Phase-controlled inversion gas-bearing favorable zone prediction method

Abstract

The invention discloses a phase-controlled inversion gas-bearing favorable region prediction method, which comprises the following steps of: 1) acquiring an Lg (Rt) simulation data volume; 2) establishing a gas-containing favorable area division standard; 3) drawing a sandstone Lg (Rt) average value plane graph; 4) performing advantage interval statistics and abnormal value processing on the favorable area Lg (Rt); 5) drawing a planar graph of the gas-bearing knowledge factor Ig; 6) a favorable area predictive factor Ie plane graph; and 7) identifying a gas-containing favorable area. According to the method, the resistivity is highly sensitive to the gas-bearing property of the reservoir, but the geophysical prospecting prediction method is poor in resistivity prediction effect and is difficult to be used for exploration and development practice. According to the innovation achievement, the resistivity obtained after logarithm taking is simulated through the waveform constraint phase control inversion technology, the simulation effect is good, the resistivity curve can be applied to earthquake gas-water prediction, and by constructing the gas-bearing prediction factor Ig, the precision of the resistivity parameter for reservoir gas-bearing judgment is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of gas-bearing property prediction in the field of oil and gas exploration and development, and in particular to a phase-controlled inversion gas-bearing favorable area prediction method. Background Art

[0002] The prediction of favorable gas-bearing areas in lithologic gas reservoirs requires solving three problems: the first is the accurate prediction of reservoir thickness and physical properties; the second is the effective identification of reservoir gas content; and the third is the comprehensive identification of reservoir favorable areas by combining reservoir gas content with other geological factors.

[0003] In terms of reservoir thickness and physical property prediction, various inversion technologies are currently used, which are mainly divided into two categories: seismic trace-based inversion and model-based inversion. The resolution of seismic trace-based inversion is relatively low, generally believed to be around λ / 8, but the prediction effect is good, and the inversion combination can be more realistic seismic data information. The resolution of model-based inversion is relatively high, and the apparent resolution is equivalent to the seismic sampling rate, but the prediction effect is poor, and the inversion result is greatly affected by the model.

[0004] In terms of reservoir gas content identification, the more commonly used ones are frequency-based seismic attributes, absorption and attenuation-based seismic attributes, AVO analysis, elastic wave impedance inversion and elastic parameter inversion. Frequency-based seismic attributes and absorption and attenuation-based seismic attributes have low resolution, many influencing factors, and are easily disturbed by surrounding rock layers, and have poor effects on the identification of gas content in thin reservoirs. Elastic wave impedance inversion and elastic parameter inversion can be used to predict the gas content of thin reservoirs, but they also have their own shortcomings. First, thin reservoir prediction usually uses model inversion, and its high-frequency and low-frequency information outside the seismic frequency band comes from the extrapolation and interpolation of logging data. The accuracy of extrapolation and interpolation directly affects the accuracy of the inversion results; second, the formation elastic parameter information that can be reflected by seismic data has strong multi-solution characteristics when using seismic data to obtain seismic elastic parameter information. Third, elastic parameters may not be very sensitive to the gas content of some formations, and it is difficult to identify gas and water through elastic parameters.

[0005] In terms of comprehensive prediction of favorable gas-bearing areas, the commonly used method is to superimpose favorable areas of various results such as reservoir thickness, physical properties, and gas content, and take the common favorable areas as favorable gas-bearing areas. This method has strong multi-solution and uncertainty when predicting reservoir thickness, physical properties, and gas content, and does not consider the mutual influence between parameters, and sets the threshold value of favorable areas for each parameter. In actual applications, it is found that there is a difference between the favorable area of ​​a single parameter and the actual situation, and the distribution of the superimposed gas-bearing favorable area of ​​multiple parameters is very different from the actual situation. The practicality of the prediction of the favorable gas-bearing area of ​​multiple parameters is poor. In addition, directly superimposing the coordinates of the favorable area of ​​a single parameter requires a large workload of map compilation and revision, which is easily interfered by human subjective factors, and the results lack quantitative evaluation information on favorable and unfavorable gas-bearing areas.

[0006] The prediction of favorable gas-bearing areas in the prior art is usually: 1. Use inversion technology to predict the thickness, physical properties, and gas-bearing planar distribution of reservoirs; 2. Combined with the test production data, the thickness, physical properties and gas content plane diagrams are divided into respective favorable areas; 3. Superimpose the favorable areas of thickness, physical properties and gas content on a flat surface to obtain a comprehensive favorable area.

[0007] This method has strong multi-solution and uncertainty when predicting reservoir thickness, physical properties, and gas content. It also does not consider the mutual influence between parameters and sets the threshold value of favorable areas for each parameter. In actual applications, it is found that the single-parameter favorable area is different from the actual one, and the distribution of the superimposed gas-bearing favorable area of ​​multiple parameters is very different from the actual one. The practicality of the multi-parameter comprehensive gas-bearing favorable area is poor. In addition, directly superimposing the coordinates of the single-parameter favorable area requires a lot of work in compiling and revising maps, which is easily interfered by human subjective factors. The results lack quantitative evaluation information on gas-bearing favorable and unfavorable areas.

[0008] The key to accurate prediction of favorable gas-bearing areas lies in three aspects. First, identify the sensitive parameters of gas-bearing properties and accurately predict the sensitive parameters by combining well and seismic data; second, determine the division criteria of favorable gas-bearing areas and analyze the key influencing parameters; third, optimize the combination of key parameters, find the best method to predict gas-bearing areas, determine the division criteria of favorable gas-bearing areas, and accurately characterize favorable gas-bearing areas.

[0009] Therefore, it is an urgent problem for technical personnel in this field to provide a phase-controlled inversion gas-bearing favorable zone prediction method with good simulation effect, high accuracy and the ability to more reasonably divide gas-bearing favorable zones. Summary of the invention

[0010] The present invention aims to provide a phase-controlled inversion gas-bearing favorable zone prediction method, which improves the resistivity prediction accuracy through waveform-constrained phase-controlled inversion technology, and further improves the accuracy of gas-bearing discrimination and gas-bearing favorable zone prediction through gas-bearing discrimination factors and gas-bearing favorable zone prediction factors. It can better solve the gas-water identification and gas-bearing favorable zone prediction problems in low-porosity gas reservoirs. After actual application in a dense sandstone high-water-content gas reservoir, the gas-bearing favorable zone discrimination compliance rate has been increased from less than 60% to more than 75%.

[0011] In order to achieve the above-mentioned object of the invention, the technical solution of the present invention is as follows: A phase-controlled inversion gas-bearing favorable zone prediction method comprises the following steps: 1) Obtain the Lg (Rt) simulation data volume, obtain simulation parameters through waveform constrained phase-controlled inversion parameter test, carry out resistivity waveform constrained phase-controlled simulation through waveform constrained phase-controlled inversion method, and obtain the Lg (Rt) simulation data volume; 2) Establishing the criteria for dividing favorable gas-bearing areas; 3) Draw a plane map of the average Lg (Rt) value of sandstone, select multiple different gamma and velocity thresholds, use the gamma inversion body and velocity inversion body as constraints, extract the average Lg (Rt) value that meets the conditions within the time window of the target layer, and draw multiple plane maps of the average Lg (Rt) value of sandstone; 4) Statistics of the dominant intervals of Lg (Rt) values ​​in favorable areas and processing of abnormal values, statistical analysis of the dominant intervals of Lg (Rt) value distribution in gas-bearing favorable areas [V L , V H ], analyze the prediction effect of the sandstone Lg (Rt) average plane map extracted by different threshold values, count the gas content prediction compliance rate, and screen out the sandstone Lg (Rt) average plane map with the highest compliance rate; Lg(Rt)>V H The filling is low, so that the low value area on the Lg (Rt) average value plane is the gas-unfavorable area, and the high value area is the gas-favorable area. The high value unfavorable area is filled with the following formula: F[Lg(Rt)]=V L -C1*[Lg(Rt)-V H ] Among them, V L —Lg (Rt) low value threshold of gas-bearing favorable area, V H —Lg (Rt) high value threshold of gas-bearing favorable area, C1—correction coefficient, taken as 0.5-2; 5) Gas content knowledge factor I g Plane drawing, according to the sandstone Lg (Rt) average plane map, select the Lg (Rt) average value in the plane map below 90% of the sample points with a relatively low value as the Lg (Rt0) value to calculate the gas content discrimination factor I g , draw the gas content discrimination factor I g Floor plan; Gas content determination factor: I g =Lg(Rt)- Lg(Rt0)= Lg(Rt / Rt0) 6) Favorable Area Prediction Factor I e Plane drawing, combined with reservoir thickness prediction results, calculate gas-bearing favorable area prediction factors, and draw gas-bearing favorable area prediction factors I e Floor plan, I e =H×[Lg(Rt)- Lg(Rt0)]= H×I g Where: H is the reservoir thickness; 7) Identify favorable gas-bearing areas and calculate the prediction compliance rate of favorable gas-bearing areas.

[0012] Further, the simulation parameters in step 1) include the relevant time window length, the number of lateral smoothing lines, and the number of lateral smoothing channels.

[0013] Further, the waveform-constrained phased inversion method in step 1) includes waveform-constrained modeling inversion and waveform-indicating inversion.

[0014] Further, during the simulation in step 1), the resistivity is logarithmically processed. Since the resistivity amplitude varies greatly, after taking the logarithm, its correlation with the seismic waveform is better and the simulation effect is更佳.

[0015] Further, the discrimination criteria for gas-bearing favorable areas and favorable wells in step 2) are as follows: a. All tested pure gas wells are considered to be in gas-bearing favorable areas and are favorable wells; b. The estimated cumulative production of dynamic type I and type II wells is statistically analyzed and arranged from high to low. The lowest value of the first 70 - 90% is set as the low threshold of the estimated cumulative production in the gas-bearing favorable area; the open flow potential threshold value of dynamic type I wells is taken as A1, and the open flow potential threshold value of dynamic type II wells is taken as A2. The maximum value of the first 60 - 90% of the tested water volumes of dynamic type II wells arranged from small to large is B2; if the open flow potential is greater than A1, its estimated cumulative production can reach the estimated cumulative production threshold in the gas-bearing favorable area, and wells with an open flow potential greater than A1 can be considered favorable wells; if the open flow potential is greater than A2 and less than A1, and the tested water volume < B2, the estimated cumulative production of more than 70% of such wells can reach the estimated cumulative production threshold in this gas-bearing favorable area, and such wells are also considered favorable wells; c. For tested gas-water producing wells, if the open flow potential > A1, it is considered to be in a gas-bearing favorable area and is a favorable well; d. For tested gas-water producing wells, when A1 < open flow potential < A2 and the tested water production < B2, it is considered to be in a gas-bearing favorable area and is a favorable well.

[0016] Further, the selection of the gamma and velocity thresholds in step 3) is specifically to refer to the upper limit of the gamma value and the lower limit of the velocity value for well logging reservoir identification. Considering the systematic error influence between well logging data and seismic inversion data, within the range of gamma value ±15 API and velocity value ±150 m / s near the well logging identification threshold, multiple different gamma and velocity thresholds are selected.

[0017] Further, the dominant interval [V L , V H of the Lg(Rt) value distribution in the gas-bearing favorable area in step 4) is specifically as follows: On the average Lg(Rt) value plan of sandstone, the Lg(Rt) value distribution of each well in the study area is statistically analyzed. When 70 - 90% of the dynamic type I and type II wells are in the interval [V L , V H , then the [V L, V H ] is the dominant range of Lg (Rt) value distribution in gas-bearing favorable areas.

[0018] Furthermore, the statistical gas content prediction compliance rate in step 4) is specifically that the favorable well is located at [V L , V H ] interval is in compliance, and the unfavorable well is in [V L , V H ]Outside the range is in compliance.

[0019] Furthermore, the prediction compliance rate of the favorable gas-bearing area in step 7) is specifically calculated based on the prediction factor I of the favorable area. e On the plane map, count the favorable wells and unfavorable wells I e Value distribution, determine the favorable gas-bearing area I e The lower value threshold is set, and the gas-bearing areas are divided into favorable and unfavorable gas-bearing areas based on this. The prediction compliance rate is statistically analyzed. If the favorable wells are in the favorable area, it is compliant; if the unfavorable wells are in the unfavorable area, it is compliant; otherwise, it is not compliant.

[0020] Furthermore, the determination e The specific value threshold is: in favorable wells, I e The values ​​are arranged from large to small, and the minimum value of the top 65%-85% and the unfavorable well I e Select multiple I values ​​between the first 65%-85% of the maximum values ​​in ascending order e value, statistically predict the compliance rate, and select the I with the highest compliance rate e The value is the threshold.

[0021] Beneficial effects of the present invention: 1. In the present invention, resistivity is highly sensitive to gas content in reservoirs, but the resistivity prediction effect of geophysical prediction methods is poor and difficult to be used in exploration and development practices. This innovative achievement uses waveform-constrained phase-controlled inversion technology to simulate the logarithmic resistivity, and the simulation effect is good, so that the resistivity curve can be applied to seismic gas and water prediction.

[0022] 2. In the present invention, by constructing a gas content prediction factor I g , further improving the accuracy of resistivity parameters in determining reservoir gas content.

[0023] 3. In the present invention, the favorable gas-bearing area should not only have good gas-bearing properties but also have a certain thickness of reservoir development. By constructing the prediction factor I of the favorable gas-bearing area e , which comprehensively utilizes the information of reservoir thickness and reservoir resistivity, can more reasonably divide the favorable gas-bearing areas than using the resistivity parameter alone.

[0024] 4. The existing gas-bearing favorable zone division method can generally only divide the favorable zone and the unfavorable zone through multi-parameter comprehensive division. The gas-bearing favorable zone discrimination factor I proposed in the present invention e It can not only qualitatively divide the favorable gas-bearing areas, but also identify the favorable area through the factor I e The value provides a quantitative reference for the level of favorable gas-bearing areas, which is more conducive to practical application and the method is more practical.

[0025] 5. The method used in the present invention to divide favorable wells, unfavorable wells, favorable gas-bearing areas, and unfavorable gas-bearing areas by using dynamic data and estimated cumulative production has strong guiding significance for gas-water identification research and well location target demonstration. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 The present invention is a flow chart of the method for predicting favorable gas-bearing areas by phase-controlled inversion.

[0027] Figure 2 This is a simulated profile diagram of Lg (Rt) obtained by waveform-constrained phase-controlled inversion in Example 1 of the present invention.

[0028] Figure 3 This is a comparison diagram of the profile effects of conventional model inversion and waveform constrained phase-controlled inversion in Example 1 of the present invention.

[0029] Figure 4 This is a plane diagram of the average value of Lg (Rt) of sandstone in Example 1 of the present invention.

[0030] Figure 5 The gas content discrimination factor I of Example 1 of the present invention g Floor plan.

[0031] Figure 6 The prediction factor I of the favorable gas-bearing area in Example 1 of the present invention is e Floor plan.

[0032] Figure 7 The gas content discrimination factor I of Example 2 of the present invention g Floor plan.

[0033] Figure 8 The prediction factor I of the gas-bearing favorable area in Example 2 of the present invention is e Floor plan.

[0034] Fig. 9 The prediction factor I of the gas-bearing favorable area in Example 3 of the present invention is e Floor plan.

[0035] Fig.10 This is the predicted distribution diagram of the reservoir thickness of the lower subsection of Box 8 in Example 1 of the present invention.

[0036] Fig.11 This is the predicted distribution map of reservoir thickness of the Shan 1 section in Example 2 of the present invention. DETAILED DESCRIPTION

[0037] The present invention is further described in detail below in conjunction with examples, but the embodiments of the present invention are not limited thereto.

[0038] Example 1 In this example, the lower subsection of box 8 in a certain block is selected to predict the favorable gas-bearing area. Figure 1 As shown, this embodiment provides a phase-controlled inversion gas-bearing favorable zone prediction method, which specifically includes the following steps: 1) The relevant time window length is 100ms, the number of horizontal smooth lines is 7, and the number of horizontal smooth traces is 7. The time window is 20ms upward from the bottom of Box 7 to 20ms downward from the bottom of Benxi. The waveform constrained phase-controlled inversion is carried out on the logarithmic resistivity curve to simulate the spatial distribution of the logarithmic resistivity curve. The Lg (Rt) simulation profile obtained by the waveform constrained phase-controlled inversion is as follows Figure 2 As shown in the figure, the comparison of the profile effect between the conventional model inversion and the waveform constrained phase control inversion is shown in Figure 3 As shown by Figure 3 It can be seen that waveform-constrained phase-controlled inversion can better reflect the changes in seismic waveforms to reflect the changes in sedimentary phases, which is more in line with the change law of sedimentary phases and has better advantages in predicting favorable gas-bearing areas. 2) Establish the criteria for identifying favorable gas-bearing areas and favorable wells based on test data, dynamic classification, cumulative production data, etc. The specific criteria are as follows: a. All the pure gas wells tested are regarded as favorable wells, which are considered to be in favorable gas-bearing areas; b. According to the estimated cumulative production of dynamic I and II wells, more than 75% of the dynamic I and II wells in the study area have a cumulative production of more than 12 million cubic meters, so 12 million cubic meters is used as the lower threshold of the cumulative production value of the favorable gas-bearing area; wells with an open flow rate greater than 150,000 cubic meters / day have an estimated cumulative production of more than 12 million cubic meters, so they are classified as favorable wells; wells with a 50,000 cubic meter / day < open flow rate < 150,000 cubic meters / day and a tested water production of < 5 cubic meters / day, more than 70% of these wells have an estimated cumulative production of more than 12 million cubic meters, and are also classified as favorable wells; c. Establish a correlation between the test production and the cumulative production. For a well producing both gas and water, if the unobstructed flow rate is >150,000 cubic meters per day, it is a favorable well located in a favorable gas-bearing area; d. Test gas-water co-production wells, 50,000 cubic meters / day < unobstructed flow < 150,000 cubic meters / day, test water production < 5 cubic meters / day, it is a favorable well, located in a favorable gas-bearing area; 3) The upper limit of the gamma value for reservoir identification in the Box 8 section of the reference area is 90 API, and the lower limit of the velocity value is 4700 m / s. In the range of gamma value 80-100 API and velocity value 4600-4800 m / s, the gamma threshold values ​​of 90 API and 95 API are selected, and the velocity threshold values ​​of 4700 m / s and 4750 m / s are selected. Under the constraints of the existing gamma inversion body and velocity inversion body, the average value of the Lg (Rt) inversion body sample points that meet the conditions is extracted, and the plane map is drawn; 4) Statistical analysis of the distribution of Lg (Rt) values ​​in gas-bearing favorable areas. More than 75% of the dynamic I and II wells in the area are in the [1.45, 1.75] interval, which can be used as the Lg (Rt) value distribution advantage interval in gas-bearing favorable areas. The prediction effect of extracting Lg (Rt) value plane maps with different gamma threshold values ​​of 90API and 95API, and velocity threshold values ​​of 4700m / s and 4750m / s was analyzed. It is believed that the plane map extracted with gamma 95API and velocity 4750m / s as the threshold has a good gas-bearing prediction effect. The sandstone Lg (Rt) average plane map with the highest compliance rate is selected as follows: Figure 4 As shown; Fill the unfavorable area with Lg (Rt)>1.75 with low value, so that the low value area on the Lg (Rt) value plane is the gas-bearing unfavorable area, and the high value area is the gas-bearing favorable area. The high value unfavorable area is filled with the following formula: F[Lg(Rt)]=V L -C1*[Lg(Rt)-V H ] Among them, V L —Lg (Rt) low value threshold of gas-bearing favorable area, take 1.45, V H —Lg (Rt) high value threshold of gas-bearing favorable area, taken as 1.75, C1—correction coefficient, taken as 1; 5) The low threshold of Lg (Rt) in gas-bearing favorable areas is 1.45. The values ​​of the sandstone Lg (Rt) plane map are basically above 1. 1 is selected as the Lg (Rt0) value to calculate the gas-bearing discriminant factor I g , draw the gas content discrimination factor I g Floor plan Figure 5 As shown; Gas content determination factor: I g =Lg(Rt)- Lg(Rt0)= Lg(Rt / Rt0) 6) Combined with the existing reservoir thickness prediction plan of the lower subsection of Box 8, as shown in Fig.10 As shown in the figure, the prediction factor of favorable gas-bearing area is calculated and the prediction factor I of favorable gas-bearing area is drawn. e Floor plan Figure 6 As shown, Prediction factors of favorable gas-bearing areas: I e=H×[Lg(Rt)- Lg(Rt0)]= H×[Lg(Rt / Rt0)] Where, H is the reservoir thickness; 7) On the plane diagram of the prediction factors of the favorable gas-bearing area, the I values ​​of favorable wells and unfavorable wells are counted. e The distribution range of values, the statistical results show that more than 80% of the favorable wells I e The value is greater than 4, and more than 80% of the unfavorable wells I e The value is less than 6, when I e When the threshold value is 5, the gas-bearing prediction compliance rate is the highest, so 5 is used as the threshold for dividing the gas-bearing favorable area in the lower subsection of Box 8.

[0039] Since many wells in the study area produce from both the Box 8 lower subsection and the Shan 1 section, the coincidence rate of the Box 8 lower subsection was not calculated separately. Example 3 will show the overall coincidence rate of the Box 8 lower subsection + Shan 1 section.

[0040] Example 2 In this example, a certain block Shan 1 section is selected to predict the favorable gas-bearing area. Figure 1 As shown, this embodiment provides a phase-controlled inversion gas-bearing favorable zone prediction method, which specifically includes the following steps: 1) The relevant time window length is 100ms, the number of horizontal smooth lines is 7, and the number of horizontal smooth traces is 7. The time window is 20ms upward from the bottom of Box 7 to 20ms downward from the bottom of Benxi. The waveform constrained phase-controlled inversion is carried out on the logarithmic resistivity curve to simulate the spatial distribution of the logarithmic resistivity curve. 2) Establish the criteria for identifying favorable gas-bearing areas and favorable wells based on test data, dynamic classification, cumulative production data, etc. The specific criteria are as follows: a. All the pure gas wells tested are regarded as favorable wells, which are considered to be in favorable gas-bearing areas; b. According to the estimated cumulative production of dynamic I and II wells, more than 75% of the dynamic I and II wells in the study area have a cumulative production of more than 12 million cubic meters, so 12 million cubic meters is used as the lower threshold of the cumulative production value of the favorable gas-bearing area; wells with an open flow rate greater than 150,000 cubic meters / day have an estimated cumulative production of more than 12 million cubic meters, so they are classified as favorable wells; wells with a 50,000 cubic meter / day < open flow rate < 150,000 cubic meters / day and a tested water production of < 5 cubic meters / day, more than 70% of these wells have an estimated cumulative production of more than 12 million cubic meters, and are also classified as favorable wells; c. Establish a correlation between the test production and the cumulative production. For a well producing both gas and water, if the unobstructed flow rate is >150,000 cubic meters per day, it is a favorable well located in a favorable gas-bearing area; d. Test gas-water co-production wells, 50,000 cubic meters / day < unobstructed flow < 150,000 cubic meters / day, test water production < 5 cubic meters / day, it is a favorable well, located in a favorable gas-bearing area; 3) The upper limit of the gamma value for reservoir identification in the Shan 1 section of the reference area is 95 API, and the lower limit of the velocity value is 4750 m / s (where do the values ​​come from). Between the gamma value of 95-105 API and the velocity value of 4650-4850 m / s, the gamma threshold values ​​of 95 API and 100 API are selected, and the velocity threshold values ​​of 4750 m / s and 4800 m / s are selected. Under the constraints of the existing gamma inversion body and velocity inversion body, the average value of the Lg (Rt) inversion body sample points that meet the conditions is extracted, and the plane map is drawn; 4) Statistical analysis of the distribution of Lg (Rt) values ​​in gas-bearing favorable areas shows that more than 75% of the dynamic I and II wells in the area are in the [1.55, 1.75] interval, which can be used as the Lg (Rt) value distribution advantage interval in gas-bearing favorable areas. The prediction effect of extracting Lg (Rt) value plane maps with different gamma threshold values ​​of 95API and 100API and velocity threshold values ​​of 4750m / s and 4800m / s is analyzed, and it is believed that the plane map extracted with gamma 100API and velocity 4800m / s as the threshold has a good gas-bearing prediction effect; Fill the unfavorable area with Lg (Rt)>1.75 with low value, so that the low value area on the Lg (Rt) value plane is the gas-bearing unfavorable area, and the high value area is the gas-bearing favorable area. The high value unfavorable area is filled with the following formula: F[Lg(Rt)]=V L -C1*[Lg(Rt)-V H ] Among them, V L —The lower threshold of Lg (Rt) in gas-bearing favorable areas is 1.55, V H —Lg (Rt) high value threshold of gas-bearing favorable area, taken as 1.75, C1—correction coefficient, taken as 1; 5) The low threshold of Lg (Rt) in gas-bearing favorable areas is 1.55. The values ​​of the sandstone Lg (Rt) plane map are basically above 1.1. 1.1 is selected as the Lg (Rt0) value to calculate the gas-bearing discriminant factor I g , draw the gas content discrimination factor I g Floor plan Figure 7 shown.

[0041] Gas content determination factor: I g =Lg(Rt)- Lg(Rt0)= Lg(Rt / Rt0) 6) Combined with the existing Shan 1 reservoir thickness prediction plan, Fig.11 As shown in the figure, the prediction factor of favorable gas-bearing area is calculated and the prediction factor I of favorable gas-bearing area is drawn. e Floor plan Figure 8 shown.

[0042] Prediction factors of favorable gas-bearing areas: I e=H×[Lg(Rt)- Lg(Rt0)]= H×[Lg(Rt / Rt0)] Where H is the reservoir thickness; 7) On the plane map of the prediction factors of the favorable area, the I values ​​of the wells in the favorable gas-bearing area and the wells in the unfavorable gas-bearing area are counted. e The distribution range of values, the statistical results show that more than 70% of the favorable wells have I e The value is greater than 2, while more than 70% of unfavorable wells are less than 4. When the Ie threshold value is 3, the gas-bearing prediction compliance rate is the highest. Therefore, 3 is used as the threshold for dividing the gas-bearing favorable area of ​​Shan 1 section.

[0043] Example 3 In this example, a certain block, the lower sub-section of Box 8 to the section of Mountain 1, is selected to predict the favorable gas-bearing area. Figure 1 As shown, this embodiment provides a phase-controlled inversion gas-bearing favorable zone prediction method, which specifically includes the following steps: 1) Combine the I of Example 1 and Example 2 e Add the value plane graph to get the box 8 lower subsection ~ mountain 1 section I e The value plane is as follows Fig. 9 shown.

[0044] 2) Count the I of favorable wells and unfavorable wells on the plane map respectively e The statistical results show that more than 70% of the favorable wells are in the lower subsection of He 8~Shan 1 section I e Value greater than 4, more than 70% of the unfavorable wells are from the lower subsection of Box 8 to the section I of Mountain 1 e The value is less than 6, when I e When the threshold value is 5, the gas prediction coincidence rate is the highest. Therefore, 5 is used as the threshold value for dividing the favorable and unfavorable areas of the lower sub-member of He 8 to the Shan 1 member. e Value threshold.

[0045] 3) Will I e >5.0 is regarded as a favorable gas-bearing area, I e <5.0 and above is an unfavorable gas-bearing zone, and the gas-bearing property prediction statistics are in line with the statistics. Among the 305 wells involved in the statistics, 233 wells are in line with the statistics, with a compliance rate of 76.4%.

[0046] It is understood that the present invention is described by some embodiments, and it is known to those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the scope of protection of the present invention.

Claims

1. A method for predicting favorable gas-bearing areas by phase-controlled inversion, characterized in that: Including the following steps: 1) Obtain the Lg(Rt) simulation data volume. Through waveform-constrained phased inversion parameter tests, obtain the simulation parameters. Conduct resistivity waveform-constrained phased simulation using the waveform-constrained phased inversion method to obtain the Lg(Rt) simulation data volume; 2) Establish the criteria for dividing gas-bearing favorable areas; 3) Draw the plane map of the average value of sandstone Lg(Rt). Select multiple different gamma and velocity thresholds. Constrained by the gamma inversion volume and velocity inversion volume, extract the average value of Lg(Rt) that meets the conditions within the time window of the target layer, and draw multiple plane maps of the average value of sandstone Lg(Rt); 4) Statistics of the dominant intervals of Lg (Rt) values ​​in favorable areas and processing of abnormal values, statistical analysis of the dominant intervals of Lg (Rt) value distribution in gas-bearing favorable areas [V L , V H ], analyze the prediction effect of the sandstone Lg (Rt) average plane map extracted by different threshold values, count the gas content prediction compliance rate, and screen out the sandstone Lg (Rt) average plane map with the highest compliance rate; Lg(Rt)>V H The filling is low, so that the low value area on the Lg (Rt) average value plane is the gas-unfavorable area, and the high value area is the gas-favorable area. The high value unfavorable area is filled with the following formula: F[Lg(Rt)]=V L -C1*[Lg(Rt)-V H ] Among them, V L —Lg (Rt) low value threshold of gas-bearing favorable area, V H —Lg (Rt) high value threshold of gas-bearing favorable area, C1—correction coefficient, taken as 0.5-2; 5) Gas content knowledge factor I g Plane drawing, according to the sandstone Lg (Rt) average plane map, select the Lg (Rt) average value in the plane map below 90% of the sample points with a relatively low value as the Lg (Rt0) value to calculate the gas content discrimination factor I g , draw the gas content discrimination factor I g Floor plan; Gas content determination factor: I g =Lg(Rt)- Lg(Rt0)= Lg ( Rt / Rt0) 6) Favorable Area Prediction Factor I e Plane map drawing, combined with reservoir thickness prediction results, calculate gas-bearing favorable area prediction factor I e , delineate the prediction factor I of gas-bearing favorable areas e Floor plan, I e =H×[Lg(Rt)- Lg(Rt0)]= H×I g where H is the reservoir thickness; 7) Identify gas-bearing favorable areas and statistically calculate the coincidence rate of gas-bearing favorable area prediction.

2. The prediction method according to claim 1, characterized in that: The simulation parameters in step 1) include the relevant time window length, the number of lateral smoothing lines, and the number of lateral smoothing channels.

3. The prediction method according to claim 1, characterized in that: The waveform-constrained phased inversion method in step 1) includes waveform-constrained modeling inversion and waveform-indicating inversion.

4. The prediction method according to claim 1, characterized in that: During the simulation in step 1), logarithmic processing is performed on the resistivity.

5. The prediction method according to claim 1, characterized in that: The discrimination criteria for gas-bearing favorable areas and favorable wells in step 2) are as follows: a. All tested pure gas wells are considered to be in gas-bearing favorable areas and are favorable wells; b. Statistically calculate the predicted cumulative production of dynamic type I and type II wells, arrange the predicted cumulative production from high to low, and set the lowest value of the first 70-90% as the low threshold of the predicted cumulative production in the gas-bearing favorable area; the open flow potential threshold of dynamic type I wells is taken as A1, the open flow potential threshold of dynamic type II wells is taken as A2, and the maximum value of the first 60-90% of the tested water volumes of dynamic type II wells arranged from small to large is taken as B2; wells with an open flow potential greater than A1 and whose predicted cumulative production can reach the lowest predicted cumulative production threshold in the gas-bearing favorable area are all favorable wells; when the open flow potential is greater than A2, less than A1, and the tested water volume < B2, the cumulative production of more than 70% of such wells can reach this threshold, and such wells are also considered favorable wells; c. For tested gas-water producing wells with an open flow potential > A1, they are considered to be in gas-bearing favorable areas and are favorable wells; d. For tested gas-water producing wells with A1 > open flow potential > A2 and the tested water production < B2, they are considered to be in gas-bearing favorable areas and are favorable wells.

6. The prediction method according to claim 1, characterized in that: The selection of gamma and velocity thresholds in step 3) is specifically as follows: Refer to the upper limit of the gamma value and the lower limit of the velocity value for logging reservoir identification. Considering the systematic error impact between logging data and seismic inversion data, select multiple different gamma and velocity thresholds within the range of ±15 API for the gamma value and ±150 m / s for the velocity value near the logging identification threshold.

7. The prediction method according to claim 1, characterized in that: In the step 4), the Lg (Rt) value distribution advantage interval [V L , V H Specifically, the distribution of Lg (Rt) values ​​of each well in the study area was statistically analyzed on the average plane of sandstone Lg (Rt). When 70-90% of the dynamic I and II wells were in [V L , V H ] interval, it is considered that the [V L , V H ] is the dominant range of Lg (Rt) value distribution in gas-bearing favorable areas.

8. The prediction method according to claim 1, characterized in that: The statistical gas content prediction compliance rate in step 4) is specifically that the favorable well is located at [V L , V H ] interval is in compliance, and the unfavorable well is in [V L , V H ]Outside the range is in compliance.

9. The prediction method according to claim 1, characterized in that: The statistical gas-bearing favorable zone prediction compliance rate in step 7) is specifically calculated based on the favorable zone prediction factor I e On the plane map, count the favorable wells and unfavorable wells I e Value distribution, determine the favorable gas-bearing area I e The lower value threshold is set, and the gas-bearing areas are divided into favorable and unfavorable gas-bearing areas based on this. The prediction compliance rate is statistically analyzed. If the favorable wells are in the favorable area, it is compliant; if the unfavorable wells are in the unfavorable area, it is compliant; otherwise, it is not compliant.

10. The prediction method according to claim 9, characterized in that: The determination e The specific value threshold is: in favorable wells, I e The values ​​are arranged from large to small, and the minimum value of the top 65%-85% and the unfavorable well I e Select multiple I values ​​between the first 65%-85% of the maximum values ​​in ascending order e value, statistically predict the compliance rate, and select the I with the highest compliance rate e The value is the threshold.

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