PREDICTION OF STUCK PIPE USING ARTIFICIAL NEURAL NETWORK: A CASE STUDY ON NIGER DELTA FIELDS OF NIGERIA
Drilling is a process that involves the procurement of natural resources such as oil and gas which holds prime importance in today’s world, Drilling practices abounds with a number of complications and an efficient way of dealing with such problems is key to the continuity of the process.
One of such problems is stuck pipe, stuck pipe is a common problem in the industry and it accounts for major rig time loss known as Non Productive Time (NPT) and also accounts for billions of dollars wasted annually in the petroleum industry.
The purpose of this project to implement a powerful machine learning tool known as the Artificial Neural Network in the prediction of stuck pipe using Niger Delta fields as a case study,
The ANN is a Matlab built in function and computational system inspired by the structure, processing method and learning ability of the human brain.
The ANN has the ability to take multiple inputs ( plastic viscosity, yield point and gel strength at 10 seconds and 10 minutes), a target ( mud weight ) to produce a single output which is the prediction of the occurrence of stuck pipe. This was successfully carried in this research study. It is therefore shown in this study that the ANN can be successfully used to predict the occurrence of stuck pipe. Thus, they can be utilized with real-time data representing the results on a log viewer which can help reduce the occurrence of getting stuck while drilling and all the complications that comes with this occurrence.