A Simulation coupling method for surface production system optimization is developed as part of an integrated operations (IO) project that aims to support the field rejuvenation program for a mature field. The objective is to unlock surface constraints and optimize field production via a production network-to-process facility coupling method. This method allows cross-discipline engineers to collaborate and perform comprehensive well deliverability assessment, evaluate network back-pressure and various facility constraints. Such collaboration also promotes the efficiency of optimization process. The well-pipeline production network and surface facility simulation models are coupled within a single application in which the hydraulic and thermal streams are tied for modeling consistency. These are configured at the upstream end of the separator system where common boundaries are solved sequentially. A "loop-back" approach is applied to impose facility constraints to the network and the well performance will be assessed based on its response to the system back-pressure and constraints. The coupled model is optimized by a neural-network solver where constraints are set up based on operation requirement such as flaring limit, process limits, gas lift requirement, erosional velocity limit, etc. Thorough analysis can be performed by incorporating and understanding the interactions between parameters and variables of the production system starting from the well, and progressing to the pipeline network, and to the processing facility. This allows personnel from multiple domains to collaborate and achieve the following: Restrategizing the separator pressure system to meet the production target while embarking on the vision of operating with zero gas flaring. The sensitivities of production network potential against surface capacity can be performed to identify the potential optimized operating setpoints.Reducing production deferment during prolonged operation equipment upset (i.e., when pump, compressor, or separator are shut down for maintenance). The deferment can be minimized by re-routing of production and/or re-allocating the gas lift distribution based on availability.Anticipating potential operational interruptions if operating setpoints of the production network and/or facility system are changed. These changes can be due to operational requirement or production enhancement initiatives. The coupling method provides critical insights to uncover opportunities of optimizing field production and minimizing production upset and interruption. The integrated operation improves the optimization process by promoting the collaboration of multiple domains. The outcome of the coupling method should be used as basis for further transient analysis check prior to field implementation, which is an additional key facet to its technical viability in terms of operational safety and avoidance to potential risk of production interruption.
CNV field in offshore Vietnam is experiencing excessive surface back pressure due to extended production pipeline and increasing field gas-oil ratio (GOR), which not only constraints the production from existing wells but also creates a challenge in evaluating production gain from future development activities. Therefore, it is critical to properly account the back pressure effect to generate a reliable long term production forecast for further investment decision. This paper describes the details of integrating subsurface dynamic reservoir simulation model with surface network simulation model to holistically assess the impact of back pressure. The conventional method of using standalone dynamic simulation model is compared against the integrated model. The well control mode in the reservoir model is updated with the response of the network model, which consist of wells, topside piping, facility equipment and export pipelines. With this approach, the surface pressure constraints and responses will be captured, and the reservoir, well and network performance will be impacted accordingly. A unified field management is designed using an advanced orchestration engine to control the well operating conditions, schedule well activities and activation of equipment in the operational cycle. Thorough assessment can be performed with the inclusion of accounting interactions between reservoir and network parameters. This integrated modelling workflow allows multiple domains of reservoir engineering, production engineering and engineering contractors to collaborate and achieve a better understanding of the impact of surface back pressure by producing a representative forecast of production profile. To address the back pressure problem in the current facility, debottleneck the surface network and improve production was evaluated by installation of additional surface equipments such as booster pump and compressor. In general, the integrated model provides critical insights to the field development planning, evaluation for de-bottle necking surface system and production optimization. There is lack of publication on the successful usage of the integrated surface network with subsurface dynamic simulation as it is uncommon for this feature in conventional modelling workflows. This paper describes the successful case of the implementation of an integrated simulation modelling workflow to simulate long term surface back pressure effect, back pressure from additional production into the system, and benefits of new surface equipment installation. Highly efficient and accurate prediction tool was developed in the scope of this study.
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.
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