P. Mielke

Processing of rock core microtomography images

The abilities of machine learning algorithms to process X-ray microtomographic rock images were determined. The study focused on the use of unsupervised, supervised, and ensemble clustering techniques, to segment X-ray computer microtomography rock images and to estimate the pore spaces and pore size diameters in the rocks. The unsupervised k-means technique gave the fastest processing time and the supervised least squares support vector machine technique gave the slowest processing time. Multiphase assemblages of solid phases (minerals and finely grained minerals) and the pore phase were found on visual inspection of the images. In general, the accuracy in terms of porosity values and pore size distribution was found to be strongly affected by the feature vectors selected. Relative porosity average value of 15.92?1.77% retrieved from all the seven machine learning algorithm is in very good agreement with the experimental results of 17?2%, obtained using gas pycnometer. Of the supervised techniques, the least square support vector machine technique is superior to feed forward artificial neural network because of its ability to identify a generalized pattern. In the ensemble classification techniques boosting technique converged faster compared to bragging technique. The k-means technique outperformed the fuzzy c-means and self-organized maps techniques in terms of accuracy and speed. Testing of machine learning algorithms to process X-ray CT rock images.Unsupervised, supervised, and ensemble clustering techniques were applied.k-Means technique is the fastest in terms of CPU performance.

Thermo-Triax: An Apparatus for Testing Petrophysical Properties of Rocks Under Simulated Geothermal Reservoir Conditions

A Thermo-Triax apparatus has been developed to facilitate research on petrophysical properties of rock samples under simulated geothermal reservoir conditions. The apparatus consists of control systems for vertical stress and horizontal confining pressure, a pair of independent pore pressure controllers for applying different upstream, and downstream pore pressures at bottom and top of rock specimens, an external heater and a data logging system. Permeability of rocks is measured using steady state and transient flow methods. The thermal expansion of metallic parts in the triaxial cell and the error introduced into the readings of the extensometers at high temperatures are calibrated via experiments on an aluminum specimen with known coefficient of thermal expansion. The possibilities of studying the effect of stress and temperature on permeability and compressibilities of porous rocks with the Thermo-Triax apparatus are presented with first data. The change of pore volume during the non-isothermal process between adjacent temperature levels as well as along the measurement of permeability at leveled temperatures is interpreted and calibrated. The thermal expansion of mineral grains during heating is verified with the data of pore volume change and the magnitude of thermal expansion of mineral grains is estimated and compared with reported values. The permeability measurements along different heating paths can be used to verify the temperature dependency of stress-dependent rock properties.

Comparison of Micro X-ray Computer Tomography Image Segmentation Methods: Artificial Neural Networks Versus Least Square Support Vector Machine

Micro X-ray computer tomography (XCT) is a powerful non-destructive method for obtaining information about rock structures and mineralogy. A new methodology to obtain porosity from 2D XCT digital images using artificial neural network and least square support vector machine is demonstrated following these steps: the XCT image was first preprocessed, thereafter clustering algorithms such as K-means, Fuzzy c-means and self-organized maps was used for image segmentation. Then artificial neural network was applied for image classification. For comparison, least square support vector machine approach was used for classification labeling of the scan images. The methodology shows how artificial neural network and least square support vector machine deals with outliers and artifact which are caused by beam hardening artifact and the curse of dimensionality problem. Furthermore, the percentages of correctness it classifies pore-space and the uncertainties within which porosity can be estimated.