TEM-function explained

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]

TEM = (k k _{r})/(\phi \mu)

in which

k is the absolute permeability, k _{r} 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.

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 an extension of RQI (rock quality index) for single-phase systems.

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

Averagekr = (\sum ^{n TEM} _{i})/(\sum ^{n\left( (k)/(\phi \mu)} _{i =1} \right) _{i}) = (\sum ^{n\left( (k k _{r})/(\phi \mu)} _{i =1} \right) _{i})/(\sum ^{n\left( (k)/(\phi \mu)} _{i =1} \right) _{i})

See also

• Lak wettability index
• USBM wettability index

Notes and 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. 10.1016/j.petrol.2019.04.044. 149962022.
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. 5. 3. 210–226. 10.1016/j.ptlrs.2020.06.003. free.
3. Mirzaei-Paiaman. A.. 2019. New Concept of Dynamic Rock Typing and Necessity of Modifying Current Reservoir Simulators. SPE Review London. 6 August 2020. 7–10.
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. 6. 16–25. 10.1016/j.ptlrs.2020.08.001. free.
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. 10.1016/j.petrol.2019.106209. 198327827.
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. 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. 10.1016/j.petrol.2019.106341. 202080296.
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. 10.1016/j.petrol.2019.06.039. 197095683.
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. 10.1016/j.petrol.2020.107022. 214481575.
10. Book: Huang. R.. 2020 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS). Research on Dynamic Simulation System of Multidimensional Reservoirs. 96–99. 2020. 10.1109/ICPICS50287.2020.9202339. 978-1-7281-9874-3. 221914057.