154 results about "Logging while drilling" patented technology

The invention discloses a logging-while-drilling reservoir parameter dynamic prediction method based on double attention convolution LSTM, and the method comprises the steps: obtaining logging-while-drilling data and drilling data in real time, and carrying out the preprocessing of the data; establishing a DTW dynamic time window model based on a gradual transition strategy, and adaptively adjusting the length of an input sequence according to the stratum change speed; a double attention convolution LSTM model is combined with the DTW dynamic time window model to establish a feature-time double attention mechanism, and two dimensions of features and time are adaptively fused based on the convolution LSTM; setting a buffer area, storing data and controlling data quality; setting an online increment fine tuning strategy; and performing instance training, and performing multi-angle evaluation on the performance of the double attention convolution LSTM model by adopting multiple indexes. According to the scheme, the influence of features and time on the prediction accuracy is fully considered, and the prediction accuracy is improved.

The invention discloses a while-drilling electromagnetic wave advanced detection sensitivity analysis method, and belongs to the field of oil-gas exploration and development. The method comprises the following steps: constructing a while-drilling electromagnetic wave advanced detection model, quantifying the influence of different measurement parameters on advanced detection signals, then analyzing the response characteristics of the advanced detection signals, extracting key sensitivity factors, and defining a single-parameter sensitivity function and a multi-parameter coupling sensitivity function; the sensitivity of advanced detection signals is qualitatively and quantitatively analyzed from the four angles of the distance to the interface, the formation resistivity, the interlayer and the stratigraphic dip angle, so that the while-drilling electromagnetic wave advanced detection range is optimized, and the adaptability in the well logging process is enhanced. And meanwhile, the advanced detection second-order sensitivity is approximately solved by adopting a central difference formula, influence factors suffered by advanced detection signals are analyzed from a numerical angle, the interaction of multi-parameter coupling is analyzed, and a scientific basis is provided for electromagnetic wave logging while drilling accurate inversion and instrument design.

The invention relates to the technical field of logging while drilling, and particularly provides a logging while drilling electromagnetic wave resistivity logging inversion method. The method comprises the following steps: constructing an XGBoost feature screening module, and optimizing a data set through the XGBoost feature screening module to obtain an optimized data set; according to the method, a BiLSTM-Transformer model is constructed, an optimized data set is input into the model, an inversion result is obtained, the inversion precision and the calculation efficiency are improved, and inversion of the target stratum resistivity, the adjacent layer surrounding rock resistivity and the boundary distance under the anisotropic stratum model is achieved.

The invention provides a petroleum drilling geomechanical characteristic estimation method based on deep learning, and aims to solve the problems of high measurement cost and poor real-time performance of shear wave velocity Vs in traditional geomechanical analysis by fusing logging while drilling LWD data and real-time drilling engineering parameters. According to the method, a Transformer model is adopted to carry out depth correction on logging-while-drilling data, depth offset of a drill bit and a logging sensor is eliminated, and real-time drilling parameters such as torque T, bit pressure WOB and drilling speed ROP are combined and input into a multi-layer perceptron MLP network to predict the shear wave speed Vs. And further calculating key geomechanical parameters based on the predicted shear wave velocity Vs. In practical application, the method improves the shear wave velocity prediction accuracy to 97.2%, the mean absolute error MAE of the shear modulus is reduced from 0.186 to 0.059, and the bulk modulus is reduced from 0.189 to 0.040. The method can be used for outputting stratum elastic parameters, optimizing bit pressure, pre-warning well wall instability and adjusting a well track, well drilling safety and efficiency are improved, and meanwhile dependence on an expensive well logging technology is reduced.

Systems and methods include obtaining well log data and core sample data of a subsurface formation; generating, based on the well log data and the core sample data, an unconfined compressive strength log for the subsurface formation; using an unsupervised machine learning model to form rock type clusters based on the unconfined compressive strength log and the well log data; forming a training dataset including the well log data, the training dataset labeled based on the rock type clusters; training a supervised machine learning model using the training dataset. While drilling a well in the subsurface formation, logging-while-drilling data is obtained from drilling equipment used to drill the well; and rock types in the subsurface formation are determined using the supervised machine learning model and the logging-while-drilling data.

The invention discloses a formation pressure prediction method and device, electronic equipment and a storage medium. The method comprises the steps that logging-while-drilling data of a target area are acquired; performing variable sequence characteristic quantification on the continuous time sequence logging-while-drilling data by using a recurrent neural network to obtain sequence characteristics; on the basis of the sequential features, weighted values of all logging parameters in the logging-while-drilling data are constructed through an attention mechanism, and then stratum deposition features are constructed through a preset weight parameter matrix; inputting the stratum deposition characteristics into a prediction model to predict and obtain a stratum pressure prediction value of the target area; wherein the prediction model is obtained based on a full-connection network through training of formation feature representation marked with actual formation pressure. The method can efficiently and accurately realize formation pressure prediction, and can be widely applied to the technical field of data prediction.

The invention discloses a rotary steerable drilling trajectory prediction method based on deep learning and knowledge distillation, and belongs to the technical field of drilling trajectory prediction, and the method comprises the following steps: collecting rotary steerable drilling site ground data and while-drilling well logging data, and carrying out data preprocessing; constructing a teacher model and training; the teacher model comprises a well drilling feature enhancement module and a sequence change prediction module; a teaching assistant model is constructed and trained, and the teacher model is assisted to carry out multi-interlayer data adaptive compression; a composite knowledge distillation framework and a student model are built, the student model is trained, and the trained student model is a lightweight borehole trajectory prediction model; during field application, ground data and logging-while-drilling data are preprocessed and then sent into the lightweight well track prediction model, a well track prediction value in the rotary steering drilling process is obtained, and therefore drilling operation parameters are adjusted in time according to a prediction result. According to the method, high-precision and light-weight well track prediction is realized.

The invention relates to the technical field of logging-while-drilling instruments, in particular to a signal construction method based on an azimuth logging-while-drilling instrument. The method comprises the following steps: forming a plurality of groups of measurement modes under different source distances on the basis of a multi-transmitting multi-receiving antenna system of a while-drilling azimuth logging instrument, and compensating a calculation definition formula of a voltage component; symmetrical compensation combinations are added on multiple groups of measurement modes under different source distances, symmetrical positive compensation and symmetrical inverse compensation are carried out on signals, and a combined strategy of multi-source-distance focusing and symmetrical compensation combinations is constructed; according to the joint strategy, the geological signal is defined, the error is reduced, and the measurement precision is improved.

The invention discloses a reservoir fracture distribution prediction method and system based on big data, and relates to the technical field of oil and gas field development, and the method comprises the steps of multi-source data collection and fusion, multi-scale feature extraction and optimization, hybrid model construction and three-dimensional fracture probability body generation, and fracture distribution prediction and visual display. According to the method, prediction blind areas are reduced by collecting multi-source heterogeneous data, physical constraints are achieved by introducing a Griffith criterion so as to enhance geological rationality, dynamic data and static geological data of fracturing construction are subjected to combined modeling, dynamic fusion of multi-source data is achieved, timeliness of crack prediction is improved, in addition, the physical constraint hybrid model is adopted, and the prediction efficiency is improved. And spatial characteristics and dynamic response are considered through a CNN-GBDT hybrid architecture, a fracture mechanics criterion is embedded to enhance interpretability, real-time access of logging-while-drilling data in the drilling process is supported, and key technical support can be provided for unconventional oil and gas development.

The embodiment of the invention discloses a quality control method and device for logging-while-drilling data and computing equipment. The method comprises the following steps: acquiring actually measured signal values of different sampling orientations acquired by the electromagnetic wave logging-while-drilling instrument at a target depth; performing waveform fitting on the actually measured signal values of different sampling orientations to generate a waveform coefficient corresponding to the target depth; fitting signal values of different sampling directions are generated based on the waveform coefficients; extracting a waveform baseline from the waveform coefficient, processing the measured signal value by using the waveform baseline to obtain a corrected measured signal value, and processing the fitted signal value by using the waveform baseline to obtain a corrected fitted signal value; calculating a data error of the sampling orientation according to the corrected actual measurement signal value and the corrected fitting signal value of the sampling orientation; and generating a quality index value of the target depth according to the data errors of different sampling orientations at the target depth. According to the scheme, accurate quality detection and accurate control of the logging-while-drilling data can be achieved, and the subsequent logging data analysis accuracy is improved.

According to the logging-while-drilling data lag compensation method based on deep learning provided by the invention, the prediction precision of the lag logging-while-drilling data is improved by combining the drilling engineering data and the historical logging-while-drilling data. According to the method, an encoder-decoder neural network model based on a self-attention mechanism is utilized, lag logging-while-drilling data is predicted in combination with drilling engineering features, and prediction precision is improved through a convolutional layer. Results show that when the method is used for predicting the gamma curve in the logging-while-drilling data, the root mean square error within the prediction range of 30 meters is lower than 0.589, and the average absolute percentage error is lower than 1.587. The problem of lag of the logging while drilling data is effectively solved, the operation risk under the complex stratum condition is reduced, the drilling strategy can be optimized in real time, and the safety and efficiency of the drilling process are improved.

The present application relates to the field of logging while drilling and intelligent steering control technology in oil and gas drilling engineering, in particular to a rotary steering drilling adaptive trajectory control method for image processing. It comprises: receiving original logging while drilling image data frames of downhole transmission channels; generating a high-resolution predicted geological texture map; synchronously generating a feature uncertainty distribution map representing the pixel probability distribution characteristics in the high-resolution predicted geological texture map; calculating the matching degree of the high-resolution predicted geological texture map and a preset stress-induced artifact feature library; generating a sensor contamination marker signal; calling a safety baseline geological model constructed before drilling; calculating the geological texture structure difference value relative to the safety baseline geological model; generating a geological illusion entropy; receiving the geological illusion entropy and the sensor contamination marker signal; and driving a downhole steering tool according to the activated mode. The present application ensures that the steering instruction is always based on the real existing geological information, and avoids the deviation of the drilling trajectory caused by the fictitious texture.

The invention discloses a section cluster optimization design method based on geological guidance mechanical specific energy intelligent correction, which comprises the following steps: determining a target stable point set according to a multi-scale local stability index in each lithologic layer section, and obtaining the lithologic layer section by performing lithologic layered clustering on logging while drilling data along a horizontal well section and corresponding well depth data; in each lithologic layer section, according to the mechanical specific energy median and / or the mechanical specific energy prediction result of the target stable point, performing layered correction on the original mechanical specific energy, the mechanical specific energy prediction result being obtained based on prediction of the target stable point set; according to the geological dessert labels and the corresponding well depth data, fracturing sections are divided; and in the fracturing section, according to the mechanical specific energy after layering correction and the geological dessert label, a cluster arrangement strategy is formulated, and perforation clusters are optimally designed based on the cluster arrangement strategy. According to the method, the mechanical specific energy can be corrected accurately and efficiently at low cost, so that the accuracy and practicability of fracturing segment cluster optimization design can be improved based on the corrected mechanical specific energy.

The invention discloses a while-drilling azimuth acoustic logging data acquisition method, and belongs to the technical field of while-drilling logging. The invention discloses a while-drilling azimuth acoustic logging data acquisition method. The method comprises the following steps: S1, acquiring natural gamma intensity, formation resistivity, three-axis vibration signals and a tool face angle of a current depth formation; s2, geological events of the current stratum are recognized according to the natural gamma intensity, the stratum resistivity and the three-axis vibration signals, and the geological events comprise a first geological event and a second geological event; s3, a first collection strategy is executed on the first geological event to collect waveform data, a second collection strategy is executed on the second geological event to collect waveform data, the first collection strategy and the second collection strategy both adopt an equal azimuth interval collection scheme, and the azimuth interval of the first collection strategy is larger than that of the second collection strategy; and S4, transmitting the waveform data acquired by the first acquisition strategy to a time difference statistical value, compressing the waveform data acquired by the second acquisition strategy, and preferentially transmitting the waveform data.

The present invention provides a method for quantitatively evaluating the sweet spot quality of conglomerate horizontal wells based on logging-while-drilling (LWD) data. Because some wells lack completion electrical logging data and dipole acoustic wave data, density, P-wave transit time, and S-wave transit time data cannot be directly obtained. LWD and gas logging data must be used to model and effectively model density, acoustic wave, and S-wave transit time. Based on the calculated density, acoustic wave, and S-wave transit time, geological parameters such as porosity, saturation, and permeability can be calculated. Based on S-wave transit time, density, and P-wave transit time, reservoir engineering parameters such as Young's modulus, Poisson's ratio, brittleness index, and maximum and minimum principal stresses can be calculated. The quality of the horizontal well sweet spot can be quantitatively evaluated by comprehensively utilizing the geological sweet spot and engineering sweet spot parameters.

The present invention proposes a three-dimensional vibration fiber grating sensor for a while-drilling logging engineering parameter pup joint, which belongs to the technical field of fiber grating sensors; it comprises a hollow shell, and the two ends of the shell extending along a first preset direction are respectively provided with a fiber input port and a fiber output port, the first preset direction is the length extension direction of the shell, and the shell is fixedly connected to the engineering parameter pup joint; three support components are sequentially arranged in the shell along the first preset direction, and the three support components are all provided with a through embedding groove, and the normal directions of the end faces of the three support components with the embedding grooves are different from each other; a bend-insensitive optical fiber is inserted into the shell and passes through the fiber input port, the embedding grooves on the three support components and the fiber output port in sequence, and the parts of the bend-insensitive optical fiber passing through the three support components are all provided with a grid area; wherein the grid area and the support component in which it is located together constitute a sensing body, and the sensing body is used to obtain vibration signals of different axes during the process of while-drilling logging.

The application discloses a downhole multi-parameter logging device and belongs to the technical field of logging while drilling, comprising a drill collar, a battery module and a circuit module are respectively arranged at the two ends of the inner cavity of the drill collar and are axially fixed through locking modules, a metal centralizer is fixedly arranged in the inner cavity of the drill collar, a plurality of independent centralizing blocks are arranged at the center of the metal centralizer, the drill collar comprises a drill collar body, an antenna housing, a strip-shaped magnetic core and a coil, and stable signal transmission is guaranteed, the battery module comprises annular batteries and butterfly springs, can be replaced on site and has a damping function, the circuit module comprises resistivity, azimuth gamma and inclination measurement modules, is integrally designed and has strong anti-interference performance, the metal centralizer realizes self-adaptive centralizing through centrifugal force and hydraulic damping, and the independent centralizing blocks can compensate temperature errors, the device is stable in structure, is suitable for complex well conditions, is convenient to maintain and can realize accurate and synchronous measurement of multiple parameters, thereby providing reliable data support for logging while drilling.

The invention provides a while-drilling radioactive logging system, a logging method and a drilling tool assembly, and belongs to the technical field of oil-gas exploration, and the system comprises at least one perovskite semiconductor detector which is used for directly absorbing gamma rays from a stratum and generating corresponding electric pulse signals; the electronics module is electrically connected with the perovskite semiconductor detector and used for processing the electric pulse signal to obtain while-drilling radioactive logging data, and the while-drilling radioactive logging data comprises at least one of natural gamma counting data, natural gamma energy spectrum data and gamma-gamma density data; the protection packaging body comprises a metal shock-resistant and high-pressure-resistant shell, a heat insulation and heat preservation layer and a heat channel which are arranged in the metal shock-resistant and high-pressure-resistant shell, and a high shock-resistant and impact-resistant connecting piece for fixing internal devices, and the protection packaging body is used for accommodating the perovskite semiconductor detector and the electronics module; and the safety and the accuracy of radioactive logging while drilling are improved.

The present invention discloses an adaptive scale coarsening method for logging-while-drilling curves, belonging to the field of logging-while-drilling data processing in oil and gas exploration and development, including: intelligently analyzing logging curves through deep learning technology and dynamically adjusting the coarsening strategy to achieve the collaborative optimization of data dimensionality reduction, noise filtering, and retention of key geological features. The present invention is applicable to multiple scenarios such as intelligent directional drilling, reservoir parameter calculation, 3D geological modeling, and well-seismic joint inversion, and involves technical fields such as signal processing, deep learning, and geological data analysis. The cross-integration of these fields makes the adaptive coarsening of logging curves possible, laying a technical foundation for improving the efficiency of logging data processing and the accuracy of geological interpretation.

The invention discloses a drilling risk prediction method based on geophysical data and related equipment. The method comprises the steps that geophysical parameters in the seabed drilling process are collected in real time through logging while drilling equipment; preprocessing the geophysical parameters to generate target logging data; the target logging data are constructed into a space sequence with the depth as the axis, and a dynamic extrapolation model is trained and generated by using a preset time sequence prediction algorithm based on the space sequence of the drilled stratum; inputting real-time target logging data into the dynamic extrapolation model, and predicting to obtain stratum parameters of an undrilled stratum within a preset distance in front of the drill bit; and coupling the stratum parameters with a preset geomechanical model to generate a dynamic safe drilling window containing a drilling parameter safe range. The method can effectively predict front geological anomalies, remarkably improves the safety and operation efficiency of seabed oil and gas drilling, and can be widely applied to the technical field of seabed oil and gas resource exploration and development.

The invention provides a logging-while-drilling high-speed transmission system and method based on multi-modal combination, and belongs to the technical field of oil exploration and logging-while-drilling, the logging-while-drilling high-speed transmission system comprises an underground acquisition unit, a transmission unit and a ground decoding unit, the underground acquisition unit continuously acquires multiple types of stratum parameters in the drilling process of a drilling tool and generates standardized data frames, and the transmission unit transmits the standardized data frames to the ground decoding unit; the transmission unit selects different transmission modes to transmit standardized data frames in real time, the ground decoding unit comprises an independent decoding layer, a redundancy check layer and a fusion layer, and the independent decoding layer performs wavelet transformation on the standardized data frames to separate modal signals corresponding to the transmission modes; the redundancy check layer marks conflict data with low consistency by calculating correlation among different modal signals and repairs the conflict data, the fusion layer constructs a probability fusion model based on a Bayesian network, and output data of the redundancy check layer is input into the probability fusion model to obtain decoded stratum parameters. According to the invention, efficient and reliable transmission of underground data can be effectively improved.

The invention discloses a complex structure stratum while-drilling azimuth electromagnetic wave logging parameterization inversion method, and belongs to the field of oil exploration and development. The method comprises the following steps: s1, modeling a complex geologic structure, constructing a 2D complex geologic model, and parameterizing the 2D complex geologic model; s2, analyzing response characteristics of the 2D complex geological model according to the 2D complex geological model established in the s1, and carrying out influence factor and sensitivity analysis; s3, obtaining observation data according to response characteristics of s2, and constructing an objective function; and s4, in combination with s2 and s3, performing inversion on the parameterized 2D complex geological model by using a PT-MCMC inversion method to realize fine reconstruction of the complex stratum. According to the method, fine reconstruction of the complex stratum is achieved, good inversion precision is achieved, and the characterization capacity and the calculation efficiency are both considered. The method provides support for real-time geosteering, reservoir evaluation, layer interface prediction and the like of oil and gas fields.

The present disclosure relates to a method and device for identifying tectonic coal based on borehole gamma data. The method comprises: acquiring gamma curve data and mechanical drilling speed data collected in real time during the process of logging while drilling; preprocessing the gamma curve data and the mechanical drilling speed data to obtain depth sequence data; performing segmentation processing on the depth sequence data to obtain a plurality of data sample units; for each data sample unit, extracting a plurality of sample features for representing the response mode of tectonic coal from the data sample unit, fusing the plurality of sample features to obtain a multi-parameter fusion feature vector; and inputting the multi-parameter fusion feature vector into a trained machine learning classification model to obtain a classification result output by the machine learning classification model. The present scheme improves the accuracy and efficiency of tectonic coal identification.

This utility model discloses a remote gamma measurement device for logging while drilling. The remote gamma measurement device includes: a pressure-resistant cylinder, a mounting frame, a main control and power board, a gamma sensor, a metal shielding sleeve, a shielding layer, a first connector assembly, and a second connector assembly. The mounting frame is located inside the pressure-resistant cylinder; the main control and power board is located on the mounting frame; the gamma sensor is located on the mounting frame and electrically connected to the main control and power board, and the gamma sensor includes a photomultiplier tube and a NaI crystal; the metal shielding sleeve is fitted over the photomultiplier tube; the shielding layer is located inside the pressure-resistant cylinder and is used to shield the peripheral area of ​​the gamma sensor; the first connector assembly is located on the pressure-resistant cylinder and electrically connected to the main control and power board; the second connector assembly is located on the pressure-resistant cylinder and electrically connected to the gamma sensor. This utility model's remote gamma measurement device for logging while drilling can reduce the influence of external signals on the photomultiplier tube and improve the reliability of the remote gamma measurement device for logging while drilling.

The utility model discloses a dismounting and mounting tool for a centralizer of a logging-while-drilling instrument, and solves the technical problem that the centralizer is difficult to dismount and mount quickly. The dismounting and mounting tool comprises a pushing sleeve which is used for being arranged on a logging instrument in a sleeving mode and abutting against a centralizer; the connecting rod is coaxially arranged in the pushing sleeve and used for being connected with a logging instrument; the hydraulic cylinder is arranged on the connecting rod; the piston end of the hydraulic cylinder can push the pushing sleeve to move in the axial direction of the connecting rod so that the pushing sleeve can push the centralizer to move in the axial direction of the logging instrument, and therefore the centralizer can be disassembled and assembled. According to the utility model, the centering device can be quickly disassembled and assembled, so that the working efficiency is improved.

The application discloses a titanium-copper alloy pipe and a preparation method thereof. The titanium-copper alloy pipe comprises the following components in mass percent: Ti: 2.3-3.4%, Cr: 0.02-0.2%, Ni: 0.02-0.5%, Al: 0.15-0.20%, rare earth elements: 0-0.05%, the rare earth elements being at least one of Ce, La, Sc and Y, the balance being Cu and inevitable impurities, and Cu+Ti≥96.0%. The preparation method comprises the following steps: titanium-copper ingot casting, homogenization heat treatment, hole drilling and breaking down, pickling, quenching, rolling, aging hardening treatment, nondestructive testing, water pressure detection, wall thickness measurement and the like. The titanium-copper alloy pipe has a tensile strength of 1100-1250 MPa, a yield strength of 1050-1100 MPa, an elongation after fracture of ≥7.0%, and a hardness HRC of 38-42, and meets the requirements of the strength and corrosion resistance of a pressure-resistant outer cylinder for a logging-while-drilling device.

The invention relates to a horizontal well type structure for a thin oil layer and a guiding method. Acquiring a thin oil layer top boundary line, a thin oil layer bottom boundary line, geologic survey features above the thin oil layer, geologic survey features below the thin oil layer, geologic survey features of the thin oil layer and remote exploration data within a normal distance from a central axis of a well track of the detection section by using data acquired during drilling of the detection section; a production section is drilled, a window entering point of the production section is determined according to the thin oil layer top boundary line, the thin oil layer bottom boundary line and the thin oil layer geological survey characteristics, geological logging data of the production section in front of the window entering point are compared with remote exploration data, and then the while-drilling geosteering tool is calibrated; and according to the geologic survey features above the thin oil layer and the geologic survey features below the thin oil layer, the drilling well is guided and adjusted, so that the horizontal section behind the window entering point extends in the thin oil layer. The detection section is used for detecting the position of the thin oil layer and the geological information of the reservoir and calibrating the while-drilling instrument, so that the borehole is prevented from penetrating through the upper and lower boundaries of the hydrocarbon reservoir.

Embodiments of the application disclose a method and device for quality control of logging-while-drilling data and a computing device. The method comprises: obtaining measured signal values of different sampling directions collected by a logging-while-drilling electromagnetic wave logging instrument at a target depth; performing waveform fitting on the measured signal values of different sampling directions to generate waveform coefficients corresponding to the target depth; generating fitted signal values of different sampling directions based on the waveform coefficients; extracting a waveform baseline from the waveform coefficients, processing the measured signal values using the waveform baseline to obtain corrected measured signal values, and processing the fitted signal values using the waveform baseline to obtain corrected fitted signal values; calculating data errors of the sampling directions according to the corrected measured signal values and the corrected fitted signal values of the sampling directions; and generating a quality index value of the target depth according to the data errors of different sampling directions at the target depth. The method can achieve accurate detection and control of the quality of logging-while-drilling data and improve the accuracy of subsequent logging data analysis.

A method includes obtaining, with an edge computing device disposed in a bottom hole assembly of a drilling system, logging while drilling data. The method further includes determining, with the edge computing device, at least one interpreted quantity of a subsurface using one or more artificial intelligence (AI) models processing the logging while drilling (LWD) data. The method further includes determining, with the edge computing device, an optimal set of data transfer parameters for a telemetry system governed by a set of data transfer parameters, to telemeter the at least one interpreted quantity, where the set of data transfer parameters are continually updated to the optimal set of data transfer parameters while the logging while drilling data is being acquired. The method further includes transmitting, with the edge computing device, one or more guidance control parameters to a geosteering system based on the at least one interpreted quantity.

The invention relates to the technical field of logging while drilling, and discloses a stratum simulation box for a lateral resistivity logging while drilling tool and a design method of the stratum simulation box. The design method comprises the following steps: determining an electrode structure of an azimuth electrode system of the lateral resistivity logging-while-drilling tool; determining a resistance network of the stratum simulation box based on an electrode structure and a working principle of the azimuth electrode system; according to the resistance network of the stratum simulation box and a working principle, determining a calculation scheme of each resistance parameter in the resistance network of the stratum simulation box; and determining each resistance value of the stratum simulation box resistance network under the typical stratum condition by utilizing a three-dimensional finite element program. The formation simulation box for the while-drilling lateral resistivity logging instrument is designed and generated through the method, various formations simulated by the formation simulation box can be measured by the while-drilling lateral resistivity logging instrument indoors, the working state of the instrument can be checked conveniently, and the instrument can be debugged, maintained and calibrated conveniently; and the time for repeatedly testing and verifying the performance of the instrument is greatly saved.