An image processing and deep learning method for identifying different types of rock images was proposed.Preprocessing,such as rock image acquisition,gray scaling,Gaussian blurring,and feature dimensionality reduction...An image processing and deep learning method for identifying different types of rock images was proposed.Preprocessing,such as rock image acquisition,gray scaling,Gaussian blurring,and feature dimensionality reduction,was conducted to extract useful feature information and recognize and classify rock images using Tensor Flow-based convolutional neural network(CNN)and Py Qt5.A rock image dataset was established and separated into workouts,confirmation sets,and test sets.The framework was subsequently compiled and trained.The categorization approach was evaluated using image data from the validation and test datasets,and key metrics,such as accuracy,precision,and recall,were analyzed.Finally,the classification model conducted a probabilistic analysis of the measured data to determine the equivalent lithological type for each image.The experimental results indicated that the method combining deep learning,Tensor Flow-based CNN,and Py Qt5 to recognize and classify rock images has an accuracy rate of up to 98.8%,and can be successfully utilized for rock image recognition.The system can be extended to geological exploration,mine engineering,and other rock and mineral resource development to more efficiently and accurately recognize rock samples.Moreover,it can match them with the intelligent support design system to effectively improve the reliability and economy of the support scheme.The system can serve as a reference for supporting the design of other mining and underground space projects.展开更多
Lithology identification is a critical aspect of geoenergy exploration,including geothermal energy development,gas hydrate extraction,and gas storage.In recent years,artificial intelligence techniques based on drill c...Lithology identification is a critical aspect of geoenergy exploration,including geothermal energy development,gas hydrate extraction,and gas storage.In recent years,artificial intelligence techniques based on drill core images have made significant strides in lithology identification,achieving high accuracy.However,the current demand for advanced lithology identification models remains unmet due to the lack of high-quality drill core image datasets.This study successfully constructs and publicly releases the first open-source Drill Core Image Dataset(DCID),addressing the need for large-scale,high-quality datasets in lithology characterization tasks within geological engineering and establishing a standard dataset for model evaluation.DCID consists of 35 lithology categories and a total of 98,000 high-resolution images(512×512 pixels),making it the most comprehensive drill core image dataset in terms of lithology categories,image quantity,and resolution.This study also provides lithology identification accuracy benchmarks for popular convolutional neural networks(CNNs)such as VGG,ResNet,DenseNet,MobileNet,as well as for the Vision Transformer(ViT)and MLP-Mixer,based on DCID.Additionally,the sensitivity of model performance to various parameters and image resolution is evaluated.In response to real-world challenges,we propose a real-world data augmentation(RWDA)method,leveraging slightly defective images from DCID to enhance model robustness.The study also explores the impact of real-world lighting conditions on the performance of lithology identification models.Finally,we demonstrate how to rapidly evaluate model performance across multiple dimensions using low-resolution datasets,advancing the application and development of new lithology identification models for geoenergy exploration.展开更多
Bayesian-optimized lithology identification has important basic geological research significance and engineering application value,and this paper proposes a Bayesian-optimized lithology identification method based on ...Bayesian-optimized lithology identification has important basic geological research significance and engineering application value,and this paper proposes a Bayesian-optimized lithology identification method based on machine learning of rock visible and near-infrared spectral data.First,the rock spectral data are preprocessed using Savitzky-Golay(SG)smoothing to remove the noise of the spectral data;then,the preprocessed rock spectral data are downscaled using Principal Component Analysis(PCA)to reduce the redundancy of the data,optimize the effective discriminative information,and obtain the rock spectral features;finally,a Bayesian-optimized lithology identification model is established based on rock spectral features,optimize the model hyperparameters using Bayesian optimization(BO)algorithm to avoid the combination of hyperparameters falling into the local optimal solution,and output the predicted type of rock,so as to realize the Bayesian-optimized lithology identification.In addition,this paper conducts comparative analysis on models based on Artificial Neural Network(ANN)/Random Forest(RF),dimensionality reduction/full band,and optimization algorithms.It uses the confusion matrix,accuracy,Precison(P),Recall(R)and F_(1)values(F_(1))as the evaluation indexes of model accuracy.The results indicate that the lithology identification model optimized by the BO-ANN after dimensionality reduction achieves an accuracy of up to 99.80%,up to 99.79%and up to 99.79%.Compared with the BO-RF model,it has higher identification accuracy and better stability for each type of rock identification.The experiments and reliability analysis show that the Bayesian-optimized lithology identification method proposed in this paper has good robustness and generalization performance,which is of great significance for realizing fast,accurate and Bayesian-optimized lithology identification in tunnel site.展开更多
In order to achieve the large-scale application of manufactured sand in railway high-strength concrete structure,a series of high-strength manufactured sand concrete(HMC)are prepared by taking the manufactured sand li...In order to achieve the large-scale application of manufactured sand in railway high-strength concrete structure,a series of high-strength manufactured sand concrete(HMC)are prepared by taking the manufactured sand lithology(tuff,limestone,basalt,granite),stone powder content(0,5%,10%,15%)and concrete strength grade(C60,C80,C100)as variables.The evolution of mechanical properties of HMC and the correlation between cubic compressive strength and other mechanical properties are studied.Compared to river sand,manufactured sand enhances the cubic compressive strength,axial compressive strength and elastic modulus of concrete,while its potential microcracks weaken the flexural strength and splitting tensile strength of concrete.Stone powder content displays both positive and negative effects on mechanical properties of HMC,and the stone powder content is suggested to be less than 10%.The empirical formulas between cubic compressive strength and other mechanical properties are proposed.展开更多
The efficiency of tunnel excavation,rock strength,stability of surrounding rock,and underground engineering disasters are closely related to lithology.Accurately identifying lithology is a necessary prerequisite for i...The efficiency of tunnel excavation,rock strength,stability of surrounding rock,and underground engineering disasters are closely related to lithology.Accurately identifying lithology is a necessary prerequisite for intelligent,safe,and efficient tunnel construction.The design of conventional recognition models heavily relies on experience and extensive calculations.To develop a model suitable for deployment on construction sites and capable of accurate lithology identification,a fast search method for lithology identification models is proposed.This method integrates geological knowledge,apparent feature extraction techniques,and search algorithms.An efficient feature extraction super network using multi-scale geological features of rock surface is constructed,a model evaluation method that comprehensively considers accuracy and latency is developed,and differential evolution algorithm is used to search for the optimal model parameters.Experiments demonstrate that the proposed method enables the model to evolve faster and more accurately,and eventually a model(LithoNet)suitable for lithological classification is found.It only takes 2.10 ms to infer an image of 224×224,which is 57.25%faster than MobileNet v3 and 62.83%faster than ShuffleNet V2.The F1-score of LithoNet is 0.9874,surpassing classical models such as EfficientNetV2-S.LithoNet can be easily deployed on portable devices,effectively promoting the intelligence and accuracy of lithology identification at engineering sites.展开更多
Ecological security provides the basis of maintaining both a sustainable regional ecosystem and economic development.However,few studies have focused on how the features such as topography and geomorphology,lithologic...Ecological security provides the basis of maintaining both a sustainable regional ecosystem and economic development.However,few studies have focused on how the features such as topography and geomorphology,lithologic stratigraphic assemblages,and geohazard distribution affect the construction of ecological security patterns and the layout of optimization measures.In order to comprehensively reveal the key areas and key objects of ecological restoration in karst basins,this study takes the Beipan River Basin(BRB)as an example,constructs an ecological security pattern(ESP)based on the methods of morphological spatial pattern analysis(MSPA),landscape connectivity analysis and circuit theory,and lays out the optimization measures in combination with the spatial distribution characteristics of topographic and geomorphological differences and lithological stratigraphic combinations.The results show that 151 ecological sources,343 ecological corridors,121 pinch points and 178 barriers constitute the ESP of the BRB.Lithology is closely related to the spatial distribution characteristics of ecological source sites.Level 1 and 2 ecological sources(The ecological sources were categorized into level 1,level 2,and level 3 source from high to low importance.)are concentrated in the Emeishan basalt region of the upstream and the clastic and impure carbonate rock region of the downstream part of the BRB;level 3ecological sources are concentrated in the carbonate rock region of the midstream.Taking into account the outstanding ecological problems in the basin,and based on the characteristics of lithology and geohazard distribution,we propose the optimization scheme of“three axes,three zones and multiple points”for the ESP and the layout of specific measures of the BRB.The results can provide scientific references for maintaining ecological security maintenance in karst ecologically fragile areas.展开更多
Imaging hyperspectral technology has distinctive advantages of non-destructive and non-contact measurement,and the integration of spectral and spatial data.These characteristics present new methodologies for intellige...Imaging hyperspectral technology has distinctive advantages of non-destructive and non-contact measurement,and the integration of spectral and spatial data.These characteristics present new methodologies for intelligent geological sensing in tunnels and other underground engineering projects.However,the in situ acquisition and rapid classification of hyperspectral images in underground still faces great challenges,including the difficulty in obtaining uniform hyperspectral images and the complexity of deploying sophisticated models on mobile platforms.This study proposes an intelligent lithology identification method based on partition feature extraction of hyperspectral images.Firstly,pixel-level hyperspectral information from representative lithological regions is extracted and fused to obtain rock hyperspectral image partition features.Subsequently,an SG-SNV-PCA-DNN(SSPD)model specifically designed for optimizing rock hyperspectral data,performing spectral dimensionality reduction,and identifying lithology is integrated.In an experimental study involving 3420 hyperspectral images,the SSPD identification model achieved the highest accuracy in the testing set,reaching 98.77%.Moreover,the speed of the SSPD model was found to be 18.5%faster than that of the unprocessed model,with an accuracy improvement of 5.22%.In contrast,the ResNet-101 model,used for point-by-point identification based on non-partitioned features,achieved a maximum accuracy of 97.86%in the testing set.In addition,the partition feature extraction methods significantly reduce computational complexity.An objective evaluation of various models demonstrated that the SSPD model exhibited superior performance,achieving a precision(P)of 99.46%,a recall(R)of 99.44%,and F1 score(F1)of 99.45%.Additionally,a pioneering in situ detection work was carried out in a tunnel using underground hyperspectral imaging technology.展开更多
Machine learning algorithms are widely used to interpret well logging data.To enhance the algorithms'robustness,shuffling the well logging data is an unavoidable feature engineering before training models.However,...Machine learning algorithms are widely used to interpret well logging data.To enhance the algorithms'robustness,shuffling the well logging data is an unavoidable feature engineering before training models.However,latent information stored between different well logging types and depth is destroyed during the shuffle.To investigate the influence of latent information,this study implements graph convolution networks(GCNs),long-short temporal memory models,recurrent neural networks,temporal convolution networks,and two artificial neural networks to predict the microbial lithology in the fourth member of the Dengying Formation,Moxi gas field,central Sichuan Basin.Results indicate that the GCN model outperforms other models.The accuracy,F1-score,and area under curve of the GCN model are 0.90,0.90,and 0.95,respectively.Experimental results indicate that the time-series data facilitates lithology prediction and helps determine lithological fluctuations in the vertical direction.All types of logs from the spectral in the GCN model and also facilitates lithology identification.Only on condition combined with latent information,the GCN model reaches excellent microbialite classification resolution at the centimeter scale.Ultimately,the two actual cases show tricks for using GCN models to predict potential microbialite in other formations and areas,proving that the GCN model can be adopted in the industry.展开更多
In this paper, we derive an approximation of the SS-wave reflection coefficient and the expression of S-wave ray elastic impedance （SREI） in terms of the ray parameter. The SREI can be expressed by the S-wave incide...In this paper, we derive an approximation of the SS-wave reflection coefficient and the expression of S-wave ray elastic impedance （SREI） in terms of the ray parameter. The SREI can be expressed by the S-wave incidence angle or P-wave reflection angle, referred to as SREIS and SREIP, respectively. Our study using elastic models derived from real log measurements shows that SREIP has better capability for lithology and fluid discrimination than SREIS and conventional S-wave elastic impedance （SEI）. We evaluate the SREIP feasibility using 25 groups of samples from Castagna and Smith （1994）. Each sample group is constructed by using shale, brine-sand, and gas-sand. Theoretical evaluation also indicates that SRE1P at large incident angles is more sensitive to fluid than conventional fluid indicators. Real seismic data application also shows that SRE1P at large angles calculated using P-wave and S-wave impedance can efficiently characterize tight gas-sand.展开更多
Studies on landslides by the 2008 Wenchuan earthquake showed that topography was of great importance in amplifying the seismic shaking, and among other factors, lithology and slope structure controlled the spatial occ...Studies on landslides by the 2008 Wenchuan earthquake showed that topography was of great importance in amplifying the seismic shaking, and among other factors, lithology and slope structure controlled the spatial occurrence of slope failures. The present study carried out experiments on four rock slopes with steep angle of 60° by means of a shaking table. The recorded Wenchuan earthquake waves were scaled to excite the model slopes. Measurements from accelerometers installed on free surface of the model slope were analyzed, with much effort on timedomain acceleration responses to horizontal components of seismic shaking. It was found that the amplification factor of peak horizontal acceleration, RPHA, was increasing with elevation of each model slope, though the upper and lower halves of the slope exhibited different increasing patterns. As excitation intensity was increased, the drastic deterioration of the inner structure of each slope caused the sudden increase of RPHA in the upper slope part. In addition, the model simulating the soft rock slope produced the larger RPHA than the model simulating the hard rock slope by a maximum factor of 2.6. The layered model slope also produced the larger RPHA than the homogeneous model slope by a maximum factor of 2.7. The upper half of a slope was influenced more seriously by the effect of lithology, while the lower half was influenced more seriously by the effect of slope structure.展开更多
The northeastern margin of the South China Sea (SCS), developed from continental rifting and breakup, is usually thought of as a non-volcanic margin. However, post-spreading volcanism is massive and lower crustal high...The northeastern margin of the South China Sea (SCS), developed from continental rifting and breakup, is usually thought of as a non-volcanic margin. However, post-spreading volcanism is massive and lower crustal high-velocity anomalies are widespread, which complicate the nature of the margin here. To better understand crustal seismic velocities, lithology, and geophysical properties, we present an S-wave velocity (VS) model and a VP/VS model for the northeastern margin by using an existing P-wave velocity (VP) model as the starting model for 2-D kinematic S-wave forward ray tracing. The Mesozoic sedimentary sequence has lower VP/VS ratios than the Cenozoic sequence;in between is a main interface of P-S conversion. Two isolated high-velocity zones (HVZ) are found in the lower crust of the continental slope, showing S-wave velocities of 4.0–4.2 km/s and VP/VS ratios of 1.73–1.78. These values indicate a mafic composition, most likely of amphibolite facies. Also, a VP/VS versus VP plot indicates a magnesium-rich gabbro facies from post-spreading mantle melting at temperatures higher than normal. A third high-velocity zone (VP : 7.0–7.8 km/s;VP/VS: 1.85–1.96), 70-km wide and 4-km thick in the continent-ocean transition zone, is most likely to be a consequence of serpentinization of upwelled upper mantle. Seismic velocity structures and also gravity anomalies indicate that mantle upwelling/ serpentinization could be the most severe in the northeasternmost continent-ocean boundary of the SCS. Empirical relationships between seismic velocity and degree of serpentinization suggest that serpentinite content decreases with depth, from 43% in the lower crust to 37% into the mantle.展开更多
An intelligent lithology identification method is proposed based on deep learning of the rock microscopic images.Based on the characteristics of rock images in the dataset,we used Xception,MobileNet_v2,Inception_ResNe...An intelligent lithology identification method is proposed based on deep learning of the rock microscopic images.Based on the characteristics of rock images in the dataset,we used Xception,MobileNet_v2,Inception_ResNet_v2,Inception_v3,Densenet121,ResNet101_v2,and ResNet-101 to develop microscopic image classification models,and then the network structures of seven different convolutional neural networks(CNNs)were compared.It shows that the multi-layer representation of rock features can be represented through convolution structures,thus better feature robustness can be achieved.For the loss function,cross-entropy is used to back propagate the weight parameters layer by layer,and the accuracy of the network is improved by frequent iterative training.We expanded a self-built dataset by using transfer learning and data augmentation.Next,accuracy(acc)and frames per second(fps)were used as the evaluation indexes to assess the accuracy and speed of model identification.The results show that the Xception-based model has the optimum performance,with an accuracy of 97.66%in the training dataset and 98.65%in the testing dataset.Furthermore,the fps of the model is 50.76,and the model is feasible to deploy under different hardware conditions and meets the requirements of rapid lithology identification.This proposed method is proved to be robust and versatile in generalization performance,and it is suitable for both geologists and engineers to identify lithology quickly.展开更多
Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face ...Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face many challenges. This paper studies the problems of difficult feature information extraction,low precision of thin-layer identification and limited applicability of the model in intelligent lithologic identification. The author tries to improve the comprehensive performance of the lithology identification model from three aspects: data feature extraction, class balance, and model design. A new real-time intelligent lithology identification model of dynamic felling strategy weighted random forest algorithm(DFW-RF) is proposed. According to the feature selection results, gamma ray and 2 MHz phase resistivity are the logging while drilling(LWD) parameters that significantly influence lithology identification. The comprehensive performance of the DFW-RF lithology identification model has been verified in the application of 3 wells in different areas. By comparing the prediction results of five typical lithology identification algorithms, the DFW-RF model has a higher lithology identification accuracy rate and F1 score. This model improves the identification accuracy of thin-layer lithology and is effective and feasible in different geological environments. The DFW-RF model plays a truly efficient role in the realtime intelligent identification of lithologic information in closed-loop drilling and has greater applicability, which is worthy of being widely used in logging interpretation.展开更多
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
Since the Early Cenozoic,the Philippine Sea Plate(PSP)has undergone a complex tectonic evolution.During this period the Parece Vela Basin(PVB)was formed by seafloor spreading in the back-arc region of the proto-Izu-Bo...Since the Early Cenozoic,the Philippine Sea Plate(PSP)has undergone a complex tectonic evolution.During this period the Parece Vela Basin(PVB)was formed by seafloor spreading in the back-arc region of the proto-Izu-Bonin-Mariana(IBM)arc.However,until now,studies of the geological,geophysical,and tectonic evolution of the PVB have been rare.In this study,we obtained in situ trace element and major element compositions of minerals in basalts collected from two sites in the southern part of the PVB.The results reveal that the basalts from site CJ09-63 were likely formed via~10%partial melting of spinel-garnet lherzolite,while the basalts from site CJ09-64 were likely formed via 15%–25%partial melting of garnet lherzolite.The order of mineral crystallization for the basalts from site CJ09-64 was olivine,spinel,clinopyroxene,and plagioclase,while the plagioclase in the basalts from site CJ09-63 crystallized earlier than the clinopyroxene.Using a plagioclase-liquid hygrometer and an olivine-liquid oxybarometer,we determined that the basalts in this study have high H2O contents and oxygen fugacities,suggesting that the magma source of the Parece Vela basalts was affected by subduction components,which is consistent with the trace element composition of whole rock.展开更多
Based on three typical mediums(sandy loam, loam and sandy clay loam) in Hebei Plain, this paper designs phreatic evaporation experiments under different lithology and phreatic depth. Based on the analysis of experimen...Based on three typical mediums(sandy loam, loam and sandy clay loam) in Hebei Plain, this paper designs phreatic evaporation experiments under different lithology and phreatic depth. Based on the analysis of experimental data, the phreatic evaporation law and influencing factors of three mediums were studied. The results showed that:(1) The shallower the phreatic depth, the larger the phreatic evaporation.(2) Sandy clay loam has the biggest response to the increase of the phreatic depth, sandy loam is the second and loam is the smallest.(3) The limit depth of phreatic evaporation of sandy clay loam is about 3 m and that of loam and sandy loam is about 2 m and 3 m, seperately.(4) By fitting the daily evaporation of phreatic water and phreatic depth, the results showed that sandy loam and sandy clay loam are exponential functions and loam is power functions.展开更多
Based on the specialty of major rock-forning minerals and elementary composition of metamorphite,the detailed and systematical review,analysis and summary are completed for a series of lithology and reservoir fracture...Based on the specialty of major rock-forning minerals and elementary composition of metamorphite,the detailed and systematical review,analysis and summary are completed for a series of lithology and reservoir fracture identification teehnologies with logging that are recent years.Research shows that nuclear logging series in conventional logging are more favorable to identify the metanorphie lithology.ECS(elemental capturespectrosoopy)and other new logging lechnologies can be applied to identify metamorphic lithology.Due to theolex and di-verse metamorphic lithologies,the correspending reservoir identification standard should be established for metamorphic reservoir identificat lithology identification,The applicable conventional logging methods for metamorphic reservoir fracture identification mainly incldle dual lat ging,scoustic logging,dual lateral logging-microspherical focus,borehole diameter logging,natural gamma ray spectrology baging,etc.In additie acoustic-resistivity imaging logging,multipolar array acoustic logging,cross dipole acoustic logging and other new logging tec nologies with unique ad-vantages are increasingly applied for metamorphic reservoir fractureidentification.Currently,there are no gener appli sandards for logging i-dentification of metamorphic lithology and reservoir fracture.The specific metamorphic reservoir development a field actual data in specific ar-eas should be considered to study the logging identification and evaluation.展开更多
The primary objective of this study is to investigate the porosity-depth trends of shales and sands and how they affect lithologies. Compaction curves from well logs of five wells were determined using interval transi...The primary objective of this study is to investigate the porosity-depth trends of shales and sands and how they affect lithologies. Compaction curves from well logs of five wells were determined using interval transit time from sonic logs. The depth of investigation lies between 1087 m and 2500 m. Based on the shale and sand trend modeling, the study intends to determine the model to be used for lithology prediction at various depths given the interplay between shale and sand compaction. The improved understanding of the physical properties of shales and sands as a function of burial depth was demonstrated, in conjunction with a good understanding of how compaction affects lithology. The compaction curve for shale and sand lithologies varies with shale being parabolic in form, and sands with linear and exponential in nature. Plots of sonic porosity against depth show great dispersion in porosity values while plotting porosity values against depth for different lithologies produced well-defined porosity trends. This shows decrease in porosity with depth. The negative porosity trend is less marked in sandstones, and faster in shale which suggests that it is possible to make accurate porosity predictions using compaction trend. The porosity trend showed exponential relationship at small depth less than 2500m. The linear and exponential models are not dependable at large depth. The result shows that the compaction models applicable for sandstones do not necessary apply for shales.展开更多
The Paleogene Shahejie Formation in the KL16 oilfield, Bohai bay, is characterized by a thinly interbedded mixed sedimentary system, with complex sedimentary facies, lithologic types and distributions. It is hard for ...The Paleogene Shahejie Formation in the KL16 oilfield, Bohai bay, is characterized by a thinly interbedded mixed sedimentary system, with complex sedimentary facies, lithologic types and distributions. It is hard for conventional logging methods to identify the lithology therein. In order to solve the difficulty in lithologic identification of mixed sedimentary system, analyses based on graph data base using elemental capture energy spectrum log have been proposed. Due to the different composition for the various minerals, we innovatively established the molar numbers of silicon, calcium, magnesium, and aluminum as characteristic parameters for sandstone, limestone, dolomite, and mudstone, and a graph clustering analysis method was applied to identify lithology. Considering the seismic waveforms corresponding to lithologic impedance of reservoir, three seismic phases were identified by neural network clustering analysis of seismic waveform, and the seismic attributes with high sensitivity to reservoir thickness were then selected to realize the fine description of the mixed carbonate-siliciclastic reservoir. Drilling results confirmed that the sedimentary facies were accurately identified, with reservoir prediction accuracy reaching up to 80%. Under the guidance of reservoir research, the oil-in-place discovered in the oilfield were estimated to be more than 5 million tonnes. This technology provides reference for the exploration and development of oilfields of mixed sedimentary system.展开更多
1 Introduction Dolomite[Ca Mg(CO3)2],a common mineral in carbonate rocks,can be found in various geological settings from Precambrian to modern age,and is widely reported in almost all sedimentary and digenetic
基金financially supported by the National Science and Technology Major Project——Deep Earth Probe and Mineral Resources Exploration(No.2024ZD1003701)the National Key R&D Program of China(No.2022YFC2905004)。
文摘An image processing and deep learning method for identifying different types of rock images was proposed.Preprocessing,such as rock image acquisition,gray scaling,Gaussian blurring,and feature dimensionality reduction,was conducted to extract useful feature information and recognize and classify rock images using Tensor Flow-based convolutional neural network(CNN)and Py Qt5.A rock image dataset was established and separated into workouts,confirmation sets,and test sets.The framework was subsequently compiled and trained.The categorization approach was evaluated using image data from the validation and test datasets,and key metrics,such as accuracy,precision,and recall,were analyzed.Finally,the classification model conducted a probabilistic analysis of the measured data to determine the equivalent lithological type for each image.The experimental results indicated that the method combining deep learning,Tensor Flow-based CNN,and Py Qt5 to recognize and classify rock images has an accuracy rate of up to 98.8%,and can be successfully utilized for rock image recognition.The system can be extended to geological exploration,mine engineering,and other rock and mineral resource development to more efficiently and accurately recognize rock samples.Moreover,it can match them with the intelligent support design system to effectively improve the reliability and economy of the support scheme.The system can serve as a reference for supporting the design of other mining and underground space projects.
基金support from the National Natural Science Foundation of China(Nos.U24B2034,U2139204)the China Petroleum Science and Technology Innovation Fund(2021DQ02-0501)the Science and Technology Support Project of Langfang(2024011073).
文摘Lithology identification is a critical aspect of geoenergy exploration,including geothermal energy development,gas hydrate extraction,and gas storage.In recent years,artificial intelligence techniques based on drill core images have made significant strides in lithology identification,achieving high accuracy.However,the current demand for advanced lithology identification models remains unmet due to the lack of high-quality drill core image datasets.This study successfully constructs and publicly releases the first open-source Drill Core Image Dataset(DCID),addressing the need for large-scale,high-quality datasets in lithology characterization tasks within geological engineering and establishing a standard dataset for model evaluation.DCID consists of 35 lithology categories and a total of 98,000 high-resolution images(512×512 pixels),making it the most comprehensive drill core image dataset in terms of lithology categories,image quantity,and resolution.This study also provides lithology identification accuracy benchmarks for popular convolutional neural networks(CNNs)such as VGG,ResNet,DenseNet,MobileNet,as well as for the Vision Transformer(ViT)and MLP-Mixer,based on DCID.Additionally,the sensitivity of model performance to various parameters and image resolution is evaluated.In response to real-world challenges,we propose a real-world data augmentation(RWDA)method,leveraging slightly defective images from DCID to enhance model robustness.The study also explores the impact of real-world lighting conditions on the performance of lithology identification models.Finally,we demonstrate how to rapidly evaluate model performance across multiple dimensions using low-resolution datasets,advancing the application and development of new lithology identification models for geoenergy exploration.
基金support from the National Natural Science Foundation of China(Grant Nos:52379103 and 52279103)the Natural Science Foundation of Shandong Province(Grant No:ZR2023YQ049).
文摘Bayesian-optimized lithology identification has important basic geological research significance and engineering application value,and this paper proposes a Bayesian-optimized lithology identification method based on machine learning of rock visible and near-infrared spectral data.First,the rock spectral data are preprocessed using Savitzky-Golay(SG)smoothing to remove the noise of the spectral data;then,the preprocessed rock spectral data are downscaled using Principal Component Analysis(PCA)to reduce the redundancy of the data,optimize the effective discriminative information,and obtain the rock spectral features;finally,a Bayesian-optimized lithology identification model is established based on rock spectral features,optimize the model hyperparameters using Bayesian optimization(BO)algorithm to avoid the combination of hyperparameters falling into the local optimal solution,and output the predicted type of rock,so as to realize the Bayesian-optimized lithology identification.In addition,this paper conducts comparative analysis on models based on Artificial Neural Network(ANN)/Random Forest(RF),dimensionality reduction/full band,and optimization algorithms.It uses the confusion matrix,accuracy,Precison(P),Recall(R)and F_(1)values(F_(1))as the evaluation indexes of model accuracy.The results indicate that the lithology identification model optimized by the BO-ANN after dimensionality reduction achieves an accuracy of up to 99.80%,up to 99.79%and up to 99.79%.Compared with the BO-RF model,it has higher identification accuracy and better stability for each type of rock identification.The experiments and reliability analysis show that the Bayesian-optimized lithology identification method proposed in this paper has good robustness and generalization performance,which is of great significance for realizing fast,accurate and Bayesian-optimized lithology identification in tunnel site.
基金Funded by the National Natural Science Foundation of China(Nos.U1934206,52108260)China Academy of Railway Sciences Fund(No.2021YJ078)+1 种基金Railway Engineering Construction Standard Project(No.2023-BZWW-006)New Cornerstone Science Foundation through the XPLORER PRIZE。
文摘In order to achieve the large-scale application of manufactured sand in railway high-strength concrete structure,a series of high-strength manufactured sand concrete(HMC)are prepared by taking the manufactured sand lithology(tuff,limestone,basalt,granite),stone powder content(0,5%,10%,15%)and concrete strength grade(C60,C80,C100)as variables.The evolution of mechanical properties of HMC and the correlation between cubic compressive strength and other mechanical properties are studied.Compared to river sand,manufactured sand enhances the cubic compressive strength,axial compressive strength and elastic modulus of concrete,while its potential microcracks weaken the flexural strength and splitting tensile strength of concrete.Stone powder content displays both positive and negative effects on mechanical properties of HMC,and the stone powder content is suggested to be less than 10%.The empirical formulas between cubic compressive strength and other mechanical properties are proposed.
基金financial support from the National Natural Science Foundation of China(Grant Nos.52279103 and 52379103)the Natural Science Foundation of Shandong Province(Grant No.ZR2023YQ049).
文摘The efficiency of tunnel excavation,rock strength,stability of surrounding rock,and underground engineering disasters are closely related to lithology.Accurately identifying lithology is a necessary prerequisite for intelligent,safe,and efficient tunnel construction.The design of conventional recognition models heavily relies on experience and extensive calculations.To develop a model suitable for deployment on construction sites and capable of accurate lithology identification,a fast search method for lithology identification models is proposed.This method integrates geological knowledge,apparent feature extraction techniques,and search algorithms.An efficient feature extraction super network using multi-scale geological features of rock surface is constructed,a model evaluation method that comprehensively considers accuracy and latency is developed,and differential evolution algorithm is used to search for the optimal model parameters.Experiments demonstrate that the proposed method enables the model to evolve faster and more accurately,and eventually a model(LithoNet)suitable for lithological classification is found.It only takes 2.10 ms to infer an image of 224×224,which is 57.25%faster than MobileNet v3 and 62.83%faster than ShuffleNet V2.The F1-score of LithoNet is 0.9874,surpassing classical models such as EfficientNetV2-S.LithoNet can be easily deployed on portable devices,effectively promoting the intelligence and accuracy of lithology identification at engineering sites.
基金jointly supported by the Key Project of the Natural Science Foundation of Guizhou Province(No.Qiankehe Jichu-ZK[2023]Zhongdian027)the Project of the Science and Technology Innovation Base Construction of Guizhou Province(No.Qiankehe Zhongyindi[2023]005)Philosophy and Social Science Planning Subjects in Guizhou Province in 2022(No.22GZYB53)。
文摘Ecological security provides the basis of maintaining both a sustainable regional ecosystem and economic development.However,few studies have focused on how the features such as topography and geomorphology,lithologic stratigraphic assemblages,and geohazard distribution affect the construction of ecological security patterns and the layout of optimization measures.In order to comprehensively reveal the key areas and key objects of ecological restoration in karst basins,this study takes the Beipan River Basin(BRB)as an example,constructs an ecological security pattern(ESP)based on the methods of morphological spatial pattern analysis(MSPA),landscape connectivity analysis and circuit theory,and lays out the optimization measures in combination with the spatial distribution characteristics of topographic and geomorphological differences and lithological stratigraphic combinations.The results show that 151 ecological sources,343 ecological corridors,121 pinch points and 178 barriers constitute the ESP of the BRB.Lithology is closely related to the spatial distribution characteristics of ecological source sites.Level 1 and 2 ecological sources(The ecological sources were categorized into level 1,level 2,and level 3 source from high to low importance.)are concentrated in the Emeishan basalt region of the upstream and the clastic and impure carbonate rock region of the downstream part of the BRB;level 3ecological sources are concentrated in the carbonate rock region of the midstream.Taking into account the outstanding ecological problems in the basin,and based on the characteristics of lithology and geohazard distribution,we propose the optimization scheme of“three axes,three zones and multiple points”for the ESP and the layout of specific measures of the BRB.The results can provide scientific references for maintaining ecological security maintenance in karst ecologically fragile areas.
基金support from the National Natural Science Foundation of China(Grant Nos.52379103,52279103)the Natural Science Foundation of Shandong Province(Grant No.ZR2023YQ049).
文摘Imaging hyperspectral technology has distinctive advantages of non-destructive and non-contact measurement,and the integration of spectral and spatial data.These characteristics present new methodologies for intelligent geological sensing in tunnels and other underground engineering projects.However,the in situ acquisition and rapid classification of hyperspectral images in underground still faces great challenges,including the difficulty in obtaining uniform hyperspectral images and the complexity of deploying sophisticated models on mobile platforms.This study proposes an intelligent lithology identification method based on partition feature extraction of hyperspectral images.Firstly,pixel-level hyperspectral information from representative lithological regions is extracted and fused to obtain rock hyperspectral image partition features.Subsequently,an SG-SNV-PCA-DNN(SSPD)model specifically designed for optimizing rock hyperspectral data,performing spectral dimensionality reduction,and identifying lithology is integrated.In an experimental study involving 3420 hyperspectral images,the SSPD identification model achieved the highest accuracy in the testing set,reaching 98.77%.Moreover,the speed of the SSPD model was found to be 18.5%faster than that of the unprocessed model,with an accuracy improvement of 5.22%.In contrast,the ResNet-101 model,used for point-by-point identification based on non-partitioned features,achieved a maximum accuracy of 97.86%in the testing set.In addition,the partition feature extraction methods significantly reduce computational complexity.An objective evaluation of various models demonstrated that the SSPD model exhibited superior performance,achieving a precision(P)of 99.46%,a recall(R)of 99.44%,and F1 score(F1)of 99.45%.Additionally,a pioneering in situ detection work was carried out in a tunnel using underground hyperspectral imaging technology.
基金supported by National Natural Science Foundation of China(Nos.41872150,U2344209 and U19B6003)the PetroChina Southwest Oil and Gasfield Company(No.2020-54365)。
文摘Machine learning algorithms are widely used to interpret well logging data.To enhance the algorithms'robustness,shuffling the well logging data is an unavoidable feature engineering before training models.However,latent information stored between different well logging types and depth is destroyed during the shuffle.To investigate the influence of latent information,this study implements graph convolution networks(GCNs),long-short temporal memory models,recurrent neural networks,temporal convolution networks,and two artificial neural networks to predict the microbial lithology in the fourth member of the Dengying Formation,Moxi gas field,central Sichuan Basin.Results indicate that the GCN model outperforms other models.The accuracy,F1-score,and area under curve of the GCN model are 0.90,0.90,and 0.95,respectively.Experimental results indicate that the time-series data facilitates lithology prediction and helps determine lithological fluctuations in the vertical direction.All types of logs from the spectral in the GCN model and also facilitates lithology identification.Only on condition combined with latent information,the GCN model reaches excellent microbialite classification resolution at the centimeter scale.Ultimately,the two actual cases show tricks for using GCN models to predict potential microbialite in other formations and areas,proving that the GCN model can be adopted in the industry.
基金sponsored by National Natural Science Fund Projects (No.41204072 and No.U1262208)Research Funds Provided to New Recruitments of China University of Petroleum-Beijing (YJRC-2011-03)Science Foundation of China University of Petroleum-Beijing (YJRC-2013-36)
文摘In this paper, we derive an approximation of the SS-wave reflection coefficient and the expression of S-wave ray elastic impedance （SREI） in terms of the ray parameter. The SREI can be expressed by the S-wave incidence angle or P-wave reflection angle, referred to as SREIS and SREIP, respectively. Our study using elastic models derived from real log measurements shows that SREIP has better capability for lithology and fluid discrimination than SREIS and conventional S-wave elastic impedance （SEI）. We evaluate the SREIP feasibility using 25 groups of samples from Castagna and Smith （1994）. Each sample group is constructed by using shale, brine-sand, and gas-sand. Theoretical evaluation also indicates that SRE1P at large incident angles is more sensitive to fluid than conventional fluid indicators. Real seismic data application also shows that SRE1P at large angles calculated using P-wave and S-wave impedance can efficiently characterize tight gas-sand.
基金financially supported by the National Basic Research Program "973" Project of the Ministry of Science and Technology of the People’s Republic of China (Grant No. 2013CB733200)the National Science Found for Distinguished Young Scholars of China (Grant No. 41225011)the Chang Jiang Scholars Program of China and the open fund on "Research on largescale landslides triggered by the Wenchuan earthquake" provided by the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection
文摘Studies on landslides by the 2008 Wenchuan earthquake showed that topography was of great importance in amplifying the seismic shaking, and among other factors, lithology and slope structure controlled the spatial occurrence of slope failures. The present study carried out experiments on four rock slopes with steep angle of 60° by means of a shaking table. The recorded Wenchuan earthquake waves were scaled to excite the model slopes. Measurements from accelerometers installed on free surface of the model slope were analyzed, with much effort on timedomain acceleration responses to horizontal components of seismic shaking. It was found that the amplification factor of peak horizontal acceleration, RPHA, was increasing with elevation of each model slope, though the upper and lower halves of the slope exhibited different increasing patterns. As excitation intensity was increased, the drastic deterioration of the inner structure of each slope caused the sudden increase of RPHA in the upper slope part. In addition, the model simulating the soft rock slope produced the larger RPHA than the model simulating the hard rock slope by a maximum factor of 2.6. The layered model slope also produced the larger RPHA than the homogeneous model slope by a maximum factor of 2.7. The upper half of a slope was influenced more seriously by the effect of lithology, while the lower half was influenced more seriously by the effect of slope structure.
基金South China Sea Institute of Oceanology (SCSIO) for providing R/V Shiyan-2 to carry out this experiment,sponsored by Oceanographic Research Vessel Sharing Plan (NORC2016-08) of National Natural Science Foundation of Chinafunded by National Natural Science Foundation of China (Grant Nos. 41776057, 41761134051, 91858213, 41730532 and 91428039)
文摘The northeastern margin of the South China Sea (SCS), developed from continental rifting and breakup, is usually thought of as a non-volcanic margin. However, post-spreading volcanism is massive and lower crustal high-velocity anomalies are widespread, which complicate the nature of the margin here. To better understand crustal seismic velocities, lithology, and geophysical properties, we present an S-wave velocity (VS) model and a VP/VS model for the northeastern margin by using an existing P-wave velocity (VP) model as the starting model for 2-D kinematic S-wave forward ray tracing. The Mesozoic sedimentary sequence has lower VP/VS ratios than the Cenozoic sequence;in between is a main interface of P-S conversion. Two isolated high-velocity zones (HVZ) are found in the lower crust of the continental slope, showing S-wave velocities of 4.0–4.2 km/s and VP/VS ratios of 1.73–1.78. These values indicate a mafic composition, most likely of amphibolite facies. Also, a VP/VS versus VP plot indicates a magnesium-rich gabbro facies from post-spreading mantle melting at temperatures higher than normal. A third high-velocity zone (VP : 7.0–7.8 km/s;VP/VS: 1.85–1.96), 70-km wide and 4-km thick in the continent-ocean transition zone, is most likely to be a consequence of serpentinization of upwelled upper mantle. Seismic velocity structures and also gravity anomalies indicate that mantle upwelling/ serpentinization could be the most severe in the northeasternmost continent-ocean boundary of the SCS. Empirical relationships between seismic velocity and degree of serpentinization suggest that serpentinite content decreases with depth, from 43% in the lower crust to 37% into the mantle.
基金support from the National Natural Science Foundation of China(Grant Nos.52022053 and 52009073)the Natural Science Foundation of Shandong Province(Grant No.ZR201910270116).
文摘An intelligent lithology identification method is proposed based on deep learning of the rock microscopic images.Based on the characteristics of rock images in the dataset,we used Xception,MobileNet_v2,Inception_ResNet_v2,Inception_v3,Densenet121,ResNet101_v2,and ResNet-101 to develop microscopic image classification models,and then the network structures of seven different convolutional neural networks(CNNs)were compared.It shows that the multi-layer representation of rock features can be represented through convolution structures,thus better feature robustness can be achieved.For the loss function,cross-entropy is used to back propagate the weight parameters layer by layer,and the accuracy of the network is improved by frequent iterative training.We expanded a self-built dataset by using transfer learning and data augmentation.Next,accuracy(acc)and frames per second(fps)were used as the evaluation indexes to assess the accuracy and speed of model identification.The results show that the Xception-based model has the optimum performance,with an accuracy of 97.66%in the training dataset and 98.65%in the testing dataset.Furthermore,the fps of the model is 50.76,and the model is feasible to deploy under different hardware conditions and meets the requirements of rapid lithology identification.This proposed method is proved to be robust and versatile in generalization performance,and it is suitable for both geologists and engineers to identify lithology quickly.
基金financially supported by the National Natural Science Foundation of China(No.52174001)the National Natural Science Foundation of China(No.52004064)+1 种基金the Hainan Province Science and Technology Special Fund “Research on Real-time Intelligent Sensing Technology for Closed-loop Drilling of Oil and Gas Reservoirs in Deepwater Drilling”(ZDYF2023GXJS012)Heilongjiang Provincial Government and Daqing Oilfield's first batch of the scientific and technological key project “Research on the Construction Technology of Gulong Shale Oil Big Data Analysis System”(DQYT-2022-JS-750)。
文摘Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face many challenges. This paper studies the problems of difficult feature information extraction,low precision of thin-layer identification and limited applicability of the model in intelligent lithologic identification. The author tries to improve the comprehensive performance of the lithology identification model from three aspects: data feature extraction, class balance, and model design. A new real-time intelligent lithology identification model of dynamic felling strategy weighted random forest algorithm(DFW-RF) is proposed. According to the feature selection results, gamma ray and 2 MHz phase resistivity are the logging while drilling(LWD) parameters that significantly influence lithology identification. The comprehensive performance of the DFW-RF lithology identification model has been verified in the application of 3 wells in different areas. By comparing the prediction results of five typical lithology identification algorithms, the DFW-RF model has a higher lithology identification accuracy rate and F1 score. This model improves the identification accuracy of thin-layer lithology and is effective and feasible in different geological environments. The DFW-RF model plays a truly efficient role in the realtime intelligent identification of lithologic information in closed-loop drilling and has greater applicability, which is worthy of being widely used in logging interpretation.
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 Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology(Qingdao)(No.2022QNLM050201-3)the National Natural Science Foundations of China(Grants Nos.41230960,41322036,41776070)+1 种基金Aoshan Talents Program of Pilot National Laboratory for Marine Science and Technology(Qingdao)(QNLM2015ASTPES16)Taishan Scholarship from Shandong Province.
文摘Since the Early Cenozoic,the Philippine Sea Plate(PSP)has undergone a complex tectonic evolution.During this period the Parece Vela Basin(PVB)was formed by seafloor spreading in the back-arc region of the proto-Izu-Bonin-Mariana(IBM)arc.However,until now,studies of the geological,geophysical,and tectonic evolution of the PVB have been rare.In this study,we obtained in situ trace element and major element compositions of minerals in basalts collected from two sites in the southern part of the PVB.The results reveal that the basalts from site CJ09-63 were likely formed via~10%partial melting of spinel-garnet lherzolite,while the basalts from site CJ09-64 were likely formed via 15%–25%partial melting of garnet lherzolite.The order of mineral crystallization for the basalts from site CJ09-64 was olivine,spinel,clinopyroxene,and plagioclase,while the plagioclase in the basalts from site CJ09-63 crystallized earlier than the clinopyroxene.Using a plagioclase-liquid hygrometer and an olivine-liquid oxybarometer,we determined that the basalts in this study have high H2O contents and oxygen fugacities,suggesting that the magma source of the Parece Vela basalts was affected by subduction components,which is consistent with the trace element composition of whole rock.
基金supported by the basic research fund of the GAGS(YYWF201624)Hebei graduate's innovative funding(CXZZSS20181)
文摘Based on three typical mediums(sandy loam, loam and sandy clay loam) in Hebei Plain, this paper designs phreatic evaporation experiments under different lithology and phreatic depth. Based on the analysis of experimental data, the phreatic evaporation law and influencing factors of three mediums were studied. The results showed that:(1) The shallower the phreatic depth, the larger the phreatic evaporation.(2) Sandy clay loam has the biggest response to the increase of the phreatic depth, sandy loam is the second and loam is the smallest.(3) The limit depth of phreatic evaporation of sandy clay loam is about 3 m and that of loam and sandy loam is about 2 m and 3 m, seperately.(4) By fitting the daily evaporation of phreatic water and phreatic depth, the results showed that sandy loam and sandy clay loam are exponential functions and loam is power functions.
文摘Based on the specialty of major rock-forning minerals and elementary composition of metamorphite,the detailed and systematical review,analysis and summary are completed for a series of lithology and reservoir fracture identification teehnologies with logging that are recent years.Research shows that nuclear logging series in conventional logging are more favorable to identify the metanorphie lithology.ECS(elemental capturespectrosoopy)and other new logging lechnologies can be applied to identify metamorphic lithology.Due to theolex and di-verse metamorphic lithologies,the correspending reservoir identification standard should be established for metamorphic reservoir identificat lithology identification,The applicable conventional logging methods for metamorphic reservoir fracture identification mainly incldle dual lat ging,scoustic logging,dual lateral logging-microspherical focus,borehole diameter logging,natural gamma ray spectrology baging,etc.In additie acoustic-resistivity imaging logging,multipolar array acoustic logging,cross dipole acoustic logging and other new logging tec nologies with unique ad-vantages are increasingly applied for metamorphic reservoir fractureidentification.Currently,there are no gener appli sandards for logging i-dentification of metamorphic lithology and reservoir fracture.The specific metamorphic reservoir development a field actual data in specific ar-eas should be considered to study the logging identification and evaluation.
文摘The primary objective of this study is to investigate the porosity-depth trends of shales and sands and how they affect lithologies. Compaction curves from well logs of five wells were determined using interval transit time from sonic logs. The depth of investigation lies between 1087 m and 2500 m. Based on the shale and sand trend modeling, the study intends to determine the model to be used for lithology prediction at various depths given the interplay between shale and sand compaction. The improved understanding of the physical properties of shales and sands as a function of burial depth was demonstrated, in conjunction with a good understanding of how compaction affects lithology. The compaction curve for shale and sand lithologies varies with shale being parabolic in form, and sands with linear and exponential in nature. Plots of sonic porosity against depth show great dispersion in porosity values while plotting porosity values against depth for different lithologies produced well-defined porosity trends. This shows decrease in porosity with depth. The negative porosity trend is less marked in sandstones, and faster in shale which suggests that it is possible to make accurate porosity predictions using compaction trend. The porosity trend showed exponential relationship at small depth less than 2500m. The linear and exponential models are not dependable at large depth. The result shows that the compaction models applicable for sandstones do not necessary apply for shales.
文摘The Paleogene Shahejie Formation in the KL16 oilfield, Bohai bay, is characterized by a thinly interbedded mixed sedimentary system, with complex sedimentary facies, lithologic types and distributions. It is hard for conventional logging methods to identify the lithology therein. In order to solve the difficulty in lithologic identification of mixed sedimentary system, analyses based on graph data base using elemental capture energy spectrum log have been proposed. Due to the different composition for the various minerals, we innovatively established the molar numbers of silicon, calcium, magnesium, and aluminum as characteristic parameters for sandstone, limestone, dolomite, and mudstone, and a graph clustering analysis method was applied to identify lithology. Considering the seismic waveforms corresponding to lithologic impedance of reservoir, three seismic phases were identified by neural network clustering analysis of seismic waveform, and the seismic attributes with high sensitivity to reservoir thickness were then selected to realize the fine description of the mixed carbonate-siliciclastic reservoir. Drilling results confirmed that the sedimentary facies were accurately identified, with reservoir prediction accuracy reaching up to 80%. Under the guidance of reservoir research, the oil-in-place discovered in the oilfield were estimated to be more than 5 million tonnes. This technology provides reference for the exploration and development of oilfields of mixed sedimentary system.
基金financially supported by Natural Science Foundation of China (NSFC, No. 41272115)
文摘1 Introduction Dolomite[Ca Mg(CO3)2],a common mineral in carbonate rocks,can be found in various geological settings from Precambrian to modern age,and is widely reported in almost all sedimentary and digenetic
