A study of calculating formation parameter by using neural network --a case study in Yunlin

碩士 === 國立成功大學 === 資源工程學系碩博士班 === 90 === Abstract In well log analysis, some empirical formula or parameters, depending on regional geology, are required. In order to avoid using these empirical formula or parameters, a neural network model may be used. So the purpose of this study is to develop a n...

Full description

Bibliographic Details
Main Authors: Ming-Hui Chou, 周明輝
Other Authors: Zsay-Shing Lin
Format: Others
Language:zh-TW
Published: 2002
Online Access:http://ndltd.ncl.edu.tw/handle/95560247115181484694
id ndltd-TW-090NCKU5397016
record_format oai_dc
spelling ndltd-TW-090NCKU53970162016-06-27T16:08:57Z http://ndltd.ncl.edu.tw/handle/95560247115181484694 A study of calculating formation parameter by using neural network --a case study in Yunlin 利用類神經網路計算地層參數之研究-雲林地區案例分析 Ming-Hui Chou 周明輝 碩士 國立成功大學 資源工程學系碩博士班 90 Abstract In well log analysis, some empirical formula or parameters, depending on regional geology, are required. In order to avoid using these empirical formula or parameters, a neural network model may be used. So the purpose of this study is to develop a neural network, using well logging data, for estimating formation parameters. For developing a neural network, the data collected in this study includes: Gamma Ray Log(GR)、Deep Induction Log (ILD)、Borehole Compensated Sonic Log (BHC)、and Formation Density Log(FDC) from the wells of THS-2, THS-4, THS-5, and THS-8, for the depth between 30-1500 meters from Tai- His area in Taiwan. The input variables of the neural network developed in this study are: depth, GR, BHC, FDC, and ILD. And the output variables include: shale content, porosity, and formation resistivity. The trained data (shale content, porosity, and formation resistivity) for the neural network are from the results of well log analysis by Chinese Petroleum Corporation for THS-5 well. Then the neural network model developed is used to analyze and calculate the formation parameters of the other wells in Tai- His area (THS-2、THS-4、 and THS-8 wells). The results from neural network analysis of the THS-2 well (depth between 61m to 1537m) are as follows: the shale content in each layer of formation is between 12.26% and 40.90%. The formation is shaly sand. The average porosity is between 18.84% and 37.51%. And the formation resistivity is between 0.691ohm-m and 95.79ohm-m. The formation parameter for the THS-4 well (depth between 182m to 1347m) is as follows: the shale content in each layer of formation is between 10.36% and 44.42%. The formation is shaly sand. The average porosity is between 26.44% and 37.19%. And the formation resistivity is between 1.32ohm-m and 79.99ohm-m. For THS-8 well (depth between 310m to 1448m) the shale content in each layer of formation is between 14.22% and 33.83%. The formation is shaly sand. The average porosity is between 12.87% and 49.43%. And the formation resistivity is between 1.86ohm-m to 30.94ohm-m. In this study, the calculated porosity is pretty high (between 30% and 41%) in deep formation (for the depth greater than 700m). The reason for the high porosity in deep formation may be because the porosity of training data is from shallow formation which is the weak and unconsolidate. Zsay-Shing Lin 林再興 2002 學位論文 ; thesis 107 zh-TW
collection NDLTD
language zh-TW
format Others
sources NDLTD
description 碩士 === 國立成功大學 === 資源工程學系碩博士班 === 90 === Abstract In well log analysis, some empirical formula or parameters, depending on regional geology, are required. In order to avoid using these empirical formula or parameters, a neural network model may be used. So the purpose of this study is to develop a neural network, using well logging data, for estimating formation parameters. For developing a neural network, the data collected in this study includes: Gamma Ray Log(GR)、Deep Induction Log (ILD)、Borehole Compensated Sonic Log (BHC)、and Formation Density Log(FDC) from the wells of THS-2, THS-4, THS-5, and THS-8, for the depth between 30-1500 meters from Tai- His area in Taiwan. The input variables of the neural network developed in this study are: depth, GR, BHC, FDC, and ILD. And the output variables include: shale content, porosity, and formation resistivity. The trained data (shale content, porosity, and formation resistivity) for the neural network are from the results of well log analysis by Chinese Petroleum Corporation for THS-5 well. Then the neural network model developed is used to analyze and calculate the formation parameters of the other wells in Tai- His area (THS-2、THS-4、 and THS-8 wells). The results from neural network analysis of the THS-2 well (depth between 61m to 1537m) are as follows: the shale content in each layer of formation is between 12.26% and 40.90%. The formation is shaly sand. The average porosity is between 18.84% and 37.51%. And the formation resistivity is between 0.691ohm-m and 95.79ohm-m. The formation parameter for the THS-4 well (depth between 182m to 1347m) is as follows: the shale content in each layer of formation is between 10.36% and 44.42%. The formation is shaly sand. The average porosity is between 26.44% and 37.19%. And the formation resistivity is between 1.32ohm-m and 79.99ohm-m. For THS-8 well (depth between 310m to 1448m) the shale content in each layer of formation is between 14.22% and 33.83%. The formation is shaly sand. The average porosity is between 12.87% and 49.43%. And the formation resistivity is between 1.86ohm-m to 30.94ohm-m. In this study, the calculated porosity is pretty high (between 30% and 41%) in deep formation (for the depth greater than 700m). The reason for the high porosity in deep formation may be because the porosity of training data is from shallow formation which is the weak and unconsolidate.
author2 Zsay-Shing Lin
author_facet Zsay-Shing Lin
Ming-Hui Chou
周明輝
author Ming-Hui Chou
周明輝
spellingShingle Ming-Hui Chou
周明輝
A study of calculating formation parameter by using neural network --a case study in Yunlin
author_sort Ming-Hui Chou
title A study of calculating formation parameter by using neural network --a case study in Yunlin
title_short A study of calculating formation parameter by using neural network --a case study in Yunlin
title_full A study of calculating formation parameter by using neural network --a case study in Yunlin
title_fullStr A study of calculating formation parameter by using neural network --a case study in Yunlin
title_full_unstemmed A study of calculating formation parameter by using neural network --a case study in Yunlin
title_sort study of calculating formation parameter by using neural network --a case study in yunlin
publishDate 2002
url http://ndltd.ncl.edu.tw/handle/95560247115181484694
work_keys_str_mv AT minghuichou astudyofcalculatingformationparameterbyusingneuralnetworkacasestudyinyunlin
AT zhōumínghuī astudyofcalculatingformationparameterbyusingneuralnetworkacasestudyinyunlin
AT minghuichou lìyònglèishénjīngwǎnglùjìsuàndecéngcānshùzhīyánjiūyúnlíndeqūànlìfēnxī
AT zhōumínghuī lìyònglèishénjīngwǎnglùjìsuàndecéngcānshùzhīyánjiūyúnlíndeqūànlìfēnxī
AT minghuichou studyofcalculatingformationparameterbyusingneuralnetworkacasestudyinyunlin
AT zhōumínghuī studyofcalculatingformationparameterbyusingneuralnetworkacasestudyinyunlin
_version_ 1718324111027994624