Patent ID: 11880776
Assignee: INSTITUTE OF GEOLOGY AND GEOPHYSICS, CHINESE ACADEMY OF SCIENCES
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A graph neural network (GNN)-based prediction system for total organic carbon (TOC) in shale, comprising an operation terminal, a data terminal, and a data processing server, wherein the operation terminal, the data terminal and the data processing server are connected through a communication link, wherein
the operation terminal is configured to select logging curves of a target well location in a target shale bed from the data terminal and input or select a weight matrix between the logging curves;
one or more data terminals are configured to store logging curves of different well locations;
the data processing server is configured to acquire the logging curves selected by the operation terminal, acquire TOC of the target well location through a trained GNN model, and transmit the TOC to the operation terminal;
the logging curves refer to formation response signals acquired during drilling and development of a shale gas or shale oil well, and the logging curves comprise a radioactive uranium logging curve, a radioactive thorium logging curve, a radioactive potassium logging curve, an acoustic velocity logging curve, a compensated neutron logging curve, a density logging curve, a lithology density logging curve, a deep investigation double lateral resistivity logging curve, a shallow investigation double lateral resistivity logging curve, and a gamma logging curve;
a method for acquiring the TOC of the target well location comprises: preprocessing a plurality of logging curves to acquire a plurality of standardized logging curves, windowing the plurality of standardized logging curves, and inputting windowed logging curves and a preset weight matrix between the logging curves into a trained GNN-based TOC prediction network to acquire the TOC of the target well location;
the method for acquiring the TOC of the target well location further comprises a first step of analyzing sensitivity values of the logging curves, comprising:
S400A: carrying out, by a trained graph convolutional neural network (GCNN), gradient back-propagation based on the TOC of the target well location to acquire a sensitivity value of each of the logging curves to predicted TOC;
the method for acquiring the TOC of the target well location further comprises a second step of analyzing the sensitivity values of the logging curves, comprising:
S400B: dividing the plurality of standardized logging curves into a plurality of combinations of logging curves; training a GNN-based TOC prediction network corresponding to the plurality of combinations of logging curves to acquire TOC predicted by the plurality of combinations of logging curves; comparing the TOC predicted by the plurality of combinations of logging curves with measured TOC of a TOC measuring point of a known well location; and analyzing sensitivity values of the plurality of combinations of logging curves to the predicted TOC to select a combination of logging curves of the plurality of combinations of logging curves with a highest sensitivity value to the predicted TOC and inputting the selected combination of logging curves into the GNN-based TOC prediction network to acquire a final TOC,
wherein the trained GNN-based TOC prediction network is acquired through training by the following steps:
A100: acquiring standardized logging curves of the known well location;
A200: acquiring a plurality of TOC measuring points of the known well location by analyzing TOC of a core extracted during drilling of the known well location and acquiring the measured TOC;
A300: windowing the logging curve according to a position of the TOC measuring point of the known well location, wherein a depth corresponding to a center of a data window after windowing is the position of the TOC measuring point;
A400: presetting the weight matrix between the logging curves;
A500: dividing the windowed logging curves, the weight matrix between the logging curves, and the measured TOC into a training set and a verification set;
A600: inputting the training set and the weight matrix between the logging curves into a GNN-based TOC prediction network; extracting, by a graph-based convolution kernel of the logging curves, a characteristic of a training sample; and outputting predicted TOC of the training set based on the characteristic of the training sample;
A700: calculating a loss function of the training set based on the predicted TOC of the training set and the measured TOC and optimizing a network parameter by a stochastic gradient descent algorithm;
A800: inputting, in each iteration, the verification set and the weight matrix into the GNN-based TOC prediction network and outputting predicted TOC of the verification set and calculating a loss function of the verification set based on the predicted TOC of the verification set and the measured TOC; and
A900: repeating steps A600 to A800 until the loss function of the training set is lower than a preset threshold or the loss function of the verification set shows an upward trend to acquire the trained GNN-based TOC prediction network; and
wherein the graph-based convolution kernel comprises a plurality of spatial-temporal convolution blocks;
each of the plurality of spatial-temporal convolution blocks comprises two spatial-temporal-convolution blocks (ST-Cony Blocks) and a fully connected output layer, wherein the two ST-Cony Blocks and the fully connected output layer are sequentially connected;
each of the two ST-Cony Blocks comprises a first temporal gated-convolution (Temporal Gated-Cony) block, a spatial graph-convolution (Spatial Graph-Cony) block, and a second Temporal Gated-Cony block, wherein the first Temporal Gated-Cony block, the Spatial Graph-Cony block and the second Temporal Gated-Cony block are sequentially connected; and
for each of the first Temporal Gated-Cony block and the second Temporal Gated-Cony block, a 1-dimensional causal convolution is implemented through a gated linear unit (GLU).