TEM-function

In petroleum engineering, TEM (True Effective Mobility), also called TEM-function, is a criterion to characterize dynamic two-phase flow characteristics of rocks (or dynamic rock quality). [1][2][3][4][5][6][7][8][9][10] TEM is a function of relative permeability, porosity, absolute permeability and fluid viscosity, and can be determined for each fluid phase separately. TEM-function has been derived from Darcy's law for multiphase flow. [1]

in which k is the absolute permeability, kr is the relative permeability, φ is the porosity, and μ is the fluid viscosity. Rocks with better fluid dynamics (i.e., experiencing a lower pressure drop in conducting a fluid phase) have higher TEM versus saturation curves. Rocks with lower TEM versus saturation curves resemble low quality systems.[1]

TEM-function in analyzing Relative permeability data is analogous with Leverett J-function in analyzing Capillary pressure data. Furthermore, TEM-function in two-phase flow systems is extension of RQI (Rock Quality Index) for single-phase systems. [1]

Also, TEM-function can be used for averaging relative permeability curves (for each fluid phase, separately (i.e., water, oil, gas, CO2)).[1]

References
  1. Mirzaei-Paiaman, A.; Saboorian-Jooybari, H.; Chen, Z.; Ostadhassan, M. (2019). "New technique of True Effective Mobility (TEM-Function) in dynamic rock typing: Reduction of uncertainties in relative permeability data for reservoir simulation". Petroleum Research. 179: 210–227. doi:10.1016/j.petrol.2019.04.044.
  2. Mirzaei-Paiaman, A.; Asadolahpour, S.R.; Saboorian-Jooybari, H.; Chen, Z.; Ostadhassan, M. (2020). "A new framework for selection of representative samples for special core analysis". Petroleum Research. doi:10.1016/j.ptlrs.2020.06.003.
  3. Mirzaei-Paiaman, A. (2019). "New Concept of Dynamic Rock Typing and Necessity of Modifying Current Reservoir Simulators" (PDF). SPE Review London: 7–10. Retrieved 6 August 2020.
  4. Faramarzi-Palangar, M. (2020). "Investigating dynamic rock quality in two-phase flow systems using TEM-function: A comparative study of different rock typing indices". Petroleum Research. doi:10.1016/j.ptlrs.2020.08.001.
  5. Wang, R. (2019). "Grid density overlapping hierarchical algorithm for clustering of carbonate reservoir rock types: A case from Mishrif Formation of West Qurna-1 oilfield, Iraq". Journal of Petroleum Science and Engineering. 182: 106209. doi:10.1016/j.petrol.2019.106209.
  6. Noorbakhsh, A. (2020). "Field Production Optimization Using Sequential Quadratic Programming (SQP) Algorithm in ESP-Implemented Wells, A Comparison Approach". Journal of Petroleum Science and Technology. Retrieved 6 August 2020.
  7. Nazari, M.H. (2019). "Investigation of factors influencing geological heterogeneity in tight gas carbonates, Permian reservoir of the Persian Gulf". Journal of Petroleum Science and Engineering. 183: 106341. doi:10.1016/j.petrol.2019.106341.
  8. Liu, Y. (2019). "Petrophysical static rock typing for carbonate reservoirs based on mercury injection capillary pressure curves using principal component analysis". Journal of Petroleum Science and Engineering. 181: 106175. doi:10.1016/j.petrol.2019.06.039.
  9. Shakiba, M. (2020). "An experimental investigation of the proportion of mortar components on physical and geomechanical characteristics of unconsolidated artificial reservoir sandstones". Journal of Petroleum Science and Engineering. 189: 107022. doi:10.1016/j.petrol.2020.107022.
  10. Huang, R. (2020). "Research on Dynamic Simulation System of Multidimensional Reservoirs". 2020 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS). doi:10.1109/ICPICS50287.2020.9202339.


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