Chinese Academy of Sciences

Beijing Eswin Computing Technology Co., Ltd.

Haining Eswin Computing Technology Co., Ltd.

Wenjing Cao

Qingyun Di

Yongyou Yang

Fei Tian

Wenhao Zheng

Bo Zhao

Qiqiang Han

The present invention is in the field of geological measurement, and particularly relates to a method and system for intelligent removal of multi-dimensional logging data outliers, aiming to solve the problem that it is difficult to achieve stable parameter acquisition in the drilling process when there are more outliers in the prior art. The present invention comprises: acquiring a multi-dimensional logging curve; obtaining two-dimensional logging curve data by using the Umap dimensionality reduction algorithm to perform dimensionality reduction; establishing a first data point index of the multi-dimensional logging curve and the two-dimensional logging curve data; obtaining valid logging data by processing outliers with an isolation forest algorithm based on the two-dimensional logging curve data; acquiring a second data point index based on the first data point index and valid logging data; finding marked data points where the second data point index exists in the multi-dimensional logging curve data.

Method and system for intelligent removal of multi-dimensional logging data outliers

The present invention belongs to the technical field of geological exploration deep Earth. The system of the present invention is configured to: collect seismic data, and preprocess original seismic data to obtain standardized gamma ray logging parameters; extract features from the standardized gamma ray logging parameters to obtain a variance attribute curve while drilling; obtaining a formation thickness prediction model of each set based on the variance attribute curve while drilling; acquire frequency-division seismic data based on the standardized seismic data by means of a frequency-division processing method based on wavelet transform; acquire optimal seismic frequency band information based on a predicted formation thickness value of each set; establish a geological prediction model of the current formation based on the frequency-division seismic data, the optimal seismic frequency band information, historical gamma ray logging parameters and the standardized gamma ray logging parameters.

System for updating clastic rock lithology model by well-to-seismic integration with deep earth oil and gas precision navigation

A sand shale formation lithology evaluation method and system for precise deep oil and gas navigation aims to solve the problem in the prior art, that is, the accuracy of the logging-while-drilling (LWD) azimuthal resistivity is insufficient due to an equipment or technology deficiency. The method includes: acquiring density distribution data, gamma distribution data, and resistivity distribution data of a target location; amplifying the data to acquire amplified logging distribution data; clustering the data to acquire clustered logging data; adding stratigraphic information to the clustered data; performing dimensionality reduction by a principal component analysis (PCA) method, and taking dimensionality-reduced data as a weight of azimuthal logging data to acquire an LWD feature dataset; predicting missing LWD photoelectric data through the LWD feature dataset; and acquiring a formation lithology evaluation result based on an LWD photoelectric data prediction curve. The method and system improves the accuracy of LWD lithology evaluation.

Sand shale formation lithology evaluation method and system for precise deep oil and gas navigation

A sand shale formation physical property evaluation method and system for precise deep oil and gas navigation aims to solve the problem that the prior art cannot acquire a real-time and accurate logging-while-drilling (LWD) Rt curve. The method includes: acquiring basic data of a target well location as well as basic data and an LWD resistivity (Rt) of an adjacent well; dividing the data into different groups; retaining data with a maximum correlation value with the LWD Rt in each group of data; eliminating outliers, and performing standardization; constructing a two-dimensional input feature map by taking the correlation value and standardized data as a weight; acquiring an LWD Rt prediction curve based on the two-dimensional input feature map; calculating a hydrocarbon parameter in a window based on the LWD Rt prediction curve; and locating an area with a high hydrocarbon potential based on the hydrocarbon parameter at each position.

Sand shale formation physical property evaluation method and system for precise deep oil and gas navigation

The present invention is in the field of geological exploration and particularly relates to a method and system for gamma ray (GR) while drilling parameter data optimization for deep-filed oil and gas precise navigation, aiming to solve the problem of insufficient measurement accuracy of the depth of a well in the existing drilling technology. The present invention comprises: eliminating outliers from the obtained GR while drilling parameter data and determining the rationality of the eliminated outlier; performing derivation on a real-time updated variance attribute while drilling curve to obtain a real-time updated formation change-point detection result; searching the formation change-point detection result of a pre-drilling predicted well curve; and correcting the drilling depth by comparing the two formation change-point detection results to obtain the accurate depth while drilling.

Method and system for GR LWD curve optimization for deep-field oil and gas precise navigation

Disclosed is a method and an apparatus for backlight control and a display device. The apparatus comprises: a block brightness module for calculating an initial brightness value of each backlight block in a backlight source according to initial image data; a filtering module for performing spatial filtering and temporal filtering on the initial brightness value of each backlight block to obtain an adjusted brightness value of each backlight block; a control module for generating a brightness control signal according to the adjusted brightness value of each backlight block to adjust brightness of the backlight source. An intermediate brightness value of each backlight block is no less than the initial brightness value of that backlight block. Based on backlight adjustment for reducing power consumption, display effect reduction on a portion of image due to uneven brightness of the portion of the image during the spatial filtering is avoided.

Method and apparatus for backlight control and display device

The present invention belongs to the field of deep navigation, and in particular to a high-precision modeling method and system of three-dimensional velocity field for precise navigation of deep oil and gas. The present invention includes: extracting wave impedance characteristics based on standardized well logging data to obtain a wave impedance curve; acquiring a velocity vector of each location at current depth, to obtain a one-dimensional velocity curve; based on the one-dimensional velocity curve, training a velocity value prediction model based on wave impedance at the current depth; performing wave impedance inversion based on the wave impedance curve at the current depth to obtain a wave impedance inversion data volume; and based on the wave impedance inversion data volume, through the velocity value prediction model based on wave impedance at the current depth, acquiring a three-dimensional high-accuracy velocity volume.

High-precison modeling method and system of three-dimensional velocity field for precise navigation of deep oil and gas

The present invention belongs to the field of geologic survey, and particularly relates to a real-time calibration method and system of acoustic logging data while drilling for precise navigation of deep oil and gas, aiming to solve a problem of inability to automatically identify stratum boundaries to adapt to geology at various different depths in an existing drilling process. The method of the present invention includes: acquiring standardized acoustic parameter data while drilling; acquiring time-depth relationship of historical acoustic curve data of drilled wells, and current actual drilling time-depth relationship; performing interpolation based on the current actual drilling time-depth relationship, to acquire interpolated current actual drilling time-depth relationship; correcting the interpolated current actual drilling time-depth relationship; and completing current precise depth correction based on the current actual drilling acoustic curve.

Real-time calibration method and system of acoustic logging data while drilling for precise navigation of deep oil and gas

A precise deep oil and gas navigation system based on structural evaluation of a sand/shale formation, and a device are provided. The precise deep oil and gas navigation system is implemented by: acquiring basic data of a target well location as well as basic data and an acoustic LWD curve of an adjacent well; dividing a basic data group; retaining extremely strongly correlated data and strongly correlated data of the basic data group, and performing dimensionality reduction; constructing a three-dimensional fusion feature data volume based on a dimensionality-reduced fusion feature parameter and a time-frequency spectrum; predicting a missing curve according to the three-dimensional fusion feature data volume; and correcting a stratigraphic structure model based on the missing curve to guide the design of a drilling trajectory.

Precise deep oil and gas navigation system based on structural evaluation of sand/shale formation, and device

The present disclosure relates to a method and a system for updating a three-dimensional lithology model for precision navigation of deep oil and gas in real time, aiming to solve the problem that an existing while drilling model is difficult to adapt to a real-time update of the model in a strong heterogeneous reservoir, which leads to a decrease in accuracy of an established prediction model and a lack of a drilling ratio. The present disclosure includes: dividing pre-processed seismic data into three pieces of single-frequency seismic waveform data by a wavelet frequency division method; selecting an optimal single-frequency combination according to a correlation coefficient between gamma ray prediction curves and measured curves at different depths; and computing a single-frequency gamma ray prediction model while drilling, updated with drilling, and a full-band gamma ray distribution model while drilling, updated with drilling, by means of the optimal single-frequency combination.

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Beijing, China

Wenjing Cao