Research on spacing optimization and density inversion for ultra-slim X-ray density logging tool

doi:10.7623/syxb202503006

Acta Petrolei Sinica ›› 2025, Vol. 46 ›› Issue (3): 563-573.DOI: 10.7623/syxb202503006

• PETROLEUM EXPLORATION • Previous Articles

Research on spacing optimization and density inversion for ultra-slim X-ray density logging tool

Fan Jilin^1,2, Jin Ya³, Li Min³, Zhang Qiong¹, Yang Ziyu¹, Wang Rongpu², Hui Dangtao²

1. School of Automation Engineering, University of Electronic Science and Technology of China, Sichuan Chengdu 611731, China;
2. China National Logging Corporation, Shaanxi Xi'an 710077, China;
3. China Oilfield Services Limited, Hebei Sanhe 065201, China

Received:2024-07-11 Revised:2024-10-08 Published:2025-04-03

超细径X射线密度测井仪的源距优化与密度反演

范继林^1,2, 金亚³, 李敏³, 张琼¹, 杨籽钰¹, 王荣谱², 惠党涛²

1. 电子科技大学自动化工程学院四川成都 611731;
2. 中国石油集团测井有限公司陕西西安 710077;
3. 中海油田服务股份有限公司河北三河 065201

作者简介:范继林,男,1996年5月生,2023年获中国石油大学(华东)博士学位,现为电子科技大学和中国石油集团测井有限公司联合培养博士后,主要从事核测井理论方法与技术研究。Email:fanjl1996@163.com
基金资助:
国家自然科学基金项目(No.U23B20151)和四川省自然科学基金青年科学基金项目(2025ZNSFSC1168)资助。

Abstract

Abstract: As oil and gas exploration continuously progresses, much attention is paid on the research of the optimization design and measurement methods of the next-generation controllable source X-ray density logging tools. Taking the existing ultra-slim gamma-ray density logging tool for an example, it is re-designed by replacing gamma-ray source with X-ray source. The research mainly focuses on the following points:(1) to simulate the generation mechanism of X-ray with different energies; (2) to study the optimal spacing of X-ray density logging tool when comprehensively considering the account detection efficiency, density measurement sensitivity, detection depth and other factors; (3) to carry out X-ray density measurements using multi-variable forward and inversion methods, and further compare with gamma density logging results. The results indicate that the performance of X-ray density logging tool can equal to or even exceed that of gamma-ray density logging tool by adjusting the source-detector spacings of near and far detectors to 110 mm and 290 mm, respectively. Compared with gamma-ray density logging, X-ray density logging is characterized by higher formation sensitivity, vertical resolution and formation density measurement accuracy. In addition, X-ray density logging tool can improve measurement accuracy of photoelectric absorption cross-section index by 0.15 b/e in condition of heavy mud cake. In conclusion, these findings provide certain technical support for the prospective design of X-ray density logging tool and the accuracy measurement of formation density.

Key words: X-ray density logging, tool optimization design, formation density calculation, Monte Carlo method, gamma-ray density logging

摘要： 随着油气勘探的不断深入,新一代可控源X射线密度测井仪器的优化设计和测量方法研究备受关注。以现有的超细径伽马射线密度测井仪为例,将伽马射线源替换为X射线源并进行仪器的重新设计,研究的重点内容包括:①模拟不同能量X射线的产生机理;②综合考虑探测效率、密度测量灵敏度、探测深度等因素,研究X射线密度测井仪的最佳源距;③采用多变量正反演方法进行X射线密度测量,并与伽马射线密度测井仪的结果进行对比。研究结果表明,通过将近探测器和远探测器的源距分别调整为110 mm和290 mm,X射线密度测井仪的性能可以与伽马射线密度测井仪相当甚至超过伽马射线密度测井仪。与伽马射线密度测井仪相比,X射线密度测井仪具有更高的地层灵敏度、垂直分辨率和地层密度测量准确度。此外,在重泥饼条件下,X射线密度测井仪将光电截面吸收指数的测量准确度提升了0.15 b/e。研究成果为X射线密度测井仪的前瞻性设计和地层密度的准确测量提供了一定的技术支持。

关键词: X射线密度测井, 仪器优化设计, 地层密度计算, 蒙特卡罗方法, 伽马射线密度测井

CLC Number:

P631.8

Fan Jilin, Jin Ya, Li Min, Zhang Qiong, Yang Ziyu, Wang Rongpu, Hui Dangtao. Research on spacing optimization and density inversion for ultra-slim X-ray density logging tool[J]. Acta Petrolei Sinica, 2025, 46(3): 563-573.

范继林, 金亚, 李敏, 张琼, 杨籽钰, 王荣谱, 惠党涛. 超细径X射线密度测井仪的源距优化与密度反演[J]. 石油学报, 2025, 46(3): 563-573.

References

[1] JIA Chengzao,PANG Xiongqi,SONG Yan.Whole petroleum system and ordered distribution pattern of conventional and unconventional oil and gas reservoirs[J].Petroleum Science,2023,20(1):1-19.
[2] 雍锐,吴建发,吴伟,等.四川盆地寒武系筇竹寺组页岩气勘探发现及其意义[J].石油学报,2024,45(9):1309-1323.
YONG Rui,WU Jianfa,WU Wei,et al.Exploration discovery of shale gas in the Cambrian Qiongzhusi Formation of Sichuan Basin and its significance[J].Acta Petrolei Sinica,2024,45(9):1309-1323.
[3] 王冰,邱楠生,王祥,等.库车坳陷克拉苏-依奇克里克构造带构造挤压型超压识别与计算[J].石油学报,2022,43(8):1107-1121.
WANG Bing.QIU Nansheng,WANG Xiang,et al.Identification and calculation of tectonic compression overpressure of Kelasu-Yiqikelike tectonic belt in Kuqa depression[J].Acta Petrolei Sinica,2022,43(8):1107-1121.
[4] 席胜利,闫伟,刘新社,等.鄂尔多斯盆地天然气勘探新领域、新类型及资源潜力[J].石油学报,2024,45(1):33-51.
XI Shengli,YAN Wei,LIU Xinshe,et al.New fields,new types and resource potentials of natural gas exploration in Ordos Basin[J].Acta Petrolei Sinica,2024,45(1):33-51.
[5] KING III G,BECKER A J,CORRIS G W,et al.Density logging using an electron linear accelerator as the X-ray source[J].Nuclear Instruments and Methods in Physics Research Section B:Beam Interactions with Materials and Atoms,1987,24-25:990-994.
[6] BAYLESS J R,BURKHART C P,KUTHI A.Advances in X-ray and neutron source technologies for logging applications[C]//Proceedings of SPWLA 34th Annual Logging Symposium.Calgary:SPWLA,1993:SPWLA-1993-Y.
[7] BADRUZZAMAN A.An assessment of fundamentals of nuclear-based alternatives to conventional chemical source bulk density measurement[J].Petrophysics,2014,55(5):415-434.
[8] 张锋,李亚芬,信毅,等.基于X射线和伽马源的密度测井数值模拟[J].中国石油大学学报(自然科学版),2018,42(1):60-66.
ZHANG Feng,LI Yafen,XIN Yi,et al.Numerical simulation of density logging based on X-ray and gamma ray sources[J].Journal of China University of Petroleum (Edition of Natural Science),2018,42(1):60-66.
[9] 张泉滢,刘国斌,袁超,等.基于井下X射线源的密度测井仪器参数优化设计[J].中国石油大学学报(自然科学版),2024,48(1):115-123.
ZHANG Quanying,LIU Guobin,YUAN Chao,et al.Parameter optimization design of density logging tool based on underground X-ray source[J].Journal of China University of Petroleum (Edition of Natural Science),2024,48(1):115-123.
[10] 于华伟,杨争春,刘超卓,等.X射线密度测井中X射线管高压对密度测量精度的影响[J].核技术,2021,44(11):110401.
YU Huawei,YANG Zhengchun,LIU Chaozhuo,et al.Effects of X-ray tube high-voltages on the density measurement accuracy in X-ray density logging[J].Nuclear Techniques,2021,44(11):110401.
[11] SIMON M,TKABLADZE A,BEEKMAN S,et al.A revolutionary X-ray tool for true sourceless density logging with superior performance[C]//Proceedings of SPWLA 59th Annual Logging Symposium.London:SPWLA,2018:SPWLA-2018-G.
[12] ZHANG Feng,FAN Jilin,LIU Yingming,et al.A new algorithm for evaluating reservoir density based on X-ray logging technology[J].Applied Radiation and Isotopes,2021,175:109793.
[13] 武蕙琳,李雨莲,金亚,等.基于数值模拟的X射线密度测井仪器源距设计[J].核技术,2022,45(10):100402.
WU Huilin,LI Yulian,JIN Ya,et al.A design of source spacing of the X-ray density logging tool based on numerical simulation[J].Nuclear Techniques,2022,45(10):100402.
[14] BECKER A J,BOYCE J R,CORRIS G W,et al.Detection of scattered X-rays from an electron linac in a borehole[J].Nuclear Instruments and Methods in Physics Research Section B:Beam Interactions with Materials and Atoms,1987,24-25:995-998.
[15] KNOLL G F.Radiation detection and measurement[M].4th ed.Hoboken:John Wiley & Sons,2010.
[16] EYL K A,CHAPELLAT H,CHEVALIER P,et al.High-resolution density logging using a three detector device[C]//Proceedings of SPE Annual Technical Conference and Exhibition.New Orleans:SPE,1994:SPE-28407-MS.
[17] 房鸿,李耀宗,黄悦.新型X射线管的研制与性能测试[J].真空,2005,42(5):36-38.
FANG Hong,LI Yaozong,HUANG Yue.R&D and performance of new X-ray tubes[J].Vacuum,2005,42(5):36-38.
[18] 杜月珍.小焦点X射线管研制开发中的探索与实践[J].中国医疗器械杂志,2004,28(2):109-110.
DU Yuezhen.Exploration and practice in the development of X-ray tubes with small focuses[J].Chinese Journal of Medical Instrumentation,2004,28(2):109-110.
[19] 王虎,刘睿,岳爱忠,等.350 kV高压X射线密度测井探测深度特性及影响[J].核技术,2023,46(10):100502.
WANG Hu,LIU Rui,YUE Aizhong,et al.Depth-of-investigation characteristics and influence of 350-kV high-voltage X-ray density logging[J].Nuclear Techniques,2023,46(10):100502.
[20] 胡广春,龚建,刘晓亚,等.体源探测效率计算及修正方法研究[J].核电子学与探测技术,2005,25(6):798-802,791.
HU Guangchun,GONG Jian,LIU Xiaoya,et al.Study of calculation and correction method of detection efficiency of the volume source[J].Nuclear Electronics & Detection Technology,2005,25(6):798-802,791.
[21] 周悦,于华伟,陈翔鸿,等.基于X射线源的密度测井近源距研究[J].核技术,2018,41(12):120401.
ZHOU Yue,YU Huawei,CHEN Xianghong,et al.Study on near detector-source spacing of density logging based on X-ray source[J].Nuclear Techniques,2018,41(12):120401.
[22] 范继林,张锋,田立立,等.X射线密度测井能谱分析及响应特性研究[J].测井技术,2021,45(3):246-252.
FAN Jilin,ZHANG Feng,TIAN Lili,et al.Energy spectrum analysis and response characteristics of X-ray density logging[J].Well Logging Technology,2021,45(3):246-252.

Research on spacing optimization and density inversion for ultra-slim X-ray density logging tool

超细径X射线密度测井仪的源距优化与密度反演

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	Deng Ya, Li Yong, Zhang Wenqi, Hu Dandan, Xiao Cong, Xu Jiacheng, Liu Dawang, Zhang Leifu, Wang Yuning. Well log correlation and extraction method based on feature points and its application on determining free water level [J]. Acta Petrolei Sinica, 2023, 44(4): 626-635.
[2]	Li Chaoliu, Wu Hongliang, Li Xia, Hu Falong, Tian Han, Huang Yuyue, Yuan Chao. A new data processing method of array induction logging for inclined electrical anisotropic formation [J]. Acta Petrolei Sinica, 2022, 43(10): 1439-1449.
[3]	Yao Donghua, Zhou Lihong, Wang Wen'ge, Han Guomeng, Pu Xiugang, Song Yanjie, Xu Chengwu. Logging evaluation of composite sweet spots for shale oil: a case study of Member 2 of Kongdian Formation in Cangdong sag [J]. Acta Petrolei Sinica, 2022, 43(7): 912-924.
[4]	Hu Song, Wang Min, Liu Weinan, Wang Bing. Anisotropy correction method for acoustic time difference in horizontal shale wells and its application [J]. Acta Petrolei Sinica, 2022, 43(1): 58-66.
[5]	Wu Hongliang, Liu Peng, Wu Lihong, Xiao Chengwen, Li Yusheng, Feng Zhou, Xia Shouji. Extraction method of reflected S-wave near borehole in carbonate fracture-cavity reservoirs [J]. Acta Petrolei Sinica, 2021, 42(10): 1337-1345,1394.
[6]	Li Ning, Xu Binsen, Wu Hongliang, Feng Zhou, Li Yusheng, Wang Kewen, Liu Peng. Application status and prospects of artificial intelligence in well logging and formation evaluation [J]. Acta Petrolei Sinica, 2021, 42(4): 508-522.
[7]	Yuan Ye, Li Ning, Wang Caizhi, Liu Yingming, Li Weizhong, Feng Zhou, Fu Haicheng. A visualization and analysis method of augmented dimension of multi-well and multi-dimensional heterogeneous data crossplot [J]. Acta Petrolei Sinica, 2020, 41(9): 1100-1108.
[8]	Gao Jianshen, Song Yang, Liu Yanping, Zhu Kairan, Liu Xin. A four-parameter calculation method of oil-based mud electric imaging logging based on concave electrode pairs in low-resistivity formation [J]. Acta Petrolei Sinica, 2020, 41(8): 960-968.
[9]	Wu Hongliang, Liu Peng, Wang Kewen, Xiao Chengwen, Feng Zhou, Xin Yi, Duan Wenxing, Li Yusheng. A method for recovery of reflection amplitude in dipole acoustic reflection imaging [J]. Acta Petrolei Sinica, 2020, 41(4): 457-466.
[10]	Feng Chong, Wang Qingbin, Tan Zhongjian, Dai Liming, Liu Xiaojian, Zhao Meng. Logging classification and identification of complex lithologies in volcanic debris-rich formations: an example of KL16 oilfield [J]. Acta Petrolei Sinica, 2019, 40(S2): 91-101.
[11]	Wang Qinghui, Feng Jin, Guan Yao, Shi Lei, Pan Weiguo, Zhou Kaijin, Yang Qing, Song Wei. Permeability logging evaluation method of low-porosity low-permeability sandstone reservoirs based on dynamic data: a case study of Paleogene strata in Lufeng sag [J]. Acta Petrolei Sinica, 2019, 40(s1): 206-216.
[12]	Li Chaoliu, Hu Falong, Yuan Chao, Li Xia, Li Changxi, Liu Xuefeng. Multi-mineral components of tight reservoirs determined by joint inversion of nuclear magnetic resonance and conventional logs [J]. Acta Petrolei Sinica, 2018, 39(9): 1019-1027.
[13]	Huang Wei, Zhang Xiaoli, Li Hao, Qi Panwen, Wang Jun. Interpretation model of organic carbon content of shale in Member 7 of Yanchang Formation, central-southern Ordos Basin [J]. Acta Petrolei Sinica, 2015, 36(12): 1508-1515.
[14]	Luo Renze, He Guolin, Bao Ke. Mixed-domain Fourier finite-difference prestack depth migration based on coefficient optimization [J]. Acta Petrolei Sinica, 2015, 36(12): 1553-1558.
[15]	Shang Fuhua, Yuan Ye, Wang Caizhi, Cao Maojun, Feng Zhou. A logging data processing method of intelligent optimization logging interpretation model based on knowledge-base [J]. Acta Petrolei Sinica, 2015, 36(11): 1449-1456.