Thin-section Images Research Articles

Relationship between tight reservoir diagenesis and hydrocarbon accumulation: An example from the early Cretaceous Fuyu reservoir in the Daqing oil field, Songliao Basin, China

It is crucial to determine the sequence of reservoir tightening and hydrocarbon accumulation for analyzing the accumulation dynamics, oil migration mode, and oil distribution pattern of tight oil reservoirs. The tight sandstone reservoirs in the Fuyu reservoir occur in both the Daqing Anticline (DA) and Sanzhao Sag (SS), which were used as examples for the qualitative and semiquantitative analyses conducted herein. By integrating the porosity and permeability data, thin section imaging, cathode luminescence, scanning electronic microscopy, and energy dispersive spectra, the diagenesis processes including compaction, calcite cementation and replacement, illite precipitation, quartz overgrowth, and dissolution were analyzed. Typical diagenetic processes and successive orders were established to have occurred in the following sequence: (1) compaction, early calcite cementation, and chlorite coating; (2) dissolution, quartz overgrowth, and kaolinite filling pores; (3) late calcite cementation, calcite replacing feldspar, and quartz; and (4) late illite (feldspar illitization). Utilizing fluid inclusion, isotopic analysis of C and O, and burial history, calcite cementation was determined to be related to the thermal decarboxylation of organic matter, showing that the dominant forming-temperature spanned 60°C–100 °C and that a large amount of calcite cement formed before the Middle-Nenjiang stage (the Early Campanian). Illite precipitation was determined to be related to feldspar illitization with a dominant forming temperature spanning 120°C–140 °C. The illite was mainly developed in the Late-Mingshui stage (the Late Campanian) in the DA. In the SS, illite was formed between the Late-Nenjiang stage (the Middle Campanian) and Late-Mingshui stage. The Fuyu reservoir experienced three–four periods of hydrocarbon charging, with the main charging period occurring in the Late Cretaceous (Late-Mingshui stage). The Fuyu reservoir became tight (porosity <12%) in the DA and SS in the Late-Mingshui and Late-Nenjiang stages, respectively. The formation of calcite and illite cementation and the tightening of the Fuyu reservoir occurred before the main hydrocarbon charging period, indicating that tightening occurred before oil accumulation. • Typical diagenetic processes and successive orders of the Fuyu oil reservoir are established. • The reservoir became tight in Daqing Anticline and Sanzhao Sag in Late Mingshui Stage and Late Nenjiang Stage, respectively. • The occurrence of calcite and illite cementation and the tightening of the Fuyu reservoir were before the main hydrocarbon charging period.

X-Ray Tomography-Based Microstructure Representation in the Snow Microwave Radiative Transfer Model

The modular Snow Microwave Radiative Transfer (SMRT) model simulates microwave scattering behavior in snow via different selectable theories and snow microstructure representations, which is well suited to intercomparisons analyses. Here, five microstructure models were parameterized from X-ray tomography and thin-section images of snow samples and evaluated with SMRT. Three field experiments provided observations of scattering and absorption coefficients, brightness temperature, and/or backscatter with the increasing complexity of snowpack. These took place in Sodankylä, Finland, and Weissfluhjoch, Switzerland. Simulations of scattering and absorption coefficients agreed well with observations, with higher errors for snow with predominantly vertical structures. For simulation of brightness temperature, difficulty in retrieving stickiness with the Sticky Hard Sphere microstructure model resulted in relatively poor performance for two experiments, but good agreement for the third. Exponential microstructure gave generally good results, near to the best performing models for two field experiments. The Independent Sphere model gave intermediate results. New Teubner–Strey and Gaussian Random Field models demonstrated the advantages of SMRT over microwave models with restricted microstructural geometry. Relative model performance is assessed by the quality of the microstructure model fit to micro-computed tomography (CT) data and further improvements may be possible with different fitting techniques. Careful consideration of simulation stratigraphy is required in this new era of high-resolution microstructure measurement as layers thinner than the wavelength introduce artificial scattering boundaries not seen by the instrument.

Geoscience Fieldwork in the Age of COVID-19 and Beyond: Commentary on the Development of a Virtual Geological Field Trip to Whitefish Falls, Ontario, Canada

In response to the COVID-19 pandemic and resultant cancelation of geoscience fieldwork, as well as outstanding accessibility issues inherent in conducting fieldwork, we developed a virtual geological fieldtrip (VFT) to the Huronian age deposits in the Whitefish Falls area, Ontario, Canada. This region is a geologically significant site in which many Ontario universities conduct undergraduate teaching due to the high-quality exposures. In this contribution, we describe and comment on the development of this openly available resource, the motivations in doing so, the challenges faced, its pedagogical impact and relevance, as well as provide suggestions to others in the development of such resources. Our multimedia VFT combines 360° imagery, georeferenced data on integrated maps, and multi-scale imagery (aerial/drone, outcrop, and thin section images). The VFT was built using the Esri Storymaps platform, and thus offers us the opportunity to review the effectiveness of building such resources using this medium, as well as our approach to doing so. We conclude that the Esri Storymaps platform provides a sound medium for the dissemination of multimedia VFTs, but that some aspects of in-person fieldwork remain hard to replicate. Most notably, this affects “hands on experience” and specific activities such as geological mapping. In addition, while VFTs alleviate some accessibility barriers to geoscience fieldwork, substantial barriers remain that should remain the focus of both pedagogical and geoscience work.

Bi-Fractal Characterization of the Pore Network of Tight Sandstone

Fluid seepage performance and accumulation in tight sandstone is a critical research topic for in-depth exploration and development, closely related to the heterogeneity of the pore network. The fractal characterization is one of the most compelling and direct ways for quantitative investigation of heterogeneity. However, only one kind of fractal is used in most studies, and the differences and relations between different fractal dimensions are rarely discussed. This paper chose one of the most representative tight sandstone formations in China, the second member of the Xujiahe Formation, as the research object. First, based on physical analysis and XRD analysis, we carried out a qualitative investigation on pore structure utilizing thin-section and scanning electron microscopy. Then, detailed pore structure parameters were obtained using high-pressure mercury intrusion (HPMI). Lastly, we combined two-dimensional fractal analysis on thin-section images and three-dimensional fractal analysis on HPMI data to characterize the pore network heterogeneity quantitatively. The Xu2 tight sandstone is mainly medium- to fine-grained lithic feldspathic sandstone or feldspathic lithic sandstone with low porosity and permeability. Also, the Xujiahe tight sandstone is mainly composed of quartz, feldspar, and clay. The pore types of Xu2 tight sandstones are primarily intergranular pores, micro-fractures, and intra- and intergranular dissolution pores. Moreover, most of the micro-fractures in gas-bearing formation are open-ended, while most are filled by clay minerals in the dry formation. The r50 (median pore radius) is the most sensitive parameter to seepage capability (permeability) and gas-bearing status. The 2D fractal dimension (Ds) of gas-bearing samples is significantly larger than that of dry samples, while the 3D fractal dimension (D1, D2) of gas-bearing samples is lower than that of dry samples. There is a strong negative correlation between D2 and gas-bearing status, permeability, quartz content, and r50, but a positive correlation between Ds and these parameters. D2 represents the heterogeneity of pore space, while the Ds indicates the development of the pore network. Tectonic movements that generate micro-fractures and clay cementation that blocks the seepage channels are the two main controlling factors on fractal dimensions. Combining 2D and 3D fractal analysis could give a more in-depth investigation of pore structure.

Characterization of damage processes in Montney siltstone under triaxial compression using acoustic emission and diagnostic imaging

SUMMARY Crack nucleation and rock failure processes in a fine-grained siltstone (Montney Formation) under triaxial compression are investigated using combined diagnostic techniques, including ultrasonic-wave measurement, acoustic-emission (AE) monitoring, computed tomography (CT) scanning, and thin-section imaging. The sample displays a weak-to-moderate inherent seismic anisotropy and noticeable stress-induced anisotropy prior to failure. No AE event was detected until the applied axial stress reached 95 per cent of the peak value. The signal-to-noise ratio is relatively low, however, and detectable AE events are more diffuse than those observed in highly brittle rocks. The AE locations correlate with a shear fracture zone imaged by CT scanning. AE moment-tensor analysis reveals that events with larger relative magnitudes are characterized by high volumetric (tensile or compressive) components, and the initiation of the failure zone is dominated by combined shear-tensile (ST) failure. Stress inversion of the AE events with high tensile components is in good agreement with the known applied stress. Microscopic imaging of thin sections from the failed sample shows that the failure zone is an en echelon structure consisting of a major fracture with branching micro and minor cracks. This failure mechanism is consistent with a ST source mechanism and is interpreted to be associated with the fine granular structure and mineral composition of Montney siltstone.

Deep learning-based thin-section MRI reconstruction improves tumour detection and delineation in pre- and post-treatment pituitary adenoma

Even a tiny functioning pituitary adenoma could cause symptoms; hence, accurate diagnosis and treatment are crucial for management. However, it is difficult to diagnose a small pituitary adenoma using conventional MR sequence. Deep learning-based reconstruction (DLR) using magnetic resonance imaging (MRI) enables high-resolution thin-section imaging with noise reduction. In the present single-institution retrospective study of 201 patients, conducted between August 2019 and October 2020, we compared the performance of 1 mm DLR MRI with that of 3 mm routine MRI, using a combined imaging protocol to detect and delineate pituitary adenoma. Four readers assessed the adenomas in a pairwise fashion, and diagnostic performance and image preferences were compared between inexperienced and experienced readers. The signal-to-noise ratio (SNR) was quantitatively assessed. New detection of adenoma, achieved using 1 mm DLR MRI, was not visualised using 3 mm routine MRI (overall: 6.5% [13/201]). There was no significant difference depending on the experience of the readers in new detections. Readers preferred 1 mm DLR MRI over 3 mm routine MRI (overall superiority 56%) to delineate normal pituitary stalk and gland, with inexperienced readers more preferred 1 mm DLR MRI than experienced readers. The SNR of 1 mm DLR MRI was 1.25-fold higher than that of the 3 mm routine MRI. In conclusion, the 1 mm DLR MRI achieved higher sensitivity in the detection of pituitary adenoma and provided better delineation of normal pituitary gland than 3 mm routine MRI.

Tomographic refractive index profiling of direct laser written waveguides.

The fabrication of complex integrated photonic devices via direct laser writing is a powerful and rapidly developing technology. However, the approach is still facing several challenges. One of them is the reliable quantitative characterization of refractive index (RI) changes induced upon laser exposure. To this end, we develop a tomographic reconstruction algorithm following a modern optimization approach, relying on accelerated proximal gradient descent, based on intensity images only. Very recently, such algorithms have become the state of the art in the community of bioimaging, but have never been applied to direct laser written structures such as waveguides. We adapt the algorithm to our concern of characterizing these translation-invariant structures and extend it in order to jointly estimate the aberrations introduced by the imaging system. We show that a correct estimation of these aberrations is necessary to make use of data recorded at larger angles and that it can increase the fidelity of the reconstructed RI profiles. Moreover, we present a method allowing to cross-validate the RI reconstructions by comparing en-face widefield images of thin waveguide sections with matching simulations based on the retrieved RI profile.

Grain-size effect on dynamic behavior of sandstone based on high-speed 3D-DIC technique

The grain size effect on the dynamic behavior of sandstone was investigated through the compression tests on coarse-grained (CG), medium-grained (MG) and fine-grained (FG) sandstones by split Hopkinson pressure bar (SHPB) tests under the strain rates of 69–83 s–1 based on the thin section and electron scanning microscopic (SEM) images analysis, the CG, MG and FG sandstone were mainly composed by quartz with the average grain size of 200–500, 90–500 and 55–120 µm, respectively. With the increasing grain size, the percentage of clay mineral was decreased correspondingly from 8% to 1%. During the dynamic compression, two high-speed cameras were applied to capture the deformation of sandstone at frame rate of 2×105 s–1 and resolution of 256×256. The real-time strain fields of rock were obtained by high-speed three-dimensional digital image correlation (3D-DIC) technique, the dynamic deformative properties, particularly the lateral strain of the specimen, were extracted by averaging the lateral strain field by pixels. The fracturing behavior of three sandstones was analyzed through the strain localization evolution within the strain fields. Results show that the critical strain rate for reversible release of elastic strain energy increases with the decreasing grain size. The dynamic strength ascends along with the reduction of grain size, while the strain rate sensitivity to the dynamic strength has an opposite trend. Compared to the quasi-static case, the dynamic elastic modulus increases by 2–3 times for MG and FG sandstone, particularly 5 times for CG sandstone. The Poisson’s ratio under dynamic loading in FG sandstone is grown by 25%, but drops at 70% of the static one in MG sandstone. The crack primarily generates inside the specimen and propagates to the surface of the specimen afterwards. The crack development is advanced under dynamic loadings, where the normalized stress threshold for crack initiation in FG sandstone is only 10%. Based on the microscopic analysis, mineral structure and clay percentage dominate the dynamic property and fracturing behavior of sandstone, respectively.

A Clinical Study Investigating Whether the Tongue-Out Position Improves the Quality of the Anatomical Appearance of the Pharynx on CT Imaging.

Objective: To evaluate the effect of using the tongue-out position on the quality of the anatomical appearance of the pharynx on computed tomography (CT) images.Methods: The data from enhanced CT thin-section images of the head and neck in 119 cases scanned were retrospectively analyzed. The cases were divided into two groups based on the position of the tip of the tongue on the images: the tongue-out group (63 cases) and non-tongue-out group (56 cases). Two observers separately evaluated the anatomy of the soft palate, uvula, palatine tonsils, epiglottis, epiglottic fossa, pyriform fossa, arytenoid folds, and tongue on all images. The Kappa test was applied to assess the consistency of scores between the two observers. In the case of data that satisfied the normal distribution, the significance of the difference in the average scores between the two groups was tested using an independent samples t-test with a value of p > 0.05. In the case of data that did not satisfy the normal distribution, the Mann–Whitney U test was adopted to test the significance of the difference in the average scores between the two groups using a value of p < 0.05. The number of cases with swallowing artifacts on the CT images in both groups was statistically analyzed and the chi-square test was used to determine whether the difference in the incidence of artifacts between the two groups was significant.Results: The Kappa test showed good consistency between the two observers scoring of the soft palate, uvula, epiglottis, epiglottic fossa, pyriform fossa, aryepiglottic folds, and tongue. The image scores of the soft palate, uvula, epiglottis, epiglottic fossa, and tongue in the tongue-out group vs. the non-tongue-out group did not satisfy the normal distribution. The Mann–Whitney U test showed that the differences in the image scores between the two groups were statistically significant in all cases (p < 0.05). The incidence of swallowing artifacts in the tongue-out group and the non-tongue-out group was 15 and 32%, respectively. The result of the chi-square test showed that the difference in the incidence of swallowing artifacts between the two groups was statistically significant (p = 0.037).Conclusion: The tongue-out position facilitated an improvement in the CT appearance of pharyngeal anatomy and was associated with a reduction in the incidence of swallowing artifacts.

Ceramic Fabric Classification of Petrographic Thin Sections with Deep Learning

Classification of ceramic fabrics has long held a major role in archaeological pursuits. It helps answer research questions related to ceramic technology, provenance, and exchange and provides an overall deeper understanding of the ceramic material at hand. One of the most effective means of classification is through petrographic thin section analysis. However, ceramic petrography is a difficult and often tedious task that requires direct observation and sorting by domain experts. In this paper, a deep learning model is built to automatically recognize and classify ceramic fabrics, which expedites the process of classification and lessens the requirements on experts. The samples consist of images of petrographic thin sections under cross-polarized light originating from the Cocal-period (AD 1000–1525) archaeological site of Guadalupe on the northeast coast of Honduras. Two convolutional neural networks (CNNs), VGG19 and ResNet50, are compared against each other using two approaches to partitioning training, validation, and testing data. The technique employs a standard transfer learning process whereby the bottom layers of the CNNs are pre-trained on the ImageNet dataset and frozen, while a single pooling layer and three dense layers are added to ‘tune’ the model to the thin section dataset. After selecting fabric groups with at least three example sherds each, the technique can classify thin section images into one of five fabric groups with over 93% accuracy in each of four tests. The current results indicate that deep learning with CNNs is a highly accessible and effective method for classifying ceramic fabrics based on images of petrographic thin sections and that it can likely be applied on a larger scale.

AUTOMATED AISI 317L SS THIN SECTION IMAGE POROSITY RECOGNITION AND SEMI-AUTOMATED PRIORITIZATION FOR PREDICTING THE YIELDING POSITION BY MEANS OF IMAGE PROCESSING

Porosity is a common phenomenon in fabricated parts, which cause stress concentration and leads to yielding, brittle fraction and fatigue of structures which makes porosity recognition and analyzing very important to increase efficiency and decrease the defects of manufactured parts. Many researches have been done in order to detecting and recognizing pores of material by different numerical and experimental methods. In this paper, an algorithm is developed in order to detect the porosity of thin section images automatically and prioritize them for a semi-automatically prediction of yielding position. The algorithm results is the following parameters in a porous material: number of pores, position and size of pores, maximum and minimum distances of pores both from each other and from boundaries. The algorithm shows an accuracy of 83% in the prediction of the results for simulated finite element method (FEM) tensile tests, which makes the algorithm creditable to be used as a non-destructive testing method.

Pretraining Convolutional Neural Networks for Mudstone Petrographic Thin-Section Image Classification

Convolutional neural networks (CNN) are currently the most widely used tool for the classification of images, especially if such images have large within- and small between- group variance. Thus, one of the main factors driving the development of CNN models is the creation of large, labelled computer vision datasets, some containing millions of images. Thanks to transfer learning, a technique that modifies a model trained on a primary task to execute a secondary task, the adaptation of CNN models trained on such large datasets has rapidly gained popularity in many fields of science, geosciences included. However, the trade-off between two main components of the transfer learning methodology for geoscience images is still unclear: the difference between the datasets used in the primary and secondary tasks; and the amount of available data for the primary task itself. We evaluate the performance of CNN models pretrained with different types of image datasets—specifically, dermatology, histology, and raw food—that are fine-tuned to the task of petrographic thin-section image classification. Results show that CNN models pretrained on ImageNet achieve higher accuracy due to the larger number of samples, as well as a larger variability in the samples in ImageNet compared to the other datasets evaluated.

Added value in stroke imaging: accuracy and utility of additional coronal diffusion-weighted imaging

To evaluate the value of adding additional coronal diffusion-weighted imaging with the same section thickness as standard axial images to improve detection of small infarcts. Axial and coronal diffusion-weighted images (4 or 5 mm section thickness, 1 mm gap) were studied retrospectively in two rounds of data collection. During the first round, two radiologists identified sub-centimetre infarcts on only axial images during one sitting, and only coronal images during a second sitting. During the second round, the two radiologists were asked to identify infarcts on only axial images during one sitting, and on both axial and coronal images simultaneously during the second sitting. An expert reviewer determined true infarcts and artefacts. Relative contrast-to-noise ratios (rCNR) and relative mean region of interest (rROI) within each lesion were calculated. During the first round, sensitivity for infarct detection for the two radiologists was 92.7% and 100% on axial and 95.1% and 92.7% on coronal, respectively. During the second round, sensitivity improved from 88.9% to 98.1% for both radiologists (p=0.03). Specificity improved but did not reach statistical significance (p=0.06 and 0.12). False-negative and false-positive lesions had lower rCNR and rROI values. Including both axial and coronal DWI images with the same section thickness in the stroke protocol improves detection of small infarcts, which can be misdiagnosed on a single imaging plane. A second imaging plane is particularly useful for subtle infarcts, even without acquiring thin-section images.

Simultaneous multielement imaging of liver tissue using laser ablation inductively coupled plasma mass spectrometry

Analysis of the spatial distribution of metals, metalloids, and non-metals in biological tissues is of significant interest in the life sciences, helping to illuminate the function and roles these elements play within various biological pathways. Chemical imaging methods are commonly employed to address biological questions and reveal individual spatial distributions of analytes of interest. Elucidation of these spatial distributions can help determine key elemental and molecular information within the respective biological specimens. However, traditionally utilized imaging methods prove challenging for certain biological tissue analysis, especially with respect to applications that require high spatial resolution or depth profiling. Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) has been shown to be effective for direct elemental analysis of solid materials with high levels of precision. In this work, chemical imaging using LA-ICP-MS has been applied as a powerful analytical methodology for the analysis of liver tissue samples. The proposed analytical methodology successfully produced both qualitative and quantitative information regarding specific elemental distributions within images of thin tissue sections with high levels of sensitivity and spatial resolution. The spatial resolution of the analytical methodology was innovatively enhanced, helping to broaden applicability of this technique to applications requiring significantly high spatial resolutions. This information can be used to further understand the role these elements play within biological systems and impacts dysregulation may have.

Classification of plutonic rock types using thin section images with deep transfer learning

Classification of rocks is one of the basic parts of geological research and is a difficult task due to the heterogeneous properties of rocks. This process is time consuming and requires sufficiently knowledgeable and experienced specialists in the field of petrography. This paper has a novelty in classifying plutonic rock types for the first time using thin section images; and proposes an approach that uses the deep learning method for automatic classification of 12 types of plutonic rocks. Convolutional neural network based DenseNet121, which is one of the deep learning architectures, is used to extract the features from thin section images of rocks; and the classification process is carried out with a single layer fully connected neural network. The deep learning model is trained and tested on 2400 images. AUC, accuracy, precision, recall and f1-score are used as performance measure. The proposed approach classifies plutonic rock images on the test set with an average accuracy of 97.52% and a maximum of 98.19%. Thus, the applied deep transfer learning is promising in geosciences and can be used to identify rock types quickly and accurately.

Geometric and Fractal Characterization of Pore Systems in the Upper Triassic Dolomites Based on Image Processing Techniques (Example from Žumberak Mts, NW Croatia)

Karst aquifers are important sources of thermal and groundwater in many parts of the world, such as the Alpine–Dinaric–Carpathian region in Europe. The Upper Triassic dolomites are regionally recognized thermal and groundwater aquifers but also hydrocarbon reservoirs. They are characterized by predominantly fractured porosity, but the actual share of depositional and diagenetic porosity is rarely investigated. In this research, we presented the geometric characterization of the measured microporosity of the Upper Triassic dolomites of the Žumberak Mts (Croatia), through thin-section image processing and particle analysis techniques. Pore parameters were analyzed on microphotographs of impregnated thin sections in scale. A total of 2267 pores were isolated and analyzed. The following parameters were analyzed: pore area, pore perimeter, circularity, aspect ratio (AR), roundness, solidity, Feret AR, compactness, and fractal dimension. Furthermore, porosity was calculated based on the pore portion in each image. The effective porosity on rock samples was determined using saturation and buoyancy techniques as an accompanying research method. We analyzed distributions of each parameter, their correlation, and most of the parameters are characterized by an asymmetric or asymmetric normal distribution. Parameters that quantify pore irregularities have similar distributions, and their values indicate the high complexity of the pore geometry, which can significantly impact permeability.

Cryosectioning and Immunostaining of Xenopus Embryonic Tissues.

The Xenopus embryo is a classical vertebrate model for molecular, cellular, and developmental biology. Despite many advantages of this organism, such as large egg size and external development, imaging of early embryonic stages is challenging because of nontransparent cytoplasm. Staining and imaging of thin tissue sections is one way to overcome this limitation. Here we describe a step-by-step protocol that combines cryosectioning of gelatin-embedded embryos with immunostaining and imaging. The purpose of this protocol is to examine various cellular and tissue markers after the manipulation of protein function. This protocol can be performed within a 2-d period and allows detection of many antigens by immunofluorescence.

A pilot study on coarse ware ceramic fabrics from the Ayios Vasileios Survey Project (Greece)

In this article, we present the results of our pilot study on coarse ware ceramic fabrics from the Ayios Vasileios Survey Project (Laconia, Greece). The aim of this pilot was to explore the potential of optical fabric analysis on coarse wares on the basis of (mineral) inclusions detectable by eye or under modest magnification. We aimed to answer the following question: can we discern Bronze Age coarse wares from Byzantine/Early Modern coarse wares by means of this technique? We studied 177 ceramic fragments by eye and by means of a stereo microscope. This resulted in the description of 51 different provisional fabrics. Only a few of these fabrics could be assigned to a specific time period with certainty, based on a consistent dating of the sherds by the ceramic specialists, who looked at shape, decoration and fabric. Most of the fabrics seem to consist of sherds stemming from various time periods. A comparison between our provisional fabric groups and those published by other researchers in Laconia shows that possible connections or matches between fabrics made by us should be considered either as tentative or as unreliable beyond the level of argued assumptions. To arrive at more reliable ceramic fabric connections, or the identification of similar fabrics, it will be necessary to publish not only textual descriptions and images of thin sections—as seems to be the common approach—but also series of high-resolution pictures of sherds and their fresh sections, as has been done in this article, together with more detailed descriptions of these sherds.

Empirical rock physics relationships on carbonate dry-frame elastic properties

Laboratory tests were conducted to analyze the ultrasonic velocity response to the pressure change in dry carbonate rocks from the Weyburn oilfield, Canada. Twenty-four samples are from seven wells with helium porosities ranging from 1% to 29%. Thin-section images, SEM and mercury intrusion porosimetry were performed to show their inner structures and pore throat size distributions. P- and S-wave velocities (Vp and Vs) measurements were first done under hydrostatic loading and then while unloading, with confining pressures varying between 3 and 35 MPa. The results indicate that Vp and Vs in these samples follow a linear relation independent of the pressure change. The ratio Vp/Vs is more responsive to pressure change irrespective of the pore volume. One-third of the carbonate samples show abnormal Vp/Vs reduction with the increase in the effective pressure. The pressure dependence of velocities (PDV) of Weyburn carbonate rocks varies widely even for samples from the same formation with similar sedimentary history. Samples with loosely packed crystals and/or relatively large dominant pore diameter have higher PDV. The exponential empirical model V = A-Ctext{e}^{DP_{text{e}}} was tested; therein, V is the elastic wave velocity, Pe is the effective confining pressure, and A, C and D are the best fitting coefficients determined by curve fitting. The model gives good fits for most of the Weyburn carbonate samples. From a statistical point of view, there is no difference between the Vp- and Vs-derived exponential coefficient D.

Tight sandstone reservoir sensitivity and damage mechanism analysis: A case study from Ordos Basin, China and implications for reservoir damage prevention

Analysis of reservoir sensitivity to velocity, water, salt, acid, alkali and stress is critical for reservoir protection. To study the tight sandstone reservoir sensitivity at different formation depths (effective stress) and formation water conditions (pH, salinity, and fluid velocity), a series of dynamic core flow tests under different pH, salinity, acid, and effective stress conditions were performed on samples from tight sandstone reservoirs of the Upper Triassic Yanchang 8 (T3y8) Member and conventional reservoirs of the Middle-Lower Jurassic Yan'an 9 (J1-2y9) Member in the Ordos Basin. The results indicate that, compared with the conventional reservoirs, the tight sandstone reservoirs are more sensitive to velocity and stress, less sensitive to water, alkali and salinity, and respond better to acid fracturing. In addition, the critical conditions (salinity, velocity, pH, and stress) for pumping drilling, completion, and fracturing fluids into tight sandstone reservoirs were investigated. A combination of scanning electron microscopy coupled with energy-dispersive spectrometry (SEM-EDS), cathodoluminescence (CL), casting thin section (CTS) and nuclear magnetic resonance (NMR) images, high-pressure mercury injection capillary pressure (MICP) measurements as well as X-ray fluorescence spectral (XRF) analyses were employed to analyze the damage mechanisms of the conventional reservoirs (J1-2y9) and tight sandstone reservoirs (T3y8) caused by fluid invasion. The results suggest that reservoir sensitivity is primarily conditioned by the composition of detrital components and interstitial fillings, petrophysical properties, pore-throat structure, and diagenetic facies. All these factors control the sensitivity types and extent of the reservoirs. Our results indicate that the poorer the reservoir physical properties, the stronger the reservoir heterogeneity and sensitivity, implying that tight sandstone reservoirs are more susceptible to changes in fluids than conventional reservoirs. This study offers insights into the reservoir damage types and helps to improve the design and implementation of protection measures for tight sandstone reservoir exploration.