Saudi Arabian Oil Company (20240255668). GEOSTEERING USING IMPROVED DATA CONDITIONING simplified abstract
Contents
GEOSTEERING USING IMPROVED DATA CONDITIONING
Organization Name
Inventor(s)
Klemens Katterbauer of Dhahran (SA)
Abdallah A. Alshehri of Dhahran (SA)
Alberto Marsala of Venezia (IT)
Ali Abdallah Alyousef of Dhahran (SA)
GEOSTEERING USING IMPROVED DATA CONDITIONING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240255668 titled 'GEOSTEERING USING IMPROVED DATA CONDITIONING
The patent application describes systems and methods for geosteering using improved data conditioning. This involves estimating physical parameters from remote sensing data, preprocessing the data, and training neural networks to convert these parameters into rock characteristics and reconciled physical parameters.
- Estimating physical parameters from remote sensing data
- Preprocessing estimated physical parameters
- Training three neural networks
- Converting estimated physical parameters into rock characteristics
- Converting rock characteristics into reconciled physical parameters
- Geosteering a well based on interpreted subsurface geology
Potential Applications: - Oil and gas exploration - Geothermal energy production - Environmental monitoring
Problems Solved: - Improved accuracy in geosteering - Enhanced understanding of subsurface geology - Efficient resource extraction
Benefits: - Increased productivity in drilling operations - Cost savings through optimized geosteering - Minimized environmental impact
Commercial Applications: - Oil and gas companies - Geothermal energy companies - Environmental consulting firms
Questions about Geosteering Using Improved Data Conditioning: 1. How does geosteering benefit the oil and gas industry? 2. What are the key advantages of using neural networks in geosteering applications?
Frequently Updated Research: - Advances in remote sensing technology - Neural network optimization for geosteering applications
Original Abstract Submitted
systems and methods for geosteering using improved data conditioning are disclosed. the methods include estimating physical parameters from a training dataset including remote sensing data; preprocessing the estimated physical parameters; training a first neural network; training a second neural network; training a third neural network; converting estimated physical parameters into the rock characteristics with the first neural network; and converting rock characteristics into reconciled physical parameters with the second neural network. the methods further include obtaining new remote sensing data; estimating new estimated physical parameters from the new remote sensing data; converting new estimated physical parameters into new reconciled physical parameters with the third neural network; and performing geosteering of a well based on a subsurface geology interpreted from the new reconciled physical parameters.
