The information from sparsely logged wellbores is currently under-utilized in reservoir simulation models and their proxies using deep and machine learning (DL/ML).This is particularly problematic for large heterogene...The information from sparsely logged wellbores is currently under-utilized in reservoir simulation models and their proxies using deep and machine learning (DL/ML).This is particularly problematic for large heterogeneous gas/oil reservoirs being considered for repurposing as gas storage reservoirs for CH_(4),CO_(2) or H_(2) and/or enhanced oil recovery technologies.Lack of well-log data leads to inadequate spatial definition of complex models due to the large uncertainties associated with the extrapolation of petrophysical rock types (PRT) calibrated with limited core data across heterogeneous and/or anisotropic reservoirs.Extracting well-log attributes from the few well logs available in many wells and tying PRT predictions based on them to seismic data has the potential to substantially improve the confidence in PRT 3D-mapping across such reservoirs.That process becomes more efficient when coupled with DL/ML models incorporating feature importance and optimized,dual-objective feature selection techniques.展开更多
Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental con...Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental concept drift,gradually alter the behavior or structure of processes,making their detection and localization a challenging task.Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift,particularly from a control-flow perspective.The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs,with a specific emphasis on the structural evolution of control-flow semantics in processes.We propose DriftXMiner,a control-flow-aware hybrid framework that combines statistical,machine learning,and process model analysis techniques.The approach comprises three key components:(1)Cumulative Drift Scanner that tracks directional statistical deviations to detect early drift signals;(2)a Temporal Clustering and Drift-Aware Forest Ensemble(DAFE)to capture distributional and classification-level changes in process behavior;and(3)Petri net-based process model reconstruction,which enables the precise localization of structural drift using transition deviation metrics and replay fitness scores.Experimental validation on the BPI Challenge 2017 event log demonstrates that DriftXMiner effectively identifies and localizes gradual and incremental process drift over time.The framework achieves a detection accuracy of 92.5%,a localization precision of 90.3%,and an F1-score of 0.91,outperforming competitive baselines such as CUSUM+Histograms and ADWIN+Alpha Miner.Visual analyses further confirm that identified drift points align with transitions in control-flow models and behavioral cluster structures.DriftXMiner offers a novel and interpretable solution for incremental concept drift detection and localization in dynamic,process-aware systems.By integrating statistical signal accumulation,temporal behavior profiling,and structural process mining,the framework enables finegrained drift explanation and supports adaptive process intelligence in evolving environments.Its modular architecture supports extension to streaming data and real-time monitoring contexts.展开更多
Electromagnetic technology used in logging while drilling(LWD) provides the resistivity distribution around a borehole within a range of several tens of meters.However,a blind zone appears in front of the drill bit wh...Electromagnetic technology used in logging while drilling(LWD) provides the resistivity distribution around a borehole within a range of several tens of meters.However,a blind zone appears in front of the drill bit when operating in high-angle wells,limiting the ability to detect formations ahead of the drill bit.Look-ahead technology addresses this issue and substantially enhances the proactive capability of geological directional drilling.In this study,we examine the detection capabilities of various component combinations of magnetic dipole antenna.Based on the sensitivity of each component to the axial information,a coaxial component is selected as a boundary indicator.We investigate the impact of various factors,such as frequency and transmitter and receiver(TR) distance,under different geological models.This study proposes 5 and 20 kHz as appropriate frequencies,and 10-14 and 12-17 m as suitable TR distance combinations.The accuracy of the numerical calculation results is verified via air-sea testing,confirming the instrument's detection capability.A test model that eliminated the influence of environmental factors and seawater depth is developed.The results have demonstrated that the tool can recognize the interface between layers up to 21.6 m ahead.It provides a validation idea for the design of new instruments as well as the validation of detection capabilities.展开更多
Well logging technology has accumulated a large amount of historical data through four generations of technological development,which forms the basis of well logging big data and digital assets.However,the value of th...Well logging technology has accumulated a large amount of historical data through four generations of technological development,which forms the basis of well logging big data and digital assets.However,the value of these data has not been well stored,managed and mined.With the development of cloud computing technology,it provides a rare development opportunity for logging big data private cloud.The traditional petrophysical evaluation and interpretation model has encountered great challenges in the face of new evaluation objects.The solution research of logging big data distributed storage,processing and learning functions integrated in logging big data private cloud has not been carried out yet.To establish a distributed logging big-data private cloud platform centered on a unifi ed learning model,which achieves the distributed storage and processing of logging big data and facilitates the learning of novel knowledge patterns via the unifi ed logging learning model integrating physical simulation and data models in a large-scale functional space,thus resolving the geo-engineering evaluation problem of geothermal fi elds.Based on the research idea of“logging big data cloud platform-unifi ed logging learning model-large function space-knowledge learning&discovery-application”,the theoretical foundation of unified learning model,cloud platform architecture,data storage and learning algorithm,arithmetic power allocation and platform monitoring,platform stability,data security,etc.have been carried on analysis.The designed logging big data cloud platform realizes parallel distributed storage and processing of data and learning algorithms.The feasibility of constructing a well logging big data cloud platform based on a unifi ed learning model of physics and data is analyzed in terms of the structure,ecology,management and security of the cloud platform.The case study shows that the logging big data cloud platform has obvious technical advantages over traditional logging evaluation methods in terms of knowledge discovery method,data software and results sharing,accuracy,speed and complexity.展开更多
Karst fractures serve as crucial seepage channels and storage spaces for carbonate natural gas reservoirs,and electrical image logs are vital data for visualizing and characterizing such fractures.However,the conventi...Karst fractures serve as crucial seepage channels and storage spaces for carbonate natural gas reservoirs,and electrical image logs are vital data for visualizing and characterizing such fractures.However,the conventional approach of identifying fractures using electrical image logs predominantly relies on manual processes that are not only time-consuming but also highly subjective.In addition,the heterogeneity and strong dissolution tendency of karst carbonate reservoirs lead to complexity and variety in fracture geometry,which makes it difficult to accurately identify fractures.In this paper,the electrical image logs network(EILnet)da deep-learning-based intelligent semantic segmentation model with a selective attention mechanism and selective feature fusion moduledwas created to enable the intelligent identification and segmentation of different types of fractures through electrical logging images.Data from electrical image logs representing structural and induced fractures were first selected using the sliding window technique before image inpainting and data augmentation were implemented for these images to improve the generalizability of the model.Various image-processing tools,including the bilateral filter,Laplace operator,and Gaussian low-pass filter,were also applied to the electrical logging images to generate a multi-attribute dataset to help the model learn the semantic features of the fractures.The results demonstrated that the EILnet model outperforms mainstream deep-learning semantic segmentation models,such as Fully Convolutional Networks(FCN-8s),U-Net,and SegNet,for both the single-channel dataset and the multi-attribute dataset.The EILnet provided significant advantages for the single-channel dataset,and its mean intersection over union(MIoU)and pixel accuracy(PA)were 81.32%and 89.37%,respectively.In the case of the multi-attribute dataset,the identification capability of all models improved to varying degrees,with the EILnet achieving the highest MIoU and PA of 83.43%and 91.11%,respectively.Further,applying the EILnet model to various blind wells demonstrated its ability to provide reliable fracture identification,thereby indicating its promising potential applications.展开更多
Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current t...Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current techniques,such as multimineral petrophysical analysis,offer details into mineralogical distribution.However,it is inherently time-intensive and demands substantial geological expertise for accurate model evaluation.Furthermore,traditional machine learning techniques often struggle to predict mineralogy accurately and sometimes produce estimations that violate fundamental physical principles.To address this,we present a new approach using Physics-Integrated Neural Networks(PINNs),that combines data-driven learning with domain-specific physical constraints,embedding petrophysical relationships directly into the neural network architecture.This approach enforces that predictions adhere to physical laws.The methodology is applied to the Broom Creek Deep Saline aquifer,a CO_(2) sequestration site in the Williston Basin,to predict the volumes of key mineral constituents—quartz,dolomite,feldspar,anhydrite,illite—along with porosity.Compared to traditional artificial neural networks (ANN),the PINN approach demonstrates higher accuracy and better generalizability,significantly enhancing predictive performance on unseen well datasets.The average mean error across the three blind wells is 0.123 for ANN and 0.042 for PINN,highlighting the superior accuracy of the PINN approach.This method reduces uncertainties in reservoir characterization by improving the reliability of mineralogy and porosity predictions,providing a more robust tool for decision-making in various subsurface geoscience applications.展开更多
We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpr...We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.展开更多
In Dagang Oilfield in China,the utilization of the KCl polymer water-based drilling fluid(WBDF) in middeep exploration/appraisal wells presents a challenge in simultaneously optimizing resistivity logging accuracy and...In Dagang Oilfield in China,the utilization of the KCl polymer water-based drilling fluid(WBDF) in middeep exploration/appraisal wells presents a challenge in simultaneously optimizing resistivity logging accuracy and wellbore stability.To address this,it is necessary to conduct geology-engineering integration studies.Based on the formation resistivity,an analytical model was developed to assess the impact of KCl concentration in the WBDF on array induction logging response accuracy.The maximum permissible KCl concentration for the target formations was determined,and technical strategies were proposed to maintain wellbore stability at a reduced KCl concentration.After that,considering the inhibitory,encapsulating,and plugging effects,a low-KCl-concentration WBDF was optimized and applied.Model calculations demonstrate that increasing KCl concentration in the WBDF decreases resistivity,thereby reducing logging accuracy.To maintain a logging accu racy of ≥80%,the upper limits for KCl concentration in the WBDF are 4.8%,4.2%,and 3.6% for the 3rd Member of the Dongying Formation,the 1st and 2nd members of the Shahejie Formation,respectively.Cuttings recovery experiments revealed that a minimum KCl concentration of 3% is required to ensure basic shale inhibition.A combination of 3% KCl with 1% polyamine inhibitor yielded cuttings recovery and shale stability index comparable to those achieved with 7% KCl alone,and the shale inhibition performance was further enhanced with the addition of an encapsulator.The optimized WBDF has been successfully deployed in exploration/appraisal wells across multiple blocks within Dagang Oilfield,resulting in superior wellbore stability during ope rations.Furthermore,the electric logging interpre tation coincidence rate improved from 68.1% to 89.9%,providing robust te chnical support for high-quality drilling and accurate reservoir evaluation in exploration/appraisal wells.展开更多
Conventional borehole image log interpretation of linear fractures on volcanic rocks,represented as sinusoids on unwrapped cylinder projections,is relatively straight-forward,however,interpreting non-linear rock struc...Conventional borehole image log interpretation of linear fractures on volcanic rocks,represented as sinusoids on unwrapped cylinder projections,is relatively straight-forward,however,interpreting non-linear rock structures and complex facies geometries can be more challenging.To characterize diverse volcanic paleoenvironments related to the formation of the South American continent,this study presents a new methodology based on image logs,petrography,seismic data,and outcrop analogues.The presented methodology used pseudo-boreholes images generated from outcrop photographs with typical igneous rock features worldwide simulating 2D unwrapped cylinder projections of a 31 cm(12.25 in)diameter well.These synthetic images and standard outcrop photographs were used to define morphological patterns of igneous structures and facies for comparison with wireline borehole image logs from subsurface volcanic and subvolcanic units,providing a“visual scale”for geological evaluation of volcanic facies,significantly enhancing the identification efficiency and reliability of complex geological structures.Our analysis focused on various scales of columnar jointing and pillow lava lobes with additional examples including pahoehoe lava,ignimbrite,hyaloclastite,and various intrusive features in Campos,Santos,and Parnaíba basins in Brazil.This approach increases confidence in the interpretation of subvolcanic,subaerial,and subaqueous deposits.The image log interpretation combined with regional geological knowledge has enabled paleoenvironmental insights into the rift magmatism system related to the breakup of Gondwana with associated implications for hydrocarbon exploration.展开更多
Pharmaceutical pollution is becoming an increasing threat to aquatic environments since inactive compounds do not break down,and the drug products are accumulated in living organisms.The ability of a drug to dissolve ...Pharmaceutical pollution is becoming an increasing threat to aquatic environments since inactive compounds do not break down,and the drug products are accumulated in living organisms.The ability of a drug to dissolve in water(i.e.,LogS)is an important parameter for assessing a drug’s environmental fate,biovailability,and toxicity.LogS is typically measured in a laboratory setting,which can be costly and time-consuming,and does not provide the opportunity to conduct large-scale analyses.This research develops and evaluates machine learning models that can produce LogS estimates and may improve the environmental risk assessments of toxic pharmaceutical pollutants.We used a dataset from the ChEMBL database that contained 8832 molecular compounds.Various data preprocessing and cleaning techniques were applied(i.e.,removing the missing values),we then recorded chemical properties by normalizing and,even,using some feature selection techniques.We evaluated logS with a total of several machine learning and deep learning models,including;linear regression,random forests(RF),support vector machines(SVM),gradient boosting(GBM),and artificial neural networks(ANNs).We assessed model performance using a series of metrics,including root mean square error(RMSE)and mean absolute error(MAE),as well as the coefficient of determination(R^(2)).The findings show that the Least Angle Regression(LAR)model performed the best with an R^(2) value close to 1.0000,confirming high predictive accuracy.The OMP model performed well with good accuracy(R^(2)=0.8727)while remaining computationally cheap,while other models(e.g.,neural networks,random forests)performed well but were too computationally expensive.Finally,to assess the robustness of the results,an error analysis indicated that residuals were evenly distributed around zero,confirming the results from the LAR model.The current research illustrates the potential of AI in anticipating drug solubility,providing support for green pharmaceutical design and environmental risk assessment.Future work should extend predictions to include degradation and toxicity to enhance predictive power and applicability.展开更多
Commercially managed forests are often poor in terms of biodiversity.Saproxylic beetle species could be a useful bioindicating group for the conservation of forest stands.In recent decades,oak stands have been affecte...Commercially managed forests are often poor in terms of biodiversity.Saproxylic beetle species could be a useful bioindicating group for the conservation of forest stands.In recent decades,oak stands have been affected by a wide range of factors that have intensified stand decline.Saproxylic beetle richness was investigated in declining oak stands that have been consequently targeted for clearcutting due to concerns about insect pest outbreaks.The research was conducted at six managed oak forests,where we compared beetle occurrences in declining stands and in healthy stands that did not show any symptoms of decline.Beetles were collected using window traps placed on the basal and mid-trunk sections of trees.A total of 2925 adults belonging to 239 saproxylic beetle species were captured,of which 56 species are on the IUCN Red List.The results show that declining stands were richer in saproxylic species,and that the diversity of beetle species was greater in these stands.Approximately 1.4 times more species were caught within declining stands than in healthy ones(1.6 times for Red List species).Declining stands hosted more pest species(e.g.,cambiophagous and xylophagous species).However,only low numbers of these species were recorded in these stands.In summary,results of this study suggest that decline of managed oak stands is creating a wide spectrum of habitats for many saproxylic species.Thus,salvage logging of declining oak trees can represent a natural trap and reduce local beetle biodiversity,mainly for saproxylic,endangered or low-mobility species that would be attracted by new suitable habitats.展开更多
Logs and sawnwood play an important and fundamental role in the development of China's timber industry and are also China's major imports.This study explores the impact of economic policy uncertainty(EPU)on Ch...Logs and sawnwood play an important and fundamental role in the development of China's timber industry and are also China's major imports.This study explores the impact of economic policy uncertainty(EPU)on China's log and sawnwood trade by empirically analyzing the panel data of China's major trading partner countries with these two types of forest products from 2001 to 2022.The results show that the economic policy uncertainty of trading partner countries has a significant promotion effect on China's log and sawnwood trade,while China's economic policy has a significant negative effect on China's log and sawnwood trade.In terms of products,the impact of economic policy uncertainty in trading partner countries on China's sawnwood exports is significantly positive,while the impact on log exports is negative and insignificant.The per capita income of trading partner countries has a positive and significant impact on the trade of logs and sawnwood,while China's per capita income has a negative and significant impact on the trade of logs and sawnwood.The impact of real exchange rate on trade in sawnwood and total trade in logs and sawnwood is significantly positive,while the impact on trade in logs is positive but not significant.The per capita forest area ratio has a negative and significant effect on China's log imports,sawnwood imports and total imports of both logs and sawnwood.There are differences in the extent to which economic policy uncertainty affects China's trade in logs and sawnwood with developed and developing trading partners,with the overall effect on China's trade with developed trading partners being smaller than that with developing trading partners.展开更多
This study explores the application of machine learning techniques for predicting permeability,porosity,and flow zone indicator(FZI)in carbonate reservoirs using well log data,aiming to overcome the limitations of tra...This study explores the application of machine learning techniques for predicting permeability,porosity,and flow zone indicator(FZI)in carbonate reservoirs using well log data,aiming to overcome the limitations of traditional empirical methods.Six machine learning algorithms are utilized:support vector machine(SVM),backpropagation(BP)neural network,gaussian process regression(GPR),extreme gradient boosting(XGBoost),K-nearest neighbor(KNN),and random forest(RF).The methodology involves classifying pore-permeability types based on the flow index,leveraging logging curves and geological data.Models are trained using seven logging parameters—spectral gamma rays(SGR),uranium-free gamma rays(CGR),photoelectric absorption cross-section index(PE),lithologic density(RHOB),acoustic transit time(DT),neutron porosity(NPHI),and formation true resistivity(RT)—along with corresponding physical property labels.Machine learning models are trained and evaluated to predict carbonate rock properties.The results demonstrate that GPR achieves the highest accuracy in porosity prediction,with a coefficient of determination(R~2)value of 0.7342,while RF proves to be the most accurate for permeability prediction.Despite these improvements,accurately predicting lowpermeability zones in heterogeneous carbonate rocks remains a significant challenge.Application of cross-validation techniques optimized the performance of GPR,resulting in an accuracy index(ACI)value of 0.9699 for porosity prediction.This study provides a novel framework that leverages machine learning techniques to improve the characterization of carbonate reservoirs.展开更多
As computer data grows exponentially,detecting anomalies within system logs has become increasingly important.Current research on log anomaly detection largely depends on log templates derived from log parsing.Word em...As computer data grows exponentially,detecting anomalies within system logs has become increasingly important.Current research on log anomaly detection largely depends on log templates derived from log parsing.Word embedding is utilized to extract information from these templates.However,this method neglects a portion of the content within the logs and confronts the challenge of data imbalance among various log template types after parsing.Currently,specialized research on data imbalance across log template categories remains scarce.A dual-attention-based log anomaly detection model(LogDA),which leveraged data imbalance,was proposed to address these issues in the work.The LogDA model initially utilized a pre-trained model to extract semantic embedding from log templates.Besides,the similarity between embedding was calculated to discern the relationships among the various templates.Then,a Transformer model with a dual-attention mechanism was constructed to capture positional information and global dependencies.Compared to multiple baseline experiments across three public datasets,the proposed approach could improve precision,recall,and F1 scores.展开更多
A cased well model consists of a coaxial tank and casing,which houses coaxially installed transmitting and receiving coils.The transmitting coil is excited by the current produced by the transmitting circuit,and trans...A cased well model consists of a coaxial tank and casing,which houses coaxially installed transmitting and receiving coils.The transmitting coil is excited by the current produced by the transmitting circuit,and transient electromagnetic responses occur in the casing,including direct coupling and casing responses.As the range between the transmitting and receiving coils increases,direct coupling responses decay rapidly,are less than the casing response at 0.3 m,and disappear at 0.7 m.By contrast,a casing response increases rapidly and then declines slowly after reaching a peak and changes little within a specifi c range.The peak decreases slowly with range.The continuous addition of water to the tank causes slight changes in transient electromagnetic responses,so the diff erence which are subtracted from the response without water is used.Moreover,the diff erences at the time of rapid increase in response and the time of rapid decrease in response are large,forming a peak and a trough.Given that the conductivity of water in a full tank changes after the addition of salt,the diff erence in the peak is linear with the increase in the conductivity of water.This result provides an experimental basis for the design of a transient electromagnetic logging instrument that measures the conductivity of formation in cased well.展开更多
Neutron well logging,using instruments equipped with neutron source and detectors(e.g.,^(3)He-tubes,Nal,BGO),plays a key role in lithological differentiation,porosity determination,and fluid property evaluation in the...Neutron well logging,using instruments equipped with neutron source and detectors(e.g.,^(3)He-tubes,Nal,BGO),plays a key role in lithological differentiation,porosity determination,and fluid property evaluation in the petroleum industry.The growing trend of multifu nctional neutron well logging,which enables simultaneous extraction of multiple reservoir characteristics,requiring high-performance detectors capable of withstanding high-temperature downhole conditions,limited space,and instrument vibrations,while also detecting multiple particle types.The Cs_(2)LiYCl_(6):Ce^(3+)(CLYC)elpasolite scintillator demonstrates excellent temperature resistance and detection efficiency,making it become a promising candidate for leading the development of the novel neutron-based double-particle logging technology.This study employed Monte Carlo simulations to generate equivalent gamma spectra and proposed a pulse shape discrimination simulation method based on theoretical analysis and probabilistic iteration.The performance of CLYC was compared to that of common detectors in terms of physical properties and detection efficiency.A double-particle pulsed neutron detection system for porosity determination was established,based on the count ratio of equivalent gamma rays from the range of 2.95-3.42 MeVee energy bins.Results showed that CLYC can effectively replace ^(3)He-tubes for porosity measurement,providing consistent responses.This study offers numerical simulation support for the design of future neutron well logging tools and the application of double-particle detectors in logging systems.展开更多
文摘The information from sparsely logged wellbores is currently under-utilized in reservoir simulation models and their proxies using deep and machine learning (DL/ML).This is particularly problematic for large heterogeneous gas/oil reservoirs being considered for repurposing as gas storage reservoirs for CH_(4),CO_(2) or H_(2) and/or enhanced oil recovery technologies.Lack of well-log data leads to inadequate spatial definition of complex models due to the large uncertainties associated with the extrapolation of petrophysical rock types (PRT) calibrated with limited core data across heterogeneous and/or anisotropic reservoirs.Extracting well-log attributes from the few well logs available in many wells and tying PRT predictions based on them to seismic data has the potential to substantially improve the confidence in PRT 3D-mapping across such reservoirs.That process becomes more efficient when coupled with DL/ML models incorporating feature importance and optimized,dual-objective feature selection techniques.
文摘Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental concept drift,gradually alter the behavior or structure of processes,making their detection and localization a challenging task.Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift,particularly from a control-flow perspective.The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs,with a specific emphasis on the structural evolution of control-flow semantics in processes.We propose DriftXMiner,a control-flow-aware hybrid framework that combines statistical,machine learning,and process model analysis techniques.The approach comprises three key components:(1)Cumulative Drift Scanner that tracks directional statistical deviations to detect early drift signals;(2)a Temporal Clustering and Drift-Aware Forest Ensemble(DAFE)to capture distributional and classification-level changes in process behavior;and(3)Petri net-based process model reconstruction,which enables the precise localization of structural drift using transition deviation metrics and replay fitness scores.Experimental validation on the BPI Challenge 2017 event log demonstrates that DriftXMiner effectively identifies and localizes gradual and incremental process drift over time.The framework achieves a detection accuracy of 92.5%,a localization precision of 90.3%,and an F1-score of 0.91,outperforming competitive baselines such as CUSUM+Histograms and ADWIN+Alpha Miner.Visual analyses further confirm that identified drift points align with transitions in control-flow models and behavioral cluster structures.DriftXMiner offers a novel and interpretable solution for incremental concept drift detection and localization in dynamic,process-aware systems.By integrating statistical signal accumulation,temporal behavior profiling,and structural process mining,the framework enables finegrained drift explanation and supports adaptive process intelligence in evolving environments.Its modular architecture supports extension to streaming data and real-time monitoring contexts.
基金co-funded by the National Key Research and Development Program of China under Grant (2019YFA0708301)the CAS Project for Young Scientists in Basic Research (Grant No.YSBR-082)Research Instrument and Equipment Development Project of Chinese Academy of Sciences (GJJSTD20210008)。
文摘Electromagnetic technology used in logging while drilling(LWD) provides the resistivity distribution around a borehole within a range of several tens of meters.However,a blind zone appears in front of the drill bit when operating in high-angle wells,limiting the ability to detect formations ahead of the drill bit.Look-ahead technology addresses this issue and substantially enhances the proactive capability of geological directional drilling.In this study,we examine the detection capabilities of various component combinations of magnetic dipole antenna.Based on the sensitivity of each component to the axial information,a coaxial component is selected as a boundary indicator.We investigate the impact of various factors,such as frequency and transmitter and receiver(TR) distance,under different geological models.This study proposes 5 and 20 kHz as appropriate frequencies,and 10-14 and 12-17 m as suitable TR distance combinations.The accuracy of the numerical calculation results is verified via air-sea testing,confirming the instrument's detection capability.A test model that eliminated the influence of environmental factors and seawater depth is developed.The results have demonstrated that the tool can recognize the interface between layers up to 21.6 m ahead.It provides a validation idea for the design of new instruments as well as the validation of detection capabilities.
基金supported By Grant (PLN2022-14) of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (Southwest Petroleum University)。
文摘Well logging technology has accumulated a large amount of historical data through four generations of technological development,which forms the basis of well logging big data and digital assets.However,the value of these data has not been well stored,managed and mined.With the development of cloud computing technology,it provides a rare development opportunity for logging big data private cloud.The traditional petrophysical evaluation and interpretation model has encountered great challenges in the face of new evaluation objects.The solution research of logging big data distributed storage,processing and learning functions integrated in logging big data private cloud has not been carried out yet.To establish a distributed logging big-data private cloud platform centered on a unifi ed learning model,which achieves the distributed storage and processing of logging big data and facilitates the learning of novel knowledge patterns via the unifi ed logging learning model integrating physical simulation and data models in a large-scale functional space,thus resolving the geo-engineering evaluation problem of geothermal fi elds.Based on the research idea of“logging big data cloud platform-unifi ed logging learning model-large function space-knowledge learning&discovery-application”,the theoretical foundation of unified learning model,cloud platform architecture,data storage and learning algorithm,arithmetic power allocation and platform monitoring,platform stability,data security,etc.have been carried on analysis.The designed logging big data cloud platform realizes parallel distributed storage and processing of data and learning algorithms.The feasibility of constructing a well logging big data cloud platform based on a unifi ed learning model of physics and data is analyzed in terms of the structure,ecology,management and security of the cloud platform.The case study shows that the logging big data cloud platform has obvious technical advantages over traditional logging evaluation methods in terms of knowledge discovery method,data software and results sharing,accuracy,speed and complexity.
基金the National Natural Science Foundation of China(42472194,42302153,and 42002144)the Fundamental Research Funds for the Central Univer-sities(22CX06002A).
文摘Karst fractures serve as crucial seepage channels and storage spaces for carbonate natural gas reservoirs,and electrical image logs are vital data for visualizing and characterizing such fractures.However,the conventional approach of identifying fractures using electrical image logs predominantly relies on manual processes that are not only time-consuming but also highly subjective.In addition,the heterogeneity and strong dissolution tendency of karst carbonate reservoirs lead to complexity and variety in fracture geometry,which makes it difficult to accurately identify fractures.In this paper,the electrical image logs network(EILnet)da deep-learning-based intelligent semantic segmentation model with a selective attention mechanism and selective feature fusion moduledwas created to enable the intelligent identification and segmentation of different types of fractures through electrical logging images.Data from electrical image logs representing structural and induced fractures were first selected using the sliding window technique before image inpainting and data augmentation were implemented for these images to improve the generalizability of the model.Various image-processing tools,including the bilateral filter,Laplace operator,and Gaussian low-pass filter,were also applied to the electrical logging images to generate a multi-attribute dataset to help the model learn the semantic features of the fractures.The results demonstrated that the EILnet model outperforms mainstream deep-learning semantic segmentation models,such as Fully Convolutional Networks(FCN-8s),U-Net,and SegNet,for both the single-channel dataset and the multi-attribute dataset.The EILnet provided significant advantages for the single-channel dataset,and its mean intersection over union(MIoU)and pixel accuracy(PA)were 81.32%and 89.37%,respectively.In the case of the multi-attribute dataset,the identification capability of all models improved to varying degrees,with the EILnet achieving the highest MIoU and PA of 83.43%and 91.11%,respectively.Further,applying the EILnet model to various blind wells demonstrated its ability to provide reliable fracture identification,thereby indicating its promising potential applications.
基金the North Dakota Industrial Commission (NDIC) for their financial supportprovided by the University of North Dakota Computational Research Center。
文摘Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current techniques,such as multimineral petrophysical analysis,offer details into mineralogical distribution.However,it is inherently time-intensive and demands substantial geological expertise for accurate model evaluation.Furthermore,traditional machine learning techniques often struggle to predict mineralogy accurately and sometimes produce estimations that violate fundamental physical principles.To address this,we present a new approach using Physics-Integrated Neural Networks(PINNs),that combines data-driven learning with domain-specific physical constraints,embedding petrophysical relationships directly into the neural network architecture.This approach enforces that predictions adhere to physical laws.The methodology is applied to the Broom Creek Deep Saline aquifer,a CO_(2) sequestration site in the Williston Basin,to predict the volumes of key mineral constituents—quartz,dolomite,feldspar,anhydrite,illite—along with porosity.Compared to traditional artificial neural networks (ANN),the PINN approach demonstrates higher accuracy and better generalizability,significantly enhancing predictive performance on unseen well datasets.The average mean error across the three blind wells is 0.123 for ANN and 0.042 for PINN,highlighting the superior accuracy of the PINN approach.This method reduces uncertainties in reservoir characterization by improving the reliability of mineralogy and porosity predictions,providing a more robust tool for decision-making in various subsurface geoscience applications.
基金supported by National Key Research and Development Program (2019YFA0708301)National Natural Science Foundation of China (51974337)+2 种基金the Strategic Cooperation Projects of CNPC and CUPB (ZLZX2020-03)Science and Technology Innovation Fund of CNPC (2021DQ02-0403)Open Fund of Petroleum Exploration and Development Research Institute of CNPC (2022-KFKT-09)
文摘We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.
基金supported by the National Natural Science Foundation of China (52474024)the National Oil&Gas Major Project of China (2025ZD1403200)the Program for Scientific Research Innovation Team of Young Scholars in Colleges and Universities of Shandong Province"Innovation Team of Deepwater Wellbore Fluids"(2022KJ069)。
文摘In Dagang Oilfield in China,the utilization of the KCl polymer water-based drilling fluid(WBDF) in middeep exploration/appraisal wells presents a challenge in simultaneously optimizing resistivity logging accuracy and wellbore stability.To address this,it is necessary to conduct geology-engineering integration studies.Based on the formation resistivity,an analytical model was developed to assess the impact of KCl concentration in the WBDF on array induction logging response accuracy.The maximum permissible KCl concentration for the target formations was determined,and technical strategies were proposed to maintain wellbore stability at a reduced KCl concentration.After that,considering the inhibitory,encapsulating,and plugging effects,a low-KCl-concentration WBDF was optimized and applied.Model calculations demonstrate that increasing KCl concentration in the WBDF decreases resistivity,thereby reducing logging accuracy.To maintain a logging accu racy of ≥80%,the upper limits for KCl concentration in the WBDF are 4.8%,4.2%,and 3.6% for the 3rd Member of the Dongying Formation,the 1st and 2nd members of the Shahejie Formation,respectively.Cuttings recovery experiments revealed that a minimum KCl concentration of 3% is required to ensure basic shale inhibition.A combination of 3% KCl with 1% polyamine inhibitor yielded cuttings recovery and shale stability index comparable to those achieved with 7% KCl alone,and the shale inhibition performance was further enhanced with the addition of an encapsulator.The optimized WBDF has been successfully deployed in exploration/appraisal wells across multiple blocks within Dagang Oilfield,resulting in superior wellbore stability during ope rations.Furthermore,the electric logging interpre tation coincidence rate improved from 68.1% to 89.9%,providing robust te chnical support for high-quality drilling and accurate reservoir evaluation in exploration/appraisal wells.
文摘Conventional borehole image log interpretation of linear fractures on volcanic rocks,represented as sinusoids on unwrapped cylinder projections,is relatively straight-forward,however,interpreting non-linear rock structures and complex facies geometries can be more challenging.To characterize diverse volcanic paleoenvironments related to the formation of the South American continent,this study presents a new methodology based on image logs,petrography,seismic data,and outcrop analogues.The presented methodology used pseudo-boreholes images generated from outcrop photographs with typical igneous rock features worldwide simulating 2D unwrapped cylinder projections of a 31 cm(12.25 in)diameter well.These synthetic images and standard outcrop photographs were used to define morphological patterns of igneous structures and facies for comparison with wireline borehole image logs from subsurface volcanic and subvolcanic units,providing a“visual scale”for geological evaluation of volcanic facies,significantly enhancing the identification efficiency and reliability of complex geological structures.Our analysis focused on various scales of columnar jointing and pillow lava lobes with additional examples including pahoehoe lava,ignimbrite,hyaloclastite,and various intrusive features in Campos,Santos,and Parnaíba basins in Brazil.This approach increases confidence in the interpretation of subvolcanic,subaerial,and subaqueous deposits.The image log interpretation combined with regional geological knowledge has enabled paleoenvironmental insights into the rift magmatism system related to the breakup of Gondwana with associated implications for hydrocarbon exploration.
文摘Pharmaceutical pollution is becoming an increasing threat to aquatic environments since inactive compounds do not break down,and the drug products are accumulated in living organisms.The ability of a drug to dissolve in water(i.e.,LogS)is an important parameter for assessing a drug’s environmental fate,biovailability,and toxicity.LogS is typically measured in a laboratory setting,which can be costly and time-consuming,and does not provide the opportunity to conduct large-scale analyses.This research develops and evaluates machine learning models that can produce LogS estimates and may improve the environmental risk assessments of toxic pharmaceutical pollutants.We used a dataset from the ChEMBL database that contained 8832 molecular compounds.Various data preprocessing and cleaning techniques were applied(i.e.,removing the missing values),we then recorded chemical properties by normalizing and,even,using some feature selection techniques.We evaluated logS with a total of several machine learning and deep learning models,including;linear regression,random forests(RF),support vector machines(SVM),gradient boosting(GBM),and artificial neural networks(ANNs).We assessed model performance using a series of metrics,including root mean square error(RMSE)and mean absolute error(MAE),as well as the coefficient of determination(R^(2)).The findings show that the Least Angle Regression(LAR)model performed the best with an R^(2) value close to 1.0000,confirming high predictive accuracy.The OMP model performed well with good accuracy(R^(2)=0.8727)while remaining computationally cheap,while other models(e.g.,neural networks,random forests)performed well but were too computationally expensive.Finally,to assess the robustness of the results,an error analysis indicated that residuals were evenly distributed around zero,confirming the results from the LAR model.The current research illustrates the potential of AI in anticipating drug solubility,providing support for green pharmaceutical design and environmental risk assessment.Future work should extend predictions to include degradation and toxicity to enhance predictive power and applicability.
基金supported by grant No.QL24020204funded by the Ministry of Agriculture of the Czech Republic and Internal Grant Agency of Faculty of Forestry and Wood Science CZU(reg.no.A_01_22)"EVA 4.0"No.CZ.02.1.01/0.0/0.0/16_019/0000803 financed by OP RDE.
文摘Commercially managed forests are often poor in terms of biodiversity.Saproxylic beetle species could be a useful bioindicating group for the conservation of forest stands.In recent decades,oak stands have been affected by a wide range of factors that have intensified stand decline.Saproxylic beetle richness was investigated in declining oak stands that have been consequently targeted for clearcutting due to concerns about insect pest outbreaks.The research was conducted at six managed oak forests,where we compared beetle occurrences in declining stands and in healthy stands that did not show any symptoms of decline.Beetles were collected using window traps placed on the basal and mid-trunk sections of trees.A total of 2925 adults belonging to 239 saproxylic beetle species were captured,of which 56 species are on the IUCN Red List.The results show that declining stands were richer in saproxylic species,and that the diversity of beetle species was greater in these stands.Approximately 1.4 times more species were caught within declining stands than in healthy ones(1.6 times for Red List species).Declining stands hosted more pest species(e.g.,cambiophagous and xylophagous species).However,only low numbers of these species were recorded in these stands.In summary,results of this study suggest that decline of managed oak stands is creating a wide spectrum of habitats for many saproxylic species.Thus,salvage logging of declining oak trees can represent a natural trap and reduce local beetle biodiversity,mainly for saproxylic,endangered or low-mobility species that would be attracted by new suitable habitats.
文摘Logs and sawnwood play an important and fundamental role in the development of China's timber industry and are also China's major imports.This study explores the impact of economic policy uncertainty(EPU)on China's log and sawnwood trade by empirically analyzing the panel data of China's major trading partner countries with these two types of forest products from 2001 to 2022.The results show that the economic policy uncertainty of trading partner countries has a significant promotion effect on China's log and sawnwood trade,while China's economic policy has a significant negative effect on China's log and sawnwood trade.In terms of products,the impact of economic policy uncertainty in trading partner countries on China's sawnwood exports is significantly positive,while the impact on log exports is negative and insignificant.The per capita income of trading partner countries has a positive and significant impact on the trade of logs and sawnwood,while China's per capita income has a negative and significant impact on the trade of logs and sawnwood.The impact of real exchange rate on trade in sawnwood and total trade in logs and sawnwood is significantly positive,while the impact on trade in logs is positive but not significant.The per capita forest area ratio has a negative and significant effect on China's log imports,sawnwood imports and total imports of both logs and sawnwood.There are differences in the extent to which economic policy uncertainty affects China's trade in logs and sawnwood with developed and developing trading partners,with the overall effect on China's trade with developed trading partners being smaller than that with developing trading partners.
基金funded by Fundamental Research Funds for the Central Universities(No.00007851)。
文摘This study explores the application of machine learning techniques for predicting permeability,porosity,and flow zone indicator(FZI)in carbonate reservoirs using well log data,aiming to overcome the limitations of traditional empirical methods.Six machine learning algorithms are utilized:support vector machine(SVM),backpropagation(BP)neural network,gaussian process regression(GPR),extreme gradient boosting(XGBoost),K-nearest neighbor(KNN),and random forest(RF).The methodology involves classifying pore-permeability types based on the flow index,leveraging logging curves and geological data.Models are trained using seven logging parameters—spectral gamma rays(SGR),uranium-free gamma rays(CGR),photoelectric absorption cross-section index(PE),lithologic density(RHOB),acoustic transit time(DT),neutron porosity(NPHI),and formation true resistivity(RT)—along with corresponding physical property labels.Machine learning models are trained and evaluated to predict carbonate rock properties.The results demonstrate that GPR achieves the highest accuracy in porosity prediction,with a coefficient of determination(R~2)value of 0.7342,while RF proves to be the most accurate for permeability prediction.Despite these improvements,accurately predicting lowpermeability zones in heterogeneous carbonate rocks remains a significant challenge.Application of cross-validation techniques optimized the performance of GPR,resulting in an accuracy index(ACI)value of 0.9699 for porosity prediction.This study provides a novel framework that leverages machine learning techniques to improve the characterization of carbonate reservoirs.
基金funded by the Hainan Provincial Natural Science Foundation Project(Grant No.622RC675)the National Natural Science Foundation of China(Grant No.62262019).
文摘As computer data grows exponentially,detecting anomalies within system logs has become increasingly important.Current research on log anomaly detection largely depends on log templates derived from log parsing.Word embedding is utilized to extract information from these templates.However,this method neglects a portion of the content within the logs and confronts the challenge of data imbalance among various log template types after parsing.Currently,specialized research on data imbalance across log template categories remains scarce.A dual-attention-based log anomaly detection model(LogDA),which leveraged data imbalance,was proposed to address these issues in the work.The LogDA model initially utilized a pre-trained model to extract semantic embedding from log templates.Besides,the similarity between embedding was calculated to discern the relationships among the various templates.Then,a Transformer model with a dual-attention mechanism was constructed to capture positional information and global dependencies.Compared to multiple baseline experiments across three public datasets,the proposed approach could improve precision,recall,and F1 scores.
基金supported by the National Natural Science Foundation of China (grant nos. 42074137)。
文摘A cased well model consists of a coaxial tank and casing,which houses coaxially installed transmitting and receiving coils.The transmitting coil is excited by the current produced by the transmitting circuit,and transient electromagnetic responses occur in the casing,including direct coupling and casing responses.As the range between the transmitting and receiving coils increases,direct coupling responses decay rapidly,are less than the casing response at 0.3 m,and disappear at 0.7 m.By contrast,a casing response increases rapidly and then declines slowly after reaching a peak and changes little within a specifi c range.The peak decreases slowly with range.The continuous addition of water to the tank causes slight changes in transient electromagnetic responses,so the diff erence which are subtracted from the response without water is used.Moreover,the diff erences at the time of rapid increase in response and the time of rapid decrease in response are large,forming a peak and a trough.Given that the conductivity of water in a full tank changes after the addition of salt,the diff erence in the peak is linear with the increase in the conductivity of water.This result provides an experimental basis for the design of a transient electromagnetic logging instrument that measures the conductivity of formation in cased well.
基金the support of the National Natural Science Foundation of China(42174147,42474155)the Scientific and Technological Innovation Projects of Laoshan Laboratory(LSKJ20220347)。
文摘Neutron well logging,using instruments equipped with neutron source and detectors(e.g.,^(3)He-tubes,Nal,BGO),plays a key role in lithological differentiation,porosity determination,and fluid property evaluation in the petroleum industry.The growing trend of multifu nctional neutron well logging,which enables simultaneous extraction of multiple reservoir characteristics,requiring high-performance detectors capable of withstanding high-temperature downhole conditions,limited space,and instrument vibrations,while also detecting multiple particle types.The Cs_(2)LiYCl_(6):Ce^(3+)(CLYC)elpasolite scintillator demonstrates excellent temperature resistance and detection efficiency,making it become a promising candidate for leading the development of the novel neutron-based double-particle logging technology.This study employed Monte Carlo simulations to generate equivalent gamma spectra and proposed a pulse shape discrimination simulation method based on theoretical analysis and probabilistic iteration.The performance of CLYC was compared to that of common detectors in terms of physical properties and detection efficiency.A double-particle pulsed neutron detection system for porosity determination was established,based on the count ratio of equivalent gamma rays from the range of 2.95-3.42 MeVee energy bins.Results showed that CLYC can effectively replace ^(3)He-tubes for porosity measurement,providing consistent responses.This study offers numerical simulation support for the design of future neutron well logging tools and the application of double-particle detectors in logging systems.
