Patent AU-2018424207-B2 (AU)
Title: Porous medium permeability prediction method based on machine image intelligent learning
Inventor: LIU JIANGFENG
IPC: G06T1/00
Filed: 2018-09-07
Granted: 2020-10-29
Abstract: i N j 9(- WJP? I IN -Ef |EP $ l 
(10)9)) 
() Pd'VT WO 2020/015086 A1 
2020 4T1f - P23 (23.01.2020) WIPO I PCT 
(51) ) pj9 4T1 }% : (72) & )A: J1id(LIU, Jiangfeng); +P4I 
G06T 7/00 (2017.01) 1 r1 ]T i Li Y 1 Jiangsu 221116 (CN)o 
(21) ) PCT/CN2018/104640 Illt t(CAO, Xulou); + T )413 r I Ni Li 
Ex * 1 I Jiangsu 221116 (CN)0 o I, Yh 
(22) M pj: $igH : 2018 9 19 7 H (07.09.2018) (CHEN, Shijie); F Pr Li E 
(25)$ : F §1 M , Jiangsu 221116 (CN)o   W M (HUANG, 
Bingxiang); FP4 i H iTM LfX1, 
(26) zf i : F Jiangsu 221116 (CN)0 o Afi t (CHEN, Zherui); 
(30) I fR: + 4l I i & lf i ,X tMI Jiangsu 
201810788898.1 2018 4 7A]18H (18.07.2018) CN 221116 (CN) &)tJ-(SONG, Shuaibing); +[ 
(71) $ifA : *- M r Lt (C (CHINA UNIVERSITY 4 IT NN r ,I Jiangsu 221116 
OF MINING AND TECHNOLOGY) [CN/CN]; ± (CN) o 58I f(NI,Hongyang); F lll 
41l 3 )H1 r4 L [ fit 1 , Jiangsu rNH W #Ex 1 , Jiangsu 221116 (CN)o 
221116 (CN)o 4 ')11 Mi * r (74) (*I4 :j h P1J /, $(74)N F 
15 (XUZHOU USURE MINING TECHNOLOGY CO., 1) (NANJING JINGWEI PATENT & TRADEMARK 
LTD) [CN/CN]; + T )H1 AGENCYCO.,LTD); + ilT M$riP X 
99 -$ u *r X \J W{ A X i 710, WY179912 B) , Jiangsu210005 (CN)o 
Jiangsu221116 (CN)o 
(54) Title: POROUS MEDIUM PERMEABILITY PREDICTION METHOD BASED ON INTELLIGENT MACHINE IMAGE 
LEARNING 
cc 
SEMf-MR AA BB 
AA SEM scanner 
BB Computer 
CC Result output display device 
(57) Abstract: Disclosed is a porous medium permeability prediction methodbased on intelligent machine image learning. The method 
comprises: selecting multiple groups of porous medium materials of the same type but different dry densities, and determining the real 
permeability of each group of porous medium materials; scanning each group of porous medium materials using an SEM to obtain 
SEM images thereof, and then calculating a gray average, a gray variance, an image energy, an image entropy and a fractal dimension 
of each of the SEM images; using an extreme learning machine neural network model to perform training and learning on five image 
I feature parameters of each of the SEM images and the real permeability corresponding thereto to determine a change relationship 
between the five image feature parameters and the real permeability; and during prediction, inputting parameters of the SEM image of 
a porous medium material of unknown permeability, the extreme learning machine neural network model being able to predict thereby 
the permeability of the porous medium material. The present invention is simple to operate and has a short test period, achieves a high 
Accuracy of predicted permeability and is low in test costs.
W O 2020/015086 A 1 |||||111I||I|I||I|||||I|||||I|I|I|I|111|||IIII |||||||||||||||||||||||||||| 
(81) 1 M5 (4jI M hR HA, n- tf 
Ef)f) : AE, AG, AL, AM, AO, AT, AU, AZ, BA, BB, BG, 
BH, BN, BR, BW, BY, BZ, CA, CH, CL, CN, CO, CR, CU, 
CZ, DE, DJ, DK, DM, DO, DZ, EC, EE, EG, ES, Fl, GB, 
GD, GE, GH, GM, GT, HN, HR, HU, ID, IL, IN, IR, IS, 
JO, JP, KE, KG, KH, KN, KP, KR, KW, KZ, LA, LC, LK, 
LR, LS, LU, LY, MA, MD, ME, MG, MK, MN, MW, MX, 
MY, MZ, NA, NG, NI, NO, NZ, OM, PA, PE, PG, PH, PL, 
PT, QA, RO, RS, RU, RW, SA, SC, SD, SE, SG, SK, SL, 
SM, ST, SV, SY, TH, TJ, TM, TN, TR, TT, TZ, UA, UG, 
US, UZ, VC, VN, ZA, ZM, ZWc 
(84) $E5(I f )] $ -$%g2% 
M): ARIPO (BW, GH, GM, KE, LR, LS, MW, MZ, 
NA, RW, SD, SL, ST, SZ, TZ, UG, ZM, ZW), RIl (AM, 
AZ, BY, KG, KZ, RU, TJ, TM), JII (AL, AT, BE, BG, 
CH, CY, CZ, DE, DK, EE, ES, Fl, FR, GB, GR, HR, HU, 
IE, IS, IT, LT, LU, LV, MC, MK, MT, NL, NO, PL, PT, 
RO, RS, SE, SI, SK, SM, TR), OAPI (BF, BJ, CF, CG, CI, 
CM, GA, GN, GQ, GW, KM, ML, MR, NE, SN, TD, TG).  
- ttK [ I d21*(3)) 
(57)iALR: aPR)]t J'$$SEHA ftIW @ L9$TL A fIAf4Ri&$, MLI Tl T 
f T -i3##i #UA M TL-lSEMj- 4 , A tW96tEN 9 MJ1 M iMT -i E&A +, EM 
$20l- )] I$ I lj mfg)M Ji]Qft$$9 tT, A j$ A ' f6jA l' ' A U)Ai