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In developing a PEP that will be an effective tool in our supply chain management tool kit, it makes sense to split the main document into several smaller independent sections One practical way of doing this is to define the work breakdown structure (WBS) of the document to reflect the split of responsibilities between different parties involved in our supply chain Different suppliers can be assigned responsibility for composing their own part of the plan In the end, the separate documents come together to form the final comprehensive document To a certain extent, this approach is always a compromise between the absolutely fluent readability of the

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The ridge line of this histogram, seen as a surface in the (RR, ) plane is then extracted (see Figure 1117) It is seen that this ridge line is roughly a straight line, that is, tted using least squares minimization in order to obtain an equation of the form = g(RR) = aRR + b The last step is to synthesize an SRMP with shape function s and regularity function g, as explained in the previous section Paths obtained in this way are shown in Figure 1118 Compare this with the graphs shown in Figure 1112, displaying true RR traces 117 Texture segmentation We will now brie y explain how multifractal analysis may be used for texture segmentation We present an application to 1D signals, namely well logs For an application to images, see [MUL 94, SAU 99]

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Figure 1118 Two forged RR intervals based on SRMP (upper curves) and estimated regularity (lower curves)

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The characterization of geological strata with the help of well logs can be used for the interpretation of a sedimentary environment of an area of interest, such as reservoirs Recent progress [SER 87] of electrofacies has enabled us to relate well logs to the sedimentary environment and to extrapolate information coming from the core of any vertical well span Electrofacies predictions are based on multivariate correlations and cluster analysis for which the entry data are conventional well logs (including sonic logs, of density and gamma) as well as the information extracted from the core analysis The microresistivity log (ML) measures the local rock wall resistivity of the wells The measure is obtained by passing an electric current in the rock, to a lateral depth of approximately 1 cm The resistivity varies according to the local porosity function and the connectivity of the pores (normally, the rock is a non-conductor and thus the current passes in the uid contained in the pores) ML contain information not only on the inclination of geological strata but also on the texture of these strata To analyze the irregular variations of ML, we may calculate texture parameters locally and at different depths in the well These can be used to obtain a well segmentation by letting [SAU 97] r(xi ) denote the signal resistivity, where xi are equidistant coordinates which measure the depth in the wells In order to emphasize the vertical variations of r(xi ), a transformed signal s (xi ) is rst de ned s (xi ) = |r(xi+1 ) r(xi )| where > 0 This transformation ampli es the small scales and eliminates any constant component of signal r(xi ) Analysis of well logs from the Oseberg reservoir in the North Sea shows that, typically, a fractal behavior is observed for lengths of about [2 cm, 20 cm] It has been.

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