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This paper presents a practical methodology of optimizing and building a detailed field surface network system by using the high-resolution reservoir simulator driven custom-made Python scripts to efficiently predict the future performance of the vast oil and gas-condensate carbonate field. All existing surface hydraulic tables are quality checked and lifting issue constraints corrected. Pressure losses at the wellhead chokes incorporated into the high-resolution reservoir simulator in the form of equation by using the custom scripts instead of a table format to calculate gas rate dependent pressure losses more precisely. Consequently, all 400+ surface production system manifolds, pipes and well chokes Horizontal Flow Performance (HFP) tables are updated and coupled to the reservoir simulator through Field Management (FM) controller which in turn generates Inflow Performance Relationship (IPR) tables for the coupled wells and passes them to solve the network. The methodology described in this paper applied for a complex field development planning of the Karachaganak. At present, reservoir management strategy requires constant balancing effort to uniformly spread gas re-injection into the lower Voidage Replacement Ratio areas in the Upper Gas-Condensate part of the reservoir due to reservoir heterogeneity. Additionally, an increase in field and wells gas-oil ratio and water-cut creates bottlenecks in the surface gathering system and requires robust solutions to decongest the surface network. Current simulation tools are not always effective due longer run times and simulation instability due to complex network system. As a solution, project-specific network balancing challenges are resolved by incorporating custom-made scripts into the high-resolution simulator. Faster and flexible integrated model based on hydraulic tables reproduced the historical pressure losses of the surface pipelines at similar resolution and generated accurate prediction profiles in a twice-quicker time than existing reservoir simulator. Overall, this approach helped to generate more stable production profiles by identifying bottlenecks in the surface network and evaluate future projects with more confidence by achieving a significant CAPEX cost savings. The comprehensive guidelines provided in this paper can aid reservoir modeling by setting up flexible integrated models to account for surface network effects. The value of incorporating Python scripts demonstrated to implement non-standard and project specific network balancing solutions leveraging on the flexibility and the openness of the modelling tool.
This paper presents a practical methodology of optimizing and building a detailed field surface network system by using the high-resolution reservoir simulator driven custom-made Python scripts to efficiently predict the future performance of the vast oil and gas-condensate carbonate field. All existing surface hydraulic tables are quality checked and lifting issue constraints corrected. Pressure losses at the wellhead chokes incorporated into the high-resolution reservoir simulator in the form of equation by using the custom scripts instead of a table format to calculate gas rate dependent pressure losses more precisely. Consequently, all 400+ surface production system manifolds, pipes and well chokes Horizontal Flow Performance (HFP) tables are updated and coupled to the reservoir simulator through Field Management (FM) controller which in turn generates Inflow Performance Relationship (IPR) tables for the coupled wells and passes them to solve the network. The methodology described in this paper applied for a complex field development planning of the Karachaganak. At present, reservoir management strategy requires constant balancing effort to uniformly spread gas re-injection into the lower Voidage Replacement Ratio areas in the Upper Gas-Condensate part of the reservoir due to reservoir heterogeneity. Additionally, an increase in field and wells gas-oil ratio and water-cut creates bottlenecks in the surface gathering system and requires robust solutions to decongest the surface network. Current simulation tools are not always effective due longer run times and simulation instability due to complex network system. As a solution, project-specific network balancing challenges are resolved by incorporating custom-made scripts into the high-resolution simulator. Faster and flexible integrated model based on hydraulic tables reproduced the historical pressure losses of the surface pipelines at similar resolution and generated accurate prediction profiles in a twice-quicker time than existing reservoir simulator. Overall, this approach helped to generate more stable production profiles by identifying bottlenecks in the surface network and evaluate future projects with more confidence by achieving a significant CAPEX cost savings. The comprehensive guidelines provided in this paper can aid reservoir modeling by setting up flexible integrated models to account for surface network effects. The value of incorporating Python scripts demonstrated to implement non-standard and project specific network balancing solutions leveraging on the flexibility and the openness of the modelling tool.
Optimization of injection strategy for the giant fractured gas-condensate Karachaganak field is the focus of this paper. Numerous development alternatives were assessed using DPDK simulation model coupled with surface network and further optimized by integration with streamline analysis. Advanced risk assessment enabled to mitigate risks associated with high uncertainties in fracture distributions and complex clinoforms within the development area. Optimization results are compared to alternative models from JV partners, considering geological, surface facilities and project uncertainties. Four main workflows have been discussed; firstly, constructing DPDK model through integration of seismic, dynamic and petrophysical data to properly characterize fracture properties and reproduce reservoir connectivity. This process has been guided by advanced Design of Experiment to manage numerous uncertainties in history match and model selection. Secondly, coupling subsurface model with the surface network simulator and addressing re-routing challenges to generate realistic forecast and efficient production system. Thirdly, devising risk management workflow to ease decision making for the placement of future gas injectors, their completion designs and gauging benefits of different conformance control options. Lastly, finalizing injection strategy through using streamline-assisted optimization workflow under geological/surface facilities/project startup uncertainties. Key observations are: Alignment of stratigraphy and enhanced permeability/fracture distribution in DPDK and SPSK models helped in achieving comparable outcomes. Adoption of advanced risk analysis and early agreement with multi-disciplinary stakeholders on subsurface and surface uncertainty parameters for multiple available models enabled generating high-quality risk assessments. Benchmarking outcomes from standalone vs. coupled models is essential step to ensure reliability of coupled models. Re-routing of wells between processing units improves recovery. Agreement with Partners on the surface simulator (ENS) integrated with different subsurface simulators allows uninterrupted analysis of information. Automating dual connection due to frequent change in boundary processing conditions accelerates delivery of results. Frequent and well-prepared engagements with stakeholders improves communication and provides better management of expectations that helped meeting project deadlines. Confining gas injection inside outboard Clinoforms farther away from fractures is the most rewarding and safest option by minimizing gas breakthroughs and improving recovery. This work was proposed by KPO, and conducted by Chevron Karachaganak support team, in part, on request of the Karachaganak Petroleum Operating Company. It was used by the Karachaganak Petroleum Operating Company and JV partnership along with alternative models to support decision-making for the next phase of phase development – the Karachaganak Expansion Project. Evolution of optimized gas injection strategy under subsurface and surface uncertainties is reported. Remedy to mitigate limitation of ENS tool in handling change of boundary processing conditions is described. The novelty of streamline tracing-assisted gas injection optimization method applied to the DPDK model for gas injection optimization is described as mean of improving the management of fractured reservoir.
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