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
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 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
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 petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on ...In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on machine learning and mud logging data is studied in this paper.This method can effectively utilize downhole parameters collected in real-time during drilling,to identify lithology in real-time and provide a reference for optimization of drilling parameters.Given the imbalance of lithology samples,the synthetic minority over-sampling technique(SMOTE)and Tomek link were used to balance the sample number of five lithologies.Meanwhile,this paper introduces Tent map,random opposition-based learning and dynamic perceived probability to the original crow search algorithm(CSA),and establishes an improved crow search algorithm(ICSA).In this paper,ICSA is used to optimize the hyperparameter combination of random forest(RF),extremely random trees(ET),extreme gradient boosting(XGB),and light gradient boosting machine(LGBM)models.In addition,this study combines the recognition advantages of the four models.The accuracy of lithology identification by the weighted average probability model reaches 0.877.The study of this paper realizes high-precision real-time lithology identification method,which can provide lithology reference for the drilling process.展开更多
Mississippi Valley-type(MVT) Zn-Pb deposits predominantly form within both orogenic forelands and fold-andthrust belts, yet the mineralization process within the latter tectonic setting remains inadequately understood...Mississippi Valley-type(MVT) Zn-Pb deposits predominantly form within both orogenic forelands and fold-andthrust belts, yet the mineralization process within the latter tectonic setting remains inadequately understood. This study, through a comprehensive review of MVT deposits across global fold-and-thrust belts, introduces a novel model elucidating the mineralization process in the context of tectonic belt evolution. It is demonstrated that during the stage Ⅰ, regional compression is introduced by early stages of plate convergence, causing the folding and thrusting and creating structural or lithological traps such as evaporite diapirs and unconformity-related carbonate dissolution-collapse structures. Thereafter, in stage Ⅱ, hydrocarbons begin to migrate and accumulate within these traps, where reduced sulfur is generated through thermochemical or bacterial sulfate reduction concurrent with or preceding Zn-Pb mineralization. In the subsequent stage Ⅲ, as plate convergence persists, the regional stress transitions from compression to transpression or extension. Under these conditions, steeply-dipping extensional faults are generated, facilitating the ascent of metalliferous brines into early-formed structural or lithological traps. Precipitation of Zn and Pb sulfides occurs through the mixing of Zn-Pb-transporting fluids with pre-existing reduced sulfur or by interaction with hydrocarbons.展开更多
Geomechanical assessment using coupled reservoir-geomechanical simulation is becoming increasingly important for analyzing the potential geomechanical risks in subsurface geological developments.However,a robust and e...Geomechanical assessment using coupled reservoir-geomechanical simulation is becoming increasingly important for analyzing the potential geomechanical risks in subsurface geological developments.However,a robust and efficient geomechanical upscaling technique for heterogeneous geological reservoirs is lacking to advance the applications of three-dimensional(3D)reservoir-scale geomechanical simulation considering detailed geological heterogeneities.Here,we develop convolutional neural network(CNN)proxies that reproduce the anisotropic nonlinear geomechanical response caused by lithological heterogeneity,and compute upscaled geomechanical properties from CNN proxies.The CNN proxies are trained using a large dataset of randomly generated spatially correlated sand-shale realizations as inputs and simulation results of their macroscopic geomechanical response as outputs.The trained CNN models can provide the upscaled shear strength(R^(2)>0.949),stress-strain behavior(R^(2)>0.925),and volumetric strain changes(R^(2)>0.958)that highly agree with the numerical simulation results while saving over two orders of magnitude of computational time.This is a major advantage in computing the upscaled geomechanical properties directly from geological realizations without the need to perform local numerical simulations to obtain the geomechanical response.The proposed CNN proxybased upscaling technique has the ability to(1)bridge the gap between the fine-scale geocellular models considering geological uncertainties and computationally efficient geomechanical models used to assess the geomechanical risks of large-scale subsurface development,and(2)improve the efficiency of numerical upscaling techniques that rely on local numerical simulations,leading to significantly increased computational time for uncertainty quantification using numerous geological realizations.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
Identification of stratigraphic interfaces and lithology is a key aspect in geological and geotechnical investigations.In this study,a monitoring while-drilling system was developed,along with a corresponding data pre...Identification of stratigraphic interfaces and lithology is a key aspect in geological and geotechnical investigations.In this study,a monitoring while-drilling system was developed,along with a corresponding data pre-processing method.The method can handle invalid drilling data generated during manual operations.The correlation between various drilling parameters was analyzed,and a database of stratigraphic interfaces and key lithology identification based on the monitoring parameters was established.The average drilling speed was found to be the most suitable parameter for stratigraphic and lithology identification,and when the average drilling speed varied over a wide range,it corresponded to a stratigraphic interface.The average drilling speeds in sandy mudstone and sandstone strata were in the ranges of 0.1e0.2 m/min and 0.2e0.29 m/min,respectively.The results obtained using the present method were consistent with geotechnical survey results.The proposed method can be used for realtime lithology identification and represents a novel approach for intelligent geotechnical surveying.展开更多
There are abundant igneous gas reservoirs in the South China Sea with significant value of research,and lithology classification,mineral analysis and porosity inversion are important links in reservoir evaluation.Howe...There are abundant igneous gas reservoirs in the South China Sea with significant value of research,and lithology classification,mineral analysis and porosity inversion are important links in reservoir evaluation.However,affected by the diverse lithology,complicated mineral and widespread alteration,conventional logging lithology classification and mineral inversion become considerably difficult.At the same time,owing to the limitation of the wireline log response equation,the quantity and accuracy of minerals can hardly meet the exploration requirements of igneous formations.To overcome those issues,this study takes the South China Sea as an example,and combines multi-scale data such as micro rock slices,petrophysical experiments,wireline log and element cutting log to establish a set of joint inversion methods for minerals and porosity of altered igneous rocks.Specifically,we define the lithology and mineral characteristics through core slices and mineral data,and establish an igneous multi-mineral volumetric model.Then we determine element cutting log correction method based on core element data,and combine wireline log and corrected element cutting log to perform the lithology classification and joint inversion of minerals and porosity.However,it is always difficult to determine the elemental eigenvalues of different minerals in inversion.This paper uses multiple linear regression methods to solve this problem.Finally,an integrated inversion technique for altered igneous formations was developed.The results show that the corrected element cutting log are in good agreement with the core element data,and the mineral and porosity results obtained from the joint inversion based on the wireline log and corrected element cutting log are also in good agreement with the core data from X-ray diffraction.The results demonstrate that the inversion technique is applicable and this study provides a new direction for the mineral inversion research of altered igneous formations.展开更多
Recently, a new promising type of marine shale gas reservoir, carbonate-rich shale, has been discovered.But the mechanical properties of this type of shale were still unrevealed and the corresponding reservoir stimula...Recently, a new promising type of marine shale gas reservoir, carbonate-rich shale, has been discovered.But the mechanical properties of this type of shale were still unrevealed and the corresponding reservoir stimulation design was lack of guidance. Using the deep downhole cores of an exploratory carbonate-rich shale gas well, the physical and mechanical parameters and failure mechanism of the whole reservoir section were acquired and evaluated systematically, by performing XRD, tri-axial compression, Brazilian splitting, and fracture toughness tests. A new model was established to evaluate the reservoir brittleness based on fracture morphology and stress-strain curve. Recommended strategy for reservoir stimulation was discussed. Results showed that(1) Carbonate-rich shale possessed high compressive strength and high Young's modulus, which were improved by 10.74% and 3.37% compared to that of siliceous shale. It featured high tensile strength and fracture toughness, with insignificant anisotropy.(2) With the content of carbonate minerals increasing, the shear failure morphology transformed from sparse and wide brittle fractures to diffusely distributed and subtle plastic cracks.(3) The brittleness index order was: siliceous shale, clay-rich shale, carbonate-rich shale, and limestone.(4) The special properties of carbonate-rich shale were rooted in the inherent feature of carbonate minerals(high strength, high elastic modulus,and cleavage structure), resulting in greater challenge in reservoirs stimulation. The above findings would promote the understanding of carbonate-rich shale reservoirs and provide reference for the optimum design of reservoir stimulation.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.
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.
基金supported by CNPC-CZU Innovation Alliancesupported by the Program of Polar Drilling Environmental Protection and Waste Treatment Technology (2022YFC2806403)。
文摘In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on machine learning and mud logging data is studied in this paper.This method can effectively utilize downhole parameters collected in real-time during drilling,to identify lithology in real-time and provide a reference for optimization of drilling parameters.Given the imbalance of lithology samples,the synthetic minority over-sampling technique(SMOTE)and Tomek link were used to balance the sample number of five lithologies.Meanwhile,this paper introduces Tent map,random opposition-based learning and dynamic perceived probability to the original crow search algorithm(CSA),and establishes an improved crow search algorithm(ICSA).In this paper,ICSA is used to optimize the hyperparameter combination of random forest(RF),extremely random trees(ET),extreme gradient boosting(XGB),and light gradient boosting machine(LGBM)models.In addition,this study combines the recognition advantages of the four models.The accuracy of lithology identification by the weighted average probability model reaches 0.877.The study of this paper realizes high-precision real-time lithology identification method,which can provide lithology reference for the drilling process.
基金funded by National Natural Science Foundation of China (Grant Nos. 42125204, 92155305, 42103068, 42372114, 42372115)。
文摘Mississippi Valley-type(MVT) Zn-Pb deposits predominantly form within both orogenic forelands and fold-andthrust belts, yet the mineralization process within the latter tectonic setting remains inadequately understood. This study, through a comprehensive review of MVT deposits across global fold-and-thrust belts, introduces a novel model elucidating the mineralization process in the context of tectonic belt evolution. It is demonstrated that during the stage Ⅰ, regional compression is introduced by early stages of plate convergence, causing the folding and thrusting and creating structural or lithological traps such as evaporite diapirs and unconformity-related carbonate dissolution-collapse structures. Thereafter, in stage Ⅱ, hydrocarbons begin to migrate and accumulate within these traps, where reduced sulfur is generated through thermochemical or bacterial sulfate reduction concurrent with or preceding Zn-Pb mineralization. In the subsequent stage Ⅲ, as plate convergence persists, the regional stress transitions from compression to transpression or extension. Under these conditions, steeply-dipping extensional faults are generated, facilitating the ascent of metalliferous brines into early-formed structural or lithological traps. Precipitation of Zn and Pb sulfides occurs through the mixing of Zn-Pb-transporting fluids with pre-existing reduced sulfur or by interaction with hydrocarbons.
基金financial support provided by the Future Energy System at University of Alberta and NSERC Discovery Grant RGPIN-2023-04084。
文摘Geomechanical assessment using coupled reservoir-geomechanical simulation is becoming increasingly important for analyzing the potential geomechanical risks in subsurface geological developments.However,a robust and efficient geomechanical upscaling technique for heterogeneous geological reservoirs is lacking to advance the applications of three-dimensional(3D)reservoir-scale geomechanical simulation considering detailed geological heterogeneities.Here,we develop convolutional neural network(CNN)proxies that reproduce the anisotropic nonlinear geomechanical response caused by lithological heterogeneity,and compute upscaled geomechanical properties from CNN proxies.The CNN proxies are trained using a large dataset of randomly generated spatially correlated sand-shale realizations as inputs and simulation results of their macroscopic geomechanical response as outputs.The trained CNN models can provide the upscaled shear strength(R^(2)>0.949),stress-strain behavior(R^(2)>0.925),and volumetric strain changes(R^(2)>0.958)that highly agree with the numerical simulation results while saving over two orders of magnitude of computational time.This is a major advantage in computing the upscaled geomechanical properties directly from geological realizations without the need to perform local numerical simulations to obtain the geomechanical response.The proposed CNN proxybased upscaling technique has the ability to(1)bridge the gap between the fine-scale geocellular models considering geological uncertainties and computationally efficient geomechanical models used to assess the geomechanical risks of large-scale subsurface development,and(2)improve the efficiency of numerical upscaling techniques that rely on local numerical simulations,leading to significantly increased computational time for uncertainty quantification using numerous geological realizations.
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.
文摘Identification of stratigraphic interfaces and lithology is a key aspect in geological and geotechnical investigations.In this study,a monitoring while-drilling system was developed,along with a corresponding data pre-processing method.The method can handle invalid drilling data generated during manual operations.The correlation between various drilling parameters was analyzed,and a database of stratigraphic interfaces and key lithology identification based on the monitoring parameters was established.The average drilling speed was found to be the most suitable parameter for stratigraphic and lithology identification,and when the average drilling speed varied over a wide range,it corresponded to a stratigraphic interface.The average drilling speeds in sandy mudstone and sandstone strata were in the ranges of 0.1e0.2 m/min and 0.2e0.29 m/min,respectively.The results obtained using the present method were consistent with geotechnical survey results.The proposed method can be used for realtime lithology identification and represents a novel approach for intelligent geotechnical surveying.
基金The project was supported by the National Natural Science Foundation of China(Grant No.42204122).
文摘There are abundant igneous gas reservoirs in the South China Sea with significant value of research,and lithology classification,mineral analysis and porosity inversion are important links in reservoir evaluation.However,affected by the diverse lithology,complicated mineral and widespread alteration,conventional logging lithology classification and mineral inversion become considerably difficult.At the same time,owing to the limitation of the wireline log response equation,the quantity and accuracy of minerals can hardly meet the exploration requirements of igneous formations.To overcome those issues,this study takes the South China Sea as an example,and combines multi-scale data such as micro rock slices,petrophysical experiments,wireline log and element cutting log to establish a set of joint inversion methods for minerals and porosity of altered igneous rocks.Specifically,we define the lithology and mineral characteristics through core slices and mineral data,and establish an igneous multi-mineral volumetric model.Then we determine element cutting log correction method based on core element data,and combine wireline log and corrected element cutting log to perform the lithology classification and joint inversion of minerals and porosity.However,it is always difficult to determine the elemental eigenvalues of different minerals in inversion.This paper uses multiple linear regression methods to solve this problem.Finally,an integrated inversion technique for altered igneous formations was developed.The results show that the corrected element cutting log are in good agreement with the core element data,and the mineral and porosity results obtained from the joint inversion based on the wireline log and corrected element cutting log are also in good agreement with the core data from X-ray diffraction.The results demonstrate that the inversion technique is applicable and this study provides a new direction for the mineral inversion research of altered igneous formations.
基金sponsored by the “National Natural Science Foundation of China” (No. U22B6003), (No. 52104010), and (No. 52104046)。
文摘Recently, a new promising type of marine shale gas reservoir, carbonate-rich shale, has been discovered.But the mechanical properties of this type of shale were still unrevealed and the corresponding reservoir stimulation design was lack of guidance. Using the deep downhole cores of an exploratory carbonate-rich shale gas well, the physical and mechanical parameters and failure mechanism of the whole reservoir section were acquired and evaluated systematically, by performing XRD, tri-axial compression, Brazilian splitting, and fracture toughness tests. A new model was established to evaluate the reservoir brittleness based on fracture morphology and stress-strain curve. Recommended strategy for reservoir stimulation was discussed. Results showed that(1) Carbonate-rich shale possessed high compressive strength and high Young's modulus, which were improved by 10.74% and 3.37% compared to that of siliceous shale. It featured high tensile strength and fracture toughness, with insignificant anisotropy.(2) With the content of carbonate minerals increasing, the shear failure morphology transformed from sparse and wide brittle fractures to diffusely distributed and subtle plastic cracks.(3) The brittleness index order was: siliceous shale, clay-rich shale, carbonate-rich shale, and limestone.(4) The special properties of carbonate-rich shale were rooted in the inherent feature of carbonate minerals(high strength, high elastic modulus,and cleavage structure), resulting in greater challenge in reservoirs stimulation. The above findings would promote the understanding of carbonate-rich shale reservoirs and provide reference for the optimum design of reservoir stimulation.
