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.
Objectives/Scope The development of Abu Dhabi's sour gas is not without its challenges. Deep drilling in some fields presents its own set of difficulties due to high temp and pressures coupled with +30% H2S and +10% CO2. Handling of these corrosive reservoir fluids both while drilling and then testing, requires deploying advanced technology to meet the specific requirements of these reservoirs, along with the infrastructure necessary to handle the toxic and corrosive products while testing in a brown field safely. Methods, Procedures, Process Developing local sour gas production is seen as the answer to resolve the ever growing energy needs for UAE but the technical requirements for the project is stretching the limits of the industry. Results, Observations, Conclusions What did we do different: Developed and implemented specific HSE procedures and SIMOPS due to close proximity with major populated facilities which could not be shut-down during the testing period. Conducted multiple audits and drills with the local authorities including Civil Defense and local Police. Additional 3rd part supervision was provided to ensure all personal are complying with the policy and procedures developed. Installed 2 green burners and 2 vertical 90 ft flare stacks at 180 degrees. This was to cater for the changing wind directions for continuous operations and as back ups. CCTV monitoring for green burners / flare stacks was conducted although this was a rigless operation 3 circles of H2S detectors and sensors were placed around the testing area and the flare stakes and green burners to detect any H2S gas. (Covering all 360° directions). Blowdown/Depressurization valve was installed at separator, storage tanks apart from Automatic and manual shutdown system upon H2S detection Installed Optic Fiber cable from wellhead to the main control room for monitoring purposes All piping connections used were flange-to-flange as welded joints could have caused stress cracking on the weak points For Sour well operation, used fully cladded X-mass tree & Inconel well completion Considered setting of compatible TTBP (Thru Tubing Bridge Plug) for staked reservoirs zonal isolation Instead of coil tubing cement plug for accurate depth calculations. Arranged Special chemical for any scale cleanout for handling of elemental Sulphur. Arab zones were stimulated with specialized acid recipe developed exclusively for this temperature, pressure and sour conditions downhole. Bottom hole measurements were recorded successfully and all the necessary data was acquired. Novel/Additive Information This paper highlights the major challenges identified and mitigated to test and produce the highly sour HPHT gas during the appraisal program complying with ADNOC 100% HSE in a brown field without disturbing the other major operations being performed in the vicinity.
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.
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