Downhole depth estimation for automated subsurface navigation

This thesis proposes and evaluates a concept for downhole depth estimation by matching subsurface measurements from two sensors in a bottom hole assembly. The application of a downhole depth estimate in automated subsurface navigation has also been demonstrated. One of the key hurdles in achieving real time subsurface navigation lies in communication bottom neck between surface and downhole. Modern measurement while drilling and logging while drilling tools measure all vital information downhole except depth. Real time availability of depth estimate in the downhole can open new doors for real time automated bit steering and optimization in well drilling operations. The downhole depth estimation concept introduced in this thesis is based on correlation of gamma ray responses from two sensors to estimate average rate of penetration at the bit. This average rate of penetration of the bit is used to estimate distance travelled by the bit. An algorithm is proposed for correlation of gamma ray sensor response. Various parameters in the algorithm are investigated and discussed in detail for optimized performance. The error in the estimation is a result of difference between average and instantaneous rate of penetration as well as wrong correlation. Synthetic sensor response is created from a gamma ray data set to evaluate the algorithm for different noise levels and count rates. The error in estimated depth due to difference between average and instantaneous rate of penetration is approximately 2.2%. The total error is observed to be less than 4% for lower statistical noise levels. To reduce the error associated with proposed system, a nuclear marker - detector system is proposed and evaluated using Monte Carlo based nuclear simulations. In the last section the application of downhole depth for automated well-plan execution through a rotary steerable system is realised.

Subject

downhole depth
drilling automation
log correlation

To reference this document use:

http://resolver.tudelft.nl/uuid:03c4abb3-1df4-4ca3-b22c-3b36e3e5c8cc

Embargo date

2014-04-08

Part of collection

Student theses

Document type

master thesis

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Files

PDF

AES_PE_13-14.pdf

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