{"title":"基于神经网络建模的煤层气储层速度、含气量预测及基于图像测井的煤层渗透率估算——以印度南卡兰普拉煤田为例","authors":"Suman Paul , Muhammad Ali , Rima Chatterjee","doi":"10.1016/j.ringps.2021.100021","DOIUrl":null,"url":null,"abstract":"<div><p>P-wave velocity and gas content of major coal seams are predicted from three wells from the South Karanpura coalfield, India. Multilayered feed-forward neural network (MLFN) model is developed for prediction P-wave velocity from two wells using three input log parameters: gamma ray, resistivity and bulk density. The model is tested on coal seams: Sayal, Balkudra, Banasgarah and Argada of three wells namely; A, B and C with satisfactory goodness of fit (R<sup>2</sup> = 0.77). The ratio of P-wave and S-wave velocities (Vp/Vs) ranges from 1.68 to 3.10 in the coal seams of this field. Cleat density using image log is obtained from four major coal seams namely; Balkudra, Kurse, Hathidari, Banasgarah varying from 1/m to 11/m. Ash content varies from 15 to 51% whereas gas content of major coal seams varies from 2.30 to 4.4 10<sup>−</sup>3 kg/m<sup>3</sup>. There is a good correlation between ash content and Vp/Vs of coal seams under the study area. MLFN model for prediction of gas content of the above-mentioned coal seams from two well is trained with input parameters such as: Vp/Vs, bulk density, cleat density and ash content. The model estimates gas content of other coal seams: Argada from well A, Hathidari from well B and Balkudra, Kurse, Banasgarah from well C with satisfactory R<sup>2</sup> = 0.77. Maximum horizontal stress orientation is observed from azimuthal shear wave anisotropy from cross multipole array acoustic (XMAC) log for a well A. It varies from N110° to N115° in these coal seams. Permeability of coal seams are computed from X-tended Range Microresistivity Imager (XRMI) tool from a well C. Permeability of coal is varying from 0.5 md in Saunda seam to 17.29 md in Banasgarah seam. The estimated permeability matches well with the pre-fracture permeability of these seams. It is observed that increase in cleat density enhances coal seam fracture permeability in coal seam.</p></div>","PeriodicalId":101086,"journal":{"name":"Results in Geophysical Sciences","volume":"7 ","pages":"Article 100021"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.ringps.2021.100021","citationCount":"4","resultStr":"{\"title\":\"Prediction of velocity, gas content from neural network modeling and estimation of coal bed permeability from image log in coal bed methane reservoirs: Case study of South Karanpura Coalfield, India\",\"authors\":\"Suman Paul , Muhammad Ali , Rima Chatterjee\",\"doi\":\"10.1016/j.ringps.2021.100021\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>P-wave velocity and gas content of major coal seams are predicted from three wells from the South Karanpura coalfield, India. Multilayered feed-forward neural network (MLFN) model is developed for prediction P-wave velocity from two wells using three input log parameters: gamma ray, resistivity and bulk density. The model is tested on coal seams: Sayal, Balkudra, Banasgarah and Argada of three wells namely; A, B and C with satisfactory goodness of fit (R<sup>2</sup> = 0.77). The ratio of P-wave and S-wave velocities (Vp/Vs) ranges from 1.68 to 3.10 in the coal seams of this field. Cleat density using image log is obtained from four major coal seams namely; Balkudra, Kurse, Hathidari, Banasgarah varying from 1/m to 11/m. Ash content varies from 15 to 51% whereas gas content of major coal seams varies from 2.30 to 4.4 10<sup>−</sup>3 kg/m<sup>3</sup>. There is a good correlation between ash content and Vp/Vs of coal seams under the study area. MLFN model for prediction of gas content of the above-mentioned coal seams from two well is trained with input parameters such as: Vp/Vs, bulk density, cleat density and ash content. The model estimates gas content of other coal seams: Argada from well A, Hathidari from well B and Balkudra, Kurse, Banasgarah from well C with satisfactory R<sup>2</sup> = 0.77. Maximum horizontal stress orientation is observed from azimuthal shear wave anisotropy from cross multipole array acoustic (XMAC) log for a well A. It varies from N110° to N115° in these coal seams. Permeability of coal seams are computed from X-tended Range Microresistivity Imager (XRMI) tool from a well C. Permeability of coal is varying from 0.5 md in Saunda seam to 17.29 md in Banasgarah seam. The estimated permeability matches well with the pre-fracture permeability of these seams. It is observed that increase in cleat density enhances coal seam fracture permeability in coal seam.</p></div>\",\"PeriodicalId\":101086,\"journal\":{\"name\":\"Results in Geophysical Sciences\",\"volume\":\"7 \",\"pages\":\"Article 100021\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/j.ringps.2021.100021\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Results in Geophysical Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666828921000122\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Results in Geophysical Sciences","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666828921000122","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Prediction of velocity, gas content from neural network modeling and estimation of coal bed permeability from image log in coal bed methane reservoirs: Case study of South Karanpura Coalfield, India
P-wave velocity and gas content of major coal seams are predicted from three wells from the South Karanpura coalfield, India. Multilayered feed-forward neural network (MLFN) model is developed for prediction P-wave velocity from two wells using three input log parameters: gamma ray, resistivity and bulk density. The model is tested on coal seams: Sayal, Balkudra, Banasgarah and Argada of three wells namely; A, B and C with satisfactory goodness of fit (R2 = 0.77). The ratio of P-wave and S-wave velocities (Vp/Vs) ranges from 1.68 to 3.10 in the coal seams of this field. Cleat density using image log is obtained from four major coal seams namely; Balkudra, Kurse, Hathidari, Banasgarah varying from 1/m to 11/m. Ash content varies from 15 to 51% whereas gas content of major coal seams varies from 2.30 to 4.4 10−3 kg/m3. There is a good correlation between ash content and Vp/Vs of coal seams under the study area. MLFN model for prediction of gas content of the above-mentioned coal seams from two well is trained with input parameters such as: Vp/Vs, bulk density, cleat density and ash content. The model estimates gas content of other coal seams: Argada from well A, Hathidari from well B and Balkudra, Kurse, Banasgarah from well C with satisfactory R2 = 0.77. Maximum horizontal stress orientation is observed from azimuthal shear wave anisotropy from cross multipole array acoustic (XMAC) log for a well A. It varies from N110° to N115° in these coal seams. Permeability of coal seams are computed from X-tended Range Microresistivity Imager (XRMI) tool from a well C. Permeability of coal is varying from 0.5 md in Saunda seam to 17.29 md in Banasgarah seam. The estimated permeability matches well with the pre-fracture permeability of these seams. It is observed that increase in cleat density enhances coal seam fracture permeability in coal seam.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
