COMPUTATION OF DREDGED MATERIAL AND SELECTION OF THE BEST KRIGING METHOD

Oladosu S.o (), Ezie V. O. and Ehigiator-Irughe R
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Oladosu S.o: Department of Geomatics, Faculty of Environmental Sciences, University of Benin, P.M.B. 1154, Edo State, Nigeria
Ezie V. O.: Department of Civil Engineering, Faculty of Engineering, University of Benin, P.M.B. 1154, Edo State, Nigeria.
Ehigiator-Irughe R: Department of Geomatics, Faculty of Environmental Sciences, University of Benin, P.M.B. 1154, Edo State, Nigeria

Earth Sciences Malaysia (ESMY), 2023, vol. 7, issue 1, 01-06

Abstract: In this study, it is demanded that a preparatory survey for an oil well location be carried out and the estimated number of dredged materials be calculated at various sections inside the perimeter of an oil mining lease (OML 38) located at Ovhor field, Niger Delta Region, Nigeria. Seven sub-sectional areas initially earmarked for dredging at predetermined depths were delineated for different purposes to accomplish the dredging task. The total sum of the seven sectional areas calculated was 89,321.31m2. Of the seven sub-sectional areas, the volume of dredged material was calculated for only five by multiplying the obtained two-dimensional areas by the designed depth. The two sub-sections exempted from dredging were because depths are appropriate at these locations. The total volume of dredged material, therefore, calculated was 56,630.73m3. Recording and monitoring of tidal observations and analysis complement bathymetry and dredging activities. Due to the rugged terrain, interpolation remains a viable option to predict spatial variability at unsampled dredged lines. To achieve this, different kriging methods were investigated before finally choosing the best. The model that made the most accurate predictions was “Indicator Kriging” with a mean error of 0.000364 and a root-mean-square-standardized error of 0.806. This result is geostatistically acceptable when predicting spatial variability at an un-sampled location. The indicator kriging approach was subsequently used to predict the spatial variability of the un-sampled locations encountered in the study area.

Keywords: Bathymetry; Dredging; GNSS; Kriging; Oil well (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:zib:zbesmy:v:7:y:2023:i:1:p:01-06

DOI: 10.26480/esmy.01.2023.01.06

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