Automatic sedimentary microfacies identification from logging curves based on deep process neural network

2018 ◽  
Vol 22 (S5) ◽  
pp. 12451-12457
Author(s):  
Hui Liu ◽  
Shaohua Xu ◽  
Xinmin Ge ◽  
Jianyu Liu ◽  
Muhammad Aleem Zahid
Keyword(s):  
Neural Network ◽  
Deep Process ◽  
Sedimentary Microfacies

Application of Self-Organizing Neural Network Method in Logging Sedimentary Microfacies Identification

2012 ◽  
Vol 616-618 ◽  
pp. 38-42 ◽  
Author(s):  
Wen Bo Li ◽  
Yun Liang Yu ◽  
Jian Qiang Wang ◽  
Ye Bai ◽  
Xin Wang
Keyword(s):  
Neural Network ◽  
Recognition Rate ◽  
Identification System ◽  
Characteristic Parameters ◽  
Network Method ◽  
Image Characteristic ◽  
Sedimentary Microfacies ◽  
Relationship Of ◽  
The Relationship ◽  
Self Organizing

Studing the identification methods of sedimentary microfacies by the implement of self-organizing neural network model. Picking up the geometrical characteristic parameters and image characteristic parameters of the logging curves. Establishing the relationship of sedimentary microfacies patterns and well logging curves shapes by characteristic parameters. Developing the Sedimentary microfacies patterns identification system and applying it to 1000 wells of the southern area of Daqing Changyuan, the recognition rate can reach 90%, it can prove the validity of the method.

Study on Artificial Neural Networks to Identify Sedimentary Microfacies

2014 ◽  
Vol 912-914 ◽  
pp. 1395-1398
Author(s):  
Wei Fu Liu ◽  
Shuang Long Liu ◽  
Li Xin Sun
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ordos Basin ◽  
Well Logging ◽  
Effective Parameters ◽  
Geological Interpretation ◽  
Artificial Neural ◽  
Sedimentary Microfacies ◽  
Local Geology ◽  
Network Pattern

Based on a number of stratigraphic sedimentary information included in log data, application of the Artificial Neural Network to identify sedimentary microfacies from well logging data can complete the series auto-interpreting. The application can improve the auto-interpreting accuracy and make us get more satisfied results. Ten parameters from the well logging curves are selected for to describing their shape characteristics when the deposition patterns of 8 in gas-bearing formation of Upper Paleozoic group, Ordos basin are studied. Effective parameters were selected on the basis of cores, and based on artificial neural network pattern recognition technique; the sedimentary microfacies of well cross section were auto-interpreted. About 300 wells and the results were interpreted by using the software. The software will be fit for the researchers who have the experiences of geological interpretation and some backgrounds of local geology.

scholarly journals Optimized algorithm for multipoint geostatistical facies modeling based on a deep feedforward neural network

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253174
Author(s):  
Jianpeng Yao ◽  
Wenling Liu ◽  
Qingbin Liu ◽  
Yuyang Liu ◽  
Xiaodong Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Optimization Design ◽  
A Priori ◽  
Training Data ◽  
Target Area ◽  
Facies Modeling ◽  
Sedimentary Characteristics ◽  
Sedimentary Microfacies ◽  
Reservoir Facies ◽  
Modeling Algorithm

Reservoir facies modeling is an important way to express the sedimentary characteristics of the target area. Conventional deterministic modeling, target-based stochastic simulation, and two-point geostatistical stochastic modeling methods are difficult to characterize the complex sedimentary microfacies structure. Multi-point geostatistics (MPG) method can learn a priori geological model and can realize multi-point correlation simulation in space, while deep neural network can express nonlinear relationship well. This article comprehensively utilizes the advantages of the two to try to optimize the multi-point geostatistical reservoir facies modeling algorithm based on the Deep Forward Neural Network (DFNN). Through the optimization design of the multi-grid training data organization form and repeated simulation of grid nodes, the simulation results of diverse modeling algorithm parameters, data conditions and deposition types of sedimentary microfacies models were compared. The results show that by optimizing the organization of multi-grid training data and repeated simulation of nodes, it is easier to obtain a random simulation close to the real target, and the simulation of sedimentary microfacies of different scales and different sedimentary types can be performed.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural
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