Permeability evaluation on oil-window shale based on hydraulic flow unit: A new approach

Abstract

Permeability is one of the most important petrophysical properties of shale reservoirs, controlling the fluid flow from the shale matrix to artificial fracture networks, the production and ultimate recovery of shale oil/gas. Various methods have been used to measure this parameter in shales, but no method effectively estimates the permeability of all well intervals due to the complex and heterogeneous pore throat structure of shale. A hydraulic flow unit (HFU) is a correlatable and mappable zone within a reservoir, which is used to subdivide a reservoir into distinct layers based on hydraulic flow properties. From these units, correlations between permeability and porosity can be established. In this study, HFUs were identified and combined with a back propagation neural network to predict the permeability of shale reservoirs in the Dongying Depression, Bohai Bay Basin, China. Well data from three locations were used and subdivided into modeling and validation datasets. The modeling dataset was applied to identify HFUs in the study reservoirs and to train the back propagation neural network models to predict values of porosity and flow zone indicator. Next, a permeability prediction method was established, and its generalization capability was evaluated using the validation dataset. The results identified five HFUs in the shale reservoirs within the Dongying Depression. The correlation between porosity and permeability in each HFU is generally greater than the correlation between the two same variables in the overall core data. The permeability estimation method established in this study effectively and accurately predicts the permeability of shale reservoirs in both cored and un-cored wells. Predicted permeability curves effectively reveal favorable shale oil/gas seepage layers and thus are useful for the exploration and the development of hydrocarbon resources in the Dongying Depression.

Cited as: Zhang, P., Lu, S., Li, J., Zhang, J., Xue, H., Chen, C. Permeability evaluation on oil-window shale based on hydraulic flow unit: A new approach. Advances in Geo-Energy Research, 2018, 2(1): 1-13, doi: 10.26804/ager.2018.01.01

References

Aguilera, R., Aguilera, M.S. The integration of capillary pressures and pickett plots for determination of flow units and reservoir containers. Paper SPE 71725 Presented at the SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, 30 September-3 October, 2001.

Aguilera, R. Flow Units: From conventional to tight-gas to shale-gas to tight-oil to shale-oil reservoirs. SPE Reserv. Eval. Eng. 2014, 17(2): 190-208.

A¨ıfa, T., Baouche, R., Baddari, K. Neuro-fuzzy system to predict permeability and porosity from well log data: A case study of Hassi RMel gas field, Algeria. J. Pet. Sci. Eng. 2014, 123: 217-229.

Al-Ajmi, F.A., Holditch, S.A. Permeability estimation using hydraulic flow units in a central arabia reservoir. Paper SPE 63254 Presented at the SPE Annual Technical Conference and Exhibition, Dallas Texas, USA, 1-4 October, 2000.

Ali, D., Ebrahim, S. Physical properties modeling of reservoirs in Mansuri oil field, Zagros region, Iran. Pet. Explor. Dev. 2016, 43(4): 611-615.

Al-Rbeawi, S., Kadhim, F. The impact of hydraulic flow unit & reservoir quality index on pressure profile and productivity index in multi-segments reservoirs. Petroleum 2017, 3(4): 414-430.

Amaefule, J.O., Altunbay, M., Tiab, D., et al. Enhanced reservoir description: Using core and log data to identify hydraulic (flow) units and predict permeability in uncored intervals/wells. Paper SPE 26436 Presented at the SPE Annual Technical Conference and Exhibition, Houston, Texas, USA, 3-6 October, 1993.

Aminian, K., Ameri, S. Application of artificial neural networks for reservoir characterization with limited data. J. Pet. Sci. Eng. 2005, 49(3): 212-222.

Baziar, S., Tadayoni, M., Nabi-Bidhendi, M., et al. Prediction of permeability in a tight gas reservoir by using three soft computing approaches: A comparative study. J. Nat. Gas Sci. Eng. 2014, 21: 718-724.

Bhattacharya, S., Byrnes, A.P., Watney, W.L., et al. Flow unit modeling and fine-scale predicted permeability validation in Atokan sandstones: Norcan east Kansas. AAPG Bull. 2008, 92(6): 709-732.

Cao, C., Li, T., Shi, J., et al. A new approach for measuring the permeability of shale featuring adsorption and ultra-low permeability. J. Nat. Gas Sci. Eng. 2016, 30: 548-556.

Carman, P.C. Fluid flow through granular beds. Chem. Eng. Res. Des. 1937, 15: 150-166.

Chen, K.G., Chen, X., Zhang, J.H. Combined methods of permeability logging evaluate in glutenite reservoirs: A case study of Badaowan Formation in Karamay Oilfield. Advance in Earth Sciences 2015, 30(7): 773-779. (in Chinese)

Chen, X.J., Yao, G.Q., Cai, J.C., et al. Fractal and multifractal analysis of different hydraulic flow units based on micro-CT images. J. Nat. Gas Sci. Eng. 2017, 48: 145-156.

Chen, X.J., Zhou, Y.F. Applications of digital core analysis and hydraulic flow units in petrophysical characterization. Adv. Geo-Energy Res. 2017, 1(1): 18-30.

Clarkson, C.R., Jensen, J.L., Pedersen, P.K., et al. Innovative methods for flow-unit and pore-structure analyses in a tight siltstone and shale gas reservoir. AAPG Bull. 2012, 96(2): 355-374.

Cronin, M.B., Flemings, P.B., Bhandari, A.R. Dual-permeability microstratigraphy in the Barnett shale. J. Pet. Sci. Eng. 2016, 142: 119-128.

Desouky, S.E.D.M. Predicting permeability in un-cored intervals/wells using hydraulic flow unit approach. J. Can. Pet. Technol. 2005, 44(7): 55-58.

Dou, Z.L., Kaifa, S.K.Y. A study on flow unit model and distribution of remaining oil in fluvial sandstone reservoirs of the Guantao Formation in Gudong oil field. Pet. Explor. Dev. 2000, 27(6): 50-52.

Guo, S.S., Cai, J., Zhou, J., et al. Hydraulic flow unit based permeability characterization and rapid production prediction workflow for an offshore field, South China Sea. Paper SPE 131486 Presented at the International Oil and Gas Conference and Exhibition, Beijing, China, 8-10 June, 2010.

Hamzehie, M.E., Fattahi, M., Najibi, H., et al. Application of artificial neural networks for estimation of solubility of acid gases (H2S and CO2 ) in 32 commonly ionic liquid and amine solutions. J. Nat. Gas Sci. Eng. 2015, 24: 106-114.

Hearn, C.L., Ebanks, W.J., Tye, R.S., et al. Geological factors Influencing reservoir performance of the hartzog draw field, Wyoming. J. Pet. Technol. 1984, 36(8): 1335-1344.

Hornik, K., Stinchcombe, M., White, H. Multilayer feedfor-ward networks are universal approximators. Neural Netw. 1989, 2(5): 359-366.

Jarvie, D.M., Hill, R.J., Ruble, T.E., et al. Unconventional shale-gas systems: The Mississippian Barnett Shale of north-central Texas as one model for thermogenic shale-gas assessment. AAPG Bull. 2007, 91(4): 475-499.

Jennings, J.W., Lucia, F.J. Predicting permeability from well logs in carbonates with a link to geology for interwell permeability mapping. Paper SPE 71336 Presented at the SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, 30 September-3 October, 2001.

Jiao, C.H., Xu, C.H. An approach to permeability prediction based on flow zone index. Well Logging Technology 2006, 30(4): 317-319. (in Chinese)

Kadkhodaie-Ilkhchi, R., Rezaee, R., Moussavi-Harami, R., et al. Analysis of the reservoir electrofacies in the framework of hydraulic flow units in the Whicher Range Field, Perth Basin, Western Australia. J. Pet. Sci. Eng. 2013, 111: 106-120.

Li, J.Q., Lu, S.F., Cai, Y.D., et al. Impact of coal ranks on dynamic gas flow: An experimental investigation. Fuel 2017a, 194: 17-26.

Li, J.Q., Yu, T., Liang, X., et al. Insights on the gas permeability change in porous shale. Adv. Geo-Energy Res. 2017b, 1(2): 69-73.

Lian, C.B., Li, H.L., Qu, F., et al. Prediction of porosity based on BP artificial neural network with well logging data. Natural Gas Geoscience 2006, 17(3): 382-384. (in Chinese)

Nabipour, M., Keshavarz, P. Modeling surface tension of pure refrigerants using feed-forward back-propagation neural networks. Int. J. Refrig. 2017, 75(1): 217-227.

Nelson, P.H. Pore-throat sizes in sandstones, tight sandstones, and shales. AAPG Bull. 2009, 93(3): 329-340.

Nooruddin, H.A., Hossain, M.E. Modified Kozeny-Carmen correlation for enhanced hydraulic flow unit characteri-zation. J. Pet. Sci. Eng. 2011, 80(1): 107-115.

Onuh, H.M., David, O.O., Onuh, C.Y. Modified reservoir quality indicator methodology for improved hydraulic flow unit characterization using the normalized pore throat methodology (Niger delta field as case study). J. Petrol. Explor. Prod. Technol. 2017, 7(2): 409-416.

Orodu, O.D., Tang, Z., Fei, Q. Hydraulic (Flow) Unit Determination and permeability prediction: A case study of block shen-95, Liaohe Oilfield, North-East China. J. Appl. Sci. 2009, 9(10): 1801-1816.

Rahimpour-Bonab, H., Mehrabi, H., Navidtalab, A., et al. Flow unit distribution and reservoir modelling in cretaceous carbonates of the sarvak formation, abteymour oilfield, dezful embayment, SW Iran. J. Pet. Geol. 2012, 35(3): 213-236.

Saemi, M., Ahmadi, M. Integration of genetic algorithm and a coactive neurofuzzy inference system for permeability prediction from well logs data. Transp. Porous Media 2008, 71(3): 273-288.

Sidiq, H., Amin, R., Kennaird, T. The study of relative permeability and residual gas saturation at high pressures and high temperatures. Adv. Geo-Energy Res. 2017, 1(1): 64-68.

Taghavi, A.A., Mφ rk, A., Kazemzadeh, E. Flow unit classification for geological modelling of a heteroge-neous carbonate reservoir: Cretaceous sarvak formation, dehluran field, SW Iran. J. Pet. Geol. 2007, 30(2): 129-146.

Tahmasebi, P., Hezarkhani, A. A fast and independent architecture of artificial neural network for permeability prediction. J. Pet. Sci. Eng. 2012, 86-87: 118-126.

Tiab, D., Donaldson, E.C. Petrophysics Theory and Practice of Measuring Reservoir Rock and Fluid Transport Properties. Oxford, United Kingdom, Elsevier Press, 2012.

Wu, B., Han, S.J., Xiao, J., et al. Error compensation based on BP neural network for airborne laser ranging. Optik 2016, 127(8): 4083-4088.

Xiao, L., Liu, X., Mao, Z.Q., et al. Tight-gas-sand permeability estimation from nuclear-magnetic-resonance (NMR) logs based on the hydraulic-flow-unit (HFU) approach. J. Can. Pet. Technol. 2013, 52(4): 306-314.

Xie, X.N., Fan, Z.H., Liu, X.F., et al. Geochemistry of formation water and its implication on overpressured fluid flow in the Dongying Depression of the Bohaiwan Basin, China. J. Geochem. Explor. 2006, 89(1-3): 432-435.

Yarmohammadi, S., Kadkhodaie-Ilkhchi, A., Rahimpour-Bonab, H., et al. Seismic reservoir characterization of a deep water sandstone reservoir using hydraulic and electrical flow units: A case study from the Shah Deniz gas field, the South Caspian Sea. J. Pet. Sci. Eng. 2014, 118: 52-60.

Yu, B.S. Particularity of shale gas reservoir and its evaluation. Earth Science Frontiers 2012, 19(3): 252-258.

(in Chinese) Zhang, L.P., Bai, G.P., Zhao, Y.Q. Data-processing and recognition of seepage and microseepage anomalies of acid-extractable hydrocarbons in the south slope of the Dongying depression, eastern China. Mar. Pet. Geol. 2014, 57: 385-402.

Zhang, S.W., Zhang, L.Y., Li, Z., et al. Formation conditions of Paleogene shale oil and gas in Jiyang depression. Petroleum Geology and Recovery Efficiency 2012b, 19(6): 1-5. (in Chinese)

Zhang, Y.C., Liu, C., Fan, X.M. Application of multivariate regression analysis method in porosity calculation of volcanic clastic rock. Global Geology 2012a, 31(2): 377-382. (in Chinese)

Zhang, Y.L. Porosity and permeability predictions in sand conglomerate reservoir from conventional well logs. Well Logging Technology 2005, 29(3): 212-215. (in Chinese)

Zhang, Z.J., Du, J.M., Zheng, Q., et al. The porosity interpretation model for the reservoir in Z area of Qaidam Basin. World Well Logging Technology 2016, 211(1): 37-39. (in Chinese)

Zhou, J.Y., Gui, B.W., Li, M., et al. An application of the artificial neural net dominated by lithology to permeability prediction. Acta Petrolei Sinica 2010, 31(6): 311-318. (in Chinese)