1887

Abstract

Summary

The purpose of this work is to create a learning algorithm that, based on historical data about previously drilled wells, will classify drilling problem. Such decision support system will help the engineer to intervene in the drilling process and prevent high expenses due to unproductive time and equipment repair due to a problem.

In order to solve this task, the wells were found in which problems were encountered. Calculations have been made on various machine learning algorithms to identify an algorithm that yields a minimum error rate. As a result of the project, a model based on gradient boosting was developed to classify the problems in the drilling process.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201800200
2018-04-09
2020-07-08
Loading full text...

Full text loading...

References

  1. Nybo, Roar.
    [2014] Closing the integration gap for the next generation of drilling decision support system. SPE 167864.
    [Google Scholar]
  2. Wang, Yanfang.
    [2014] Drilling Hydraulics Optimization Using Neural Networks. p. 74.
    [Google Scholar]
  3. Yuliya B.Lind and Aigul R.Kabirova.
    [2014] Artificial Neural Networks in Drilling Troubles Prediction. SPE 171274-MS.
    [Google Scholar]
  4. YashodhanGidh and HaniIbrahim
    [2012] Artificial Neural Network Drilling Parameter Optimization System Improves ROP by Predicting/Managing Bit Wear. SPE 149801.
    [Google Scholar]
  5. Jahanbakhshi, R. and Keshavarzi, R.
    [2012] Real-time Prediction of Rate of Penetration during Drilling Operation in Oil and Gas Wells. ARMA paper 12-244.
    [Google Scholar]
  6. MattHall and BrendonHall
    . [2017] Distributed collaborative prediction: Results of the machine learning contest. The Leading Edge.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201800200
Loading
/content/papers/10.3997/2214-4609.201800200
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error