This paper discusses business intelligence algorithms and data analytics capabilities of an integrated digital production platform implemented in a giant gas condensate field. The advanced workflow focuses on helping the user navigate through the bulk of data to identify patterns and make predictions utilizing exception-based intelligence alarming. This helps derive insightful findings and provides recommendations for users to make efficient business decisions for achieving field potential optimization objectives. An Integrated digital production platform within a giant gas condensate field is implemented with numerous production optimization workflows encompassing daily well and facility performance monitoring and surveillance. The data integration within the systems is enhanced by integration with powerful Business Intelligence (BI) tools, enabling users to create customized dashboards, KPI screens, and exception-based alarm screens. An additional integration to the production platform is carried out with data from real-time sources like PI Asset Framework and corporate databases, improving the integrated production system's daily well and facility surveillance capabilities. The advanced integration of BI tools provided users with various opportunities to identify bottlenecks, production improvement chances, and troubleshooting areas by capitalizing insights from various dashboards and business KPI screens. Further, integrating these dashboards with several corporate data sources and a real-time asset data framework enabled users to harness maximized information embedded in the bulk of data. This also enabled end-users to harness maximized system potential, with all information available under a single collaborative platform. The integration powered by various inbuilt complex algorithms extended scripting capabilities, and enhanced visualization assisted the asset in realizing business KPIs requirements. Business intelligence algorithms in user interface established a drill-down approach to utilize information associated with multiple variables on top of one another. This allowed for the quick identification of trends and patterns in data. The customization approach helped the user to draw maximum information out of data as per their engineering requirements and current practices. This advanced integration facilitated users to minimize their efforts in traditional data analysis such as gathering, mapping, filtering, and plotting. With the help of these powerful features embedded in an integrated platform, the user was able to drive more focus on optimization and minimize time and effort on system configuration. This unique integration was one of its kind. An online integrated digital production platform comprising of wells, networks, and various workflows was integrated with business intelligence tools, thereby providing end-users tremendous opportunities related to system optimization.
Integrated asset modeling, application of big data, and automation are among the top emerging trends in the oil and gas industry. The value associated with such implementation projects is very closely linked with the efficient use of the project management approach and a robust strategy to handle the technological challenges. This paper puts light on such initiatives implemented in a giant gas field. In this giant gas condensate field, a vast amount of data is generated and monitored on a daily basis. The frequent need to deliver the dynamic production target was driving this project implementation so that a value-driven system can be established while achieving the business KPIs. A phased approach was used to target multiple requirements into business deliverables where the early offline phases provided a robust base for full online integration. This project followed the agile approach focusing on getting insights from multiple stakeholders and domain experts and developing a lesson-learnt repository in all the project phases. The online integration solution is a critical differentiator in the workforce and process efficiency improvement. The multiple technical solution workflows helped in reducing manual efforts and streamlining the methodology in a standardized fashion. In addition, the standard project management practices, such as initializing the phases in a planned manner, followed by an interactive execution, monitoring, and controlling stages, ensured delivering project outcomes in an efficient way. This implementation also established a robust collaborative team effort to identify various different roles and responsibilities for stakeholders. This helped in the end phase when the project sustainability was essential. A strong team base maintained and updated the integrated system while delivering daily well and facility surveillance objectives and KPIs from users ranging from planning, engineering, operation, and management team. A special focus on IT team involvement throughout the project phase led to a successful data integration and diagnostic, as the core of the solution was a data-driven analytical framework integrated with multiple corporate and real-time data sources. In addition, this solution was equipped with various one-of-its-kind solution features such as business intelligence, advanced surveillance, dynamic-reservoir integration, manage-by-exception workflows, intelligence alerts, along with a strong digital framework and data architecture. The unique hybrid and agile project management approach focusing on delivering emerging trends and technologies to end-users in the most efficient way paved the way for achieving asset digitalization and standardization goals.
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.
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.
Asset engineers spend significant time in data validation on a daily basis by gathering data from multiple sources, manually collecting and analyzing these data points to deduce well behavior, and finally implementing the changes on the field. This paper proposes a closed-loop methodology that drastically reduces the time lost in low-efficiency activities, helps engineers to make faster decisions, and assists in efficiently implementing the changes in the field. This well performance evaluation starts with direct integration with the corporate database to feed the field data into a hydraulic model. Next, Pre-configured well performance limits such as reservoir parameters, well calibration parameters, and surface parameters are used to validate the input data and alert the end-user to trigger a well performance evaluation workflow. This workflow is based on a business intelligence tool that integrates statistical information with physics-based model information. Finally, after the engineer makes a holistic decision, an integrated action tracking mechanism assigns an actionable item to the field operator to close the workflow. This approach significantly reduces the time spent on data consolidation and analysis. Essentially this means more time for the engineers to focus on well behavior improvement strategies such as stimulation or re-perforation from more than three hundred strings with more than a thousand well data captured over a month. This approach is not entirely dependent on either static physics-based or statistical models; instead, this approach integrates both methods to enhance decision-making. Moreover, the dynamic behavior of the well is captured in the statistical model and validated against the estimated well behavior derived from the hydraulic model. Furthermore, the streamlined visualization tool helps engineers quickly identify well problems, such as lower productivity, reduced reservoir pressure, increased well scale, increased restrictions in the wellbore, etc. Another critical value addition of this closed-loop workflow is the actionable feedback that is well defined and stored within the system for common reference. For example, the asset engineers provide actionable feedback such as retesting requirement, well stimulation, artificial lift candidate, tubing clearance. Within the action tracking framework, field engineers can quickly filter the assigned action items to him or her for the day and take appropriate actions. This new integrated action-based closed-loop workflow significantly reduces the time spent on daily validation tasks and well performance evaluation tasks by combining the statistical and hydraulic models supported with visualization and action tracking capabilities.
The concept of integrated modeling and digital transformation has grown within the oil and gas industry over the past decade and every such digital transformation has its own set of challenges from which significant learnings can be derived to enhance the knowledge base of the industry. This paper encompasses the successful achievement journey from the UAE's first end to end standardized workflow- based digital transformation in a giant gas producing asset, where several key challenges and learnings have been summarized that are originated from a unique project for a giant gas-condensate asset. The role and importance from multiple business stakeholders such as the planning, engineering, operations and performance teams was imperative to establish a collaborative working philosophy and a detailed specification document, the end-to-end solution, functional and non-functional requirements were captured and aligned with end-user needs. Firstly, a detailed offline phase along with focused efforts in understanding data-quality and establishing representative base-models, was key to enhance the benefit-realization of the integrated platform. Secondly, the online implementation helped in achieving significant process efficiency improvement as inbuilt data validation features significantly improved the confidence of the output. The diagnostic workflows replaced the conventional spreadsheet-based approach. The digital platform works as a common reference of "truth" for everyone across the organization. It helped to produce several the business KPIs to assist the engineers in emphasizing on the problem area, such as improved well test planning.
In rapidly growing data-intensive workflow processes, one of the major challenges for engineers is to carry out the high-priority tasks aligned with the engineering and business needs of the organization. This paper presents an implementation methodology adopted to solve this problem and create a holistic action tracking system that is firmly integrated into the engineering practice and fosters improved communication within various engineering disciplines. The first step in creating the comprehensive action tracking workflow was to identify the engineering workflows that end with an actionable item. Afterward, a framework was created that integrates these action items with the corresponding workflow as per the organization structure active directory. Once these action items are created, the inbuilt reminder system notifies the user of the upcoming tasks based on the priority assigned to these items. The escalation mechanism also ensures that the manager gets a notification when actions passed the due date. Once the action items are closed, the action assignee and assigner both get a notification to close the action loop. One of the most significant benefits of such a process is that all the outcomes of the engineering workflows, such as the requirement to retest the well for production tests, allowable approval requirements, injection improvement recommendations, etc., are directly fed into the integrated action tracking mechanism. This way, the engineers do not need to use a separate system for assigning and tracking the actions. Once these actions are fed into an integrated action tracking system, the system maintains, expedites, and escalates the actions by itself. Another benefit realized by this action tracking system was that the organization structure was internally utilized within the online platform. Therefore, it helps in dynamically assigning the organization structure and creating an escalation item to the right person at the right time so that the integrity of the overall operation remains uncompromised. The business intelligence tool is integrated with the action tracking system to support monitoring and decision-making surveillance exercises of reservoir monitoring plans and allowable monitoring, for example, utilizing the inbuilt planning and performance dashboards. Another major objective achieved in this closed-loop action tracker was the improved system-based communication among various engineering disciplines as the actions are decided based on common business objectives and agreement within the digital platform One of the well-known gaps in modern production optimization solutions is integrating the engineering solutions with the actionable item. This system bridges this gap and provides a way forward for improved collaboration and fostering communication among various disciplines.
There are several operational challenges associated with a gas field producing in recycle or depletion mode, including a reasonable forecast and a robust production strategy planning. The complex reservoir dynamics further demands faster and reasonable analysis and decision-making. This paper discusses an all-inclusive integrated modeling approach to devise a production strategy incorporating the detailed compressor design requirements to ensure that a consistent production-stream is available in the long-term considering technical and economic aspects. The proposed production strategy is a two-fold approach. In the first step, the process utilizes the current reservoir simulation data in the production-forecast model. This history matched model captures the reservoir dynamics such as reservoir pressure decline and accounts for future wells drilling-requirements. However, the detailed production hydraulics in wellbore and surface facilities is not captured in the model. Further, to consider the declining well-performance and facility bottlenecks, integrated analysis is required. So, in the second step, the reservoir simulation model is dynamically integrated to take the input from the production model, encompassing detailed well and surface facility digital twins. The continuous interaction provides a highly reliable production profile that can be used to produce a production strategy of compressor design for the future. A strong interactive user-interface in the digital platform enables the user to configure various what-if scenarios efficiently, considering all anticipated future events and production conditions. The major output of the process was the accurate identification of the pressure-profile at multiple surface facility locations over the course of the production. Using the business-plan, field development strategy, production-profile, and the reservoir simulation output, reliable pressure-profiles were obtained, giving an indication of the declining pressures at gathering manifold over time. A well level production-profile-forecast helped in prioritizing wells for rerouting as well as workover requirements. As an outcome of this study, several manifolds were identified that are susceptible to high-pressure decline caused by declining reservoir pressures. To capture this pressure decline, a compressor mechanism was put in place to transfer the fluid to its delivery point. As this study utilizes several timesteps for the production forecast estimation, flexible routine options are also provided to the engineers to ensure that backpressure is minimized to avoid a larger back pressure on the wells for quick gains. This solution improves the efficiency of the previous approaches that were entirely relying on the reservoir simulation model to capture the pressure decline at the wellhead to forecast the compressor needs. In this methodology, the pressure profile at each node was captured to simulate a real production scenario. This holistic approach is in line with Operator's business plan strategy to identify the needs of external energy-source to avoid production-deferral.
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