In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on ...In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on machine learning and mud logging data is studied in this paper.This method can effectively utilize downhole parameters collected in real-time during drilling,to identify lithology in real-time and provide a reference for optimization of drilling parameters.Given the imbalance of lithology samples,the synthetic minority over-sampling technique(SMOTE)and Tomek link were used to balance the sample number of five lithologies.Meanwhile,this paper introduces Tent map,random opposition-based learning and dynamic perceived probability to the original crow search algorithm(CSA),and establishes an improved crow search algorithm(ICSA).In this paper,ICSA is used to optimize the hyperparameter combination of random forest(RF),extremely random trees(ET),extreme gradient boosting(XGB),and light gradient boosting machine(LGBM)models.In addition,this study combines the recognition advantages of the four models.The accuracy of lithology identification by the weighted average probability model reaches 0.877.The study of this paper realizes high-precision real-time lithology identification method,which can provide lithology reference for the drilling process.展开更多
Distinguishing between web traffic generated by bots and humans is an important task in the evaluation of online marketing campaigns.One of the main challenges is related to only partial availability of the performanc...Distinguishing between web traffic generated by bots and humans is an important task in the evaluation of online marketing campaigns.One of the main challenges is related to only partial availability of the performance metrics:although some users can be unambiguously classified as bots,the correct label is uncertain in many cases.This calls for the use of classifiers capable of explaining their decisions.This paper demonstrates two such mechanisms based on features carefully engineered from web logs.The first is a man-made rule-based system.The second is a hierarchical model that first performs clustering and next classification using human-centred,interpretable methods.The stability of the proposed methods is analyzed and a minimal set of features that convey the classdiscriminating information is selected.The proposed data processing and analysis methodology are successfully applied to real-world data sets from online publishers.展开更多
On March 3,2024,the prototype permeability logging instrument independently developed in China successfully completed its first downhole test in Ren 91 standard well in PetroChina Huabei Oilfield.In the open hole sect...On March 3,2024,the prototype permeability logging instrument independently developed in China successfully completed its first downhole test in Ren 91 standard well in PetroChina Huabei Oilfield.In the open hole section at a depth of 3925 metres and at a temperature of 148℃,the device collected high-quality permeability logging data.This marks a key technological breakthrough from 0 to 1 in permeability logging,and lays the foundation for the next step in developing a complete set of permeability logging equipment.展开更多
Identification of reservoir types in deep carbonates has always been a great challenge due to complex logging responses caused by the heterogeneous scale and distribution of storage spaces.Traditional cross-plot analy...Identification of reservoir types in deep carbonates has always been a great challenge due to complex logging responses caused by the heterogeneous scale and distribution of storage spaces.Traditional cross-plot analysis and empirical formula methods for identifying reservoir types using geophysical logging data have high uncertainty and low efficiency,which cannot accurately reflect the nonlinear relationship between reservoir types and logging data.Recently,the kernel Fisher discriminant analysis(KFD),a kernel-based machine learning technique,attracts attention in many fields because of its strong nonlinear processing ability.However,the overall performance of KFD model may be limited as a single kernel function cannot simultaneously extrapolate and interpolate well,especially for highly complex data cases.To address this issue,in this study,a mixed kernel Fisher discriminant analysis(MKFD)model was established and applied to identify reservoir types of the deep Sinian carbonates in central Sichuan Basin,China.The MKFD model was trained and tested with 453 datasets from 7 coring wells,utilizing GR,CAL,DEN,AC,CNL and RT logs as input variables.The particle swarm optimization(PSO)was adopted for hyper-parameter optimization of MKFD model.To evaluate the model performance,prediction results of MKFD were compared with those of basic-kernel based KFD,RF and SVM models.Subsequently,the built MKFD model was applied in a blind well test,and a variable importance analysis was conducted.The comparison and blind test results demonstrated that MKFD outperformed traditional KFD,RF and SVM in the identification of reservoir types,which provided higher accuracy and stronger generalization.The MKFD can therefore be a reliable method for identifying reservoir types of deep carbonates.展开更多
System logs,serving as a pivotal data source for performance monitoring and anomaly detection,play an indispensable role in assuring service stability and reliability.Despite this,the majority of existing log-based an...System logs,serving as a pivotal data source for performance monitoring and anomaly detection,play an indispensable role in assuring service stability and reliability.Despite this,the majority of existing log-based anomaly detection methodologies predominantly depend on the sequence or quantity attributes of logs,utilizing solely a single Recurrent Neural Network(RNN)and its variant sequence models for detection.These approaches have not thoroughly exploited the semantic information embedded in logs,exhibit limited adaptability to novel logs,and a single model struggles to fully unearth the potential features within the log sequence.Addressing these challenges,this article proposes a hybrid architecture based on amultiscale convolutional neural network,efficient channel attention and mogrifier gated recurrent unit networks(LogCEM),which amalgamates multiple neural network technologies.Capitalizing on the superior performance of robustly optimized BERT approach(RoBERTa)in the realm of natural language processing,we employ RoBERTa to extract the original word vectors from each word in the log template.In conjunction with the enhanced Smooth Inverse Frequency(SIF)algorithm,we generate more precise log sentence vectors,thereby achieving an in-depth representation of log semantics.Subsequently,these log vector sequences are fed into a hybrid neural network,which fuses 1D Multi-Scale Convolutional Neural Network(MSCNN),Efficient Channel Attention Mechanism(ECA),and Mogrifier Gated Recurrent Unit(GRU).This amalgamation enables themodel to concurrently capture the local and global dependencies of the log sequence and autonomously learn the significance of different log sequences,thereby markedly enhancing the efficacy of log anomaly detection.To validate the effectiveness of the LogCEM model,we conducted evaluations on two authoritative open-source datasets.The experimental results demonstrate that LogCEM not only exhibits excellent accuracy and robustness,but also outperforms the current mainstream log anomaly detection methods.展开更多
In the traditional well log depth matching tasks,manual adjustments are required,which means significantly labor-intensive for multiple wells,leading to low work efficiency.This paper introduces a multi-agent deep rei...In the traditional well log depth matching tasks,manual adjustments are required,which means significantly labor-intensive for multiple wells,leading to low work efficiency.This paper introduces a multi-agent deep reinforcement learning(MARL)method to automate the depth matching of multi-well logs.This method defines multiple top-down dual sliding windows based on the convolutional neural network(CNN)to extract and capture similar feature sequences on well logs,and it establishes an interaction mechanism between agents and the environment to control the depth matching process.Specifically,the agent selects an action to translate or scale the feature sequence based on the double deep Q-network(DDQN).Through the feedback of the reward signal,it evaluates the effectiveness of each action,aiming to obtain the optimal strategy and improve the accuracy of the matching task.Our experiments show that MARL can automatically perform depth matches for well-logs in multiple wells,and reduce manual intervention.In the application to the oil field,a comparative analysis of dynamic time warping(DTW),deep Q-learning network(DQN),and DDQN methods revealed that the DDQN algorithm,with its dual-network evaluation mechanism,significantly improves performance by identifying and aligning more details in the well log feature sequences,thus achieving higher depth matching accuracy.展开更多
Pore pressure(PP)information plays an important role in analysing the geomechanical properties of the reservoir and hydrocarbon field development.PP prediction is an essential requirement to ensure safe drilling opera...Pore pressure(PP)information plays an important role in analysing the geomechanical properties of the reservoir and hydrocarbon field development.PP prediction is an essential requirement to ensure safe drilling operations and it is a fundamental input for well design,and mud weight estimation for wellbore stability.However,the pore pressure trend prediction in complex geological provinces is challenging particularly at oceanic slope setting,where sedimentation rate is relatively high and PP can be driven by various complex geo-processes.To overcome these difficulties,an advanced machine learning(ML)tool is implemented in combination with empirical methods.The empirical method for PP prediction is comprised of data pre-processing and model establishment stage.Eaton's method and Porosity method have been used for PP calculation of the well U1517A located at Tuaheni Landslide Complex of Hikurangi Subduction zone of IODP expedition 372.Gamma-ray,sonic travel time,bulk density and sonic derived porosity are extracted from well log data for the theoretical framework construction.The normal compaction trend(NCT)curve analysis is used to check the optimum fitting of the low permeable zone data.The statistical analysis is done using the histogram analysis and Pearson correlation coefficient matrix with PP data series to identify potential input combinations for ML-based predictive model development.The dataset is prepared and divided into two parts:Training and Testing.The PP data and well log of borehole U1517A is pre-processed to scale in between[-1,+1]to fit into the input range of the non-linear activation/transfer function of the decision tree regression model.The Decision Tree Regression(DTR)algorithm is built and compared to the model performance to predict the PP and identify the overpressure zone in Hikurangi Tuaheni Zone of IODP Expedition 372.展开更多
The study integrates both the geological and geophysical mapping techniques for groundwater potential studies at Ekwegbe-Agu and the environs, Enugu state, Nigeria for optimal citing of borehole. Located in the Anambr...The study integrates both the geological and geophysical mapping techniques for groundwater potential studies at Ekwegbe-Agu and the environs, Enugu state, Nigeria for optimal citing of borehole. Located in the Anambra Basin between latitudes 6˚43'N and 6˚47'N and longitudes 7˚28'E and 7˚32'E, it is stratigraphycally underlain by, from bottom to top, the Enugu/Nkporo, Mamu and Ajali Formation respectively, a complex geology that make citing of productive borehole in the area problematic leading to borehole failure and dry holes due to inadequate sampling. The study adopted a field and analytic sampling approach, integrating field geological, electrical resistivity and self-potential methods. The software, SedLog v3.1, InterpexIx1Dv.3, and Surfer v10 were employed for the data integration and interpretation. The result of the geological field and borehole data shows 11 sedimentary facies consisting of sandstone, shales and heterolith of sandstone/shale, with the aquifer zone mostly prevalent in the more porous sand-dominated horizons. Mostly the AK and HK were the dominant curve types. An average of 6 geo-electric layers were delineated across all transects with resistivity values ranging from 25.42 - 105.85 Ωm, 186.38 - 3383.3 Ωm, and 2992 - 6286.4 Ωm in the Enugu, Mamu and Ajali Formations respectively. The resistivity of the main aquifer layer ranges from 1 to 500 Ωm. The aquifer thickness within the study area varies between 95 and 140 m. The western and northwestern part of the study area which is underlain mainly by the Ajali Formation showed the highest groundwater potential in the area and suitable for citing productive boreholes.展开更多
Volcanic oil and gas reservoirs are generally buried deep,which leads to a high whole-well coring cost,and the degree of development and size of reservoirs are controlled by volcanic facies.Therefore,accurately identi...Volcanic oil and gas reservoirs are generally buried deep,which leads to a high whole-well coring cost,and the degree of development and size of reservoirs are controlled by volcanic facies.Therefore,accurately identifying volcanic facies by logging curves not only provides the basis of volcanic reservoir prediction but also saves costs during exploration.The Songliao Basin is a‘fault-depression superimposed’composite basin with a typical binary filling structure.Abundant types of volcanic lithologies and facies are present in the Lishu fault depression.Volcanic activity is frequent during the sedimentary period of the Huoshiling Formation.Through systematic petrographic identification of the key exploratory well(SN165C)of the Lishu fault-depression,which is a whole-well core,it is found that the Huoshiling Formation in SN165C contains four facies and six subfacies,including the volcanic conduit facies(crypto explosive breccia subfacies),explosive facies(pyroclastic flow and thermal wave base subfacies),effusive facies(upper and lower subfacies),and volcanogenic sedimentary facies(pyroclastic sedimentary subfacies).Combining core,thin section,and logging data,the authors established identification markers and petrographic chart logging phases,and also interpreted the longitudinal variation in volcanic petro-graphic response characteristics to make the charts more applicable to this area's volcanic petrographic interpretation of the Huoshiling Formation.These charts can provide a basis for the further exploration and development of volcanic oil and gas in this area.展开更多
Logging data and its interpretation results are one of the most important basic data for understanding reservoirs and oilfield development. Standardized and unified logging interpretation results play a decisive role ...Logging data and its interpretation results are one of the most important basic data for understanding reservoirs and oilfield development. Standardized and unified logging interpretation results play a decisive role in fine reservoir description and reservoir development. Aiming at the problem of the conflict between the development effect and the initial interpretation result of Yan 9 reservoir in Hujianshan area of Ordos Basin, by combining the current well production performance, logging, oil test, production test and other data, on the basis of making full use of core, coring, logging, thin section analysis and high pressure mercury injection data, the four characteristics of reservoir are analyzed, a more scientific and reasonable calculation model of reservoir logging parameters is established, and the reserves are recalculated after the second interpretation standard of logging is determined. The research improves the accuracy of logging interpretation and provides an effective basis for subsequent production development and potential horizons.展开更多
Forests promote the conservation of biodiversity and also play a crucial role in safeguarding theenvironment against erosion,landslides,and climate change.However,illegal logging remains a significant threatworldwide,...Forests promote the conservation of biodiversity and also play a crucial role in safeguarding theenvironment against erosion,landslides,and climate change.However,illegal logging remains a significant threatworldwide,necessitating the development of automatic logging detection systems in forests.This paper proposesthe use of long-range,low-powered,and smart Internet of Things(IoT)nodes to enhance forest monitoringcapabilities.The research framework involves developing IoT devices for forest sound classification andtransmitting each node’s status to a gateway at the forest base station,which further sends the obtained datathrough cellular connectivity to a cloud server.The key issues addressed in this work include sensor and boardselection,Machine Learning(ML)model development for audio classification,TinyML implementation on amicrocontroller,choice of communication protocol,gateway selection,and power consumption optimization.Unlike the existing solutions,the developed node prototype uses an array of two microphone sensors forredundancy,and an ensemble network consisting of Long Short-Term Memory(LSTM)and ConvolutionalNeural Network(CNN)models for improved classification accuracy.The model outperforms LSTM and CNNmodels when used independently and also gave 88%accuracy after quantization.Notably,this solutiondemonstrates cost efficiency and high potential for scalability.展开更多
基金supported by CNPC-CZU Innovation Alliancesupported by the Program of Polar Drilling Environmental Protection and Waste Treatment Technology (2022YFC2806403)。
文摘In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on machine learning and mud logging data is studied in this paper.This method can effectively utilize downhole parameters collected in real-time during drilling,to identify lithology in real-time and provide a reference for optimization of drilling parameters.Given the imbalance of lithology samples,the synthetic minority over-sampling technique(SMOTE)and Tomek link were used to balance the sample number of five lithologies.Meanwhile,this paper introduces Tent map,random opposition-based learning and dynamic perceived probability to the original crow search algorithm(CSA),and establishes an improved crow search algorithm(ICSA).In this paper,ICSA is used to optimize the hyperparameter combination of random forest(RF),extremely random trees(ET),extreme gradient boosting(XGB),and light gradient boosting machine(LGBM)models.In addition,this study combines the recognition advantages of the four models.The accuracy of lithology identification by the weighted average probability model reaches 0.877.The study of this paper realizes high-precision real-time lithology identification method,which can provide lithology reference for the drilling process.
基金supported by the ABT SHIELD(Anti-Bot and Trolls Shield)project at the Systems Research Institute,Polish Academy of Sciences,in cooperation with EDGE NPDRPMA.01.02.00-14-B448/18-00 funded by the Regional Development Fund for the development of Mazovia.
文摘Distinguishing between web traffic generated by bots and humans is an important task in the evaluation of online marketing campaigns.One of the main challenges is related to only partial availability of the performance metrics:although some users can be unambiguously classified as bots,the correct label is uncertain in many cases.This calls for the use of classifiers capable of explaining their decisions.This paper demonstrates two such mechanisms based on features carefully engineered from web logs.The first is a man-made rule-based system.The second is a hierarchical model that first performs clustering and next classification using human-centred,interpretable methods.The stability of the proposed methods is analyzed and a minimal set of features that convey the classdiscriminating information is selected.The proposed data processing and analysis methodology are successfully applied to real-world data sets from online publishers.
文摘On March 3,2024,the prototype permeability logging instrument independently developed in China successfully completed its first downhole test in Ren 91 standard well in PetroChina Huabei Oilfield.In the open hole section at a depth of 3925 metres and at a temperature of 148℃,the device collected high-quality permeability logging data.This marks a key technological breakthrough from 0 to 1 in permeability logging,and lays the foundation for the next step in developing a complete set of permeability logging equipment.
基金supported by the National Natural Science Foundation of China(No.U21B2062)the Natural Science Foundation of Hubei Province(No.2023AFB307)。
文摘Identification of reservoir types in deep carbonates has always been a great challenge due to complex logging responses caused by the heterogeneous scale and distribution of storage spaces.Traditional cross-plot analysis and empirical formula methods for identifying reservoir types using geophysical logging data have high uncertainty and low efficiency,which cannot accurately reflect the nonlinear relationship between reservoir types and logging data.Recently,the kernel Fisher discriminant analysis(KFD),a kernel-based machine learning technique,attracts attention in many fields because of its strong nonlinear processing ability.However,the overall performance of KFD model may be limited as a single kernel function cannot simultaneously extrapolate and interpolate well,especially for highly complex data cases.To address this issue,in this study,a mixed kernel Fisher discriminant analysis(MKFD)model was established and applied to identify reservoir types of the deep Sinian carbonates in central Sichuan Basin,China.The MKFD model was trained and tested with 453 datasets from 7 coring wells,utilizing GR,CAL,DEN,AC,CNL and RT logs as input variables.The particle swarm optimization(PSO)was adopted for hyper-parameter optimization of MKFD model.To evaluate the model performance,prediction results of MKFD were compared with those of basic-kernel based KFD,RF and SVM models.Subsequently,the built MKFD model was applied in a blind well test,and a variable importance analysis was conducted.The comparison and blind test results demonstrated that MKFD outperformed traditional KFD,RF and SVM in the identification of reservoir types,which provided higher accuracy and stronger generalization.The MKFD can therefore be a reliable method for identifying reservoir types of deep carbonates.
基金supported by the Science and Technology Program State Grid Corporation of China,Grant SGSXDK00DJJS2250061.
文摘System logs,serving as a pivotal data source for performance monitoring and anomaly detection,play an indispensable role in assuring service stability and reliability.Despite this,the majority of existing log-based anomaly detection methodologies predominantly depend on the sequence or quantity attributes of logs,utilizing solely a single Recurrent Neural Network(RNN)and its variant sequence models for detection.These approaches have not thoroughly exploited the semantic information embedded in logs,exhibit limited adaptability to novel logs,and a single model struggles to fully unearth the potential features within the log sequence.Addressing these challenges,this article proposes a hybrid architecture based on amultiscale convolutional neural network,efficient channel attention and mogrifier gated recurrent unit networks(LogCEM),which amalgamates multiple neural network technologies.Capitalizing on the superior performance of robustly optimized BERT approach(RoBERTa)in the realm of natural language processing,we employ RoBERTa to extract the original word vectors from each word in the log template.In conjunction with the enhanced Smooth Inverse Frequency(SIF)algorithm,we generate more precise log sentence vectors,thereby achieving an in-depth representation of log semantics.Subsequently,these log vector sequences are fed into a hybrid neural network,which fuses 1D Multi-Scale Convolutional Neural Network(MSCNN),Efficient Channel Attention Mechanism(ECA),and Mogrifier Gated Recurrent Unit(GRU).This amalgamation enables themodel to concurrently capture the local and global dependencies of the log sequence and autonomously learn the significance of different log sequences,thereby markedly enhancing the efficacy of log anomaly detection.To validate the effectiveness of the LogCEM model,we conducted evaluations on two authoritative open-source datasets.The experimental results demonstrate that LogCEM not only exhibits excellent accuracy and robustness,but also outperforms the current mainstream log anomaly detection methods.
基金Supported by the China National Petroleum Corporation Limited-China University of Petroleum(Beijing)Strategic Cooperation Science and Technology Project(ZLZX2020-03).
文摘In the traditional well log depth matching tasks,manual adjustments are required,which means significantly labor-intensive for multiple wells,leading to low work efficiency.This paper introduces a multi-agent deep reinforcement learning(MARL)method to automate the depth matching of multi-well logs.This method defines multiple top-down dual sliding windows based on the convolutional neural network(CNN)to extract and capture similar feature sequences on well logs,and it establishes an interaction mechanism between agents and the environment to control the depth matching process.Specifically,the agent selects an action to translate or scale the feature sequence based on the double deep Q-network(DDQN).Through the feedback of the reward signal,it evaluates the effectiveness of each action,aiming to obtain the optimal strategy and improve the accuracy of the matching task.Our experiments show that MARL can automatically perform depth matches for well-logs in multiple wells,and reduce manual intervention.In the application to the oil field,a comparative analysis of dynamic time warping(DTW),deep Q-learning network(DQN),and DDQN methods revealed that the DDQN algorithm,with its dual-network evaluation mechanism,significantly improves performance by identifying and aligning more details in the well log feature sequences,thus achieving higher depth matching accuracy.
文摘Pore pressure(PP)information plays an important role in analysing the geomechanical properties of the reservoir and hydrocarbon field development.PP prediction is an essential requirement to ensure safe drilling operations and it is a fundamental input for well design,and mud weight estimation for wellbore stability.However,the pore pressure trend prediction in complex geological provinces is challenging particularly at oceanic slope setting,where sedimentation rate is relatively high and PP can be driven by various complex geo-processes.To overcome these difficulties,an advanced machine learning(ML)tool is implemented in combination with empirical methods.The empirical method for PP prediction is comprised of data pre-processing and model establishment stage.Eaton's method and Porosity method have been used for PP calculation of the well U1517A located at Tuaheni Landslide Complex of Hikurangi Subduction zone of IODP expedition 372.Gamma-ray,sonic travel time,bulk density and sonic derived porosity are extracted from well log data for the theoretical framework construction.The normal compaction trend(NCT)curve analysis is used to check the optimum fitting of the low permeable zone data.The statistical analysis is done using the histogram analysis and Pearson correlation coefficient matrix with PP data series to identify potential input combinations for ML-based predictive model development.The dataset is prepared and divided into two parts:Training and Testing.The PP data and well log of borehole U1517A is pre-processed to scale in between[-1,+1]to fit into the input range of the non-linear activation/transfer function of the decision tree regression model.The Decision Tree Regression(DTR)algorithm is built and compared to the model performance to predict the PP and identify the overpressure zone in Hikurangi Tuaheni Zone of IODP Expedition 372.
文摘The study integrates both the geological and geophysical mapping techniques for groundwater potential studies at Ekwegbe-Agu and the environs, Enugu state, Nigeria for optimal citing of borehole. Located in the Anambra Basin between latitudes 6˚43'N and 6˚47'N and longitudes 7˚28'E and 7˚32'E, it is stratigraphycally underlain by, from bottom to top, the Enugu/Nkporo, Mamu and Ajali Formation respectively, a complex geology that make citing of productive borehole in the area problematic leading to borehole failure and dry holes due to inadequate sampling. The study adopted a field and analytic sampling approach, integrating field geological, electrical resistivity and self-potential methods. The software, SedLog v3.1, InterpexIx1Dv.3, and Surfer v10 were employed for the data integration and interpretation. The result of the geological field and borehole data shows 11 sedimentary facies consisting of sandstone, shales and heterolith of sandstone/shale, with the aquifer zone mostly prevalent in the more porous sand-dominated horizons. Mostly the AK and HK were the dominant curve types. An average of 6 geo-electric layers were delineated across all transects with resistivity values ranging from 25.42 - 105.85 Ωm, 186.38 - 3383.3 Ωm, and 2992 - 6286.4 Ωm in the Enugu, Mamu and Ajali Formations respectively. The resistivity of the main aquifer layer ranges from 1 to 500 Ωm. The aquifer thickness within the study area varies between 95 and 140 m. The western and northwestern part of the study area which is underlain mainly by the Ajali Formation showed the highest groundwater potential in the area and suitable for citing productive boreholes.
基金Supported by projects of the National Natural Science Foundatio n of China(Nos.41972313,41790453).
文摘Volcanic oil and gas reservoirs are generally buried deep,which leads to a high whole-well coring cost,and the degree of development and size of reservoirs are controlled by volcanic facies.Therefore,accurately identifying volcanic facies by logging curves not only provides the basis of volcanic reservoir prediction but also saves costs during exploration.The Songliao Basin is a‘fault-depression superimposed’composite basin with a typical binary filling structure.Abundant types of volcanic lithologies and facies are present in the Lishu fault depression.Volcanic activity is frequent during the sedimentary period of the Huoshiling Formation.Through systematic petrographic identification of the key exploratory well(SN165C)of the Lishu fault-depression,which is a whole-well core,it is found that the Huoshiling Formation in SN165C contains four facies and six subfacies,including the volcanic conduit facies(crypto explosive breccia subfacies),explosive facies(pyroclastic flow and thermal wave base subfacies),effusive facies(upper and lower subfacies),and volcanogenic sedimentary facies(pyroclastic sedimentary subfacies).Combining core,thin section,and logging data,the authors established identification markers and petrographic chart logging phases,and also interpreted the longitudinal variation in volcanic petro-graphic response characteristics to make the charts more applicable to this area's volcanic petrographic interpretation of the Huoshiling Formation.These charts can provide a basis for the further exploration and development of volcanic oil and gas in this area.
文摘Logging data and its interpretation results are one of the most important basic data for understanding reservoirs and oilfield development. Standardized and unified logging interpretation results play a decisive role in fine reservoir description and reservoir development. Aiming at the problem of the conflict between the development effect and the initial interpretation result of Yan 9 reservoir in Hujianshan area of Ordos Basin, by combining the current well production performance, logging, oil test, production test and other data, on the basis of making full use of core, coring, logging, thin section analysis and high pressure mercury injection data, the four characteristics of reservoir are analyzed, a more scientific and reasonable calculation model of reservoir logging parameters is established, and the reserves are recalculated after the second interpretation standard of logging is determined. The research improves the accuracy of logging interpretation and provides an effective basis for subsequent production development and potential horizons.
基金funded by Climate Change AI(2023 innovation grant-https://www.climatechange.ai/innovation_grants).
文摘Forests promote the conservation of biodiversity and also play a crucial role in safeguarding theenvironment against erosion,landslides,and climate change.However,illegal logging remains a significant threatworldwide,necessitating the development of automatic logging detection systems in forests.This paper proposesthe use of long-range,low-powered,and smart Internet of Things(IoT)nodes to enhance forest monitoringcapabilities.The research framework involves developing IoT devices for forest sound classification andtransmitting each node’s status to a gateway at the forest base station,which further sends the obtained datathrough cellular connectivity to a cloud server.The key issues addressed in this work include sensor and boardselection,Machine Learning(ML)model development for audio classification,TinyML implementation on amicrocontroller,choice of communication protocol,gateway selection,and power consumption optimization.Unlike the existing solutions,the developed node prototype uses an array of two microphone sensors forredundancy,and an ensemble network consisting of Long Short-Term Memory(LSTM)and ConvolutionalNeural Network(CNN)models for improved classification accuracy.The model outperforms LSTM and CNNmodels when used independently and also gave 88%accuracy after quantization.Notably,this solutiondemonstrates cost efficiency and high potential for scalability.
