Introduction:The Craig Hospital Inventory of Environmental Factors instrument (CHIEF) is one of the few tools to assess the environmental barriers. The purpose of this study was to translate long and short CHIEF into Hindi language, and to determine its validity and reliability.Design and Setting:The study design was observational case series with repeated measures. It was carried out at Indian Spinal Injuries Centre New Delhi, a specialized center for rehabilitation for spinal cord injury.Methods:The CHIEF instrument was translated from English to Hindi based on the Beaton guidelines for the cross-cultural adaptation of health status measures. The Hindi version of the CHIEF instrument was then administered on a convenience sample of 30 spinal cord injured subjects. Its content validity, internal consistency, test-rest reliability (intraclass correlation coefficient [ICC] 2,1), standard error of measurement (SEM), and minimum detectable change (MDC) were determined for both the longer and shorter version.Results:The mean ± SD of total of Hindi-CHIEF instrument, longer version was 1.44 ± 0.82 and total score of the shorter version was 1.07 ± 0.66. The content validity determined by the content validity ratio was found to be 1 for all the items except item number 5, 11, and 12. The content validity index was 0.97 for the longer version and for the shorter version it was 0.98. Internal consistency, Cronbach's α value was found to be 0.92 and test-retest value (ICC 2,1) was 0.80 (P < 0.001). The MDC was found to be 0.99 and SEM was 0.36 for the longer version. The Cronbach's α was 0.731, ICC 2,1 was 0.63 (P < 0.001), SEM was 0.24, and MDC was 0.66 for the shorter version.Conclusion:The Hindi translated version of the CHIEF scale has acceptable content validity and reliability. It can be used to assess environmental barriers perceived by spinal cord injury patients.
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.
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.
One of the critical aspects of production optimization and planning is to meet the production targets or to meet some operational requirements such as workovers or maintenance activities. This paper demonstrates how an advanced integration in a digital platform, coupled with a predictive analytical model for choke performance and intelligent alarming, can significantly help asset in production planning and cost optimization while accurately regulating the field rates. First, the bulk of well-test data from corporate databases is integrated into an advanced digital platform with an automated well-test validation workflow. The workflow output provides the choke tuning factors for each test while validating the well-test parameters. The digital platform provided the initial data check to ensure the validated tests with choke tuning factors were processed for further regression analysis. The network model in the digital platform for the entire asset was run for a predefined set of iterations to generate the representative choke tuning factors for each well, based on production test parameters and flow line pressure constraints. The regression analysis output was used to predict the choke sizes for different inflow performance rates and various operating wellhead pressures and vice versa. The predictive choke analytical model outputs were utilized to predict the choke size for a set of well parameters, such as rates and wellhead pressures, based on historical well performance. The choke sizes predicted could be used to identify preferred wells in an area to be controlled to achieve production targets, minimizing the operational effort and time. The predictive choke model with intelligent alarm feature provided users instantaneous insight into underperforming and overperforming wells, assisting them to take further actions in an effective way. The other intelligent alarms worked in combination to detect lifting problems associated with wells more efficiently, such as the liquid loading intelligent alarm. The predictive model was also valuable for efficient production planning in terms of setting the quarterly well allowable, choke sizes, or performing field capacity tests to meet the business production target on field & well level and to analyze short-term and medium-term forecast cases using an automated reservoir integration workflow in the digital platform. This was helpful in planning ahead of time for future operations and saving a significant amount of time and effort for engineers and the operation team. This specialized approach of predictive choke performance modeling in a digital platform provided asset a robust tool to plan and optimize their field production while leveraging the power of data-driven digital platforms consisting of closed-loop automated engineering workflows. The accuracy of prediction proved significant cost optimization and proactive planning, where the bulk of data was handled effectively and efficiently to identify production optimization opportunities and field bottlenecks.
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 describes accurate, efficient, and time-saving methodology for achieving the Business target by determining well allowable using advanced, integrated, and automated work-process for a gas condensate field with more than 350 (producing and injecting) well strings from a multi-layered reservoir, having varied reservoir characteristics. This paper will also illustrate challenges and enhancement opportunities toward full smart field applications. Integrated asset operation modeling (IAOM) within a digital framework provides automation to the engineering and analytical approach of allowable rate calculations. The approach comprises 3 step calculation process to determine the Well targets/allowable. Firstly, using the shareholder/reservoir management guideline along with calibrated well models for calculating the well's technical rate. Secondly, calculation of the well and reservoir available/potential rate using the well technical rates, reservoir target, and an inbuilt analytical solver. Thirdly, determination of the well allowable rate by conjugation of various well production components, including wellbore dynamics (Inflow performance and Well performance) and surface constraints. In a digital platform, this automated "Well allowable" workflow has enabled engineers and operators to determine the true potential of wells and reservoirs, thus overcoming potential challenges of computational time saving and identification of cost improvement opportunities. The use of the automated workflow has reduced the time to compute well allowable rates by more than 90% for a gas condensate field with more than 350 (producing and injecting) strings. Implementing this workflow prevented engineers from performing a tedious manual calculation on a well-by-well basis, allowing engineers to focus on engineering and analytical problems. Additionally, this efficient engineering approach provided the user with key information associated with the well's performance under various guideline indexes such as well available/potential rates, well technical rate, reservoir available rate, and rate to maintain drawdown/ minimum Bottom-hole Pressure. This advanced workflow computes the rate that can be delivered from each well corresponding to each guideline and constraint, thereby providing key inputs to various business objective scenarios for production efficiency improvement. Post-implementation, some challenges turned into opportunities to ensure the full and smooth implementation of the generated production scenarios adhering to the gas demand fluctuation. The accuracy and robustness of advanced and automated workflow of setting well allowable /production scenarios empower users to establish well performance and deliverability with a solid engineering analysis base, thereby providing key opportunities for saving cost computational time and assuring short-term production mandate deliverables. This approach supports standardization of the work process across the organization and a minimum of $ 2.8M value proposition from manpower time saving over 5 years.
Digital oil fields have seen major advancements over the past ten years, with the goal of integrating and optimizing the loops of production operation, production optimization, well and reservoir surveillance based on real-time data and model-based workflow automation capabilities. This paper discusses how ADNOC onshore has successfully implemented model-based digital oil field workflows in all its producing fields and describes the process of migrating these workflows to a data-driven platform for improved decision making. In the existing workflows, data-driven diagnostic analytics are applied to validate well performance and accelerate the process of identifying underperforming wells and inefficiencies. These data-driven diagnostic analytics were implemented on a digital oilfield workflow platform where data is aggregated from disparate data sources consisting of non-real time well data, well events, well test history, MPFM, interpreted PTA, reservoir simulation, well integrity and wells tie-in data, along with continuous real-time sensor and model-generated data. The analytics are mapped with workflows and asset hierarchy. The linear regression method is used to forecast trends for water cut and GOR based on historical data. Diagnostic analytics have been successfully configured for a giant onshore field having more than a thousand wells and multiple reservoirs. The alarm diagnostic map is generated based on tolerance and difference with exceptions. The solution framework has a common data abstraction layer and integration. A built-in visualization engine allows customization based on user preferences, linking multiple screens and analytics. Well test validations are improved for non-instrumented wells by using diagnostic based on more than 10 years of well test history. Well level allocation analytics allow comparisons between real-time export meter and terminal figures at the same timestamp, based on well models. For model calibration, wellhead pressure estimation from the last valid model was introduced. Well surveillance and management diagnostic analyze wells which are operating on critical/sub-critical condition and increasing water cut based on models and measured data. The combination of reservoir simulation data, PTA, bottom-hole surveys and estimated data from well models provides insights to validate quality of simulation data and reduce uncertainty in well models. Compartment and reservoir-wise VRR diagnostic enable asset operators to take faster remedial actions for reservoir performance management. These analytics complemented traditional model-based automated workflows for identifying wells for optimization. A digital oilfield solution platform has been leveraged to implement diagnostic analytics in the first phase and to provide a road map to migrate it to next-generation data-driven platform that has more predictive capabilities. This paper discusses solutions and data integration frameworks, analytics visualization, integration with model-based workflows, value cases and the road map ahead.
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