Reef-bank reservoirs are an important target for petroleum exploration in marine carbonates and also an essential supplemental area for oil and gas production in China. Due to the diversity of reservoirs and the extre...Reef-bank reservoirs are an important target for petroleum exploration in marine carbonates and also an essential supplemental area for oil and gas production in China. Due to the diversity of reservoirs and the extreme heterogeneity of reef-banks, it is very difficult to discriminate the sedimentary facies and lithologies in reef-bank reservoirs using conventional well logs. The borehole image log provides clear identification of sedimentary structures and textures and is an ideal tool for discriminating sedimentary facies and lithologies. After examining a large number of borehole images and cores, we propose nine typical patterns for borehole image interpretation and a method that uses these patterns to discriminate sedimentary facies and lithologies in reeI^bank reservoirs automatically. We also develop software with user-friendly interface. The results of applications in reef-bank reservoirs in the middle Tarim Basin and northeast Sichuan have proved that the proposed method and the corresponding software are quite effective.展开更多
Much study has been done in the study area linking Vertical Electrical Sounding (VES) interpreted results to lithologies in the subsurface though only tend to indicate the vertical changes with the aim of mapping the ...Much study has been done in the study area linking Vertical Electrical Sounding (VES) interpreted results to lithologies in the subsurface though only tend to indicate the vertical changes with the aim of mapping the occurrence of groundwater aquifers. Several boreholes have been drilled in the study area, though not much has been done to compare the vertical and lateral lithologic changes in the study area. This research is based on VES modelled geoelectric layers compared from point to point and using borehole logs as control data to establish inferences of certain lithology in the subsurface. The inversion of each VES curve was obtained using an AGI Earth Imager ID inversion automated computer program and resistivities and thicknesses of a geoelectric model were estimated. The analyzed VES data interpretation achieved using the curve matching technique resulted in mapping the subsurface of the area as portraying H-type;ρ<sub>1</sub> > ρ<sub>2</sub> ρ<sub>3</sub>, K-type;ρ<sub>1</sub> ρ<sub>2</sub> > ρ<sub>3</sub>, A-type;ρ<sub>1</sub> ρ<sub>2</sub> ρ<sub>3</sub>, Q-type;ρ<sub>1</sub> > ρ<sub>2</sub> > ρ<sub>3</sub>, representing 3-Layer subsurface and subsequently a combination of HK, HA and KHK types of curves representing 4-Layer and 5-Layer in the subsurface. The analysis further deployed the use of the surfer software capabilities which combined the VES data to generate profiles running in the west-east and the north-south direction. A closer analysis of the curve types indicates that there exists a sequence showing a shifting of the order of arrangement between the west and the east fragments which incidentally coincides with VES points 8, 9 and 10 in the West-East profiles. The lateral change is noted from the types of curves established and each curve indicates a vertical change in the subsurface. Control log data of lithologies from four boreholes BH1, BH2, BH3 and BH5 to show a qualification that different resistivity values portent different lithologies. Indeed, an analysis at borehole BH3 lithologies is dominated by either compacted rocks or soils, insinuating a scenario of compression experienced in this part of the subsurface which confirmed compression of subsurface formations. A correlation of the VES curve types and their change from one point to another in the study area are evident. This change supported by the surfer generated profiles from the modeled VES data show that there exists and inferred fault line running in the north-south in the area. The inferred fault line by VES mapping, is magnificently outlined by the geological map. There is exuded evidence from this study that the application of VES is able to help map the lateral and the vertical changes in the subsurface of any area but the evidence of the specific lithologies has to be supported by availability of borehole log control data. The VES data was able to enumerate vertical layering of lithologies, lateral changes and even mapping vertical fault line in the study area.展开更多
The use of gravity data has demonstrated capability for monitoring lithological changes on a large scale as a consequence of differentiating basement and sedimentary of buried valleys. Gravity anomalies are associated...The use of gravity data has demonstrated capability for monitoring lithological changes on a large scale as a consequence of differentiating basement and sedimentary of buried valleys. Gravity anomalies are associated with lateral contrasts in density and therefore deformation by faulting or folding will be manifested if accompanied by lateral density changes, otherwise, the vice versa is true. The study’s objective is to evaluate the effectiveness of gravity method in establishing different lithologies in an area. The study has revealed that regional anomaly gravity map presents high anomalies in the Northern region in the NW-SE trend and low anomalies in the southern trend in NW-SE, while the residual anomaly gravity map shows different trends for the low and high gravity anomalies. The gravity anomalies are well interpreted in line with the lithologies of the study area rather than the deformation of the same lithologies. There are observed high values of gravity anomaly values (ranging from -880.2 to -501.2 g.u.) where there are eolian unconsolidated rocks overlying the basement compared to low gravity anomaly values (ranging from -1338.9 to -1088.7 g.u.) where the andesites, trachytes and phonolites overly the basement. The different regional gravity anomalies relate well with different rock densities in the study area along the line profile for radially averaged power spectrum. The gravity highs are noted in the eastern point and are associated with andesites, trachytes, basalts and igneous rocks, while the gravity lows are associated with sandstone, greywacke, arkose, and eolian unconsolidated rock. The utilization of the information from the Power spectrum analysis demonstrates that the depth to the deepest basement rock is 12.8 km which is in the eastern flank, while the shallowest to the basement of 1.1 km to the western flank.展开更多
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
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
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
Harrat Lunayyir,a volcanic field in western Saudi Arabia,exhibits diverse geomorphological and topographical features shaped by volcanic,tectonic,and climatic processes.This study integrates field observations,remote ...Harrat Lunayyir,a volcanic field in western Saudi Arabia,exhibits diverse geomorphological and topographical features shaped by volcanic,tectonic,and climatic processes.This study integrates field observations,remote sensing,and GIS analysis to investigate the spatial distribution and relationships between volcanic landforms,lava flows,and topographical variation result obtained is a morphological classification of the cinder cones of Harrat Lunayyir,which can be sub-divided into four types:tephra rings,horseshoe-shaped volcanoes,multiple volcanoes and volcanoes without craters.All of these are monogenetic volcanoes,unlike central volcanoes(stratovolcanoes)which live for tens or hundreds of thousands of years and erupt numerous times.In Harrat Lunayyir,there is a clear dominance of arched horseshoe-shaped volcanoes(58)over ring-shaped cinder cones(10),A1_symmetric cones(circular,uniform cinder cones with a single crater)(32),A2_asymmetric cones(elongated,irregular cones and may feature one or more craters)(8),volcanoes without craters(55)and multiple volcanoes(20).The classification presented in this paper makes it possible to include all morphological types of volcanoes found in the region.This fact also renders the present classification a useful tool to apply in other,both insular and continental volcanic areas to eventually analyze and systematize the study of eruptive edifices with similar traits.Hence,this research will explore the standard physical volcanology literature so as to follow accepted definitions.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
The Edikan Mine,which consists of Fobinso and Esuajah gold deposits,lies within the Asankrangwa Gold Belt of the Birimian Supergroup in the Kumasi Basin.The metasedimentary rocks in the Basins and the faulted metavolc...The Edikan Mine,which consists of Fobinso and Esuajah gold deposits,lies within the Asankrangwa Gold Belt of the Birimian Supergroup in the Kumasi Basin.The metasedimentary rocks in the Basins and the faulted metavolcanic rocks in the Belts that make up the Birimian Supergroup were intruded by granitoids during the Eburnean Orogeny.This research aims to classify granitoids in the Edikan Mine and ascertain the petrogenetic and geochemical characteristics of some auriferous granitoids in the wider Kumasi Basin,Ghana,to understand the implications for geodynamic settings.A multi-methods approach involving field studies,petrographic studies,and whole-rock geochemical analysis was used to achieve the goal of the study.Petrographic studies revealed a relatively high abundance of plagioclase and a low percentage of K-feldspars(anorthoclase and orthoclase)in the Fobinso samples,suggesting that the samples are granodioritic in nature,while the Esuajah samples showed relatively low plagioclase abundance and a high percentage in K-feldspars,indicating that they are granitic.The granitoids from the study areas are co-magmatic.The granitoids in Esuajah and Fobinso are generally enriched in large ion lithophile elements and light rare earth elements than high field strength elements,middle rare earth elements,and heavy rare earth elements,indicating mixing with crustal sources during the evolution of the granitoids.The granitoids were tectonically formed in a syn-collisional+VAG setting,which implies that they were formed in the subduction zone setting.Fobinso granodiorites showed S-type signatures with evidence of extensive crustal contamination,while the Esuajah granites showed I-type signatures with little or no crustal contamination and are peraluminous.Gold mineralization in the study area is structurally and lithologically controlled with shear zones,faulting,and veining as the principal structures controlling the mineralization.The late-stage vein,V3,in the Edikan Mine is characterized by a low vein angle and is mineralized.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
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
Rock classification plays a crucial role in various fields such as geology,engineering,and environmental studies.Employing deep learning AI(artificial intelligence)methods has a high potential to significantly improve...Rock classification plays a crucial role in various fields such as geology,engineering,and environmental studies.Employing deep learning AI(artificial intelligence)methods has a high potential to significantly improve the accuracy and efficiency of this task.The paper delves into the exploration of two cuttingedge AI techniques,namely Mask DINO and Mask R-CNN(convolutional neural network),as means to identify rock weathering grades and rock types.The results demonstrate that Mask DINO,which is a Detection Transformer(DETR),outperforms Mask R-CNN for the aforementioned purposes.Mask DINO achieved f-1 scores of 91% and 86% in weathering grade detection and rock type detection,as opposed to the Mask R-CNN's f-1 scores of 84% and 75%,respectively.These findings underscore the substantial potential of employing DETR algorithms like Mask DINO for automatic classification of both rock type and weathering states.Although the study examines only two AI models,the data processing and other techniques developed in this study may serve as a foundation for future advancements in the field.By incorporating these advanced AI techniques,logging personnel can obtain valuable references to aid their work,ultimately contributing to the advancement of geological and related fields.展开更多
Accurate acquisition of the lithological composition of a tunnel face is crucial for efficient tunneling and hazard prevention in large-diameter slurry shield tunnels.While widely applied,current data-driven methods o...Accurate acquisition of the lithological composition of a tunnel face is crucial for efficient tunneling and hazard prevention in large-diameter slurry shield tunnels.While widely applied,current data-driven methods often face challenges such as indirect prediction,data sparsity,and data drift,which limit their accuracy and generalizability.This study develops an integrated method that combines a knowledge-driven method to directly compute distribution patterns of lithological components,which are used as a priori knowledge to guide the development of a data-driven method.Coupled Markov chain(CMC)and deep neural networks(DNNs)serve as the knowledge-driven and data-driven components,respectively.Additionally,a dynamic prediction strategy is proposed,where the model is continuously optimized as construction progresses and training samples accumulate,rather than being statically trained on post-construction data,as is common in data-driven methods.Finally,the proposed method is evaluated using a real-world project.The evaluation results show that the integrated method outperforms both individual data-and knowledge-driven methods,demonstrating higher predictive performance,greater stability,and greater robustness to data scarcity and data drift.Furthermore,the dynamic prediction strategy better captures the effects of gradual data accumulation and lithological spatial variability on prediction performance during construction,providing new insights for real-time prediction in practical tunneling applications.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
Coal-measure gas is a primary target with significant potential for the exploration of unconventional hydrocarbon resources.However,the spatiotemporal distribution and combination patterns of multi-type coal-measure g...Coal-measure gas is a primary target with significant potential for the exploration of unconventional hydrocarbon resources.However,the spatiotemporal distribution and combination patterns of multi-type coal-measure gases are yet to be clarified,directly impeding the sweet spot evaluation and exploration deployment of coal-measure gas.This study discussed the characteristics and distribution patterns of coal-measure gases in the Daniudi gas field in northeastern Ordos Basin,China,with abundant drilling data.The results indicate that the coal seams variably thin upward and are mainly seen in the first and second members of the Taiyuan Formation(also referred to as the Tai 1 and Tai 2 members,respectively)and the first member of the Shanxi Formation(Shan 1 Member).Nos.8,5 and 3 coal seams are laterally continuous,and significantly thicker in its southern part compared to the northern part.Moreover,carbonaceous mudstones and shales are better developed in the southern part,where limestones are only observed in the Tai 1 Member.Based on the main lithological types,we identified three lithologic roofs of coal seams,that is,limestone,mudstone,and sandstone,which determine the spatiotemporal distribution of coal-measure gases.Besides bauxite gas in the Benxi Formation,the coal-measure gases include tight-sand gas,coalbed methane(CBM),coal-measure shale gas,and tight-limestone gas,with CBM typically associated with coal-measure shale gas.The combinations of different types of coal-measure gas vary across different layers and regions.Tight-sand gas is well-developed in areas where tight sandstones are in contact with coal-measures.From the Taiyuan to the Shanxi formations,CBM gradually transitions into a combination of CBM and coal-measure shale gas,and coal-measure shale gas.Nos.8 and 5 coal seams in low-lying areas exhibit favorable gas-bearing properties due to their large thickness and favorable roof lithologies,serving as prospective play fairways.Mudstone and limestone roofs are more conducive to achieving good gas-bearing properties.The direct contact between sandstones and coal seams tends to result in the formation of tight-sand gas and a reduced gas content of CBM.While focusing on single types of gas reservoirs such as CBM and tight-sand gas,it is essential to consider the concurrent exploration of various coal-measure gas combinations to discover more additional gas resources and guide exploration deployment.展开更多
Objective The aim was to reveal relationship between lithological character soil and productivity of Cunninghamia lanceolata and lay a foundation for systemic management of C. lanceolata fast-growing and high yield pl...Objective The aim was to reveal relationship between lithological character soil and productivity of Cunninghamia lanceolata and lay a foundation for systemic management of C. lanceolata fast-growing and high yield plantation. Method By using experimental ecology method and variance analysis, the biomass and growth of planting eleven years＇ C. lanceolata on the soils with six different lithologicel characters were studied. Result The effects of soils with six different lithological characters on the height, diameter growth and biomass of C. lanceolata were different, in which the growth order of C. lanceolata was： Feldspathic quartzy sandstone （ average height 523. 270 cm, average diameter 4.720 cm, average individual biomass 5.059kg） 〉 Basalt （ average height 511. 570 cm, average diameter 4.650 cm, average individual biomass 4.848 kg） 〉 Quartzy sandstone 〉 Blastopsammite 〉 The Quarternary Period red clay 〉 Coal-series siliceous sand-shale, and the difference was smaller between the last two lithological characters. Conclusion Feldspathic quartzy sandstone and Basalt are beneficial to C. lanceolata.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
The rapid changing near source, multi-stream depositional environment of conglomerate reservoirs leads to severe heterogeneity, complex lithology and physical properties, and large changes of oil layer resistivity. Qu...The rapid changing near source, multi-stream depositional environment of conglomerate reservoirs leads to severe heterogeneity, complex lithology and physical properties, and large changes of oil layer resistivity. Quantitative evaluation of water-flooded layers has become an important but difficult focus for secondary development of oilfields. In this paper, based on the analysis of current problems in quantitative evaluation of water-flooded layers, the Kexia Group conglomerate reservoir of the Sixth District in the Karamay Oilfield was studied. Eight types of conglomerate reservoir lithology were identified effectively by a data mining method combined with the data from sealed coring wells, and then a multi-parameter model for quantitative evaluation of the water-flooded layers of the main oil-bearing lithology was developed. Water production rate, oil saturation and oil productivity index were selected as the characteristic parameters for quantitative evaluation of water-flooded layers of conglomerate reservoirs. Finally, quantitative evaluation criteria and identification rules for water-flooded layers of main oil-bearing lithology formed by integration of the three characteristic parameters of water-flooded layer and undisturbed formation resistivity. This method has been used in evaluation of the water-flooded layers of a conglomerate reservoir in the Karamay Oilfield and achieved good results, improving the interpretation accuracy and compliance rate. It will provide technical support for avoiding perforation of high water-bearing layers and for adjustment of developmental programs.展开更多
Calculating the mineral composition is a critical task in log interpretation. Elementalcapture spectroscopy （ECS） log provides the weight percentages of twelve common elements,which lays the foundation for the accur...Calculating the mineral composition is a critical task in log interpretation. Elementalcapture spectroscopy （ECS） log provides the weight percentages of twelve common elements,which lays the foundation for the accurate calculation of mineral compositions. Previousprocessing methods calculated the formation composition via the conversion relation betweenthe formation chemistry and minerals. Thus, their applicability is limited and the methodprecision is relatively low. In this study, we present a multimineral optimization processingmethod based on the ECS log. We derived the ECS response equations for calculating theformation composition, then, determined the logging response values for the elements ofcommon minerals using core data and theoretical calculations. Finally, a software modulewas developed. The results of the new method are consistent with core data and the meanabsolute error is less than 10%.展开更多
基金sponsored by the National S&T Major Special Project(No.2008ZX05020-01)
文摘Reef-bank reservoirs are an important target for petroleum exploration in marine carbonates and also an essential supplemental area for oil and gas production in China. Due to the diversity of reservoirs and the extreme heterogeneity of reef-banks, it is very difficult to discriminate the sedimentary facies and lithologies in reef-bank reservoirs using conventional well logs. The borehole image log provides clear identification of sedimentary structures and textures and is an ideal tool for discriminating sedimentary facies and lithologies. After examining a large number of borehole images and cores, we propose nine typical patterns for borehole image interpretation and a method that uses these patterns to discriminate sedimentary facies and lithologies in reeI^bank reservoirs automatically. We also develop software with user-friendly interface. The results of applications in reef-bank reservoirs in the middle Tarim Basin and northeast Sichuan have proved that the proposed method and the corresponding software are quite effective.
文摘Much study has been done in the study area linking Vertical Electrical Sounding (VES) interpreted results to lithologies in the subsurface though only tend to indicate the vertical changes with the aim of mapping the occurrence of groundwater aquifers. Several boreholes have been drilled in the study area, though not much has been done to compare the vertical and lateral lithologic changes in the study area. This research is based on VES modelled geoelectric layers compared from point to point and using borehole logs as control data to establish inferences of certain lithology in the subsurface. The inversion of each VES curve was obtained using an AGI Earth Imager ID inversion automated computer program and resistivities and thicknesses of a geoelectric model were estimated. The analyzed VES data interpretation achieved using the curve matching technique resulted in mapping the subsurface of the area as portraying H-type;ρ<sub>1</sub> > ρ<sub>2</sub> ρ<sub>3</sub>, K-type;ρ<sub>1</sub> ρ<sub>2</sub> > ρ<sub>3</sub>, A-type;ρ<sub>1</sub> ρ<sub>2</sub> ρ<sub>3</sub>, Q-type;ρ<sub>1</sub> > ρ<sub>2</sub> > ρ<sub>3</sub>, representing 3-Layer subsurface and subsequently a combination of HK, HA and KHK types of curves representing 4-Layer and 5-Layer in the subsurface. The analysis further deployed the use of the surfer software capabilities which combined the VES data to generate profiles running in the west-east and the north-south direction. A closer analysis of the curve types indicates that there exists a sequence showing a shifting of the order of arrangement between the west and the east fragments which incidentally coincides with VES points 8, 9 and 10 in the West-East profiles. The lateral change is noted from the types of curves established and each curve indicates a vertical change in the subsurface. Control log data of lithologies from four boreholes BH1, BH2, BH3 and BH5 to show a qualification that different resistivity values portent different lithologies. Indeed, an analysis at borehole BH3 lithologies is dominated by either compacted rocks or soils, insinuating a scenario of compression experienced in this part of the subsurface which confirmed compression of subsurface formations. A correlation of the VES curve types and their change from one point to another in the study area are evident. This change supported by the surfer generated profiles from the modeled VES data show that there exists and inferred fault line running in the north-south in the area. The inferred fault line by VES mapping, is magnificently outlined by the geological map. There is exuded evidence from this study that the application of VES is able to help map the lateral and the vertical changes in the subsurface of any area but the evidence of the specific lithologies has to be supported by availability of borehole log control data. The VES data was able to enumerate vertical layering of lithologies, lateral changes and even mapping vertical fault line in the study area.
文摘The use of gravity data has demonstrated capability for monitoring lithological changes on a large scale as a consequence of differentiating basement and sedimentary of buried valleys. Gravity anomalies are associated with lateral contrasts in density and therefore deformation by faulting or folding will be manifested if accompanied by lateral density changes, otherwise, the vice versa is true. The study’s objective is to evaluate the effectiveness of gravity method in establishing different lithologies in an area. The study has revealed that regional anomaly gravity map presents high anomalies in the Northern region in the NW-SE trend and low anomalies in the southern trend in NW-SE, while the residual anomaly gravity map shows different trends for the low and high gravity anomalies. The gravity anomalies are well interpreted in line with the lithologies of the study area rather than the deformation of the same lithologies. There are observed high values of gravity anomaly values (ranging from -880.2 to -501.2 g.u.) where there are eolian unconsolidated rocks overlying the basement compared to low gravity anomaly values (ranging from -1338.9 to -1088.7 g.u.) where the andesites, trachytes and phonolites overly the basement. The different regional gravity anomalies relate well with different rock densities in the study area along the line profile for radially averaged power spectrum. The gravity highs are noted in the eastern point and are associated with andesites, trachytes, basalts and igneous rocks, while the gravity lows are associated with sandstone, greywacke, arkose, and eolian unconsolidated rock. The utilization of the information from the Power spectrum analysis demonstrates that the depth to the deepest basement rock is 12.8 km which is in the eastern flank, while the shallowest to the basement of 1.1 km to the western flank.
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(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.
文摘Harrat Lunayyir,a volcanic field in western Saudi Arabia,exhibits diverse geomorphological and topographical features shaped by volcanic,tectonic,and climatic processes.This study integrates field observations,remote sensing,and GIS analysis to investigate the spatial distribution and relationships between volcanic landforms,lava flows,and topographical variation result obtained is a morphological classification of the cinder cones of Harrat Lunayyir,which can be sub-divided into four types:tephra rings,horseshoe-shaped volcanoes,multiple volcanoes and volcanoes without craters.All of these are monogenetic volcanoes,unlike central volcanoes(stratovolcanoes)which live for tens or hundreds of thousands of years and erupt numerous times.In Harrat Lunayyir,there is a clear dominance of arched horseshoe-shaped volcanoes(58)over ring-shaped cinder cones(10),A1_symmetric cones(circular,uniform cinder cones with a single crater)(32),A2_asymmetric cones(elongated,irregular cones and may feature one or more craters)(8),volcanoes without craters(55)and multiple volcanoes(20).The classification presented in this paper makes it possible to include all morphological types of volcanoes found in the region.This fact also renders the present classification a useful tool to apply in other,both insular and continental volcanic areas to eventually analyze and systematize the study of eruptive edifices with similar traits.Hence,this research will explore the standard physical volcanology literature so as to follow accepted definitions.
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.
文摘The Edikan Mine,which consists of Fobinso and Esuajah gold deposits,lies within the Asankrangwa Gold Belt of the Birimian Supergroup in the Kumasi Basin.The metasedimentary rocks in the Basins and the faulted metavolcanic rocks in the Belts that make up the Birimian Supergroup were intruded by granitoids during the Eburnean Orogeny.This research aims to classify granitoids in the Edikan Mine and ascertain the petrogenetic and geochemical characteristics of some auriferous granitoids in the wider Kumasi Basin,Ghana,to understand the implications for geodynamic settings.A multi-methods approach involving field studies,petrographic studies,and whole-rock geochemical analysis was used to achieve the goal of the study.Petrographic studies revealed a relatively high abundance of plagioclase and a low percentage of K-feldspars(anorthoclase and orthoclase)in the Fobinso samples,suggesting that the samples are granodioritic in nature,while the Esuajah samples showed relatively low plagioclase abundance and a high percentage in K-feldspars,indicating that they are granitic.The granitoids from the study areas are co-magmatic.The granitoids in Esuajah and Fobinso are generally enriched in large ion lithophile elements and light rare earth elements than high field strength elements,middle rare earth elements,and heavy rare earth elements,indicating mixing with crustal sources during the evolution of the granitoids.The granitoids were tectonically formed in a syn-collisional+VAG setting,which implies that they were formed in the subduction zone setting.Fobinso granodiorites showed S-type signatures with evidence of extensive crustal contamination,while the Esuajah granites showed I-type signatures with little or no crustal contamination and are peraluminous.Gold mineralization in the study area is structurally and lithologically controlled with shear zones,faulting,and veining as the principal structures controlling the mineralization.The late-stage vein,V3,in the Edikan Mine is characterized by a low vein angle and is mineralized.
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supported by the Construction Industry Council(Grant No.CICR/01/22)the support from the General Research Fund(Grant No.17206822)of the Research Grants Council(Hong Kong).
文摘Rock classification plays a crucial role in various fields such as geology,engineering,and environmental studies.Employing deep learning AI(artificial intelligence)methods has a high potential to significantly improve the accuracy and efficiency of this task.The paper delves into the exploration of two cuttingedge AI techniques,namely Mask DINO and Mask R-CNN(convolutional neural network),as means to identify rock weathering grades and rock types.The results demonstrate that Mask DINO,which is a Detection Transformer(DETR),outperforms Mask R-CNN for the aforementioned purposes.Mask DINO achieved f-1 scores of 91% and 86% in weathering grade detection and rock type detection,as opposed to the Mask R-CNN's f-1 scores of 84% and 75%,respectively.These findings underscore the substantial potential of employing DETR algorithms like Mask DINO for automatic classification of both rock type and weathering states.Although the study examines only two AI models,the data processing and other techniques developed in this study may serve as a foundation for future advancements in the field.By incorporating these advanced AI techniques,logging personnel can obtain valuable references to aid their work,ultimately contributing to the advancement of geological and related fields.
基金supported by the Beijing Natural Science Foundation(Grant No.8252012)the National Natural Science Foundation of China(Grant No.52378475).
文摘Accurate acquisition of the lithological composition of a tunnel face is crucial for efficient tunneling and hazard prevention in large-diameter slurry shield tunnels.While widely applied,current data-driven methods often face challenges such as indirect prediction,data sparsity,and data drift,which limit their accuracy and generalizability.This study develops an integrated method that combines a knowledge-driven method to directly compute distribution patterns of lithological components,which are used as a priori knowledge to guide the development of a data-driven method.Coupled Markov chain(CMC)and deep neural networks(DNNs)serve as the knowledge-driven and data-driven components,respectively.Additionally,a dynamic prediction strategy is proposed,where the model is continuously optimized as construction progresses and training samples accumulate,rather than being statically trained on post-construction data,as is common in data-driven methods.Finally,the proposed method is evaluated using a real-world project.The evaluation results show that the integrated method outperforms both individual data-and knowledge-driven methods,demonstrating higher predictive performance,greater stability,and greater robustness to data scarcity and data drift.Furthermore,the dynamic prediction strategy better captures the effects of gradual data accumulation and lithological spatial variability on prediction performance during construction,providing new insights for real-time prediction in practical tunneling applications.
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funded by SINOPEC Science and Technology Research Program(No.P23206No.P23230).
文摘Coal-measure gas is a primary target with significant potential for the exploration of unconventional hydrocarbon resources.However,the spatiotemporal distribution and combination patterns of multi-type coal-measure gases are yet to be clarified,directly impeding the sweet spot evaluation and exploration deployment of coal-measure gas.This study discussed the characteristics and distribution patterns of coal-measure gases in the Daniudi gas field in northeastern Ordos Basin,China,with abundant drilling data.The results indicate that the coal seams variably thin upward and are mainly seen in the first and second members of the Taiyuan Formation(also referred to as the Tai 1 and Tai 2 members,respectively)and the first member of the Shanxi Formation(Shan 1 Member).Nos.8,5 and 3 coal seams are laterally continuous,and significantly thicker in its southern part compared to the northern part.Moreover,carbonaceous mudstones and shales are better developed in the southern part,where limestones are only observed in the Tai 1 Member.Based on the main lithological types,we identified three lithologic roofs of coal seams,that is,limestone,mudstone,and sandstone,which determine the spatiotemporal distribution of coal-measure gases.Besides bauxite gas in the Benxi Formation,the coal-measure gases include tight-sand gas,coalbed methane(CBM),coal-measure shale gas,and tight-limestone gas,with CBM typically associated with coal-measure shale gas.The combinations of different types of coal-measure gas vary across different layers and regions.Tight-sand gas is well-developed in areas where tight sandstones are in contact with coal-measures.From the Taiyuan to the Shanxi formations,CBM gradually transitions into a combination of CBM and coal-measure shale gas,and coal-measure shale gas.Nos.8 and 5 coal seams in low-lying areas exhibit favorable gas-bearing properties due to their large thickness and favorable roof lithologies,serving as prospective play fairways.Mudstone and limestone roofs are more conducive to achieving good gas-bearing properties.The direct contact between sandstones and coal seams tends to result in the formation of tight-sand gas and a reduced gas content of CBM.While focusing on single types of gas reservoirs such as CBM and tight-sand gas,it is essential to consider the concurrent exploration of various coal-measure gas combinations to discover more additional gas resources and guide exploration deployment.
基金Supported by the National Key Technology R&D Program during the11~(th)Five-years Plan(2006BAD24B0301)~~
文摘Objective The aim was to reveal relationship between lithological character soil and productivity of Cunninghamia lanceolata and lay a foundation for systemic management of C. lanceolata fast-growing and high yield plantation. Method By using experimental ecology method and variance analysis, the biomass and growth of planting eleven years＇ C. lanceolata on the soils with six different lithologicel characters were studied. Result The effects of soils with six different lithological characters on the height, diameter growth and biomass of C. lanceolata were different, in which the growth order of C. lanceolata was： Feldspathic quartzy sandstone （ average height 523. 270 cm, average diameter 4.720 cm, average individual biomass 5.059kg） 〉 Basalt （ average height 511. 570 cm, average diameter 4.650 cm, average individual biomass 4.848 kg） 〉 Quartzy sandstone 〉 Blastopsammite 〉 The Quarternary Period red clay 〉 Coal-series siliceous sand-shale, and the difference was smaller between the last two lithological characters. Conclusion Feldspathic quartzy sandstone and Basalt are beneficial to C. lanceolata.
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.
文摘The rapid changing near source, multi-stream depositional environment of conglomerate reservoirs leads to severe heterogeneity, complex lithology and physical properties, and large changes of oil layer resistivity. Quantitative evaluation of water-flooded layers has become an important but difficult focus for secondary development of oilfields. In this paper, based on the analysis of current problems in quantitative evaluation of water-flooded layers, the Kexia Group conglomerate reservoir of the Sixth District in the Karamay Oilfield was studied. Eight types of conglomerate reservoir lithology were identified effectively by a data mining method combined with the data from sealed coring wells, and then a multi-parameter model for quantitative evaluation of the water-flooded layers of the main oil-bearing lithology was developed. Water production rate, oil saturation and oil productivity index were selected as the characteristic parameters for quantitative evaluation of water-flooded layers of conglomerate reservoirs. Finally, quantitative evaluation criteria and identification rules for water-flooded layers of main oil-bearing lithology formed by integration of the three characteristic parameters of water-flooded layer and undisturbed formation resistivity. This method has been used in evaluation of the water-flooded layers of a conglomerate reservoir in the Karamay Oilfield and achieved good results, improving the interpretation accuracy and compliance rate. It will provide technical support for avoiding perforation of high water-bearing layers and for adjustment of developmental programs.
基金sponsored by the National S&T Major Special Project(No.2011ZX05020-008)
文摘Calculating the mineral composition is a critical task in log interpretation. Elementalcapture spectroscopy （ECS） log provides the weight percentages of twelve common elements,which lays the foundation for the accurate calculation of mineral compositions. Previousprocessing methods calculated the formation composition via the conversion relation betweenthe formation chemistry and minerals. Thus, their applicability is limited and the methodprecision is relatively low. In this study, we present a multimineral optimization processingmethod based on the ECS log. We derived the ECS response equations for calculating theformation composition, then, determined the logging response values for the elements ofcommon minerals using core data and theoretical calculations. Finally, a software modulewas developed. The results of the new method are consistent with core data and the meanabsolute error is less than 10%.
