Two techniques of preprocessing data from core plugs have been investigated to enhance the quality of synthetic permeability estimation from conventional logs by use of artificial neural networks (ANNs). A first technique consists of “cleaning” the core-plug data set by removing the measurements deemed log-incompatible (i.e., those from plugs corresponding to log measurements affected by shoulder-bed effect or layers with thickness below the vertical log resolution). The second technique relies on building high-resolution digital models of cored intervals by use of a process-oriented-modeling (POM) approach—the core model is populated with permeability values from core plugs and then upscaled to a log-equivalent support volume.

Synthetic permeability curves estimated with these techniques have been compared to synthetic permeability curves estimated without core-data preprocessing and to permeability estimated directly from core plugs and properly calibrated permeability curves from a nuclear magnetic resonance (NMR) log tool in a turbidite reservoir, the ground truth value being represented by actual dynamic data. Results highlight that core-to-log scale effects play a major role in the permeability estimation from conventional logs and show that the proposed preprocessing techniques can be effective in improving permeability prediction, because they significantly reduce cross-scaling problems related to the differences in support volumes.

Strengths and weaknesses of the two preprocessing approaches also have been compared. The first technique is faster, but its application is strongly constrained by the statistical and geological representativeness of the selected data set. This is because some lithologies go underrepresented so as to question the use of estimation tools like ANNs. Conversely, the POM preprocessing technique is more time-consuming and needs detailed core descriptions, but has the great advantage of supplying—starting from core data only—a reliable permeability curve that retains its validity at the log scale.

Mauro Cozzi, Livio Ruvo, SPE, Paolo Scaglioni, and Anna Maria Lyne, Eni E&P

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