This paper elaborates on the concept of successfully applying one combined platform that includes gas condensate dynamic simulation models, surface network, and individual well models interacting and running sequentially within a closed loop. The study also highlights the value created by integrating dynamic modelling, simulation data, history matching (covering gas condensate reservoirs consisting of gas producers and injectors under the recycle mode) with continuously calibrated well and network models, thereby allowing end-users to make the best use of an integrated system for their dynamic production forecasting. The dynamic reservoir integration methodology incorporates as a first step the data coming from the reservoir simulator model as the main source of reservoir parameters to build a comprehensive system for enhancing production forecasting profiles. In an automatic routine, the simulation data provides the Inflow Performance Relationship, which gets transferred to the well's models, so a well performance curve (WPC) can be generated automatically. Once the latter is generated, it gets transferred to a recycle production-injection network model where a user-configured surface network scenario optimizes in an IAOM (Integrated Asset Operation Model) environment to calculate the rates corresponding to each well taking into consideration distinct constraints. The rates generated are transferred back to the reservoir simulator as well control parameters to initialize the next step of the loop and begin the process under updated conditions. The number of steps, termed as the schedule of the run, are determined by the user based on the forecasting objectives. From the practical point of view, this dynamic reservoir integration mainly targets at getting the best possible assessment from the available data, assumptions, and constraints. The value generated by having a dynamic integration, including all main components of the field/reservoir production, initially relies on the accurate understanding of the dynamic behavior of the hydrocarbon reservoir in order to predict future performance under different development and production approaches. There are several reasons why an integrated approach proved to have strong value creation: Reliable evaluation of the entire production system from reservoir to processing facilities. Continuous assessment of well and network performance. Verifying consistency of data reducing uncertainties. Minimizing underlying assumptions and constraints. It is worth mentioning that during this implementation, the entire system employed compositional models where a high number of components and pseudo components were part of the system, and the thermodynamic behavior added further rigor to the overall calculations. This advanced methodology of carrying out dynamic integration of surface to sub-surface in a production platform framework enhances various key factors of numerical simulation, such as run time estimation, optimal incorporation of surface parameters, identifying gaps between the surface and sub-surface system and enabling the user to perform key business scenarios in an efficient and flexible workflow-based production platform system.
This paper demonstrates the successful implementation of an advanced integration of production system for well and facility surveillance, which incorporates the utilization of automated workflows, intelligent exception-based dashboards and KPI (Key Performance Indicator) screens for a giant gas condensate field for a major E&P operator. The incorporated case studies highlight how analytical tools, automation, and data integration enable proactive evaluation of the facility surveillance and reservoir management strategy, thereby supporting efficient and faster decision-making to achieve the desired target while extending the life of the wells. In the current scenario, strong and effective reservoir management demands continuous, rigorous, and proactive reservoir surveillance tools. An intelligent integrated production optimization framework is being utilized as a single platform to combine information, processes, and multidisciplinary knowledge in a systematic and efficient manner. This allows management and end-users to proactively maintain engineering focus on problematic wells diagnostic and opportunity generation that support the execution of the business plan in a more coherent and reliable fashion. Using the real-time measurements in conjunction with an IAOM (Integrated Asset Operation Model) framework and collaborative feedback from the engineers, intelligent dashboards, and screens were designed to highlight the well and facility-related problems and highlighting decremental well behavior or facility bottlenecks. Most of the intensive computational technical calculation and data aggregation from different sources, such as well testing and real-time production and injection measurements, are integrated with automatic workflows and displayed in visualization dashboards; thus, users can carry out purposeful analysis utilizing information in processed data to realize potential value. These dashboards measure the true well and facility performance towards operational objectives and production targets. Intelligent KPIs also helps in identifying well health-status, potential risks, and mitigate them for continuous improvement of short- and long-term recovery factors to obtain an optimum reservoir energy balance daily. In case of unexpected well performance behaviors, the dashboards provide crucial data insights, highlighting the root cause of bottlenecks. Additionally, the integrated models help in providing the recommendation for troubleshooting and support engineers in devising mitigation plan. In order to have control over the large fields containing hundreds of wells, having integrated well and facility surveillance is the need of the hour. The advanced workflow and intelligent dashboards implemented in the asset tremendously helped in reducing the operational cost and increasing the average uptime per well per field, thereby providing opportunities to management and end-users to leverage the utmost value from a digital framework implementation for the purpose of daily well and facility surveillance.
Forecasting oil and gas production for a well or reservoir is one of the most valuable tasks of a reservoir engineer. This paper elaborates on the assessment of production targets deliverability using a dynamic and integrated approach to perform short term production forecasting. The case also studies the seamless integration of sub-surface with well and facility network models providing options to examine the feasibility of production plans. The principal approach employed in the methodology comprises an automated workflow, which includes reservoir simulation data, wells, and network models enclosed in a dynamic loop, where workflow iteration takes place until the production target is achieved. Within this implementation, the process allows the estimation of short-term production forecasts mainly used for optimizing production operations and business planning, among other tasks. Some of the main steps followed in order to assess the feasibility of the production targets are: Well, Network and Reservoir data QA/QC and further alignmentNarrowing down of gaps between the surface and sub-surface systemIntegration among the several data-driven sourcesIteration of the overall process allowing minimal human intervention Throughout this implementation, it was clearly appreciated that production forecasting represents a highly complex task due to the number of different components included in an integrated system and their intrinsic interconnection, where essentially every piece of the calculation influences others. The case study highlighted how performing a dynamic reservoir integration run in an integrated digital production system can help engineers to provide a way to check the feasibility of short-term production targets while considering full surface system configuration. Moreover, the integrated production system provided flexibility in terms of setting up forecast scenarios in an efficient manner, thereby minimizing users' time and efforts in data handling and driving maximum user focus on results and analysis. A dedicated forecast server helped in achieving run performance, thereby enabling the user to carry out various what-if scenarios in a short amount of time. The case studies also discuss a few key challenges encountered during the process that represented a difficulty in overcoming unless addressed in an integrated collaborative system: Data size and complexityLack of data and/or data inconsistencySurface and Sub-surface model configuration for dynamic integrationGaps between surface and sub-surface performances at initial time step. The application of this integrated and automated workflow approach improved confidence in the reservoir target deliverables by providing robust data management and better predictions resulting from evaluating the entire system (including the performance of wells and reservoirs at the same time). This helped in saving user analysis time significantly by avoiding the process of analyzing all the sections of the system in isolated silos, which is usually the approach followed by many operators with large amounts of wells.
Within a complex and dynamic production system with several operational challenges, maintaining a steady stream of throughput to meet the production targets based on the day to day well availability is a key business driver. This paper discusses an all-inclusive integrated modeling approach to evaluate the supply side of the production value chain, i.e. reservoir & well deliverability and the demand side, i.e. production targets. The process starts with the representative inflow reservoir performance and well performance generation. In the second step, the key business requirements are applied as quantified parameters such as shareholder guidelines, minimum well production, and maximum drawdown. The most conservative figure was taken to ensure the long-term reservoir health. Subsequently, the target production was estimated from each reservoir based on the current strategic business plan. Lastly, an allocation mechanism was applied, honoring the required target-production and the well capacity to give a unique solution. The major output of the entire process was achieved by estimating the well targets and probable shortfalls, honoring the process constraints within the production system. Also, the output of this target estimation was transferred to the surface network simulation to consider the back pressure impact and provide adequate outputs such as choke settings and wellhead pressure settings. This outlines process provides a standardized approach that is utilized to cater the several business needs, such as minimizing the liquid loading, optimizing the drawdown to maintain a stable reservoir performance, and health. Starting from producing layers to the delivery point, this process uses an integrated approach encompassing the various components in a complex production system such as reservoir capacities, fluid composition, well behavior and network capacities to assure a representative forecast. This approach is crucial in a gas-producing operating asset as the fluctuation in demand can be easily fulfilled using a seamlessly integrated approach that takes care of the dynamic operations variables such as well availability, surface facility back pressure, etc. in a single platform. The approach improves the efficiency of target estimation significantly as the previous tiresome work of updating the simulation models and running the isolated calculation have been replaced with few clicks within the workflow. This holistic approach is in line with the overall corporate strategy of integrated reservoir management (IRM) guidelines ensuring the long-term development plan and strategy is inherent to the overall process.
Meeting the gas production target while maximizing the total gas condensate and hence the revenue from condensate reservoirs is one of the key business drivers for an operating company. This paper describes a comprehensive simulation process to strategize production optimization, which helps in meeting the target molar fraction at the delivery point from the overall asset producing from complex reservoirs with varying fluid properties and achieving the overall target of lean gas production. This process uses an integrated approach encompassing the various nodes in a production system, starting from reservoir to the export system to assure a representative and accurate prediction. In the first step, using the representative fluid model and the desired target production from the field, the well capacity-based rates are allocated to the individual production strings. In the second step, a component level optimizer is used to estimate the contribution of each well based on the well production stream composition. In the third and final step, this contributed production figure is fed back to the surface network hydraulic simulator to assess the back-pressure impact on the overall production and the achievable field-target. The objective of maximizing the condensate production was fulfilled considering the provided constraints of operating guidelines, reservoir, wells, and surface facility capacities. Two different scenario runs were put together where the target gas production was achieved while increasing the condensate production by maximizing the condensate specific molar components and minimizing the heavy molar components. The expected condensate production was forecasted to increase by 5% in the scenario. As the predictive hydraulic model is seamlessly integrated with the true field operating conditions, the outlined optimization process ensured that various business-scenarios accurately forecast the system behavior under various operating conditions. In the scenario, the forecast was able to maximize the condensate while meeting the production target within 1% tolerance limit. The outlined approach provides a clear step by step standardized process approach which can be expanded to cater to the other business needs, such as minimizing the H2S for reducing the corrosion problem, minimizing the C7+ components to produce a high-quality condensate. Such a framework based standardized methodology incorporates a seamless integration between the molar composition optimization and the associated hydraulic calculations-based optimization. Solving the both objective optimization functions simultaneously, this approach provides a novel way to address a vital industry business objective.
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