Sea Bed Logging Applications: Predicting Hydrocarbon Depth Using Mathematical Equations

Hanita Daud; Radzuan Razali; Vijanth Asirvadam

doi:10.4028/www.scientific.net/AMM.789-790.560

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 789-790Sea Bed Logging Applications: Predicting...

Sea Bed Logging Applications: Predicting Hydrocarbon Depth Using Mathematical Equations

Abstract:

The aim of this research is to develop 1-D mathematical equation that relates Normalized mean Square Error (NMSE) to depth of hydrocarbon (HC) for data processed using cubic spline interpolation in sea bed logging (SBL) application. Simulations were conducted using CST software that replicated real SBL environment to generate synthetic data. Frequency of 0.25Hz was used and sediment thicknesses were varied from 1000m to 3000m and incremented at every 250m. Data collected were interpolated using cubic spline and NMSE were calculated between original and interpolated data. Mathematical equation that relates NMSE (y) and depth of hydrocarbon (x) was constructed in terms of linear, exponential, logarithm and polynomial degree 2, 3, 4 and R² were calculated for each equation. Mathematical equation using exponential function was adopted because it has reasonably high R² and can be inversed easily. Average percentage error was calculated between calculated and measured data to achieve 5 % or lower values. If achievable, this model was accepted as forward mathematical model for SBL. Otherwise, the coefficients were adjusted and the processes repeated until minimum average error was achieved.

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Periodical:

Applied Mechanics and Materials (Volumes 789-790)

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560-565

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https://doi.org/10.4028/www.scientific.net/AMM.789-790.560

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Online since:

September 2015

Authors:

Hanita Daud, Radzuan Razali, Vijanth Asirvadam

Keywords:

Cubic Spline Interpolation, Normalized Mean Square Error, Sea Bed Logging

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References

[1] 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.

Google Scholar

[2] F. Roth, F. A. Maao. Improving Seabed Logging Sensitivity in Shallow Water Through Up-Down Separation. Trondhiem, Norway : EGM 2007 International Workshop, (2007).

DOI: 10.3997/2214-4609-pdb.166.b_op_09

Google Scholar

[3] A. Bhuiyan, T. wicklund, S. Johansen. High Resistivity Anomalies at Modgunn Arch in the Norwegian Sea. Technical Article, first break volume 24, January (2006).

DOI: 10.3997/1365-2397.2006001

Google Scholar

[4] S.E. Johansen, H.E.F. Amundsen, T. Rosten, S. Ellingsurd, T. Eidismo and A.H. Bhuyian. Subsurface Hydrocarbons detected by electromagnetic sounding. First Break, 2005. - Vol. 23.

DOI: 10.3997/1365-2397.2005005

Google Scholar

[5] K.C. Chiadikobi, O.I. Chiaghanam, A.O. Omoboriowo, M.V. Etukudoh, N.A. Okafor. Detection of Hydrocarbon Reservoirs using Controlled Source Electromagnetic (CSEM) method in the BETA field, Deep-water Offshore Niger Delta, Nigeria. International Journal of Science in Emerging Technology, Vol 3, No 1 January, (2012).

Google Scholar

[6] H. Daud, N. Yahya, M.Y. Nayan, V. Sagayan, M. Talib. A scaled experiment for verification of SPLINE interpolation technique for sea bed logging method. IEEE Symposium on Industrial Electronics and Applications, ISIEA 2011, art. no. 6108724 , pp.320-325.

DOI: 10.1109/isiea.2011.6108724

Google Scholar

[7] H. Daud, N. Yahya, M.Y. Nayan, V. Sagayan, M. Talib. A scaled experiment 2 for verification of SPLINE Interpolation Technique for sea bed logging method. 2011 IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2011, art. no. 6190500 , pp.80-85.

DOI: 10.1109/iccsce.2011.6190500

Google Scholar

[8] L. -J. Gelius. Multi-component Processing of Sea Bed Logging Data. Department of Geoscience, University of Oslo, Norway, PIERS ONLINE, Vol. 2, No 6 , (2006).

DOI: 10.2529/piers060822070325

Google Scholar

[9] A. Perry Fischer. New EM technology offerings are growing quickly. WorldOil Vol. 226, No. 6, (2005).

Google Scholar

[10] S. Ellingsrud, T. Eidesmo, M.C. Sinha, L.M. MacGregor, S.C. Constable, 2002, Remote Sensing of Hydrocarbon Layers by Sea Bed Logging (SBL): Results from a Cruise Offshore Angola, Leading Edge 20 (10), pp.972-982.

DOI: 10.1190/1.1518433

Google Scholar

[11] G. Wolberg. Monotonic Cubic Spline Interpolation. Department of Computer Science, City College of New York, CUNY.

Google Scholar

[12] C. Gerald, P. Wheatley, Applied Numerical Analysis, Addison-Wesley, (1994).

Google Scholar