Paper Info
Title
Deep Match between Geology Reports and Well Logs Using Spatial Information
Abstract
In the shale oil and gas industry, operators are looking toward big data and new analytics tools and techniques to optimize operations and reduce cost. Formation evaluation is one of the most crucial steps before the fracturing operation. To assist engineers in understanding the subsurface and in turn make optimal operations, we focus on learning semantic relations between geology reports and well logs, which are collected during down-hole drilling. The challenges are how to represent the features of the geology reports and the well logs collected at measured depths and how to effectively embed them into a common feature space. We propose both linear and nonlinear (artificial neural network) models to achieve such an embedding. Extensive validations are conducted on public well data of North Dakota in the United States. We empirically discover that both geology reports and well logs follow a neighborhood property measured by geological distance. We show that this spatial information is highly effective in both the linear and nonlinear models and our nonlinear model with the spatial information performs the best among the state-of-the-art methods. This research is expected to be integrated into a customized solution for Hitachi regarding shale oil and gas.
Year
DOI
Venue
2016
10.1145/2983323.2983352
ACM International Conference on Information and Knowledge Management
Keywords
Field
DocType
Geology Report,Well Log,Neural Network,Learning to Match,Information Retrieval
Spatial analysis,Data mining,Nonlinear system,Computer science,Artificial intelligence,Formation evaluation,Analytics,Artificial neural network,Feature vector,Information retrieval,Well logging,Big data,Machine learning
Conference
Citations 
PageRank 
References 
1
0.36
18
Authors
6
Name
Order
Citations
PageRank
Bin Tong1408.11
Martin Klinkigt 291.84
Makoto Iwayama343687.03
Yoshiyuki Kobayashi 491.50
Anshuman Sahu 550.79
Ravigopal Vennelakanti6153.01