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With a vast reservoir with a complex and dynamic system production system containing more than 300 wells, both producers and injectors, keeping track of the field operational activity like Reservoir Monitoring Plan (RMP) jobs can lead to sub-utilization, confusion, lack of efficiency, and loss of time. This paper describes an integrated and collaborated method that supports monitoring and execution of field activity tasks utilizing the integrated production optimization platform and the business intelligence tools. Reservoir monitoring planning (RMP) is one of the critical workflows used to ensure the smooth execution of tasks at the required time. This enables the user to plan future tasks based on the reservoir behavior and have a quick comparison between actual and planned tasks. The process starts with inputting the planned tasks into the integrated system, categorizing the tasks based on types, and assigning the executors. The system sends reminders/notifications of the planned task approaching the task due date to all the stakeholders. It also provides an automated direct summary/bird's eye view utilizing the business intelligence tool. Using an integrated asset operation model (IAOM) solution in a digital platform, this planning and monitoring workflow has enabled the users to establish a standardized and unified central repository for the tasks to ensure the single source of truth. With the help of this advanced workflow, inter-departmental communication gaps have been reduced tremendously, thus enabling better execution, analysis, gaps, or bottleneck identification. The automated summary dashboard contains the comparison of the actual status of tasks versus planned tasks. This helps in optimal facility utilization based on dynamic RMP monitoring. Additionally, the integrated solution for planner, performer & approver enabled the users to prioritize the activity based on bottlenecks faced during the past months and reduce the times used to update the monitoring Excel sheets. This outlining process provides a standardized approach across the assets, leading to improved tracking efficiency, minimizing the time spent on manual monitoring, planning, and receiving automatic reminders to avoid delay in the planned tasks, which assisted the users in focusing on production optimization and solving different bottlenecks. This reservoir monitoring and planning approach aligns with the overall corporate strategy of using an integrated asset operation Model (IAOM) for providing end users with tremendous opportunities related to system optimization. This also supports the users’ drive to switch the approach from individual people oriented to standardized process oriented. This approach supports standardization of the work process across the organization and a minimum of $ 700K value proposition from manpower time saving over five years.
With a vast reservoir with a complex and dynamic system production system containing more than 300 wells, both producers and injectors, keeping track of the field operational activity like Reservoir Monitoring Plan (RMP) jobs can lead to sub-utilization, confusion, lack of efficiency, and loss of time. This paper describes an integrated and collaborated method that supports monitoring and execution of field activity tasks utilizing the integrated production optimization platform and the business intelligence tools. Reservoir monitoring planning (RMP) is one of the critical workflows used to ensure the smooth execution of tasks at the required time. This enables the user to plan future tasks based on the reservoir behavior and have a quick comparison between actual and planned tasks. The process starts with inputting the planned tasks into the integrated system, categorizing the tasks based on types, and assigning the executors. The system sends reminders/notifications of the planned task approaching the task due date to all the stakeholders. It also provides an automated direct summary/bird's eye view utilizing the business intelligence tool. Using an integrated asset operation model (IAOM) solution in a digital platform, this planning and monitoring workflow has enabled the users to establish a standardized and unified central repository for the tasks to ensure the single source of truth. With the help of this advanced workflow, inter-departmental communication gaps have been reduced tremendously, thus enabling better execution, analysis, gaps, or bottleneck identification. The automated summary dashboard contains the comparison of the actual status of tasks versus planned tasks. This helps in optimal facility utilization based on dynamic RMP monitoring. Additionally, the integrated solution for planner, performer & approver enabled the users to prioritize the activity based on bottlenecks faced during the past months and reduce the times used to update the monitoring Excel sheets. This outlining process provides a standardized approach across the assets, leading to improved tracking efficiency, minimizing the time spent on manual monitoring, planning, and receiving automatic reminders to avoid delay in the planned tasks, which assisted the users in focusing on production optimization and solving different bottlenecks. This reservoir monitoring and planning approach aligns with the overall corporate strategy of using an integrated asset operation Model (IAOM) for providing end users with tremendous opportunities related to system optimization. This also supports the users’ drive to switch the approach from individual people oriented to standardized process oriented. This approach supports standardization of the work process across the organization and a minimum of $ 700K value proposition from manpower time saving over five years.
This paper demonstrates the use of an integrated production optimization platform to determine the well performance for gas condensate wells in a statistical approach to increase the data accuracy for reservoir studies, simulate the field limitations, and provide recommendations for production optimization in a multilayered large carbonate reservoir field. This case involves wells under recycle and natural decline with challenges in the evaluation of well performance where the bulk of the information is available in multiple data sources The first elemental block in establishing the well performance of a gas condensate well is to determine and simulate its fluid behavior. Based on the PVT reports and subsurface fluid studies, compositional PVT models are built and matched with experimental data analyzing representative phase envelop properties and relevant Equation of State (EOS). The next step incorporates the utilization of representative physics-based well models in an integrated system to determine the reservoir and well deliverability. Finally, by applying a detailed statistical approach to the production well test history, models are calibrated in order to predict the performance of the gas condensate wells. Tuning of compositional PVT models established the EOS to be incorporated in predicting the fluid behavior and integrating representative PVT models with well models to determine such behavior along the fluid path. Using the statistical approach, the poor well measurements were identified, facilitating the well-performance and deliverability calculation. In addition, the use of representative models helped in increasing the accuracy of identifying well performance. During this study, two different methodologies were identified based on the reservoir management guidelines. Firstly, for the recycle reservoir in which, the decline of reservoir pressure is arrested using gas Injection. Secondly, for the depletion reservoir, in which the reservoir pressure declines rapidly. For the recycle reservoir, it was statistically identified that the reservoir pressure was declining at less than 4%. Therefore, the acceptance criteria for the operating envelope for each well was defined using the reservoir decline of less than 4%. Similarly, for the depletion reservoir, the pressure was declining between 7% and 10%. Thus, the operating envelope's acceptance criteria were defined using the max reservoir decline tolerance of 10%. The above-identified criteria were incorporated into the integrated model framework to validate the well performance generated from the well tests. Implementing this specialized engineering approach in an integrated model framework considerably reduces the time required by engineers to validate the production well tests and provides higher levels of accuracy for production optimization, voidage replacement ratio calculation, daily rate estimation, and surveillance.
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