In order to increase drilling speed in deep complicated formations in Kela-2 gas field, Tarim Basin, Xinjiang, west China, it is important to predict the formation lithology for drilling bit optimization. Based on the...In order to increase drilling speed in deep complicated formations in Kela-2 gas field, Tarim Basin, Xinjiang, west China, it is important to predict the formation lithology for drilling bit optimization. Based on the conventional back propagation （BP） model, an improved BP model was proposed, with main modifications of back propagation of error, self-adapting algorithm, and activation function, also a prediction program was developed. The improved BP model was successfully applied to predicting the lithology of formations to be drilled in the Kela-2 gas field.展开更多
With the rapid development of the Internet,many enterprises have launched their network platforms.When users browse,search,and click the products of these platforms,most platforms will keep records of these network be...With the rapid development of the Internet,many enterprises have launched their network platforms.When users browse,search,and click the products of these platforms,most platforms will keep records of these network behaviors,these records are often heterogeneous,and it is called log data.To effectively to analyze and manage these heterogeneous log data,so that enterprises can grasp the behavior characteristics of their platform users in time,to realize targeted recommendation of users,increase the sales volume of enterprises’products,and accelerate the development of enterprises.Firstly,we follow the process of big data collection,storage,analysis,and visualization to design the system,then,we adopt HDFS storage technology,Yarn resource management technology,and gink load balancing technology to build a Hadoop cluster to process the log data,and adopt MapReduce processing technology and data warehouse hive technology analyze the log data to obtain the results.Finally,the obtained results are displayed visually,and a log data analysis system is successfully constructed.It has been proved by practice that the system effectively realizes the collection,analysis and visualization of log data,and can accurately realize the recommendation of products by enterprises.The system is stable and effective.展开更多
The paper explores the technique of three-dimensional modelling and volu-metric analysis of reservoir sands in the DINO Field onshore Niger Delta,Ni-geria to enhance reservoir production capability of a hydrocarbon fi...The paper explores the technique of three-dimensional modelling and volu-metric analysis of reservoir sands in the DINO Field onshore Niger Delta,Ni-geria to enhance reservoir production capability of a hydrocarbon field.De-spite advances in the application of Seismic Data Interpretation,results from that alone fails to provide detailed and trusted volumetric assessment hence a method that integrates seismic data modeling is introduced to enhance reliance on the results.An interpretation and modeling approach that integrated seis-mic data and well data from four wells was employed.Results of the well-to-seismic tie revealed that there is good correspondence between the seismic and the well data in matching the reservoir geometry.From the wireline log signa-tures,some sand horizons were identified,among which the X-reservoir sand which was the hydrocarbon bearing sands.The gross thickness of the X-reser-voir ranges from 71 m to 106 m.The X-reservoir model shows a major growth fault trending East-West and an antithetic fault trending Northwest-Southeast.The positions of the four wells are on the structural highs within the model.Compartments were delineated with additional well positions identified on the model to enhance hydrocarbon recovery.The oil water contact was at the depth of 3825 m on the footwall and 3835 m on the hanging wall due to the downward displacement caused by growth fault trending in East-West direction.Estimate of the volume of hydrocarbon in place revealed that X-reservoir has approxi-mately 2 million Stock Tank Barrel of Oil.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
The complexity of alarm detection and diagnosis tasks often results in a lack of alarm log data.Due to the strong rule associations inherent in alarm log data,existing data augmentation algorithms cannot obtain good r...The complexity of alarm detection and diagnosis tasks often results in a lack of alarm log data.Due to the strong rule associations inherent in alarm log data,existing data augmentation algorithms cannot obtain good results for alarm log data.To address this problem,this paper introduces a new algorithm for augmenting alarm log data,termed APRGAN,which combines a generative adversarial network(GAN)with the Apriori algorithm.APRGAN generates alarm log data under the guidance of rules mined by the rule miner.Moreover,we propose a new dynamic updating mechanism to alleviate the mode collapse problem of the GAN.In addition to updating the real reference dataset used to train the discriminator in the GAN,we dynamically update the parameters and the rule set of the Apriori algorithm according to the data generated in each epoch.Through extensive experimentation on two public datasets,it is demonstrated that APRGAN surpasses other data augmentation algorithms in the domain with respect to alarm log data augmentation,as evidenced by its superior performance on metrics such as BLEU,ROUGE,and METEOR.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
Valuable industrial oil and gas were discovered in the formations of Ordovician, Carboniferous and Triassic of the Tahe （塔河） oilfield, Xinjiang （新疆）, China. The Carboniferous formations contain several oil- an...Valuable industrial oil and gas were discovered in the formations of Ordovician, Carboniferous and Triassic of the Tahe （塔河） oilfield, Xinjiang （新疆）, China. The Carboniferous formations contain several oil- and gas-bearing layers. The lateral distribution of Carboniferous reservoir is unstable, and thin layers are crossbedded. This makes it difficult to do lateral formations＇ contrast and reservoir prediction, so it is necessary to develop a method that can achieve reservoir lateral contrast and prediction by using multi-well logging data and seismic data. To achieve reservoir lateral contrast and prediction at the Carboniferous formations of the Tahe oilfield, processing and interpretation of logging data from a single well were done first. The processing and interpretation include log pretreatment, en- vironmental correction and computation of reservoir＇s parameters （porosity, clay content, water saturation, etc.）. Based on the previous work, the data file of logging information of multi-well was formed, and then the lateral distribution pictures （2D and 3D pictures of log curves and reservoir parameters） can be drawn. Comparing multi-well＇s logging information, seismic profiles and geological information （sedimentary sign）, the reservoir of the Carboniferous in the Tahe oilfield can be contrasted and pre- dicted laterally. The sand formation of Carboniferous can be subdivided. The results of reservoir contrast and prediction of the Carboniferous formations show that 2D and 3D pictures of multi-weU reser- voir parameters make the lateral distribution of reservoir and oil-bearing sand very clear, the connectedness of the reservoir of neighboring wells can be analyzed, and five sand bodies can be identified based on the reservoir＇s lateral distribution, geological information and seismic data.展开更多
Seismic inversion and basic theory are briefly presented and the main idea of this method is introduced. Both non-linear wave equation inversion technique and Complete Utilization of Samples Information (CUSI) neural ...Seismic inversion and basic theory are briefly presented and the main idea of this method is introduced. Both non-linear wave equation inversion technique and Complete Utilization of Samples Information (CUSI) neural network analysis are used in lithological interpretation in Jibei coal field. The prediction results indicate that this method can provide reliable data for thin coal exploitation and promising area evaluation.展开更多
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
The recognition and contrast of bed sets in parasequence is difficult in terrestrial basin high-resolution sequence stratigraphy. This study puts forward new methods for the boundary identification and contrast of bed...The recognition and contrast of bed sets in parasequence is difficult in terrestrial basin high-resolution sequence stratigraphy. This study puts forward new methods for the boundary identification and contrast of bed sets on the basis of manifold logging data. The formation of calcareous interbeds, shale resistivity differences and the relation of reservoir resistivity to altitude are considered on the basis of log curve morphological characteristics, core observation, cast thin section, X-ray diffraction and scanning electron microscopy. The results show that the thickness of calcareous interbeds is between 0.5 m and 2 m, increasing on weathering crusts and faults. Calcareous interbeds occur at the bottom of a distributary channel and the top of a distributary mouth bar. Lower resistivity shale （4-5 Ω · m） and higher resistivity shale （〉 10Ω·m） reflect differences in sediment fountain or sediment microfacies. Reservoir resistivity increases with altitude. Calcareous interbeds may be a symbol of recognition for the boundary of bed sets and isochronous contrast bed sets, and shale resistivity differences may confirm the stack relation and connectivity of bed sets. Based on this, a high-resolution chronostratigraphic frame- work of Xi-1 segment in Shinan area, Junggar basin is presented, and the connectivity of bed sets and oil-water contact is confirmed. In this chronostratigraphic framework, the growth order, stack mode and space shape of bed sets are qualitatively and quantitatively described.展开更多
Assessment of reservoir and fracture parameters is necessary to optimize oil production,especially in heterogeneous reservoirs.Core and image logs are regarded as two of the best methods for this aim.However,due to co...Assessment of reservoir and fracture parameters is necessary to optimize oil production,especially in heterogeneous reservoirs.Core and image logs are regarded as two of the best methods for this aim.However,due to core limitations,using image log is considered as the best method.This study aims to use electrical image logs in the carbonate Asmari Formation reservoir in Zagros Basin,SW Iran,in order to evaluate natural fractures,porosity system,permeability profile and heterogeneity index and accordingly compare the results with core and well data.The results indicated that the electrical image logs are reliable for evaluating fracture and reservoir parameters,when there is no core available for a well.Based on the results from formation micro-imager(FMI)and electrical micro-imager(EMI),Asmari was recognized as a completely fractured reservoir in studied field and the reservoir parameters are mainly controlled by fractures.Furthermore,core and image logs indicated that the secondary porosity varies from 0%to 10%.The permeability indicator indicates that zones 3 and 5 have higher permeability index.Image log permeability index shows a very reasonable permeability profile after scaling against core and modular dynamics tester mobility,mud loss and production index which vary between 1 and 1000 md.In addition,no relationship was observed between core porosity and permeability,while the permeability relied heavily on fracture aperture.Therefore,fracture aperture was considered as the most important parameter for the determination of permeability.Sudden changes were also observed at zones 1-1 and 5 in the permeability trend,due to the high fracture aperture.It can be concluded that the electrical image logs(FMI and EMI)are usable for evaluating both reservoir and fracture parameters in wells with no core data in the Zagros Basin,SW Iran.展开更多
In this paper we have developed a data logging and monitoring system, we validated the system by comparing the result from it with the existing one and found that the system performs slightly better than the existing ...In this paper we have developed a data logging and monitoring system, we validated the system by comparing the result from it with the existing one and found that the system performs slightly better than the existing work in the same area. This implies that the data logger and monitoring system is good and can be used to monitor solar energy variables even at the comfort of our homes. We fitted a model to the generated data and found that the meteorological variables considered accounted for 99.88% of the power output in the rainy seasons while 0.12% of the variation was not explained due to other factors. Solar panels inclined at an angle of 5° (Tilt) and facing South Pole perform optimally.展开更多
Hydrocarbon reservoir beds have been delineated using direct hydrocarbon indicator on seismic sections as well as well logs data in X field, Onshore Niger Delta. The research methodology involved horizon interpretatio...Hydrocarbon reservoir beds have been delineated using direct hydrocarbon indicator on seismic sections as well as well logs data in X field, Onshore Niger Delta. The research methodology involved horizon interpretation to produce sub-surface structure map. Geophysical well log signatures were employed in identifying hydrocarbon bearing sand. The well-to-seismic tie revealed that the reservoir tied directly with hydrocarbon indicator (bright spot) on the seismic sections. The major structure responsible for the hydrocarbon entrapment is anticline. The crest of the anticline from the depth structural map occurs at 3450 metres.展开更多
日志主要记录软硬件的运行信息,通过查看系统日志,可以找到系统出现的问题及原因,确保系统的稳定性和正常运行。日志解析的目的是将半结构化的原始日志解析为可阅读的日志模板,现有解析方法往往只注重于对原始日志的解析,而忽略了后期...日志主要记录软硬件的运行信息,通过查看系统日志,可以找到系统出现的问题及原因,确保系统的稳定性和正常运行。日志解析的目的是将半结构化的原始日志解析为可阅读的日志模板,现有解析方法往往只注重于对原始日志的解析,而忽略了后期模板处理,导致结果的精度不能进一步提高。自此,提出了一种日志解析方法FMLogs(logs parsing based on frequency and MinHash algorithm)。该方法通过设计正则表达式和调节阈值参数以获得最佳性能,同时采用了字符级频率统计和MinHash方法对长度相同和不同的日志模板进行合并。FMLogs在七个真实数据集上进行了广泛的实验,取得了0.924的平均解析准确率和0.983的F 1-Score。实验结果表明,FMLogs是一种有效的日志解析方法,在解析日志的同时具有较高的准确性和效率,并能保证性能的稳定。展开更多
文摘In order to increase drilling speed in deep complicated formations in Kela-2 gas field, Tarim Basin, Xinjiang, west China, it is important to predict the formation lithology for drilling bit optimization. Based on the conventional back propagation （BP） model, an improved BP model was proposed, with main modifications of back propagation of error, self-adapting algorithm, and activation function, also a prediction program was developed. The improved BP model was successfully applied to predicting the lithology of formations to be drilled in the Kela-2 gas field.
基金supported by the Huaihua University Science Foundation under Grant HHUY2019-24.
文摘With the rapid development of the Internet,many enterprises have launched their network platforms.When users browse,search,and click the products of these platforms,most platforms will keep records of these network behaviors,these records are often heterogeneous,and it is called log data.To effectively to analyze and manage these heterogeneous log data,so that enterprises can grasp the behavior characteristics of their platform users in time,to realize targeted recommendation of users,increase the sales volume of enterprises’products,and accelerate the development of enterprises.Firstly,we follow the process of big data collection,storage,analysis,and visualization to design the system,then,we adopt HDFS storage technology,Yarn resource management technology,and gink load balancing technology to build a Hadoop cluster to process the log data,and adopt MapReduce processing technology and data warehouse hive technology analyze the log data to obtain the results.Finally,the obtained results are displayed visually,and a log data analysis system is successfully constructed.It has been proved by practice that the system effectively realizes the collection,analysis and visualization of log data,and can accurately realize the recommendation of products by enterprises.The system is stable and effective.
文摘The paper explores the technique of three-dimensional modelling and volu-metric analysis of reservoir sands in the DINO Field onshore Niger Delta,Ni-geria to enhance reservoir production capability of a hydrocarbon field.De-spite advances in the application of Seismic Data Interpretation,results from that alone fails to provide detailed and trusted volumetric assessment hence a method that integrates seismic data modeling is introduced to enhance reliance on the results.An interpretation and modeling approach that integrated seis-mic data and well data from four wells was employed.Results of the well-to-seismic tie revealed that there is good correspondence between the seismic and the well data in matching the reservoir geometry.From the wireline log signa-tures,some sand horizons were identified,among which the X-reservoir sand which was the hydrocarbon bearing sands.The gross thickness of the X-reser-voir ranges from 71 m to 106 m.The X-reservoir model shows a major growth fault trending East-West and an antithetic fault trending Northwest-Southeast.The positions of the four wells are on the structural highs within the model.Compartments were delineated with additional well positions identified on the model to enhance hydrocarbon recovery.The oil water contact was at the depth of 3825 m on the footwall and 3835 m on the hanging wall due to the downward displacement caused by growth fault trending in East-West direction.Estimate of the volume of hydrocarbon in place revealed that X-reservoir has approxi-mately 2 million Stock Tank Barrel of Oil.
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supported by the National Key Research and Development Program of China under Grant No.2019YFB-2103202.
文摘The complexity of alarm detection and diagnosis tasks often results in a lack of alarm log data.Due to the strong rule associations inherent in alarm log data,existing data augmentation algorithms cannot obtain good results for alarm log data.To address this problem,this paper introduces a new algorithm for augmenting alarm log data,termed APRGAN,which combines a generative adversarial network(GAN)with the Apriori algorithm.APRGAN generates alarm log data under the guidance of rules mined by the rule miner.Moreover,we propose a new dynamic updating mechanism to alleviate the mode collapse problem of the GAN.In addition to updating the real reference dataset used to train the discriminator in the GAN,we dynamically update the parameters and the rule set of the Apriori algorithm according to the data generated in each epoch.Through extensive experimentation on two public datasets,it is demonstrated that APRGAN surpasses other data augmentation algorithms in the domain with respect to alarm log data augmentation,as evidenced by its superior performance on metrics such as BLEU,ROUGE,and METEOR.
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 Petroleum and Geological Bureau of SINOPEC,China (No. 200002)
文摘Valuable industrial oil and gas were discovered in the formations of Ordovician, Carboniferous and Triassic of the Tahe （塔河） oilfield, Xinjiang （新疆）, China. The Carboniferous formations contain several oil- and gas-bearing layers. The lateral distribution of Carboniferous reservoir is unstable, and thin layers are crossbedded. This makes it difficult to do lateral formations＇ contrast and reservoir prediction, so it is necessary to develop a method that can achieve reservoir lateral contrast and prediction by using multi-well logging data and seismic data. To achieve reservoir lateral contrast and prediction at the Carboniferous formations of the Tahe oilfield, processing and interpretation of logging data from a single well were done first. The processing and interpretation include log pretreatment, en- vironmental correction and computation of reservoir＇s parameters （porosity, clay content, water saturation, etc.）. Based on the previous work, the data file of logging information of multi-well was formed, and then the lateral distribution pictures （2D and 3D pictures of log curves and reservoir parameters） can be drawn. Comparing multi-well＇s logging information, seismic profiles and geological information （sedimentary sign）, the reservoir of the Carboniferous in the Tahe oilfield can be contrasted and pre- dicted laterally. The sand formation of Carboniferous can be subdivided. The results of reservoir contrast and prediction of the Carboniferous formations show that 2D and 3D pictures of multi-weU reser- voir parameters make the lateral distribution of reservoir and oil-bearing sand very clear, the connectedness of the reservoir of neighboring wells can be analyzed, and five sand bodies can be identified based on the reservoir＇s lateral distribution, geological information and seismic data.
文摘Seismic inversion and basic theory are briefly presented and the main idea of this method is introduced. Both non-linear wave equation inversion technique and Complete Utilization of Samples Information (CUSI) neural network analysis are used in lithological interpretation in Jibei coal field. The prediction results indicate that this method can provide reliable data for thin coal exploitation and promising area evaluation.
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This paper is supported by the Main Project of the National Tenth Five-Year Plan .
文摘The recognition and contrast of bed sets in parasequence is difficult in terrestrial basin high-resolution sequence stratigraphy. This study puts forward new methods for the boundary identification and contrast of bed sets on the basis of manifold logging data. The formation of calcareous interbeds, shale resistivity differences and the relation of reservoir resistivity to altitude are considered on the basis of log curve morphological characteristics, core observation, cast thin section, X-ray diffraction and scanning electron microscopy. The results show that the thickness of calcareous interbeds is between 0.5 m and 2 m, increasing on weathering crusts and faults. Calcareous interbeds occur at the bottom of a distributary channel and the top of a distributary mouth bar. Lower resistivity shale （4-5 Ω · m） and higher resistivity shale （〉 10Ω·m） reflect differences in sediment fountain or sediment microfacies. Reservoir resistivity increases with altitude. Calcareous interbeds may be a symbol of recognition for the boundary of bed sets and isochronous contrast bed sets, and shale resistivity differences may confirm the stack relation and connectivity of bed sets. Based on this, a high-resolution chronostratigraphic frame- work of Xi-1 segment in Shinan area, Junggar basin is presented, and the connectivity of bed sets and oil-water contact is confirmed. In this chronostratigraphic framework, the growth order, stack mode and space shape of bed sets are qualitatively and quantitatively described.
基金financial and data support from NISOC Oil Company.
文摘Assessment of reservoir and fracture parameters is necessary to optimize oil production,especially in heterogeneous reservoirs.Core and image logs are regarded as two of the best methods for this aim.However,due to core limitations,using image log is considered as the best method.This study aims to use electrical image logs in the carbonate Asmari Formation reservoir in Zagros Basin,SW Iran,in order to evaluate natural fractures,porosity system,permeability profile and heterogeneity index and accordingly compare the results with core and well data.The results indicated that the electrical image logs are reliable for evaluating fracture and reservoir parameters,when there is no core available for a well.Based on the results from formation micro-imager(FMI)and electrical micro-imager(EMI),Asmari was recognized as a completely fractured reservoir in studied field and the reservoir parameters are mainly controlled by fractures.Furthermore,core and image logs indicated that the secondary porosity varies from 0%to 10%.The permeability indicator indicates that zones 3 and 5 have higher permeability index.Image log permeability index shows a very reasonable permeability profile after scaling against core and modular dynamics tester mobility,mud loss and production index which vary between 1 and 1000 md.In addition,no relationship was observed between core porosity and permeability,while the permeability relied heavily on fracture aperture.Therefore,fracture aperture was considered as the most important parameter for the determination of permeability.Sudden changes were also observed at zones 1-1 and 5 in the permeability trend,due to the high fracture aperture.It can be concluded that the electrical image logs(FMI and EMI)are usable for evaluating both reservoir and fracture parameters in wells with no core data in the Zagros Basin,SW Iran.
文摘In this paper we have developed a data logging and monitoring system, we validated the system by comparing the result from it with the existing one and found that the system performs slightly better than the existing work in the same area. This implies that the data logger and monitoring system is good and can be used to monitor solar energy variables even at the comfort of our homes. We fitted a model to the generated data and found that the meteorological variables considered accounted for 99.88% of the power output in the rainy seasons while 0.12% of the variation was not explained due to other factors. Solar panels inclined at an angle of 5° (Tilt) and facing South Pole perform optimally.
文摘Hydrocarbon reservoir beds have been delineated using direct hydrocarbon indicator on seismic sections as well as well logs data in X field, Onshore Niger Delta. The research methodology involved horizon interpretation to produce sub-surface structure map. Geophysical well log signatures were employed in identifying hydrocarbon bearing sand. The well-to-seismic tie revealed that the reservoir tied directly with hydrocarbon indicator (bright spot) on the seismic sections. The major structure responsible for the hydrocarbon entrapment is anticline. The crest of the anticline from the depth structural map occurs at 3450 metres.
文摘日志主要记录软硬件的运行信息,通过查看系统日志,可以找到系统出现的问题及原因,确保系统的稳定性和正常运行。日志解析的目的是将半结构化的原始日志解析为可阅读的日志模板,现有解析方法往往只注重于对原始日志的解析,而忽略了后期模板处理,导致结果的精度不能进一步提高。自此,提出了一种日志解析方法FMLogs(logs parsing based on frequency and MinHash algorithm)。该方法通过设计正则表达式和调节阈值参数以获得最佳性能,同时采用了字符级频率统计和MinHash方法对长度相同和不同的日志模板进行合并。FMLogs在七个真实数据集上进行了广泛的实验,取得了0.924的平均解析准确率和0.983的F 1-Score。实验结果表明,FMLogs是一种有效的日志解析方法,在解析日志的同时具有较高的准确性和效率,并能保证性能的稳定。
