Characteristics of nano-sized pore-throat in unconventional tight reservoir rocks and its scientific value

2015 ◽  
Vol 32 (3) ◽  
pp. 257 ◽  
Author(s):  
Zhi Yang ◽  
Caineng Zou ◽  
Songtao Wu ◽  
Shizhen Tao ◽  
Lianhua Hou ◽  
...  
2014 ◽  
Vol 54 (2) ◽  
pp. 539
Author(s):  
Sam Yang ◽  
Yudan Wang ◽  
Sherry Mayo ◽  
Andrew Tulloh ◽  
Keyu Liu ◽  
...  

A data-constrained modelling (DCM) approach has been developed at CSIRO, which enables 3D characterisation of pores and mineral phase distributions using quantitative multi-energy synchrotron CT. For a tight reservoir, such as a carbonate limestone or a shale rock, DCM can generate microscopic partial volume distributions of materials and pores which are the effects of the fine length scales below X-ray CT resolution. Using this information, a quantitative relation between recoverable reserve and pore-throat size can be established for a rock sample. The technique can also be used for characterisation of other unconventional reservoir rocks.


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Hamed Sanei

Abstract This paper presents a new schematic model for generation and timing of multiple phases of solid bitumen throughout the continuum of organic matter maturation in source and tight reservoir rocks. Five distinct stages in the evolution of solid bitumen are proposed: (1) diagenetic solid bitumen (or degraded bituminite), which is not a secondary maceral resulting from the thermal cracking of kerogen. Instead it is derived from degradation of bituminite in the diagenesis stage (Ro < 0.5%); (2) initial-oil solid bitumen, is a consolidated form of early catagenetically generated bitumen at the incipient oil window (Ro ~ 0.5–0.7%); (3) primary-oil solid bitumen is derived from thermally generated bitumen and crude oil in the primary oil window (Ro ~ 0.7–1.0%); (4) late-oil solid bitumen (solid-wax) is derived from the waxy bitumen separated from the mature paraffinic heavy oil in the primary- and late-oil windows; and (5) pyrobitumen, which is mainly a non-generative solid bitumen, is evolved from thermal cracking of the remaining hydrocarbon residue and other types of solid bitumen in the dry gas window and higher temperature (Ro > 1.4%). This model shows concurrence of multi-populations solid bitumen with oil, bitumen, and other phases of fluid hydrocarbon residue during most of the maturity continuum.


2015 ◽  
Vol 1094 ◽  
pp. 385-388
Author(s):  
Qi Li ◽  
Li You Ye ◽  
Wei Guo An

In condition of bound water, bound water is distributed on surface of pore throat in the form of water film in low permeability and tight sandstone gas reservoir, so bound water reduces the seepage space of the gas and makes gas to occur Special seepage law. This article design physical simulation research experiment about gas seepage law in containing water reservoir. Experimental results explain: Gas seepage curve existed obvious non-linear seepage region in low permeability reservoir, gas slippage effect happens in the low-pressure region, and high-speed non-Darcy seepage happens in the high-pressure region. With the limit of water and pore throat in tight reservoir, gas hardly occurs specific non-linear seepage phenomenon. The critical water saturation which causes gas effective permeability sudden changing is around 30% in low permeability and tight reservoir. The research result has important theoretical significance on establishing corresponding percolation model of single well productivity and efficient development of low permeability and tight sandstone gas reservoir.


1995 ◽  
Author(s):  
Cengiz Satik ◽  
Roland N. Horne ◽  
Yanis C. Yortsos

1999 ◽  
Vol 2 (02) ◽  
pp. 161-168 ◽  
Author(s):  
Z.R. Liang ◽  
P.C. Philippi ◽  
C.P. Fernandes ◽  
F.S. Magnani

Summary The main purpose of the present work is to predict the permeability of a porous medium from its three-dimensional (3D) porous structure network. In this work, 3D porous structure is reconstructed by the truncated Gaussian method using Fourier transform and starting from a 2D binary image obtained from a thin section of a porous sample. The skeleton of the 3D porous structure provides a way of visualizing the graph of the pore network. It is determined using a thinning algorithm, which is conceived to preserve topology. It gives both visual and quantitative information about the connectivity of the pore space, the coordination number for every node and local hydraulic radius. Once the network of the pore structure is obtained, the macroscopic transport properties, such as the permeability, can be predicted. The method is applied to a 500 mD Berea sandstone and the predicted permeability is in good agreement with the experimental value and empirical correlations. Introduction The prediction of equilibrium and transport properties of porous media is a long-standing problem of great theoretical and practical interest, particularly in petroleum reservoir engineering.1 Past theoretical attempts to derive macroscopic transport coefficients from the microstructure of porous media entailed a simplified representation of the pore space, often as a bundle of capillary tubes.1–3 These models have been widely applied because of their convenience and familiarity to the engineers. But they do have some limitations. For example, they are not well suited for describing the effect of the pore space interconnectivity and long range correlation in the system. Network models have been advanced to describe phenomena at the microscopic level and have been extended in the last few years to describe various phenomena at the macroscopic level. These models are mostly based on a network representation of the porous media in which larger pores (pore bodies) are connected by narrower pores (pore throats). Network models represent the most important and widely used class of geometric models for porous media.2 A network is a graph consisting of a set of nodes or sites connected by a set of links or bonds. The nodes can be chosen deterministically or randomly as in the realization of a Poisson or other stochastic point process. Similarly the links connecting different nodes may be chosen according to some deterministic or random procedure. Finally, the nodes are dressed with convex sets such as spheres representing pore bodies, and the bonds are dressed with tubes providing a connecting path between the pore bodies. The original idea of representing a porous structure by a network is rather old, but it was only in the early 1980s that systematic and rigorous procedures were developed to map, in principle, any disordered rock onto an equivalent random network of bonds and sites. Once this mapping is complete one can study a given phenomenon in porous media in great detail.3 Dullien1 reviewed the details of various pore-scale processes, including detailed descriptions of many aspects of network models. The most important features of pore network geometry and topology that affect fluid distribution and flow in reservoir rocks are the pore throat and pore body size distributions, the pore body-to-pore throat size aspect ratio and the pore body coordination number.4 These data have been tentatively assumed in the previous works. The extension of these techniques to real porous media has been complicated by the difficulty in describing the complex three-dimensional (3D) pore structure of real porous rocks. Information about the pore structure of reservoir rocks is often obtained from mercury intrusion and sorption isotherm. Mercury intrusion and sorption isotherm data provide statistical information about the pore throat size distribution, or, more correctly, the distribution of the volumes that may be invaded within specified pore throat sizes. Advanced techniques such as microcomputed tomography5 and serial sectioning6,7 do provide a detailed description of the 3D pore structures of rocks. Recently, image analysis methods used over pictures of highly polished surfaces of porous materials (e.g., Refs. 8-10), taken with an electron scanning microscope have been used to describe the porous structure. Image analysis techniques such as opening (2D and 3D)11,13 and median line graphs (2D)13 were developed. Information on porous structure is obtained from the analysis of 2D binary images. For isotropic media, a 3D microstructure may be reconstructed from any statistically homogeneous 2D section. The general objective of a reconstructed porous structure is to mimic more closely the geometry of real media. This method has been previously applied to the prediction of important petrophysical and reservoir engineering properties, such as permeability8 and formation factor14 with reasonable success. Thovert et al.15 used the reconstructed porous structure and developed thinning algorithms to obtain the graph of the 3D pore structure. Some topological characteristics such as the number of loops were derived. Bakke and O/ren16 generated 3D pore networks based on numerical modeling of the main sandstone forming geological processes. Absolute and relative permeability were computed for a Bentheimer sandstone. However, although their algorithms worked well on their models, the problem of connectivity preservation for a 3D thinning algorithm appears to be only correctly taken into account by Ma,17 who proposed sufficient conditions for providing a 3D thinning algorithm to preserve connectivity.


2014 ◽  
Vol 962-965 ◽  
pp. 16-20
Author(s):  
Yi Si Dong ◽  
Xing He Yu ◽  
Zhi Hao Yang ◽  
Fang Zeng ◽  
Ying Li ◽  
...  

Based on geological background, this study is to understand the potential of tight oil of Qingshankou Formation in Songliao Basin by comparing lithofacies features, oil-generating conditions and reservoir characteristics. Hundreds of samples are analyzed to derive geochemical parameters, such as organic richness, kerogen type, and source rock maturity. The results indicate that source rocks of Qingshankou Formation are organic rich, contain oil-prone kerogen, and are thermally mature. The tight reservoir of Qingshankou Formation has complicated pore throat structure, abundant fractures, and an beneficial place for oil accumulation.


SPE Journal ◽  
2021 ◽  
pp. 1-20
Author(s):  
Andrey Kazak ◽  
Kirill Simonov ◽  
Victor Kulikov

Summary The modern focused ion beam-scanning electron microscopy (FIB-SEM) allows imaging of nanoporous tight reservoir-rock samples in 3D at a resolution up to 3 nm/voxel. Correct porosity determination from FIB-SEM images requires fast and robust segmentation. However, the quality and efficient segmentation of FIB-SEM images is still a complicated and challenging task. Typically, a trained operator spends days or weeks in subjective and semimanual labeling of a single FIB-SEM data set. The presence of FIB-SEM artifacts, such as porebacks, requires developing a new methodology for efficient image segmentation. We have developed a method for simplification of multimodal segmentation of FIB-SEM data sets using machine-learning (ML)-based techniques. We study a collection of rock samples formed according to the petrophysical interpretation of well logs from a complex tight gas reservoir rock of the Berezov Formation (West Siberia, Russia). The core samples were passed through a multiscale imaging workflow for pore-space-structure upscaling from nanometer to log scale. FIB-SEM imaging resolved the finest scale using a dual-beam analytical system. Image segmentation used an architecture derived from a convolutional neural network (CNN) in the DeepUNet (Ronneberger et al. 2015) configuration. We implemented the solution in the Pytorch® (Facebook, Inc., Menlo Park, California, USA) framework in a Linux environment. Computation exploited a high-performance computing system. The acquired data included three 3D FIB-SEM data sets with a physical size of approximately 20 × 15 × 25 µm with a voxel size of 5 nm. A professional geologist manually segmented (labeled) a fraction of slices. We split the labeled slices into training, validation, and test data. We then augmented the training data to increase its size. The developed CNN delivered promising results. The model performed automatic segmentation with the following average quality indicators according to test data: accuracy of 86.66%, precision of 54.93%, recall of 83.76%, and F1 score of 55.10%. We achieved a significant boost in segmentation speed of 14.5 megapixel (MP)/min. Compared with 0.18 to 1.45 MP/min for manual labeling, this yielded an efficiency increase of at least 10 times. The presented research work improves the quality of quantitative petrophysical characterization of complex reservoir rocks using digital rock imaging. The development allows the multiphase segmentation of 3D FIB-SEM data complicated with artifacts. It delivers correct and precise pore-space segmentation, resulting in little turn-around-time saving and increased porosity-data quality. Although image segmentation using CNNs is mainstream in the modern ML world, it is an emerging novel approach for reservoir-characterizationtasks.


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