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
Delivery: Email
Number of Pages: 70
Price: 3000 NGN
In Stock
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