Speaker
Description
The knowledge of pore size distribution and absolute permeability of porous media are key for a proper petrophysical characterization of a reservoir, which directly impacts the accuracy of hydrocarbon reserves estimation and the production strategies optimization. For the Oil and Gas exploration industry, downhole measurements are done to infer several properties and to perform a formation evaluation based on petrophysical analysis and interpretation of such data. Among them, Nuclear Magnetic Resonance (NMR) is becoming a key logging tool since it is sensitive to the saturating fluids only, i.e. it is not affected by the matrix minerals. Hence, it is ideal for the direct determination of porosity, for example. The main application of NMR on porous media study is based on the measurement of a magnetic relaxation process of the fluid´s nuclear spins, in this case those from the hydrogens that constitute the hydrocarbon chains and the formation brine. Such relaxation process, in porous media, is proportional to the pore sizes and to a parameter called surface relaxivity, which scales the intensity of the matrix-fluid interaction. Hence, to a proper characterization of pore sizes based on NMR relaxation measurements, surface relaxivity must be properly determined [1].
In this work, we formulate and solve an inverse problem to obtain the suface relaxivity parameter as a function of pore sizes for 14 sedimentary rock cores: 7 sandstones and 7 carbonates. In this methodology, micro-tomographic images of the rock samples are the input for simulations that applies Random Walk (RW) and a genetic algorithm to infer a surface relaxivity function that varies with the size of the pores. Hence, a distribution of pore sizes can be obtained from this function when correlated to experimental NMR data measured from the same samples.
Using NMR data, absolute permeability is modeled as a linear product of porosity and the mean relaxation time value, i.e. NMR data is used as a proportionality parameter to the pore sizes [2]. Applying the inverse problem developed here, the pore size mean value can be calculated with higher accuracy, with also increased the absolute permeability prediction. The estimated absolute permeability results were compared to experimental data measured from the same samples.
References
[1] Benavides, F., Leiderman, R., Souza, A., Carneiro, G., Bagueira, R. 2017. Estimating the Surface Relaxivity as a Function of Pore Size from NMR T2 Distributions and Micro-tomographic Images. Computers & Geosciences, v. 106, p. 200-208.
[2] Souza, A., Carneiro, G., Zielinski, L., Polinski, R., Schwartz, L., Hürlimann, M.D., Boyd, A., Rios, E.H., Santos, B.C.C., Trevizan, W.A., Machado, V.F., Azeredo, R.B.V. 2013. Permeability Prediction Improvement Using 2D NWR Diffusion-T2 Maps, in: SPWLA 54th Annual Logging Symposium. Society of Petrophysicists and Well-Log Analysts.
| Participation | In-Person |
|---|---|
| Country | Brazil |
| MDPI Energies Student Poster Award | No, do not submit my presenation for the student posters award. |
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