Estimation and correct determination of vitrinite equivalent reflectance in rock is crucial for the assessment of the source rock in both conventional and unconventional hydrocarbon deposits. These parameters can be determined in laboratories on rock samples. Laboratory measurements provide only point information. However, the use of well logs could overcome discontinuities in the data and provide parameters throughout a study interval. Attention has been paid to the estimation of TOC based on well logs. Vitrinite equivalent reflectance estimation is less well discussed and most papers reported cases with high TOC content in analyzed deposits. In this paper, the estimation of improved Ro is presented using a calculated maturity indicator with well logs. As the organic matter content is not high, additional steps were required for the calculation. To improve the quality of the fit and to find similar intervals, the data were grouped using cluster and neural network analysis. The next step was to use the resistivity log to improve the obtained maturity indicator. Due to the changing properties of kerogen with the type and degree of thermal maturity, this approach turned out to be reliable. The use of resistivity significantly increased the correlation coefficient and reduced errors. The method was tested on two wells with different type and maturity of kerogen. The obtained results are satisfactory, which makes it possible to use the method even in formations with a low organic matter content.
This article presents a novel methodology for data integration including laboratory data, the results of standard well logging measurements and interpretation and the interpretation of XRMI imager data for determination of the porosity and permeability of the fracture system in carbonate rock. An example of the results of the micro computed tomography applied for carbonate rock is included. Data were obtained on the area of the Polish Lowland Zechstein Main Dolomite formation. The input set of data included the results of mercury injection porosimetry (MICP), thin section and polished section analysis, well logging measurements and comprehensive interpretation and micro computed tomography. The methodology of the macrofractures’ analysis based on borehole wall imagery as well as estimation of their aperture was described in detail. The petrophysical characteristics of the fracture systems were analyzed as an element of standard interpretation of well logging data along a carbonate formation. The results of permeability determination, with micro-, mezzo- and macrofractures’ presence in the rock taken into consideration, were compared with outcomes of the drill stem tests (DSTs).
Permeability is a property of rocks which refers to the ability of fluids to flow through each substance. It depends on several factors as pore shape and diameter. Also the presence and type of clay has a large influence on the permeability value. Permeability can be measured on rock sample in the laboratory by injecting fluid through the rock under known condition, but this provides only point information. Due to the dependence of the parameter on many factors, the deterministic estimation of permeability based on laboratory measurement and well logs is problematic. Many empirical methods for determining permeability are available in the literature and interpretation systems. An interesting approach to the problem is the use of artificial neural networks based on laboratory measurement and modern, high-resolution logging tools. The authors decided to use MLP artificial neural networks, which allow permeability estimation and can be used both in the test well and applied to neighbouring wells. The network was checked in several variants. Obtained results show the legitimacy of using artificial neural networks in the issue of estimating permeability. However, they also show limitations resulting from the lack of accurate data or influence of geological setting and processes.
Computed X-ray tomography (CT), together with pulse and pressure decay permeability methods were used to evaluate a formula for absolute reservoir permeability. For this reason, 62 core samples representing geological material of tight, gas-bearing sandstones, mudstones, limestones, and dolostones were studied. Samples were divided into two groups with lower and higher permeability values. Images of the pore space were processed and interpreted to obtain geometrical parameters of the objects (pores, microfractures) with 0.5 × 0.5 × 0.5 µm3 voxel size. Statistical methods, which included basic statistical analysis, linear regression, and multiple linear regression analysis, were combined to evaluate the formula for absolute permeability. It appeared that the following parameters: Feret Breadth/Volume, Flatness/Anisotropy, Feret Max/Flatness, moments of inertia around middle principal axis I2/around longest principal axis I3, Anisotropy/Flatness, Flatness/Anisotropy provided the best results. The presented formula was obtained for a large set of data and is based only on the geometric parameters of the pore space. The novelty of the work is connected with the estimation of absolute permeability using only data from the CT method for tight rocks.
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