Date of Graduation
Statler College of Engineering and Mineral Resources
Petroleum and Natural Gas Engineering
H. I. Bilgesu.
In this study a new methodology was developed to predict the drilling parameters using the Artificial Neural Network. Three models were developed to predict bit type, rate of penetration (ROP), and cost-per-foot (cost/ft), respectively.;The prediction of bit type and other drilling parameters from the current available data is an important criterion in selecting the most cost efficient bit. History of bit runs plays an important factor in bit selection and bit design. Based on field data, the selection of bit type can be accomplished by the use of a neural network as an alternative bit selection method.;Three drilling parameters were modeled with data from different fields located in Kuwait. Results show that the drilling parameters of the new well can be predicted with the neural network models developed from the previous wells, a cost efficient alternative.
Al-Rashidi, Abdulrahman F., "Designing neural networks for the prediction of the drilling parameters for Kuwait oil and gas fields" (2000). Graduate Theses, Dissertations, and Problem Reports. 1101.