Development of methods for automation of multi-well logging data interpretation and core analysis

UDK: 550.83.05
DOI: 10.24887/0028-2448-2018-6-54-57
Key words: processing of geophysical well logging and core data, logging data interpretation, core analysis, petrophysical modeling, quality check, calibration, log splicing, normalization, petrophysical functions, interpretation model
Authors: L.R. Minikeeva (BashNIPIneft LLC, RF, Ufa; Bashkir State University, RF, Ufa), O.V. Nadegdin (BashNIPIneft LLC, RF, Ufa), E.R. Nugumanov (BashNIPIneft LLC, RF, Ufa), A.V. Markov (BashNIPIneft LLC, RF, Ufa), D.V. Efimov (BashNIPIneft LLC, RF, Ufa)

The process of interpretation and petrophysical modeling of well logging methods includes great number of stages from initial work of data preparing to development of petrophysical models. The number of data analysis steps performed depends on data quality, the complexity of geological section under study, required level of detail of the analysis. To build self-consistent model specialist has to gather mix of information from all analyzed wells. During interpretation modeling of well logging methods, they face routine tasks that take a lot of time. Therefore, it is imperative to develop methods and automate the process of multi-well analysis and well logging and core data interpretation.

During this work, the procedures of multi-well analysis and well logging and core data interpretation were developed and automated. As a result, there was implemented analytical application to execute different operations that specialist usually performs during interpretation modeling of well logging. Also we gave examples of different technologies of multi-well data processing at the stages of analysis and interpretation of well logging and core data: initial data unification, quality check, depth matching of well logging curves (single- an multi-type), calibration, splicing of well logging curves, normalization of well logging curves, depth matching of permeability and porosity properties, development of consistent with logging and core data petrophysical functions and interpretation models including parameters adaptation.

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