CN115857015A - Method for quantitatively predicting distribution of tuff in volcanic formation - Google Patents
Method for quantitatively predicting distribution of tuff in volcanic formation Download PDFInfo
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- CN115857015A CN115857015A CN202211618881.4A CN202211618881A CN115857015A CN 115857015 A CN115857015 A CN 115857015A CN 202211618881 A CN202211618881 A CN 202211618881A CN 115857015 A CN115857015 A CN 115857015A
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Abstract
The invention discloses a method for quantitatively predicting distribution of tuff in volcanic formations, which relates to the technical field of quantitatively predicting distribution of tuff and comprises the following steps: s1, analyzing logging response characteristics of volcanic rocks with different lithologies in a drilled volcanic formation, and searching or constructing a tuff characterization curve; s2, performing seismic attribute calculation such as 90-degree phase shift and relative wave impedance by using three-dimensional seismic data; s3, extracting seismic attributes of well side channels and calculating correlation of the sillimanite characterization curves; s4, performing error test according to the attributes in the front of the sequence to obtain the top n seismic attributes with the minimum error; and S5, carrying out deep learning according to the first n seismic attributes with the minimum error to obtain a tuff characterization three-dimensional data body. Through the mode, the invention provides the method for quantitatively predicting the spatial distribution of the tuff by utilizing the three-dimensional seismic data, and the spatial distribution range of the tuff in the volcanic stratum can be accurately predicted.
Description
Technical Field
The invention relates to the technical field of quantitative prediction of tuff distribution, in particular to a method for quantitatively predicting tuff distribution in a volcanic formation.
Background
The tuff is a kind of heavy volcaniclastic rock, is the transitional lithology between volcanic rock and sedimentary rock, is formed under the dual action of volcanic activity and transformation action, and the research shows that the tuff has poor storage effect, and the elastic parameter characteristics of speed, density and the like are very close to those of the volcanic tuff reservoir with good storage condition, so the recognition of the volcanic rock reservoir is greatly interfered. As the tuff is widely distributed in the volcanic basin, the prediction of the spatial distribution of the tuff in the volcanic stratum is of great importance for researching volcanic rock reservoirs and reducing the multi-solution of earthquake prediction reservoirs.
In the research on the current situation at home and abroad, relevant experiments and research progresses on the pore structure characteristics and the fluidity characteristics of the tuff are provided. In the aspect of geophysical well logging, lithology of the tuff and electrical well logging curve feature recognition research are deep, and a plurality of related recognition methods based on well logging curve intersection charts, machine learning, neural networks and the like are provided. However, the research on the tuff is limited to lithology identification and well log identification, and the research on quantitative spatial prediction of the tuff in volcanic formations is lacked.
Therefore, a method for quantitatively predicting distribution of tuff in volcanic formations is proposed to solve the above problems.
Disclosure of Invention
The invention aims to provide a method for quantitatively predicting distribution of tuff in volcanic formations, so as to solve the technical problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
a method for quantitatively predicting distribution of tuff in a volcanic formation comprises the following steps:
s1, analyzing logging response characteristics of volcanic rocks with different lithologies in a drilled volcanic formation, and searching or constructing a tuff characterization curve;
s2, performing seismic attribute calculation such as 90-degree phase shift and relative wave impedance by using the three-dimensional seismic data to obtain a plurality of seismic attribute data volumes;
s3, extracting seismic attributes of well side channels and a tuff characterization curve to calculate correlation, and sequencing according to the correlation;
s4, performing error test according to the attributes in the front of the sequence to obtain the top n seismic attributes with the minimum error;
and S5, carrying out deep learning according to the first n seismic attributes with the minimum error to obtain a tuff characterization three-dimensional data body.
Further, the method comprises a step S6 of analyzing the spatial position of the tuff on the three-dimensional tuff data body according to the value range of the tuff characterization curve, and extracting the plane distribution diagram of the tuff according to the top bottom or the secondary top bottom of the volcanic mechanism.
Furthermore, the sedimentary tuff is characterized by low resistivity on the logging curve, the gamma value is distributed in a certain interval and is lower than volcanic lava, so that a curve for distinguishing lithology is constructed, and the formula for constructing lithology indicator factors is as follows:
wherein GRJ is a curve obtained by performing region consistency correction on GR of a research region, and LLD is a resistivity logging curve; b is the threshold value of the gamma curve dividing the tuff and the lava in the region, and a is the threshold value of the resistivity curve dividing the tuff and the tuff in the region.
Further, in step S5, a nonlinear relation is established between the tuff characterization curve and the first 3 seismic attributes with the minimum error, and deep learning is performed by using the nonlinear relation to obtain a three-dimensional data body characterizing the tuff.
Advantageous effects
The invention can establish a correlation relationship by establishing the characteristics of the drilled tuff and the seismic attributes of the well side channels, and apply the correlation relationship to the seismic attribute body calculated by the three-dimensional seismic data through deep learning, thereby obtaining the three-dimensional data body representing the tuff.
The invention provides a method for quantitatively predicting the spatial distribution of tuff by using three-dimensional seismic data, which can more accurately predict the spatial distribution range of the tuff in a volcanic stratum.
The method has the characteristics of seismic attribute determination, quick realization and accurate identification, is applied to predicting the spatial distribution of the tufa in the south China, and has proved to have an effect.
The method carries out deep learning by establishing the relationship between the seismic attribute and the lithology characterization curve of the tuff, and carries out extrapolation prediction on the lithology on the well under the seismic transverse change trend, so that the extrapolation prediction of the method accords with the transverse change rule of lithology combination on seismic reflection and the attribute thereof, and simultaneously can relatively accurately reflect the transverse change rule of the volcanic rock.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
FIG. 1 is a flow chart of a method for quantitatively predicting tuff distribution in a volcanic formation.
FIG. 2 is a gamma resistivity cross-plot.
FIG. 3 is a constructed tuff characterization curve.
FIG. 4 is a plan view of the distribution of the tuff.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The present invention will be further described with reference to the following examples.
Example 1
The embodiment provides a method for quantitatively predicting distribution of tuff in a volcanic formation, and with reference to fig. 1, the method comprises the following steps:
s1, analyzing logging response characteristics of volcanic rocks with different lithologies in a drilled volcanic formation, and searching or constructing a tuff characterization curve;
the sedimentary tuff is characterized by low resistivity on the logging curve, the gamma value is distributed in a certain interval and is lower than volcanic lava, so that a curve for distinguishing lithology is constructed, and the formula for constructing lithology indicator factors is as follows:
wherein GRJ is a curve obtained by performing region consistency correction on GR of a research region, and LLD is a resistivity logging curve; b is the threshold value of the gamma curve dividing the tuff and the lava in the region, and a is the threshold value of the resistivity curve dividing the tuff and the tuff in the region;
FIG. 2 is a gamma resistivity cross-plot showing tuff when the resistivity is high and the gamma value is high; when the resistivity is small and the gamma is small, the volcanic lava is obtained; when the resistivity is high and the gamma value is low, the volcanic lava is mainly used; when the resistivity is small and the gamma value is large, the limestone is mainly precipitated;
FIG. 3 is a constructed tuff characterization curve with low values (black parts) to distinguish tuff;
s2, performing seismic attribute calculation such as 90-degree phase shift and relative wave impedance by using three-dimensional seismic data to obtain multiple seismic attribute data volumes;
s3, extracting seismic attributes of well side channels and a tuff characterization curve to calculate correlation, and sequencing according to the correlation;
| seismic attribute | Correlation | Sorting |
| 90 phase shift | 0.64 | 1 |
| Relative wave impedance | 0.62 | 2 |
| Instantaneous amplitude | 0.58 | 3 |
| … | … | … |
S4, performing error test according to the attributes in the front of the sequence to obtain the top n seismic attributes with the minimum error;
| number of seismic attributes | Error of the measurement | Sorting |
| 3 | 0.34 | 1 |
| 2 | 0.37 | 2 |
| 5 | 0.41 | 3 |
| … | … | … |
S5, performing deep learning according to the first n seismic attributes with the minimum error to obtain a tuff representation three-dimensional data volume; analyzing the spatial position of the tufa on the three-dimensional data body of the tufa according to the value range of the characterization curve of the tufa, and extracting a plane distribution map of the tufa according to the top bottom or the periodic top bottom of the volcanic mechanism;
preferably, a nonlinear relation is established by utilizing the tuff characterization curve and the first 3 seismic attributes with the minimum error, and deep learning is carried out by utilizing the nonlinear relation to obtain a three-dimensional data body for characterizing the tuff;
the principle of the invention is as follows: because the lithology of different volcanic rocks has different elastic parameter characteristics, different lithology and lithology combination changes show as different seismic waveform characteristics, the relationship between the lithology characteristic curve and the seismic attribute is established by constructing the lithology characteristic curve of the tuff, deep learning is carried out by preferably selecting various seismic attributes with high correlation with the lithology characteristic curve, and finally the spatial distribution of the tuff in the volcanic stratum is predicted;
the invention can establish a correlation relationship by establishing the characteristics of the drilled tuff and the seismic attributes of the well side channels, and apply the correlation relationship to the seismic attribute body calculated by the three-dimensional seismic data through deep learning, thereby obtaining the three-dimensional data body representing the tuff.
The invention provides a method for quantitatively predicting the spatial distribution of tuff by using three-dimensional seismic data, which can more accurately predict the spatial distribution range of the tuff in a volcanic stratum.
The method has the characteristics of seismic attribute determination, quick realization and accurate identification, is applied to predicting the spatial distribution of the tuff in the south China, and has proved to have the effect.
The method carries out deep learning by establishing the relation between the seismic attributes and the lithologic character characterization curve of the tuff, and extrapolates and predicts the lithologic character on the well under the seismic transverse change trend, so that the method extrapolates and predicts the transverse change rule of lithologic character combination on seismic reflection and the attributes thereof, and can relatively accurately reflect the transverse change rule of the volcanic rock.
In the description herein, references to the description of "one embodiment," "an example," "a specific example," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (4)
1. A method for quantitatively predicting distribution of tuff in volcanic formations is characterized by comprising the following steps: the method comprises the following steps:
s1, analyzing logging response characteristics of volcanic rocks with different lithologies in a drilled volcanic formation, and searching or constructing a tuff characterization curve;
s2, performing seismic attribute calculation such as 90-degree phase shift and relative wave impedance by using the three-dimensional seismic data to obtain a plurality of seismic attribute data volumes;
s3, extracting seismic attributes of well side channels and a tuff characterization curve to calculate correlation, and sequencing according to the correlation;
s4, performing error test according to the attributes in the front of the sequence to obtain the top n seismic attributes with the minimum error;
and S5, performing deep learning according to the first n seismic attributes with the minimum error to obtain the sillimanite representation three-dimensional data body.
2. The method for quantitatively predicting distribution of tuff in volcanic formations according to claim 1, wherein: and S6, analyzing the spatial position of the tuff on the three-dimensional tuff data body according to the value range of the tuff characterization curve, and extracting a plane distribution diagram of the tuff according to the top bottom or the periodic top bottom of the volcanic mechanism.
3. The method for quantitatively predicting distribution of tuff in a volcanic formation as claimed in claim 1, wherein: the sedimentary tuff is characterized by low resistivity on the logging curve, the gamma value is distributed in a certain interval and is lower than volcanic lava, so that a curve for distinguishing lithology is constructed, and the formula for constructing lithology indicator factors is as follows:
wherein GRJ is a curve obtained by performing region consistency correction on GR of a research region, and LLD is a resistivity logging curve; b is the threshold value of the gamma curve dividing the tuff and the lava in the region, and a is the threshold value of the resistivity curve dividing the tuff and the tuff in the region.
4. The method for quantitatively predicting distribution of tuff in volcanic formations according to claim 1, wherein: and S5, establishing a nonlinear relation by using the tuff characterization curve and the first 3 seismic attributes with the minimum error, and performing deep learning by using the nonlinear relation to obtain a three-dimensional data body for characterizing the tuff.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106707340A (en) * | 2016-12-13 | 2017-05-24 | 中国石油天然气股份有限公司大港油田分公司 | Method for predicting volcanic rock facies |
| CN108931815A (en) * | 2017-05-24 | 2018-12-04 | 中国石油化工股份有限公司 | A kind of hierarchical identification method of lithology |
| CN109388816A (en) * | 2017-08-07 | 2019-02-26 | 中国石油化工股份有限公司 | A kind of hierarchical identification method of complex lithology |
| US20190293815A1 (en) * | 2016-05-25 | 2019-09-26 | Schlumberger Technology Corporation | Elastic parameter estimation |
| CN110687599A (en) * | 2018-07-04 | 2020-01-14 | 中国石油天然气股份有限公司 | Well control self-coding lithology identification method for igneous rock development area |
| CN111577263A (en) * | 2019-02-18 | 2020-08-25 | 中国石油化工股份有限公司 | Tuff logging identification method |
| US20210026031A1 (en) * | 2019-07-26 | 2021-01-28 | Yangtze University | Dolomite reservoir prediction method and system based on well and seismic combination, and storage medium |
-
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- 2022-12-15 CN CN202211618881.4A patent/CN115857015B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190293815A1 (en) * | 2016-05-25 | 2019-09-26 | Schlumberger Technology Corporation | Elastic parameter estimation |
| CN106707340A (en) * | 2016-12-13 | 2017-05-24 | 中国石油天然气股份有限公司大港油田分公司 | Method for predicting volcanic rock facies |
| CN108931815A (en) * | 2017-05-24 | 2018-12-04 | 中国石油化工股份有限公司 | A kind of hierarchical identification method of lithology |
| CN109388816A (en) * | 2017-08-07 | 2019-02-26 | 中国石油化工股份有限公司 | A kind of hierarchical identification method of complex lithology |
| CN110687599A (en) * | 2018-07-04 | 2020-01-14 | 中国石油天然气股份有限公司 | Well control self-coding lithology identification method for igneous rock development area |
| CN111577263A (en) * | 2019-02-18 | 2020-08-25 | 中国石油化工股份有限公司 | Tuff logging identification method |
| US20210026031A1 (en) * | 2019-07-26 | 2021-01-28 | Yangtze University | Dolomite reservoir prediction method and system based on well and seismic combination, and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| BUZHOU HUANG ET AL.: "Characteristics of log responses and lithology determination of igneous rock reservoirs", JOURNAL OF GEOPHYSICS AND ENGINEERING, pages 51 * |
| 冉启全等: "基于神经网络的多属性火成岩岩性反演技术", 西南石油学院学报, vol. 28, no. 1, pages 5 - 8 * |
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