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
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
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
Coaly source rocks have attracted considerable attention for their significant hydrocarbon generation potential in recent years. However, limited study is performed on utilizing geochemical data and well log data to e...Coaly source rocks have attracted considerable attention for their significant hydrocarbon generation potential in recent years. However, limited study is performed on utilizing geochemical data and well log data to evaluate coaly hydrocarbon source rocks. In this study, geochemical data and well log data are selected from two key wells to conduct an evaluation of coaly hydrocarbon source rocks of Jurassic Kezilenuer Formation in Kuqa Depression of Tarim Basin. Initially, analysis was focused on geochemical parameters to assess organic matter type, source rock quality, and hydrocarbon generation potential.Lithology types of source rocks include mudstone, carbonaceous mudstone and coal. The predominant organic matter type identified was Type Ⅲ and Type Ⅱ_(2), indicating a favorable hydrocarbon generation potential. Well log data are integrated to predict total organic carbon(TOC) content, and the results indicate that multiple regression method is effective in predicting TOC of carbonaceous mudstone and coal. However, the ΔlgR method exhibited limited predictive capability for mudstone source rock.Additionally, machine learning methods including multilayer perceptron neural network(MLP), random forest(RF), and extreme gradient boosting(XGBoost) techniques are employed to predict TOC of mudstone source rock. The XGBoost performs best in TOC prediction with correlation coefficient(R2) of 0.9517, indicating a close agreement between measured and predicted TOC values. This study provides a reliable prediction method of coaly hydrocarbon source rocks through machine learning methods, and will provide guidance for resource assessment.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
Pore pressure is a decisive measure to assess the reservoir’s geomechanical properties,ensures safe and efficient drilling operations,and optimizes reservoir characterization and production.The conventional approache...Pore pressure is a decisive measure to assess the reservoir’s geomechanical properties,ensures safe and efficient drilling operations,and optimizes reservoir characterization and production.The conventional approaches sometimes fail to comprehend complex and persistent relationships between pore pressure and formation properties in the heterogeneous reservoirs.This study presents a novel machine learning optimized pore pressure prediction method with a limited dataset,particularly in complex formations.The method addresses the conventional approach's limitations by leveraging its capability to learn complex data relationships.It integrates the best Gradient Boosting Regressor(GBR)algorithm to model pore pressure at wells and later utilizes ContinuousWavelet Transformation(CWT)of the seismic dataset for spatial analysis,and finally employs Deep Neural Network for robust and precise pore pressure modeling for the whole volume.In the second stage,for the spatial variations of pore pressure in the thin Khadro Formation sand reservoir across the entire subsurface area,a three-dimensional pore pressure prediction is conducted using CWT.The relationship between the CWT and geomechanical properties is then established through supervised machine learning models on well locations to predict the uncertainties in pore pressure.Among all intelligent regression techniques developed using petrophysical and elastic properties for pore pressure prediction,the GBR has provided exceptional results that have been validated by evaluation metrics based on the R^(2) score i.e.,0.91 between the calibrated and predicted pore pressure.Via the deep neural network,the relationship between CWT resultant traces and predicted pore pressure is established to analyze the spatial variation.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
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
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
The characterization of subsurface formations via the analysis of near-wellbore velocity profiles represents a crucial method in geophysical exploration.This technique enables the evaluation of key parameters,includin...The characterization of subsurface formations via the analysis of near-wellbore velocity profiles represents a crucial method in geophysical exploration.This technique enables the evaluation of key parameters,including rock brittleness,wellbore stability,fracturing effects,and invasion extent,thereby enhancing comprehension of formation structures and informing exploration strategies.However,traditional near-wellbore formation velocity imaging methods exhibit two principal limitations.First,these methods lack azimuthal sensitivity,yielding results averaged across all directions.Second,they are computationally intensive and impractical for well-site environments.To address these drawbacks,we developed a rapid 3D velocity imaging method for array acoustic logging instru ments equipped with azimuthal receivers,capable of producing 3D imaging results efficiently.The workflow entails the following steps:(1)Band-pass filtering of logging data to mitigate scattered wave interference caused by formation heterogeneity near the wellbore;(2)combination of receivers with varying detection ranges in each direction to derive radial velocity sequences,followed by integration of ray-tracing theory to obtain 2D velocity distributions;and(3)synthesis of final 3D velocity imaging results via interpolation of these 2D datasets.In the velocity sequence extraction process,we significantly reduced the computational load by employing an adaptive time window,ensuring rapid and stable application in well-site settings.We utilized the finite difference method to construct well models with heteroge neous formations.The compressional and shear wave 3D velocity imaging results derived from synthetic data correlated with the model,demonstrating the azimuthal sensitivity of our proposed method.Furthermore,we applied this method to a well in West China,successfully identifying the azimuth of nearwellbore anisotropy.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
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 (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 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 Science Foundation of China University of Petroleum(Beijing)(No.2462023QNXZ010).
文摘Coaly source rocks have attracted considerable attention for their significant hydrocarbon generation potential in recent years. However, limited study is performed on utilizing geochemical data and well log data to evaluate coaly hydrocarbon source rocks. In this study, geochemical data and well log data are selected from two key wells to conduct an evaluation of coaly hydrocarbon source rocks of Jurassic Kezilenuer Formation in Kuqa Depression of Tarim Basin. Initially, analysis was focused on geochemical parameters to assess organic matter type, source rock quality, and hydrocarbon generation potential.Lithology types of source rocks include mudstone, carbonaceous mudstone and coal. The predominant organic matter type identified was Type Ⅲ and Type Ⅱ_(2), indicating a favorable hydrocarbon generation potential. Well log data are integrated to predict total organic carbon(TOC) content, and the results indicate that multiple regression method is effective in predicting TOC of carbonaceous mudstone and coal. However, the ΔlgR method exhibited limited predictive capability for mudstone source rock.Additionally, machine learning methods including multilayer perceptron neural network(MLP), random forest(RF), and extreme gradient boosting(XGBoost) techniques are employed to predict TOC of mudstone source rock. The XGBoost performs best in TOC prediction with correlation coefficient(R2) of 0.9517, indicating a close agreement between measured and predicted TOC values. This study provides a reliable prediction method of coaly hydrocarbon source rocks through machine learning methods, and will provide guidance for resource assessment.
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funded by the Basic Science Centre Project of the National Natural Science Foundation of China(Grant No.72088101)supported by the Higher Education Commission,Pakistan(Grant No.20-14925/NRPU/R&D/HEC/2021-2021)+1 种基金the Researchers Supporting Project Number(Grant No.RSP2025R351)King Saud University,Riyadh,Saudi Arabia,for funding this research article.
文摘Pore pressure is a decisive measure to assess the reservoir’s geomechanical properties,ensures safe and efficient drilling operations,and optimizes reservoir characterization and production.The conventional approaches sometimes fail to comprehend complex and persistent relationships between pore pressure and formation properties in the heterogeneous reservoirs.This study presents a novel machine learning optimized pore pressure prediction method with a limited dataset,particularly in complex formations.The method addresses the conventional approach's limitations by leveraging its capability to learn complex data relationships.It integrates the best Gradient Boosting Regressor(GBR)algorithm to model pore pressure at wells and later utilizes ContinuousWavelet Transformation(CWT)of the seismic dataset for spatial analysis,and finally employs Deep Neural Network for robust and precise pore pressure modeling for the whole volume.In the second stage,for the spatial variations of pore pressure in the thin Khadro Formation sand reservoir across the entire subsurface area,a three-dimensional pore pressure prediction is conducted using CWT.The relationship between the CWT and geomechanical properties is then established through supervised machine learning models on well locations to predict the uncertainties in pore pressure.Among all intelligent regression techniques developed using petrophysical and elastic properties for pore pressure prediction,the GBR has provided exceptional results that have been validated by evaluation metrics based on the R^(2) score i.e.,0.91 between the calibrated and predicted pore pressure.Via the deep neural network,the relationship between CWT resultant traces and predicted pore pressure is established to analyze the spatial variation.
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supported by the National Natural Science Foundation of China(Grant Nos.42174129 and 41374143)the project of"Vice President of Science and Technology"of Changping District,Beijing(Grant No.202502003019)。
文摘The characterization of subsurface formations via the analysis of near-wellbore velocity profiles represents a crucial method in geophysical exploration.This technique enables the evaluation of key parameters,including rock brittleness,wellbore stability,fracturing effects,and invasion extent,thereby enhancing comprehension of formation structures and informing exploration strategies.However,traditional near-wellbore formation velocity imaging methods exhibit two principal limitations.First,these methods lack azimuthal sensitivity,yielding results averaged across all directions.Second,they are computationally intensive and impractical for well-site environments.To address these drawbacks,we developed a rapid 3D velocity imaging method for array acoustic logging instru ments equipped with azimuthal receivers,capable of producing 3D imaging results efficiently.The workflow entails the following steps:(1)Band-pass filtering of logging data to mitigate scattered wave interference caused by formation heterogeneity near the wellbore;(2)combination of receivers with varying detection ranges in each direction to derive radial velocity sequences,followed by integration of ray-tracing theory to obtain 2D velocity distributions;and(3)synthesis of final 3D velocity imaging results via interpolation of these 2D datasets.In the velocity sequence extraction process,we significantly reduced the computational load by employing an adaptive time window,ensuring rapid and stable application in well-site settings.We utilized the finite difference method to construct well models with heteroge neous formations.The compressional and shear wave 3D velocity imaging results derived from synthetic data correlated with the model,demonstrating the azimuthal sensitivity of our proposed method.Furthermore,we applied this method to a well in West China,successfully identifying the azimuth of nearwellbore anisotropy.
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.
