Distribution, health and ecological risk assessments of trace elements in Nigerian oil sands

The Nigerian oil sands represent the largest oil sand deposit in Africa, yet there is little published information on the distribution and potential health and ecological risks of trace elements in the oil resource. In the present study, we investigated the distribution pattern of 18trace elements(including biophile and chalcophile elements) as well as the estimated risks associated with exposure to these elements. The results of the study indicated that Fe was the most abundant element, with a mean concentration of 22,131 mg/kg while Br had the lowest mean concentration of 48 mg/kg. The high occurrence of Fe and Ti suggested a possible occurrence of ilmenite(Fe TiO_(3)) in the oil sands. Source apportionment using positive matrix factorization showed that the possible sources of detected elements in the oil sands were geogenic, metal production, and crustal. The contamination factor, geo-accumulation index, modified degree of contamination, pollution load index, and Nemerow pollution index indicated that the oil sands are heavily polluted by the elements. Health risk assessment showed that children were relatively more susceptible to the potentially toxic elements in the oil sands principally via ingestion exposure route(HQ > 1E-04). Cancer risks from inhalation are unlikely due to CR < 1E-06 but ingestion and dermal contact pose severe risks(CR > 1E-04). The high concentrations of the elements pose serious threats due to the potential for atmospheric transport, bioaccessibility, and bioavailability.

An Innovative Deep Architecture for Flight Safety Risk Assessment Based on Time Series Data

With the development of the integration of aviation safety and artificial intelligence,research on the combination of risk assessment and artificial intelligence is particularly important in the field of risk management,but searching for an efficient and accurate risk assessment algorithm has become a challenge for the civil aviation industry.Therefore,an improved risk assessment algorithm(PS-AE-LSTM)based on long short-term memory network(LSTM)with autoencoder(AE)is proposed for the various supervised deep learning algorithms in flight safety that cannot adequately address the problem of the quality on risk level labels.Firstly,based on the normal distribution characteristics of flight data,a probability severity(PS)model is established to enhance the quality of risk assessment labels.Secondly,autoencoder is introduced to reconstruct the flight parameter data to improve the data quality.Finally,utilizing the time-series nature of flight data,a long and short-termmemory network is used to classify the risk level and improve the accuracy of risk assessment.Thus,a risk assessment experimentwas conducted to analyze a fleet landing phase dataset using the PS-AE-LSTMalgorithm to assess the risk level associated with aircraft hard landing events.The results show that the proposed algorithm achieves an accuracy of 86.45%compared with seven baseline models and has excellent risk assessment capability.

Stroke Risk Assessment Decision-Making Using a Machine Learning Model:Logistic-AdaBoost

Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.

Machine Learning-Based Decision-Making Mechanism for Risk Assessment of Cardiovascular Disease

Cardiovascular disease(CVD)has gradually become one of the main causes of harm to the life and health of residents.Exploring the influencing factors and risk assessment methods of CVD has become a general trend.In this paper,a machine learning-based decision-making mechanism for risk assessment of CVD is designed.In this mechanism,the logistics regression analysismethod and factor analysismodel are used to select age,obesity degree,blood pressure,blood fat,blood sugar,smoking status,drinking status,and exercise status as the main pathogenic factors of CVD,and an index systemof risk assessment for CVD is established.Then,a two-stage model combining K-means cluster analysis and random forest(RF)is proposed to evaluate and predict the risk of CVD,and the predicted results are compared with the methods of Bayesian discrimination,K-means cluster analysis and RF.The results show that thepredictioneffect of theproposedtwo-stagemodel is better than that of the comparedmethods.Moreover,several suggestions for the government,the medical industry and the public are provided based on the research results.

Health risk assessment of trace metal(loid)s in agricultural soils based on Monte Carlo simulation coupled with positive matrix factorization model in Chongqing, southwest China

This study aimed to investigate the pollution characteristics, source apportionment, and health risks associated with trace metal(loid)s(TMs) in the major agricultural producing areas in Chongqing, China. We analyzed the source apportionment and assessed the health risk of TMs in agricultural soils by using positive matrix factorization(PMF) model and health risk assessment(HRA) model based on Monte Carlo simulation. Meanwhile, we combined PMF and HRA models to explore the health risks of TMs in agricultural soils by different pollution sources to determine the priority control factors. Results showed that the average contents of cadmium(Cd), arsenic (As), lead(Pb), chromium(Cr), copper(Cu), nickel(Ni), and zinc(Zn) in the soil were found to be 0.26, 5.93, 27.14, 61.32, 23.81, 32.45, and 78.65 mg/kg, respectively. Spatial analysis and source apportionment analysis revealed that urban and industrial sources, agricultural sources, and natural sources accounted for 33.0%, 27.7%, and 39.3% of TM accumulation in the soil, respectively. In the HRA model based on Monte Carlo simulation, noncarcinogenic risks were deemed negligible(hazard index <1), the carcinogenic risks were at acceptable level(10^(-6)<total carcinogenic risk ≤ 10^(-4)), with higher risks observed for children compared to adults. The relationship between TMs, their sources, and health risks indicated that urban and industrial sources were primarily associated with As, contributing to 75.1% of carcinogenic risks and 55.7% of non-carcinogenic risks, making them the primary control factors. Meanwhile, agricultural sources were primarily linked to Cd and Pb, contributing to 13.1% of carcinogenic risks and 21.8% of non-carcinogenic risks, designating them as secondary control factors.

Risk Assessment of Deep-Water Horizontal X-Tree Installation

Due to the high potential risk and many influencing factors of subsea horizontal X-tree installation,to guarantee the successful completion of sea trials of domestic subsea horizontal X-trees,this paper established a modular risk evaluation model based on a fuzzy fault tree.First,through the analysis of the main process oftree down and combining the Offshore&Onshore Reliability Data(OREDA)failure statistics and the operation procedure and the data provided by the job,the fault tree model of risk analysis of the tree down installation was established.Then,by introducing the natural language of expert comprehensive evaluation and combining fuzzy principles,quantitative analysis was carried out,and the fuzzy number was used to calculate the failure probability of a basic event and the occurrence probability of a top event.Finally,through a sensitivity analysis of basic events,the basic events of top events significantly affected were determined,and risk control and prevention measures for the corresponding high-risk factors were proposed for subsea horizontal X-tree down installation.

IoT Smart Devices Risk Assessment Model Using Fuzzy Logic and PSO

Increasing Internet of Things(IoT)device connectivity makes botnet attacks more dangerous,carrying catastrophic hazards.As IoT botnets evolve,their dynamic and multifaceted nature hampers conventional detection methods.This paper proposes a risk assessment framework based on fuzzy logic and Particle Swarm Optimization(PSO)to address the risks associated with IoT botnets.Fuzzy logic addresses IoT threat uncertainties and ambiguities methodically.Fuzzy component settings are optimized using PSO to improve accuracy.The methodology allows for more complex thinking by transitioning from binary to continuous assessment.Instead of expert inputs,PSO data-driven tunes rules and membership functions.This study presents a complete IoT botnet risk assessment system.The methodology helps security teams allocate resources by categorizing threats as high,medium,or low severity.This study shows how CICIoT2023 can assess cyber risks.Our research has implications beyond detection,as it provides a proactive approach to risk management and promotes the development of more secure IoT environments.

Trends and hotspots in gastrointestinal neoplasms risk assessment: A bibliometric analysis from 1984 to 2022

BACKGROUND Gastrointestinal neoplasm(GN)significantly impact the global cancer burden and mortality,necessitating early detection and treatment.Understanding the evolution and current state of research in this field is vital.AIM To conducts a comprehensive bibliometric analysis of publications from 1984 to 2022 to elucidate the trends and hotspots in the GN risk assessment research,focusing on key contributors,institutions,and thematic evolution.METHODS This study conducted a bibliometric analysis of data from the Web of Science Core Collection database using the"bibliometrix"R package,VOSviewer,and CiteSpace.The analysis focused on the distribution of publications,contributions by institutions and countries,and trends in keywords.The methods included data synthesis,network analysis,and visualization of international collaboration networks.RESULTS This analysis of 1371 articles on GN risk assessment revealed a notable evolution in terms of research focus and collaboration.It highlights the United States'critical role in advancing this field,with significant contributions from institutions such as Brigham and Women's Hospital and the National Cancer Institute.The last five years,substantial advancements have been made,representing nearly 45%of the examined literature.Publication rates have dramatically increased,from 20 articles in 2002 to 112 in 2022,reflecting intensified research efforts.This study underscores a growing trend toward interdisciplinary and international collaboration,with the Journal of Clinical Oncology standing out as a key publication outlet.This shift toward more comprehensive and collaborative research methods marks a significant step in addressing GN risks.CONCLUSION This study underscores advancements in GN risk assessment through genetic analyses and machine learning and reveals significant geographical disparities in research emphasis.This calls for enhanced global collaboration and integration of artificial intelligence to improve cancer prevention and treatment accuracy,ultimately enhancing worldwide patient care.

Safety Risk Assessment Analysis of Bridge Construction Using Backpropagation Neural Network

The evaluation of construction safety risks has become a crucial task with the increasing development of bridge construction.This paper aims to provide an overview of the application of backpropagation neural networks in assessing safety risks during bridge construction.It introduces the situation,principles,methods,and advantages,as well as the current status and future development directions of backpropagation-related research.

Risk Assessment and Control in Medical Investment,Merger,and Acquisition

The risk assessment and control of medical investment,merger,and acquisition are crucial topics within the medical industry,encompassing various aspects of investment,merger,and acquisition within this sector.The process primarily targets the unique nature and associated risks of the medical industry,focusing on effective risk management and control strategies to facilitate the smooth progression of investment,merger,and acquisition activities.

Health risk assessment of heavy metals in soils and crops in a mining area(Au-Ag-Cutrona-oil et al.) of the Nanyang Basin, Henan Province, China

Heavy metal distribution in mining areas has always been a hot research topic due to the special environment of these areas. This study aims to explore the impact of heavy metal pollution on soils and crops in the study area, ensure the safety of local crops and the health of local residents, and provide a basis for the subsequent environmental restoration and the prevention and control of environmental pollution. Based on the analysis of the heavy metal concentrations in local soils and crops, the study investigated the spatial distribution, pollution degrees, and potential ecological risks of heavy metals in the farmland of a mining area in the southeastern Nanyang Basin, Henan province, China explored the sources of heavy metals and assessed the health risks caused by crop intake. The results of this study are as follows. The root soils of crops in the study area suffered heavy metal pollution to varying degrees. The degree of heavy metal pollution in maize fields is higher than that in wheat fields, and both types of fields suffer the most severe Cd pollution. Moreover, the root soils of different crops suffer compound pollution.The root soils in the maize fields suffer severe compound pollution at some sampling positions, whose distribution is similar to that of the mining area. Cd poses the highest potential ecological risks among all heavy metals, and the study area mainly suffers low and moderate comprehensive potential ecological risks. The principal component analysis(PCA) shows that the distribution of Zn, Cd, Pb, and As in soils of the study area is mainly affected by anthropogenic factors such as local mining activities;the distribution of Cr and Ni is primarily controlled by the local geological background;the distribution of Hg is mainly affected by local vehicle exhaust emissions, and the distribution of Cu is influenced by both human activities and the geological background. Different cereal crops in the study area are polluted with heavy metals dominated by Cd and Ni to varying degrees, especially wheat. As indicated by the health risk assessment results, the intake of maize in the study area does not pose significant human health risks;however, Cu has high risks to human health, and the compound heavy metal pollution caused by the intake of wheat in the study area poses risks to the health of both adults and children. Overall, the soils and crops in the study area suffer a high degree of heavy metal pollution, for which mining activities may be the main reason.

Oil Spill Risk Assessment of Offshore Pipeline in the Bohai Sea Under the Perspective of Ecological Protection

In recent decades,the exploration and development of marine oil and gas resources have increased significantly to meet the increasing energy demand of mankind.The Bohai Sea is a semi-closed continental sea that has a weak water exchange capacity and high ecological fragility.However,at present,more than 200 oil platforms have been built in the Bohai Sea,with more than 270 offshore oil pipelines having a length exceeding 1600 km.The oil spill pollution of offshore platforms has a great impact on the marine environment and ecosystems.Therefore,a comprehensive assessment of its risks is of great practical significance.This paper systematically constructs a comprehensive oil spill risk assessment model that combines the oil spill risk probability model and the ocean hydrodynamic model.This paper uses the Bohai Sea offshore pipeline as an example to assess its oil spill risk.The high-risk-value areas of the Bohai Sea offshore pipeline are mainly distributed at the bottom of Liaodong Bay,the bottom of Bohai Bay,near the Caofeidian area,and the northern part of the Yellow River Estuary.

Distribution and risk assessment of heavy metals in surface sediments of coastal mudflats on Leizhou Peninsula,China

Mudflats play a vital role in maintaining the dynamic balance between sea and land.To understand the characteristics,sources,and pollution risks of six heavy metals(As,Cd,Cr,Cu,Hg,and Pb)in the coastal mudflats on the Leizhou Peninsula,257 surface sediment samples were studied using mathematical statistics,correlation analysis,and factor analysis.The results show that the overall concentrations of these heavy metals are low although there are several high abnormal points in the local areas.The strong correlation between these heavy metals indicates that the sources of some of the metals are similar,yet their elemental combinations in different cities(counties)varied.According to the calculated enrichment factor(EF),anthropogenic activity-induced heavy metals were determined in order of decreasing influence:As,Cd,Pb,Cr,Cu,and Hg.The low EF values of Hg indicate that it does not present as a contaminant in the study area,while low values of Cr and Cu from the Lianjiang City suggest that these two metals were also attributed to natural sources.The presence of As,Cd,Cr,Cu,and Pb from the remaining cities(counties)should be influenced by anthropogenic activities.The overall potential ecological risk index indicates that the ecological risks posed by the six analyzed heavy metals to the Leizhou Peninsula mudflats,in order of decreasing risk,are Cd,As,Hg,Pb,Cu,and Cr.It is noteworthy that only Cd in Lianjiang City demonstrated substantial ecological risk.Other examined heavy metals in other cities of the study area showed slight ecological risk.

Detection and Analysis of Pesticide Residues in Chinese Chives and Risk Assessment of Dietary Intake

[Objectives]This study was conducted to understand the status of pesticide residues and dietary intake risk of Chinese chives in Tangshan area. [Methods] Sixty eight kinds of pesticide residues in 415 Chinese chive samples collected from Tangshan area were qualitatively and quantitatively determined by high-performance liquid chromatography-tandem mass spectrometry(HPLC-MS/MS) and gas chromatography(GC) in 2020. [Results] The results showed that 41 kinds of pesticide residues were detected in the 415 Chinese chive samples, and the detection rate was 69.4%(288/415), and there was a combination of pesticides in many samples. According to the National Food Safety Standard―Maximum Residue Limits of Pesticides in Food(GB 2763-2019), the residues of 12 pesticides exceeded the maximum residue limits(MRLs), and the unqualified rate was 38.07%(158/415). The highest detection rate of clothianidin was 41.20%(171/415), but there was no MRL in GB 2763-2019. The next was procymidone, the detection rate of which was 35.42%(147/415), and the over-standard rate was 30.12%(125/415). Forbidden and restricted pesticides were detected in some samples. According to the dietary exposure risk assessment, the NEDI/ADI values were all less than 1 and the intake risk was within acceptable range. In Tangshan area, the types of pesticides used in Chinese chive production are complex, and there are risks of multi-residue pollution and use of banned and restricted pesticides and unregistered pesticides. It is suggested that routine monitoring of pesticide residues and management of pesticide use should be strengthened to ensure the quality and safety of agricultural products. [Conclusions] This study provides a theoretical basis for the safe production of Chinese chive and the standardized and rational use of pesticides.

Pollution and health risk assessment of heavy metals in waters around mine sites of Elazig(Eastern Turkey)

Turkey’s Eastern Anatolia Region is the oldest known mineral mining area of Maden and Alacakaya.Chromite production in the Alacakaya field constitutes 50% of the country’s exports,and copper mines in the Maden region account for approximately 12% of the country’s copper production.There is a risk of water pollution due to significant mine waste which affects the Inci and Maden rivers.The water needs of many settlements are met from these streams,which run through these two mine sites.This study investigated the water pollution in the rivers.25 water samples were collected during the dry and rainy periods,and the Al,Cr,Cu,Fe,Li,Mn,Ni,Pb,Sr and Zn contents of these samples were examined in terms of health.Evaluation of element concentrations and creation of spatial distribution maps were performed using ArcGIS software.Spatial distribution maps,correlation and cluster analysis indicate that the source of heavy metals observed in waters is mine fields.The heavy metal content of the samples is higher in the dry period,the high concentration values are obtained from the mine sites,the decrease in the concentrations throughout the flow during the rainy period,are indicators of the effect of the mines on the water pollution.As a result of the comparison from the analysis results of water samples with World Health Organization(WHO),Environmental Protection Agency(EPA)and European Commission(EC)standards,the element values of Al,Cr,Fe,Mn,Ni and Pb exceeded the permissible values for health.The concentrations of these elements for dry period samples are:0-6.411 mg L^(-1),0.006-0.235 mg L^(-1),0-13.433 mg L^(-1),0-0.316 mg L^(-1),0-0.495 mg L^(-1),0-0.065mg L^(-1),and for rainy period samples are 0-1.698mg/L,0-0.2 mg L^(-1),0-9.033 mg L^(-1),0-0.173 mg L^(-1),0-0.373 mg L^(-1),0-0.034 mg L^(-1),respectively.Although the waters in the region are polluted by heavy metals,it has been determined that there is no noncarcinogenic hazard as a result of the calculation of the hazard index(HI<1)by ingestion and dermal contact within the scope of human health risk assessment.This study will be beneficial as it draws attention to the prevention of the negative effects of water pollution,which may cause serious health problems in the future as a result of mining activities in the region.

A methodological framework of landslide quantitative risk assessment in areas with incomplete historical landslide information

Landslide risk assessment(LRA)is of great significance to hazard prevention and mitigation.However,the historical landslide information is incomplete in most areas,which makes the landslide quantitative risk assessment(LQRA)extremely difficult.This research proposed a set of frameworks for LQRA,so as to achieve LQRA in areas with incomplete historical landslide information.Firstly,we constructed the convolutional neural network(CNN)model suitable for landslide susceptibility assessment(LSA)by studying the structure and hyperparameters optimization of CNN.Secondly,we proposed a method to calculate the temporal probability by using the Poisson model based on the time range of historical landslides occurrence,and then conducted landslide hazard assessment(LHA).Then,we established a mathematical model for landslide intensity of shallow landslide based on landslide area and slope,aiming at solving the problem that it is difficult to calculate landslide intensity due to the lack of landslide volume and velocity.Based on the landslide intensity and the hazard-resistant capacity of the element at risk,we assessed the landslide vulnerability.Finally,population risk map and economic risk map are obtained based on the landslide hazard,vulnerability,and estimated value of the elements at risk.The proposed LQRA framework was applied to Tumen City,China for testing and field validation.From the results,the CNN model built can help improve the accuracy of LSA.The proposed temporal probability calculation method is conducive to the completion of LHA in areas with incomplete historical landslide information.The established landslide intensity mathematical model has certain credibility.Since the landslide risk map is obtained through appropriate simplification and substitution estimation,its final value cannot be used as an accurate prediction of future losses,but it can be used as a reference for the extent of potential losses,so as to determine the areas where hazard prevention and mitigation measures need to be taken.

Risk assessment and landslide prevention design using numerical modelling——A case study in Qingliu,China

Numerous Quaternary deposits are existed in the mountainous areas of Southwest China,especially in the transition zone between the QinghaiTibet Plateau and the Sichuan Basin,where strong tectonic movements and frequent climatic changes increase the potential landslides.The possible deformation and failure process of potential landslides and their impacts on the surrounding environment are important research topics.Field investigation and monitoring indicate that the Qingliu landslide in Xiameng town,Li County,Sichuan Province,China has been continuously deforming since August 2020.The deformation zone has a maximum deformation depth of approximately 18.9m,a total area of 54,628 m2,and a volume of 34.0×104 m3,which seriously threatens infrastructure projects and dwellings.As a result,understanding the Qingliu landslide evolution process,assessing the hazard risk,and planning disaster prevention measures are of great significance for reducing disaster loss.In this study,the mass movement process and hazard risk of the Qingliu landslide are evaluated,and the effects of different prevention measures are compared and discussed.By using the depth-integrated method,the mass movement of the Qingliu landslide is analyzed.The numerical simulation results indicate that the maximum velocity of the Qingliu landslide is approximately 37.5 m/s,and the duration of the landslide is approximately 90s.The simulated landslide can eventually form a deposited mass with a maximum deposit thickness of 19.4 m and an area of approximately 60,168.3 m2,thereby blocking the river and burying dwellings.Furthermore,a risk assessment of the Qingliu landslide under different forms of protection measures is also produced and discussed by considering the hazard level and economic vulnerability level of the affected area.Setting three layers of anti-slide piles on the deformation zone to reduce the hazard risk of the Qingliu landslide is a better choice.Our results may be useful for planning prevention measures and improving disaster emergency response systems.

Risk assessment of mountain tourism on the Western Sichuan Plateau,China

As an important site for tourism activities,mountainous areas may generate greater tourism risks than plain areas due to potential natural disasters,social issues,scenic area management,and tourist behavior.Western Sichuan Plateau is mostly mountainous area and tourism is its pillar industry,Therefore,the assessment of the tourism risks on the Western Sichuan Plateau is of academic value and practical significance.In this study,we use statistical and remote sensing data,fishbone diagram,and the entropy weighting method to construct a tourism risk evaluation model and classify risks into different levels,and we also use a geographic information system(GIS)for spatial mapping to quantify and spatialize the results.The objectives are 1)to identify the risk sources in the Western Sichuan Plateau and analyze their causal mechanisms,precisely reveal the distribution of tourism risks in the study area;2)improve the precision of tourism risk evaluation in scenic areas and analyze the causes and spatial distribution patterns of tourism risks and propose targeted management measures.This study found that the evaluation results of the four elements of hazard,exposure,vulnerability,and disaster prevention and mitigation capacity on the Western Sichuan Plateau showed significant spatial variability,depending on the natural conditions and the quantity difference of tourism resources in different regions.In addition,the tourism risk is low in most areas of the Western Sichuan Plateau,and disaster prevention and mitigation capacity is higher in areas with high tourism risk where attractions are densely populated and tourism is concentrated.Our study can provide a reference for future analyses of tourism risks in mountainous tourist areas such as in China and worldwide.

Risk pre-assessment method for regional drilling engineering based on deep learning and multi-source data

Accurately predicting downhole risk before drilling in new exploration areas is one of the difficulties.Using intelligent algorithms to explore the complex relationship between multi-source data and downhole risk is a hot research topic and frontier in this field.However,due to the small number and uneven distribution of drilled wells in new exploration areas and the lack of sample data related to risk,the training model has insufficient generalization ability,and thus the prediction is not effective.In this paper,a drilling risk profile(depth domain)rich in geological and engineering information is constructed by introducing a quantitative evaluation method for drilling risk of drilled wells,which can provide sufficient risk sample data for model training and thus solve the small sample problem.For the problem of uneven distribution of drilling wells in new exploration areas,the concept of virtual wells and their deployment methods were proposed.Besides,two methods for calculating rock mechanical parameters of virtual wells were proposed,and the accuracy and applicability of the two methods are analyzed.The LSTM deep learning model was optimized to tap the quantitative relationship between drilling risk profiles and multi-source data(e.g.,seismic,logging,and rock mechanical parameters).The model was validated to have an average relative error of 9.19%.The quantitative prediction of the drilling risk profile of the virtual well was achieved using the trained LSTM model and the calculation of the relevant parameters of the virtual well.Finally,based on the sequential Gaussian simulation method and the risk distribution of drilled and virtual wells,a regional 3D drilling risk model was constructed.The analysis of real cases shows that the addition of virtual wells can significantly improve the identification of regional drilling risks and the prediction accuracy of pre-drill drilling risks in unexplored areas can be improved by up to 21%compared with the 3D risk model constructed based on drilled wells only.

A reliability-oriented genetic algorithm-levenberg marquardt model for leak risk assessment based on time-frequency features

Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses,a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected incidents.The fast and accurate leak detection methods are essential for maintaining pipeline safety in pipeline reliability engineering.Current oil pipeline leakage signals are insufficient for feature extraction,while the training time for traditional leakage prediction models is too long.A new leak detection method is proposed based on time-frequency features and the Genetic Algorithm-Levenberg Marquardt(GA-LM)classification model for predicting the leakage status of oil pipelines.The signal that has been processed is transformed to the time and frequency domain,allowing full expression of the original signal.The traditional Back Propagation(BP)neural network is optimized by the Genetic Algorithm(GA)and Levenberg Marquardt(LM)algorithms.The results show that the recognition effect of a combined feature parameter is superior to that of a single feature parameter.The Accuracy,Precision,Recall,and F1score of the GA-LM model is 95%,93.5%,96.7%,and 95.1%,respectively,which proves that the GA-LM model has a good predictive effect and excellent stability for positive and negative samples.The proposed GA-LM model can obviously reduce training time and improve recognition efficiency.In addition,considering that a large number of samples are required for model training,a wavelet threshold method is proposed to generate sample data with higher reliability.The research results can provide an effective theoretical and technical reference for the leakage risk assessment of the actual oil pipelines.