MSE based drilling optimization using neural network simulaton
Abstract
The method disclosed receives a data stream from an MWD system and determines the response of a specific energy (SE) relationship and a rate of penetration (ROP) relationship respectively to variables controllable by the operator, in order to enable operation at a lowest SE, or a highest Rate-of-Penetration (ROP) to SE ratio. The method utilizes artificial neural networks trained by MWD data to deduce a depth-of-cut and torque based on relationships manifesting between the various data points collected, and an SE equation and a predicted ROP is evaluated over a series of probable operating points. The method continuously gathers and analyzes MWD data during the drilling operation and allows an operator to manage the controllable parameters such that operation at the lowest SE or highest ROP or ROP to SE ratio can be achieved during the drilling operation.
- Inventors:
- Issue Date:
- Research Org.:
- US Department of Energy (USDOE), Washington DC (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1525026
- Patent Number(s):
- 10221671
- Application Number:
- 14/799,753
- Assignee:
- U.S. Department of Energy (Washington, DC)
- Patent Classifications (CPCs):
-
G - PHYSICS G06 - COMPUTING G06N - COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
E - FIXED CONSTRUCTIONS E21 - EARTH DRILLING E21B - EARTH DRILLING, e.g. DEEP DRILLING
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 2015-07-15
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Zhang, Wu. MSE based drilling optimization using neural network simulaton. United States: N. p., 2019.
Web.
Zhang, Wu. MSE based drilling optimization using neural network simulaton. United States.
Zhang, Wu. Tue .
"MSE based drilling optimization using neural network simulaton". United States. https://www.osti.gov/servlets/purl/1525026.
@article{osti_1525026,
title = {MSE based drilling optimization using neural network simulaton},
author = {Zhang, Wu},
abstractNote = {The method disclosed receives a data stream from an MWD system and determines the response of a specific energy (SE) relationship and a rate of penetration (ROP) relationship respectively to variables controllable by the operator, in order to enable operation at a lowest SE, or a highest Rate-of-Penetration (ROP) to SE ratio. The method utilizes artificial neural networks trained by MWD data to deduce a depth-of-cut and torque based on relationships manifesting between the various data points collected, and an SE equation and a predicted ROP is evaluated over a series of probable operating points. The method continuously gathers and analyzes MWD data during the drilling operation and allows an operator to manage the controllable parameters such that operation at the lowest SE or highest ROP or ROP to SE ratio can be achieved during the drilling operation.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {3}
}