CN109283577B - Seismic horizon calibration method - Google Patents
Seismic horizon calibration method Download PDFInfo
- Publication number
- CN109283577B CN109283577B CN201710596550.8A CN201710596550A CN109283577B CN 109283577 B CN109283577 B CN 109283577B CN 201710596550 A CN201710596550 A CN 201710596550A CN 109283577 B CN109283577 B CN 109283577B
- Authority
- CN
- China
- Prior art keywords
- seismic
- horizon
- calibration
- work area
- time window
- 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
- 238000000034 method Methods 0.000 title claims abstract description 50
- 238000012545 processing Methods 0.000 claims description 19
- 238000004458 analytical method Methods 0.000 claims description 14
- 238000013517 stratification Methods 0.000 claims description 11
- 238000010206 sensitivity analysis Methods 0.000 claims description 8
- 238000005553 drilling Methods 0.000 claims description 3
- 239000003550 marker Substances 0.000 claims description 3
- 239000010410 layer Substances 0.000 description 43
- 239000004576 sand Substances 0.000 description 15
- 230000000994 depressogenic effect Effects 0.000 description 14
- 239000003921 oil Substances 0.000 description 10
- 235000008302 Chamaenerion angustifolium Nutrition 0.000 description 9
- 241000721098 Epilobium Species 0.000 description 9
- 238000011160 research Methods 0.000 description 5
- 238000001228 spectrum Methods 0.000 description 5
- 238000011161 development Methods 0.000 description 4
- 230000035945 sensitivity Effects 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000000605 extraction Methods 0.000 description 3
- 239000004215 Carbon black (E152) Substances 0.000 description 2
- 238000005056 compaction Methods 0.000 description 2
- 229930195733 hydrocarbon Natural products 0.000 description 2
- 150000002430 hydrocarbons Chemical class 0.000 description 2
- 241000282320 Panthera leo Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000008021 deposition Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- ZINJLDJMHCUBIP-UHFFFAOYSA-N ethametsulfuron-methyl Chemical compound CCOC1=NC(NC)=NC(NC(=O)NS(=O)(=O)C=2C(=CC=CC=2)C(=O)OC)=N1 ZINJLDJMHCUBIP-UHFFFAOYSA-N 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 238000013508 migration Methods 0.000 description 1
- 230000005012 migration Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 239000003129 oil well Substances 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
- 238000005096 rolling process Methods 0.000 description 1
- 239000002356 single layer Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/30—Analysis
- G01V1/307—Analysis for determining seismic attributes, e.g. amplitude, instantaneous phase or frequency, reflection strength or polarity
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/284—Application of the shear wave component and/or several components of the seismic signal
- G01V1/286—Mode conversion
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/61—Analysis by combining or comparing a seismic data set with other data
Landscapes
- Engineering & Computer Science (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Acoustics & Sound (AREA)
- Environmental & Geological Engineering (AREA)
- Geology (AREA)
- General Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Geophysics (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The invention relates to a seismic horizon calibration method, which is characterized in that on the basis of a horizon vertical calibration result obtained by the conventional vertical horizon calibration method, an optimal reservoir calibration position is determined according to the optimal matching relation between the thickness of each single-well target reservoir and seismic attribute information in the transverse direction. The invention provides a method for combining vertical calibration and transverse calibration, which is characterized in that a sliding time window is adopted to search the optimal matching point of the thickness parameter of a multi-well reservoir in the transverse direction and the seismic sensitive attribute, and the optimal matching point is used as a final calibration result, is suitable for the condition that a plurality of wells exist in a seismic work area, and improves the calibration precision of a thin layer.
Description
Technical Field
The invention belongs to the technical field of reservoir prediction and lithologic oil and gas reservoir analysis in the exploration and development processes of oil and gas fields, and particularly relates to a seismic horizon calibration method.
Background
Lithologic hydrocarbon reservoir exploration and development become an important field of current oil and gas field exploration and development, people develop various research works such as attribute extraction and analysis, pre-stack/post-stack seismic attribute inversion and the like in order to obtain more direct and detailed reservoir lithologic, physical property and oil-gas-containing information from seismic data, but any seismic information and attribute extraction aiming at a target reservoir cannot be calibrated by a position between the seismic information and logging information. That is to say, it is a very important basic work to utilize the logging information to mark the seismic horizon, because it is the precondition of reservoir prediction research in the exploration and development of lithologic hydrocarbon reservoir, it is the bridge connecting seismic data and reservoir.
Currently, there are two main methods for horizon calibration: one is the horizon calibration by making the relative comparison between the acoustic synthetic seismic record and the well side seismic channel; the other is direct horizon scaling with VSP recording. The methods all belong to vertical calibration methods, namely the horizon is calibrated sequentially from shallow to deep along the borehole diameter, and the methods are frequently multi-solvable and limited when solving the complex practical problem.
Problems with synthetic recording horizon calibration:
1. the reflection coefficient of the wave impedance interface for making the synthetic record is calculated according to the acoustic logging information and the density logging information, but the two items of oil well information can be influenced by mud pollution and the structure of a well wall;
2. the conventional synthetic seismic recording layer position calibration technology adopts a one-dimensional convolution model under the assumed condition of a horizontal layered medium, and does not consider the influence of factors such as seismic wave transmission loss, multiples, converted longitudinal waves and the like on well-side seismic channels;
3. when the time migration seismic data are used for horizon calibration, the reflection data are considered to come from the position right below the position of the reflection data, and the drift of seismic imaging rays in the transverse direction is not considered, so that the difference exists between the synthetic seismic record and the horizontal contrast of the well-side seismic channel;
4. the logging velocity is the parawell vertical interval velocity, while the surface seismic has the root mean square velocity of the incident angle. Therefore, in the deep-time conversion of well information, a closure difference with ground seismic data occurs, and closure can be performed only by fine adjustment of a velocity field, however, in actual work, a thin interbed often represents a weak reflected wave with poor continuity, and the above velocity fine adjustment can cause calibration ambiguity.
VSP is the most accurate in various vertical horizon calibration methods, but the following problems also exist:
1. VSP velocity is the vertical average velocity of the stratum, seismic data is the root-mean-square velocity with an incidence angle, and the corresponding time waveforms of the VSP velocity and the seismic data are inconsistent;
2. the VSP logging detector is arranged in a well, half-wave loss does not exist, the ground geophone is buried on the ground and positioned on a half-space interface of an elastic medium, half-wave loss exists, and the polarities of the VSP logging detector and the ground geophone are inconsistent.
In summary, the existing vertical calibration method is only suitable for a single well, and when a plurality of wells exist in a work area, the existing vertical calibration method has inherent problems and multi-resolution, so that the calibration results of the single wells for the same target reservoir are not completely consistent, and the seismic horizon calibration precision is influenced.
With the gradual transfer of the oil and gas exploration target to the lithologic oil and gas reservoir and the thin layer, the precision of the conventional seismic horizon calibration method is difficult to meet the requirement, and how to improve the precision of seismic horizon calibration has very important significance for improving the correctness of reservoir prediction and lithologic interpretation.
Disclosure of Invention
The invention aims to provide a seismic horizon calibration method, which is used for solving the problems that when a plurality of wells exist in a work area, the conventional vertical seismic horizon calibration method for synthetic records and the like has the defects of low precision, multiple resolvability and the like, and cannot meet the requirements of thin reservoir prediction and lithologic oil and gas reservoir research.
In order to solve the technical problem, the invention provides a seismic horizon calibration method, which comprises the following steps:
1) According to the post-stack pure wave data and the logging data of the seismic processing results, establishing well-seismic time-depth relations of all wells in the seismic work area, and performing vertical horizon calibration to obtain a seismic horizon vertical calibration result of each single well to a target layer in the seismic work area;
2) Tracking and explaining the target layer to obtain a target layer seismic explanation result; determining the seismic attribute of the seismic work area sensitive to the reservoir thickness by using an attribute sensitivity analysis method; taking the target layer seismic interpretation result as the center of a time window, and extracting seismic attributes of the seismic work area transverse distribution along the target layer according to the set size of the time window; sliding the time window up and down in the center of the time window, and extracting the seismic attributes of the target layer transverse distribution corresponding to the time window sliding each time;
3) Performing intersection analysis on the extracted data of all seismic sensitive attributes and the sandstone reservoir thickness of the target horizon by using an intersection analysis method, determining correlation coefficients between the seismic sensitive attributes and the sandstone reservoir thickness according to the results of the intersection analysis, comparing all the obtained correlation coefficients, and correcting the seismic horizon vertical calibration result by using the time window sliding quantity corresponding to the correlation coefficient with the largest value to obtain the final calibration result of the seismic horizon; the sliding amount of the time window is the sliding amount relative to the center of the time window when the time window is slid.
The time window size is determined according to the sandstone reservoir thickness of the target horizon and the inherent resolution of seismic data.
The attribute sensitivity analysis method comprises the following steps: and performing attribute sensitivity analysis by combining with a seismic model forward modeling according to the post-stack pure wave data, geological stratification data and logging data of the seismic processing results of the seismic work area and the sandstone reservoir thickness of each single well to the target position, thereby determining the seismic attribute of the seismic work area sensitive to the sandstone reservoir thickness.
The seismic attribute is seismic wave impedance.
The step of tracking and interpreting the target layer and acquiring the seismic interpretation result of the target layer comprises the following steps: and according to the geological stratification data of the seismic work area and the reflection positions of the single wells in the seismic horizon vertical calibration result, which are calibrated on the seismic section for the target layer, finely tracking and explaining the layer of the target layer in the seismic work area on the pure wave data body after the seismic processing result is stacked, so that a target layer seismic explanation result is obtained.
The sandstone reservoir thickness is determined according to geological stratification data and logging data of the seismic work area.
And 1) generating synthetic seismic records of all single wells in the seismic work area according to the well logging curve subjected to standardization processing and the extracted seismic wavelets, and establishing well-seismic time-depth relations of all the single wells in the seismic work area.
The process of obtaining the normalized well log comprises the steps of: and carrying out standardized editing processing on the logging curves of all the drilling wells in the earthquake work area according to the logging curve characteristic response values of the marker layer of the earthquake work area.
The seismic wavelets are extracted from post-stack pure wave data of the seismic processing results and the well log of the normalization processing.
The beneficial effects of the invention are: on the basis of a horizon vertical calibration result obtained by using the conventional vertical horizon calibration method, an optimal reservoir calibration position is determined according to the optimal matching relation between the thickness of each single-well target reservoir in the transverse direction and seismic attribute information. The invention provides a method for combining vertical calibration and transverse calibration, which is characterized in that a sliding time window is adopted to search the optimal matching point of the thickness parameter of a multi-well reservoir in the transverse direction and the seismic sensitive attribute, and the optimal matching point is used as a final calibration result, is suitable for the condition that a plurality of wells exist in a seismic work area, and improves the calibration precision of a thin layer.
Drawings
FIG. 1 is a block diagram of the technical solution of the present invention;
fig. 2 is a histogram of the eastone depression willow-push log before consistency correction;
FIG. 3 is a histogram of the consistency of the Dongpowenghshun Lowe log;
fig. 4 is a plot of the eastern depressed willow herb composition record calibration;
fig. 5 is a plot of eastone depression willow-actuation depression-depth relationship;
fig. 6 is a time window sliding map of eastone depressed willowherb extraction seismic attributes;
fig. 7 is a plot of eastern depressed willow-depressed reservoir thickness versus seismic sensitivity attributes;
FIG. 8 is a graph of transverse horizon scaling correlation coefficients for an eastone depressed willowherb sub-depression;
fig. 9 is a plot of the wave impedance of an eastone depressed Liutun minor-urheen 114-1 well.
Detailed Description
The following further describes embodiments of the present invention with reference to the drawings.
And obtaining the vertical calibration result of the seismic horizon of each single well to the target layer in the seismic work area by using a vertical horizon calibration method.
Tracking and explaining a target layer to obtain a target layer seismic explanation result; determining the seismic attribute of the seismic work area sensitive to the reservoir thickness by using an attribute sensitivity analysis method; taking the target layer seismic interpretation result as the center of a time window, and extracting seismic sensitivity attributes of the seismic work area transverse distribution along the target layer according to the set size of the time window; and sliding the time window up and down in the center of the time window, and extracting the seismic sensitivity attribute of the target layer transverse distribution corresponding to the time window sliding each time.
Performing intersection analysis on the extracted data of all seismic sensitive attributes and the sandstone reservoir thickness of the target layer by using an intersection analysis method, determining correlation coefficients between the seismic sensitive attributes and the sandstone reservoir thickness according to the result of the intersection analysis, comparing all obtained correlation coefficients, and correcting the seismic layer vertical calibration result by using the time window sliding quantity corresponding to the correlation coefficient with the maximum value to obtain the final calibration result of the seismic layer; the sliding amount of the time window is the sliding amount relative to the center of the time window when the time window is slid.
According to the method, on the basis of vertical calibration of each single well of the target reservoir, the optimal reservoir calibration position is determined by utilizing the optimal matching relation between the thickness of each single well target reservoir in the transverse direction and the seismic attribute information.
The meaning of the optimal matching relation between the thickness of each single-well target reservoir stratum and the seismic attribute information in the transverse direction is that the extracted sensitive seismic attribute has the maximum correlation coefficient with the reservoir thickness of the multi-well target reservoir stratum only under the condition of correct horizon calibration, and the correlation coefficient is reduced when the thickness deviates from a correct calibration position.
The following description of the embodiments of the present invention will be made in further detail with reference to the examples of lateral calibration in the prediction of eastone depressed salsa three-well reservoir and the accompanying drawings, and as can be seen from fig. 1, the specific steps of the present invention are as follows:
1. acquiring post-stack pure wave data, geological stratification data and well logging curves of seismic processing results in an industrial area to be inverted, namely eastone depressed willow-silvicultaneous sand.
1.1 well log standardization treatment: and carrying out standardized editing processing on the logging curves of all the drilling wells in the work area according to the characteristic response values of the logging curves of the seismic work area marker layer. The method comprises the following specific steps:
the method comprises the following steps of utilizing a histogram method to carry out sound wave curve standardization editing treatment in two steps, firstly selecting a standard layer which can be contrastingly tracked, and selecting a single-layer or a layer group which has obvious lithology and electrical characteristics and stable deposition and has a certain thickness, and a mudstone layer section which is close to or on an inversion target layer section and is drilled by a plurality of wells in a work area; and secondly, making a total logging response frequency histogram of all the single-well standard layers and a logging response frequency histogram of the single-well standard layers by using a cross-over analysis technology. As shown in fig. 2, the histogram of the uniformity of the eastern depressed willowherb log shown in fig. 3 was obtained by normalizing the log obtained in step 1 with the curve values corresponding to the characteristic peaks read from fig. 2 as a standard, and by using the curve values as a standard, the histogram of the uniformity of the eastern depressed willowherb log shown in fig. 3 was obtained.
1.2 extracting seismic wavelets: and (3) extracting seismic wavelets by using the post-stack pure wave data of the well-side seismic processing result and the logging data obtained in the step 1.1. The method comprises the following specific steps:
firstly, the amplitude spectrum and the phase spectrum of the seismic wavelet are extracted by using the stacked pure wave data of the seismic processing result of the east Purpdepressed willow-tunny sub-depression obtained in the step 1 and the logging curve obtained in the step 1.1, and then the seismic wavelet extracted in the east Purpdepressed willow-tunny sub-depression impedance inversion shown in the figure 4 is synthesized by using the information of the amplitude spectrum and the phase spectrum. The length of the wavelets is determined by trial and error based on factors such as signal-to-noise ratio and frequency characteristics of the data. The seismic wavelet length extracted from the Donpu-depressed-Horn-sub-depression impedance inversion according to FIG. 4 is typically around 120ms, with a computation time window at least 3 times the seismic wavelet length.
2. And establishing a well seismic time-depth relation, and carrying out vertical horizon calibration. And (3) applying the seismic wavelets extracted in the step (1.2) and the well logging curves subjected to the standardized editing processing in the step (1.1) to generate synthetic seismic records of all wells in the seismic work area, and establishing well-seismic time-depth relations of all wells in the seismic work area to obtain a seismic horizon vertical calibration result of each well relative to a target layer. The method comprises the following specific steps:
2.1 establish the well-seismic depth relationship for all individual wells in the eastone depressed willow-herb sub-region. Using the seismic wavelets extracted in step 1.2 and the standardized well log obtained in step 1.1 to make a calibration plot of the eastern depressed saloon secondary depression record shown in fig. 4, we obtained the well seismic time-depth relationship shown in fig. 5 for all wells in eastern depressed saloon three, where the dashed line represents the average time-depth relationship. In fig. 4, the correlation coefficient is expressed by color, warm tones such as yellow represent high correlation coefficient, cool tones such as blue and green represent low correlation coefficient, and the correlation coefficient is generally required to be more than 0.7.
2.2, by using the well seismic time-depth relation of each single well obtained in the step 2.1 and the geological stratification data obtained in the step 1, calibrating the reflection position of the horizon interface of each single well target on the seismic section, and thus obtaining a vertical calibration result, wherein characters on the right side are horizons and time depths on the left side are calibrated as shown in fig. 4.
3. And (2) counting the sandstone reservoir thickness data of 12 sand groups in each single well sand three in the work area such as Hu 114, hu 99, hu 101, hu 115, hu 98 wells and the like by using the geological stratification data obtained in the step (1) and the logging data obtained in the step (1.1), and loading the sandstone reservoir thickness data into the work area in a scattered point data mode.
4. Fine horizon interpretation:
and (2) according to geological stratification data of all wells in the Dongpo depressed willow-herb sub-region obtained in the step 1 and the reflection positions of the 12 sand group layers in the Sanzhong sand in the step 2 marked on each well on the seismic section, carrying out fine tracking interpretation on the 12 sand group layers in the Dongpo depressed willow-herb sub-region on a pure wave data body after seismic processing result stacking to obtain an interpretation result of the 12 sand group seismic layers in the Sanzhong sand, wherein the interpretation result reaches 1 multiplied by 1 interpretation density.
5. Determining the sensitive attribute:
carrying out comprehensive research and analysis on stacked pure wave data, geological stratification data of 12 sand groups in sand three, the work area logging data obtained in step 1.1 and the reservoir thickness data of 12 sand groups in each single-well sand three obtained in step 3 in the east-Puyun-Liu-minor-depressed work area obtained in step 1, and carrying out attribute sensitivity analysis by combining with forward modeling of a seismic model to determine that the seismic attribute of the seismic work area sensitive to the reservoir thickness is a wave impedance attribute.
6. The thickness of a single sand body of 12 sand groups in sand III in the region is generally 3-16m, the time window is determined to be 20ms by considering the inherent resolution and the frequency spectrum calculation precision of seismic data, the seismic sensitivity attribute, namely the wave impedance amplitude value, which is transversely distributed in the whole work area is extracted in the time window along the target layer by taking the seismic interpretation result of the 12 sand groups in sand III in the step 4 as the center of the time window, and the wave impedance amplitude value is transversely and continuously distributed in the whole work area.
7. The size of the time window is kept unchanged for 20ms, 2ms is taken as a unit, the time window is slid up and down by taking the 12 interpretation layers in sand three as the center, as shown in fig. 6, the sliding quantity is +/-2 ms each time, and the wave impedance amplitude values distributed transversely in the whole work area are extracted once along the target layer respectively after the time window is slid once.
8. And (4) performing intersection analysis on the reservoir thickness data of the three middle positions of the single well sand in the work area obtained in the step (3) and the wave impedance amplitude values of the 12 sand groups in the sand III, which are extracted in the step (6) and the step (7) and have different time window sliding quantities respectively to obtain a plurality of wave impedance amplitude values and a reservoir thickness data intersection graph obtained by a plurality of wells, and corresponding correlation coefficient values, namely polynomial regression coefficient values. The regression coefficient values reflect the degree of correlation between seismic attributes having different time window slips for the lateral distribution and the multi-well data. Fig. 7 is a graph of the extracted wave impedance amplitude value with the time window sliding up for 2ms, which is a plot of reservoir thickness with a polynomial regression coefficient of 83.6, which is the maximum on the regression coefficient curve.
9. Drawing a curve by taking the time window sliding quantity as an abscissa and taking the polynomial regression coefficient as an ordinate to obtain a curve graph of the horizontal horizon calibration polynomial regression coefficient of 12 sand groups in Liutun minor-depression sand III in the figure 8, searching the time window sliding quantity corresponding to the maximum value of the correlation coefficient curve to be-2 ms, and correcting the vertical calibration result obtained by the conventional vertical horizon calibration method by using the time window sliding quantity to obtain the final calibration result of the target horizon.
Therefore, the method has the advantages that the traditional vertical calibration method is combined with the new transverse calibration method, on the basis of vertical calibration of multi-well single-well synthetic record, the sliding time window is adopted to search the optimal matching point of the thickness parameter of the reservoir of the 12 sand groups in the sand three wells and the seismic sensitive attribute, namely the wave impedance attribute, in the transverse direction, the vertical calibration result is corrected upwards for 2ms and serves as the final calibration result of the 12 sand groups in the sand three wells, and the thin layer calibration precision is further improved. The problems that the thin layer calibration precision of the vertical calibration method is low and the calibration results are often inconsistent when multiple wells exist are effectively solved. The precision of the seismic horizon calibration result obtained by the method is obviously improved compared with the conventional vertical calibration method, the multi-well calibration consistency is particularly met, the thin layer calibration is improved, and a good foundation is laid for reservoir prediction and lithologic oil and gas reservoir research.
On the basis of transverse fine calibration of the Dongpuxuan willow-herb sub-depression reservoir, reservoir prediction is carried out by adopting a de-compaction wave impedance inversion technology, fine lithologic trap drawing is carried out on the stratum in Sanzhong according to de-compaction wave impedance data, and the lithologic oil-gas reservoir reserves are found to be 604.6 multiplied by 10 in scale 4 The method is characterized in that a lithologic oil reservoir rolling exploratory well 114-1 well which is deployed as shown in figure 9 is drilled to meet an oil layer of 22.5m/11, 9.8 tons of industrial oil flow with daily yield is obtained, the visible reservoir prediction coincidence rate on a wave impedance section is high and can reach 85%, the accuracy of geological recognition is verified, and a solid foundation is laid for reservoir prediction, correct identification and lithologic trap carving.
Claims (8)
1. A seismic horizon calibration method is characterized by comprising the following steps:
1) According to the post-stack pure wave data and the logging data of the seismic processing results, establishing well-seismic time depth relations of all wells in the seismic work area, and carrying out vertical horizon calibration to obtain a seismic horizon vertical calibration result of each single well for a target layer in the seismic work area;
2) Tracking and explaining the target layer to obtain a target layer seismic explanation result; determining the seismic attribute of the seismic work area sensitive to the reservoir thickness by using an attribute sensitivity analysis method; taking the target layer seismic interpretation result as the center of a time window, and extracting seismic attributes of the seismic work area transverse distribution along the target layer according to the set size of the time window; sliding a time window up and down in the center of the time window, and extracting the seismic attributes of the transverse distribution of the target layer corresponding to the sliding time window each time; the attribute sensitivity analysis method comprises the following steps: performing attribute sensitivity analysis according to the post-stack pure wave data, geological stratification data and logging data of the seismic processing results of the seismic work area and the sandstone reservoir thickness of each single well to the target horizon by combining with seismic model forward modeling, thereby determining the seismic attribute of the seismic work area sensitive to the sandstone reservoir thickness; 3) Performing intersection analysis on the extracted data of all seismic sensitive attributes and the sandstone reservoir thickness of the target horizon by using an intersection analysis method, determining correlation coefficients between the seismic sensitive attributes and the sandstone reservoir thickness according to the results of the intersection analysis, comparing all the obtained correlation coefficients, and correcting the seismic horizon vertical calibration result by using the time window sliding quantity corresponding to the correlation coefficient with the largest value to obtain the final calibration result of the seismic horizon; the sliding amount of the time window is the sliding amount relative to the center of the time window when the time window is slid.
2. The seismic horizon calibration method of claim 1, wherein the time window size is determined according to the sandstone reservoir thickness of the target horizon and the inherent resolution of the seismic data.
3. The seismic horizon calibration method of claim 1, wherein the seismic attribute is seismic wave impedance.
4. The seismic horizon calibration method of claim 1, wherein the step of performing trace interpretation on the target horizon and obtaining a target horizon seismic interpretation result comprises: and according to the geological stratification data of the seismic work area and the reflection positions of the single wells in the seismic horizon vertical calibration result, which are calibrated on the seismic section for the target layer, finely tracking and explaining the layer of the target layer in the seismic work area on the pure wave data body after the seismic processing result is stacked, so that a target layer seismic explanation result is obtained.
5. The seismic horizon calibration method of any one of claims 1 to 2, wherein the sandstone reservoir thickness is determined from geological stratification data and well log data for the seismic work zone.
6. The seismic horizon calibration method according to claim 1, characterized in that in step 1), synthetic seismic records of all single wells in a seismic work area are generated according to the well logging curves subjected to standardization processing and the extracted seismic wavelets, and well-seismic time-depth relations of all the single wells in the seismic work area are established.
7. The method of seismic horizon calibration according to claim 6, wherein the process of obtaining the normalized well log comprises the steps of: and carrying out standardized editing processing on the logging curves of all the drilling wells in the earthquake work area according to the logging curve characteristic response values of the marker layer of the earthquake work area.
8. The seismic horizon calibration method of claim 6, wherein the seismic wavelets are extracted from post-stack pure wave seismic processing results and the normalized processed log.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710596550.8A CN109283577B (en) | 2017-07-20 | 2017-07-20 | Seismic horizon calibration method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710596550.8A CN109283577B (en) | 2017-07-20 | 2017-07-20 | Seismic horizon calibration method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109283577A CN109283577A (en) | 2019-01-29 |
| CN109283577B true CN109283577B (en) | 2023-02-28 |
Family
ID=65184562
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710596550.8A Active CN109283577B (en) | 2017-07-20 | 2017-07-20 | Seismic horizon calibration method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109283577B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109188520B (en) * | 2018-09-17 | 2020-05-08 | 中国石油天然气股份有限公司 | Thin reservoir thickness prediction method and device |
| CN109932749B (en) * | 2019-03-05 | 2020-12-01 | 中国石油天然气股份有限公司 | Well-seismic calibration method and device |
| CN112269212B (en) * | 2020-10-20 | 2024-07-26 | 中国石油天然气集团有限公司 | Method, device, equipment and medium for determining seismic interpretation horizon of logging small layering |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| BRPI0503813A (en) * | 2005-09-08 | 2007-05-15 | Petroleo Brasileiro Sa | geophysical method for extracting geological information from the treatment of seismic data ranges |
| CN101013161A (en) * | 2007-01-15 | 2007-08-08 | 中国石油大港油田勘探开发研究院 | Seismic exploration position calibration method based on prestack wave field simulation |
| CN102109613A (en) * | 2009-12-23 | 2011-06-29 | 中国石油天然气股份有限公司 | Method for determining effective thickness of target reservoir under complex geological conditions |
| CN103412332A (en) * | 2013-01-22 | 2013-11-27 | 中国地质大学（北京） | Method for quantitative calculation of thickness of thin reservoir layer |
| CN105093306A (en) * | 2014-05-15 | 2015-11-25 | 中国石油化工股份有限公司 | Method for automatic interpretation and thickness calculation of reservoir in geophysical exploration |
| CN105353407A (en) * | 2015-10-28 | 2016-02-24 | 中国石油化工股份有限公司 | Post-stack earthquake wave impedance inversion method |
| WO2016041189A1 (en) * | 2014-09-19 | 2016-03-24 | 杨顺伟 | Method for evaluating shale gas reservoir and seeking desert area |
| CN105759312A (en) * | 2016-03-01 | 2016-07-13 | 中国海洋石油总公司 | Reservoir characteristic method well-seismic calibration method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1412575A (en) * | 2001-10-11 | 2003-04-23 | 中国石油天然气集团公司西北地质研究所 | Oil-gas detection method based on multiphase medium theory |
-
2017
- 2017-07-20 CN CN201710596550.8A patent/CN109283577B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| BRPI0503813A (en) * | 2005-09-08 | 2007-05-15 | Petroleo Brasileiro Sa | geophysical method for extracting geological information from the treatment of seismic data ranges |
| CN101013161A (en) * | 2007-01-15 | 2007-08-08 | 中国石油大港油田勘探开发研究院 | Seismic exploration position calibration method based on prestack wave field simulation |
| CN102109613A (en) * | 2009-12-23 | 2011-06-29 | 中国石油天然气股份有限公司 | Method for determining effective thickness of target reservoir under complex geological conditions |
| CN103412332A (en) * | 2013-01-22 | 2013-11-27 | 中国地质大学（北京） | Method for quantitative calculation of thickness of thin reservoir layer |
| CN105093306A (en) * | 2014-05-15 | 2015-11-25 | 中国石油化工股份有限公司 | Method for automatic interpretation and thickness calculation of reservoir in geophysical exploration |
| WO2016041189A1 (en) * | 2014-09-19 | 2016-03-24 | 杨顺伟 | Method for evaluating shale gas reservoir and seeking desert area |
| CN105353407A (en) * | 2015-10-28 | 2016-02-24 | 中国石油化工股份有限公司 | Post-stack earthquake wave impedance inversion method |
| CN105759312A (en) * | 2016-03-01 | 2016-07-13 | 中国海洋石油总公司 | Reservoir characteristic method well-seismic calibration method |
Non-Patent Citations (4)
| Title |
|---|
| 东濮凹陷前梨园地区储层预测研究与应用;周玲等;《内蒙古石油化工》;20060225(第07期);第86-87页 * |
| 横波速度正演在东濮凹陷浊积砂储层预测中的应用;范凌霄;《石油地质与工程》;20170531;第62-67页 * |
| 测井与地震信息标定研究;凌云，等;《石油地球物理勘探》;20040215(第01期);第68-75页 * |
| 高精度三维地震资料解释技术在查干凹陷勘探中的应用;梁国胜，等;《内蒙古石油化工》;20101231;第142-145页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109283577A (en) | 2019-01-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113759425B (en) | Method and system for evaluating filling characteristics of deep paleo-karst reservoir stratum by well-seismic combination | |
| CN101738639B (en) | Method for improving computing precision of rock fracture parameters | |
| CN109541685B (en) | River channel sand body identification method | |
| CN109470187B (en) | Reservoir thickness prediction method based on three seismic attributes | |
| CN105093306A (en) | Method for automatic interpretation and thickness calculation of reservoir in geophysical exploration | |
| CN109283577B (en) | Seismic horizon calibration method | |
| CN114994758B (en) | Wave impedance extraction and structure characterization method and system for carbonate rock fracture control reservoir | |
| CN111239824A (en) | Volcanic lithofacies prediction method | |
| CN108226997A (en) | Seismic facies division method based on pre-stack seismic data | |
| CN114609675A (en) | Quantitative recovery method for carbonate rock stratum sedimentary micro-landform based on high-frequency cycle | |
| CN116856845A (en) | Tight sandstone gas reservoir horizontal well geosteering method | |
| CN110927817B (en) | Method for optimally designing and controlling thin-layer superposed riverway horizontal well track | |
| CN110727030B (en) | Reservoir prediction method based on wavelet multi-extreme characteristic joint analysis | |
| CN107132574B (en) | Prediction method for lithologic combination of marine stratum clastic rocks | |
| CN112558180A (en) | Method for rapidly detecting seismic horizon calibration accuracy by using horizontal isochronous surface | |
| CN113589365B (en) | Reservoir pinch-out line description method based on time-frequency domain information | |
| CN114482995A (en) | Fine determination method for argillaceous content of fine-grain sediment | |
| CN113514884A (en) | Compact sandstone reservoir prediction method | |
| CN110389381B (en) | Sand reservoir prediction method and device based on seismic attributes | |
| CN112147676A (en) | Method for predicting thickness of coal bed and gangue | |
| CN112147701B (en) | Seismic waveform driven high-resolution seismic inversion method | |
| CN113311482B (en) | High-resolution medium-deep reservoir prediction method based on prestack spectrum inversion optimization | |
| CN110703326B (en) | FVO inversion method based on small and medium offset gathers | |
| CN111812743B (en) | Identification method of single sand body of reservoir | |
| RU2201606C1 (en) | Method of typification and correlation of oil and gas productive rocks by borehole spectral-time parameters |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: 457001 Zhongyuan Road 277, Puyang City, Henan Province Applicant after: CHINA PETROLEUM & CHEMICAL Corp. Applicant after: CHINA PETROLEUM & CHEMICAL CORPORATION ZHONGYUAN OILFIELD BRANCH EXPLORATION AND DEVELOPMENT Research Institute Address before: 100728 Chaoyangmen North Street, Chaoyang District, Chaoyang District, Beijing Applicant before: CHINA PETROLEUM & CHEMICAL Corp. Applicant before: CHINA PETROLEUM & CHEMICAL CORPORATION ZHONGYUAN OILFIELD BRANCH EXPLORATION AND DEVELOPMENT RESEARCH INSTITUTE |
|
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |