A method for predicting oil and gas based on big data of waveform structure characteristics

Abstract

The present invention discloses a method for predicting oil and gas based on waveform structure feature big data, which relates to the technical field of oil and gas prediction, including: field collection of seismic big data body; routine processing of big data body; establishment of amplitude waveform data body prediction mathematical model; real-time selection of control parameters; determination of abnormal value segment of waveform structure feature; modification of mathematical model; dimensionless processing of waveform structure feature abnormal value correlation analysis, solving the two-level difference in the structural feature abnormal value correlation coefficient; solving the correlation coefficient and correlation degree of structural feature anomaly; predicting the quantitative discrimination standard of favorable oil and gas content and the determination of classification level; determining the profile position and plane distribution range of favorable oil and gas layer segment. The method of the present invention realizes the unification of vertical and horizontal algorithms and division standards in oil and gas reservoir prediction, and the linkage comparison of graphs and tables, which minimizes the influence of human factors, thereby significantly improving the success rate of pre-drilling oil and gas prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas prediction, and in particular to a method for predicting oil and gas based on waveform structure feature big data. Background Art

[0002] At present, the main task of geophysical exploration is to provide effective evidence for oil and gas exploration and development, with the goal of finding oil and gas rich areas. Since the survey area of ​​seismic exploration data is large and the amount of data is huge, it can be studied in a continuous manner. However, as the most basic data for oil and gas exploration and development, the various information extracted from a large amount of data is not yet sufficient. People use seismic data for oil and gas prediction technology, such as bright spot technology, pattern recognition technology, AVO technology, neural network technology and multi-attribute seismic oil and gas pre-drilling oil and gas prediction technology. Usually, they are displayed by color maps with color scale classification after extracting different seismic physical parameters, and then reasoning, analyzing and judging different color maps. Although the color map is beautiful, people can identify the direction, shape, fault, type and continuity of the stratum from the plane map or the cross-section map, but the success rate of identifying oil and gas using color maps is not high. Due to the low degree of direct correlation between the characteristics of different oil and gas zones, there is uncertainty in the discrimination of oil and gas, so the credibility is not high.

[0003] Therefore, it is urgent to study new oil and gas detection methods to improve the success rate of drilling. Summary of the invention

[0004] In order to solve the above technical problems, the present invention discloses a method for predicting oil and gas based on big data of waveform structure characteristics. The present invention studies the spatiotemporal relationship of the waveform structure characteristics of seismic data, more accurately identifies oil and gas layers on seismic data, finds out the boundaries of oil and gas layers, and quantitatively determines the distribution area and thickness of oil and gas layers under the condition of referring to other limited geological data, and finally calculates the reserves.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for predicting oil and gas based on waveform structure feature big data includes the following steps:

[0007] (1) Field acquisition of 2D or 3D seismic data volumes;

[0008] (2) Perform routine indoor processing of 2D or 3D seismic data volumes;

[0009] (3) Selection of control parameters for special processing of waveform structural characteristics of seismic data volumes in real time;

[0010] (4) modifying the prediction mathematical model, including abnormal values ​​of waveform structure characteristics and abnormal characteristics of control parameters;

[0011] (5) Special processing is performed on the structural characteristics of the seismic data volume to establish a mathematical model for amplitude waveform data volume prediction;

[0012] (6) determining the outlier segments of the waveform structure characteristics;

[0013] (7) Perform dimensionless processing on the correlation analysis of the outlier values ​​of the waveform structural characteristics, and solve the two-level difference in the correlation coefficient of the outlier values ​​of the structural characteristics;

[0014] (8) Based on the two-level difference, the correlation coefficient and correlation degree of the structural feature anomaly are solved;

[0015] (9) Determination of quantitative criteria for predicting favorable oil and gas content and classification levels;

[0016] (10) Determine the cross-sectional location and planar distribution range of favorable oil and gas-bearing strata.

[0017] Optionally, in step (2), conventional processing includes noise removal, dynamic correction, static correction, stacking processing, etc. In step (3), special processing includes slope, angle, amplitude anomaly processing, etc.

[0018] Optionally, in step (3), the basic principle of the prediction mathematical model is: within a phase axis range of the seismic trace, the exponential fitting formula (1) is used to fit the background of the seismic trace x(t), so that the statistical distribution of the vertical integral fitting residual of x(t) satisfies the Gaussian distribution law. Formula (1) is:

[0019] x(t)=μexp(-a(t-t0)) (1)

[0020] Among them, α is the data behavior development coefficient, μ is the grey action;

[0021] This residual distribution is consistent with the least square fitting residual distribution assumption during wave impedance inversion (similar to vertical integration). However, since it is a local fit, it is different from the wave impedance inversion of the entire curve, which requires a good low-frequency component. The local spectrum of the fitting model is a low-pass spectrum with a symmetric center of 0:

[0022]

[0023] Among them, X(w) is the local spectrum of the seismic trace, F(x(t)) represents the Fourier transform, and according to the autoregressive operator spectrum decomposition and O'Doherty formula, we get:

[0024]

[0025] Among them, r n is the reflection coefficient of the upper layer, assuming:

[0026]

[0027] That is, the reflection coefficient r n The spectrum is a low-pass spectrum.

[0028] Optionally, in step (9),

[0029] The criteria include:

[0030] The smoothness of the abnormal characteristics of the waveform structure of the seismic data volume;

[0031] The magnitude of the abnormal value of the seismic data volume waveform structure and its relationship with the numerical magnitude of the surrounding structure;

[0032] Comparison results of seismic data volume waveform structure anomalies and well correlation degree;

[0033] The classification levels include:

[0034] The relative error of the quantitative identification mark of the type I waveform structure feature is greater than 85%;

[0035] The relative error of the quantitative identification mark of type II waveform structure characteristics is 65%-85%;

[0036] The relative error of the quantitative identification mark of the type III waveform structure characteristics is 50%-65%;

[0037] The relative error of the quantitative identification mark of the type IV waveform structure feature is less than 50%;

[0038] Among them, Class I and Class II oil and gas bearing areas are the main areas where oil companies deploy efficient well locations in oil fields.

[0039] The beneficial effect of the present invention is that the present invention first assumes that the seismic waveform is locally composed of background and anomaly; the background is fitted with an exponential function, and the integration of the fitting residual along the depth conforms to the least squares principle. The physical meaning of the background is the tail of the limited frequency band response of the direct overlying layer, and the physical meaning of the anomaly is the reflection that changes relatively quickly and has a large numerical arrangement structure feature performance; then, vector correlation analysis is performed to predict the cross-sectional position and plane distribution range of the oil and gas reservoir. The method realizes the unification of the vertical and horizontal algorithms and division standards in the prediction of oil and gas reservoirs, and the linkage comparison of graphs and tables, which minimizes the influence of human factors, thereby significantly improving the success rate of pre-drilling oil and gas prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 The oil and gas flow chart is predicted by the big data research of the waveform structure characteristics of seismic data volume of the present invention;

[0041] Figure 2 A comparative analysis diagram of seismic data volume waveform structures of two different CDP points in adjacent time periods in one embodiment of the present invention;

[0042] Figure 3 This is a conventionally processed stacked seismic profile of the Puguang gas field through the Pg5 well in one embodiment of the present invention, the abscissa is the cdp number, the ordinate is the time, the unit is ms, and the coordinates of the abscissa are usually at the top of the picture;

[0043] Figure 4 This is a graph showing the slope and angle changes of the waveform structure characteristics of the seismic data of the target layer of the Puguang gas field through the Pg4 well in one embodiment of the present invention, where the horizontal axis is the cdp number and the vertical axis is the time in ms. The coordinates of the horizontal axis are usually at the top of the picture;

[0044] Figure 5 This is a waveform structure characteristic change diagram of 11 channels near the target layer beside the Pg5 well in Puguang gas field in one embodiment of the present invention, the horizontal axis is the amplitude value, the vertical axis is the time, and the unit is ms;

[0045] Figure 6 This is a cross-sectional diagram of the waveform structure characteristics of the seismic data volume of the gas layer of the Puguang gas field through the Pg2 well in one embodiment of the present invention, the horizontal axis is the cdp number, the vertical axis is the time, and the unit is ms;

[0046] Figure 7 This is a cross-sectional model diagram of the waveform structure characteristics of the original seismic data volume of the Puguang gas field through the Puguang 1dx well in one embodiment of the present invention;

[0047] Figure 8 This is a cross-sectional model diagram of the waveform structure characteristics of the original seismic data volume of the Puguang gas field through the Pg2 well in one embodiment of the present invention;

[0048] Fig. 9 This is a cross-sectional model diagram of the waveform structure characteristics of the original seismic data volume of the Puguang gas field through the Pg4 well in one embodiment of the present invention;

[0049] Fig.10 This is a cross-sectional model diagram of the waveform structure characteristics of the original seismic data volume of the Puguang gas field through the Pg3 well in one embodiment of the present invention;

[0050] Fig.11 This is a cross-sectional diagram of the waveform structure characteristics of the seismic data volume of the gas layer of the Puguang gas field through the wells in one embodiment of the present invention;

[0051] Fig.12 This is a superimposed distribution diagram of abnormal values ​​of the waveform structure characteristics of seismic data in the entire Puguang gas field in one embodiment of the present invention;

[0052] Fig.13 It is a boundary map of the predicted favorable gas layer distribution range in the entire Puguang gas field in one embodiment of the present invention;

[0053] Fig.14It is a classification and comprehensive boundary map of the abnormal value distribution of the seismic data volume waveform structure characteristics of the entire area of ​​the Changxing Formation-Feixianguan Formation (T1f1-T1f4) of the Puguang gas field in one embodiment of the present invention;

[0054] Fig.15 A comparison diagram of the distribution of abnormal values ​​of the structure characteristics of the seismic data volume of the Puguang gas field and the producing gas-bearing area in one embodiment of the present invention;

[0055] Fig.16 This is an abnormal cross-section diagram of the waveform structure characteristics of the seismic data volume of the Puguang gas field through the Pg7 well in one embodiment of the present invention;

[0056] Fig.17 A planar distribution diagram of exploration wells in the Puguang gas field in one embodiment of the present invention;

[0057] Fig.18 A planar position distribution diagram of the development wells of the Puguang gas field in one embodiment of the present invention;

[0058] Fig.19 In one embodiment of the present invention, the Puguang gas field has different structural characteristics through well P301-1 + well P301-2 + well P301-3 + well P301-4 and well P301-5. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] A method for predicting oil and gas based on big data of waveform structure characteristics, such as Figure 1 As shown, the following steps are included:

[0061] (1) Field acquisition of 2D or 3D seismic data volumes

[0062] (2) Perform routine indoor processing of 2D or 3D seismic data volumes

[0063] The seismic data elements of each seismic channel are not isolated, but have certain relationships among them. The relationship between these data elements is called the seismic data volume structure. The so-called seismic data volume waveform structure characteristics refer specifically to the waveform characteristics displayed by the discrete data points of each seismic channel arranged in time order, such as Figure 2As shown, there are two forms of expression: single-channel and multi-channel. The structural characteristics of the seismic data volume of a single channel refer to the single-channel waveform characteristics displayed by the discrete data points of each seismic channel arranged in time sequence; the structural characteristics of the seismic data volume of multiple channels refer to the structural characteristics of the adjacent data point sets of many waveform groups displayed by the discrete data points between channels arranged in time sequence. The application of seismic data waveform structural characteristics to predict oil and gas layers is to extract the amplitude value of each seismic channel, study the relationship between the combination and arrangement characteristics of the data and the oil and gas content (such as inflection point, slope, angle, etc.), and finally achieve the purpose of predicting oil and gas reservoirs. There are two meanings here: 1. The time sequence arrangement of data points in the vertical direction; 2. The waveform space combination change characteristics in the horizontal direction. With the time and space relationship of seismic data points, the waveform structural characteristics of the seismic data volume can be studied.

[0064] Specifically, for the structural characteristics of a single-channel seismic data volume, Figure 2 As shown in the figure, the figure is extracted from a seismic survey line and two seismic traces with high magnification. The horizontal axis is the reflection amplitude value, and the vertical axis is the time t0. The y1 and y2 data tables on the right side of the seismic trace are the amplitude values ​​corresponding to the actual time t0 after SEGY conversion by cdp882 and cdp884, respectively. The sampling rate is 2ms. By comparing the two seismic traces, we can see that the waveforms of the seismic traces are different in the time period of 2430-2445ms. The trough is at 2440ms on the left side of the figure, and the peak is at the right side of the figure, and the slopes of the waveforms are different. In this time period, not only the waveform is different, but also the structure of the seismic data body is different in time and space; in addition, the values ​​at the adjacent time points of 2454ms (left) and 2460ms (right) are basically the same, 1172.1 and 1172 respectively, but their combination and arrangement are different. At 2454ms on the left, the value decreases to 4.9347 upwards and increases to 2287.1 downwards, and at 2460ms on the right, the value decreases to 1157.9 upwards and decreases to 814.97 downwards. The values ​​are basically the same, but the arrangement is different, which also reflects the difference in the data structure in time and space in the seismic data body.

[0065] (3) Selection of control parameters for special processing of the waveform structure characteristics of seismic data volumes in real time, including analysis, processing, induction and summary of parameter characteristics such as amplitude, frequency, inflection point, angle and slope of waveform structure characteristics

[0066] The prediction mathematical model of this embodiment is for carbonate reservoirs, and the control parameters include:

[0067] ①. Carbonate reservoir bedrock density control parameters:

[0068] Maximum density: 2.87 (conventional dolomite density, maximum density setting, g / cm 3 );

[0069] Density minimum: 2.71 (conventional limestone density, minimum setting, g / cm 3 );

[0070] ②、Oil and gas saturation control parameters:

[0071] Maximum oil and gas saturation: 95%;

[0072] The median oil and gas saturation is 50%;

[0073] Minimum oil and gas saturation: 5%;

[0074] The oil and gas saturation is adjusted and controlled at every 5% increase: 5%, 10%, 15%, ..., 85%, 90%, 95% respectively, so as to observe the changing rules of the waveform structure characteristics of the seismic data body;

[0075] ③. Effective frequency band control parameters:

[0076] Maximum frequency: 60hz;

[0077] Minimum frequency: 5hz;

[0078] Effective main frequency range: 15-45hz;

[0079] The frequency bands need to be adjusted and controlled according to different sub-layers;

[0080] ④. Numerical control parameters of earthquake amplitude

[0081] Maximum amplitude: 2.12082E+4;

[0082] Minimum amplitude: -2.10469E+4;

[0083] Amplitude average: -7.48034E-1;

[0084] The control parameters of the seismic amplitude numerical value are completely applied according to the actual numerical values ​​of the seismic data body, and are adjusted and controlled according to the actual numerical values ​​of different small layer sections.

[0085] (4) modifying the prediction mathematical model, including abnormal values ​​of waveform structure characteristics and abnormal characteristics of control parameters;

[0086] (5) Special processing of the structural characteristics of seismic data volumes to establish a mathematical model for amplitude waveform data volume prediction

[0087] The prediction of oil and gas by the structural characteristics of seismic data bodies meets the principle of grey prediction model. Because the relationship between the abnormal structural characteristics of local seismic data bodies and oil and gas is not a one-to-one deterministic relationship, but an uncertain grey relationship, it is very appropriate to use grey system theory to conduct research.

[0088] By combining the grey system theory with the basic theory of structural feature anomalies of local seismic data, the basic principle of the prediction mathematical model is: within the range of one event axis of the seismic trace, the exponential fitting formula (1) is used to fit the background of the seismic trace x(t), so that the statistical distribution of the vertical integral fitting residual of x(t) satisfies the Gaussian distribution law. Formula (1) is:

[0089] x(t)=μexp(-a(t-t0)) (1)

[0090] Among them, α is the data behavior development coefficient, μ is the grey action;

[0091] This residual distribution is consistent with the least square fitting residual distribution assumption during wave impedance inversion (similar to vertical integration). However, since it is a local fit, it is different from the wave impedance inversion of the entire curve, which requires a good low-frequency component. The local spectrum of the fitting model is a low-pass spectrum with a symmetric center of 0:

[0092]

[0093] Among them, X(w) is the local spectrum of the seismic trace, F(x(t)) represents the Fourier transform, and according to the autoregressive operator spectrum decomposition and O'Doherty formula, we get:

[0094]

[0095] Among them, r n is the reflection coefficient of the upper layer, assuming:

[0096]

[0097] That is, the reflection coefficient r n The spectrum is a low-pass spectrum. This embodiment is equivalent to using the influence of oil and gas in the formation on the amplitude and spectrum of the seismic trace to detect the fast-changing reflections (gray outliers) below the slowly changing (vertical) overburden as much as possible, and then perform gray correlation analysis on these reflections to achieve the purpose of oil and gas prediction.

[0098] (6) determining the outlier segments of the waveform structure characteristics;

[0099] (7) Perform dimensionless processing on the correlation analysis of the outlier values ​​of the waveform structural characteristics, and solve the two-level difference in the correlation coefficient of the outlier values ​​of the structural characteristics;

[0100] (8) Based on the second-level difference, the correlation coefficient and correlation degree of the structural feature anomaly are solved;

[0101] (9) Determination of quantitative criteria for predicting favorable oil and gas content and classification levels;

[0102] The criteria include:

[0103] The smoothness of the abnormal characteristics of the waveform structure of the seismic data volume;

[0104] The magnitude of the abnormal value of the seismic data volume waveform structure and its relationship with the numerical magnitude of the surrounding structure;

[0105] Comparison results of seismic data volume waveform structure anomalies and well correlation degree;

[0106] The classification levels include:

[0107] The relative error VSDS% of the quantitative identification mark of type I waveform structure characteristics is greater than 85%;

[0108] The relative error VSDS% of the quantitative identification mark of type II waveform structure characteristics is 65%-85%;

[0109] The relative error VSDS% of the quantitative identification mark of the waveform structure characteristics of Class III is 50%-65%;

[0110] The relative error VSDS% of the quantitative identification mark of the type IV waveform structure feature is less than 50%;

[0111] Note: Relative error VSDS% = (waveform structure characteristic value - original seismic trace data value) / waveform structure characteristic value × 100%.

[0112] Among them, Class I and Class II oil and gas bearing areas are the main areas where oil companies deploy efficient well locations in oil fields.

[0113] (10) Determine the cross-sectional location and planar distribution range of favorable oil and gas-bearing strata.

[0114] Application Examples

[0115] Seismic waveform structural characteristics and its relationship with oil and gas in Puguang gas field

[0116] The seismic data volume structural feature analysis method is applied to conduct a practical analysis of the temporal and spatial changes of the seismic data volume, and provide the corresponding oil and gas prediction results. Firstly, a typical single well is selected to conduct a quantitative analysis of the seismic data volume structural feature model, and the relationship between structural features and oil and gas is established. Figure 3 This is a variable-area seismic profile of the Puguang gas field through the Pg5 well. Due to the overlapping of wave peaks, it is impossible to show the subtle changes in waveforms between different seismic traces, nor to distinguish the differences in waveforms between the target layer and other layers. The strong amplitude peak reflects the changes between the rock layer and the surrounding rock, rather than the changes in gas and water content in the same rock layer. The bipolar seismic profile enhances the optical dynamic range, thereby improving the effect of judging the amplitude anomaly range, but it also increases the uncertainty factors in identifying the oil-gas-water relationship in some aspects. Figure 4This is a local enlarged section of the target layer of the Pg4 well in the Puguang gas field. When the seismic wave changes from the crest to the trough or from the trough to the crest, the slope of the waveform curve changes rapidly, appearing chaotic and irregular. Through the qualitative analysis of the seismic wave slope, we can clearly see the changes in the seismic waveform inside and outside the target layer of the same seismic trace. Figure 5 The following are 10 seismic waveforms of the target layer near the well in Puguang Gas Field. Table 1 Quantitative calculation of the corresponding slope values ​​in the 10 target layers near the Pg5 well in Puguang Gas Field. It more clearly illustrates the subtle changes in waveforms and seismic data body structure characteristics between different seismic traces, and also fully illustrates that different waveforms must have different seismic data structures. By calculating and analyzing the slope of seismic waves of the same target layer, the lateral changes in the target layer can be better found, which is more conducive to the improvement of seismic lateral resolution.

[0117] Table 1 Changes in the waveform structure slope near the first cdp662 target layer near the Pg5 well in Puguang gas field

[0118]

[0119] Secondly, applying the quantitative analysis results of the single-well seismic data volume structural characteristic model, a quantitative model of the seismic data volume structural characteristics and anomaly profiles of Pg2, Pg3, Pg4, Pg5, Pg6 and Pg7 wells in the Puguang gas field were established. Figure 3 The stacked seismic profile of the Puguang gas field through the main survey line of the Pg5 well is shown. The well point is located on the anticline structure. Figure 4 This is a cross-sectional model diagram of the seismic data structure characteristics of the Pg4 well. In the gas-bearing layer section, the data structure characteristics of the seismic trace vary greatly (its slope and angle vary greatly), and the consistency from top to bottom is poor, with no regularity to follow. In the gas-free layer section, the seismic trace does not change much from top to bottom, and both the slope and angle are relatively regular and consistent. This shows that the gas content of the formation affects the changes in the structural characteristics of the seismic data body, and the seismic data structural characteristics of the gas-free layer section vary differently. At this time, the size of the waveform and amplitude values ​​are both unpredictable, which also shows that there is a difference between data structure changes and waveform changes. Figure 6 It more clearly reflects the abnormal changes in the data structure of the gas-bearing layer in the vertical direction, and thus the abnormal data structure boundary of the gas-bearing layer can be delineated.

[0120] Numerical characteristics of seismic data volume structure prediction model

[0121] Table 2 is a table of abnormal values ​​(GM abnormal values) of seismic data volume structure characteristics of gas layers passing through the Pg1 well in Puguang Gas Field. In the table, t represents time (ms), 1-9 in the first row are time series, and bold fonts are gray abnormal values ​​of abnormal segments. Among them, the distribution range of abnormal values ​​of the waveform structure characteristics of seismic data volume passing through the Pg1 well is 17.58-21.83 (dimensionless).

[0122] From the distribution of abnormal values ​​of waveform structural characteristics of seismic data in Table 2, it can be clearly seen that no matter it is a gas layer confirmed by drilling or a predicted gas layer, they all have obvious abnormal value segments of structural characteristics; if there is no obvious abnormal value segment of seismic data structural characteristics, it means that there is no oil and gas. The seismic traces processed in the gas-bearing layer section of the known well show obvious gray anomalies. The geological meaning of these abnormal value segments may be an abnormal body, but the connotation cannot be expressed. Whether it is a lithological anomaly or an anomaly caused by oil content, further correlation analysis is required; through the comparative interpretation of actual drilling, the abnormal body is generally reflected as a stronger phase axis on the seismic profile, and some are reflected in a weaker phase, but for the convenience of tracking, comparison and drawing, it is still marked on the strong phase.

[0123] Table 2 Abnormal values ​​of waveform structure of seismic data of Puguang gas field through Pg1dx well

[0124]

[0125] Graphical features of the seismic data volume waveform structure prediction model (I) Profile features of the seismic data volume waveform structure prediction model

[0126] The research on the cross-well profile characteristics of the seismic data volume structure prediction model includes the original seismic data volume structure characteristic profile model diagram at each well point of Pg1dx, Pg2, Pg3 and Pg4 in Puguang Gas Field and the analysis and research on the seismic data volume structure characteristic attribute profile diagram of gas layer and non-gas layer of Pg1dx, Pg2, Pg3 and Pg4 wells, such as Figure 7-10 As shown in Figure 2, in the gas-bearing layer, the slope and angle change greatly, with no regularity or poor regularity. They are mainly distributed in the T1f4-T1f1 layer vertically. Figure 7-Figure 9 In the interval without oil and gas, the slope and angle change in a relatively regular manner, such as Fig.10 shown. Fig.11This is a cross-sectional diagram (broken line) of the structural characteristics of the seismic data volume of the gas layer in the Puguang gas field. From the abnormal cross-sectional diagram of the structural characteristics of the seismic data volume after seismic data processing, it can be seen that the boundaries of the structural characteristics of the seismic data volume on the section are clear, and the vertical thin layers are separated and clear. After comparing with the results of some newly deployed wells after drilling, the consistency is very good, which shows the effectiveness and reliability of the structural characteristic method to predict oil layers. For example, both Pg5 and Pg6 wells have achieved good results. Through further in-depth research on the abnormal characteristics of the seismic data volume structure, the results show that:

[0127] 1) The structural anomaly characteristics of the overall seismic data volume are basically consistent with the results of the actual development main area, and the details within the layer are clearly portrayed.

[0128] 2) The structural anomaly characteristics of the seismic data volume in the main area are mainly distributed in a gourd shape from northeast to southwest, and the distribution characteristics of the gas layers actually developed are also the same.

[0129] 3) The resolution of structural anomaly features of the seismic data volume is improved without destroying the resolution of the original three-dimensional seismic data. The prediction effect of the three small layers in the study area is good, and the vertical thin gas layers are clearly separated.

[0130] 4) In the original 27.2km 2 New wells were deployed in favorable gas-bearing system units with expanded gas-bearing area, and good results were achieved (Pg1dx well, Pg2 well, Pg3 well, Pg5 well and Pg6 well), which demonstrated the effectiveness and reliability of the structural characteristic method in predicting oil reservoirs.

[0131] 2. Planar characteristics of the seismic data volume waveform structure prediction model

[0132] Figure 12-13 This is a superposition diagram of the abnormal value distribution of the seismic data volume structure characteristics of the three gas layers of the Feixianguan Formation (T1f) of the Lower Triassic System and the Changxing Formation (P2c) of the Upper Permian System in the Puguang Gas Field (T1f1 is taken down to 150ms, and T1f4 is taken up to 30ms). The processing and interpretation work area ranges from ILN: 355-1035; XLN: 520-885; area 156km 2 , the distribution range of abnormal values ​​of seismic data volume structure characteristics in the whole area is 200-680, and the distribution range of main abnormal values ​​is 400-680. It can be seen that the Puguang gas field has strong gas distribution characteristics, which are mainly distributed in Pg2 well-Pg4 well-Pg1dx well, and are distributed in the north-south-east-west direction in a belt-like manner on the plane, especially in the Pg2 well area. The seismic data volume structure characteristics are most obvious.

[0133] Brief description of the prediction model of seismic data volume waveform structure characteristics

[0134] from Figure 7-Figure 10 The cross-section model diagram of the Puguang gas field through the well point and Figure 12-13It can be seen from the plane model diagram of the structural characteristics of the seismic data volume in the whole area that in the gas-bearing layer section, the characteristics of the seismic traces vary greatly, and the slope and angle are inconsistent from top to bottom; in the gas-free layer section, the seismic traces do not change much from top to bottom, and both the slope and the angle are relatively regular and consistent, which shows that the gas content in the stratum affects the changes in the structural characteristics of the seismic data volume, and the seismic data structural characteristics of the gas-free layer section change little; and the waveform and amplitude value are both changeable, which means that there is a difference between data structure changes and waveform changes.

[0135] In summary, in this area, the structural characteristics of seismic data corresponding to the oil and gas-bearing strata at known well sections can be summarized as follows:

[0136] 1. The seismic trace shows obvious gray structural anomalies;

[0137] 2. The seismic waveform is multi-peak and does not return to zero;

[0138] 3. The slope of the seismic reflection waveform changes greatly from top to bottom;

[0139] These characteristics are consistent with the seismic reflection characteristics of oil and gas reservoirs in other regions, indicating that the seismic data in this area meet the prerequisites for the use of seismic structural characteristics analysis technology. Therefore, this technology can be used to analyze the three-dimensional data of this area, and combined with the results of closure analysis, the spatial distribution of oil layers can be predicted, and the distribution area and layer of oil-bearing layers can be delineated.

[0140] Usually, in the oil-bearing well section, the seismic waveform has a multi-peak phenomenon that does not return to zero (positive peaks appear continuously, and the peaks are not smooth), while the oil-free well section has a single peak phenomenon (peaks and troughs appear alternately, and the peaks are smooth). In this area, through vector correlation analysis, the seismic waveform of the oil-bearing well section has a multi-peak phenomenon that does not return to zero (positive peaks appear continuously, and the peaks are not smooth). From these profiles, it can be seen that the seismic waveform characteristics of the oil-bearing well section in this area also have the common characteristics of other regions.

[0141] The seismic gray structure analysis shows that the seismic gray anomaly is obvious at the designated well location and its surroundings. By calculating the distribution of each anomaly value and tracing the profile, its distribution range can be closed. The profile time is calibrated according to the actual calculation results. The reading method is based on Table 2. The structural characteristics of the seismic wave data body are consistent with the structural characteristics of the reflection wave data body of the oil and gas reservoir at the known well.

[0142] The three favorable gas layers predicted in Puguang Gas Field are the Changxing Formation (T1f1-P2c), the first and second segments of the Feixianguan Formation (T1f1-T1f3), and the third segment of the Feixianguan Formation (T1f3-T1f4). The conservatively predicted gas-bearing areas of each layer are 20.28km 2 (Changxing Group), 33.32km 2(Feixianguan Group Sections 1 and 2), 31.57km 2 (Member 3 of the Feixianguan Formation). The conservative maximum single-layer area among the three gas-bearing areas is 33.32km 2 The optimistic single-layer maximum area among the three gas-bearing areas is 40.27 km 2 , see Table 3.

[0143] Table 3 Gas-bearing area and abnormal value distribution range of different types of Feixianguan Formation and Changxing Formation gas reservoirs

[0144]

[0145] Classification criteria for predicting favorable gas-bearing range

[0146] The seismic data volume structure feature method is used to predict the gas strata in Puguang Gas Field. The main task is to predict the possible gas-bearing trap range of Puguang Gas Field based on the actual situation of existing drilling data (such as Pg1dx well, Pg2 well, Pg3 well, Pg4 well, Pg5 well, Pg6 well and Pg7 well, etc.). The purpose is to improve the drilling success rate and provide accurate data for geological reserve estimation and reserve evaluation of the gas field.

[0147] The classification standards for the predicted favorable gas-bearing range of Puguang Gas Field are as follows:

[0148] 1. The smoothness of the structural anomaly characteristics of the seismic data volume;

[0149] 2. The magnitude of structural anomalies in the seismic data volume and its relationship with the magnitude of surrounding structural values;

[0150] 3. Comparison results of structural anomalies and well correlation.

[0151] The classified horizon is the entire Feixianguan Formation, along the horizon from 30 ms above T1f4 to 150 ms below T1f1, including part of the Changxing Formation in the Puguang 2 block.

[0152] Classification of predicted favorable gas-bearing range

[0153] Based on the above quantitative analysis of the seismic data volume structural characteristic model of Pg2 well, Pg3 well, Pg4 well, Pg5 well, Pg6 well and Pg7 well in the entire Puguang gas field, finally, the quantitative identification mark of the seismic data volume structural characteristics for predicting favorable gas-bearing layers in the Changxing Formation-Feixianguan Formation (P2c-T1f4) of the entire gas-bearing layer section of the Puguang gas field was established. The entire Puguang gas field is divided into four categories, and the abnormal values ​​of each type of gas-bearing areas are: in the Class I area, the abnormal value VSDS of the structural characteristics is greater than 580, and the relative error VSDS% of the structural characteristics is greater than 85%; in the Class II area, the abnormal value VSDS of the structural characteristics is 420-580, and the relative error VSDS% of the structural characteristics is 65%-85%; in the Class III area, the abnormal value VSDS of the structural characteristics is 360-580, and the relative error VSDS% of the structural characteristics is 50%-65%; in the Class IV area, the abnormal value VSDS of the structural characteristics is less than 360, and the relative error VSDS% of the structural characteristics is less than 50%, as shown in Table 4. Among them, the Class I+II gas-bearing areas are the main areas for the deployment of efficient well locations.

[0154] Table 4 Abnormal classification of waveform structure characteristics of seismic data in the whole Puguang gas field

[0155]

[0156] Note: VSDS% = (structural characteristic value - original seismic trace value) / structural characteristic value × 100%

[0157] According to the above classification standards, the gas-bearing traps in the Puguang gas field are divided into three categories: one in category I, one in category II, and one in category III. Fig.14 As shown, they are:

[0158] Type I: mainly distributed in the Pg2 well area, in blocky distribution, located at the top of the Puguang structure;

[0159] Type II: Mainly distributed between the Pg2 well area and the Pg1dx and Pg4 wells, in a ring-shaped distribution, with the northern wing of the structure eroded, so it can also be called a semi-ring-shaped distribution;

[0160] Class III: Mainly distributed in Pg2 well, Pg1dx well and between Pg4 well and Pg3 well, in a ring-shaped distribution. Pg2 well, Pg1 well and Puguang 4 area within the ring are mainly Class I reservoirs and Class II reservoirs. Outside the ring, that is, the vast area outside Pg3 well.

[0161] Through comparative evaluation of favorable gas-bearing areas in the Puguang gas field, the overall structural anomaly is consistent with the current drilling situation. Fig.15As shown, the gas-bearing boundary extends to the Pg7 well fault in the west, to the phase change line in the south, to the Pg8 well-Pg9 well-Puguang 101 well line in the east, and to the Pg3 well in the north. The entire gas-bearing area is located in the structural high part; the gas-water transition zone in the Pg2 well block is near the Pg8 well-Puguang 304-1 well-Pg10 well-Puguang 101 well; the gas-water transition zone in the Pg3 well block is near the Puguang 7 side 1 well-Puguang 106-2H well-Pg3 well, which is consistent with the prediction results.

[0162] The classification of favorable gas-bearing range levels is predicted based on the structural anomalies of the seismic data body, and is completely classified according to the structural anomaly characteristics of the seismic data body, which is different from other geological classification methods; the entire Feixianguan Formation is classified from 30ms above T1f4 to 150ms below T1f1; of course, Class I contains Class II, but Class I is the main one; similarly, Class II contains Class I, but Class II is the main one.

[0163] Application effect evaluation

[0164] In the Pg7 well, the Feixianguan Formation-Changxing Formation in the vertical well location predicted before drilling has no obvious abnormal seismic data body structure characteristics. It is outside the abnormality of the favorable gas distribution range III ( Fig.14 ), after drilling, there is no obvious gas layer in the Feixianguan Formation-Changxing Formation at the vertical well location ( Fig.16 ), and then through side drilling, the perforation section 5484.7-5503.0m was perforated in the second section of the Feixianguan Formation in the limestone and dolomite, and good results were achieved, with a daily gas production of 57.34×10 4 m 3 / d, the daily gas production of the Feixianguan Formation Section 3 was 9.63×10 4 m 3 / d.

[0165] In addition, in the Puguang gas field study area, there is a main block with obvious abnormal seismic data structure characteristics. Subsequently, four exploration wells, Puguang 5, 6, 8 and 9, were deployed, and thick gas layers were obtained in the Feixianguan Formation-Changxing Formation ( Fig.17 ), among which, the thickness of the superimposed gas layer of Feixianguan Formation-Changxing Formation in Pg5 well is 366.3m; the thickness of the superimposed gas layer of Feixianguan Formation-Changxing Formation in Pg6 well is 411.2m; the thickness of the superimposed gas layer of Feixianguan Formation-Changxing Formation in Pg8 well is 102.6m; the thickness of the superimposed gas layer of Feixianguan Formation-Changxing Formation in Pg9 well is 204.2m. After testing of Pg5 and Pg6 wells, Pg5 well is in Fei3 section, with a testing depth of 4830-4868m and a gas production of 15.52×10 4 m 3 / d; Changxing Formation of Pg5 well, test depth 5141-5243.8m, gas production 67.42×10 4 m 3 / d. In the Fei3 section, Fei1-2 upper section and Fei1-2 middle section of Pg6 well, the test depths were 4850.7-4892.8m, 4992.5-5158m and 5030-5180m, respectively, and the test gas production was 40.22×10 4 m 3 / d,128.15×10 4 m 3 / d and 75.25×10 4 m 3 / d.

[0166] The research results are closely integrated with production, and the locations of 38 development wells in Puguang Gas Field provided by Zhongyuan Oilfield are analyzed. Fig.18 As shown in the figure, according to the structural characteristics of the seismic data body and combined with other research results, the well location and well trajectory are optimized in a timely manner, and suggestions for changing the well location are also proposed for individual well points, such as well p303-2, well p304-3, well p305-2 and well 204-2H. Fig.19 This is a structural feature anomaly profile of the Puguang Gas Field through wells P301-1, P301-2, P301-3, P301-4 and P301-5. The wellhead is located on the Pg6 well. Although they are all located at structural highs and in high gas-bearing areas, due to the strong heterogeneity of the Puguang Gas Field, there is not gas at structural highs or in high gas-bearing areas. If the P301-3 well is offset to the left, the effect will not be very good. The 38 development wells in the Puguang Gas Field are P101, P102, P103, P104, P202, P204, P301, P302, P303, P304, P305, etc. Finally, the success rate of the 38 development wells in the Puguang Gas Field reached 100%.

[0167] In summary, the method of the present invention is completely applicable to the prediction of gas content in the Feixianguan Formation T1f of the Lower Triassic and the Changxing Formation P2c of the Upper Permian in the Puguang gas field. This can be confirmed by both the structural characteristic profile model of the original seismic data of 7 wells and the structural characteristic numerical model of the seismic data volume of 7 wells. Moreover, the structural characteristic profile model of the seismic data volume and the numerical changes of the seismic data volume structure characteristics of the gas-bearing layer section are relatively large.

[0168] The seismic data volume structure model of the Feixianguan Formation gas layer in the Puguang gas field has obvious characteristics, and the predicted three favorable gas-bearing layers (traps) are reliable. Because, through the interpretation and comparison of 4 wells, it can be seen that the gas layers actually drilled in the 4 wells are basically consistent with the interpreted layers of the processed seismic data volume structure characteristics, which can prove that the research results have a certain degree of reliability.

[0169] In the whole area (150km 2) research, and further in-depth gas classification research was conducted after drilling Pg5, Pg6 and Pg7 wells. According to the previous research results, the main gas layers of Puguang gas field were mainly distributed in the Pg2 well area, and the Changxing formation still has a large gas distribution area, which can reach 25.46km 2 Wells Pg5 and Pg6 are located within the main gas layers of the Puguang gas field, and well Pg7 is located outside the main gas layers of the Puguang gas field. The drilling results have been confirmed. Therefore, the prediction of the gas content of the Puguang gas field based on the structural characteristics of seismic data is feasible.

[0170] By processing the structural characteristics of the seismic data of the Puguang gas field and comparing the interpretation results of the Feixianguan Formation and Changxing Formation gas layers, it is believed that this method has the following characteristics in predicting oil and gas layers in this area:

[0171] (1) Predictions are made based on the structural characteristics of seismic data rather than well models;

[0172] (2) The prediction process is strictly controlled by the time sequence;

[0173] (3) The results are oil and gas results rather than general seismic attributes. They are comparable to other methods (inversion, attribute research results) and can complement each other to eliminate some of their limitations.

[0174] Therefore, the method of predicting gas layers based on the structural characteristics of seismic data can be used as one of the gas layer prediction methods in this area and can be promoted and applied. The prediction results can serve as a reference for pre-drilling oil and gas prediction.

[0175] According to the structural characteristics of the seismic data of the Puguang gas field, there are two types of favorable gas-bearing areas in the Feixianguan Formation and Changxing Formation: the area is 34.63 km 2 , within the most favorable gas distribution range of Class I and Class II, the area is 40.27km 2 , part of which is on the Class III gas-water boundary; but regardless of the area taken as 34.63km 2 or 40.27km 2 , all of which are well-founded because the completed Pg5, Pg6 and Pg7 wells have confirmed the accuracy of the prediction.

[0176] The research results are quite complex, which is specifically reflected in the abnormal display of seismic data volume structure characteristics in some parts; this may be caused by the presence of multiple seismic data volume structure systems and good reservoir physical properties, and the standard division of each oil and gas block is different. Since some small parts of the Puguang gas field have high lithology anomalies, such as the abnormal changes in seismic data volume structure characteristics in the Pg3 well area, which are mainly caused by lithology, it is necessary to be cautious when using other attributes to predict oil and gas.

[0177] Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by technicians in this technical field within the essential scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A method for predicting oil and gas based on waveform structure feature big data, characterized in that: The steps include: (1) Field acquisition of 2D or 3D seismic data volumes; (2) Perform routine indoor processing of 2D or 3D seismic data volumes; (3) Selection of control parameters for special processing of waveform structural characteristics of seismic data volumes in real time; (4) Special processing is performed on the structural characteristics of the seismic data volume to establish a mathematical model for amplitude waveform data volume prediction; (5) Modify the prediction mathematical model, including abnormal values ​​of waveform structure characteristics and abnormal characteristics of control parameters; (6) determining the outlier segments of the waveform structure characteristics; (7) Perform dimensionless processing on the correlation analysis of the outlier values ​​of the waveform structural characteristics, and solve the two-level difference in the correlation coefficient of the outlier values ​​of the structural characteristics; (8) Based on the two-level difference, the correlation coefficient and correlation degree of the structural feature anomaly are solved; (9) Determination of quantitative criteria for predicting favorable oil and gas content and classification levels; (10) Determine the cross-sectional location and planar distribution range of favorable oil and gas-bearing strata; In step (2), conventional processing includes noise removal, dynamic correction, static correction, and stacking processing; In steps (3) and (4), special processing includes: slope, angle, and amplitude anomaly processing; In step (3), the control parameters include amplitude, frequency, inflection point, angle and slope; The basic principle of the prediction mathematical model is: within a seismic trace event axis range, the exponential fitting formula (1) is used to fit the background of the seismic trace x(t), so that the statistical distribution of the vertical integral fitting residual of x(t) satisfies the Gaussian distribution law. Formula (1) is: x(t)=μexp(-a(t-t0)) (1) Among them, α is the data behavior development coefficient, μ is the grey action; The local spectrum of the fitted model is a low-pass spectrum with a symmetric center of 0: Among them, X(w) is the local spectrum of the seismic trace, F(x(t)) represents the Fourier transform, and according to the autoregressive operator spectrum decomposition and O'Doherty formula, we get: Among them, r n is the reflection coefficient of the upper layer, assuming: That is, the reflection coefficient r n The spectrum of is a low-pass spectrum; In step (9), The criteria include: The smoothness of the abnormal characteristics of the waveform structure of the seismic data volume; The magnitude of the seismic data volume waveform structure anomaly and its relationship with the magnitude of the surrounding structure values; the comparison results of the seismic data volume waveform structure anomaly and the degree of correlation with the well; The classification levels include: The relative error of the quantitative identification mark of the type I waveform structure feature is greater than 85%; The relative error of the quantitative identification mark of type II waveform structure characteristics is 65%-85%; The relative error of the quantitative identification mark of the type III waveform structure characteristics is 50%-65%; The relative error of the quantitative identification mark of the type IV waveform structure feature is less than 50%.

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