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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 Topicive 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.

Over several years the petroleum industry has been facing challenges associated with stuck pipe. Stuck pipe has caused a major drilling cost for the drilling industry worldwide and various cost estimates carried out have indicated that the cost of fixing stuck pipe issues exceeds $250 million per year (Bradley et al., 1991).
Problems of stuck pipe can range from minor inconveniences to increase in drilling cost up to major complications which will lead to altered drilling due to the inability to drill when this occurs resulting in major time loss.
A major key to the reduction of this phenomenon is the ability to correctly or even better, accurately predict the occurrence of stuck pipe.
Generally, stuck pipe is described as any restriction of upward or downward movement of drill string and/or pipe rotation and leads to a situation where the pipe cannot be freed from the hole without damaging the pipe, and without exceeding the drill rigs maximum allowed hook load. The portion of the drill string that cannot be rotated or moved vertically is known as the stuck pipe.
There are several causes of stuck pipe which include poor hole cleaning, key sitting, collapsed casing, junk, cement related problems, mobile formation, geo-pressured formation, fractured formation. However, the causes of stuck pipe can be classified under two broad categories which are mechanical and differential sticking………………………

1.3 Aims and Objective
This aim of this research project is
 To predict occurrence of stuck pipe in the Niger Delta by examining, analyzing drilling parameters gotten from some daily drilling reports within the Niger Delta fields using the Artificial Neural Network.

To achieve this aim, my objectives include to:
 Identify, study, analyze and understand the causes the causes of stuck pipe
 Analyze the difference between drilling parameters of stuck and non- stuck wells to enable proper prediction of probability of getting stuck
 Implement the ANN in predicting pipe sticking in Niger Delta fields.
1.4 Problem Statement
The purpose of his project is to utilize a powerful machine learning tool, i.e. the ANN to predict the occurrence of stuck pipe which has been a problem in the petroleum industry since the inception of drilling and has led to a lot of cost due fishing operation and time lost in addressing stuck pipe incidences.
This research intends to:
 analyze the importance of each mud parameter to determine their contribution to pipe sticking to aid in stuck pipe prediction
 predict the occurrence of stuck pipe to eschew the risk associated with drilling and excessive drilling.
 avoid or reduce to the barest minimum the amount of money used in drilling.
 avoid cost spent on fishing lost down hole tools.

1.5 Scope of Study
There are several causes of pipe sticking, however this project will be narrowed down to differential sticking, and how ANN can be used to predict its occurrence using data acquired from Niger Delta Fields.
1.6 Justification
The technique of stuck pipe prediction using ANN can be utilized for calculating the risk of stuck pipe either mechanically or differentially before the operation. It is therefore very essential to predict the occurrence of getting stuck because if the drilling personnel know that the parameters used for drilling can cause pipes to get stuck, the parameters will be adjusted and that will result in a smooth operation.
In the comparison between wells that were drilled successfully without getting stuck and wells that had pipe sticking problem, it was clear that some parameters clearly different, therefore this research entails analyzing those differences and using them to predict the occurrence of stuck pipe.
This project will help the petroleum industry and the economy to reduce the amount spent on the already very expensive drilling operations. This will be done by reducing the need for retrieving lost down hole tools to due to stuck pipe which is a very expensive operation known as fishing, and will also reduce the Non Topicive Time (NPT) spent of suck pipe problems.
1.7 Materials and Methodology
For the purpose of this project, a reliable approach for prediction of pipe sticking which is based on ANN is presented. In this method, our system possesses multiple inputs which include the plastic viscosity of the mud, the yield point, the mud weight and the gel strength, etc., and one output, which is the probability of pipe sticking.

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