Interpolation and fitting the data to produce the interpolating curves or fitting curves are important in Oil and Gas (O&G) industry. Data interpolation is useful for scientific visualization for data interpretation. One of the efficient methods for data interpolation is cubic spline function. In this paper two types of cubic spline will be used for data interpolation. The first one is cubic spline interpolation with 2 C continuity. Meanwhile the second spline is Piecewise Cubic Hermite Spline (PCHIP) with 1 C continuity. Numerical comparison between both cubic splines and linear splines will be discussed in details. From all numerical results, it was indicated that cubic spline gives good results.

The singular value decomposition (SVD) is an effective tool to reconstruct the image approximately towards the original image. This paper will introduce and explores image reconstruction by applying the SVD on gray-scale image. As quality measurements, we used Compression Ratio (CR) and Root-Mean Squared Error (RMSE). The results indicated that for certain images the value of k is smaller than for other images. The value of k is defined as the rank for the closet matrix and the constant integer k can be chosen expectantly less than diagonal matrix n, and the digital image corresponding to outer product expansion, Qk still have very close to the original image.