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Establishing an initial permeability model based on a conventional porosity permeability plot using core analysis data from all cored study wells.
The width of the uncertainty band related to average permeability is addressed by analysing core analysis data from the local Stenlille field with several cored and logged wells located closely together.
The conclusions of fine interpretation are compared with the core analysis data and well testing data.
Poor planning and ill-considered programme design are the two principal reasons why so much core analysis data are unreliable or unrepresentative.
Core analysis data, log data and seismic data were used to build a 3D shale lithofacies from core to well and finally to regional scale.
Petrophysical well-logs were integrated with detailed core descriptions, thin sections and core analysis data into provide insight to the reservoir characteristics, pore structure features, and depositional environments.
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According to the architecture, the system is divided into the human-computer interaction, functional implementation, core analysis and data supporting layer, and the complete system architecture is established.
It needs to be determined by special core analysis (SCAL) data.
Petrophysical groups (PG) were identified based on the core analysis (MICP data).
Traditional rock typing methodologies for carbonate reservoirs typically are based on the routine core analysis (RCA) data with well logs and a small subset of MICP tests.
A method using artificial neural networks (ANNs)—simple mathematical implementations of human brain activity shows promise, but this method also has its own disadvantages, like failing to succeed when limited core plug analysis data are available.
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