CN112485844B - Volcanic lithology lithofacies distribution prediction method and prediction device - Google Patents
Volcanic lithology lithofacies distribution prediction method and prediction device Download PDFInfo
- Publication number
- CN112485844B CN112485844B CN201910866166.4A CN201910866166A CN112485844B CN 112485844 B CN112485844 B CN 112485844B CN 201910866166 A CN201910866166 A CN 201910866166A CN 112485844 B CN112485844 B CN 112485844B
- Authority
- CN
- China
- Prior art keywords
- volcanic
- lithofacies
- lithology
- well
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V11/00—Prospecting or detecting by methods combining techniques covered by two or more of main groups G01V1/00 - G01V9/00
Landscapes
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Geophysics (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The application provides a volcanic lithology lithofacies distribution prediction method and a prediction device, wherein the volcanic lithology lithofacies distribution prediction method comprises the steps of obtaining multiphase logging data and seismic data, wherein the multiphase logging data comprise: single-well lithofacies and inter-well facies, the single-well lithofacies being used to obtain qualitative volcanic lithofacies data; the interwell phase qualitatively characterizes the lithology of the volcanic rock according to single well logging data; the seismic data comprises seismic phases, and the seismic phases are used for determining single-layer lithofacies boundaries in a seismic mode; and establishing an intersection diagram of the volcanic institution according to the multiphase logging data and the seismic data. The method solves the problems that the logging interpretation technology in the prior art cannot accurately divide the inter-well lithofacies types and the volcanic rock is difficult to identify in the favorable reservoir lithofacies.
Description
Technical Field
The application relates to the technical field of geophysics for petroleum exploration and development, in particular to a volcanic lithology distribution prediction method and a volcanic lithology distribution prediction device.
Background
Volcanic gas reservoirs, which are a special type of gas reservoirs, have gradually become important exploration targets and oil and gas reserves growing points in China. The lithology and lithofacies of the volcanic rock reservoir change rapidly, and the control factors of oil and gas reservoir are complex, so that the identification of the lithofacies of the volcanic rock favorable reservoir becomes the current research difficulty.
In the prior art, the method identifies the favorable reservoir rock phases of the volcanic rock by adopting a logging interpretation technology, namely the conventional logging interpretation technology qualitatively indicates the geological information of the reservoir rock phases near the volcanic rock well hole by acquiring each macroscopic physical property (such as a natural gamma curve (GR), an Acoustic Curve (AC), a resistance curve (RT) and the like) of single-well stratum rock, so as to divide the rock phase types of the reservoir.
As shown in fig. 1, taking a volcanic gas reservoir as an example, a well zone is selected according to a logging interpretation technology, a natural gamma curve (GR) is taken as an abscissa, and a resistance curve (RT) is taken as an ordinate to establish a plate for identifying the lithofacies among volcanic wells, so that the neutral overflow phase, the volcanic sediment phase and the equal lithofacies distribution areas invaded by the volcanic sediment phase can be roughly distinguished from fig. 1, the purpose of qualitatively identifying the lithofacies is basically achieved, but quantitative characterization of different lithofacies cannot be performed.
As the output of the volcanic rock changes along with the change of volcanic eruption characteristics and the lithology of the volcanic rock reservoir change quickly, the volcanic gas reservoir with large well spacing and irregular well pattern development can be divided by the conventional well logging interpretation technology, the accuracy of the division of each lithofacies can not be ensured, and the difficulty of the volcanic rock in the identification of the lithofacies of the reservoir is increased.
Disclosure of Invention
The application mainly aims to provide a volcanic lithology lithofacies distribution prediction method and a volcanic lithofacies distribution prediction device, which are used for solving the problems that the logging interpretation technology in the prior art cannot accurately divide the types of lithofacies among wells and the volcanic rock is difficult to identify in favorable reservoir lithofacies.
In order to achieve the above object, according to one aspect of the present application, there is provided a volcanic lithology lithofacies distribution prediction method including acquiring multiphase well logging data and seismic data, wherein the multiphase well logging data includes: single-well lithofacies and inter-well facies, wherein the single-well lithofacies are used for acquiring qualitative volcanic lithofacies data; the inter-well facies qualitatively characterize volcanic lithology according to single-well logging data to communicate single-well lithofacies; the seismic data comprises seismic facies, wherein the seismic facies are used for determining single-layer lithofacies boundaries in a seismic mode; establishing an intersection graph of the volcanic mechanism according to the multiphase logging data and the seismic data, wherein the intersection graph comprises at least one of the following volcanic mechanisms: channel phase, overflow phase, burst phase, secondary volcanic intrusion phase; and predicting lithologic lithofacies distribution of the volcanic institutions according to the intersection map.
Further, where the multiphase well log data includes a log natural gamma value and the seismic data includes a layer velocity parameter, establishing an intersection of volcanic mechanisms includes establishing an intersection of volcanic mechanisms with the log natural gamma value as an abscissa of the intersection of volcanic mechanisms and with the layer velocity parameter as an ordinate of the intersection of volcanic mechanisms.
Further, predicting the lithology petrography distribution of the volcanic mechanism from the intersection graph includes predicting the lithology petrography distribution of the volcanic mechanism from the layer velocity parameter.
Further, the layer velocity parameters are corrected in the seismic data using the multiphase log data.
Further, the layer speed parameter V is calculated using the following formula int ：Wherein n is the type of rock radiation; h i Well depth in units of radiation: m; alpha is a sound wave time difference correction factor, and the range of alpha is 0.5<α<3；S i A sensitivity factor corresponding to a certain element; l (L) i Span for sonic logging, units: m; t (T) i As acoustic time difference, unit: s.
Further, after the lithologic facies of the volcanic mechanism are predicted, the volcanic lithology facies distribution prediction method further comprises correcting lithology facies distribution prediction of the volcanic institution through drilling lithology facies calibration.
Further, correcting lithology distribution prediction of the volcanic mechanism comprises establishing a lithology transverse quantitative prediction standard by establishing a quantitative relation between lithology and root mean square amplitude attribute and fused curve inversion wave impedance attribute, and inverting lithology three-dimensional space distribution.
Further, the lithology distribution prediction of the volcanic mechanism is corrected to obtain the corresponding volcanic lithology with wave impedance from big to small, which is sequentially as follows: an invaded phase, an exploded phase, a medium overflow phase, and a volcanic clastic deposit phase.
According to another aspect of the present application, there is provided a volcanic lithology lithofacies distribution prediction apparatus, including an acquisition module configured to acquire multiphase well log data and seismic data, wherein the multiphase well log data includes: single-well lithofacies and inter-well facies, wherein the single-well lithofacies are used for acquiring qualitative volcanic lithofacies data; the inter-well facies qualitatively characterize volcanic lithology according to single-well logging data to communicate single-well lithofacies; the seismic data comprises seismic facies, wherein the seismic facies are used for determining single-layer lithofacies boundaries in a seismic mode; the building module is used for building an intersection graph of the volcanic mechanism according to the multiphase logging data and the seismic data, wherein the intersection graph comprises at least one of the following volcanic mechanisms: channel phase, overflow phase, burst phase, secondary volcanic intrusion phase; and the prediction module is used for predicting lithologic lithology distribution of the volcanic institution according to the intersection map.
Further, in the case where the multiphase well logging data includes a logging natural gamma value and the seismic data includes a layer velocity parameter, the establishing module is configured to establish an intersection map of the volcanic mechanism with the logging natural gamma value as an abscissa of the intersection map of the volcanic mechanism and with the layer velocity parameter as an ordinate of the intersection map of the volcanic mechanism.
By applying the technical scheme of the application, obvious density difference between different volcanic rocks (such as acid volcanic rocks and basic volcanic rocks) is considered, and a specific parameter in the seismic data is sensitive to volcanic rock responses of different lithofacies.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:
FIG. 1 illustrates an intersection of volcanic mechanisms established based on existing logging interpretation parameters;
FIG. 2 illustrates a flow chart of screening parameters in multiphase log data and seismic data involved in a volcanic lithology lithofacies distribution prediction method according to an alternative embodiment of the present application;
FIG. 3 shows an intersection of volcanic mechanisms established according to the volcanic lithology lithofacies distribution prediction method provided by the application;
fig. 4 shows a plane distribution of the facies of a volcanic gas reservoir.
Fig. 5 shows a flow chart of a volcanic lithology lithofacies distribution prediction method according to an alternative embodiment of the application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the application, its application, or uses. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Fig. 5 is a flowchart of a volcanic lithology lithofacies distribution prediction method according to an embodiment of the present application, as shown in fig. 5, the method comprising the steps of:
step S102, multiphase logging data and seismic data are acquired, wherein the multiphase logging data comprise: single-well lithofacies and inter-well facies, wherein the single-well lithofacies are used for acquiring qualitative volcanic lithofacies data; the inter-well facies qualitatively characterize volcanic lithology according to single-well logging data to communicate single-well lithofacies; the seismic data comprises seismic facies, wherein the seismic facies are used for determining single-layer lithofacies boundaries in a seismic mode;
step S104, establishing an intersection graph of the volcanic mechanism according to the multiphase logging data and the seismic data, wherein the intersection graph comprises at least one of the following volcanic mechanism: channel phase, overflow phase, burst phase, secondary volcanic intrusion phase;
and S106, predicting lithologic lithology distribution of the volcanic institutions according to the intersection map.
Through the steps, before the intersection map of the volcanic mechanism is established, seismic data sensitive to the volcanic rock responses of different lithofacies and parameters in multiphase logging data are collected, so that the subsequently established intersection map can effectively divide the lithofacies types of the lithofacies between wells, and the distribution of the lithofacies of the volcanic rock is quantitatively identified; in addition, through combining the intersection diagram of the volcanic mechanism established by each parameter in the multiphase logging data and the seismic data, the inter-well lithofacies of the volcanic favorable reservoir are distinguished more obviously, the accuracy of the lithofacies division is improved, the difficulty of the volcanic favorable reservoir lithofacies identification is reduced, and important basis is provided for the subsequent volcanic favorable reservoir spread prediction and well position deployment, so that the problems in the prior art are solved.
The method disclosed by the application combines multiphase logging data and seismic data to quantitatively identify the lithofacies among volcanic wells based on the fact that the obvious density difference exists between different acidic volcanic rocks and basic volcanic rocks, so that a specific parameter in the seismic data is sensitive to volcanic rock responses of different lithofacies.
In the application, the single-well lithofacies are qualitative volcanic lithofacies data obtained in the drilling process, and the inter-well lithofacies are qualitatively characterized according to the single-well logging data, so that the single-well lithofacies are communicated, and further, the result of the inter-well volcanic lithofacies qualitative division is obtained.
It should be noted that in the present application, the multiphase logging data and the seismic data may each include different types of parameters, for example, the logging parameters in the multiphase logging data may include a natural gamma curve (GR), a sonic curve (AC), a resistance curve (RT), and a densityA degree curve (DEN), the seismic parameters in the seismic data including a layer velocity (V int ) In an alternative embodiment, parameters in the multiphase log data and the seismic data need to be screened prior to establishing the intersection map of the volcanic institution, the parameter screening process being shown in fig. 2. The intersection of volcanic mechanisms in accordance with the present application is shown in FIG. 2 as a plate.
In fig. 2, the seismic parameters in the seismic data are corrected by the logging parameters in the multiphase logging data to obtain single seismic parameters, and when the single logging parameters in the multiphase logging data are orthogonal to the single seismic parameters in the seismic data, the inter-well lithology plate coincidence rate is more than 80%; when the coincidence rate of the inter-well lithofacies patterns is less than or equal to 80%, single logging parameters or single seismic parameters need to be replaced until the coincidence rate of the inter-well lithofacies patterns is more than 80%, so that single logging parameters in multiphase logging data and single seismic parameters in seismic data are determined; in addition, taking into account that the acid lithofacies in the volcanic rock contain higher radioactive substances, the reservoir lithofacies of the volcanic rock are characterized by using logging natural gamma values which are more sensitive to the volcanic rock; meanwhile, as the acid volcanic rock has obvious density difference to the basic volcanic rock, the amplitude difference is obvious when the earthquake logging is interpreted, namely the response to the layer speed parameter is sensitive, so the layer speed parameter is used as the ordinate of an intersection graph of the volcanic mechanism; and when the single logging parameter is a logging natural gamma value and the single seismic parameter is a layer velocity parameter, the coincidence rate of the inter-well lithofacies pattern is more than 80%, and then an intersection map of the volcanic mechanism is established according to the logging natural gamma value and the layer velocity parameter.
As shown in table 1, which shows the log natural gamma values and layer velocity parameters for the same horizon screened for the a to F shot regions according to the screening flow diagram in fig. 2:
TABLE 1 identification parameter table for lithofacies logging of certain volcanic gas reservoir
| Sequence number | Lithofacies | Horizon layer | GR[API] | Layer speed [ m/s ]] | Throwing point area |
| 1 | Medium base overflow phase | C | 8-83 | 3100-5940 | Zone A |
| 2 | Mud, carbonaceous mudstone and coal | C | 25-80 | 2530-3450 | Zone B |
| 3 | Volcanic sediment phase (e.g. curdling sand) | C | 30-92 | 3040-4450 | Region C |
| 4 | Burst phase (tuff, volcanic breccia) | C | 53-127 | 3100-4970 | Zone D |
| 5 | Invasive phase | C | >83 | 4350-5600 | Zone E |
| 6 | Acid overflow phase | C | >120 | 3340-4680 | Zone F |
According to the data in table 1, as shown in fig. 3, in the case where the multiphase log data includes a log natural gamma value and the seismic data includes a layer velocity parameter, establishing an intersection map of volcanic mechanisms includes establishing an intersection map of volcanic mechanisms with the log natural gamma value as an abscissa of the intersection map of volcanic mechanisms and with the layer velocity parameter as an ordinate of the intersection map of volcanic mechanisms. As is clear from the figure, the area a represents the medium overflow phase, the area B represents the mud, carbonaceous mudstone and coal, the area C represents the volcanic sedimentary rock, the area D represents the burst phase, the area E represents the invaded phase, and the area F represents the acid overflow phase, so that the lithofacies of the intersection map of the volcanic mechanism established according to the parameters after screening are clearly and precisely divided.
In the present application, considering that the seismic facies in the seismic data can determine a single-layer facies boundary by means of a seismic method, it is preferable that the prediction of the lithologic facies distribution of the volcanic mechanism based on the intersection map includes the prediction of the lithologic facies distribution of the volcanic mechanism based on the layer velocity parameter.
As an alternative embodiment, in the present application, the inter-volcanic rock facies can be quantitatively characterized in order to ensure a layer velocity parameter in the seismic data. For greater accuracy, the interval velocity parameters are preferably corrected in the seismic data using multiphase log data.
For example, the layer speed parameter V may be calculated using the following formula int ：Wherein n is the type of rock radiation; h i Well depth in units of radiation: m; alpha is a sound wave time difference correction factor, and the range of alpha is 0.5<α<3；S i A sensitivity factor corresponding to a certain element; l (L) i Span for sonic logging, units: m; t (T) i As acoustic time difference, unit: s.
In the present application, in order to ensure the feasibility of the volcanic lithology and lithology facies distribution prediction method, it is preferable that the volcanic lithology and lithology facies distribution prediction method further includes correcting the volcanic lithology and lithology facies distribution prediction of the volcanic institution by drilling lithology and lithology facies calibration after the volcanic institution lithology and lithology facies are predicted. Thus, the accuracy of inter-well lithofacies division of the volcanic favorable reservoir is further improved.
There are many ways of correction, one of which is exemplified in the present embodiment: correcting lithology distribution prediction of the volcanic mechanism comprises establishing a quantitative relation between lithology and root mean square amplitude attribute and fused curve inversion wave impedance attribute, establishing a lithology transverse quantitative prediction standard, and inverting lithology three-dimensional space distribution. Thus, the correction reliability of lithologic rock phase distribution prediction of the volcanic mechanism is improved.
The following describes this alternative embodiment with reference to fig. 4, and as shown in fig. 4, the wave impedance obtained by correcting the lithology facies distribution prediction of the volcanic mechanism is sequentially as follows: an invaded phase, an exploded phase, a medium overflow phase, and a volcanic clastic deposit phase. Thus, the volcanic lithology lithofacies distribution prediction method provided by the embodiment of the application can effectively divide the inter-well lithofacies of the volcanic favorable reservoir.
The volcanic lithology and lithology distribution prediction method provided by the application is successfully applied to the development of a certain volcanic gas reservoir in China, the coincidence rate of the intersection map of the volcanic mechanism established by the volcanic lithology and lithology distribution prediction method reaches 80%, the classification and the distinction of the volcanic favorable reservoir inter-well lithology (an overflow phase of an area A, an explosion phase of an area D, an intrusion phase of an area E and an acidic overflow phase of an area F) are obvious, and an important basis is provided for the favorable reservoir spreading prediction and well position deployment.
The embodiment also provides a volcanic lithology lithofacies distribution prediction device, and the modules in the device correspond to the steps in the method, and the obtained effects and the solved problems of the device are already described and are not described herein. The volcanic lithology lithofacies distribution prediction device comprises an acquisition module, wherein the acquisition module is used for acquiring multiphase logging data and seismic data, and the multiphase logging data comprise: single-well lithofacies and inter-well facies, wherein the single-well lithofacies are used for acquiring qualitative volcanic lithofacies data; the inter-well facies qualitatively characterize volcanic lithology according to single-well logging data to communicate single-well lithofacies; the seismic data comprises seismic facies, wherein the seismic facies are used for determining single-layer lithofacies boundaries in a seismic mode; the building module is used for building an intersection graph of the volcanic mechanism according to the multiphase logging data and the seismic data, wherein the intersection graph comprises at least one of the following volcanic mechanisms: channel phase, overflow phase, burst phase, secondary volcanic intrusion phase; and the prediction module is used for predicting lithologic lithology distribution of the volcanic institution according to the intersection map.
As an alternative embodiment, where the multiphase log data includes a log natural gamma value and the seismic data includes a layer velocity parameter, the establishing module is configured to establish the intersection map of the volcanic mechanism with the log natural gamma value as an abscissa of the intersection map of the volcanic mechanism and the layer velocity parameter as an ordinate of the intersection map of the volcanic mechanism.
The apparatus may be part of software or may be implemented in connection with corresponding hardware. And will not be described in detail herein.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present application. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
The relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present application unless it is specifically stated otherwise. Meanwhile, it should be understood that the sizes of the respective parts shown in the drawings are not drawn in actual scale for convenience of description. Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but should be considered part of the specification where appropriate. In all examples shown and discussed herein, any specific values should be construed as merely illustrative, and not a limitation. Thus, other examples of the exemplary embodiments may have different values. It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
Spatially relative terms, such as "above … …," "above … …," "upper surface at … …," "above," and the like, may be used herein for ease of description to describe one device or feature's spatial location relative to another device or feature as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations in use or operation in addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, elements described as "above" or "over" other devices or structures would then be oriented "below" or "beneath" the other devices or structures. Thus, the exemplary term "above … …" may include both orientations of "above … …" and "below … …". The device may also be positioned in other different ways (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present application. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein.
The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (5)
1. The volcanic lithology lithofacies distribution prediction method is characterized by comprising the following steps of:
acquiring multiphase well log data and seismic data, wherein the multiphase well log data comprises: single-well lithofacies and inter-well facies, wherein the single-well lithofacies are used for acquiring qualitative volcanic lithofacies data; the inter-well facies qualitatively characterize volcanic lithology according to single-well logging data to communicate the single-well lithofacies; the seismic data comprises a seismic phase, wherein the seismic phase is used for determining a single-layer lithofacies boundary in a seismic mode;
establishing an intersection graph of the volcanic mechanism according to the multiphase logging data and the seismic data, wherein the intersection graph comprises at least one of the following volcanic mechanism: channel phase, overflow phase, burst phase, secondary volcanic intrusion phase;
predicting lithologic lithofacies distribution of the volcanic mechanism according to the intersection map;
where the multiphase well log data includes logging natural gamma values and the seismic data includes a layer velocity parameter, establishing an intersection map of the volcanic mechanism includes:
taking the logging natural gamma value as the abscissa of the intersection graph of the volcanic institution, and,
establishing an intersection map of the volcanic mechanism by taking the layer speed parameter as an ordinate of the intersection map of the volcanic mechanism;
predicting lithologic lithology distribution of the volcanic mechanism according to the intersection graph comprises:
predicting lithologic lithofacies distribution of the volcanic mechanism according to the layer speed parameter;
the layer speed parameters are obtained by correcting the layer speed parameters in the seismic data by using the multiphase logging data;
calculating the layer speed parameter V using the formula int ：
Wherein n is the type of rock radiation; h i Well depth in units of radiation: m; alpha is a sound wave time difference correction factor, and the range of alpha is 0.5<α<3；S i A sensitivity factor corresponding to a certain element; l (L) i Span for sonic logging, units: m; t (T) i As acoustic time difference, unit: s.
2. The volcanic lithology and lithology distribution prediction method according to claim 1, wherein after predicting the lithology and lithology of the volcanic institution, the volcanic lithology and lithology distribution prediction method further comprises:
and correcting lithologic facies distribution prediction of the volcanic mechanism through drilling lithologic facies calibration.
3. The method of volcanic lithology and lithology distribution prediction according to claim 2, wherein correcting the lithology and lithology distribution prediction of the volcanic mechanism comprises:
and establishing a transverse quantitative prediction standard of the lithofacies, and inverting the three-dimensional spatial distribution of the lithofacies by establishing a quantitative relation between lithofacies and root mean square amplitude attribute and the fused curve inversion wave impedance attribute.
4. The method for predicting lithology and lithology of volcanic organization according to claim 2, wherein the wave impedance obtained by correcting the lithology and lithology distribution of volcanic organization is as follows: an invaded phase, an exploded phase, a medium overflow phase, and a volcanic clastic deposit phase.
5. A volcanic lithology lithofacies distribution prediction apparatus, comprising:
the system comprises an acquisition module for acquiring multiphase logging data and seismic data, wherein the multiphase logging data comprises: single-well lithofacies and inter-well facies, wherein the single-well lithofacies are used for acquiring qualitative volcanic lithofacies data; the inter-well facies qualitatively characterize volcanic lithology according to single-well logging data to communicate the single-well lithofacies; the seismic data comprises a seismic phase, wherein the seismic phase is used for determining a single-layer lithofacies boundary in a seismic mode;
the establishing module is used for establishing an intersection diagram of the volcanic mechanism according to the multiphase logging data and the seismic data, wherein the intersection diagram comprises at least one of the following volcanic mechanism: channel phase, overflow phase, burst phase, secondary volcanic intrusion phase;
the prediction module is used for predicting lithology and lithology distribution of the volcanic mechanism according to the intersection map;
in the case where the multiphase log data includes log natural gamma values and the seismic data includes a layer velocity parameter, the setup module is to:
taking the logging natural gamma value as the abscissa of the intersection graph of the volcanic institution, and,
establishing an intersection map of the volcanic mechanism by taking the layer speed parameter as an ordinate of the intersection map of the volcanic mechanism;
predicting lithologic lithology distribution of the volcanic mechanism according to the intersection graph comprises:
predicting lithologic lithofacies distribution of the volcanic mechanism according to the layer speed parameter;
the layer speed parameters are obtained by correcting the layer speed parameters in the seismic data by using the multiphase logging data;
calculating the layer speed parameter V using the formula int ：
Wherein n is the type of rock radiation; h i Well depth in units of radiation: m; alpha is a sound wave time difference correction factor, and the range of alpha is 0.5<α<3；S i A sensitivity factor corresponding to a certain element; l (L) i Span for sonic logging, units: m; t (T) i As acoustic time difference, unit: s.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910866166.4A CN112485844B (en) | 2019-09-12 | 2019-09-12 | Volcanic lithology lithofacies distribution prediction method and prediction device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910866166.4A CN112485844B (en) | 2019-09-12 | 2019-09-12 | Volcanic lithology lithofacies distribution prediction method and prediction device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112485844A CN112485844A (en) | 2021-03-12 |
| CN112485844B true CN112485844B (en) | 2023-10-27 |
Family
ID=74920694
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910866166.4A Active CN112485844B (en) | 2019-09-12 | 2019-09-12 | Volcanic lithology lithofacies distribution prediction method and prediction device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112485844B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5870690A (en) * | 1997-02-05 | 1999-02-09 | Western Atlas International, Inc. | Joint inversion processing method for resistivity and acoustic well log data |
| CN104360385A (en) * | 2014-11-26 | 2015-02-18 | 中国石油集团川庆钻探工程有限公司地球物理勘探公司 | Method for comprehensively establishing initial depth interval velocity model by combining seismogeology understanding |
| CN105510993A (en) * | 2015-12-23 | 2016-04-20 | 中国石油大学(北京) | Foreland basin deep buried and compressed type complex gypsum-salt rock identification and distribution prediction method |
| 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 |
| CN107422384A (en) * | 2017-03-20 | 2017-12-01 | 中石化石油工程技术服务有限公司 | A kind of method that vertical seismic profile data is imitated using well-log information |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR2909775A1 (en) * | 2006-12-11 | 2008-06-13 | Inst Francais Du Petrole | METHOD FOR CONSTRUCTING A GEOLOGICAL MODEL OF A SUB-SOIL FORMATION CONSTRAINTED BY SEISMIC DATA |
-
2019
- 2019-09-12 CN CN201910866166.4A patent/CN112485844B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5870690A (en) * | 1997-02-05 | 1999-02-09 | Western Atlas International, Inc. | Joint inversion processing method for resistivity and acoustic well log data |
| CN104360385A (en) * | 2014-11-26 | 2015-02-18 | 中国石油集团川庆钻探工程有限公司地球物理勘探公司 | Method for comprehensively establishing initial depth interval velocity model by combining seismogeology understanding |
| CN105510993A (en) * | 2015-12-23 | 2016-04-20 | 中国石油大学(北京) | Foreland basin deep buried and compressed type complex gypsum-salt rock identification and distribution prediction method |
| 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 |
| CN107422384A (en) * | 2017-03-20 | 2017-12-01 | 中石化石油工程技术服务有限公司 | A kind of method that vertical seismic profile data is imitated using well-log information |
Non-Patent Citations (2)
| Title |
|---|
| Facing geological mapping at low-latitude volcanoes: The Dona˜ Juana Volcanic Complex study-case, SW-Colombia;Natalia Pardo et al.;《Journal of Volcanology and Geothermal Research》;第385卷;第46-67页 * |
| 火成岩地震识别及构造描述方法研究;孙淑艳 等;《特种油气藏》;第10卷(第1期);第47-50、54页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112485844A (en) | 2021-03-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ezati et al. | Characterization of micro-fractures in carbonate Sarvak reservoir, using petrophysical and geological data, SW Iran | |
| US7453767B1 (en) | Method for deriving a 3D GRAZ seismic attribute file | |
| CN104502971B (en) | Method for detecting total organic carbon content and brittleness space distribution of shale reservoir | |
| Yarmohammadi et al. | An integrated approach for heterogeneity analysis of carbonate reservoirs by using image log based porosity distributions, NMR T2 curves, velocity deviation log and petrographic studies: A case study from the South Pars gas field, Persian Gulf Basin | |
| US7487047B2 (en) | Method of interpreting well data | |
| Russel-Houston et al. | Paleokarst in the Grosmont formation and reservoir implications, Saleski, Alberta, Canada | |
| Wagner et al. | Quantitative application of poststack acoustic impedance inversion to subsalt reservoir development | |
| Bredesen et al. | Seismic reservoir and source-rock analysis using inverse rock-physics modeling: A Norwegian Sea demonstration | |
| Cerveny et al. | Reducing uncertainty with fault-seal analysis | |
| Cluff et al. | Petrophysics of the Lance sandstone reservoirs in Jonah field, Sublette County, Wyoming | |
| CN112505754A (en) | Method for collaborative partitioning sedimentary microfacies by well-seismic based on high-precision sequence grid model | |
| Holden et al. | Integration of production logs helps to understand heterogeneity of Mishrif reservoir in Rumaila | |
| Bredesen et al. | Seismic characterization of geothermal sedimentary reservoirs: A field example from the Copenhagen area, Denmark | |
| Casini et al. | Results of a 3D seismic survey at the Travale (Italy) test site | |
| Glinskikh et al. | Forward modeling and inversion of induction logs from shaly sand reservoirs using petrophysical conductivity models | |
| Eisner et al. | Effective VTI anisotropy for consistent monitoring of microseismic events | |
| Niculescu et al. | Principal component analysis as a tool for enhanced well log interpretation | |
| CN112485844B (en) | Volcanic lithology lithofacies distribution prediction method and prediction device | |
| Kadhim et al. | Investigation of petrophysical properties for Yamamma carbonate formation | |
| Alfataierge et al. | 3D hydraulic fracture simulation integrated with 4D time-lapse multicomponent seismic and microseismic interpretation, Wattenberg field, Colorado | |
| George et al. | Challenges and key learning for developing tight carbonate reservoirs | |
| Sahoo* et al. | Application of Lithofacies Modelling in Enhancing the Well Productivity; An Example from Eagle Ford Shale | |
| Deepa et al. | Integrated study of a fractured granitic basement reservoir with connectivity analysis and identification of sweet spots: Cauvery Basin, India | |
| Nourollah et al. | Seal potential of shale sequences through seismic anisotropy: Case study from Exmouth Sub-basin, Australia | |
| Loutfi et al. | Permian Khuff in Abu Dhabi: Geological re-evaluation using well logs, cores, testing, and seismic data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |