Estimate of Saturation Pressures of Crude Oil by Using Ensemble-Smoother-Assisted Equation of State

The equation of state (EOS) has been extensively used to evaluate the saturation pressures of petroleum fluids. However, the accurate determination of empirical parameters in the EOS is challenging and time-consuming, especially when multiple measurements are involved in the regression process. In this work, an ensemble smoother (ES) -assisted EOS method has been proposed to compute the saturation pressure by intelligently optimizing the to-be-tuned parameters. To be specific, the to-be-tuned parameters for the Peng–Robinson EOS (PR EOS) are integrated into a model input matrix and the measured saturation pressures are collected into a model output matrix. The model input matrix is then integrally and iteratively updated with respect to the model output matrix by using the iterative ES algorithm. For convenience, an in-house module is compiled to implement the ES-assisted EOS for determining the saturation pressures of crude oils. Subsequently, the experimentally measured saturation pressures of 45 mixtures of heavy oil and solvents are used to validate the performance of the in-house module. In addition, 130 measured saturation pressures of worldwide light oil samples are collected to verify the applicability of the developed ES-assisted EOS method. The in-house module is found to be competent by not only matching 45 measured saturation pressures with a better agreement than a commercial simulator but also providing a quantitative means to analyze the uncertainties associated with the estimated model parameters and the saturation pressure. Moreover, the application of the ES-assisted EOS to 130 light oil samples distinctly demonstrates that the new method greatly improves the accuracy and reliability of the EOS regression. Consequently, the in-house module representing the ES-assisted EOS is proven as an efficient and flexible tool to determine the saturation pressure under various conditions and implement uncertain analyses associated with the saturation pressure

Improving recovery efficiency by CO2 injection at late stage of steam assisted gravity drainage

The high recovery performance of steam-assisted gravity drainage (SAGD) makes it a popular option for heavy oil resources. Currently, most of the heavy oil reservoirs developed by SAGD in China are in the late development phase, with high energy consumption due to reduced thermal efficiency. The use of SAGD wind-down processes involving CO2 in combination with steam for heavy oil recovery is considered as a viable alternative to limit energy consumption, and also reduce the amount of greenhouse gas emissions by leaving CO2 behind in the reservoir. Study reveals that the dissolution and demulsification of CO2 steam chamber temperature reaches 200 ◦C, the amount of solid phase deposition induced in crude oil can reduce the viscosity of emulsified heavy oil by more than 50%. When the by CO2 extraction is only 0.016 kg/m3 , the rock wettability changes from lipophilic to hydrophilic, and the higher the reservoir temperature, the stronger the hydrophilicity is, which reduces the adhesion power of the oil phase and facilitates the stripping of crude oil from the rock surface. Numerical simulation studies have been carried out utilizing STARS to obtain energy efficient utilization and improved steam chamber characteristics. Heat loss from SAGD baseline is 1.77 times that with CO2 injection process, but the recovery factor is only 2.48% higher. At the initial stage with CO2 injection, the steam chamber continues its lateral expanding, which increases the recovery factor at the initial stage of CO2 injection by about 6%. One year after CO2 injection, gas channeling results in lower recovery than traditional SAGD process, and 38.4% of the injected CO2 is stored in the reservoir from this study.Cited as: Gong, H., Yu, C., Jiang, Q., Su, N., Zhao, X., Fan, Z. Improving recovery efficiency by CO2 injection at late stage of steam assisted gravity drainage. Advances in Geo-Energy Research, 2022, 6(4): 276-285. https://doi.org/10.46690/ager.2022.04.0

Natural history of spontaneous aortic intramural hematoma progression: Six years follow-up with cardiovascular magnetic resonance

We described a 6 years follow-up of a spontaneous aortic intramural hematoma (IMH) with cardiovascular magnetic resonance (CMR) examination. Since multiple factors may play roles in the natural history of IMH, the patient experienced the course of progression, which included hematoma absorption, ulcer-like lesion, aneurysm and limited dissection. The initial and follow-up CMR examination included 3D CE MRA and non-enhanced "bright blood" pulse sequence. The inherent advantage of outstanding contrast with plain scan, which shorten the scan time and avoid potential risk of contrast agent, might make the fast gradient echo sequence as an alternative method when following stable IMH

Multi-contrast atherosclerosis characterization (MATCH) of carotid plaque with a single 5-min scan: technical development and clinical feasibility

BACKGROUND: Multi-contrast weighted imaging is a commonly used cardiovascular magnetic resonance (CMR) protocol for characterization of carotid plaque composition. However, this approach is limited in several aspects including low slice resolution, long scan time, image mis-registration, and complex image interpretation. In this work, a 3D CMR technique, named Multi-contrast Atherosclerosis Characterization (MATCH), was developed to mitigate the above limitations. METHODS: MATCH employs a 3D spoiled segmented fast low angle shot readout to acquire data with three different contrast weightings in an interleaved fashion. The inherently co-registered image sets, hyper T1-weighting, gray blood, and T2-weighting, are used to detect intra-plaque hemorrhage (IPH), calcification (CA), lipid-rich necrotic core (LRNC), and loose-matrix (LM). The MATCH sequence was optimized by computer simulations and testing on four healthy volunteers and then evaluated in a pilot study of six patients with carotid plaque, using the conventional multi-contrast protocol as a reference. RESULTS: On MATCH images, the major plaque components were easy to identify. Spatial co-registration between the three image sets with MATCH was particularly helpful for the reviewer to discern co-existent components in an image and appreciate their spatial relation. Based on Cohen’s kappa tests, moderate to excellent agreement in the image-based or artery-based component detection between the two protocols was obtained for LRNC, IPH, CA, and LM, respectively. Compared with the conventional multi-contrast protocol, the MATCH protocol yield significantly higher signal contrast ratio for IPH (3.1 ± 1.3 vs. 0.4 ± 0.3, p < 0.001) and CA (1.6 ± 1.5 vs. 0.7 ± 0.6, p = 0.012) with respect to the vessel wall. CONCLUSIONS: To the best of our knowledge, the proposed MATCH sequence is the first 3D CMR technique that acquires spatially co-registered multi-contrast image sets in a single scan for characterization of carotid plaque composition. Our pilot clinical study suggests that the MATCH-based protocol may outperform the conventional multi-contrast protocol in several respects. With further technical improvements and large-scale clinical validation, MATCH has the potential to become a CMR method for assessing the risk of plaque disruption in a clinical workup

DNA barcoding and comparative RNA-Seq analysis provide new insights into leaf formation using a novel resource of high-yielding Epimedium koreanum

Epimedium koreanum Nakai, a well-known traditional Chinese medicinal herb, has been widely used to treat osteoporosis and sexual dysfunction for thousands of years. However, due to the decreasing population of East Asian natural resources, yearly output of Epimedium crude herb has been in low supply year by year. In this study, an unusual variety of E. koreanum was discovered in Dunhua, Jilin Province, the northernmost area where this variety was found containing 6 individuals, with three branches that had 27 leaflets, which is much more than the typical leaflet number of 9. Firstly, the novel E. koreanum varety was identified using DNA barcodes. Then, 1171 differentially expressed genes (DEGs) were discovered through parallel RNA-seq analysis between the newly discovered variety and wild type (WT) E. koreanum plant. Furthermore, the results of bioinformatics investigation revealed that 914 positively and 619 negatively correlated genes associated with the number of leaflets. Additionally, based on RNA-Seq and qRT-PCR analysis, two homologous hub TCP genes, which were commonly implicated in plant leaf development, and shown to be up regulated and down regulated in the discovered newly variety, respectively. Thus, our study discovered a novel wild resource for leaf yield rewarding medicinal Epimedium plant breeding, provided insights into the relationship between plant compound leaf formation and gene expression of TCPs transcription factors and other gene candidates, providing bases for creating high yield cultivated Epimedium variety by using further molecular selection and breeding techniques in the future

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Determination of Klinkenberg Permeability Conditioned to Pore-Throat Structures in Tight Formations

This paper has developed a pragmatic technique to efficiently and accurately determine the Klinkenberg permeability for tight formations with different pore-throat structures. Firstly, the authors use steady-state experiments to measure the Klinkenberg permeability of 56 tight core samples under different mean pore pressures and confining pressures. Secondly, pressure-controlled mercury injection (PMI) experiments and thin-section analyses are conducted to differentiate pore-throat structures. After considering capillary pressure curve, pore types, throat size, particle composition, and grain size, the pore-throat structure in the target tight formation was classified into three types: a good sorting and micro-fine throat (GSMFT) type, a moderate sorting and micro-fine throat (MSMFT) type, and a bad sorting and micro throat (BSMT) type. This study found that a linear relationship exists between the Klinkenberg permeability and measured gas permeability for all three types of pore-throat structures. Subsequently, three empirical equations are proposed, based on 50 core samples of data, to estimate the Klinkenberg permeability by using the measured gas permeability and mean pore pressure for each type of pore-throat structure. In addition, the proposed empirical equations can generate accurate estimates of the Klinkenberg permeability with a relative error of less than 5% in comparison to its measured value. The application of the proposed empirical equations to the remaining six core samples has demonstrated that it is necessary to use an appropriate equation to determine the Klinkenberg permeability of a specific type of pore-throat structure. Consequently, the newly developed technique is proven to be qualified for accurately determining the Klinkenberg permeability of tight formations in a timely manner