Exponential Decline Research Articles

Research Methods of Main Parameter Sensitivity Differences in China’s Dynamic Oil and Gas Reserve Estimation under SEC Standards

International oil and gas companies listed in New York must publish the information of oil and gas reserves under the SEC (United States Securities and Exchange Commission) standards every year. For greatly improving the SEC reserve, the SEC reserve value and the SEC reserve substitution rate, in this article not only the SEC reserve equations have been determined but also the SEC reserve value models have been established. The SEC reserve value models have been verified as correct. Based on these models, the multivariate function calculus method, the multivariate function limit method and the function recurrence method have been adopted to research parameter sensitivity differences rules, parameter adjustment directions, parameter adjustment degrees and SEC reserve parameter linkage adjustment rules. The research is significant, because there are great differences between SEC standards and China’s in reserve management mode, reserve estimation method system and financial management system. It is just these differences that cause the frequent adjustment of SEC reserve parameters during the process of SEC reserve submissions each year. As a result, this article reaches some conclusions. Above all, the article has clarified the parameter quantitative conditions that lead to the sensitivity between the SEC reserve and the initial production to begin stronger and weaker than the sensitivity between the SEC reserve and the price in production exponential, hyperbolic and harmonic decline types. Furthermore, the article has clarified the parameter quantitative conditions that lead to the sensitivity between the SEC reserve value and the initial production to begin stronger and weaker than the sensitivity between the SEC reserve value and the price in common production exponential decline types. Moreover, the article has clarified reserve parameter linkage adjustment rules and found the most significant parameter whose least adjustment will cause the largest reserve increase. In addition, the function calculus method adopted to disclose reserve parameter sensitivity rules will expand the parameter sensitivity analysis method that took the previous statistical mapping method as the main analysis method.

A direct comparison of the optically stimulated luminescent properties of BeO and Al2O3 for clinical in-vivo dosimetry

Optically stimulated luminescence dosimetry is a relatively recent field of in-vivo dosimetry in clinical radiotherapy, developing over the last 20 years. As a pilot study, this paper presents a direct comparison between the sensitivity variance with use, stability of measurement and linearity of the current clinical standard Al2O3:C and a potential alternative, beryllium oxide. A set of ten optically stimulated luminescence dosimeters (OSLD), including five of each type, were used simultaneously and irradiated on a Versa HD linear accelerator. Having similar sensitivity, while Al2O3:C showed a relatively stable signal response from initial use, BeO was found to have a higher response to the same dose. However, BeO displayed a strong exponential decline from initial signal response following a model of Respons{e}_{BeO}=(0.55pm 0.05){e}^{-left(0.40pm 0.05right)x}+(0.54pm 0.01), reaching stability after approximately 10 irradiation cycles. BeO was shown to have potentially higher accuracy than Al2O3:C, with less variation between individual doses. Both OSLD showed good linearity between 0.2–5.0 Gy. Between these bounds, Al2O3:C demonstrated a strong linear response following the trend Dose_{Al_{2}O_{3}, group(adj)}=(1.00pm 0.09)x-(0.02pm 0.04),{text{Gy}}, however beyond this showed deviation from linearity, resulting in a measured dose of 12.0pm 0.2 Gy at 10.0 Gy dose delivery. BeO showed strong linearity across the full examined range of 0.2–10.0 Gy with following a model of Dos{e}_{BeO, ind}=(0.98pm 0.01)x+(0.04pm 0.01) Gy with a recorded dose at 10.0 Gy delivery as 9.9pm 0.1 Gy. In conclusion, BeO does show large variance in sensitivity between individual OSLD and a considerable initial variance and decline in dose–response, however after pre-conditioning and individual normalisation to offset OSLD specific sensitivity BeO provides not only a viable alternative to Al2O3:C, but potentially provide higher accuracy, precision and reproducibility for in-vivo dosimetry.

The Marinoan cap carbonate of Svalbard: Syngenetic marine dolomite with 17O‐anomalous carbonate‐associated sulphate

AbstractTwo cap carbonates overlying Cryogenian panglacial deposits are found in North‐East Svalbard of which the younger (635 Ma) forms the base of the Ediacaran Period. It is represented by a transgressive succession in which laminated dolostone, typically around 20 m thick (Member D1), is succeeded transitionally by a similar thickness of impure carbonates (Member D2). In Spitsbergen, there is evidence of microbially influenced sediment stabilisation and carbonate precipitation in the lower part of D1, whilst the upper part of D1 and D2 show centimetre‐decimetre‐scale graded units with undulatory lamination interpreted as evidence of storm activity. Carbonate originated as possible freshwater whitings, as well as microbial precipitates. Exhumed and eroded hardgrounds display replacive 10–30 μm dolomite crystals with cathodoluminescence characteristics consistent with early diagenetic manganese and iron reduction. Regionally, carbon isotope values consistently decrease by around 2‰ from around −3‰ over 30 m of section which is both a temporal and a bathymetric signal, but not a global one. An exponential decline in carbonate production predicted by box models is fitted by a semi‐quantitative sedimentation model. A mass‐anomalous 17O depletion in carbonate‐associated sulphate in dolomite, inherited from precursor calcite, decreases from −0.6 to −0.3‰ in the basal 15 m of section and then approaches background values. The post‐glacial anomalous 17O depletion in carbonate‐associated sulphate and barite elsewhere has been interpreted in terms of ultra‐high pCO2 at the onset of deglaciation. Such anomalies, with larger amplitude, have been reported in Svalbard from underlying lacustrine and tufaceous limestones representing a hyperarid glacial environment. The anomalous sulphate could be produced contemporarily, or the internally drained landscape may have continued to release 17O‐anomalous sulphate as it was transgressed during cap carbonate deposition. The late Cryogenian to earliest Ediacaran record in Svalbard provides the most complete record of the basal 17O‐depletion event in the world.

Long short-term memory suggests a model for predicting shale gas production

Predicting the production behaviors of shale gas wells is of great importance for further developing future unconventional hydrocarbon strategies. An accurate prediction production, as well as reliable shale gas production models, are required to fully understand the shale gas exploitation budget. However, a major problem with classical analytic methods is the insufficient accuracy of the existing models, the time-consuming collection of historical production data, and the costly computational expense. To minimize this problem, a combination of the exponential smoothing method, autoregressive integrated moving average (ARIMA) model, and long short-term memory (LSTM) model was proposed to provide robust support for the production behaviors of shale gas. In this paper, we employed shale gas well production data to establish a database for model training and optimized the predicted model. Hereby, we sought to evaluate the production data predicted by conventional analytical methods, the exponential smoothing method, the ARIMA model, and the LSTM model. Shortly afterward, we objectively compared the predicted results obtained by the novel LSTM model and traditional analytical methods, such as Arps, stretched exponential decline (SEPD), and the Duong model. Herein, we compared the computational cost between the LSTM model and traditional numerical simulation. The combined interpretation of the proposed model demonstrates that the LSTM model achieved scientific accuracy and outstanding results in both short-term and long-term predictions, and realized production prediction of the adjacent well, with excellent agreement with the real shale gas production and a low error, making it an effective tool in forecasting shale gas production. This assay could be used as a potential approach for evaluating deep learning in the petroleum industry and for predicting the future production of unconventional hydrocarbons.

Accelerating decline of an important wintering population of the critically endangered Spoon-billed Sandpiper Calidris pygmaea at Sonadia Island, Bangladesh

The critically endangered Spoon-billed Sandpiper Calidris pygmaea breeds in Russia and winters between Bangladesh and China. World population estimates during 2014–2019 suggested a decline at a mean rate of 8% per year. Several surveys of local populations also indicated declines. We report a rapid decline of another local population of the species based on a 9-year series of annual surveys during the boreal winter at Sonadia Island, Chattogram Division, Bangladesh. We made bounded-count estimates of local populations of the Spoon-billed Sandpiper and 25 other shorebird species based on monthly counts in each of nine winters (2012/2013 to 2020/2021). For three of these winters (2017/2018 to 2019/2020), we also made Lincoln–Petersen estimates of the local population of Spoon-billed Sandpipers using resightings and scan surveys of individually marked birds. Population and trend estimates for the two methods were similar during the 3-year period when results from both were available. Bounded-count estimates of Spoon-billed Sandpipers declined markedly over the 9-year period. Analysis of combined data from both methods indicated that an exponential decline at a mean rate of 9.5% per year during the period 2012/2013 to 2017/2018 was followed by a much more rapid decline at 49.1% per year during 2018/2019 to 2020/2021. Bounded-count estimates of the combined population of 25 other shorebird species in each winter showed no decline during the 9-year period, which suggests that the decline in the local population of the Spoon-billed Sandpiper was not part of a general decline in populations of wintering shorebirds due to local factors. Estimates of the mean trend of other Spoon-billed Sandpiper local populations and of the world population all indicate declines at broadly similar rates. We recommend immediate reassessment of threats faced by the Spoon-billed Sandpiper and improved conservation interventions.

Kinetics of Plant‐derived Heteropolysaccharide Bioabsorption Characterized by Fluorescence‐based Microfluidics System

Plant‐derived structural polymer, pectin, is the “glue” between plant cells, a biomaterial with promising performance as a sealant on visceral organs. However, the nature of bioabsorption and biodurability of pectin remains unclear. Here, we investigate the feasibility of a quantitative microfluidic perfusion assay based on photochemical quenching, to define the kinetics of pectin bioabsorption.We hypothesize that fluorescent tracers embedded into pectin biopolymers will reliably reflect predictable kinetics patterns of bioabsorption including predictable trends when modifying the chemical species used in fluorescence quenching experiments.A fluorescent tracer, Fluorescein Sodium, was embedded into high methoxyl citrus pectin. Dried Fluorescein pectin films were placed into 24‐well plates, to monitor degradation via parallel microfluidics perfusion. This perfusion was performed via micro‐perfused exogenous 0.4% Trypan Blue, an anionic hydrophilic azo dye, which was delivered into porous matrix interfacing on pectin in each well. To assess biodurability, the fluorescence of perfused pectin under quenching by dye was monitored over time using a multi‐well plate fluorescence reader (Cytofluor 4000, PerSeptive Biosystems).Our data show that consistent trends in fluorescence result during quenching experiments. When un‐perfused, Fluorescein‐embedded pectin exhibited statistically insignificant change in quantum yield over 1 week (p=0.457), suggesting that results were not impacted by substantial photobleaching over the course of experimentation. To minimize interfacial flow artifact, 6µm porous matrix provided an effective interface for observable perfusion of azo dye into pectin. During micro‐perfusion, serial measurements of fluorescence in Fluorescein pectin demonstrated exponential decline in fluorescence intensity. This quenching of fluorescent signal from Fluorescein correlated with the progressive bioabsorption of the pectin polymer. Absorption was feasibly modulated by adjusting dye volumes used in perfusion.In this in vitro assay, plant‐derived pectin exhibits predictable decay characteristics and bioabsorption kinetics when incorporated with fluorescent tracers. These tracers not only provide a quantitative measure of biodurability, but also an opportunity for guided biochemical tuning of the complex biomaterial, to improve its performance as a potential therapeutic sealant.

Suppressing the Effect of the Wetting Layer through AlAs Capping in InAs/GaAs QD Structures for Solar Cells Applications.

Recently, thin AlAs capping layers (CLs) on InAs quantum dot solar cells (QDSCs) have been shown to yield better photovoltaic efficiency compared to traditional QDSCs. Although it has been proposed that this improvement is due to the suppression of the capture of photogenerated carriers through the wetting layer (WL) states by a de-wetting process, the mechanisms that operate during this process are not clear. In this work, a structural analysis of the WL characteristics in the AlAs/InAs QD system with different CL-thickness has been made by scanning transmission electron microscopy techniques. First, an exponential decline of the amount of InAs in the WL with the CL thickness increase has been found, far from a complete elimination of the WL. Instead, this reduction is linked to a higher shield effect against QD decomposition. Second, there is no compositional separation between the WL and CL, but rather single layer with a variable content of InAlGaAs. Both effects, the high intermixing and WL reduction cause a drastic change in electronic levels, with the CL making up of 1–2 monolayers being the most effective configuration to reduce the radiative-recombination and minimize the potential barriers for carrier transport.

Aggregated transfer factor of 137Cs in edible wild plants and its time dependence after the Fukushima Dai-ichi nuclear accident

We obtained the aggregated transfer factor (Tag) for 10 common edible wild plant species (four perennial spermatophytes, bamboo shoot, two tree species, and three perennial pteridophytes) in northeastern Japan. Measurement of Tag was carried out in 2012–2019 and we also used publicly available data for 2012–2019: food monitoring data and total deposition data from an airborne survey. The Tag obtained from actual measurements agreed well with Tag values calculated from the publicly available data. The sampling locations were only identified at the municipal level and uncertainty of the deposition for the publicly available data, and thus Tag values showed substantial variation. The Tag of the perennial spermatophytes, including bamboo shoot, and perennial pteridophytes showed single exponential decline with effective half-lives of approximately 2 years, whereas those of tree species did not show distinct temporal change. These results imply that data since 2014 are applicable for Tag estimation for long-term potential ingestion dose in the future to the public because of the slow decline. The calculated Tag values of all species for 2014–2019 ranged from 6.1 × 10−5 to 5.2 × 10−3 m2/kg-fresh mass. The maximum Tag value was observed for the tree koshiabura (Chengiopanax sciadophylloides) and the minimum value was observed for the perennial spermatophyte giant butterbur (Petasites japonica). Tree species showed higher Tag than spermatophyte and pteridophyte perennials.

Stress-sensitivity of fracture conductivity of Tuscaloosa Marine Shale cores

Production decline from wells producing from shale rocks is typically quite rapid. One possible factor responsible for such decline is loss in fracture conductivity. This is true in the case of the Tuscaloosa Marine shale reservoir. In this work, the stress-dependent fracture conductivity of TMS core samples is investigated using five propped cores under varying stress conditions. In addition, the effects of static rock mechanical properties and rock mineralogy on conductivity decline are investigated. The mineralogy of each core sample was determined by X-ray Diffraction analysis. Fracture conductivity was measured using a Hassler-type pressure core holder with a pressure rating of 15,000 psi (103.42 MPa). Estimated rock mechanical properties showed that shale anisotropy could affect fracture conductivity. The time decline in conductivity revealed two possible decline trends, a) decline from the reduction in induced fracture width, and b) decline from the healing of developed micro-cracks. An exponential decline in fracture conductivity with increasing confinement pressure was observed with an average decline rate constant of 3.15 × 10 − 10 mPa −1 . While rock mineralogy did not have any clear relationship with conductivity decline, the effects of rock mechanical properties were only important at 10.34 MPa confinement pressure and above. The observation showed that a high Young's modulus correlate to high conductivity, whereas a high Poisson's ratio correlate to low conductivity. This study provides useful observations for optimizing fracture design to enhance well productivity in the TMS. • Fracture conductivity decline followed an exponential trend. • Micro-cracks formed in the rock samples which later contributed to fluid flow. • Reduction in fracture width is controlled by proppant embedment for the TMS. • Fracture width reduction and healing of micro-cracks causes conductivity decline.

Analysis of the atmospheric visibility influencing factors under sea-land breeze circulation.

Factors influencing atmospheric visibility (VIS) in coastal areas are more complex than those for inland and far oceans owing to the complex circulation and aerosol sources. This study analyzed the factors influencing VIS under sea-land breeze circulation (SLBC) for different external aerosol sources based on field survey data in southern Chinese coastal areas. First, SLBC characteristics observed during the experiment period showed that on SLBC days, sea breeze occurs more frequently (∼50%) than land breeze (∼27%), and the wind speed (WS) is generally small, with a mean sea and land breeze WSs of ∼2.18 m/s and ∼2.38 m/s, respectively. Then, analysis of factors influencing VIS was conducted for different land/sea breeze conditions and external aerosol source conditions indicated by the HYSPLIT4 model simulations. Results showed that the aerosol particle number concentration (PNC) and relative humidity (RH) both had negative correlations with VIS, while only very weak relationships between WS and VIS were found, possibly due to small WSs on SLBC days or because local aerosols were not pure marine aerosols. Further two-factor analysis of VIS showed that the power-law function relating VIS with PNC in each RH bin ranges from ∼-0.3 to ∼-1.5, and VIS exhibited sharper exponential decline with increasing PNC under high RH. A new method of retrieving aerosol-extinction hygroscopic growth factor (fext) with the measured VIS, RH, and PNC was developed to investigate the optical hygroscopic growth property of aerosols. Results show that aerosols in the study area have similar fext under different land/sea breeze and external aerosol source conditions; the deliquescence RH of aerosols is ∼60%, suggesting that mainly polluted marine aerosol was observed during experiments in this area.

Forecast of Gas Production from Deep Offshore Field Using Exponential Decline Curve Analysis Technique – A Case Study of a Typical Niger Delta Field

The Niger Delta region of Nigeria has been a focal point in Nigeria’s Oil and gas Industry, being the epicenter of oil and gas activities and thus a has drawn a lot of scholarly attention in the quest to gain more insight into the wealth of the resources underneath. This work uses data from existing gas deep offshore production field to forecast the gas production using the decline curve technique. The result shows an annual gas production of 146BCF and a steady decline rate of 0.15BCF/Year and a total of 1.151 TCF of gas throughout the life of the field. Key Words: Forecast, Offshore, Natural gas, Production rate, Gas field.

Influence of cyclical hygrothermal aging on mechanical response and structural durability of composite bolted interference-fit joints

The paper deals with the long-term performance and durability of composite bolted interference-fit joints subjected to harsh environmental loads in flight-cycles. An accelerated cyclical hygrothermal aging spectrum with repeated temperature and moisture variation is specially designed to simulate real service condition. The single-lap composite joints with various stacking sequences, interference-fit sizes and tightening torques are aged in designed hygrothermal environments with different time duration, in which the material deterioration, stress relaxation and intrinsic frequency of joints are monitored to characterize structural evolution. Tensile tests are conducted to aged composite joints to obtain joint stiffness, bearing strength and failure modes to quantitatively evaluate their residual mechanical properties and structural degradation regulation. The results show that the moisture absorption rate presents a sharp rise and slow decent trend with a peek value of 0.213%. The aging process causes weathering phenomenon and reduces the structural benefits induced by such assembly technologies as interference fitting and bolt preloading. The reduction of joint stiffness and bearing strength exhibits a negative exponential decline trend with the growth of aging time. The matrix plasticization and interlaminar debonding are main hygrothermal damages. The safety factor of 0.7 to 0.8 is suggested on bearing strength value to reach hygrothermal aging bearing strength allowable in joint application.

Simulated impacts of vertical distributions of black carbon aerosol on meteorology and PM<sub>2.5</sub> concentrations in Beijing during severe haze events

Abstract. Vertical profiles of black carbon (BC) play a critical role in modifying the meteorological conditions such as temperature, planetary boundary layer height (PBLH), and regional circulation, which influence surface layer concentrations of PM2.5 (particulate matter with a diameter of 2.5 µm or less; the surface layer covers from 0 to 79.5 m). However, BC vertical profiles in current models usually have large uncertainties. In this study, by using measurements of BC vertical profiles in Beijing collected by King Air 350 aircraft and the Weather Research and Forecasting with Chemistry model (WRF-Chem) coupled with an improved integrated process (IPR) analysis scheme, we investigated the direct radiative effect (DRE) of BC with different vertical profiles on meteorology and PM2.5 concentrations in Beijing during two severe haze events (11–12 and 16–19 December 2016). Compared with measurements in Beijing, the model overestimated BC concentrations by 87.4 % at the surface and underestimated BC mass by 14.9 % at altitudes of 300–900 m as averaged over the two pollution events. The BC DRE with the default vertical profiles from the model heated the air around 300 m altitude, but the warming would be stronger when BC vertical profiles were modified for each day using the observed data during the two severe haze events. Accordingly, compared to the simulation with the default vertical profiles of BC, PBLH was reduced further by 24.7 m (6.7 %) and 6.4 m (3.8 %) in Beijing in the first and second haze events, respectively, with the modified vertical profiles, and hence the surface layer PM2.5 concentrations were higher by 9.3 µg m−3 (4.1 %) and 5.5 µg m−3 (3.0 %) over central Beijing, owing to increased positive contributions of vertical mixing and chemical processes. Furthermore, we quantified by sensitivity experiments the roles of BC vertical profiles with six exponential decline functions (C(h)=C0×e-h/hs and hs​​​​​​​ = 0.35, 0.48, 0.53, 0.79, 0.82, and 0.96) parameterized on the basis of the observations. A larger hs means less BC at the surface and more BC in the upper atmosphere, resulting in less solar radiation reaching the ground and consequently a stronger cooling at the surface (+0.21 with hs of 0.35 vs. −0.13∘ with hs of 0.96). Our results indicate that it is very important to have accurate vertical profiles of BC in simulations of meteorology and PM2.5 concentrations during haze events.

Estimated ultimate recovery prediction of fractured horizontal wells in tight oil reservoirs based on deep neural networks

Accurate estimated ultimate recovery prediction of fractured horizontal wells in tight reservoirs is crucial to economic evaluation and oil field development plan formulation. Advances in artificial intelligence and big data have provided a new tool for rapid production prediction of unconventional reservoirs. In this study, the estimated ultimate recovery prediction model based on deep neural networks was established using the data of 58 horizontal wells in Mahu tight oil reservoirs. First, the estimated ultimate recovery of oil wells was calculated based on the stretched exponential production decline model and a five-region flow model. Then, the calculated estimated ultimate recovery, geological attributes, engineering parameters, and production data of each well were used to build a machine learning database. Before the model training, the number of input parameters was reduced from 14 to 9 by feature selection. The prediction accuracy of the model was improved by data normalization, the early stopping technique, and 10-fold cross validation. The optimal activation function, hidden layers, number of neurons in each layer, and learning rate of the deep neural network model were obtained through hyperparameter optimization. The average determination coefficient on the testing set was 0.73. The results indicate that compared with the traditional estimated ultimate recovery prediction methods, the established deep neural network model has the strengths of a simple procedure and low time consumption, and the deep neural network model can be easily updated to improve prediction accuracy when new well information is obtained. Cited as: Luo, S., Ding, C., Cheng, H., Zhang, B., Zhao, Y., Liu, L. Estimated ultimate recovery prediction of fractured horizontal wells in tight oil reservoirs based on deep neural networks. Advances in Geo-Energy Research, 2022, 6(2): 111-122. https://doi.org/10.46690/ager.2022.02.04

Length dependence of magnetoresistance in organic spin valves

With the Su–Schrieffer–Heeger model and Green's function method, the length dependence of magnetoresistance in organic spin valves is calculated in the frame of tunneling transport. Based on different energy level alignments between the molecule and the electrodes, the length effect on the magnetoresistance is investigated in three transport schemes, barrier tunneling, resonant tunneling, and transition between them. In the first scheme, a length-induced exponential or linear decline of the magnetoresistance is obtained. An oscillation and nonlinear dependence are observed in the second and third schemes. The mechanism is explained by investigating the transmission spectra at different lengths, where the evolution of the efficient transmission in the bias window with length differs in different schemes. The results agree with many experimental measurements qualitatively.

XXL-HSC: An updated catalogue of high-redshift (z ≥ 3.5) X-ray AGN in the XMM-XXL northern field

X-rays offer a reliable method to identify active galactic nuclei (AGNs). However, in the high-redshift Universe, X-ray AGNs are poorly sampled due to their relatively low space density and the small areas covered by X-ray surveys. In addition to wide-area X-ray surveys, it is important to have deep optical data in order to locate the optical counterparts and determine their redshifts. In this work, we built a high-redshift (z ≥ 3.5) X-ray-selected AGN sample in the XMM-XXL northern field using the most updated [0.5–2 keV] catalogue along with a plethora of new spectroscopic and multi-wavelength catalogues, including the deep optical Subaru Hyper Suprime-Cam (HSC) data, reaching magnitude limits i ∼ 26 mag. We selected all the spectroscopically confirmed AGN and complement this sample with high-redshift candidates that are HSC g- and r-band dropouts. To confirm the dropouts, we derived their photometric redshifts using spectral energy distribution techniques. We obtained a sample of 54 high-z sources (28 with spec-z), the largest in this field so far (almost three times larger than in previous studies), and we estimated the possible contamination and completeness. We calculated the number counts (log N-log S) in different redshift bins and compared our results with previous studies and models. We provide the strongest high-redshift AGN constraints yet at bright fluxes (f0.5 − 2 keV > 10−15 erg s−1 cm−2). The samples of z ≥ 3.5, z ≥ 4, and z ≥ 5 are in agreement with an exponential decline model similar to that witnessed at optical wavelengths. Our work emphasises the importance of using wide-area X-ray surveys with deep optical data to uncover high-redshift AGNs.

Machine learning based rate decline prediction in unconventional reservoirs

The main objective of this paper is to develop a novel machine learning based model that can accurately predict the decline curves and EUR (Estimated Ultimate Recovery) for new wells based on the data collected from nearby wells. This is because decline curves are easier and faster alternative to complex reservoir simulators which perform computationally expensive operations. In contrast to this, decline curves require only a few parameters in the equation which can be easily collected from the existing data of the wells. In this study, first we collected the well data corresponding to well parameters such as initial monthly oil flow rate ( q i ), well completion parameters (i.e., no. of fracturing stages, completed length, amount of proppant used, amount of fracturing fluid used), well location parameters (TVD Heel-Toe Difference), reservoir fluid properties (Oil API Gravity, initial 24 h period Gas-Oil Ratio (GOR), initial 24 h period Gas Produced, initial 24 h period Oil Produced), flowing tubing pressure, casing pressure tubing size,choke size from publicly available databases of the Eagle Ford Shale formation Texas RRC (Railroad Commission of Texas). Wells were selected randomly and only those wells were finally included for the study whose data of the all the required parameters were available. The model parameters were estimated by fitting the production data to the decline curve models. Artificial Neural Network (ANN) was employed to build Machine learning models as a function of the above well parameters for the corresponding model parameters. The decline curves for new or existing wells were rapidly predicted using these models. In order to estimate the predictive accuracy of these models when applied to new or test wells cross validation technique such as k-fold cross validation was employed. These models were also used to predict EUR for the test wells. Additionally, feature selection was also done using algorithms such as Chi Square Test (χ2) and Minimum Redundancy Maximum Relevance (MRMR) Algorithm to determine the relative importance of predictor variables in predicting EUR. The predictor variables were successfully linked to SEDM (Stretched Exponential Decline Model) decline curve parameters (n and τ) in a random set of oil field well data. The relative influences of various well parameters were also examined to determine the hidden relationship between them. The novelty in this study lies in the algorithm and dataset that we used for the rate decline prediction in Eagle Ford data set. Although, this paper has referenced some previous papers where machine learning has been used to make prediction, but this paper presents use of new algorithm as well as a new dataset. As more data gets available, there is definitely extra room for further data analysis and improved results using machine learning methods. In future, the number of wells can be increased and the updated results can again be submitted after investing more time. It should be noted here that data downloading and preparing takes most of the time for such study especially when dealing with oil and gas data.

Decline of Anti-SARS-CoV-2 IgG Antibody Levels 6 Months after Complete BNT162b2 Vaccination in Healthcare Workers to Levels Observed Following the First Vaccine Dose.

Research on post-vaccination antibody dynamics has become pivotal in estimating COVID-19 vaccine efficacy. We studied anti-SARS-CoV-2 Spike RBD IgG levels in 587 healthcare workers (2038 sera) who completed BNT162b2 vaccination. Average antibody titer 3 weeks after the first dose in COVID-19-naïve participants (median 873.5 AU/mL) was 18-fold higher than the test threshold, with a significant increase 1 month (median 9927.2 AU/mL) and an exponential decrease 3 (median 2976.7 AU/mL) and 6 (median 966.0 AU/mL) months after complete vaccination. Participants with a history of COVID-19 prior to vaccination showed significantly higher antibody levels, particularly after the first dose (median 14,280.2 AU/mL), with a slight decline 1 month (median 12,700.0 AU/mL) and an exponential decline in antibody titers 3 (median 4831.0 AU/mL) and 6 (median 1465.2 AU/mL) months after vaccination. Antibody levels of COVID-19-naïve subjects after the first dose were moderately correlated with age (r = −0.4). Multivariate analysis showed a strong independent correlation between IgG levels 6 months after vaccination and both IgG titers after the first dose and 1 month after vaccination (R2 = 0.709). Regardless of pre-vaccination COVID-19 history, IgG levels 6 months after vaccination were comparable to antibody levels reached by COVID-19-naïve participants after the first vaccine dose.

АНАЛИЗ ТРЕНДОВ ПРОИЗВОДСТВЕННОГО ТРАВМАТИЗМА В РОССИИ ЗА 2000-2020 ГОДЫ

The analysis of trends in industrial injuries indicators for 2000-2020 has been carried out. in Russia and revealed their exponential decline. The adequacy of the obtained models 98-99% allows them to be used for forecasting. It was found that with an increase in funding for labor protection measures more than 14 thousand rubles / person. there is no significant decrease in the values of indicators

Estimating antigenic distances between GII.4 human norovirus strains

Human norovirus (HuNoV) is an etiological agent of acute gastroenteritis. Viral protein 1 (VP1) is the major capsid protein and the determinant of antigenicity in HuNoV. GII.4 has been the dominant VP1-genotype in most of HuNoV seasons. Here an attempt was made to estimate the magnitude of antigenic difference, defined as the antigenic distance, between GII.4 HuNoV strains from the comparison of amino acid sequences for VP1. Antigenic distance was estimated more accurately assuming an exponential decline in the amount of increase in the antigenic distance along with an increment in the number of amino acid differences than assuming a constant increase. Although antigenic sites A, C, D, E, and G appeared to be the major determinant of antigenicity, estimation accuracy was sometimes improved by adding other regions. These results suggested that it may be suitable to construct a model based on the entire region of VP1 for estimating antigenic distances between GII.4 HuNoV strains.