449 results about "Mudrock" patented technology

The invention relates to the technical field of underground cavern construction, and discloses a layered rock mass cavern excavation parameter dynamic optimization decision system and method, and the system comprises a rock stratum database building module, an excavation simulation module and an excavation model optimization module. The method comprises the following steps: collecting basic data such as engineering geological conditions, rock mechanical properties and ground stress distribution; establishing an initial rock stratum parameter database through a three-dimensional ground stress test and rock mass permeability characteristic analysis; establishing a stratified rock mass excavation simulation model, simulating a failure mode of a stratified rock stratum under excavation disturbance, and analyzing stress redistribution and a potential failure area; determining a cavern axis direction, an excavation sequence and support parameters according to a simulation result; acquiring surrounding rock deformation, loosening circle expansion and seepage characteristic change; and utilizing a damage proximity index to predict the distribution of the loose circles, and optimizing the stratified rock mass excavation simulation model. The method can adapt to various complex working conditions from a sand shale interbed to an ultra-deep fractured stratum and the like.

The invention relates to an intelligent stratum lithology identification method based on logging data. The method comprises the following steps: collecting the logging data; preprocessing the data; extracting features; an LSTM-CRF model is adopted, training set data serve as input of the LSTM model, the CRF model is applied to output of the LSTM model, and stratum lithology is output; and evaluating the model. The method combines the long-term dependence of the recurrent neural network and a deep learning model of the sequence labeling capability of the CRF, and deduces the formation lithology of clay, sandstone, glutenite, limestone, mudstone and dolomite through logging data of natural gamma, sound wave, resistivity and the like. According to the method, the features are automatically extracted from the logging data through a machine learning algorithm, and the lithology identification is carried out by using the features, so that the identification accuracy can be effectively improved; the method does not depend on geological conditions of a specific region, is suitable for logging data of different regions, and has wide applicability.

The invention relates to the technical field of geological condition monitoring, in particular to a sand shale settlement monitoring system and method.The sand shale settlement monitoring system comprises an FLAC3D simulation assembly, a monitoring module, a data fusion module, an early warning module and a comparison module, various monitoring data of a sand shale stratum are collected in real time through the monitoring module, and the timeliness and accuracy of the data are ensured. And the data fusion module performs fusion processing on various monitoring data, so that the representativeness and effectiveness of the data are improved, and a future settlement trend and potential risks are predicted by using an FLAC3D simulation component. The early warning module timely notifies a related person in charge, and the comparison module compares and verifies a prediction result with actual monitoring data, evaluates prediction accuracy and optimizes model parameters. By integrating a plurality of monitoring means, a data fusion technology and a numerical simulation analysis method, real-time monitoring, accurate prediction and timely early warning of sand shale stratum settlement are realized, and the comprehensiveness and accuracy of prediction are improved.

The invention provides an analysis method for multi-cluster fracturing crossing in a horizontal well section of a sand shale reservoir, which comprises the following steps of: based on a boundary element theory, considering three-dimensional fracture stress interference and flow distribution of each cluster, and establishing a full-three-dimensional fracture propagation numerical model of segmented multi-cluster fracturing stress interference and flow distribution of a tight gas horizontal well; coupling the wellbore flow model and the crack propagation numerical model through bottom hole pressure to obtain a coupling model; based on a three-dimensional boundary element theory, aiming at the coupling model, discretizing different fracture forms into rectangular units, and establishing a three-dimensional boundary element stress interference calculation model through a superposition principle; under the condition that geological factors are kept constant, the influence of different construction parameters on crack propagation and layer crossing behaviors is analyzed, a crack layer crossing main control factor is obtained, and the conditions and boundaries of segmented multi-cluster fracturing layer crossing of the sand-mud interbed horizontal well are determined. The method provides a basis for optimization of crack parameters and construction parameters.

The invention relates to the technical field of drilling equipment, in particular to an impact composite drill bit which comprises a drill bit body, a rotating shaft, a center wheel and main blades, a mounting groove with an upward opening is formed in the center of the upper end of the drill bit body, the horizontally-arranged rotating shaft is rotatably mounted in the mounting groove, and the center wheel is fixedly mounted on the outer side of the middle of the rotating shaft. The impact composite drill bit is reasonable and compact in structure, during use, the lower end of the impact composite drill bit is connected with a pipe string and then descends into a target position in a well, when the drill bit body makes contact with a rock stratum, the center wheel starts to rotate, the section of the center wheel is an arc triangle, and when the center wheel rotates, the high point and the low point of the section triangle of the center wheel are continuously switched; the main cutting teeth on the main cutter wings can impact the rock stratum along with conversion of the high point and the low point, the rock stratum crushing effect is achieved, the working efficiency of the impact composite drill bit is improved, the friction force in the center of the impact composite drill bit can be reduced when drilling is conducted in the mudstone stratum, and therefore the drilling efficiency is improved.

The invention relates to the technical field of geological sequestration, in particular to a method and a system for predicting breakthrough pressure of a CO2 geological sequestration mudstone cover layer. The prediction method comprises the following steps: acquiring a mercury injection curve of the CO2 geological sequestration mudstone cover layer; obtaining a calibrated mercury injection curve by using the mercury injection curve; based on the calibrated mercury injection curve, a relation curve of the porosity and the mercury injection pressure is obtained; obtaining a predicted value of the breakthrough pressure through the relation curve; and predicting the breakthrough pressure of the CO2 geological sequestration mudstone cover layer according to the predicted value. On the basis of deep research and wide coverage of characteristics of various mudstone samples, a scientific and reasonable prediction model is established, and low-cost, high-aging and high-precision prediction of the breakthrough pressure of the mudstone cover layer is realized.

The invention discloses a method for predicting a favorable area of a thin-layer stacked sand body, and relates to the technical field of exploration and development of oil and gas reservoirs, and the method comprises the steps: generating a synthetic seismic record through well-seismic combination by using interval transit time and a density curve, aligning the synthetic seismic record with an actual seismic record, and building a three-dimensional frame model of a target layer of a research area; performing abnormal value elimination and standardization processing on the logging curve, making a statistical histogram, comparing peak separation degrees of sandstone and mudstone, and selecting the most sensitive curve to distinguish the sandstone and mudstone; and realizing well-side seismic trace waveform dynamic clustering analysis through singular value decomposition, and determining the number of effective samples. Based on the result of waveform indication simulation, the method is combined with the optimized seismic attribute to predict the thin-layer stacked sand body together, a systematic method is provided, the multiplicity of solutions of predicting the thin-layer stacked favorable sand body through the seismic attribute and the uncertainty of depicting the sand body by the sedimentary facies in the area with few wells can be reduced, and the prediction accuracy of the thin-layer stacked favorable sand body is improved. Therefore, the precision and reliability of thin-layer stacked sand body prediction are improved.

The invention provides a geophysical recognition method for medium-deep under-compacted low-speed mudstone, which comprises the following steps of: obtaining representative longitudinal wave velocity, transverse wave velocity and density of low-speed mudstone and gas-bearing sandstone in logging information through statistical analysis of data, and designing a theoretical model of a superposition relationship between the low-speed mudstone and the gas-bearing sandstone in combination with geological information, so as to obtain the geophysical recognition result of the low-speed mudstone and the gas-bearing sandstone. Performing convolution forward modeling and pre-stack forward modeling on the model, respectively calculating to obtain post-stack seismic attributes and pre-stack seismic attributes of the model, performing sensitive attribute optimization on the attributes, taking the obtained sensitive attributes as learning samples, and identifying the superposition relationship by using probability self-organizing neural network clustering. According to the method, the pre-stack attributes are added into sensitive attribute optimization, so that the fuzzy self-organizing neural network can more accurately recognize the gas-bearing sandstone and low-speed mudstone stacking relation, the low-speed mudstone and the gas-bearing sandstone can be effectively distinguished, and support is provided for subsequent effective reservoir prediction.

The invention belongs to the technical field of bottom plate mudstone treatment, and particularly relates to a rapid reinforcing material and method for bottom plate mudstone. The rapid reinforcing material for the bottom plate mudstone comprises the following components in parts by mass: a material A: 40-60 parts of sulphoaluminate cement, 15-40 parts of Portland cement and 1-2 parts of a naphthalene water reducer, and further comprises an initiator, a cross-linking agent and a redox agent; and a material B: 80-100 parts of gypsum, 10-30 parts of acrylamide and 2-4 parts of an aluminate coupling agent. According to the rapid reinforcing material for the bottom plate mudstone, the on-site argillization bottom plate can be hardened on site, a bottom plate hardening layer meeting the bearing condition is formed, the labor cost and the time cost caused by cleaning of bottom plate argillization substances are reduced, the passing smoothness is guaranteed, the project progress is accelerated, and the project benefits and the economic benefits are improved.

The invention discloses a mudstone lateral expansive force tester and a mudstone roadbed deformation calculation method, the mudstone lateral expansive force tester comprises a fixing system and a measuring system, and the mudstone roadbed deformation calculation method comprises the step of calculating mudstone vertical elastic extrusion deformation and mudstone center soil body bending deformation. The mudstone lateral expansive force tester disclosed by the invention can quickly measure the lateral expansive force of mudstone under humidification deformation, the structure is simple, the applicability is wide, and when the mudstone roadbed deformation is calculated based on the lateral expansive force, the calculation error can be further reduced, and the actual expansive deformation can be accurately estimated.

The invention discloses a neural network-based coal mine drainage and drainage quantity prediction and drill site optimization design method, which comprises the following steps of: deeply analyzing stratum characteristics such as sand shale proportion and lithology category by introducing a space occurrence characteristic quantitative model and a convolutional neural network; historical drainage water volume data and real-time monitoring information are dynamically learned in combination with the bidirectional long-short-term memory network, and the prediction precision is remarkably improved. Meanwhile, a drill site adaptive optimization module is constructed based on a reinforcement learning algorithm, and the drill hole spacing, depth and layout mode are automatically generated according to a prediction result. According to the method, sand shale category division and space occurrence characteristics are incorporated into a neural network input layer, and the problem that lithology heterogeneity modeling is insufficient in a traditional method is solved; and through dynamic coupling prediction and design links, real-time optimization of drilling parameters is realized, and the universality and flexibility of the technology are remarkably improved. The invention provides an efficient, accurate and economical technical means for coal mine water disaster prevention and control.

The embodiment of the invention provides a mudstone interlayer prediction method and device, electronic equipment and a storage medium. The method comprises the steps of performing high-resolution frequency extraction processing on pre-stack sub-angle gather data; pre-stack seismic elastic inversion of well logging constraint is carried out by using high longitudinal and transverse resolutions of well logging and earthquakes; determining independent and sensitive reservoir elastic parameters of the reservoir predicted by the mudstone interlayer; the selected reservoir elastic parameters serve as independent variables, a mudstone factor recognition function of the mudstone interlayer is constructed, and a calculation model of the mudstone factor recognition function of the mudstone interlayer is established; and according to the established mudstone factor identification function model and the selected reservoir elastic parameter inversion data as variable parameters, a division standard is established, and mudstone interlayer prediction is completed. According to the method, the earthquake identification capability of the mudstone interlayer can be improved, the accuracy of turbidite sandstone earthquake prediction can be improved, and the method has great application values for analyzing the connectivity of oil reservoir sand bodies, optimizing an oil reservoir development scheme and improving the oil reservoir recovery efficiency.

The invention relates to the technical field of oil-gas exploration and development, in particular to a block formation pressure prediction system and method based on three-dimensional seismic information. Comprising a data acquisition module, a depth domain conversion module, a three-dimensional grid chemical engineering area modeling module, a dynamic lithology quantity version generation module, a thinning survey line compaction modeling module, a three-dimensional pressure field calculation module and a dynamic regulation and control module. The global space coverage is realized by constructing a three-dimensional grid model with a well position mark, and the limitation of two-dimensional data is eliminated; pure mudstone node data is processed by adopting a thinning measuring line strategy controlled by structural features, so that the calculation load is remarkably reduced; a reliable data source is screened based on a dynamic lithology probability threshold, and the pressure prediction precision is improved in combination with a sandstone region lithology correction coefficient; the prediction result is verified in a closed-loop mode through the real-time drilling DC index, and a control instruction is triggered. According to the method, the technical bottlenecks of insufficient space coverage, low calculation efficiency and large prediction deviation of the complex geological structure area are effectively solved.

The invention provides a coarse-grained soil strength prediction method considering permeation erosion and mudstone fine grain content, and belongs to the technical field of soil strength prediction. Aiming at the defects in the prior art, the influence of penetration erosion on the shearing performance of coarse-grained soil with different mudstone fine grain contents is considered, and the siliceous mudstone coarse-grained soil strength prediction model considering the penetration erosion effect is constructed, so that the strength prediction precision can be improved; therefore, a better guiding effect is provided for design, construction and operation maintenance of the coarse-grained soil highway subgrade in the mountainous area.

The invention discloses a tunnel blasting vibration prediction method suitable for a sand shale interbed area, and mainly relates to the technical field of tunnel blasting. Comprising the following steps: S1, determining key factors influencing the vibration velocity; s2, building a blasting vibration prediction model in the sand shale interbed area; s3, carrying out data normalization processing; s4, introducing a BP neural network to train the prediction model; s5, introducing a particle swarm optimization algorithm and initializing related parameters of a particle swarm; s6, improving a particle swarm optimization algorithm; s7, a sand shale interbed tunnel blasting vibration prediction model of the improved particle swarm optimization BP neural network is obtained, the model is combined with blasting characteristic parameters of specific sand shale interbed area tunnel engineering for prediction, and a blasting effect prediction result is obtained; according to the method, important theoretical guidance can be provided for blasting vibration prediction of the stratified structure with changeable rock strata, and a practical safety evaluation tool is provided for ground surface vibration velocity control of similar projects.

The present application provides a method, device, equipment, medium and product for well logging environment correction, belonging to the technical field of oil and gas exploration and development. The technical solution provided by the embodiments of the present application differentiates shale sections and sandstone sections according to the depth curve of well logging and the corresponding first spontaneous potential curve; based on the borehole diameter curve of the collapsed section of the shale section, a first density change relationship is obtained to correct the density curve of the entire well section; on the basis of the above correction, based on the porosity curve of the sandstone section, a second density change relationship is obtained to correct the density curve of the sandstone section; by combining the curves corrected twice, overall correction of the entire well section and key correction of the sandstone section are realized, achieving accurate correction of the density curves of sandstone and shale in the case of poor quality of conventional well logging curves, large-area borehole diameter collapse, no standard layer and standard curves, thereby effectively improving the quality of well logging data.

The invention discloses a horizontal salt cavity circulating hydrogen storage and release whole process representation numerical simulation method which comprises the following steps: constructing an initial stratum model which comprises a mudstone upper layer, a rock salt interlayer and a mudstone lower layer; an ellipsoid is embedded in the initial stratum model, and a one-fourth stratum model with simplified calculation is obtained; adding a physical field module to the 1 / 4 stratum model to obtain an optimal stratum model; wherein the multi-physics field module comprises a solid mechanics physics field module, a Darcy law physics field module and a porous medium heat transfer physics field module; the optimal stratum model is subjected to grid division, the initial ground stress of the divided optimal stratum model is calculated firstly, then multi-field coupling data after the initial ground stress is added are calculated, and the multi-field coupling data comprise a temperature field, a stress field and a displacement field. The design capacity of the reservoir can be effectively increased, and the stable leakproofness of the reservoir is ensured by improving the structural form of the gas storage.

The invention discloses a cementing wall-fixing agent while drilling as well as a preparation method and an application process thereof, belongs to the technical field of oilfield chemistry, and particularly relates to preparation of the cementing wall-fixing agent while drilling. The preparation method of the while-drilling cementing wall-fixing agent comprises the step of uniformly mixing and stirring the thermoplastic resin, the curing agent and the inorganic powder according to the proportion at normal temperature and normal pressure. The wall-fixing agent material is simple and safe in production process, low in material cost and convenient for large-scale production and application, and the product can block pores, reduce the filter loss, bond sand shale strata and improve the pressure-bearing capacity of the strata, so that the aim of stabilizing the well wall is fulfilled. The invention further provides an application process of the while-drilling cementing wall-fixing agent. The application process comprises an applicable drilling fluid system and dosage and construction processes under different formation porosities and different working conditions.

The present invention provides a geophysical identification method for mid-deep undercompacted low-velocity mudstone. By statistically analyzing data, representative P-wave velocity, S-wave velocity, and density of low-velocity mudstone and gas-bearing sandstone in well logging data are obtained. Combining geological information, a theoretical model of the superimposed relationship between low-velocity mudstone and gas-bearing sandstone is designed, and convolution forward modeling and prestack forward modeling are carried out on it. The post-stack and prestack seismic attributes of the model are calculated respectively. Then, sensitive attributes are optimized from these attributes, and the obtained sensitive attributes are used as learning samples, and the superimposed relationship is identified by probability self-organizing neural network clustering. The present invention adds prestack attributes to the optimization of sensitive attributes, making the fuzzy self-organizing neural network more accurate in identifying the superimposed relationship between gas-bearing sandstone and low-velocity mudstone, effectively distinguishing low-velocity mudstone from gas-bearing sandstone, and providing support for subsequent effective reservoir prediction.

The invention provides a data-driven sand shale rock lithology prediction and uncertainty estimation method, and the method comprises the steps: 1, carrying out the rock physical analysis, and selecting a proper rock physical model; 2, a fusion method of data generated by forward modeling of the rock physical model and actual data under the physical property parameter disturbance condition is researched, and an elastic parameter data set is generated to serve as an expanded simulation training sample; 3, establishing a statistical rock physical mapping relation between reservoir sand shale and elastic parameters; and step 4, the improved deep neural network no longer outputs the lithology result but outputs the lithology probability. According to the data-driven sand shale mass lithology prediction and uncertainty estimation method, a more accurate sand shale prediction model is established by using a deep learning technology, the probability of a sand shale prediction result is given, and finally a fine statistical physical model for sand shale stratum lithology body prediction and uncertainty estimation is obtained.

The invention discloses a high-resolution seismic sequence division method for a river channel sand group, and relates to the technical field of petroleum and natural gas exploration and development. According to the method, a high-resolution sequence stratigraphy theory is taken as guidance, a flooding mudstone interface is taken as a marker bed, a high-density isochronous slice highly matched with seismic data is obtained, and an isochronous stratigraphic interface identification mark is constructed according to sand body development characteristics, so that the multiplicity of solution division of fluvial facies sand group seismic sequences is effectively reduced. According to the method, on the basis of isochronous stratigraphic division, the division scheme is adjusted according to the actual seismic homonymous axis form and the characteristics of the sand body on the well, the unit division result is more objective and fine, and therefore the method can be widely applied to seismic stratigraphic division of the fluvial facies sand group.

The present invention discloses a method and system for identifying low-velocity mudstone based on attribute clustering. It includes: by analyzing the logging data and seismic data of exploration wells in the work area, dividing the low-velocity mudstone into bright spot type and non-bright spot type, and analyzing their characteristics. Establishing a three-layer medium model and conducting seismic forward modeling to obtain the seismic response characteristics of different models. Then, extracting and optimizing seismic attributes from the forward modeling results. Using the CLARA clustering algorithm and the PAM method to conduct clustering analysis on the attributes to identify the types of low-velocity mudstone and gas-bearing sandstone. The present invention improves the identification accuracy of low-velocity mudstone and gas-bearing sandstone, and provides technical support for exploration and well location deployment.

The present invention relates to the field of slope reinforcement technology, specifically to a rapid construction method for mudstone interlayer slope protection and reinforcement technology, which solves the problems that the existing reinforcement efficiency of slopes with mudstone interlayers through on-site concrete pouring is low, and it cannot meet the rapid reinforcement of different slopes. When the water content of the slope soil is too high, it is not convenient to maintain soil stability, which easily causes slope collapse and other problems. A mudstone interlayer slope protection and reinforcement device includes a slope body, an X-direction reinforcement mechanism and a Z-direction tensioning mechanism. A plurality of Y-direction reinforcement mechanisms are installed on one side of the slope body, and a drainage ditch is provided on the side of the bottom of the slope body. The present invention can effectively improve the slope solidification effect and construction efficiency by using an assembled method to segment and anchor the slope, and is convenient for active drainage of the slope soil to avoid slope collapse caused by excessive water content.

The invention relates to the technical field of methods for obtaining formation pressure through oil drilling, in particular to a method for obtaining formation pressure based on wellbore pressure difference and gas logging total hydrocarbon response characteristics, which comprises the following steps of: collecting and arranging logging data, logging gas logging total hydrocarbon data, drilling fluid density data and the like after three or more drilled reference wells in the same block are drilled; calculating the stratum pressure equivalent density of a reference well and the well bottom pressure difference equivalent density of mudstone sections with different burial depths according to the data, drawing a well bottom pressure difference-logging gas logging total hydrocarbon intersection trend chart, reading corresponding well bottom pressure difference equivalent density delta P coordinate data according to the trend chart, and calculating the well bottom pressure difference equivalent density delta P coordinate data according to the well bottom pressure difference-logging gas logging total hydrocarbon intersection trend chart. And finally calculating the stratum pressure equivalent density of the stratum mudstone section of the target well. According to the method, the drilled reference well and the positively-drilled target well which are located in the same block serve as processing objects, the formation pressure calculation method based on logging information and the wellbore pressure difference are combined with the gas logging total hydrocarbon response characteristics, the formation pressure of the positively-drilled target well is accurately obtained while drilling, and the method has the advantages of timeliness and accuracy.

The invention relates to a quantitative evaluation method and device for an opposite relation of two-disc same-set sand shale of a fault. The method comprises the following steps: acquiring a three-dimensional seismic data volume, drilling data and logging data of a target work area to draw a top surface structure contour map of a target sand body; carrying out reservoir rock physical analysis and reservoir inversion to draw a thickness contour map of the target sand body; analyzing and determining the coordinate, the horizontal fault displacement and the vertical fault displacement of each sampling point at the fault center of the top surface development fault of the target sand body according to the top surface construction contour map; determining coordinate data of two sand body thickness sampling points corresponding to each sampling point of the fault center so as to obtain the average sand body vertical thickness of the two sand body thickness sampling points corresponding to each sampling point of the fault center; and determining the sand shale contraposition factor of the fault area corresponding to each sampling point of the fault center, and carrying out quantitative evaluation on the contraposition relation of the two sets of sand shale in the same set. The method can quantitatively, rapidly and accurately complete the quantitative characterization and evaluation of the contraposition relation of the two-disc same-set sand shale of the fault.

The present invention relates to a method for predicting sandstone reservoirs in coal-bearing strata, belonging to the technical field of predicting sandstone reservoirs in coal-bearing strata. The present invention utilizes the difference in frequency attenuation of seismic waves propagating in sandstone and mudstone in coal-bearing strata. First, forward modeling is performed on the well logging data of the wells with measured sandstone reservoirs obtained to obtain the corresponding pre-stack gathers; then, the well logging data of the sandstone section in the well is replaced with mudstone well logging data, and forward modeling is performed again using the replaced data to obtain the corresponding pre-stack gathers; frequency analysis is performed on the pre-stack gathers obtained before and after replacement to determine the attenuation of the reflected waves in the sandstone section; the pre-stack gathers of the target area to be predicted are obtained, and sandstone prediction is performed on the target area according to the attenuation of the reflected waves in the sandstone section. The present invention does not require complex post-stack data processing, reduces the errors caused by data processing, and improves the prediction accuracy.

The present invention discloses a method, system, device and medium for intelligent prediction of low-velocity mudstone, including: calibrating the target layer by combining well logging data, and dividing the layer into background mudstone, low-velocity mudstone, gas-bearing sandstone and dry-layer sandstone. Based on the division results, a large number of datasets for neural network training are generated through geological body evolution. A convolutional neural network (CNN) is constructed, and the input data is normalized to prevent gradient explosion. By training the model, an optimized neural network model is obtained. Using the AVO prestack inversion technology, the longitudinal and transverse wave velocities and density parameters are extracted from the prestack seismic data to assist in the identification of low-velocity mudstone. The processed data is input into the trained model to achieve intelligent prediction of low-velocity mudstone. The present invention can effectively improve the identification accuracy of medium-deep reservoirs, avoid misjudgment caused by the "bright spot trap" of low-velocity mudstone, and provide reliable technical support for oil and gas exploration.

The utility model discloses a large longitudinal slope water-rich sand mudstone interbedding shield segment floating model test system. The large longitudinal slope water-rich sand mudstone interbedding shield segment floating model test system comprises a main body box, a ground stress loading system, a power system, a shield system, a grouting system, a water injection system, a monitoring system and a gradient leveling system. A shield system and model soil are contained in the main body box; the ground stress loading system and the water injection system are used for simulating a water-rich sand shale interbed environment by applying stress and water pressure to the model soil in the main body box; the power system can carry out construction simulation of different gradients by adjusting the tunneling direction of the shield shell, and meanwhile, the grouting system carries out grouting on the gap between the shield shell and the duct piece. The large longitudinal slope water-rich sand mudstone interbed shield wall post-grouting visualization and duct piece floating integrated model test is realized, and a shield tunnel wall post-grouting filling project in a water-rich sand mudstone environment can be reduced in size; and the duct piece floating rule and the tunnel longitudinal deformation mechanism in the shield tunneling process are obtained by feeding back the large longitudinal slope water-rich sand mudstone interbed physical field in real time.

The present invention relates to the field of disintegration testing technology, specifically a device for testing the disintegration characteristics of expansive mudstone, comprising a flat plate, a turntable rotatably connected to the top of the flat plate, a test box mounted on the turntable, an opening on one side of the test box, a plurality of water inlet holes on the other side of the test box, the test box filled with expansive mudstone, an optical fiber sensor embedded in the expansive mudstone, a water injection device mounted on the top side of the turntable, the water injection device injecting water into the expansive mudstone through the water inlet holes, a bracket mounted on the flat plate, a pressure device slidably connected to the bracket. The present invention has the following beneficial effects: the sedimentation and disintegration performance of the expansive mudstone can be tested, and the water injection position of the expansive mudstone can be changed, thereby testing the influence of moisture at different positions on the disintegration characteristics of the expansive mudstone. At the same time, the opening on one side of the test box is provided, so that there is no restrictive force on the expansive mudstone at this location. After applying a thrust in this direction to the expansive mudstone, the unilateral disintegration of the expansive mudstone can be tested.

The invention relates to the technical field of oil exploration, in particular to a complex shale strata lithology identification method based on logging information. The method comprises the following steps: determining lithology and classification of a coring sample through experimental means: mudstone, shale, siltstone, silty mudstone and marl-containing rock; logging curve response characteristics of different lithology in the shale bed series are extracted according to the lithology analysis result, and an average value is calculated; and performing fitting processing on a logging response parameter mean value or a parameter value after standardization of a mean value range, amplifying logging curve response characteristics of different lithologies, and further performing effective identification. According to the method for identifying the lithology of the complex shale bed series based on the logging information provided by the invention, aiming at the complex shale bed series, various lithology identification in the complex shale bed series can be accurately completed, so that the dessert section of the shale oil and gas reservoir is optimized, and the identification accuracy is improved.