Method and machine-readable medium for data-centric drilling hazard prediction

Abstract

A method includes inputting a dataset including data about a wellbore, a geographical area, a hydrocarbon formation, or any combination thereof, to one or more artificial intelligence models, generating a prediction, via the one or more artificial intelligence models, of a probability of a hazard event for the wellbore, an impending hazard event during active drilling of the wellbore, an optimal location for the wellbore in the geographical area, or any combination thereof, and performing or modifying drilling operations in response to the prediction generated by the one or more artificial intelligence models.

The invention claimed is: 
     
         1 . A method comprising:
 inputting a dataset comprising data about a wellbore, a geographical area, a hydrocarbon formation, or any combination thereof, to one or more artificial intelligence models;   generating a prediction, via the one or more artificial intelligence models, of a probability of a hazard event for the wellbore, an impending hazard event during active drilling of the wellbore, an optimal location for the wellbore in the geographical area, or any combination thereof; and   performing or modifying drilling operations in response to the prediction generated by the one or more artificial intelligence models.   
     
     
         2 . The method of  claim 1 , wherein the data comprises a drilling report containing hazard event information, static data about the wellbore, sensor data, or any combination thereof. 
     
     
         3 . The method of  claim 1 , further comprising:
 collecting data about one or more existing wellbores, one or more drilled geographical areas, one or more drilled hydrocarbon formation, or any combination thereof;   mining, sorting, and filtering the data to correlate the data to a previous hazard event within the one or more existing wellbores;   flagging the previous hazard event and quantifying the severity of the previous hazard event;   generating a training dataset comprising data sorted by depth, time, and event; and   training the one or more artificial intelligence models on the training dataset and the previous hazard event.   
     
     
         4 . The method of  claim 3 , further comprising:
 training the one or more artificial intelligence models on feedback comprising the prediction generated by the one or more artificial intelligence models and an outcome of the performed or modified drilling operations.   
     
     
         5 . The method of  claim 3 , wherein mining the data comprises using natural language processing algorithms for extracting contextual information. 
     
     
         6 . The method of  claim 3 , wherein the one or more artificial intelligence models are trained using one or more machine learning techniques selected from the group consisting of logistic regression, naïve Beyes, k-nearest neighbor, decision tree, Ada boost, deep neural network, random forest, and any combination thereof. 
     
     
         7 . The method of  claim 3 , wherein filtering the data comprises identifying a change in data within a predefined range and excluding data correlated to expected drilling events. 
     
     
         8 . The method of  claim 1 , wherein the drilling operations are performed or modified by the one or more artificial intelligence models in response to the prediction generated by the one or more artificial intelligence models. 
     
     
         9 . The method of  claim 1 , further comprising generating a warning alert to an operator or a drilling crew about the probability of the hazard event or the impending hazard event. 
     
     
         10 . A system comprising:
 a data processing application comprising a data mining module and a data filter module for receiving, extracting, and filtering a dataset;   an artificial intelligence model training application comprising one or more machine learning training modules; and   a hazard prediction application configured to predict a hazard event within a wellbore utilizing one or more artificial intelligence models.   
     
     
         11 . The system of  claim 10 , wherein the hazard prediction application comprises a real-time warning module configured to predict the hazard event during active drilling, a predictive warning module configured to determine a probability of a future hazard event in the wellbore, a planning module configured to predict an optimal well location in a geographical area, or any combination thereof. 
     
     
         12 . The system of  claim 10 , further comprising one or more sensors within the wellbore configured to provide real-time readings to the system. 
     
     
         13 . The system of  claim 10 , further comprising a database storing historical data comprising hazard event reports, static wellbore data, sensor readings, or any combination thereof from existing wellbores. 
     
     
         14 . The system of  claim 13 , wherein the historical data stored on the database is input to the artificial intelligence model training application for training or updating the one or more artificial intelligence models. 
     
     
         15 . The system of  claim 10 , further comprising one or more pieces of drilling equipment, wherein operations of the one or more pieces of drilling equipment are controlled or modified by the hazard prediction application upon predicting the hazard event.