Spatial estimation of transmissivity using artificial neural network

Ground Water

Volumn 37, Issue 3, 1999, Pages 458-464

  Mukhopadhyay, Amitabha ^a  

^a KUWAIT INSTITUTE FOR SCIENTIFIC RESEARCH   (Kuwait)

Author keywords

[No Author keywords available]

Indexed keywords

AQUIFERS; INTERPOLATION; MATHEMATICAL MODELS; NEURAL NETWORKS; PARAMETER ESTIMATION;

TRANSMISSIVITY;

GROUNDWATER FLOW;

AQUIFER; ARTIFICIAL NEURAL NETWORK; GROUNDWATER FLOW; HYDROLOGICAL MODEL; SPATIAL ANALYSIS; TRANSMISSIVITY; WELL;

KUWAIT;

EID: 0032899424     PISSN: 0017467X     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.1745-6584.1999.tb01125.x     Document Type: Article

References (20)

1. Abdul-Aziz, A., and K.V. Wong. 1992. Neural-network approach to the determination of aquifer parameters. Ground Water 30, no. 2: 164-166.
2. Aboufirassi, M., and M.A. Marino. 1984. Cokriging of aquifer transmissivities from field measurements of transmissivity and specific capacity. Mathematical Geology 16, no. 1: 19-34.
3. Ahmed, S., and G. de Marsily. 1987. Comparison of geostatistical methods for estimating transmissivity using data on transmissivity and specific capacity. Water Resources Research 23, no. 9: 1717-1737.
4. Ahmed, S., G. de Marsily, and A. Talbot. 1988. Combined use of hydraulic and electrical properties of an aquifer in a geostatistical estimation of transmissivity. Ground Water 26, no. 1: 78-86.
5. Al-Kaabi, A.O., and W.J. Lee. 1990. Using artificial neural networks to identify the well test interpretation model. SPE paper 20332 presented at the Society of Petroleum Engineers Petroleum Computer Conference, Denver, Colorado, June 25-28.
6. Allain, O.F., and R.N. Horne. 1990. Use of artificial intelligence in well test analysis. Journal of Petroleum Technology 42, no. 3: 342-349.
7. Athichanagorn, S., and R.N. Horne. 1995. Automatic parameter estimation from well test data using artificial neural network. SPE paper 30556 presented at the Society of Petroleum Engineers Annual Technical Conference and Exhibition, Dallas, Texas, October 21-25.
8. California Scientific Software. 1993. Brainmaker Professional: User's Guide and Reference Manual. Nevada City, California: California Scientific Software Press.
9. Cassiani, G., and M. A. Medina Jr. 1997. Incorporating auxiliary geophysical data into ground-water flow parameter estimation. Ground Water 35, no. 1: 79-91.
10. Christensen, S. 1997. On the strategy of estimating regional-scale transmissivity fields. Ground Water 35, no. 1: 131-139.
11. David, M. 1977. Geostatistical Ore Reserve Estimation. Amsterdam, The Netherlands: Elsevier Scientific Publishing Company.
12. Golden Software Inc. 1991. Reference manual: SURFER Version 4. Golden, Colorado: Golden Software Inc.
13. Hantush, M.S., and C.E. Jacob. 1955. Nonsteady radial flow in an infinite leaky aquifer. American Geophysical Union Transactions 36, no. 1: 95-100.
14. Lawrence, J. 1994. Introduction to Neural Networks: Design, Theory, and Applications. Nevada City, California: California Scientific Software Press.
15. Lippmann, R.P. 1987. An introduction to computing with neural nets. Institute of Electrical and Electronic Engineers Acoustic, Speech and Signal Processing Magazine 4, 4-22.
16. May, E.A. Jr., and C.H. Dagli. 1996. A parallel approach to identifying the well-test interpretation model using a neurocomputer. In Proceedings of Applications and Science of Artificial Neural Networks II, Orlando, Florida, April 9-12, Volume 2760, ed. S.K. Rogers and D.W. Ruck, 646-656. Bellingham, Washington: The International Society of Optical Engineering.
17. Mukhopadhyay, A. 1995. Distribution of transmissivity in the Dammam Limestone Formation, Kuwait. Ground Water 33, no. 5: 801-805.
18. Pucci, A.A. Jr., and J.A.E. Murashige. 1987. Applications of universal kriging to an aquifer study in New Jersey. Ground Water 25, no. 6: 672-678.
19. Theis, C.V. 1935. The relationship between the lowering of the piezometric surface and the rate and duration of discharge of a well using ground-water storage. American Geophysical Union Transactions 16, no. 2: 519-524.
20. Vemuri, V. 1988. Artificial neural networks: An introduction. In Artificial Neural Networks: Theoretical Concepts, Computer Society Press Technology Series, ed. V. Vemuri, 1-11. Washington, D.C.: The Computer Society of Institute of Electrical and Electronic Engineers.