, & Dandekar, 2020), this paper focuses on the integration of a steady-state process simulation model that provides high-fidelity thermo-physical property prediction to represent the gas treatment and LNG plant operation. This is accomplished through the Python-enabled extensibility and generic capability of the field management system. This is demonstrated on a complex LNG asset that is fed by sour gas of varying compositions from multiple reservoirs. An asset wide economics model is also incorporated in the integrated model to assess the economic performance and viability of competing strategies. The impact of changes to the wells and production network system on LNG plant operation is analyzed along with the long-term evolution of the inlet stream specifications. The end-to-end integration enables component tracking throughout the flowing system over time which is useful for contractual and environmental compliance. Integrated economics captures costs at all levels and enables the comparison of development alternatives. Flexible integration of the dedicated domain models reveals interactions that can be otherwise overlooked. The ability of the integrated field management system to allow the modeling of the sub-systems at the ‘right’ level of fidelity makes the solution versatile and adaptable. In addition, the integration of economics enables the maximization of total asset value by improving decision making.
Recovery factor in gas fields is heavily reliant on the abandonment pressure. As reservoir pressure declines and water cut increases, matured assets experience declining well productivity. Fields at a gas hub within Sarawak Basin faces immense challenge to maximize field recovery following an infill drilling evaluation study which indicated no further potential. This paper provides the details on the hub opportunity to lower the abandonment pressure identified from network modelling, optimization works and execution lessons learned. Started with the vision to sustain the gas hub production which was anticipated to cease production in year 2025, when production falls below turndown rate (TDR) of 60 MMscf/d, a gas hub network model has been setup to represent the gas hub configuration with 3 natural depletion drive gas fields tie-in to the export compressor, with the aim to assess the incremental gains and recoverables by lowering down the field abandonment pressure. Material balance (MBAL) model for the gas fields were developed and history matched with production data. The MBAL model was then incorporated into the gas hub network model in General Allocation Package (GAP) model. The network model prediction run results demonstrated up to 12% substantial gas recovery improvement, from lowering down the compressor operating envelope from 48 Barg to 20 Barg in 3 stages; 36-28-20 Barg from year 2021 to 2024, which subsequently reduces its compression capacity from 600 MMscf/d to 300-250-200 MMscf/d, via compressor change-out activities. The single to dual stage compressor change-out Phase 1 implementation has been declared a success with reserves addition beyond 60 MMBOE and prolonging the gas hub life till year 2030. Post execution field performances indicated comparable performances and volumes as per forecasted by the network model, leading to robust project economic returns. Good production attainability at 105% was achieved for the first 12 months of production. The reinstatement of 2 idle wells during the project execution has also proven the value-added benefits from the project. The fields have successfully executed compressor change-out Phase 2 in Q3 2023 and continuous evaluation using updated network model is performed to optimize the Phase 3 timing currently predicted to happen in 2026. Various useful lessons learned were captured during the phased implementation of this project, both operational and subsurface, which included lower CGR than prediction and capped flow rates due to compressor gas turbine exhaust temperature limitation. As part of a continuous improvement process, all lessons learned were documented to be integrated into the network model updates, to improve future projects and to ensure good practices are replicated for future applications in PETRONAS.
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