As part of the production optimization exercise in J field, an initiative has been taken to enhance the field production target without well intervention. J field is a mature field; the wells are mostly gas lifted, and currently it is in production decline mode. As part of this optimization exercise, a network model with multiple platforms was updated with the surface systems (separator, compressors, pumps, FPSO) and pipelines in place to understand the actual pressure drop across the system. Modelling and calibration of the well and network model was done for the entire field, and the calibrated model was used for the production optimization exercise. A representative model updated with the current operating conditions is the key for the field production and asset management. In this exercise, a multiphase flow simulator for wells and pipelines has been utilized. A total of ∼50 wells (inclusive of idle wells) has been included in the network model. Basically, the exercise started by updating the single-well model using latest well test data. During the calibration at well level, several steps were taken, such as evaluation of historical production, reservoir pressure, and well intervention. This will provide a better idea on the fine-tuning parameters. Upon completion of calibrating well models, the next level was calibration of network model at the platform level by matching against the platform operating conditions (platform production rates, separator/pipeline pressure). The last stage was performing field network model calibration to match the overall field performance. During the platform stage calibration, some parameters such as pipeline ID, horizontal flow correlation, friction factor, and holdup factor were fine-tuned to match the platform level operating conditions. Most of the wells in J field have been calibrated by meeting the success criterion, which is within +/-5% for the production rates. However, there were some challenges in matching several wells due to well test data validity especially wells located on remote platform where there is no dedicated test separator as well as the impact of gas breakthrough, which may interfere to performance of wells. These wells were decided to be retested in the following month. As for the platform level matching, five platforms were matched within +/-10% against the reported production rates. During the evaluation, it was observed there were some uncertainties in the reported water and gas rates (platform level vs. well test data). This is something that can be looked into for a better measurement in the future. By this observation, it was suggested to select Platform 1 with the most reliable test data as well as the platform rate for the optimization process and qualifying for the field trial. Nevertheless, with the representative network model, two scenarios, reducing separator pressure at platform level and gas lift optimization by an optimal gas lift rate allocation, were performed. The model predicts that a separator pressure reduction of 30 psi in Platform 1 has a potential gain of ∼300 BOPD, which is aligned with the field results. Apart from that, there was also a potential savings in gas by utilizing the predicted allocated gas lift injection rate.
Field X located in offshore South East Asia, is a deepwater, turbidite natural gas greenfield currently being developed using a subsea tieback production system. It is part of a group of fields anticipated to be developed together as a cluster. Due to the nature of this development, several key challenges were foreseen: i) subsurface uncertainty ii) production network impact on system deliverability and flow assurance iii) efficient use of high frequency data in managing production. The objective of this study was to demonstrate a flexible and robust methodology to address these challenges by integrating multiple realizations of the reservoir model with surface network models and showing how this could be link to "live" production data in the future. This paper describes the development and deployment of the solutions to overcome those challenges. Furthermore, the paper describes the results and key observations for further recommendation in moving forward to field digitalization. The process started with a quality check of the base case dynamic reservoir model to improve performance and enable multiple realization runs in a reasonable timeframe. This was followed by sensitivity and uncertainty analysis to obtain 10 realizations of the subsurface model which were integrated with the steady-state surface network model. Optimization under uncertainty was then performed on the integrated model to evaluate three illustrative development scenarios. To demonstrate extensibility, two additional candidate reservoirs for future development were also tied in to the system and modelled as a single integrated asset model to meet the anticipated gas delivery targets. Next, the subsurface model was integrated with a multiphase transient network model to show how it can be used to evaluate the risk of hydrate formation along the pipeline during planned production start-up. As a final step, in-built application programming interface (API) in the integration software was used to perform automation, enabling the integrated model to be activated and run automatically while being updated with sample "live" production data. At the conclusion of the study, the reservoir simulation performance was improved, reducing runtime by a factor of four without significant change in base case results. The results of the coupled reservoir to steady-state network simulation and optimization showed that the network could constrain reservoir deliverability by up to 4% in all realizations due to back pressure, and the most optimum development scenario was to delay first gas production and operate with shorter duration at high separator pressure. With the additional reservoirs in the integrated model, the production plateau could be extended up to 15 years beyond the base case without exceeding the specified water handling limit. For hydrates risk analysis, the differences between hydrate formation and fluid temperature indicated there was a potential risk of hydrate formation, which could be reduced by increasing inhibitor concentration. Finally, the automation process was successfully tested with sample data to generate updated production forecast profiles as the "new" production data was fed into the database, enabling immediate analysis. This study demonstrated an approach to improve forecasting and scenario evaluation by using multiple realizations of the reservoir model coupled to a surface network. The study also demonstrated that this integrated model can be carried forward to improve management of the field in the future when combined with "live" data and automation logic to create a foundation for a digital field deployment.
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