Petrophysical Characterization of Deep, Low-Permeability Carbonate Formations in Tarim Oilfield

The paper presents a petrophysical modeling and permeability analysis approach for a tight carbonate formation. The formation samples used in this study are from the Tarim oil field Yingshan formation, deposited during Ordovician in the Tarim basin of Southern Xinjiang in Northwest China. The petrophysical modeling is based on a detailed pore structure analysis utilizing thin sections, focused ion-beam SEM images, XRD, and nitrogen and helium porosimetry data. A permeability analysis was performed after the pore structure was characterized. Rock samples from three wells are analyzed. Lab experiments indicate that the samples are rich in carbonate (typically more than 90%) and experienced diagenesis characterized by cementation associated with dolomitization and healed natural fractures. No significant pore volume is observed in thin section images. Nonetheless, SEM images and nitrogen porosimetry both show that matrix pore volume consists of micro-, meso- and macro-pores. Porosimetry data indicate that most of the rock samples are rich in meso- and macropores with an effective pore size of 60-90 nanometers; 34-80% of total matrix pore volume is due to these pores, while the rest of the pore volume is due to natural fractures and larger pores that have not been captured by nitrogen porosimetry. The petrophysical analysis suggests that reliable reservoir storage and flow models to predict the well performances in the field need to be triple porosity, including re-opened fractures imbedded within a matrix that includes meso and macropores. This thesis is a preparation for the next phase of “Petrophysical Characterization of Deep Low Permeability Carbonate Formations for Fluid Storage and Transport Predictions in Tarim Oilfield” which will include a brief description of a single well numerical model with hydraulic fracture to simulate the base-case production trends from the region and compare with the wells’ performances

Petrophysical Characterization of Deep, Low-Permeability Carbonate Formations in Tarim Oilfield

The paper presents a petrophysical modeling and permeability analysis approach for a tight carbonate formation. The formation samples used in this study are from the Tarim oil field Yingshan formation, deposited during Ordovician in the Tarim basin of Southern Xinjiang in Northwest China. The petrophysical modeling is based on a detailed pore structure analysis utilizing thin sections, focused ion-beam SEM images, XRD, and nitrogen and helium porosimetry data. A permeability analysis was performed after the pore structure was characterized. Rock samples from three wells are analyzed. Lab experiments indicate that the samples are rich in carbonate (typically more than 90%) and experienced diagenesis characterized by cementation associated with dolomitization and healed natural fractures. No significant pore volume is observed in thin section images. Nonetheless, SEM images and nitrogen porosimetry both show that matrix pore volume consists of micro-, meso- and macro-pores. Porosimetry data indicate that most of the rock samples are rich in meso- and macropores with an effective pore size of 60-90 nanometers; 34-80% of total matrix pore volume is due to these pores, while the rest of the pore volume is due to natural fractures and larger pores that have not been captured by nitrogen porosimetry. The petrophysical analysis suggests that reliable reservoir storage and flow models to predict the well performances in the field need to be triple porosity, including re-opened fractures imbedded within a matrix that includes meso and macropores. This thesis is a preparation for the next phase of “Petrophysical Characterization of Deep Low Permeability Carbonate Formations for Fluid Storage and Transport Predictions in Tarim Oilfield” which will include a brief description of a single well numerical model with hydraulic fracture to simulate the base-case production trends from the region and compare with the wells’ performances

The spatial pattern and influencing factors of tourism eco-efficiency in Inner Mongolia, China

BackgroundTourism eco-efficiency is a performance basis for evaluating green total factor productivity and sustainable development.ObjectiveThe objective of this study was to measure tourism eco-efficiency in Inner Mongolia and explore its influencing factors. The aim was to provide an accurate reference for improving the quality and efficiency of tourism in Inner Mongolia and promoting the sustainable development of the regional economy and society.MethodsTourism eco-efficiency in Inner Mongolia from 2009 to 2019 was calculated using a super-slacks-based measure (SBM) model with an undesirable output. The spatial variation function was used to explore the spatial evolution pattern of tourism eco-efficiency in Inner Mongolia, and the influencing factors of the spatial evolution were analyzed by geographically weighted regression.ResultsTourism eco-efficiency in Inner Mongolia is relatively low. Eco-efficiency values among cities in Inner Mongolia vary, and their distribution is not balanced. The structural eco-efficiency of tourism in Inner Mongolia has been consistent from 2009 to 2019. The degree of homogenization in the overall direction is relatively good. Furthermore, its spatial distribution form and internal structure evolution show a certain regularity and continuity. The pattern evolution of tourism eco-efficiency in Inner Mongolia is jointly driven by the economic level, environmental regulation, industrial structure, traffic conditions, resource endowment, and tourism reception facilities. These influencing factors show obvious spatial heterogeneity.ConclusionFrom the perspective of Inner Mongolia, the difference in the tourism eco-efficiency value from 2009 to 2019 was relatively large, but the number of effective areas in the efficiency frontier generally showed a fluctuating growth trend. The range parameters of tourism eco-efficiency showed a decreasing trend, and the spatial correlation effect of tourism eco-efficiency in Inner Mongolia showed a decreasing trend under the influence of structural and spatial differentiation

Association Between the Ratio of Ovarian Stimulation Duration to Original Follicular Phase Length and In Vitro Fertilization Outcomes: A Novel Index to Optimise Clinical Trigger Time

The duration of ovarian stimulation which is largely dependent on the ovarian response to hormonal stimulation may influence in vitro fertilization (IVF) outcomes. Menstrual cycle length is potentially a good indicator of ovarian reserve and can predict ovarian response. Ovarian stimulation and the follicular phase of the menstrual cycle are both processes of follicular development. There is no published research to predict the duration of ovarian stimulation based on the length of the menstrual cycle. Our retrospective cohort study included 6110 women with regular menstrual cycles who underwent their first IVF treatment between January 2015 and October 2020. Cycles were classified according to quartiles of the ratio of ovarian stimulation duration to original follicular phase length (OS/FP). Multivariate generalized linear models were applied to assess the association between OS/FP and IVF outcomes. The odds ratio (OR) or relative risk (RR) was estimated for each quartile with the lowest quartile as the comparison group. OS/FP of 0.67 to 0.77 had more retrieved and mature oocytes (adjusted RR 1.11, 95% confidence interval [CI] 1.07–1.15, p for trend = 0.001; adjusted RR 1.14, 95% CI 1.09–1.19, p for trend = 0.001). OS/FP of 0.67 to 0.77 showed the highest rate of fertilization (adjusted OR 1.11, 95% CI 1.05–1.17, p for trend = 0.001). OS/FP > 0.77 had the lowest rate of high-quality blastocyst formation (adjusted OR 0.81, 95% CI 0.71–0.93, p for trend = 0.01). No apparent association was noted between OS/FP and clinical pregnancy, live birth, or early miscarriage rate. In conclusion, OS/FP has a significant effect on the number of oocytes, fertilization rate, and high-quality blastocyst formation rate. MCL could be used to predict the duration of ovarian stimulation with an OS/FP of 0.67 to 0.77, which provides a new indicator for the individualized clinical optimization of the trigger time

Action-Quantized Offline Reinforcement Learning for Robotic Skill Learning

The offline reinforcement learning (RL) paradigm provides a general recipe to convert static behavior datasets into policies that can perform better than the policy that collected the data. While policy constraints, conservatism, and other methods for mitigating distributional shifts have made offline reinforcement learning more effective, the continuous action setting often necessitates various approximations for applying these techniques. Many of these challenges are greatly alleviated in discrete action settings, where offline RL constraints and regularizers can often be computed more precisely or even exactly. In this paper, we propose an adaptive scheme for action quantization. We use a VQ-VAE to learn state-conditioned action quantization, avoiding the exponential blowup that comes with na\"ive discretization of the action space. We show that several state-of-the-art offline RL methods such as IQL, CQL, and BRAC improve in performance on benchmarks when combined with our proposed discretization scheme. We further validate our approach on a set of challenging long-horizon complex robotic manipulation tasks in the Robomimic environment, where our discretized offline RL algorithms are able to improve upon their continuous counterparts by 2-3x. Our project page is at https://saqrl.github.io

An automatic catchment and root irrigation device for desert trees

Now, there are 36 million km2 of deserts on the planet, a quarter of the land area, and expanding by 60,000 km2 per year. To combat climate change and achieve the carbon peaking and carbon neutrality goals, China is engaged in afforestation. However, water scarcity, strong winds and shortcomings of current irrigation methods make afforestation a major challenge. Based on the problems, we design an automatic catchment and root irrigation device for desert trees

Twisted Epithelial-to-Mesenchymal Transition Promotes Progression of Surviving Bladder Cancer T24 Cells with hTERT-Dysfunction

Human cancer cells maintain telomeres to protect cells from senescence through telomerase activity (TA) or alternative lengthening of telomeres (ALT) in different cell types. Moreover, cellular senescence can be bypassed by Epithelial-to-mesenchymal transition (EMT) during cancer progression in diverse solid tumors. However, it has not been elucidated the characteristics of telomere maintenance and progression ability after long-term culture in bladder cancer T24 cells with hTERT dysfunction.In this study, by using a dominant negative mutant human telomerase reverse transcriptase (hTERT) vector to inhibit TA in bladder cancer T24 cells, we observed the appearance of long phenotype of telomere length and the ALT-associated PML body (APB) complex after the 27(th) passage, indicating the occurrence of ALT-like pathway in surviving T24/DN868A cells with telomerase inhibition. Meanwhile, telomerase inhibition resulted in significant EMT as shown by change in cellular morphology concomitant with variation of EMT markers. Consistently, the surviving T24/DN868A cells showed increased progression ability in vitro and in vivo. In addition, we found Twist was activated to mediate EMT in surviving T24/DN868A samples.Taken together, our findings indicate that bladder cancer T24 cells may undergo the telomerase-to-ALT-like conversion and promote cancer progression at advanced stages through promoting EMT, thus providing novel possible insight into the mechanism of resistance to telomerase inhibitors in cancer treatment

Reviving Static Charts into Live Charts

Data charts are prevalent across various fields due to their efficacy in conveying complex data relationships. However, static charts may sometimes struggle to engage readers and efficiently present intricate information, potentially resulting in limited understanding. We introduce "Live Charts," a new format of presentation that decomposes complex information within a chart and explains the information pieces sequentially through rich animations and accompanying audio narration. We propose an automated approach to revive static charts into Live Charts. Our method integrates GNN-based techniques to analyze the chart components and extract data from charts. Then we adopt large natural language models to generate appropriate animated visuals along with a voice-over to produce Live Charts from static ones. We conducted a thorough evaluation of our approach, which involved the model performance, use cases, a crowd-sourced user study, and expert interviews. The results demonstrate Live Charts offer a multi-sensory experience where readers can follow the information and understand the data insights better. We analyze the benefits and drawbacks of Live Charts over static charts as a new information consumption experience