Landmark Graphics Corporation

Halliburton Energy Services, Inc.

Shashwat Verma

Samiran Roy

Sridharan Vallabhaneni

Geetha Gopakumar Nair

Rune Hobberstad

Richa Choudhary

Janvi Nayan Acharya

Gurunath Venkatarama Subrahmanya Gandikota

Pradyumna Singh Rathore

Geetha Nair

Ailneni Rakshitha Rao

Systems and methods are described using a trained ML model to monitor, detect failure within, and schedule a remediation procedure (RP) for an operating ESP within a well. ESP status data including a time series comprising ESP input variables representing ESP state are collected from a sensor. Using fuzzy logic, the ESP status data is cleaned to remove abnormal data and used to generate fuzzy logic-based labels, each representing an ESP condition associated with ESP state. The fuzzy logic-based labels are segregated into processed labels used to populate each ML model feature. A selected, trained ML model with improved accuracy for ESP monitoring, failure detection, and RP scheduling for the ESP (based on specific ML model, well, and ESP), accepts the ML model features as input. An ESP failure alert is generated by the ML model based on the ESP status data. The RP is scheduled before ESP catastrophic failure.

Hybrid ESP failure prediction using fuzzy logic for data improvement and augmentation

A method comprises receiving a time series of data values for a time window of each operational parameter of a number of operational parameters of equipment; calculating a time derivative feature that comprises a change of the data values of a first operational parameter of the number of operational parameters over the time window; and classifying, using a machine learning model and based on the time derivative feature, an operational mode of the equipment into different failure categories.

Machine learning based equipment failure prediction

A wellbore drilling system can generate a machine-learning model trained using historic drilling operation data for monitoring for a lost circulation event. Real-time data for a drilling operation can be received and the machine-learning model can be applied to the real-time data to identify a lost circulation event that is occurring. An alarm can then be outputted to indicate a lost circulation event is occurring for the drilling operation.

Early warning and automated detection for lost circulation in wellbore drilling

A system is described for determining a likelihood of a type of fluid in a subterranean reservoir. The system may include a processor and a non-transitory computer-readable medium that includes instructions executable by the processor to cause the processor to perform various operations. The processor may receive pre-stack seismic data having seismically-acquired data elements for geometric locations in a subterranean reservoir. The processor may determine, using the pre-stack seismic data, input features for each geometric location and may execute a trained model on the input features for determining a likelihood of a type of fluid in the subterranean reservoir and for determining a list of features affecting the likelihood. The processor may subsequently output the likelihood and the list of features.

Subsurface fluid-type likelihood using explainable machine learning

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Bengaluru, India

Shashwat Verma