Permeability Estimation from Stoneley Waves in Carbonate Reservoirs

Permeability is one of the petrophysical properties of oil and gas reservoirs and is defined as the ability of rock to transmit fluids through the porous media. After exploration of any reservoir, permeability information is necessary to optimize the well completion method, oil and gas production and field development. Permeability is determined by both direct and indirect methods. Direct methods are core analysis, well testing, and modular dynamic tester (MDT) and the indirect method is using well logging data such as nuclear magnetic resonance (NMR) and porosity. Determination of permeability from the Stoneley slowness is one of the indirect and continuous methods in the whole well-bore and has been chosen as the goal of this study. The result of this correlation has been plotted against other well logging data and there is a very good match between this result and other petrophysical properties. Due to the complex nature of permeability in carbonate reservoirs, most of the time there is not a good match between this parameter and other petrophysical properties. This study has been conducted on the data of a single well and correlation has been determined. The results show that in calculation of permeability from Stoneley waves, the effective parameters are porosity, lithology, Stoneley slowness and accuracy of the MDT tool. For more precise correlation in a reservoir or a specific geological area, more data from other wells or reservoirs are necessary.

Permeability Estimation from Stoneley Waves in Carbonate Reservoirs

Permeability is one of the petrophysical properties of oil and gas reservoirs and is defined as the ability of rock to transmit fluids through the porous media. After exploration of any reservoir, permeability information is necessary to optimize the well completion method, oil and gas production and field development. Permeability is determined by both direct and indirect methods. Direct methods are core analysis, well testing, and modular dynamic tester (MDT) and the indirect method is using well logging data such as nuclear magnetic resonance (NMR) and porosity. Determination of permeability from the Stoneley slowness is one of the indirect and continuous methods in the whole well-bore and has been chosen as the goal of this study. The result of this correlation has been plotted against other well logging data and there is a very good match between this result and other petrophysical properties. Due to the complex nature of permeability in carbonate reservoirs, most of the time there is not a good match between this parameter and other petrophysical properties. This study has been conducted on the data of a single well and correlation has been determined. The results show that in calculation of permeability from Stoneley waves, the effective parameters are porosity, lithology, Stoneley slowness and accuracy of the MDT tool. For more precise correlation in a reservoir or a specific geological area, more data from other wells or reservoirs are necessary.

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  • Abbott, B., Abolins, M., Abramov, V., Acharya, B., Adams, D., Adams, M. & Anderson, E. (2000). The bb production cross section and angular correlations in pp collisions at s=1.8 TeV. Physics Letters B, 487(3-4), 264-272. https://doi.org/10.1016/S0370-2693(00)00844-3
  • Ahmed, U. ,Crary, S. & Coates, G. (1991). Permeability estimation: the various sources and their interrelationships. Journal of Petroleum Technology, 43(05), 578-587.
  • Al-Adani, N. & Al-Khatib, H. (2008). The Identification of Natural Fractures in Inclined Highly Fractured Formation. 2008 CSPG CSEG CWLS Convention, Calgary, Canada, May 12-15, 2008.
  • Al-Adani, N. & Barati, A. (2003). New hydraulic unit permeability approach with DSI. SPWLA 9th Formation Evaluation Symposium of Japan (pp. 25–26).
  • Biot, M. A. (1962). Mechanics of deformation and acoustic propagation in porous media. Journal of Applied Physics, 33(4), 1482-1498.
  • Burchette, T. P. (2012). Carbonate rocks and petroleum reservoirs: a geological perspective from the industry. Geological Society, London, Special Publications, 370(1), 17-37.‏
  • Brie, A., Endo, T., Johnson, D. & Pampuri, F. (2000). Quantitative formation permeability evaluation from Stoneley waves. SPE Reservoir Evaluation & Engineering, 3(02), 109-117.
  • Doodran, R. J., Khakmardan, S., Shirazi, A. & Shirazy, A. (2020). Minimalization of Ash from Iranian Gilsonite by Froth Flotation. Journal of Minerals and Materials Characterization and Engineering, 9(1), 1-13.
  • Guan, W., Hu, H. & Wang, Z. (2013). Permeability inversion from low‐frequency seismoelectric logs in fluid‐saturated porous formations. Geophysical Prospecting, 61(1), 120-133.
  • Hodavand, M. & Moradzadeh, A., (2007). Estimation of reservoir permeability by studying Stoneley waves [Master's Thesis]. Shahrood University of Technology, Iran
  • Jafari, S., Mashohor S., Ramli A. R., & Marhaban M. H. (2012). Expert Pruning Based on Genetic Algorithm in Regression Problems. In: J.S. Pan, S. M.Chen & Nguyen N.T. (Eds.), Intelligent Information and Database Systems. ACIIDS 2012. Lecture Notes in Computer Science, vol 7198. Springer, Berlin, Heidelberg.
  • Khakmardan, S., Doodran, R. J., Shirazy, A., Shirazi, A. & Mozaffari, E. (2020). Evaluation of Chromite Recovery from Shaking Table Tailings by Magnetic Separation Method. Open Journal of Geology, 10(12), 1153-1163.
  • Khakmardan, S., Shirazi, A., Shirazy, A. & Hosseingholi, H. (2018). Copper oxide ore leaching ability and cementation behavior, mesgaran deposit in Iran. Open Journal of Geology, 8(09), 841.
  • Mosalman-nejad.H, Rezaei. m, Dehghanzadeh. m, (2008), Quantitative permeability evaluation using acoustic waves and comparison with permeability from NMR and core analysis (Kangan Formation in South Pars field). The first congress of Iranian Petroleum Engineering. 106-122
  • Neasham, J. W. (1977). The morphology of dispersed clay in sandstone reservoirs and its effect on sandstone shaliness, pore space and fluid flow properties. 52nd Annual Fall Technical Conference and Exhibition of the Society of Petroleum Engineers of AIME, Denver, Colorado.
  • Ren, X., Zhao, Y., Deng, Q., Kang, J., Li, D. & Wang, D. (2016). A relation of hydraulic conductivity—void ratio for soils based on Kozeny-Carman equation. Engineering Geology, 213, 89-97.
  • Rosenbaum, J. H. (1974). Synthetic microseismograms: Logging in porous formations. Geophysics, 39(1), 14-32.
  • Shirazi, A., Hezarkhani, A.& Shirazy, A., (2018a). Exploration Geochemistry Data-Application for Cu Anomaly Separation Based On Classical and Modern Statistical Methods in South Khorasan, Iran. International Journal of Science and Engineering Applications, 7, 39-44.
  • Shirazi, A., Hezarkhani, A. & Shirazy, A., (2018b). Remote sensing studies for mapping of iron oxide regions, South of Kerman, Iran. International Journal of Science and Engineering Applications, 7(4), 45-51.
  • Shirazi, A., Shirazy, A. & Karami, J. (2018c). Remote sensing to identify copper alterations and promising regions, Sarbishe, South Khorasan, Iran. International Journal of Geology and Earth Sciences, 4(2), 36-52.
  • Shirazi, A., Shirazy, A., Saki, S. & Hezarkhani, A. (2018d). Geostatistics studies and geochemical modeling based on core data, sheytoor iron deposit, Iran. Journal of Geological Resource and Engineering, 6, 124-133.
  • Shirazy, A., Shirazi, A., Heidarlaki, S. & Ziaii, M. (2018e). Exploratory Remote Sensing Studies to Determine the Mineralization Zones around the Zarshuran Gold Mine. International Journal of Science and Engineering Applications, 7(9), 274-279.
  • Shirazy, A., Shirazi, A., Ferdossi, M. H. & Ziaii, M. (2019). Geochemical and geostatistical studies for estimating gold grade in tarq prospect area by k-means clustering method. Open Journal of Geology, 9(6), 306-326.
  • Shirazy, A., Ziaii, M. & Hezarkhani, A. (2020a). Geochemical Behavior Investigation Based on K-means and Artificial Neural Network Prediction for Copper, in Kivi region, Ardabil province, IRAN. Iranian Journal of Mining Engineering, 14(45), 96-112.
  • Shirazy, A., Ziaii, M., Hezarkhani, A. & Timkin, T. (2020b). Geostatistical and remote sensing studies to identify high metallogenic potential regions in the Kivi area of Iran. Minerals, 10(10), 869.
  • Shirazy, A., Shirazy, A. & Nazerian, H. (2021a). Application of Remote Sensing in Earth Sciences–A Review. International Journal of Science and Engineering Applications 10(5), 45-51.
  • Shirazy, A., Shirazi, A., Nazerian, H., & Khayer, K. (2021b). Geophysical study: Estimation of deposit depth using gravimetric data and Euler method (Jalalabad iron mine, kerman province of IRAN). Open Journal of Geology, 11, 340-355.
  • Sun, Y. & Han, J. (2012), Mining heterogeneous information networks: principles and methodologies. Synthesis Lectures on Data Mining and Knowledge Discovery, 3(2), 1-159.
  • Tang, X. & Cheng, C.-H. (1996). Fast inversion of formation permeability from Stoneley wave logs using a simplified Biot-Rosenbaum model. Geophysics, 61(3), 639-645.
  • Timur, A. (1968). An investigation of permeability, porosity, & residual water saturation relationships for sandstone reservoirs. The Log Analyst, 9, 3-5.
  • Williams, J. R., Jones, C. A. & Dyke, P. T. (1984). A modeling approach to determining the relationship between erosion and soil productivity. Transactions of the ASAE, 27(1), 129-0144.
  • Zemanek, J., Angona, F., Williams, D. & Caldwell, R. L. (1984). Continuous acoustic shear wave logging. SPWLA 25th Annual Logging Symposium.