Drilling with casing monitor

Abstract

A drilling with casing monitor that receives transducer and rig data from a drilling operation, provides visualizations, and outputs a condition of a drilling with casing operation. The drilling with casing monitor includes a machine learning (ML) model that receives torque and acceleration inputs and outputs the condition of the drilling with casing operation. Systems, method, and computer-readable media implementing the model are provided.

What is claimed is: 
     
         1 . A method for drilling a well using a drilling with casing operation, comprising:
 receiving, from a transducer, data associated with a casing, the transducer data comprising torque, radial acceleration, tangential acceleration, and axial acceleration;   providing the transducer data to a drilling with casing machine learning model configured to output a condition of the drilling with casing operation, the drilling with casing machine learning model trained using a relationship between torque, radial acceleration, tangential acceleration, and axial acceleration; and   outputting a condition of the drilling with casing operation from the drilling with casing machine learning model.   
     
     
         2 . The method of  claim 1 , comprising:
 receiving data associated with a rig, the rig data comprising electronic drilling recorder (EDR) data comprising hook load, string depth, revolutions-per-minute (RPM), torque, and block height;   forming a dataset comprising at least one datum of the transducer data and at least one datum of the EDR data; and   providing the dataset to the drilling with casing machine learning model.   
     
     
         3 . The method of  claim 1 , wherein the drilling with casing machine learning model comprises an artificial neural network (ANN). 
     
     
         4 . The method of  claim 1 , comprising stopping the drilling with casing operation based on the condition. 
     
     
         5 . The method of  claim 1 , comprising adjusting the drilling with casing operation based on the condition. 
     
     
         6 . The method of  claim 1 , wherein the condition comprises a torsional vibration value above a threshold value over a threshold time period. 
     
     
         7 . The method of  claim 1 , wherein the condition comprises a lateral vibration value above a threshold value over a threshold time period. 
     
     
         8 . The method of  claim 1 , wherein the transducer data is acquired at a rate of 120 hertz (Hz). 
     
     
         9 . The method of  claim 1 , comprising providing a graph of torque, radial acceleration, tangential acceleration, and axial acceleration versus time. 
     
     
         10 . A non-transitory computer-readable storage medium having executable code stored thereon for drilling a well using a drilling with casing operation, the executable code comprising a set of instructions that causes a processor to perform operations comprising:
 receiving, from a transducer, data associated with a casing, the transducer data comprising torque, radial acceleration, tangential acceleration, and axial acceleration;   providing the transducer data to a drilling with casing machine learning model configured to output a condition of the drilling with casing operation, the drilling with casing machine learning model trained using a relationship between torque, radial acceleration, tangential acceleration, and axial acceleration; and   outputting a condition of the drilling with casing operation from the drilling with casing machine learning model.   
     
     
         11 . The non-transitory computer-readable storage medium of  claim 10 , the operations comprising:
 receiving data associated with a rig, the rig data comprising electronic drilling recorder (EDR) data comprising hook load, string depth, revolutions-per-minute (RPM), torque, and block height;   forming a dataset comprising at least one datum of the transducer data and at least one datum of the EDR data; and   providing the dataset to the drilling with casing machine learning model.   
     
     
         12 . The non-transitory computer-readable storage medium of  claim 10 , wherein the drilling with casing machine learning model comprises an artificial neural network (ANN). 
     
     
         13 . The non-transitory computer-readable storage medium of  claim 10 , the operations comprising stopping the drilling with casing operation based on the condition. 
     
     
         14 . The non-transitory computer-readable storage medium of  claim 10 , the operations comprising adjusting the drilling with casing operation based on the condition. 
     
     
         15 . The non-transitory computer-readable storage medium of  claim 10 , wherein the condition comprises a torsional vibration value above a threshold value over a threshold time period. 
     
     
         16 . The non-transitory computer-readable storage medium of  claim 10 , wherein the condition comprises a lateral vibration value above a threshold value over a threshold time period. 
     
     
         17 . A system for drilling a well using a drilling with casing operation, comprising:
 a processor;   a non-transitory computer-readable storage memory accessible by the processor and having executable code stored thereon for drilling a well using a drilling with casing operation, the executable code comprising a set of instructions that causes the processor to perform operations comprising:
 receiving, from a transducer, data associated with a casing, the transducer data comprising torque, radial acceleration, tangential acceleration, and axial acceleration; 
 providing the transducer data to a drilling with casing machine learning model configured to output a condition of the drilling with casing operation, the drilling with casing machine learning model trained using a relationship between torque, radial acceleration, tangential acceleration, and axial acceleration; and 
 outputting a condition of the drilling with casing operation from the drilling with casing machine learning model. 
   
     
     
         18 . The system of  claim 17 , the operations comprising:
 receiving data associated with a rig, the rig data comprising electronic drilling recorder (EDR) data comprising hook load, string depth, revolutions-per-minute (RPM), torque, and block height;   forming a dataset comprising at least one datum of the transducer data and at least one datum of the EDR data; and   providing the dataset to the drilling with casing machine learning model.   
     
     
         19 . The system of  claim 17 , wherein the drilling with casing machine learning model comprises an artificial neural network (ANN). 
     
     
         20 . The system of  claim 17 , the operations comprising stopping the drilling with casing operation based on the condition. 
     
     
         21 . The system of  claim 17 , wherein the condition comprises a torsional vibration value above a threshold value over a threshold time period. 
     
     
         22 . The system of  claim 17 , wherein the condition comprises a lateral vibration value above a threshold value over a threshold time period.