Warianty tytułu
Języki publikacji
Abstrakty
The identification of formation fluid properties of igneous rock reservoirs plays a vital role in the igneous rock oil/gas exploration and development. Due to the complex lithology, low porosity, low permeability and strong heterogeneity of igneous rock reservoirs, conventional geophysical logging evaluation methods are not as accurate as sandstone reservoirs in response to different fluid properties of igneous rock reservoirs. It is difficult to identify the fluid type of the formation. In order to solve this problem, with the rapid development of array acoustic logging technology, this paper proposes a new method for analyzing the time–frequency characteristics of full wave train data based on the large amount of information contained in the array acoustic wave train, hoping to more accurately identify the fluid properties of igneous rock reservoirs. This paper introduces the time slice and frequency slice of Choi–Williams distribution with different IMF components obtained by EMD method and analyzes the characteristics of time slice and frequency slice of reservoirs with different fluid properties, in order to be used for identification of formation fluid types. The results show that the time slices and frequency slices of different IMF components have significantly different characteristics for different formation fluid properties of igneous rocks. This new method can provide geophysical and geological researchers with some new information to more accurately identify the properties of formation fluids.
Czasopismo
Rocznik
Tom
Strony
1753--1768
Opis fizyczny
Bibliogr. 44 poz.
Twórcy
autor
- Key Laboratory for Evolution of Past Life and Environment in Northeast Asia, Jilin University, Ministry of Education, Changchun 130026, China
- College of Earth Sciences, Jilin University, Changchun 130021, Jilin, China
autor
- Key Laboratory for Evolution of Past Life and Environment in Northeast Asia, Jilin University, Ministry of Education, Changchun 130026, China
- College of Earth Sciences, Jilin University, Changchun 130021, Jilin, China
autor
- College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, Jilin, China, wangzw@jlu.edu.cn
autor
- College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, Jilin, China
autor
- College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, Jilin, China
autor
- College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, Jilin, China
autor
- College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, Jilin, China
Bibliografia
- 1. Abudeif AM, Attia MM, Radwan AE (2016) New simulation technique to estimate the hydrocarbon type for the two untested members of Belayim Formation in the absence of pressure data, Badri Field, Gulf of Suez. Egypt Arab J Geosci 9(3):21
- 2. Anifowose F, Abdulraheem A, Al-Shuhail A (2019) A parametric study of machine learning techniques in petroleum reservoir permeability prediction by integrating seismic attributes and wireline data. J Petrol Sci Eng 176:762–774
- 3. Aplin AC, Macquaker JHS (2011) Mudstone diversity: origin and implications for source, seal, and reservoir properties in petroleum systems. AAPG Bull 95(12):2031–2059
- 4. Archie GE (1942) The electrical resistivity log as an aid in determining some reservoir characteristics. Trans AIME 146(1):54–61
- 5. Bai Z, Tan MJ, Li GR, Shi YJ (2019) Analysis of low-resistivity oil pay and fluid typing method of Chang 8(1) Member, Yanchang Formation in Huanxian area, Ordos Basin, China. J Petrol Sci Eng 175:1099–1111
- 6. Basheer IA, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods 43(1):3–31
- 7. Chen BT (2010) Application of EMD decomposition combined with time-frequency analysis in array acoustic signal. In: Master's thesis, Jilin University
- 8. Chen J (2006. Hilbert-Huang transform and its application in signal processing. In: Master's thesis, Dalian University of Technology
- 9. Choi HI, Williams WJ (1989) Improved time-frequency representation of multicomponent signals using exponential kernels. IEEE Trans Acoust Speech Signal Process 37(6):862–871
- 10. Clavier C, Heim A, Scala C, (1976) Effect of pyrite on resistivity and other logging measurements. In: SPWLA 17th annual logging symposium, society of petrophysicists and well-log analysts
- 11. Cohen L (1966) Generalized phase-space distribution functions. J Math Phys 7(5):781–786
- 12. Das B, Chatterjee R (2018) Well log data analysis for lithology and fluid identification in Krishna-Godavari Basin. India Arab J Geosci 11(10):231
- 13. Fan XQ, Wang GW, Dai QQ, Li YF, Zhang FS, He ZB, Li QB (2019) Using image logs to identify fluid types in tight carbonate reservoirs via apparent formation water resistivity spectrum. J Petrol Sci Eng 178:937–947
- 14. Flandrin P, Rilling G, Goncalves P (2004) Empirical mode decomposition as a filter bank. IEEE Signal Process Lett 11(2):112–114
- 15. Huang NE, Zheng S, Steven R, (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear non-stationary time Series Analysis. The Royal Society press, London
- 16. Lai J, Wang GW, Fan ZY, Chen J, Wang SC, Fan XQ (2017) Sedimentary characterization of a braided delta using well logs, the upper triassic xujiahe formation in central Sichuan Basin, China. J Petrol Sci Eng 154:172–193
- 17. Li H, Misra S (2019) Long short-term memory and variational autoencoder with convolutional neural networks for generating NMR T2 distributions. IEEE Geosci Remote Sens Lett 16(2):192–195
- 18. Li X, Shi Y, Wang L, Hu S (2013) Logging identification and evaluation technique of tight sandstone gas reservoirs: taking Sulige gas field as an example. Nat Gas Geosci 24:62–68
- 19. Liu L, Sun SZ, Yu HY, Yue XT, Zhang D (2016) A modified Fuzzy C-Means (FCM) clustering algorithm and its application on carbonate fluid identification. J Appl Geophys 129:28–35
- 20. Ning QQ, Wang ZW, Yu Y, Xu FH, Wu HP (2020) Comparison of time-frequency analysis methods for acoustic full waveform based on empirical mode decomposition. Geophys Prosp Petrol 59(2):303–316
- 21. Ou WM, Wang ZW, Ning QQ, Yu Y, Xu FH (2019) Numerical simulation of borehole Stoneley wave reflection by a fracture based on variable grid spacing method. Acta Geophys 67(4):1119–1129
- 22. Poupon A, Loy ME, Tixier MP (1954) A contribution to electrical log interpretation in shaly sands. J Petrol Technol 6(06):27–34
- 23. Ramamoorthy R, Murphy III WF (1998) Fluid identification through dynamic modulus decomposition in carbonate reservoirs. In: SPWLA 39th annual logging symposium, society of petrophysicists and well-log analysts
- 24. Rong F, Chen N, Guo CJ (2018) A new method for estimating the speed curve of EEMD-HHT combined with time-frequency rearrangement. Vib Shock 37(22):81–87
- 25. Serra O (1986) Fundamentals of well-log interpretation. Elsevier Science Publishing Company Inc., New York
- 26. Sharma R, Prasad M (2009) Characterization of Heterogeneities in Carbonates. In: SEG technical program expanded abstracts. Society of Exploration Geophysicists, pp 2149–2154
- 27. Shedid SA, Saad MA (2017) Comparison and sensitivity analysis of water saturation models in shaly sandstone reservoirs using well logging data. J Petrol Sci Eng 156:536–545
- 28. Shokir EM, Alsughayer AA, Al-Ateeq A (2006) Permeability estimation from well log responses. J Can Pet Technol 45(11):41–46
- 29. Simandous P (1963) Dielectric measurements on porous media: application to the measurement of water saturations: study of the behaviour of argillaceous formations. Revue De L’institute Francais Du Petrol 18(S1):193–215
- 30. Sun B, Dunn KJ (2005) A global inversion method for multi-dimensional NMR logging. J Magn Reson 172(1):152–160
- 31. Sun B (2007) In situ fluid typing and quantification with 1D and 2D NMR logging. Magn Reson Imaging 25(4):521–524
- 32. Tan MJ, Bai Y, Zhang HT, Li GR, Wei XP, Wang AD (2020) Fluid typing in tight sandstone from wireline logs using classification committee machine. Fuel, p 271
- 33. Tan MJ, Zou YL, Zhou CC (2013) A new inversion method for (T-2, D) 2D NMR logging and fluid typing. Comput Geosci 51:366–380
- 34. Vardian M, Nasriani HR, Faghihi R, Vardian A, Jowkar S (2016) Porosity and permeability prediction from well logs using an adaptive neuro-fuzzy inference system in a naturally fractured gas-condensate reservoir. Energy Sources Part a Recovery Util Environ Effects 38(3):435–441
- 35. Wang WH, Wang PJ, Wang ZW, Xiang M, Liu JH (2020) Identifying the lithology of volcanic rocks by using the time-frequency features of array acoustic logging data. Interpret J Subsurface Char 8(3):Sl127-Sl136
- 36. Wang ZW, Liu JH, Nie CY (2008) Time-frequency analysis of array acoustic logging waveform signal based on Hilbert-Huang Transform. Earth Sci J China Univ Geosci 33(3):387–392
- 37. Wang ZW, Liu JH, Yue CW, Li XC, Li CC (2009) The filtering characteristic of HHT and its application in acoustic log waveform signal processing. Appl Geophys 6(1):8–16
- 38. Wang ZW, Wang XL, Xiang M, Liu JH, Zhang XA, Yang C (2012) Reservoir information extraction using a fractional fourier transform and a smooth pseudo Wigner-Ville distribution. Appl Geophys 9(4):391–400
- 39. White JE, Zechman RE (1968) Computed response of an acoustic logging tool. Geophysics 33(2):302–310
- 40. Wood DA (2020) Predicting porosity, permeability and water saturation applying an optimized nearest-neighbour, machine-learning and data-mining network of well-log data. J Petrol Sci Eng 184:1–17
- 41. Xiang M, Wang ZW, Qi XH (2020) Comparison and research of multidimensional analysis for array acoustic logging in fractured formations. Interpretation 8(3):SL89-SL102
- 42. Xu FH, Wang ZW, Liu JH (2018) Acoustic logging information extraction and fractural volcanic formation characteristics based on empirical mode decomposition. Geophysical Prospecting for Petroleum 57(06):936–943
- 43. Zeng LB, Su H, Tang XM, Peng YM, Gong L (2013) Fractured tight sandstone oil and gas reservoirs: a new play type in the Dongpu depression, Bohai Bay Basin. China Aapg Bull 97(3):363–377
- 44. Zhang C, Pan GC (2011) Application of probabilistic neural network technique in identifying low porosity and low permeability gas-layers in logging data, Information Technology for Manufacturing Systems II, Applied Mechanics and Materials. Trans Tech Publications Ltd., Durnten-Zurich, pp 58–60
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-ec6bc2d8-c073-4822-9bc6-cefe1486c227