CN111650647A - Acoustic logging curve reconstruction method based on seismic data constraint - Google Patents
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Abstract
The invention discloses an acoustic logging curve reconstruction method based on seismic data constraint, and relates to the field of seismic data application of oil and gas field exploration and development. Firstly, making a synthetic seismic record of a well in a research area, and carrying out time-depth relation matching; selecting well sections with good matching relation, and establishing a mathematical relation between a logging acoustic curve and a well-side seismic channel by using a neural network; applying the mathematical relationship to extrapolate an acoustic wave simulation curve of the well to be reconstructed; and (3) respectively filtering out the dominant components of the original curve and the sound-like wave curve by using a digital filtering method, and then fusing the original curve and the sound-like wave curve to finally form a reconstructed curve. The method combines the advantage of the lateral stability of the seismic data with the high-precision characteristic of a single point of the logging data, can improve the matching effect of the logging data and the seismic data, and reduces the difficulty in interpretation.
Description
Technical Field
The invention relates to the field of seismic data application of oil and gas field exploration and development.
Background
Logging and seismic joint inversion are conventional means for seismic data reservoir prediction. The method is characterized in that sound wave data are required, the function of the method is to establish a synthetic seismic record of the method and match the synthetic seismic record with a seismic time record: time depth matching and waveform amplitude matching are carried out to form time depth corresponding relation, and a geological horizon and a seismic event are calibrated, so that a reflection interface can be tracked on a seismic section to carry out structural interpretation, reservoir interpretation, modeling, inversion and the like.
However, a great deal of practice shows that due to the man-made nature of seismic data processing, the change of logging data acquisition instruments and acquisition conditions and the like, ideal matching can be rarely achieved when the synthetic seismic records are compared with the well-side seismic channels; the matching of sonic composite records to seismic records for different wells tends to vary widely laterally. Sometimes, the logging data has to be "processed" manually, such as by stretching, editing, etc. the seismic data. However, due to the processing, artificial subjective factors are increased, so that the acoustic wave curve is essentially transformed, and the time-depth relation is distorted, so that the calibration accuracy, the later inversion and the reliability of reservoir prediction are influenced.
Heretofore, some methods for reconstructing and correcting well logs have been proposed by scholars, most of which utilize other well logs to reconstruct and form a certain specified well log based on rock physics, multivariate statistical analysis and the like, and few methods for reconstructing well logs by using seismic data as constraints and improving the matching of synthetic seismic records are available.
Chinese patent publication No. CN 107255834a, published as 2017, 10 and 17, discloses a method for correcting a sonic logging curve based on seismic restraint, which relates to the field of logging data processing in exploration and development of oil and gas fields, and utilizes the thought or flow of correcting the logging curve by seismic records to convert the sonic logging data into a high-resolution synthetic record, matches the synthetic record with the seismic records at corresponding positions, optimizes a well section with high matching degree according to the matching condition, establishes a relationship between the sonic logging section and the seismic recording section, and then applies the relationship to seismic data of all wells to obtain the corrected sonic data. The invention corrects the acoustic wave curve from a brand new angle, overcomes the problem to a greater extent and obtains the acoustic wave curve reflecting relative change in the transverse direction.
The prior art represented by the above patent documents discloses correcting a log using seismic recordings. However, a detailed method for reconstructing the acoustic logging curve is not provided, and the method can also combine the advantage of lateral stability of seismic data with the high-precision characteristic of single points of logging data, and the prior art is completely silent, so that the application effect of the acoustic logging curve reconstructed by the prior art is not ideal.
Disclosure of Invention
The invention aims to provide an acoustic logging curve reconstruction method based on seismic data constraint aiming at the defects and shortcomings of the prior art.
The invention is realized by adopting the following technical scheme:
a method for reconstructing an acoustic logging curve based on seismic data constraint is characterized by comprising the following steps: firstly, making a synthetic seismic record of a well in a research area, and carrying out time-depth relation matching; selecting well sections with good matching relation, and establishing a mathematical relation between a logging acoustic curve and a well-side seismic channel by using a neural network; applying the mathematical relationship to extrapolate an acoustic wave simulation curve of the well to be reconstructed; and (3) respectively filtering out the dominant components of the original curve and the sound-like wave curve by using a digital filtering method, and then fusing the original curve and the sound-like wave curve to finally form a reconstructed curve.
The specific method comprises the following steps:
(1) making a synthetic seismic record of each well, and performing time-depth relation matching; the time-depth relationship is approximately correct as much as possible;
(2) designing a neural network to represent the mapping relation between the seismic record and the acoustic logging curve; using a well with good time-depth relation and waveform characteristic matching degree as a sample well, selecting a well section, using a seismic channel beside the well as a sample, using a corresponding acoustic curve as a label, and training a neural network;
(3) setting the original acoustic curve of the well to be reconstructed as AC for the well needing the acoustic curve reconstruction, selecting the well section needing the acoustic curve reconstruction, converting the depth range into the time range by using the initial time-depth relation, and calculating the well-side seismic channel S in the corresponding time period of the well section into the pseudo-acoustic curve AC by using the trained neural networkpred；
(4) Performing band elimination filtering on the original acoustic wave curve AC, wherein the filter is fstopTo obtain AC ═ fstop(AC); to ACpredMaking band-pass filtering with filter fpassTo obtain AC'pred＝fpass(ACpred) Wherein f isstopAnd fpassThe frequency parameters of the seismic data are determined by the dominant frequency band of the seismic data;
(5) and mixing AC 'with AC'predAdding to obtain a reconstructed curve ACmerged＝AC′+AC′pred。
In the step (1), although the longitudinal wave velocity and density are theoretically used for making the synthetic seismic record, since the density log item may have poor stability or lack of stability, the density curve may be set to be constant without using measured density data in order to unify the entire research area, and if the density data is used, all the synthetic seismic records of the well are used.
In the step (2), the neural network uses a multi-input multi-output neural network, the multi-input multi-output refers to a plurality of nodes of an input layer, and the output layer is also a plurality of nodes; the learning mode is a supervised mode, and the supervised mode is a learning mode of adjusting the weight and the threshold of the network neuron according to the difference between the prediction output and the label. The specific internal structural features are not limited.
In the step (2), the matching degree of the synthetic seismic record of the well section selected as the sample and the well-side seismic channel is as high as possible, so that the neural network is ensured to learn a stable and correct mapping relation.
In the step (4), the filter used is recommended to be an FIR digital filter, and the filter window type is Hanning window (Hanning), Hamming window (Hamming), Blackman window (Blackman), or the like.
In the step (4), the passband and stopband of the band-pass filter and the stopband of the band-stop filter are in a complementary relationship, and the band-stop filter of the original curve filters out low-frequency and high-frequency band components; the band-pass filter of the acoustic simulation curve filters out the acoustic simulation curve components corresponding to the dominant frequency band of the seismic data.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention makes the synthetic record again, can make the correlation of synthetic record and side channel of seismic well get the considerable improvement; meanwhile, the information of earthquake and well logging is utilized, so that the key information of the original well logging curve is not damaged during reconstruction, and the consistency of the synthetic seismic record and the seismic data is higher.
2. The reconstruction curve mapping relation formed by the invention is more accurate. The neural network can be used for learning the relationship which is not easy to analyze and express between the seismic data and the acoustic logging curve, and the relationship is different from the statistical fitting relationship which is mostly established only by logging items, or the curve is artificially stretched, compressed or directly changed; and the relation comprises longitudinal and transverse variability and is not a constant single relation, so that the relation between the seismic data of the whole area and the acoustic logging curve can be well represented.
3. The reconstruction curve formed by the invention combines the respective advantageous components of the seismic data and the logging data. The reconstructed acoustic wave curve not only has the high-low frequency dominant components of the original acoustic wave curve, but also has the dominant components of the pseudoacoustic wave curve derived by seismic data. The method not only ensures that the original key information is not damaged, but also can ensure that the height of the synthetic seismic record made by the reconstructed acoustic wave curve is consistent with the height of the seismic data in the transverse direction.
4. The matching of the reconstructed synthetic seismic record and the well-side seismic channel is improved. By using the reconstructed acoustic curve obtained in the non-manual modification mode, the correlation between the manufactured synthetic seismic record and the well-side seismic channel is greatly improved, and the uncertainty of horizon calibration in seismic data interpretation work is reduced.
Drawings
The invention will be described in further detail with reference to the following description taken in conjunction with the accompanying drawings and detailed description, in which:
FIG. 1 is a basic flow chart of the acoustic curve reconstruction method of the present invention;
FIG. 2 is a schematic diagram of the spectrum of the original curve and the extrapolated calculated pseudoacoustic curve and the corresponding band-stop bandpass filter when the curves of the present invention are fused;
FIG. 3 is a comparison of synthetic seismic record matching before and after reconstruction of a well acoustic curve in an actual work area.
Detailed Description
Example 1
In a preferred embodiment of the present invention, the present invention is a method for reconstructing an acoustic logging curve by combining the advantage of lateral stability of seismic data and the high-precision characteristic of a single point of logging data, so as to improve the matching effect between the logging data and the seismic data and reduce the difficulty in interpretation. Firstly, making synthetic seismic records of a well in a research area, and carrying out approximate time-depth relation matching; selecting well sections with good matching relation, and establishing a mathematical relation between a logging acoustic curve and a well-side seismic channel by using a neural network; applying the mathematical relation to extrapolate an acoustic wave simulation curve of the well to be reconstructed, respectively filtering out the dominant components of the original curve and the acoustic wave simulation curve by using a digital filtering method, and fusing the two curves to finally form a reconstructed curve; and the correlation between the synthetic record and the seismic well side channel can be improved remarkably by reproducing the synthetic record. The key of the invention is that the information of earthquake and well logging is simultaneously utilized, which not only ensures that the key information of the original well logging curve is not damaged during reconstruction, but also ensures that the consistency of the synthetic seismic record and the seismic data is higher.
Example 2
Referring to fig. 1, the best mode of the present invention includes the following steps:
(1) making a synthetic seismic record of each well, and ensuring that the time-depth relation is approximately correct as much as possible;
(2) designing a neural network to represent the mapping relation between the seismic record and the acoustic logging curve; using a well with good time-depth relation and waveform characteristic matching degree as a sample well, selecting a well section, using a seismic channel beside the well as a sample, using a corresponding acoustic curve as a label, and training a neural network;
(3) setting the original acoustic curve of the well to be reconstructed as AC for the well needing the acoustic curve reconstruction, selecting the well section needing the acoustic curve reconstruction (converting the depth range into the time range by using the initial time-depth relation), and calculating the well-side seismic channel S in the corresponding time period of the well section into the pseudo-acoustic curve AC by using the trained neural networkpred；
(4) The band elimination filtering is carried out on the AC, and the filter is fstopTo obtain AC ═ fstop(AC); to ACpredMaking band-pass filtering with filter fpassTo obtain AC'pred＝fpass(ACpred) Wherein f isstopAnd fpassThe frequency parameters of the seismic data are determined by the dominant frequency band of the seismic data;
(5) and mixing AC 'with AC'predAdding to obtain a reconstructed curve ACmerged＝AC′+AC′pred。
In the step (1), the longitudinal wave velocity and the density are theoretically required for making the synthetic seismic record, but because the density logging item is likely to have poor stability or lack, in order to unify the whole research area, the density curve can be set as a constant without using actually measured density data; if density data is to be used, then all well synthetic seismic records need to be used.
In the step (2), the neural network technology generally uses a multi-input multi-output neural network, that is, the input layer is provided with a plurality of nodes, and the output layer is also provided with a plurality of nodes; the learning mode is a supervised mode, namely a learning mode of adjusting the weight and the threshold of the network neuron according to the difference between the prediction output and the label. The specific internal structural features are not limited.
In the step (2), the matching degree of the synthetic seismic record of the well section selected as the sample and the well-side seismic channel is as high as possible, so that the neural network is ensured to learn a stable and correct mapping relation.
In the step (4), the filter used is recommended to be an FIR digital filter, and the filter window types may be Hanning window (Hanning), Hamming window (Hamming), Blackman window (Blackman), and the like.
In the step (4), the passband and the stopband of the band-pass filter and the stopband of the band-stop filter are in a complementary relationship. The band-stop filter of the original curve filters out low-frequency and high-frequency band components; the band-pass filter of the acoustic simulation curve filters out the acoustic simulation curve components corresponding to the dominant frequency band of the seismic data.
Example 3 (concrete examples of authentication)
The experimental work area is provided with 113 wells, and the post-stack seismic data is about 924Km2. Most of the synthetic seismic records of the well target interval are poor in matching correlation with the post-stack seismic records. The acoustic curves are reconstructed and the synthetic seismic records are reconstructed.
(1) Using the sonic log, the density was set to constant 1. The wavelet is theoretical Rake wavelet or wavelet extracted from seismic data, and the dominant frequency is close to the dominant frequency of the seismic data of 25 Hz. Making synthetic seismic records of all wells, matching time-depth relations with seismic channels beside the stacked seismic data wells, and preliminarily knowing the matching conditions of all wells;
(2) preferably selecting a part of wells with higher matching consistency of the synthetic seismic records and the well-side seismic channels, wherein the proportion is about 7%;
(3) designing a BP full-connection neural network structure with 5 layers in total, wherein each layer is provided with 70 nodes;
(4) selecting a target interval of a preferred well or a well section (possibly containing a nearby interval) with the best matching, and splitting a well-side seismic channel of the selected well section and a corresponding acoustic curve into a plurality of sections with the lengths of 70 (points) according to the step length of 2 (points) and the unit length of 70 (points) to form a sample set. Taking a seismic channel beside a well as the input of a neural network, taking an acoustic logging curve as a label, and training the neural network;
(5) the adjustable parameter of the BP neural network training is learning rate which is generally 0.1-0.5 and gradually decreases along with the training steps;
(6) and measuring the error of the predicted output and the label by using the root mean square error. After the error of the network training is small enough, such as below 0.03, the network is considered to have enough prediction capability, and then the training is completed;
(7) and performing pseudo-acoustic curve calculation on the well needing to reconstruct the acoustic curve: inputting the well side seismic channel of the well into the trained network, and outputting an acoustic wave simulation curve;
(8) and designing an FIR digital filter according to the dominant frequency band of the earthquake. Designing a band-pass filter, wherein the pass-band frequency is 10-20-80-90 Hz; a band-stop filter is designed, and the stop band frequency is 10-20-80-90 Hz. The window type is a Haiming window;
(9) carrying out band-elimination filtering on an original acoustic wave curve of the reconstructed well to obtain low-frequency and high-frequency components; performing band-pass filtering on the pseudo-acoustic wave curve to obtain components of the earthquake dominant frequency band range; and adding the two to obtain a final reconstruction curve.
(10) And using the reconstructed curve to make a synthetic seismic record again, and counting all wells, wherein the correlation coefficient of a target layer is improved from 0.6 to more than 0.8 on average, and the wells larger than or equal to 0.85 account for 63%.
Claims (7)
1. A method for reconstructing an acoustic logging curve based on seismic data constraint is characterized by comprising the following steps: firstly, making a synthetic seismic record of a well in a research area, and carrying out time-depth relation matching; selecting well sections with good matching relation, and establishing a mathematical relation between a logging acoustic curve and a well-side seismic channel by using a neural network; applying the mathematical relationship to extrapolate an acoustic wave simulation curve of the well to be reconstructed; and (3) respectively filtering out the dominant components of the original curve and the sound-like wave curve by using a digital filtering method, and then fusing the original curve and the sound-like wave curve to finally form a reconstructed curve.
2. The method of claim 1, wherein the method comprises: the specific method comprises the following steps:
(1) making a synthetic seismic record of each well, and performing time-depth relation matching;
(2) designing a neural network to represent the mapping relation between the seismic record and the acoustic logging curve; using a well with good time-depth relation and waveform characteristic matching degree as a sample well, selecting a well section, using a seismic channel beside the well as a sample, using a corresponding acoustic curve as a label, and training a neural network;
(3) setting the original acoustic curve of the well to be reconstructed as AC for the well needing the acoustic curve reconstruction, selecting the well section needing the acoustic curve reconstruction, converting the depth range into the time range by using the initial time-depth relation, and calculating the well-side seismic channel S in the corresponding time period of the well section into the pseudo-acoustic curve AC by using the trained neural networkpred；
(4) Performing band elimination filtering on the original acoustic wave curve AC, wherein the filter is fstopTo obtain AC ═ fstop(AC); to ACpredMaking band-pass filtering with filter fpassTo obtain AC'pred＝fpass(ACpred) Wherein f isstopAnd fpassThe frequency parameters of the seismic data are determined by the dominant frequency band of the seismic data;
(5) and mixing AC 'with AC'predAdding to obtain a reconstructed curve ACmerged＝AC′+AC′pred。
3. The method of claim 2, wherein the acoustic log reconstruction method based on seismic data constraints comprises: in the step (1), the density curve is set to be constant without using measured density data, and if the density data is used, all the well synthetic seismic records need to be used.
4. The method of claim 2, wherein the acoustic log reconstruction method based on seismic data constraints comprises: in the step (2), the neural network uses a multi-input multi-output neural network, the multi-input multi-output refers to a plurality of nodes of an input layer, and the output layer is also a plurality of nodes; the learning mode is a supervised mode, and the supervised mode is a learning mode of adjusting the weight and the threshold of the network neuron according to the difference between the prediction output and the label.
5. The method of claim 2, wherein the acoustic log reconstruction method based on seismic data constraints comprises: in the step (2), the matching degree of the synthetic seismic records of the well section selected as the sample and the well side seismic channels is as high as possible.
6. The method of claim 2, wherein the acoustic log reconstruction method based on seismic data constraints comprises: in the step (4), the filter used is recommended to be an FIR digital filter, and the type of the filter window is Hanning window, Hamming window or Blackman window.
7. The method of claim 2, wherein the acoustic log reconstruction method based on seismic data constraints comprises: in the step (4), the passband and stopband of the band-pass filter and the stopband of the band-stop filter are in a complementary relationship, and the band-stop filter of the original curve filters out low-frequency and high-frequency band components; the band-pass filter of the acoustic simulation curve filters out the acoustic simulation curve components corresponding to the dominant frequency band of the seismic data.
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