Model and method of permeability evaluation based on mud invasion effects
Zhou Feng1,2, Hu Xiang-Yun2, Meng Qing-Xin3, Hu Xu-Dong2, and Liu Zhi-Yuan4
1. School of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan), Wuhan 430074, China.
2. Institute of Geophysics and Geomatics, China University of Geosciences (Wuhan), Wuhan 430074, China.
3. College of Exploration Technology and Engineering, Shijiazhuang University of Economics, Shijiazhuang 050031, China.
4. Research Institute of Petroleum Exploration and Development, SINOPEC, Beijing 100083, China.
Abstract:
The evaluation of permeability in reservoir assessment is a complex problem. Thus, it is difficult to perform direct evaluation permeability with conventional well-logging methods. Considering that reservoir permeability significantly affects mud invasion during drilling, we derive a mathematical model to assess the reservoir permeability based on mud invasion. A numerical model is first used to simulate the process of mud invasion and mud cake growth. Then, based on Darcy’s law, an approximation is derived to associate the depth of mud invasion with reservoir permeability. A mathematical model is constructed to evaluate the reservoir permeability as a function of the mud invasion depth in time-lapse logging. Sensitivity analyses of the reservoir porosity, permeability, and water saturation are performed, and the results suggest that the proposed model and method are well suited for oil layers or oil–water layers of low porosity and low permeability. Numerical simulations using field logging and coring data suggest that the evaluated and assumed permeability data agree, validating the proposed model and method.
Zhou Feng,Hu Xiang-Yun,Meng Qing-Xin et al. Model and method of permeability evaluation based on mud invasion effects[J]. APPLIED GEOPHYSICS, 2015, 12(4): 482-492.
[1]
Ahmed, U., Crary, S. F., and Coates, G. R., 1991, Permeability estimation: the various sources and their interrelationships: Journal of Petroleum Technology, 43(5), 578-587.
[2]
Aziz, K., and Settari, A., 1979, Petroleum reservoir simulation: Applied Science Publishers Ltd., UK.
[3]
Balan, B., Mohaghegh, S., and Ameri, S., 1995, State-of-the-art in permeability determination from well log data: part 1—a comparative study, model development: SPE Eastern Regional Meeting, Morgantown, West Virginia, 33-42.
[4]
Carman, P. C., 1937, Fluid flow through granular beds: Transactions-Institution of Chemical Engineeres, 15, 150-166.
[5]
Chen, Y. H., Coates, R. T., and Chew, W. C., 2002, FDTD modeling and analysis of a broadband antenna suitable for oil-field imaging while drilling: Geoscience and Remote Sensing, 40(2), 434-442.
[6]
Chen, Y. H., and Oristaglio, M., 2002, A modeling study of borehole radar for oil-field applications: Geophysics, 67(5), 1486-1494.
[7]
Chu, Z. H., and Xie, J., 1994, The evalution of the permeability by well logging information: Petroleum exploration and prouction (in Chinese), 21(1), 46-52.
[8]
Delshad, M., and Pope, G. A., 1989, Comparison of the three-phase oil relative permeability models: Transport in Porous Media, 4(1), 59-83.
[9]
Deng, S. G., Li, Z. Q., Fan, Y. R., and Chen, H., 2010, Numerical simulation of mud invasion and its array laterolog response in deviated wells: Chinses Journal of Geophyscis (in Chinese), 53(4), 994-1000.
[10]
Dewan, J. T., and Chenvert, M. E., 1993, Mudcake buildup and invasion in low permeability formations: application to permeability determination by measurement while drilling: SPWLA 34th Annual Logging Symposium, Calgary, Alberta, 1-24.
[11]
Heigl, W. M., and Peeters, M., 2005, Can we obtain invasion depth with directional borehole radar?: Petrophysics, 46(1), 52-61.
[12]
Huang, Z. H., Shimeld, J., Williamson, M., and Katsube, J., 1996, Permeability prediction with artificial neural network modeling in the Venture gas field, offshore eastern Canada: Geophyiscs, 61(2), 422-436.
[13]
Larson, R. G., Scriven, L. E., and Davis, H. T., 1981, Percolation theory of two phase flow in porous media: Chemical Engineering Science, 36(1), 57-73.
[14]
Leverett, M. C., 1941, Capillary behavior in porous solids: Transactions of the American Institute of Mining, Metallurgical and Petroleum Engineers, 142, 152-169.
[15]
Liang, H. Y., 2013, Design of oil drilling and receiving antenna and study on the characteristics of electromagnetic wave transmission: Master Thesis, University of Electronic Science and Technology of China, Chengdu.
[16]
Liu, S. X., and Sato, M., 2002, Electromagnetic logging technique based on borehole radar: IEEE Transaction of Geoscience and Remote Sensing, 40(9), 2083-2092.
[17]
Mohaghegh, S., 2000, Virtual-intelligence applications in petroleum engineering: part 1—artificial neural networks: Journal of Petroleum Technology, 52(9), 64-72.
[18]
Salazar, J., and Torres-Verdín, C., 2008, Quantitative comparison of processes of oil- and water-based mud-filtrate invasion and corresponding effects on borehole resistivity measurements: Geophysics, 74(1), E57-E73.
[19]
Wang, J. H., Yan, J. N., Zheng, M., Feng, J., and Feng, G. B., 2009, Prediction model for invasion radius of solids and filtrate in drilling fluids: Acta Petrolei Sinica (in Chinese), 30(6), 923-926.
[20]
Wu, J. H., Torres-Verdín, C., Sepehrnoori, K., and Proett, M. A., 2005, The influence of water-base mud properties and petrophysical parameters on mudcake growth, filtrate invasion, and formation pressure: Petrophysics, 46(1), 14-32.
[21]
Yao, C. Y., and Holditch, S. A., 1993, Estimating permeability profiles using core and log data: SPE Eastern Regional Meeting, Pittsburgh, Pennsylvania, 317-322.
[22]
Yu, B. M., and Cheng, P., 2002, A fractal permeability model for bi-dispersed porous media: International Journal of Heat and Mass Transfer, 45(14), 2983-2993.