1887

Abstract

Summary

Reservoir geomechanical evaluation provides powerful insights to understand and more precisely predict the lifetime behavior of reservoir regarding to the given or desired development plan. Any geomechanical evaluation is directly based on the rock mechanical data which is taken from experimental destructive tests on intact rock samples. However in some situations preparing required undisturbed and intact rock samples is impossible, technically or financially. Investigating the relationship between some microstructural properties and key geomechanical characteristics may lead to develop some models to estimate those geomechanical parameters by thin section studies instead of destructive tests. In this study which is done on 15 carbonate plugs of Iranian gas field, first qualified plugs were chosen based on the CT-Scan images to investigation. Second thin section studies were carried out on each trim of plugs both qualitatively and quantitatively. In the next step, uniaxial compression tests were performed on the samples. Investigations results illustrate that microstructural parameters including porosity, mud percentage and anhydrite cement content are the main affecting features on unconfined compressive strength (UCS) and Young’s modulus (E) of studied carbonate samples.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201414272
2015-10-13
2024-04-24
Loading full text...

Full text loading...

References

  1. Aadnoy, B. and Looyeh, R.
    [2011] Petroleum rock mechanics: drilling operations and well design. Gulf Professional Publishing.
    [Google Scholar]
  2. Chang, C., Zoback, M.D. and Khaksar, A.
    [2006] Empirical relations between rock strength and physical properties in sedimentary rocks. Journal of Petroleum Science and Engineering, 51(3), 223–237.
    [Google Scholar]
  3. Lindqvist, J.E., Åkesson, U. and Malaga, K.
    [2007] Microstructure and functional properties of rock materials. Materials characterization, 58(11), 1183–1188.
    [Google Scholar]
  4. Manouchehrian, A., Sharifzadeh, M. and Moghadam, R.
    [2012] Application of artificial neural networks and multivariate statistics to estimate UCS using textural characteristics. International Journal of Mining Science and Technology, 22(2), 229–236.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201414272
Loading
/content/papers/10.3997/2214-4609.201414272
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error