Search

Summary Steering drilling is used for exploring oil, natural gas, and other liquid and gaseous minerals. Steering drilling consists of high-efficiency drill bits, steering power drilling tools, and logging while drilling (LWD) and is used in petroleum drilling engineering. This paper mainly discusses subhorizontal drain geosteering, one of the methods of guided subhorizontal drilling. We use the currently popular deep learning method to conduct intelligent guided drilling. Geosteering is a sequential drilling decision process under uncertain stratum environment. However, the current geosteering drilling process relies heavily on manual work and has no use of temporal context. This paper aims to solve decision-making of geosteering in deep well (between 4500 and 6000 km) or ultradeep well (between 6000 and 9000 km). To this end, we make three contributions: (1) a wide-angle eye mechanism to obtain more geological information; (2) an asymmetric peephole convolutional long short-term memory (APC-LSTM) approach for geosteering drilling decision, whose input data were assembled with the wide-angle eye mechanism; and (3) use of the deep convolution generative adversarial networks (DCGAN) model to generate simulated logging data and conduct experiments in the simulation environment to verify our proposed method. APC-LSTM can capture the spatial-temporal correlation better between different strata for decision-making. Meanwhile, the APC-LSTM drilling decision model achieved better performance than other advanced methods in two drilling data sets. Tested in a simulative drilling environment, our proposed model achieves excellent application effect. Moreover, our method has been applied to the wells of oil field in practice.

OBJECTIVE To determine the 50% effective dose (ED50) of intravenous propofol required for successfully preventing tracheal intubation response in Beagles co-induced with dexmedetomidine. ANIMALS 36 adult male Beagles PROCEDURES The dogs were randomly assigned to either group D1, group D2, or group C (received 1 µg/kg, 2 µg/kg dexmedetomidine intravenously, or the same amount of normal saline as dexmedetomidine, 10 mL). The first dog in each group received 6 mg/kg of propofol for induction. The pump speed of propofol was 600 mL/h. The dosage varied with increments or decrements of 0.5 mg/kg based on the Dixon up-and-down method. The duration of eye-opening after propofol administration was recorded. Changes in heart rate (HR) and respiratory rate (RR) were recorded at 5 timepoints: after entering the operation room and prior to propofol administration (T1), 1 and 3 min after propofol administration (T2 and T3), 3 and 5 min after intubation (T4 and T5). RESULTS The required ED50 of propofol that prevented tracheal intubation response in D1, D2, and C groups were 6.4 mg/kg (95% CI, 6.1 to 6.7 mg/kg), 5.8 mg/kg (95% CI, 5.67 to 6 mg/kg), and 8.3 mg/kg (95% CI, 8 to 8.5 mg/kg), respectively. The recovery time of group D2 was significantly longer than that of groups D1 and C (P < .05). The differences in HR among the 3 groups were significant from T2 up to T5 timepoint (P < .05). The differences in RR among the 3 groups were significant at T2 and T3 timepoints (P < .05). CLINICAL RELEVANCE Dexmedetomidine pre-injection reduces the amount of propofol required for endotracheal intubation response in Beagles, thereby reducing the respiratory inhibition induced by propofol.

In this paper, we use a surprised system to construct some exact solutions of compressible Euler equations with two and three dimension. Furthermore, we also give other exact solutions of three dimension incompressible Euler equations.

Emerged as important regulators in cancer progression, circular RNAs have been tested to participate in diverse biological processes. Former studies have suggested that circular RNA_LARP4 (circLARP4) exerts indispensable function on the development of different cancers such as gastric cancer and ovarian cancer. Nonetheless, the specific role of circLARP4 has not been discovered in ESCC. The aim of this study is to explore the biological function and regulatory mechanism of circLARP4 in ESCC. CircLARP4, miR-1323, and PTEN expression levels were quantified by RT-qPCR. CCK-8, EdU, caspase-3 activity, wound healing, transwell, and western blot assays were chosen to assess ESCC cell growth. Luciferase reporter, RIP, and RNA pull-down assays were performed to examine the interaction between miR-1323 and circLARP4 (or PTEN). CircLARP4 expression was observably downregulated in ESCC cell lines, and overexpressed circLARP4 restrained cell proliferation and migration whereas boosted cell apoptosis in ESCC. Molecular mechanism experiments revealed that circLARP4 could act as a sponge for miR-1323 and negatively modulated miR-1323 expression in ESCC. Interestingly, the repression of miR-1323 was correlated with inhibitive cell proliferation, migration, and promotive apoptosis. Besides, miR-1323 bound with PTEN, and PTEN expression was negatively regulated by miR-1323 whereas positively regulated by circLARP4 in ESCC. Moreover, rescue assays testified that miR-1323 overexpression or PTEN deficiency could countervail the function of circLARP4 overexpression on ESCC progression. More importantly, circLARP4 played an inhibitory role in PI3K/AKT pathway. CircLARP4 sponges miR-1323 and hampers tumorigenesis of ESCC through modulating PTEN/PI3K/AKT pathway.

Boltzmann equation is an equation which is related to the three variables of x, v, t. In this paper, we mainly study the space-uniform Boltzmann equation which unknown function F is not related to the position variable x. We mainly use the contraction mapping theorem to find the existence of the solution, so our mainly work is to prove the self-mapping, i.e. to prove its uniformly bounded, and then to prove the contraction mapping. There we can get the range of ||B(θ)||L1(L∞), next we can figure out the range of M and T from the conditions what we know. Finally, from these conditions, we can find the existence of the solution.

The accurate location of the rocket recovery cabin section is one of the key technologies to realize the reliable recovery rockets. Based on the multi-station time difference of arrival (TDOA) passive location principle, a technical solution for the recovery and positioning of the rocket recovery cabin section is designed. Through the active radiation signal of the recovery cabin, the whale optimization algorithm (WOA) is used to locate the position of the rocket recovery cabin section. The recovery process trajectory of the rocket's stage 1 cabin was adopted to verify the feasibility of the designed recovery positioning technical scheme.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) accounts for 90% of head and neck malignant tumors. As the early symptoms of HNSCC are not obvious, and it is prone to recurrence and metastasis, making the overall survival (OS) rate of patients very low. Existing studies have shown m6A methylation plays a crucial role in various cancers, but it is rarely studied in HNSCC. This study aimed to explore the expression of m6A methylation-related genes in HNSCC and its correlation with prognosis, and to explore its relationship with immune infiltration. METHODS: The gene expression data of HNSCC patient tumor samples (tumor =510) and adjacent normal tissue samples (normal =50) were extracted from The Cancer Genome Atlas (TCGA) database, and the expression characteristics of m6A regulatory factors were described. Kaplan-Meier survival analysis was used to analyze the relationship between m6A regulatory factors and OS and disease-specific survival (DSS). Least absolute shrinkage and selection operator (LASSO) regression was used to construct the m6A regulatory factor-HNSCC risk prediction model. In addition, the relationship between m6A methylation-related genes and tumor immune infiltration were discussed. RESULTS: The differential expression of 20 genes were identified by TCGA, and 18 genes (IGF2BP2, IGF2BP1, IGF2BP3, VIRMA, YTHDF1, YTHDF2, YTHDF3, ZC3H13, METTL14, ALKBH5, METTL3, RBMX, WTAP, YTHDC1, FTO, HNRNPC, HNRNPA2B1, and RBM15) were overexpressed in HNSCC. The survival rate of different gene expression levels was different. The high expression of YTHDC1 and YTHDC2 indicated better OS. Furthermore, for DSS, increased expression of YTHDC2 was also correlated with better clinical outcomes (P

The impact of moisture on gas adsorption capacity reduction on coal has been well recognized, and empirical correlations are widely used to quantitatively evaluate the moisture effect. However, few studies are found on fundamental modeling of the moisture effect on gas adsorption capacity. In this work, two theoretical models on the basis of the extended Langmuir theory (EL-based) and the ideal adsorbed solution theory (IAS-based) were developed to account for the gas adsorption capacity with different pressures and moisture contents. With the parameters determined from the gas adsorption on dry samples and water adsorption on samples under atmospheric conditions, both models are able to predict the gas adsorption capacity under combined effects of gas pressure and moisture content. The models were verified through a set of experimental data from a coal sample from Australia, and they were further applied to describe the methane adsorption behavior on a coal sample from New Zealand. The results demonstrat...

Data centres (DC) house electrical and electronic data processing and storage equipment. The data centre industry has grown from zero in the late 1980s into a global service provider with over 7 million sites in 2021. This rapid sectoral growth presents many challenges to environmental, social, and economic sustainability including the generation of large quantities of e-waste, (which is exacerbated by an under-developed recycling infrastructure, and a limited market for second life products) and potential threat to material supply chains, specifically Critical Raw Materials. There is an urgent need to improve sectoral sustainability, which includes development of a Circular Economy. The CEDaCI (a Circular Economy for the Data Centre Industry) project was launched in 2018 to initiate this by demonstrating its viability though business case studies, physical prototypes and digital tools, a key example being the Circular Data Centre Compass (CDCC) – a unique online tool that includes original primary source data. This tool allows users to compare and assess the overall life cycle sustainability of different servers, the criticality of embodied materials and the circularity of current and proposed designs. Therefore, the CDCC improves overall life cycle management of data centres, which increases supply chain security and reduces risk of DC service interruption.

The conventional well trajectory design mainly developed from spatial mathematical model e.g. arcs or cylindrical spiral lines method, focusing on seeking optimal kickoff point and curvature. However, the complex well trajectory design requires more sophisticated and efficient method, considering greater contact with reservoir pay zone, which means time consuming computation works based on conventional well trajectory design. The automated well trajectory planning can meet such requirements, based on reservoir area identification, reservoir matrix evaluation, wellbore trajectory path generation. The present study aims to develop this algorithm for automated drilling trajectory design without assistances from human experts. Based on the stratigraphic figure of a specific reservoir, the present algorithm is able to provide an optimal trajectory with more contact zone while still meeting the curvature requirement. An intelligent planning algorithm of drilling trajectory is proposed on basis of computer vision. Based on the stratigraphic figure of a specific reservoir, the present algorithm is able to provide an optimal trajectory with more contact zone. By converting the reservoir profile (pay zone) to digital matrix and evaluating the pay zone matrix, the optimal well path is able to be automated generated. The research finding can be potential applied in geosteering drilling or autumated drilling.

The characteristics of the massive data is inspected which is accumulated by the energy management system of industrial enterprises during the production process. They are real-time, massive, non-linear, and even unstructured, meanwhile some implicit correlation are hidden between them. These correlations have not yet been fully learned and unearthed. Here big data technology is proposed to improve the availability of energy data for the energy saving and consumption reduction. The general pattern of big data application is proposed in energy management systems. On this basis, some algorithms are applied to the actual management process, including parameter prediction, status monitoring, operation optimization and performance evaluation. Finally, two practical examples of the constructed model are presented including compressed air prediction and Operation prediction of air-conditioning system. The results show that big data technology can improve the efficiency of data acquisition, expand data applications, and exert greater data value.

Formation and lithology identification based on well logging curves reflecting geophysical response characteristics is fundamental for drilling planning and reservoir recovery. For the purpose of providing efficient, accurate, and comprehensive insights for drilling operation decisions, the present research evaluates three typical supervised learning algorithms based on machine learning, e.g. Adaboost, decision tree and support vector machine (SVM). By comparing the prediction results from three typical classification algorithms based on performance metrics such as accuracy, precision, recall, F1 score, Adaboost and decision tree are found to present more accurate prediction with relatively higher accuracy, precision, recall and F1 score. The prediction accuracy is positively related to the training data set proportion for all three approaches. Decision tree approach spends less computation time while still provides favorable prediction scores. The accuracy of prediction gradually increases as the number of logging features increases. Accuracy for most parameter combinations beyond four logging parameters can be up to 90%. The neutron porosity and the spontaneous potential are considered as the most influential parameters affecting the prediction accuracy.

Under the environment of China’s continuous promotion of energy conservation and emission reduction, the idea of on-demand energy supply of steam power system in tobacco manufacturing industry is put forward. Through the research and optimization of prediction algorithm, we analyze the historical data and energy supply process of steam energy, and establish the on-demand energy supply prediction model of steam energy, in order to realize the on-demand energy supply prediction of steam energy in tobacco manufacturing industry.

Channel fracturing generally refers to a kind of novel hydraulic fracturing treatment that relies on the intermittent pumping of proppant-laden and proppant-free fluid to generate highly conductive channels within the formation. The fracture conductivity within the proppant pillar can increase up to several folds. However, how to effectively evaluate the fracture conductivity in channel fracturing is a tough problem. Unlike conventional fracturing, there are two distinct conductive media within the fracture: proppant pack and free channel. This paper analyzes the special proppant distribution pattern of channel fracturing, and the expression of fracture permeability is derived. On the basis of Hertz contact theory and geometry of proppant embedment, the expression of fracture opening is obtained. Then the effects of proppant distribution density and proppant pillar radius on fracture conductivity are analyzed. In the subsequent chapter, the correlation between proppant pillar permeability and free channel permeability is provided. The results show that the fracture conductivity increases at first and then decreases rapidly with the increase in proppant distribution density. The conductivity factor reaches its maximum value when vertical pillar spacing is equal to horizontal spacing. The more regular the shape of the proppant pillar is, the greater the fracture permeability is. The research in this paper provides a simple method to evaluate the key parameters controlling the fracture conductivity and permeability in channel fracturing.

Hydraulic fracture is the main flow path for gas transport. The proppants are man-made material that filled in the hydraulic fractures to keep them open and allow gas flow through. The permeability change of hydraulic fracture is controlled by the combined effect of compaction and embedment. In this study, we modeled the proppant embedment as a function of effective stress by a transformed Hertz contact model and a proposed power law model which is analogous to the Oliver-Pharr model. The results illustrate that the power law relationship could better fit the experimental data, because the Hertz model becomes invalid when the embedment is large compared to the proppant size. By incorporating the power law correlation into an existing theoretical permeability model as a function of effective stress, a permeability model for the hydraulic fracture filled with proppant packs under combined effect of compaction and embedment is developed. The new model is able to adequately describe the permeability data of proppant packs confined by rock core slices. Although this study puts forward the theoretical basis of the hydraulic permeability modelling under combined effect of compaction and embedment, more fundamental studies are required to investigate the contact behaviour between the proppant packs and the fracture face under various conditions. Therefore, the permeability model could be further improved by introducing the new advanced proppant embedment correlations.

The current drilling guide operation relies on the two-way transmission of signals between the downhole drilling tools and the ground control center. However, the downhole environment is sometimes not conducive to such real-time signal transmission, and the analysis and decision-making in the ground involves complex human expert analysis and fine management. To deal with these problems, this paper proposes a downhole self-steering guided drilling method based on a reinforcement learning framework to achieve the 3D well trajectory design and control in real-time. In every time interval of the drilling process, the proposed system evaluates the drilling status and gives the adjustment action of drill bit in 3D space according to the received data, guiding the drill bit to the target reservoir without the involvement of human. The main module is a modified deep Q network using Sarsa algorithm for online self-learning. The experimental results show that after training, the drill bit is increasingly able to select control actions closer to the target reservoir. The frequency of effective actions is approximately 258% higher after the algorithm converges. The proposed system has the ability of online self-learning, which can automatically adjust the evaluation and decision models without manual monitoring.

In the transmission process of crude oil gathering system, water-assisted heat transfer is often used to avoid wax formation, and pipeline temperature and pressure control are often controlled manually. In order to improve control efficiency and save labor cost. In this paper, we propose a DQN-based algorithm. The intensive learning model completes the temperature and pressure control in the pipeline. At the same time, because these two parameters have strong coupling, which affects the global control, this paper focuses on the joint optimization of valve opening and heating furnace and pressure pump. Finally, in order to verify the effectiveness of the system, the simulation control experiment is adopted. The test results show that the system control effect is excellent and the robustness is good.

The intelligent guided drilling system adopts the precise guided drilling geological system and a new rotary steering drilling tool to achieve deep drilling intelligent cruise. It can increase the amount of oil and gas exploration and ensure safety in production. However, the geosteering problem in deep wells and ultra-deep wells is still an outstanding issue due to the hostile environment for signal transmission. In this research, an autonomous geosteering method based on deep learning model is proposed, which is able to make the strategic decision of the drill bit direction in downhole operating mode. According to the characteristics of the Logging While Drilling (LWD) data, the "Wide-angle Eye" mechanism is embedded to feel the future change of stratum ahead and give preview information to the drill bit. Consequencely, the Drilling Decision Model is designed to be a Convolutional Neural Network (ConvNet). The performance of the proposed model was validated in simulation, and the experimental results indicate that the proposed method has high accuracy and robustness, appearing an enhanced capacity to predict stratigraphic changes.

Channel fracturing revolutionarily changes continuous sanding into discontinuous distribution by wrapping proppant particles with a degradable fiber and then injecting these packs or pillars into the formation. Channel fracturing creates open channels between pillars and substantially increases the fracture conductivity. The pulse time of injecting proppants can influence pillar spacing, thereby changing the opening of the fracture. Under different formation conditions, determining a proper pulse time to improve the wellbore productivity is the key problem in channel fracturing. On the basis of Hertz contact theory, we assume that a proppant pillar is a standard cylindrical indenter. We also calculate the penetration depth beneath the original surface. In this manner, we obtain the residual fracture width under a certain confining stress. This residual fracture width serves as our optimization objective. Then, by combining the flow conservation equation, we find that a restriction relationship exists between the pillar spacing and pulse time. By controlling the variables, we adjust the parameters to find the maximum fracture width. The fracture width reaches its maximum when the horizontal spacing equals vertical spacing. A set of simulations are completed and analyzed. The results show that the pulse time, pillar spacing, and formation parameters such as the closure pressure, elasticity modulus and initial crack width, can significantly affect the supporting effectiveness of the proppant pillar. Critical points exist when these parameters increase or decrease to a certain level. For the convenience of field operation, a reference plate is provided. A proper pulse time can be determined on the plate under certain formation conditions, thereby providing theoretical guidance for actual channel fracturing.

Although many coal permeability models have been developed in the past decades to describe the coal permeability behaviour under elastic state, few of them address the coal permeability change under plastic and post-failure state which is often the case within the plastic region adjacent to the excavation face in underground coal mining. In this study, a methodology to model permeability change from elastic to post-failure state is developed by using a modified logistic growth function in conjunction with the classic exponential coal permeability correlation. The proposed coal permeability model is a function of mean effective stress which controls the coal compaction and deviatoric effective stress which controls coal fracturing. The coal permeability may increase by up to several orders of magnitude after failure and then reaches a plateau during triaxial tests. The new model is able to capture this behaviour by matching a set of permeability data in transition from elastic to post-failure state under triaxial stress conditions. This modelling approach may be used to better understand coal permeability changes associated with mining activities, which have applications in the prediction of gas emission, risk assessment of coal and gas outburst, and analysis of gas drainage near mining openings. It is anticipated that the current work may attract more attentions on coal permeability modelling under plastic condition, a critical issue for mining safety.

A large amount of experimental data for methane adsorption capacity in shale are available and the Langmuir model is capable to describe most of these adsorption isotherms. Two parameters used in the Langmuir model are the Langmuir pressure constant P L and the Langmuir volume constant V L . However, experimental data also demonstrate that the gas adsorption capacity of shale is greatly affected by temperature and the classic Langmuir model, with temperature independent V L and temperature dependent P L , could not well interpret the experimental data. This is partly attributed to the isosteric heat of adsorption, which makes V L vary with temperature. The change of V L with temperature for gas adsorption in shale and its dependency on shale properties are still not well understood. In this study, the variation characteristics of V L with temperature for gas shale are investigated through a modeling analysis on the published gas sorption data on the shales from the US, China, Canada, and etc. The classic Langmuir model is improved by considering the temperature dependent V L . The results show that the improved Langmuir model can reasonably describe the shale gas sorption data with less fitting parameters required and it allows a better understanding of gas sorption capacity under the combined effect of temperature and pressure. In addition, the model parameter analysis indicates that the adsorption capacity of shale samples with lower TOC is more likely to be affected by temperature change on the basis of the limited data available. More experiments are required to investigate how the shale properties affect the thermal impact on the gas adsorption capacity on gas shale.

Effects of copper ferrites with different prepared methods on the catalytic degradation of lignin with hydrogen peroxide as oxidant is studied. The microstructure, spectral properties and magnetic properties of copper ferrites prepared with five methods were characterized. The results showed that the microcosmic appearance of the catalysts prepared by the five methods was nanoparticles or irregular blocks. All the samples have strong magnetic properties. However the magnetic saturation intensity, residual magnetic intensity and coercivity of copper ferrite prepared by different methods are distinct. The degradation rate and the content of benzene-ring substances were the highest when lignin decomposed under the catalyst of copper ferrite prepared by ethylene glycol assisted sol-gel process, while the content of open-ring substances was the highest when lignin decomposed under the catalyst of copper ferrite made by co-precipitation. Compared with Fenton catalyst, The copper ferrite catalyst easily be separated and good repetitive catalytic performance when used in the lignin degradation. In addition, the crystal structure of copper ferrite play an important role on the catalytic degradation properties of lignin.

Smart lignin hydrogels with pH responsive and magnetic properties was prepared by free radical polymerization from lignin and acrylamide. In order to improve its swelling properties, calcium carbonate as pore-foaming agent was introduced to the polymer formulation, then magnetic particles (Fe3O4) was formed in the polymer by in situ reaction and co-precipitation. The results showed that degree of swelling of pH responsive hydrogels (PLH) containing the pore-foaming agent increased 47% compared to the original hydrogels (OLH) without pore-foaming agent, and the swelling rate of PLH reached 100% within 13.5 min under optimum pH conditions (pH = 6.8) which is a shorter amount of time, 1.5 times, compared to the OLH. The swelling-deswelling experiments showed that the hydrogels retained good reversibility and stability. Fourier transform infrared (FTIR) spectra indicated that the molecular chain of the PAM matrix hydrogels was successfully grafted on the lignin and the magnetic particles (Fe3O4) precipitated in the hydrogel. The X-ray diffraction (XRD) results showed that the magnetic particles in the hydrogels were composed of magnetite. The presence of a mesoporous structure was observed in scanning electron microscopy (SEM) images. The composition of hydrogels was analyzed by energy dispersive spectroscopy (EDS). The vibrating sample magnetometer (VSM) measurements also showed good magnetic properties.

As pore size of gas shale ranges widely, various flow types including viscous flow, slip flow and Knudsen flow could coexist during shale gas production. Currently, different models have to be applied to describe the gas transport behaviours in different flow regions and the selection of the models is highly dependent on the flow classification criteria. This also brings difficulty in selecting proper gas flow models near the flow region boundaries, which again depends on the flow classification criteria. To address this problem, this study proposes a unified method to describe the gas flow in the whole spectrum of flow regions by transforming all the gas transport equations for different flow types into a general form. The modelling results by the unified method are comparable with that by the previous models, but less input parameters are required for the unified method. Since the unified method provides a continuous permeability kea for all flow regions, it avoids discontinuity at the boundary of different flow regions caused by using piecewise flow models. The method is able to depict the gas permeability evolution with reservoir pressure change and is applied to interpret the literature permeability data and to predict the permeability variation among all the flow regions with different gas pressures and pore sizes. The results illustrate that the flow types may change within a specific pore (especially for small pores) during the pressure depletion in shale gas production and different gas transport mechanisms may coexist for some shales with typical bimodal pore size distribution under a specific gas pressure condition. This work provides a unified way to analyse gas flow behaviours and gas permeability variation in different regions and can be readily applied to the reservoir simulation models to predict the gas production behaviour especially the long term gas production behaviour.