Closed-Loop Data & Business Intelligence Driven Approach of Well Performance Evaluation to Identify Changes in Well Behavior

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.