Artificial lift

Real-Time Machine Learning Enhances Dynacard Surveillance, Predictive Analytics

This paper explores the use of machine learning in predicting pump statuses, offering probabilistic assessments for each dynacard, automating real-time analysis, and facilitating early detection of pump damage.

DynaCard model workflow.
DynaCard model workflow.
Source: SPE 224979.

Sucker rod pumping, also known as beam pumping, is the major artificial lifting method used for extracting oil from subsurface wells. Maintenance issues in downhole beam-pump components can be diagnosed using dynamometer cards that plot displacement and load. Manually collecting and analyzing these cards daily requires significant effort and time. This paper demonstrates the effectiveness of machine learning (ML) in predicting pump status, offering detailed probabilistic assessments for each dynacard, automating real-time analysis, and facilitating early detection of pump damage.

Project Workflow

Data Automation and Collection. The automation of data collection and transfer is facilitated by transferring dynacard data generated by beam pumps in the field to an open-platform-communications (OPC) server, where it is subsequently stored in a database.

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