Multi-resolution graph-based clustering analysis for lithofacies identification from well log data: Case study of intraplatform bank gas fields, Amu Darya Basin
Tian Yu1,2, Xu Hong3, Zhang Xing-Yang2, Wang Hong-Jun2, Guo Tong-Cui2, Zhang Liang-Jie2, and Gong Xing-Lin4
1. College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China.
2. Research Institute of Petroleum Exploration and Development, CNPC, Beijing 100083, China.
3. China National Oil and Gas Exploration and Development Corporation, Beijing 100034, China.
4. Amu Darya Gas Company, CNPC (Turkmenistan), Ashkhabad 744036, Turkmenistan.
Abstract：
In this study, we used the multi-resolution graph-based clustering (MRGC) method for determining the electrofacies (EF) and lithofacies (LF) from well log data obtained from the intraplatform bank gas fields located in the Amu Darya Basin. The MRGC could automatically determine the optimal number of clusters without prior knowledge about the structure or cluster numbers of the analyzed data set and allowed the users to control the level of detail actually needed to define the EF. Based on the LF identification and successful EF calibration using core data, an MRGC EF partition model including five clusters and a quantitative LF interpretation chart were constructed. The EF clusters 1 to 5 were interpreted as lagoon, anhydrite flat, interbank, low-energy bank, and high-energy bank, and the coincidence rate in the cored interval could reach 85%. We concluded that the MRGC could be accurately applied to predict the LF in non-cored but logged wells. Therefore, continuous EF clusters were partitioned and corresponding LF were interpreted, and the distribution and petrophysical characteristics of different LF were analyzed in the framework of sequence stratigraphy.
. Multi-resolution graph-based clustering analysis for lithofacies identification from well log data: Case study of intraplatform bank gas fields, Amu Darya Basin[J]. APPLIED GEOPHYSICS, 2016, 13(4): 598-607.
[1]
Ajil, K. S., Thapliyal, P. K., Shukla, M. V., Pal, P. K., Joshi, P. C., and Navalgund, R. R., 2010, A new technique for temperature and humidity profile retrieval from infrared-sounder observations using the adaptive neuro-fuzzy inference system: IEEE Transactions on Geoscience and Remote Sensing, 48(4), 1650−1659.
[2]
Alizadeh, B., Najjar, S., and Kadkhodaie-Ilkhchi, A., 2012, Artificial neural network modeling and cluster analysis for organic facies and burial history estimation using well log data: A case study of the South Pars Gas Field, Persian Gulf, Iran: Computers & Geosciences, 45(4), 261−269.
[3]
Asante, J., and Kreamer, D., 2015, A new approach to identify recharge areas in the Lower Virgin River Basin and surrounding basins by multivariate statistics: Mathematical Geosciences, 47(7), 819−842.
[4]
Canet, C., Arana, L., González-Partida, E., Pi, T., Prol-Ledesma, R. M., and Franco, S. I., 2010, A statistics-based method for the short-wave infrared spectral analysis of altered rocks: An example from the Acoculco Caldera, Eastern Trans-Mexican Volcanic Belt: Journal of Geochemical Exploration, 105(1−2), 1−10.
[5]
Chai, H., Li, N., Xiao, C. W., Liu, X. L., Li, D. L., Wang, C. Z., and Wu, D. C., 2009, Automatic discrimination of sedimentary facies and lithologies in reef-bank reservoirs using borehole image logs: Applied Geophysics, 6(1), 17−29.
[6]
Fiedler, M., 1973, Algebraic connectivity of graphs: Czechoslovak Mathematical Journal, 23(2), 298−305.
[7]
Fukunaga, K., and Hostetler, L. D., 1975, The estimation of the gradient of a density function, with applications in pattern recognition: IEEE Transactions on Information Theory, 21(1), 32−40.
[8]
Gillis, N., Kuang, D., and Park, H., 2015, Hierarchical clustering of hyperspectral images using rank-two nonnegative matrix factorization: IEEE Transactions on Geoscience & Remote Sensing, 53(4), 2066−2078.
[9]
Hatampour, A., Schaffie, M., and Jafari, S., 2015, Hydraulic flow units, depositional facies and pore type of Kangan and Dalan Formations, South Pars Gas Field, Iran: Journal of Natural Gas Science and Engineering, 23, 171−183.
[10]
Jain, A. K., Murty, M. N., and Flynn, P. J., 1999, Data clustering: A review: ACM Computing Surveys, 31(3), 264−323.
[11]
Khoshbakht, F., and Mohammadnia, M., 2012, Assessment of clustering methods for predicting permeability in a heterogeneous carbonate reservoir: Journal of Petroleum Science and Technology, 2(2), 50−57.
[12]
Lü, G. X., Liu, H. N., Deng, M. M., Wu, L., Zhang, B. Q., Zhang, X. Y., and Fei, H. Y., 2014, Large scale sub-salt carbonate gas fields exploration and development in the Amu Darya Right Bank Area: Science Press, China, 1−378.
[13]
MacQueen, J., 1967, Some Methods for classification and Analysis of Multivariate Observations, in LeCam, L. M., and Neyman, J., Eds., Proceedings of the 5th Berkeley Symposium on Mathematics Statistic and Probability, Volume 1: Statistics, University of California Press, USA, 281−297.
[14]
Nouri-Taleghani, M., Kadkhodaie-llkhchi, A., and Karimi-Khaledi, M., 2015, Determining hydraulic flow units using a hybrid neural network and multi-resolution graph-based clustering method: Case study from South Pars Gasfield, Iran: Journal of Petroleum Geology, 38(2), 177−191.
[15]
Pabakhsh, M., Ahmadi, K., Riahi, M. A., and Shahri, A. A., 2012, Prediction of PEF and LITH logs using MRGC approach: Life Science Journal, 9(4), 974−982.
[16]
Pantopoulos, G., Vakalas, I., Maravelis, A., and Zelilidis, A., 2013, Statistical analysis of turbidite bed thickness patterns from the Alpine fold and thrust belt of western and southeastern Greece: Sedimentary Geology, 294(2), 37−57.
[17]
Sfidaria, E., Kadkhodaie-Ilkhchib, A., Rahimpour-Bbonaba, H., and Soltania, B., 2014, A hybrid approach for litho-facies characterization in the framework of sequence stratigraphy: A case study from the South Pars gas field, the Persian Gulf basin: Journal of Petroleum Science and Engineering, 121(2), 87−102.
[18]
Tang, H., Meddaugh, W. S., and Toomey N., 2011, Using an artificial-neural-network method to predict carbonate well log facies Successfully: SPE Reservoir Evaluation & Engineering, 14(1), 35−44.
[19]
Tian, Y., Zhang, X. Y., Zhu, G. W., Zhang, L. J., Wu, Lei., Guo, T. C., Zhang, H. W., and Yu, X. W., 2016, Controlling effects of paleogeomorphology on intraplatform shoal reservoirs distribution and gas reservoirs characteristics: Taking intraplatform shoal gas fields of the Amu Darya Basin as examples: Natural Gas Geoscience, 27(2), 320−329.
[20]
Ward, W. O. C., Wilkinson, P. B., Chambers, J. E., Oxby, L. S., and Bai, L., 2014, Distribution-based fuzzy clustering of electrical resistivity tomography images for interface detection: Geophysical Journal International, 197(1), 310−321.
[21]
Whitcomb, K. Z., Ryan, D. P., Gelfand, M. P., and Orden A. V., 2013, Blinking statistics of small clusters of semiconductor nanocrystals: The Journal of Physical Chemistry C, 117(48), 25761−25768.
[22]
Ye, S. J., and Rabiller, P., 2000, A new tool for electro-facies analysis: Multi-Resolution Graph Based Clustering: SPWLA 41st Annual Logging Symposium, Dallas, Texas, USA, Jun 4−7.
2016, Vol. 13 
