53 results about "Resistivity inversion" patented technology

The invention discloses a subway shield section boulder refining detecting method based on resistivity trans-hole CT (computed tomography). The subway shield section boulder refining detecting method includes a scientific and reasonable drilling plane layout scheme of an unexcavated in front of a tunneling side; a detecting scheme of paving a resistivity trans-hole CT wiring net and combining two-dimensional general detecting survey with three-dimensional detailed detecting survey. Data acquisition acquires a novel combined observation mode, and more effective information related to abnormal boulder bodies can be acquired; by structuring a three-dimensional resistivity inversion target function and an inversion equation of a carrying distance weighting function, algorithms can be quickly analyzed and solved parallelly by a partial derivative matrix; a resistivity image result diagram of detection of the abnormal boulder body within a ranging range is formed, and size and spatial distribution and the like of the boulder body are deduced by being combined with geological analysis. Three-dimensional high-resolution accurate imaging of the abnormal boulder body can be realized, and power technical support is provided for safety and quick tunneling of construction tunnels by subway shield tunneling machines.

The invention discloses a resistivity anisotropy recognition method in a horizontal well stratum environment. Firstly, an azimuth natural GR and an azimuth resistivity logging are obtained through logging, and then a stratigraphic dip and a stratigraphic azimuth angle are extracted. Secondly, the instrument azimuthal resistivity logging response value at multiple spacings and different frequencies in a horizontal well are acquired through logging. Through the environmental correction and automatic layering process, a false layer is removed, and standardized logging data and a stratum layering interface are obtained. Thirdly, according to a stratigraphic dip extracting result, the layering interface, an apparent resistivity measured value and adjoining well data, a horizontal well stratum model is constructed. Finally, the stratum model is subjected to back calculation, and then an anisotropy coefficient and a stratum resistivity inversion result are outputted. According to the method, the real resistivity information and the anisotropy coefficient information of the stratum in the horizontal well environment are extracted based on azimuthal resistivity logging data while drilling, so that the existing logging evaluation data of the horizontal well are perfected. During the comprehensive interpretation and evaluation process of the logging data of the horizontal well, the reservoir oil saturation and the like can be accurately calculated based on the stratum real resistivity and the anisotropy coefficient. As a result, the method provides reliable parameters for reservoir evaluation.

The invention provides a multi-stage penetration type submarine sand wave in-situ observation device and method based on a resistivity feeler lever. The device comprises a support structure, a multi-stage penetration mechanism, an annular electrode resistivity feeler lever, and a matched observation apparatus. The principle thereof is as follows: penetration of the feeler lever is performed depending on a multi-stage penetration system triggered by characteristics of different stages of sand wave migration, observation of a potential value of a penetration region is performed through the feeler lever, a resistivity sudden change cross section elevation is extracted, and the change of a sandy seabed surface elevation with time is analyzed. The method includes implementation of the multi-stage penetration system and resistivity inversion submarine sand wave elevation change. According to the device and method, a new observation thought is provided for in-situ real-time observation of submarine sand wave migration, a new method is provided for penetration of medium and small submarine sand wave development areas, the device is simple and easy to use, and the applicable water area of in-situ real-time measurement is wide.

The embodiment of the invention provides an inversion method of a transient electromagnetic resistivity. The method is characterized in that the method comprises: (1), an initial inversion model is set, inversion is carried out on the initial inversion model, and a corrected inversion model is obtained (2), layer number refining, inversion, and correction are carried out on the corrected inversion model; (3), the step (2) is repeated until the number of times of the layer number refining of the corrected inversion model reaches a preset value or the fitting error of the corrected inversion model response and the observation data is less than a first threshold value. According to the embodiment of the invention, the method has the following beneficial effects: refining is carried out on the inversion model gradually, so that the dependency on the initial model by the inversion process is reduced and the convergence of the inversion process is improved. Moreover, the number of times of iterations is reduced to the certain extent and the inversion speed is accelerated.

The invention relates to a resistivity data and earthquake data combined speed field construction method for petroleum and natural gas exploration. Data matching of space positions of the resistivity data and the earthquake data is carried out to acquire a resistivity body in matching with a time domain and a matched resistivity inversion impedance body; an initial speed body is acquired through combined inversion of the resistivity data and the earthquake data; a speed error body is acquired through analysis and correction on an speed error; combined speed field construction is accomplished through correction on an offset data body. According to the resistivity data and earthquake data combined speed field construction method, the speed field reflects spatial distribution characteristic and speed change rules of an exploration strata, the speed value is relatively close to a real strata speed, a depth prediction error is relatively small, and a trap high point and construction can be reliably predicted.

The invention provides an evaluation method for marine controllable source electromagnetic exploration noise reduction effects, and concretely relates to an evaluation method for noise reduction effects of earth natural electromagnetic signal noise in marine controllable source electromagnetic exploration. A mean square relative error of a resistivity inversion result and a resistivity input valueare used as quantitative evaluation indexes of the noise reduction effects by combining MCSEM signal one-dimensional forward modeling, MT noise one-dimensional forward modeling, noise reduction processing and one-dimensional OCCAM inversion. MT noise can be generated by using a forward modeling means, and actual measured data of an MCSEM transmitter in a power-off time segment can be directly input to be the MT noise, and so the evaluation method can be ensured to have strong flexibility. Quantitative evaluation is carried out on the noise reduction effects of a method combining a short timewindow and pre-whitening filtering and the validity of the noise reduction effects is verified by using the evaluation method of the MT noise reduction effects provided by the invention. The noise reduction effect evaluation method provided by the invention has a wide application range, and fills the blank in the field of quantitative evaluation of noise reduction effects.

The invention discloses a high density electrical resistivity imaging method, comprising following steps: establishing a suitable mesh subdivision scheme aiming at a detection area according to topographic features and the requirement of high density electrical resistivity measurement and artificial refraction seismic survey; establishing an artificial refraction seismic data inversion model to perform inversion calculation to obtain a discrete velocity module; converting the discrete velocity module into a discrete electrical resistivity module according to the empirical equation between the velocity and the resistivity; using the obtained resistivity module as a constrained initial module of resistivity inversion to perform inversion calculation. By means of the method which is a novel technology for greatly increasing the electrical resistivity imaging precision, the electrical resistivity data and the artificial refraction seismic data are fused with each other; the processed artificial refraction seismic data is used as a prerequisite of electric resistivity imaging to perform constrained inversion calculation so that calculation time can be greatly saved under same hardware conditions and imaging precision is increased.

The invention discloses a real-time monitoring method for bad geological body grouting on the basis of four-dimensional resistivity inversion. An approximate range of a bad geological body is determined through a geophysical prospecting means, multiple drill holes are punched at a regional boundary according to the determined approximate range of the bad geological body, electrodes are arranged inthe drill holes, whole-space three-dimensional resistivity cross-hole CT fine detection is carried out, and the bad geological body is identified and located; grouting holes are formed according to adetection result, and the bad geological body is grouted and blocked; a whole-space four-dimensional resistivity cross-hole CT method is used for monitoring the whole process of grouting and blocking, and a four-dimensional resistivity inversion objective function and an inversion equation are created; results obtained through a seismic method and a radar method are used as priori information andsubstituted into the inversion equation, and inversion iteration is conducted to obtain a group of resistivity inversion results; according to imaging results, a conductivity image of dynamic changesof a monitored area is obtained, and correspondingly a migration path, deposition retention and blocking conditions of slurry veins at each stage are obtained.

The invention discloses a four-dimensional specific resistance cross-hole CT monitoring imaging method based on priori gradient constraint. Detecting is conducted through multi-element physical geography means, geological structure and environmental state information is determined, and a key monitoring area is divided according to the detecting result; drill holes are laid in the selected key monitoring area according to site conditions, electrodes are laid in the drill holes, and an observation space for four-dimensional cross-hole specific resistance CT is formed; the range of a forward andinversion model is determined in the observation space, and the distance between the electrodes and the grid size of the model constructed through forward and inversion are determined according to theneeded precision and time requirements; a full-space four-dimensional specific resistance cross-hole CT method is carried out for monitoring, and a four-dimensional specific resistance inversion objective function and the inversion equation are constructed; and an initial model of the inversion process is determined with a detected geological structure as guidance, the main moving direction of anobjective body is judged in advance to serve as priori information, inversion iteration is conducted continuously, a set of specific resistance inversion results is obtained, and a specific conductance image of dynamic change of the monitored area is obtained.

The invention provides a deep ground temperature field prediction method and device and a storage medium, and the method comprises the following steps: equally dividing each logging temperature-resistivity data pair of a research region or an adjacent region into N segments, and carrying out the normalization processing of the resistivity of each layer segment; deducing the optimal correction temperature, the intrinsic normalized resistivity and the temperature correction coefficient of different well positions and different layer sections, constructing a data set through the optimal correction temperature, the intrinsic normalized resistivity and the temperature correction coefficient and the corresponding depth, carrying out regression analysis, and obtaining changes T0 (z), rhoNT0 (z) and alphaT0 (z) along with the depth respectively; performing resistivity inversion on the electromagnetic data volume in the research area, equally dividing the electromagnetic data volume into M sections, and normalizing the resistivity of different layer sections to be rho Ninv (x, z); based on the T0 (z), the rhoNT0 (z), the alphaT0 (z) and the rhoNinv (x, z), obtaining relation characterization between normalized inversion resistivity and temperature of different nodes of different underground layer sections of the research area, and predicting distribution characteristics of the underground space temperature field of the research area according to the relation characterization. According to the method, the macroscopic resistivity characteristics of the underground medium can be accurately converted into visual temperature field distribution, the practicability is high, and the prediction range is wide and deep.

The invention provides a data depth change characteristic self-adaptive two-dimensional resistivity inversion method and system. The method comprises the steps of constructing data sets of apparent resistivity-resistivity model data pairs of different geoelectric models; constructing an adaptive convolution network with adaptively variable convolution kernel amplitudes and offsets of different layer depths according to resistivity depth change characteristics; constructing an inversion loss function carrying vertical weights of resistivity data items, training an adaptive convolution network controlled by the inversion loss function by using the data set, and establishing a mapping relationship between apparent resistivity data and a resistivity model; inputting observation apparent resistivity data into the trained adaptive convolutional network, outputting a resistivity imaging result through the established mapping relation, achieving earth surface two-dimensional resistivity deep learning inversion, and effectively improving inversion quality, especially inversion precision of a deep area.

The invention discloses a resistivity inversion initial value selection method based on array lateral logging data. The method comprises steps that forward calculation is carried out based on a radialstep stratum model, resistivity amplitude difference coefficients S1, S2, S3, S4, S5 and S6 are defined, four basic logging curves MLR1a, MLR2a, MLR3a and MLR4a corresponding to the radial step stratum model are obtained, and a change chart of resistivity amplitude difference coefficients along with the radius of a flushing zone is established; the quantitative relation between an initial value rxocz of the radius of the flushing zone and the resistivity amplitude difference coefficients is established through linear regression based on the change chart of the resistivity amplitude differencecoefficients along with the radius of the flushing zone; the MLR4a is utilized as a stratum resistivity initial value Rtcz, and a flushing zone resistivity initial value Rxocz is obtained based on pseudo-geometric factor definition. The resistivity inversion initial value selection method based on the array lateral logging data is advantaged in that deviation between the initial value and a realvalue can be effectively reduced without supplementing other logging data, and resistivity logging inversion precision and the speed can be improved.

The invention discloses a horizontal well true resistivity inversion method based on dual lateral logging. The horizontal well true resistivity inversion method comprises the steps that horizontal well dual lateral logging curve data and auxiliary logging curve data are obtained; according to logging curve data of an adjacent well or a pilot well, measured target well sections are layered, the formation interface is determined, and the initial formation resistivity of all separated layers is inversed; according to comparison of the horizontal well dual lateral logging curve data and the auxiliary logging curve data with the logging curve data of the adjacent well or the pilot well, the drilling layer section of a horizontal well is determined, and an initial formation model is built; according to the initial formation resistivity and the initial formation model, by combining dual lateral instrument parameters, dual lateral simulation curves of all the separated layers are generated; and whether the lateral simulation curves are consistent with dual lateral logging curves or not is judged, if not, according to the dual lateral logging curves and the geological characteristics, the initial formation resistivity and formation model are modified, and the final formation resistivity is output.

The invention provides a multi-scale resistivity inversion method and system based on wavelet transform. The method belongs to the field of geophysical exploration resistivity inversion. In order to solve the problem that the accurate position of a geological interface is difficult to determine, the method comprises the following steps: firstly, converting model parameters from a spatial domain toa wavelet domain through convolutional wavelet transform; obtaining wavelet domain characteristic parameter increments of different scales by solving an inversion equation in a wavelet domain, further obtaining an iterated wavelet domain characteristic parameter result, and then carrying out deconvolution transformation on the result to obtain a resistivity model of a space domain, so that the boundary of a geological target can be positioned and described with relatively high precision.

The invention discloses a four-dimensional resistivity inversion method based on dynamic adjustment of data weight values. The method includes the steps that resistance/potential difference/apparent resistivity obtained in a collecting process constitute a data set, the collected complete data set is equally divided into multiple parts, and the divided small parts of data are recombined to obtaina recombined data set; a four-dimensional resistivity inversion objective function based on dynamic adjustment of the data weight values is constructed, an appropriate data weight matrix is selected,a model parameter increment vector is solved, and a final inversion result is obtained after iterative inversion.

The embodiment of the application discloses a method and device for processing magnetotelluric data. The method comprises: collected three-dimensional magnetotelluric data are obtained and three-dimensional resistivity inversion is carried out on the three-dimensional magnetotelluric data to obtain a three-dimensional resistivity data volume; logarithm taking operation is carried out on first profile resistivity data of the three-dimensional resistivity data volume and meshing processing is carried out to obtain mesh profile data; first frequency domain bit field processing is carried out on the mesh profile data and a processing result is filtered; second frequency domain bit field processing is carried out on the filtered result to obtain target mesh profile data and low-resistance electromagnetic data are screened from the target mesh profile data to obtain low-resistance thin-layer weak information. With the processing method and device, the low-resistance thin layer can be identified.

The invention provides a semi-supervised resistivity deep learning inversion method and a semi-supervised resistivity deep learning inversion system based on an adversarial generative network and pseudo-annotation, which can realize a deep learning inversion network under the condition that part of apparent resistivity data lacks a corresponding geological model; vertical features of the electrical method data are extracted, a resistivity model is finally obtained by extracting a feature map, and the mapping relation between the apparent resistivity data and the underground resistivity geologic model is completed; meanwhile, a discriminator is added into the network structure, pseudo labeling is introduced into apparent resistivity data without corresponding geological models, the data size of labeled data is expanded, and a semi-supervised learning strategy is achieved. Through the semi-supervised deep learning electrical method data inversion network, the deep learning network inversion effect is improved when the label data is less.

The invention discloses a site pollutant description method and system based on directional smooth constraint inversion. The method comprises the following steps: acquiring resistivity difference interface information on a structural boundary of a polluted site according to drilling data and geological survey data of the polluted site; constructing a grey-scale map according to the resistivity difference interface information; quantizing the grey-scale map into a plurality of local grey-scale maps; calculating structure tensors of the plurality of local grey-scale maps, and calculating diffusion tensors and similarities according to the structure tensors; determining a final resistivity difference interface for the resistivity difference interface according to the similarity; calculating smooth weight coefficients of the final resistivity difference interface in four directions according to the diffusion tensor; constructing a four-direction weighted smooth matrix according to the smooth weight coefficients in the four directions, and substituting the four-direction weighted smooth matrix into an inversion algorithm to obtain an inversion resistivity prediction model; and determining a site pollutant distribution range according to the inversion resistivity prediction model. The method improves the accuracy of resistivity inversion, and enables the determination of the spatial distribution range of pollutants to be more accurate.

The invention relates to a new method of advance geology forecast. For a three-dimensional resistivity inversion problem, the amount of observation data is usually smaller than the number of unknown parameters, and due to errors of the observation data and other reasons, a strong correlation usually exists between equations; under the circumstance, the inversion problem is manifested as a mixed determined problem; to solve the problems, prior information needs to be applied into model construction. According to the new method of advance geology forecast, smooth constraint serves as the prior information to be applied into the inversion equation, wherein the smooth constraint means smooth transition of resistivity values of adjacent grids, that is to say, the difference between the resistivities of the adjacent grids is made extremely small, and thus a simplest three-dimensional geophysical model is constructed; the smooth constraint has natural reasonability, because the essential features of the model are maintained; due to application of the smooth constraint, the ill-posedness of the inversion equation is greatly reduced.