Implementation experience of data processing automation of rock mechanical properties laboratory studies at TATNEFT PJSC

UDK: 004.9:622.276.031.011.43

DOI: 10.24887/0028-2448-2025-7-56-60

Key words: geomechanics, rocks, core, elastic, strength, mechanical properties, studies, automation, digitalisation, processing, software

Authors: I.I. Girfanov (TatNIPIneft, RF, Almetyevsk); I.T. Usmanov (TatNIPIneft, RF, Almetyevsk); O.S. Sotnikov (TatNIPIneft, RF, Almetyevsk); M.M. Remeev (TatNIPIneft, RF, Almetyevsk); A.A. Lutfullin (TATNEFT PJSC, RF, Almetyevsk)

This paper explores the issues of processing automation of rock mechanical properties core testing used to ensure the safety of drilling, well operations, fracturing design, etc. Due to the expanded scope of studies and the increased volume of data for analysis, there is a growing need to optimise routine procedures during the pre-processing of experiment results for further analysis. The paper presents the experience of TatNIPIneft in the development of used approaches to automating the study of rock stress-related properties divided into three stages starting from fully manual data processing to the unified digital platform concept. The transition from discrete software units to an integrated solution is essential. It enables automation of routine operations, provides complex data analysis and fosters the improvement of laboratory efficiency. Development of such a solution required consideration of equipment specificities, data formats, calculation techniques and user requirements for reporting and analysis. The need to evaluate the results of core testing together with well log data requires the availability of certain digital tools. Their further improvement will help to increase efficiency of geomechanical modelling and optimise the oil and gas field development process. It is planned to extend the range of analysis tools to enable more in-depth investigation of rock mechanical behaviour patterns. Moreover, the authors consider the possibility of replicating the accumulated experience and developed algorithms applicable to other areas of core testing.

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pp. 49–52, DOI: https://doi.org/10.24887/0028-2448-2021-7-49-52