No abstract

Data monitoring in remote satellite field without any DOF platform is a challenging task but critical for ALS monitoring and optimization. In SRP wells the VFD data collection is important for analysis of downhole pump behavior and system health. SRP maintenance crew collects data from VFDs daily, but it is time consuming and can target only few wells in a day. The steps from requirement of dyna to final decision taken for ALS optimization are mobilizing team, permits approvals, download data, e-mail dynacards, dyna visualization, final decision. The problems with above process were: - Insufficient and discrete data for any post-failure analysis or ALS-optimization Minimal data to investigate the pre failure events The lack of real time monitoring was resulting in well downtime and associated production loss. The combination of IOT, Cloud Computing and Machine learning was implemented to shift from the reactive to proactive approach which helped in ALS Optimization and reduced production loss. The data was transmitted to a Cloud server and further it was transmitted to web-based app. Since thousands of Dynacards are generated in a day, hence it requires automated classification using computer driven pattern recognition techniques. The real time data is used for analysis involving basic statistic and Machine learning algorithms. The critical pump signatures were identified using machine learning libraries and email is generated for immediate action. Several informative dashboards were developed which provide quick analysis of ALS performance. The types of dashboard are as below Well Operational Status Dynacards Interpretation module SRP parameters visualization Machine Learning model calibration module Pump Performance Statistics After collection of enough data and creation of analytical dashboards on the three wells using domain knowledge the gained insights were used for ALS optimization. To keep the model in an evergreen high-confidence prediction state, inputs from domain experts are often required. After regular fine-tuning the prediction accuracy of the ML model increased to 80-85 %. In addition, system was made flexible so that a new algorithm can be deployed when required. Smart Alarms were generated involving statistic and Machine Learning by the system which gives alerts by e-mail if an abnormal behavior or erratic dynacards were identified. This helped in reduction of well downtime in some events which were treated instinctively before. The integration of domain knowledge and digitalization enables an engineer to take informed and effective decisions. The techniques discussed above can be implemented in marginal fields where DOF implementation is logistically and economically challenged. EDGE along with advanced analytics will gain more technological advances and can be used in other potential domains as well in near future.