Preventing stuck pipe events with predictive analytics and ML-based modeling
While physics-based modeling can provide a good foundation for using surface parameters to model torque and drag, physics models are unable to flag leading indicators of drilling anomalies such as stuck pipe with enough forewarning.
Predictive analytics and machine learning-based modeling can.
SparkCognition’s solution for drilling operators can:
- Predict stuck pipe with sufficient advance notice to implement mitigating action
- Save upwards of millions of dollars in drilling costs and downtime
- Significantly alleviate, if not eliminate, overall non-productive time
- Improve overall operational efficiency to meet or exceed drilling output
Predictive analytics and machine learning-based modeling have been used in past deployments to generate significant value.
A large drilling operator in the Middle East needed to decrease the impending threat of stuck pipe events in their operations. By leveraging SparkCognition’s solution, the operator was able to predict 79% of overall drilling anomalies, including stuck pipe events, with up to six hours’ advance notice, saving the company upwards of millions of dollars in drilling costs and downtime.
Read the white paper below to learn more about how predictive analytics and machine learning-based modeling can help prevent stuck pipe events.