CN113296150B - High-dimensional closed-loop network seismic inversion method under logging constraint - Google Patents
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Abstract
The invention discloses a high-dimensional closed-loop network seismic inversion method under logging constraint, belonging to the technical field of geophysical technology. The method specifically comprises the following steps: step 1: building a convolutional neural network; comprises a forward network and an inverse network; step 2: preparing training data; the method comprises logging wave impedance data, interpolation wave impedance data and synthetic seismic data; and step 3: training the network and carrying out fine adjustment: training the normal network and the inversion network, then applying one-dimensional logging data to the two-dimensional model and the three-dimensional model, and carrying out fine adjustment; and 4, step 4: prediction and evaluation: firstly, inverting seismic data; then forward modeling is carried out on the inversion result to obtain reconstructed seismic data; and finally, evaluating the effectiveness of the inversion result by using the reconstructed seismic data. According to the method, additional collection of input and reference images is not needed, good transverse continuity can be guaranteed, and the accuracy is higher than that of the traditional and other deep learning inversion methods.
Description
Technical Field
The invention relates to the technical field of geophysical, in particular to a high-dimensional closed-loop network seismic inversion method under logging constraint.
Background
In geophysical exploration, seismic inversion is used for reversely deducing subsurface physical parameters and reconstructing an internal structure of the earth through observed seismic data, wherein the reversely deduced parameters comprise speed, density, wave impedance and the like, and the parameters play an important role in positioning and evaluating oil and gas deposits and determining a drilling position. The usual inversion method is the inverse calculation as a forward algorithm. Depending on the basis of the inversion, these methods can be distinguished into model-based inversion and data-based inversion, such as trace-integral inversion[1]Recursive inversion[2]Generalized linear inversion[3]Etc., these inversion methods require hypothetical, simplified models that are generally linear, while the actual geophysical process is nonlinear, thereby introducing large inversion errors.
The deep neural network has strong feature expression and nonlinear fitting capability due to the fact that dozens or even hundreds of nonlinear layers can be stacked, and a mapping model can be learned from training data through a complex nonlinear structure[4]. Das et al[5](2018) Firstly, a network formed by two layers of one-dimensional CNN is used for learning the mapping from the seismic data to the wave impedance to obtainBetter results are achieved than with conventional methods, but this method requires a large number of label samples and is not very generalized and robust. Wang et al propose the use of Cycle-GAN[6]And a one-dimensional closed-loop network[7]And seismic inversion is carried out, and the demand of label samples is reduced by introducing label-free data in training. In a subsequent improvement, Wang et al[8]On the basis of a one-dimensional closed-loop network, bilateral filtering constraint is added, so that the inversion effect is further improved, but the transverse continuity is still poor.
The invention utilizes the high-dimensional closed-loop network to directly invert two-dimensional and three-dimensional seismic data, obtains good transverse continuity, has stronger generalization and robustness, and has more reasonable inversion result.
Reference documents:
[1]Ferguson,R.J.,&Margrave,G.F.(1996).A simple algorithm for band-limited impedance inversion.CREWES annual
[2]Lindseth,R.O.(1979).Synthetic sonic logs—A process for stratigraphic interpretation.Geophysics,44(1),3-26.
[3]Cooke D A,Schneider W A.Generalized linear inversion of reflection seismic data[J].Geophysics,1983,48(6):665-676.
[4]LeCun Y,Bengio Y,Hinton G.Deep learning[J].nature,2015,521(7553):436.
[5]Das,V.,et al.(2018).Convolutional neural network for seismic impedance inversion.SEG Technical Program Expanded Abstracts 2018,Society of Exploration Geophysicists:2071-2075.
[6].Wang,Q.Ge,W.Lu,and X.Yan,“Seismic impedance inversion basedon cycle-consistent generative adversarial network,”inSEG Technical Program Expanded Abstracts 2019.Society of Exploration Geophysi-cists,2019,pp.2498–2502
[7]Y.Wang,Q.Ge,W.Lu,and X.Yan.[2020]Well-logging constrained seismic inversion based on closed-loop convolutional neural network IEEE Transactions on Geoscience and Remote Sensing,2020.
[8]Y.Wang,Q.Wang,W.Lu and H.Li,"Physics-Constrained Seismic Impedance Inversion Based on Deep Learning,"in IEEE Geoscience and Remote Sensing Letters,doi:10.1109/LGRS.2021.3072132.
[9]H.Huang,L.Lin,R.Tong,H.Hu,Q.Zhang,Y.Iwamoto,X.Han,Y.-W.Chen,and J.Wu,[2020].Unet 3+:A full-scale connected unet for medical image segmentation.in ICASSP2020-2020IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP).IEEE,2020,pp.1055–1059.
disclosure of Invention
The invention aims to provide a high-dimensional closed-loop network seismic inversion method under logging constraint, which is characterized by comprising the following steps:
step 1: building a convolutional neural network; the network structure is a closed loop structure and comprises a forward network and an inversion network, wherein the forward network and the inversion network are both U-net + + + models, and input data and output data of the network are both two-dimensional data or three-dimensional data;
step 2: preparing training data; the training data comprises log wave impedance data, interpolated wave impedance data and synthetic seismic data; the interpolated wave impedance data is generated by the constraint of log wave impedance data and actual seismic data, the synthetic seismic data is input data of network training, and the interpolated wave impedance data is calculated by a Robinson convolution model;
and step 3: training the network and carrying out fine adjustment: training the forward network and the inversion network by using the data in the step 2 to obtain a forward model and an inversion model, then applying one-dimensional logging data to a two-dimensional model and a three-dimensional model by using a Mask technology, and finely adjusting the two-dimensional model and the three-dimensional model to obtain the trained forward network and inversion network;
and 4, step 4: prediction and evaluation: firstly, inverting the seismic data by using the inversion network trained in the step 3 to obtain a wave impedance prediction result; then forward modeling is carried out on the inversion result by using the forward modeling network trained in the step 3 to obtain reconstructed seismic data; and finally, evaluating the effectiveness of the inversion result by using the reconstructed seismic data, wherein if the error value of the reconstructed seismic data and the input seismic data is smaller, the inversion result is more effective, and otherwise, the applicability of the inversion network on the input seismic data is poor.
The step 3 specifically comprises the following substeps:
step 31: according to
Using the label seismic data to optimize forward network weight, WF is forward network weight, S is label seismic data, AI is label wave impedance data,
calculating a result for the forward network; according to
Optimizing the inversion network weights using the tag wave impedance data, WB being the inversion network weights,
calculating a result for the inversion network; while according to a closed-loop uniform loss function
Synchronously optimizing the forward network and the inversion network; wherein
For closed-loop coherent loss of unlabeled seismic data, S*Representing the non-tagged seismic data and,
closed-loop coherent loss for tagged seismic data
Closed loop coherent loss for tag wave impedance data；
Step 32: before calculating the network loss, multiplying the logging by the corresponding output of the network, and then calculating the loss reflection propagation for updating the network parameters; the loss function is:
wherein AI is*And representing the logging wave impedance, distinguishing whether a pixel point is on the logging or not by using a Mask, and setting the corresponding value of the pixel point on the logging to be 1, otherwise, setting the corresponding value to be 0.
The invention has the beneficial effects that:
according to the method, training samples are obtained through actual data synthesis, and input and reference images do not need to be collected additionally; the two-dimensional and three-dimensional inversion methods can ensure good transverse continuity; the logging data is used for fine tuning of the model, and the precision is higher than that of the traditional and other deep learning inversion methods.
Drawings
FIG. 1 is an overall flow diagram of the present invention;
FIG. 2 is a diagram of the forward and inverse network architecture of the present invention;
FIG. 3(a) is a schematic diagram of a two-dimensional Mask technique; FIG. 3(b) is a schematic diagram of a three-dimensional Mask technique;
FIG. 4(a) is actual seismic data; FIG. 4(b) is the inversion result of the method of the present invention; FIG. 4(c) is the one-dimensional closed-loop CNN inversion result; FIG. 4(d) is a comparison of waveforms on a blind well;
FIG. 5 is an inverted two-dimensional profile of the present invention on actual seismic data; FIG. 5(a) is a two-dimensional cross-sectional view of actual seismic data; FIG. 5(b) is an inverted cross-sectional view of a blind well at the well-plugging location of the present invention.
Detailed Description
The invention provides a high-dimensional closed-loop network seismic inversion method under logging constraint, and the invention is further explained by combining the attached drawings and specific embodiments.
Fig. 1 is an overall flow chart of the present invention, and the specific process is as follows:
(1) building convolutional neural networks
The network structure mainly comprises two parts: a forward network model and an inverse network model. Both the forward and inverse networks use a modified U-net + + + model, as shown in fig. 2. Mainly comprising an encoder and a decoder. The convolution layers are connected by flying leads, and the convolution kernels of the convolution layers are set to be two-dimensional in a two-dimensional method and three-dimensional in a three-dimensional method. Both the input data and the tag data are normalized to between-1 and 1 by dividing by the maximum of the absolute value. In the two-dimensional method, network input is two-dimensional data, and in the three-dimensional method, input and output are three-dimensional data.
(2) Preparing training data
The data preparation process is used to provide training data for the network training process. The method comprises actual seismic data, logging wave impedance data, interpolation wave impedance data generated by the logging data and the actual seismic data, and the interpolation wave impedance data serving as a label for training. The synthetic seismic data used for training are calculated by a corresponding interpolated wave impedance through a Robinson convolution model. The seismic data and the wave impedance data are both two-dimensional data in a two-dimensional method, and are both three-dimensional data in a three-dimensional method.
(3) Training network, fine tuning
In order to ensure the stability of the training process, the invention distinguishes two stages for training. The first stage is a training stage and the second stage is a fine tuning stage.
The first stage is as follows: forward and inverse network models are trained. The training data used in this stage is tag synthetic seismic data, corresponding tag interpolated wave impedance data, and non-tag seismic data.
According to a loss function
Optimizing forward network weights using tagged seismic data, where WFRepresenting forward network weights, S tag seismic data, AI tag wave impedance data,
representing the results of the forward network computations.
According to
Optimizing inversion network weights using tag wave impedance data, where WBThe inverse network weights are represented as a function of,
representing the inversion network computation. Meanwhile, according to a combined closed-loop consistent loss function, the loss function is:
synchronizing the optimization of the evolving and inverting networks, wherein
For closed-loop coherent loss of tag seismic data,
for closed-loop coherent loss of tag wave impedance data,
is a closed-loop coherent loss of unlabeled seismic data. The closed-loop loss consistent term expression is as follows:
wherein S*Representing unlabeled seismic data. The optimization algorithm in the training process uses the Adam algorithm.
And a second stage: and applying one-dimensional logging data to a two-dimensional model and a three-dimensional model by using a Mask technology, and finely adjusting the model. The Mask technique is shown in fig. 3(a) and 3 (b). Is a mask of the same size as the network output, two-dimensional in the two-dimensional approach, and three-dimensional in the three-dimensional approach. The value of the logging tool is l only at the corresponding position of the logging tool, and the value of the logging tool is 0 at the rest positions. Before calculating the network loss, the corresponding outputs of the logging and the network are multiplied, and then the calculated loss reflection propagation is used for updating the network parameters. The loss function is:
wherein, the Mask distinguishes whether the pixel point is on the well logging, S*Representing unlabeled seismic data, AI*Representing the log wave impedance. For the pixel point on the logging, the corresponding value is l, otherwise, the corresponding value is 0. The optimization algorithm in the training process also uses the Adam algorithm.
(4) And (6) predicting and evaluating. And obtaining the final forward network weight and the final inversion network weight through the training process. And when in prediction, the whole actual seismic data S is inverted, and the inversion result can be further evaluated by using a forward model obtained by training. If the inversion result is accurate, the reconstructed seismic data should be more consistent with the input seismic data. The smaller the error value is, the more effective the inversion result is, otherwise, the poor applicability of the inversion network on the data is.
In order to verify the effectiveness and superiority of the method, the method provided by the invention is applied to actual seismic data. The experimental platform is Intel (R) core (TM) i9-9820X CPU @3.30GHz, 64GB RAM, GeForce RTX 2080 Ti. Fig. 4 and 5 show inversion results of the invention, and it can be seen that, compared with the one-dimensional closed-loop CNN, the blind well correlation coefficient of the invention is 0.91, and the one-dimensional closed-loop CNN blind well correlation coefficient is 0.45, and meanwhile, the continuity of the invention is better.
The present invention is not limited to the above embodiments, and any changes or substitutions that can be easily made by those skilled in the art within the technical scope of the present invention are also within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (1)
1. A high-dimensional closed-loop network seismic inversion method under logging constraint is characterized by comprising the following steps:
step 1: building a convolutional neural network; the network structure is a closed loop structure and comprises a forward network and an inversion network, wherein the forward network and the inversion network are both U-net + + + models, and input data and output data of the network are both two-dimensional data or three-dimensional data;
step 2: preparing training data; the training data comprises log wave impedance data, interpolated wave impedance data and synthetic seismic data; the interpolated wave impedance data is generated by the constraint of log wave impedance data and actual seismic data, the synthetic seismic data is input data of network training, and the interpolated wave impedance data is calculated by a Robinson convolution model;
and step 3: training the network and carrying out fine adjustment: training the forward network and the inversion network by using the data in the step 2 to obtain a forward model and an inversion model, then applying one-dimensional logging data to a two-dimensional model and a three-dimensional model by using a Mask technology, and finely adjusting the two-dimensional model and the three-dimensional model to obtain the trained forward network and inversion network;
the step 3 specifically comprises the following substeps:
step 31: according to
Optimizing forward network weights, W, using tagged seismic dataFTo forward network weights, S is tag seismic data, AI is tag wave impedance data,
calculating a result for the forward network; according to
Optimizing inversion network weights, W, using tag wave impedance dataBIn order to invert the weights of the network,
calculating a result for the inversion network; while according to a closed-loop uniform loss function
Synchronously optimizing the forward network and the inversion network; wherein
For closed-loop coherent loss of unlabeled seismic data, S*Representing the non-tagged seismic data and,
for closed-loop coherent loss of tag seismic data,
closed-loop uniform loss of tag wave impedance data;
step 32: before calculating the network loss, multiplying the logging by the corresponding output of the network, and then calculating the loss reflection propagation for updating the network parameters; the loss function is:
wherein AI is*Representing the logging wave impedance, distinguishing whether a pixel point is on logging or not by a Mask, and setting the corresponding value of the pixel point on logging to be 1, otherwise, setting the corresponding value to be 0;
and 4, step 4: prediction and evaluation: firstly, inverting the seismic data by using the inversion network trained in the step 3 to obtain a wave impedance prediction result; then forward modeling is carried out on the inversion result by using the forward modeling network trained in the step 3 to obtain reconstructed seismic data; and finally, evaluating the effectiveness of the inversion result by using the reconstructed seismic data, wherein if the error value of the reconstructed seismic data and the input seismic data is smaller, the inversion result is more effective, and otherwise, the applicability of the inversion network on the input seismic data is poor.
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