Commenced in January 2007
Paper Count: 30319
Visualization of Sediment Thickness Variation for Sea Bed Logging using Spline Interpolation
Abstract:This paper discusses on the use of Spline Interpolation and Mean Square Error (MSE) as tools to process data acquired from the developed simulator that shall replicate sea bed logging environment. Sea bed logging (SBL) is a new technique that uses marine controlled source electromagnetic (CSEM) sounding technique and is proven to be very successful in detecting and characterizing hydrocarbon reservoirs in deep water area by using resistivity contrasts. It uses very low frequency of 0.1Hz to 10 Hz to obtain greater wavelength. In this work the in house built simulator was used and was provided with predefined parameters and the transmitted frequency was varied for sediment thickness of 1000m to 4000m for environment with and without hydrocarbon. From series of simulations, synthetics data were generated. These data were interpolated using Spline interpolation technique (degree of three) and mean square error (MSE) were calculated between original data and interpolated data. Comparisons were made by studying the trends and relationship between frequency and sediment thickness based on the MSE calculated. It was found that the MSE was on increasing trends in the set up that has the presence of hydrocarbon in the setting than the one without. The MSE was also on decreasing trends as sediment thickness was increased and with higher transmitted frequency.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1075090Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1332
 E. N. Kong, H. Westerdhal. Seabed Logging: A possible direct hydrocarbon for deepsea prospects using EM energy. Oslo :Oil Gas Journal, 2002. - May 13, 2002 edition.
 T. Eidesmo, et al. Sea Bed Logging (SBL), A New Method for Remote and Direct Identification of Hydrocarbon Filled Layers in Deepwater Areas using Controlled Source Electromagnetic Sounding, Technical Article, First Break Volume 20, 2002, p. 144-152.
 S. Constable,, L.J.Srnka,An Introduction to Marine Controlled Source Electromagnetic Methods for Hydrocarbon Exploration, Geophysics 72, no 2, 2007,WA3-WA12
 Dirk Smit, Pal R. Wood, Experience is crucial to expanding CSEM use, World Oil, September 2006, pg 37-43.
 S. Ellingsrud, T. Eidesmo, M. C. Sinha, L.M. MacGregor, S. C. Constable. Remote Sensing of Hydrocarbon Layers by Sea Bed Logging (SBL): Results from a Cruise Offshore Angola, Leading Edge 20(10), 2002, pp 972-982.
 Anwar Bhuiyan, Tor wicklund, Stale Johansen, High Resistivity Anomalies at Modgunn Arch in the Norwegian Sea, Technical Article, first break volume 24, January 2006
 N.O. Sadiku, "Numerical methods in Electromagnetics", second edition, Mathew, Boca Raton London New York Washington, D.C. (2001).
 Cox, C.S. Constable, S.C., Chave, A.D, Webb S.C.Controlled-source Electromagnetic Sounding of the oceanic Lithosphere, Nature Magazine, 1986, 320, pp 52-54.
 Evan S Um, David L Alumbaugh, Marine CSEM Methods on the Physics of the Marine CSEM Method, Geophys. Res. Vol.72, No. 2, pp. 13 - 18, 2007.
 N Nasir, A Shafie, H Daud, H M Zaid, N Yahya, M N Akhtar, M Khasif, Magnitude Versus Offset (MVO) Study with EM Transmitter in Different Resistive Medium, Journal of Applied Sciences 11 (7), pp. 1309-1314, 2011
 Ulaby Fawwaz T. Electromagnetics for Engineers. New Jersey : Pearson Education, 2005.
 L.M.MacGregor, M.Tompkins, Imaging Hyrocarbon Reservoirs using Marine Controlled-Source Electromagnetic Sounding, in Offshore Technology Conference, 2-5May 2005, paper OTC 17163.
 Daud, H., Yahya, N., Asirvadam, V. Development of EM simulator for sea bed logging applications using MATLAB Indian Journal of Marine Sciences 40 (2), pp. 267-274, 2011
 Sky McKinley, Megan Levine, Cubic Spline Interpolation, available online at http://online.redwoods.cc.ca.us/instruct/darnold/laproj/Fall98/SkyMeg/Proj. PDF (Accessed on 30/08/2010)
 C. de Boor, A Practical Guide to Splines, 1978, Springer-verlag, New York.
 JM. Unser, Splines: A Perfect Fit for Signal and Image Processing, 1999, IEEE Signal Processing Magazine, Vol 16, pp 22-38
 Panayiotis Foteinopoulos, Cubic spline interpolation to develop contours of large reservoirs and evaluate area and volume, 2009, Journal of Advances in Engineering Software 40, pp 23-29.
 M Sarfaz, Malik Zawwar Hussain, Data visualization using rational spline interpolation, Journal of Computational and Applied Mathematics 189 (2006) pgp 513-525.
 JM. Unser, Splines and Wavelets: New Perspectives for Pattern Recognition, 2003, pp 244-248, Springer-Verlag Berlin Heidelberg.
 E.L. Lehmann; Casella, George (1998). Theory of Point Estimation (2nd ed.). New York: Springer