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Sunday 20 May 2018

PREDICTION OF STUCK PIPE USING ARTIFICIAL NEURAL NETWORK

PREDICTION OF STUCK PIPE USING ARTIFICIAL NEURAL NETWORK: A CASE STUDY ON NIGER DELTA FIELDS OF NIGERIA
ABSTRACT
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.
CHAPTER ONE
1.0 INTRODUCTION
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.
1.1.1 Mechanical sticking:
This is the limiting or prevention of motion of the drill string by anything other than differential pressure sticking. According to drillers stuck pipe handbook (1997) by Schlumberger, Mechanical Sticking can be caused by the following:
1. Inadequate hole cleaning
2. Formation instability (brittle, sloughing, or swelling shales)
3. Key seating
4. Under gauge hole
5. Tectonically stressed formations
6. Plastic or mobile formations
7. Under pressured formations
8. Junk
9. Ledges and doglegs
10. Collapsed casing/tubing
11. Unconsolidated formations
12. Large boulders falling into the hole
13. Running large gauge tools
14. Cement blocks
15. Green cement
However, most cases of mechanical sticking can be avoided by proper well planning, optimal mud design and right directional planning.

TOPIC: PREDICTION OF STUCK PIPE USING ARTIFICIAL NEURAL NETWORK: A CASE STUDY ON NIGER DELTA FIELDS OF NIGERIA
Chapters: 1 - 5
Delivery: Email
Number of Pages: 70

Price: 3000 NGN
In Stock

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