| Popis: |
Two techniques of preprocessing data from core plugs have been investigated to enhance the quality of synthetic permeability estimation from conventional logs using Artificial Neural Networks (ANNs). A first technique consists of ‘cleaning’ the core plug data set by removing the measurements deemed as log-incompatible, i.e. those from plugs corresponding to log measurements affected by shoulder-bed effect, and those from layers with thickness below the vertical log resolution. The second technique relies on building high-resolution digital models of cored intervals using 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 the above 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 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 very effective in improving permeability prediction, as they significantly reduce cross-scaling problems related to the differences in support volumes. Strengths and weaknesses of the two preprocessing approaches have also been compared. The first technique is faster, but its application can be strongly constrained by statistical and geological representativeness of the selected data set, in the sense that some lithologies could go under-represented 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 to supply – starting from core data only – a reliable permeability curve that can be retained valid at the log scale. |
