Application of core X-ray microtomography in oilfield geology

The article presents studies devoted to the practical application of computer X-ray microtomography (micro-CT) in oilfield geology. In particular, the authors give results of using the method for sample defectoscopy before petrophysical studies in order to improve the quality of analyzes. The paper includes an example of assessing the depth of core plugging with drilling fluid; assessing the mineral composition by micro-CT; experimental core studies when modeling the thermal effect on the oil source rocks of the Bazhenov formation. The authors also examine the current state of research in the field of digital petrophysics or digital core. The study is aimed at introducing the micro-CT method into the oilfield process.

Deep learning based restoration of lost sections in Micro-CT core plugs

Lack of petrophysical information is critical for reservoirs development composed of poorly consolidated rocks or for zones bearing wells with core damaged by improper coring operations. The restoration complexity of the digital-core lost sections is associated with the need to consider an enormous amount of data from the existing core image and the necessity to include lithological expert knowledge. That makes deep learning methods a natural choice for solving such problems. We proposed, examined, and compared several deep learning methods convenient for analyzing micro-computed tomography digital core data. It was done under the most simplistic problem statement when the destroyed part (a set of slices) is completely lost. Here, we present the results of comparison interpolation/extrapolation procedures under proposed quality metrics. We discover that the variational autoencoder method can be trained to extract some petrophysical parameters from the digital core plug in an unsupervised manner.

Digital core modeling technology for determining the reservoir-capacitive properties of terrigenous reservoirs

For today digital core modelling technology is demanded and developing instrument in conducting the main reservoir-capacitive properties of terrigenous rocks. This technology is becoming more widespread in connection with the development of computer and nanotechnologies. The main attempts to apply the digital core model in practice have been undertaken in the last decade, although the first examples of its use for the analysis of reservoir rocks date back to the 80s of the last century. Improvement of digital core modeling technology will allow to cope with the problem of lack or absence of core material, as well as to solve the problems of studying loose, weakly cemented and other rocks, "problematic" of conducting physical experiments. In addition, it seems relevant to create a digital core block that fits into the general digitalization platform of technologies related to reservoir-capacitive properties in the development of hydrocarbon fields. With the use of a digital core model, it also becomes possible to effectively refine and supplement the calculated parameters in laboratory core studies, reducing the likelihood of errors in the obtained results.

Multi-Mineral Segmentation of SEM Images Using Deep Learning Techniques

The approaches of building and methods of using the digital core are currently developing rapidly. The use of these methods makes it possible to obtain petrophysical information by non-destructive methods quickly. Digital rock physics includes two main stages: constructing models and modeling various physical processes on the obtained models. Our work proposes using deep learning methods for mineral and pore space segmentation instead of classical methods such as threshold image processing. Deep neural networks have long been able to show their advantages in many areas of computer vision. This paper proposes and tests methods that help identify different minerals in images from a scanning electron microscope. We used images of rocks of the Achimov formation, which are arkoses, as samples. We tested various deep neural networks such as LinkNet, U-Net, ResUNet, and pix2pix and identified those that performed best in segmentation.

A New Method of Central Axis Extracting for Pore Network Modeling in Rock Engineering

Characterizing internal microscopic structures of porous media is of vital importance to simulate fluid and electric current flow. Compared to traditional rock mechanics and geophysical experiments, digital core and pore network modeling is attracting more interests as it can provide more details on rock microstructure with much less time needed. The axis extraction algorithm, which has been widely applied for pore network modeling, mainly consists of a reduction and burning algorithm. However, the commonly used methods in an axis extraction algorithm have the disadvantages of complex judgment conditions and relatively low operating efficiency, thus losing the practicality in application to large-scale pore structure simulation. In this paper, the updated algorithm proposed by Palágyi and Kuba was used to perform digital core and pore network modeling. Firstly, digital core was reconstructed by using the Markov Chain Monte Carlo (MCMC) method based on the binary images of a rock cutting plane taken from heavy oil reservoir sandstone. The digital core accuracy was verified by comparing porosity and autocorrelation function. Then, we extracted the central axis of the digital pore space and characterize structural parameters through geometric transformation technology and maximal sphere method. The obtained geometric parameters were further assigned to the corresponding nodes of pore and throat on the central axis of the constructed model. Moreover, the accuracy of the new developed pore network model was measured by comparing pore/throat parameters, curves of mercury injection, and oil-water relative permeability. The modeling results showed that the new developed method is generally effective for digital core and pore network simulation. Meanwhile, the more homogeneity of the rock, which means the stronger “representative” of binary map the rock cutting plane, the more accurate simulated results can be obtained.

Study of Reservoir Properties of Turonian Formation Using Digital Core Analysis

The work demonstrates results of reservoir properties evaluation using a complex of laboratory and multiscale digital core or digital rock analysis. Rock properties (including relative phase permeabilities) were studied at different scales: from nanometers to meter (whole core). For the first time, cores from Turonian formation were characterized with digital rock analysis, which provided stationary relative permeabilities for gas-water under reservoir conditions. Lab determination of relative permeabilities was rather challenging for some low-permeability samples (<0.02 md), while digital analysis was successful even for them. Gas recovery in a depletion mode from different rock types was studied on a whole core model for different capillary pressures. Such studies are not conducted in the lab.

Modern approaches to pore space scale digital modeling of core structure and multiphase flow

In current review, we consider the Russian and, mainly, international experience of the “digital core» technology, namely – the possibility of creating a numerical models of internal structure of the cores and multiphase flow at pore space scale. Moreover, our paper try to gives an answer on a key question for the industry: if digital core technology really allows effective to solve the problems of the oil and gas field, then why does it still not do this despite the abundance of scientific work in this area? In particular, the analysis presented in the review allows us to clarify the generally skeptical attitude to technology, as well as errors in R&D work that led to such an opinion within the oil and gas companies. In conclusion, we give a brief assessment of the development of technology in the near future.