A method for predicting gas-bearing favorable zones by phase-controlled inversion

Abstract

The application discloses a kind of gas-bearing favorable area prediction methods of phase-controlled inversion, comprising the following steps, 1) obtaining Lg (Rt) simulation data body;2) establish gas-bearing favorable area division standard;3) draw sandstone Lg (Rt) average value plan;4) favorable area Lg (Rt) advantage interval statistics and outlier processing;5) gas-bearing knowledge factor I g Plan drawing;6) favorable area prediction factor I e Plan;7) gas-bearing favorable area identification.In the application, resistivity is sensitive to reservoir gas-bearing property, but the resistivity prediction effect of geophysical prospecting prediction method is poor, which is difficult to be used in exploration and development practice.The innovation 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-water prediction, and the gas-bearing prediction factor I g is constructed, to further improve the accuracy of resistivity parameter for reservoir gas-bearing property discrimination.

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 method for predicting favorable gas-bearing areas. 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 gas content and other geological factors to determine favorable reservoir areas.

[0003] Currently, various inversion techniques are used to predict reservoir thickness and physical properties. These techniques fall into two main categories: seismic trace-based inversion and model-based inversion. Seismic trace-based inversion has a relatively low resolution, generally considered to be around λ / 8, but offers good prediction results and can incorporate more realistic seismic data. Model-based inversion offers a relatively high resolution, with an apparent resolution comparable to the seismic sampling rate, but offers poor prediction results, and the inversion results are significantly influenced by the model.

[0004] Frequency-based seismic attributes, absorption-attenuation-based seismic attributes, AVO analysis, elastic wave impedance inversion, and elastic parameter inversion are commonly used to identify gas-bearing properties in reservoirs. Frequency-based seismic attributes and absorption-attenuation-based seismic attributes have low resolution, are subject to numerous influencing factors, and are easily affected by interference from surrounding rock layers, making them less effective in identifying gas-bearing properties in thin reservoirs. Elastic wave impedance inversion and elastic parameter inversion can be used to predict gas-bearing properties in thin reservoirs, but they also have their own shortcomings. First, thin reservoir prediction typically uses model inversion, where high- and low-frequency information outside the seismic frequency band is derived from extrapolated and interpolated well logging data. The accuracy of this extrapolation directly affects the accuracy of the inversion results. Second, the formation elastic parameter information that seismic data can reflect is highly multi-solution prone. Third, elastic parameters may not be very sensitive to the gas-bearing properties of certain formations, making it difficult to identify gas and water using elastic parameters.

[0005] For the comprehensive prediction of favorable gas-bearing zones, the current common approach is to overlay favorable zones derived from multiple parameters, such as reservoir thickness, physical properties, and gas content, and define the common favorable zones as favorable gas-bearing zones. This approach suffers from significant ambiguity and uncertainty when predicting reservoir thickness, physical properties, and gas content. It also fails to consider the interplay between parameters and sets thresholds for favorable zones for each parameter. In practical applications, it has been found that favorable zones for a single parameter differ significantly from actual distributions, and the distribution of favorable gas-bearing zones derived from multi-parameter overlays differs significantly from actual distributions. This comprehensive multi-parameter prediction of favorable gas-bearing zones is impractical. Furthermore, directly overlaying the coordinates of favorable zones derived from single parameters requires extensive map compilation and editing, is susceptible to subjective interference, and lacks quantitative information on the evaluation of favorable and unfavorable gas-bearing zones.

[0006] The prediction of favorable gas-bearing areas in existing technologies is usually:

[0007] 1. Use inversion technology to predict reservoir thickness, physical properties, and gas-bearing planar distribution;

[0008] 2. Combined with test production data, divide favorable areas based on thickness, physical properties, and gas content planes;

[0009] 3. Superimpose the favorable areas of thickness, physical properties and gas content to obtain the comprehensive favorable area.

[0010] This method is prone to high multi-solution and uncertainty when predicting reservoir thickness, physical properties, and gas content. It also fails to consider the interplay between parameters and sets thresholds for favorable zones for each parameter. In actual applications, it has been found that favorable zones for a single parameter differ significantly from actual distributions, and the distribution of favorable gas-bearing zones derived from the superposition of multiple parameters differs significantly from actual distributions. The practicality of a comprehensive multi-parameter gas-bearing favorable zone prediction is limited. Furthermore, directly superimposing the coordinates of favorable zones for single parameters requires extensive map compilation and editing, is susceptible to interference from subjective factors, and lacks quantitative information on favorable and unfavorable gas-bearing zones.

[0011] The key to accurately predicting favorable gas-bearing zones lies in three aspects: first, identifying gas-bearing sensitive parameters and accurately predicting them through the combination of well and seismic data; second, determining the criteria for demarcating favorable gas-bearing zones and analyzing their key influencing parameters; and third, optimizing the combination of key parameters to find the optimal method for predicting gas-bearing zones, determine the criteria for demarcating favorable gas-bearing zones, and accurately characterize favorable gas-bearing zones.

[0012] Therefore, it is an urgent problem for those skilled in the art 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

[0013] This invention aims to provide a method for predicting favorable gas-bearing zones using phase-controlled inversion. This method improves resistivity prediction accuracy through waveform-constrained phase-controlled inversion technology. Furthermore, it further enhances the accuracy of gas-bearing discrimination and favorable gas-bearing zone prediction using gas-bearing discriminant factors and gas-bearing favorable zone prediction factors. This method can better address gas-water identification and gas-bearing favorable zone prediction in low-porosity gas reservoirs. In practical application in a tight sandstone, high-water-content gas reservoir, the accuracy of favorable gas-bearing zone discrimination has increased from less than 60% to over 75%.

[0014] In order to achieve the above-mentioned object of the invention, the technical solution of the present invention is as follows:

[0015] A method for predicting favorable gas-bearing areas by phase-controlled inversion includes the following steps:

[0016] 1) Obtain the Lg (Rt) simulation data volume, obtain simulation parameters through waveform-constrained phase-controlled inversion parameter tests, and conduct resistivity waveform-constrained phase-controlled simulation using the waveform-constrained phase-controlled inversion method to obtain the Lg (Rt) simulation data volume; the Lg (Rt) simulation data volume is the logarithm of the resistivity;

[0017] 2) Establishing criteria for demarcating favorable gas-bearing zones;

[0018] 3) Draw a plane map of the average Lg (Rt) value of sandstones. Select multiple different gamma and velocity thresholds, use the gamma inversion volume and velocity inversion volume 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 sandstones.

[0019] 4) Statistics of the dominant intervals of Lg (Rt) values ​​in favorable areas and treatment of abnormal values, statistical analysis of the dominant intervals of Lg (Rt) value distribution in favorable gas-bearing areas [V L , V H ], analyze the prediction effect of the sandstone Lg (Rt) average value plane map extracted with different threshold values, calculate the gas-bearing prediction compliance rate, and screen out the sandstone Lg (Rt) average value plane map with the highest compliance rate;

[0020] Lg(Rt)>V H The filling of 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:

[0021] F[Lg(Rt)]=V L -C1*[Lg(Rt)-V H ]

[0022] 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;

[0023] 5) Gas content knowledge factor I g Draw a plane diagram. According to the plane diagram of the average value of sandstone Lg (Rt), select the relative low value of the sample points with the average value of Lg (Rt) lower than 90% in the plane diagram as the Lg (Rt0) value to calculate the gas-bearing discrimination factor I. g , draw the gas content discrimination factor I g Floor plan;

[0024] Gas content discrimination factor: I g =Lg(Rt)- Lg(Rt0)= Lg(Rt / Rt0)

[0025] 6) Favorable Area Prediction Factor Ie Plane map drawing, combined with reservoir thickness prediction results, calculate gas-bearing favorable area prediction factors, and outline gas-bearing favorable area prediction factors I e Floor plan,

[0026] I e =H×[Lg(Rt)- Lg(Rt0)]= H×I g

[0027] Where: H is the reservoir thickness;

[0028] 7) Identify favorable gas-bearing areas and calculate the prediction compliance rate of favorable gas-bearing areas.

[0029] Furthermore, the simulation parameters in step 1) include the relevant time window length, the number of horizontal smoothing lines and the number of horizontal smoothing channels.

[0030] Furthermore, the waveform-constrained phase-controlled inversion method in step 1) includes waveform-constrained modeling inversion and waveform-indicating inversion.

[0031] Furthermore, in the step 1), the resistivity is processed by taking a logarithm during the simulation. The resistivity amplitude varies in a large range, and taking the logarithm thereof has a better correlation with the seismic waveform and a better simulation effect.

[0032] Furthermore, the criteria for distinguishing favorable gas-bearing areas and favorable wells in step 2) are as follows:

[0033] a. All tested pure gas wells are considered to be located in favorable gas-bearing areas and are favorable wells;

[0034] b. Statistically calculate the expected cumulative production of dynamic Class I and Class II wells, and arrange the expected cumulative production from high to low. The lowest value of the top 70-90% is set as the low threshold of the expected cumulative production in the gas-bearing favorable zone; the open flow threshold value of the dynamic Class I well is set as A1, the open flow threshold value of the dynamic Class II well is set as A2, and the maximum value of the top 60-90% of the test water volume of the dynamic Class II well arranged from small to large is B2; if the open flow rate is greater than A1, the expected cumulative production can reach the expected cumulative production threshold of the gas-bearing favorable zone, and the well with an open flow rate greater than A1 can be considered a favorable well; if the open flow rate is greater than A2 but less than A1, and the test water volume is greater than A1, the well can be considered a favorable well. <B2时,70%以上此类井的预计累产可达到该含气有利区预计累产门槛,认为此类井亦为有利井;

[0035] c. If the open flow rate of a gas-water co-producing well is greater than A1, it is considered to be in a favorable gas-bearing zone and a favorable well.

[0036] d. Test gas and water co-producing wells, A1 < unobstructed flow <A2, 测试产水量<B2,认为其处于含气有利区,为有利井。

[0037] Furthermore, the gamma and velocity thresholds in step 3) are specifically selected based on the upper gamma value limit and lower velocity value limit of the reference logging reservoir identification. Taking into account the influence of systematic errors in the logging data and the seismic inversion data, multiple different gamma and velocity thresholds are selected within the range of ±15 API for gamma values ​​and ±150 m / s for velocity values ​​near the logging identification threshold.

[0038] Furthermore, in 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 plane of the average value 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.

[0039] Furthermore, the statistical gas-bearing 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.

[0040] Furthermore, the prediction coincidence rate of favorable gas-bearing areas in step 7) is specifically calculated based on the prediction factor I in the favorable area. e On the plane map, count the I of favorable wells and unfavorable wells e Value distribution, determine the favorable gas-bearing area I e The low value threshold is set, and based on this, the gas-bearing area is divided into favorable and unfavorable gas-bearing areas. The prediction compliance rate is statistically calculated. 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.

[0041] Furthermore, the determination e The specific value threshold is: in favorable wells, I e The minimum value of the top 65%-85% of the values ​​​​arranged from large to small and the I in the unfavorable wells e Select multiple I values ​​between the first 65%-85% of the maximum values ​​arranged from small to large. e value, statistically predict the compliance rate, and select the I with the highest compliance rate e The value is the threshold.

[0042] Beneficial effects of the present invention:

[0043] 1. In this invention, resistivity is highly sensitive to reservoir gas content, but geophysical prediction methods are ineffective, making them difficult to apply in exploration and development practices. This innovative achievement utilizes waveform-constrained phase-controlled inversion technology to simulate logarithmic resistivity, achieving excellent results and enabling the application of resistivity curves in seismic gas and water prediction.

[0044] 2. In the present invention, by constructing the gas content prediction factor I g , further improving the accuracy of resistivity parameters in determining reservoir gas content.

[0045] 3. In the present invention, favorable gas-bearing areas should not only have good gas-bearing properties but also have reservoirs of a certain thickness. By constructing the prediction factor I of favorable gas-bearing areas, e The comprehensive utilization of reservoir thickness and reservoir resistivity information can more reasonably divide the favorable gas-bearing areas than using the resistivity parameter alone.

[0046] 4. The existing gas-bearing favorable zone division method can generally only divide favorable and unfavorable zones through a comprehensive multi-parameter division. The gas-bearing favorable zone discrimination factor I proposed in this invention is e Not only can the favorable gas-bearing areas be qualitatively divided, but also the favorable area discrimination factor I can be used 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.

[0047] 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

[0048] Figure 1 This is a flow chart of the phase-controlled inversion gas-bearing favorable zone prediction method of the present invention.

[0049] Figure 2 This is a simulated cross-section diagram of Lg (Rt) obtained by waveform-constrained phase-controlled inversion in Example 1 of the present invention.

[0050] Figure 3 This is a comparison diagram of the cross-sectional effects of conventional model inversion and waveform-constrained phase-controlled inversion in Example 1 of the present invention.

[0051] Figure 4 This is a plane diagram of the average value of Lg (Rt) of sandstone in Example 1 of the present invention.

[0052] Figure 5 The gas content discrimination factor I of Example 1 of the present invention is g Floor plan.

[0053] Figure 6 The prediction factor I of the favorable gas-bearing zone in Example 1 of the present invention is e Floor plan.

[0054] Figure 7 The gas content discrimination factor I in Example 2 of the present invention g Floor plan.

[0055] Figure 8 The prediction factor I of the favorable gas-bearing zone in Example 2 of the present invention is e Floor plan.

[0056] Figure 9 The prediction factor I of the favorable gas-bearing zone in Example 3 of the present invention is e Floor plan.

[0057] Figure 10 This is the predicted distribution diagram of the reservoir thickness of the lower sub-section of Box 8 in Example 1 of the present invention.

[0058] Figure 11 This is the predicted distribution map of reservoir thickness of Shan 1 section in Example 2 of the present invention. DETAILED DESCRIPTION

[0059] The present invention will be further described in detail below with reference to the examples, but the embodiments of the present invention are not limited thereto.

[0060] Example 1

[0061] In this example, the lower subsection of a certain block Box 8 is selected to predict the favorable gas-bearing area. Figure 1 As shown, this embodiment provides a method for predicting favorable gas-bearing areas through phase-controlled inversion, which specifically includes the following steps:

[0062] 1) The relevant time window length is 100ms, the number of horizontal smoothing lines is 7, and the number of horizontal smoothing 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 performed on the logarithmic resistivity curve to simulate the spatial distribution of the logarithmic resistivity curve. The Lg (Rt) simulated 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 conventional model inversion and 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, and thus reflect the changes in sedimentary facies, and is more consistent with the changing laws of sedimentary facies, and has better advantages in predicting favorable gas-bearing areas;

[0063] 2) Based on test data, dynamic classification, cumulative production data, etc., establish the criteria for identifying favorable gas-bearing areas and favorable wells. The specific criteria are as follows:

[0064] a. All tested pure gas wells are considered favorable wells and are considered to be in favorable gas-bearing areas;

[0065] b. Based on the estimated cumulative production of dynamic Class I and Class II wells, more than 75% of the dynamic Class I and Class II wells in the study area have cumulative production exceeding 12 million cubic meters, so 12 million cubic meters is used as the lower threshold for cumulative production value of favorable gas-bearing areas. Wells with an open flow rate greater than 150,000 cubic meters / day have an estimated cumulative production greater than 12 million cubic meters and are therefore classified as favorable wells. Wells with a flow rate of 50,000 cubic meters / day < open flow rate < 150,000 cubic meters / day and a tested water production rate of < 5 cubic meters / day, more than 70% of these wells have an estimated cumulative production greater than 12 million cubic meters and are also classified as favorable wells.

[0066] c. Correlate the test production with the cumulative production. For wells producing both gas and water, if the open flow rate is >150,000 cubic meters per day, it is a favorable well located in a favorable gas-bearing zone.

[0067] d. For a gas-water co-producing well, if the open flow rate is less than 50,000 cubic meters per day and less than 150,000 cubic meters per day, and the water production is less than 5 cubic meters per day, it is a favorable well located in a favorable gas-bearing zone.

[0068] 3) The upper limit of the gamma value for reservoir identification in the He 8 section of the reference area is 90 API, and the lower limit of the velocity value is 4700 m / s. Within the gamma value range of 80-100 API and the velocity value range of 4600-4800 m / s, gamma thresholds of 90 API and 95 API, and velocity thresholds of 4700 m / s and 4750 m / s are selected. Under the constraints of the existing gamma inversion volume and velocity inversion volume, the average value of the Lg (Rt) inversion volume sample points that meet the conditions is extracted and a plan map is drawn.

[0069] 4) Statistical analysis of the Lg (Rt) value distribution in gas-bearing favorable areas shows that more than 75% of the dynamic I and II wells in the area are in the [1.45, 1.75] range, which can be used as the dominant range for the Lg (Rt) value distribution in gas-bearing favorable areas. The prediction effect of extracting Lg (Rt) value plane maps with different gamma thresholds of 90 API and 95 API, and velocity thresholds of 4700 m / s and 4750 m / s was analyzed. It was found that the plane map extracted with gamma 95 API and velocity 4750 m / s as the threshold had the best gas-bearing prediction effect. The sandstone Lg (Rt) average plane map with the highest compliance rate was selected as follows: Figure 4 As shown;

[0070] Fill the unfavorable areas where Lg (Rt) > 1.75 with low values, so that the low-value areas on the Lg (Rt) value plane map are gas-bearing unfavorable areas, and the high-value areas are gas-bearing favorable areas. The high-value unfavorable areas are filled using the following formula:

[0071] F[Lg(Rt)]=V L -C1*[Lg(Rt)-V H ]

[0072] Among them, V L —The lower threshold of Lg (Rt) in gas-bearing favorable areas is 1.45, VH —Lg(Rt) high value threshold of gas-bearing favorable zone, taken as 1.75, C1—correction coefficient, taken as 1;

[0073] 5) The lower 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 out the gas content discrimination factor I g Floor plan Figure 5 As shown;

[0074] Gas content discrimination factor: I g =Lg(Rt)- Lg(Rt0)= Lg(Rt / Rt0)

[0075] 6) Combined with the existing Box 8 lower sub-member reservoir thickness prediction plan, as shown in Figure 10 As shown, the prediction factor of favorable gas-bearing area is calculated and the prediction factor of favorable gas-bearing area I is drawn. e Floor plan Figure 6 As shown,

[0076] Prediction factor of favorable gas-bearing area: I e =H×[Lg(Rt)- Lg(Rt0)]= H×[Lg(Rt / Rt0)]

[0077] Where H is the reservoir thickness;

[0078] 7) On the plane diagram of the prediction factors of favorable gas-bearing areas, calculate the I values ​​of favorable wells and unfavorable wells. 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 consistency rate is the highest, so 5 is used as the threshold for dividing the gas-bearing favorable zone in the lower subsection of Box 8.

[0079] Since many wells in the study area produce from both the Box 8 lower submember and the Shan 1 member, the coincidence rate of the Box 8 lower submember was not calculated separately. Example 3 will show the overall coincidence rate of the Box 8 lower submember + Shan 1 member.

[0080] Example 2

[0081] 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 method for predicting favorable gas-bearing areas through phase-controlled inversion, which specifically includes the following steps:

[0082] 1) The relevant time window length is 100ms, the number of horizontal smoothing lines is 7, and the number of horizontal smoothing 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 performed on the logarithmic resistivity curve to simulate the spatial distribution of the logarithmic resistivity curve.

[0083] 2) Based on test data, dynamic classification, cumulative production data, etc., establish the criteria for identifying favorable gas-bearing areas and favorable wells. The specific criteria are as follows:

[0084] a. All tested pure gas wells are considered favorable wells and are considered to be in favorable gas-bearing areas;

[0085] b. Based on the estimated cumulative production of dynamic Class I and Class II wells, more than 75% of the dynamic Class I and Class II wells in the study area have cumulative production exceeding 12 million cubic meters, so 12 million cubic meters is used as the lower threshold for cumulative production value of favorable gas-bearing areas. Wells with an open flow rate greater than 150,000 cubic meters / day have an estimated cumulative production greater than 12 million cubic meters and are therefore classified as favorable wells. Wells with a flow rate of 50,000 cubic meters / day < open flow rate < 150,000 cubic meters / day and a tested water production rate of < 5 cubic meters / day, more than 70% of these wells have an estimated cumulative production greater than 12 million cubic meters and are also classified as favorable wells.

[0086] c. Correlate the test production with the cumulative production. For wells producing both gas and water, if the open flow rate is >150,000 cubic meters per day, it is a favorable well located in a favorable gas-bearing zone.

[0087] d. For a gas-water co-producing well, if the open flow rate is less than 50,000 cubic meters per day and less than 150,000 cubic meters per day, and the water production is less than 5 cubic meters per day, it is a favorable well located in a favorable gas-bearing zone.

[0088] 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 these values ​​come from?). Within the gamma value range of 95-105 API and the velocity value range of 4650-4850 m / s, gamma thresholds of 95 API and 100 API, and velocity thresholds of 4750 m / s and 4800 m / s are selected. Under the constraints of the existing gamma inversion volume and velocity inversion volume, the average value of the Lg (Rt) inversion volume sample points that meet the conditions is extracted and a plan map is drawn.

[0089] 4) Statistical analysis of the Lg(Rt) value distribution in gas-favorable zones revealed that over 75% of the dynamic Class I and II wells in the zone fell within the [1.55, 1.75] interval, which can be considered the dominant interval for Lg(Rt) value distribution in gas-favorable zones. Analysis of the prediction performance of Lg(Rt) value plots extracted using gamma thresholds of 95 API and 100 API, and velocity thresholds of 4750 m / s and 4800 m / s revealed that plots extracted using a gamma threshold of 100 API and a velocity threshold of 4800 m / s performed best in predicting gas potential.

[0090] Fill the unfavorable areas where Lg (Rt) > 1.75 with low values, so that the low-value areas on the Lg (Rt) value plane map are gas-bearing unfavorable areas, and the high-value areas are gas-bearing favorable areas. The high-value unfavorable areas are filled using the following formula:

[0091] F[Lg(Rt)]=V L -C1*[Lg(Rt)-V H ]

[0092] 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 zone, taken as 1.75, C1—correction coefficient, taken as 1;

[0093] 5) The lower 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 out the gas content discrimination factor I g Floor plan Figure 7 shown.

[0094] Gas content discrimination factor: I g =Lg(Rt)- Lg(Rt0)= Lg(Rt / Rt0)

[0095] 6) Combined with the existing Shan 1 reservoir thickness prediction plan, Figure 11 As shown, the prediction factor of favorable gas-bearing area is calculated and the prediction factor of favorable gas-bearing area I is drawn. e Floor plan Figure 8 shown.

[0096] Prediction factor of favorable gas-bearing area: I e =H×[Lg(Rt)- Lg(Rt0)]= H×[Lg(Rt / Rt0)]

[0097] Where H is the reservoir thickness;

[0098] 7) On the plane diagram of favorable area prediction factors, calculate the I of wells in favorable gas-bearing areas and unfavorable gas-bearing areas. 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 prediction compliance rate is the highest, so 3 is used as the threshold for dividing the gas-bearing favorable area of ​​Shan 1 section.

[0099] Example 3

[0100] In this example, a certain block, the lower sub-section of He8~Shan1, is selected to predict the favorable gas-bearing area. Figure 1As shown, this embodiment provides a method for predicting favorable gas-bearing areas through phase-controlled inversion, which specifically includes the following steps:

[0101] 1) Combine the I of Example 1 and Example 2 e Add the value plane graph and get the box 8 lower sub-segment ~ mountain 1 segment I e The value plane is as follows Figure 9 shown.

[0102] 2) Count the I values ​​of favorable wells and unfavorable wells on the plane respectively e The statistical results show that more than 70% of the favorable wells are from the lower sub-member of He 8 to the I member of Shan 1. e Value greater than 4, more than 70% of the unfavorable wells He 8 lower sub-member ~ Shan 1 member I e The value is less than 6, when I e When the threshold value is 5, the gas prediction coincidence rate is the highest, so 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.

[0103] 3) Will I e >5.0 is considered as a favorable gas-bearing area, I e <5.0 or above is an unfavorable gas-bearing area, and the gas-bearing property prediction statistics are consistent with the statistics. Among the 305 wells involved in the statistics, 233 wells are consistent with the statistics, with a compliance rate of 76.4%.

[0104] It will be understood that the present invention is described by way of certain embodiments, and it will be appreciated by 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 guidance of the present invention, these features and embodiments may be modified to suit 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 intended to be protected by the present invention.

Claims

1. A method for predicting favorable gas-bearing areas using phase-controlled inversion, characterized by: It includes the following steps: 1) Obtain the Lg(Rt) simulated data volume. Through waveform-constrained phased inversion parameter tests, obtain the simulation parameters, and conduct resistivity waveform-constrained phased simulation using the waveform-constrained phased inversion method to obtain the Lg(Rt) simulated data volume; the Lg(Rt) simulated data volume is the logarithm of the resistivity. 2) Establish the criteria for dividing favorable gas-bearing areas. 3) Draw the plane map of the average value of Lg(Rt) of sandstone. Select multiple different gamma and velocity thresholds. Constrained by the gamma inversion volume and the 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 Lg(Rt) of sandstone. 4) Statistics of the dominant intervals of Lg (Rt) values ​​in favorable areas and treatment of abnormal values, statistical analysis of the dominant intervals of Lg (Rt) value distribution in favorable gas-bearing areas [V L , V H ], analyze the prediction effect of the sandstone Lg (Rt) average value plane map extracted with different threshold values, calculate the gas-bearing prediction compliance rate, and screen out the sandstone Lg (Rt) average value plane map with the highest compliance rate; Lg(Rt)>V H The filling of 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 Draw a plane diagram. According to the plane diagram of the average Lg (Rt) value of sandstone, select the relative low value of the sample points with the average Lg (Rt) value lower than 90% in the plane diagram as the Lg (Rt0) value to calculate the gas-bearing discrimination factor I. g , draw the gas content discrimination factor I g Floor plan; Gas content discrimination factor: I g =Lg(Rt)- Lg(Rt0)= Lg ( Rt / Rt0) 6) Favorable Area Prediction Factor I e Draw a plan view and calculate the gas-bearing favorable area prediction factor I by combining the reservoir thickness prediction results. e , outline 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) Identification of favorable gas-bearing areas and statistical prediction compliance of favorable gas-bearing areas: In the favorable area prediction factor I e On the plane map, count the I of favorable wells and unfavorable wells e Value distribution, determine the favorable gas-bearing area I e The low value threshold is set, and based on this, the gas-bearing area is divided into favorable and unfavorable gas-bearing areas. The prediction compliance rate is statistically calculated. 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.

2. The prediction method according to claim 1, characterized in that: In step 1), the simulation parameters 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: In step 1), the waveform-constrained phased inversion method includes waveform-constrained modeling inversion and waveform-indicated inversion.

4. The prediction method according to claim 1, wherein: In step 1), the resistivity is logarithmically processed during simulation.

5. The prediction method according to claim 1, characterized in that: In step 2), the discrimination criteria for favorable gas-bearing areas and favorable wells are as follows: a. All tested pure gas wells are considered to be in favorable gas-bearing areas and are favorable wells. b. Statistically analyze 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 favorable gas-bearing 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 favorable gas-bearing 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 favorable gas-bearing 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 favorable gas-bearing areas and are favorable wells.

6. The prediction method according to claim 1, characterized in that: In step 3), the selection of gamma and velocity thresholds 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 plane of the average value 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-bearing prediction compliance rate in step 4) is specifically as follows: 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 determination e The specific value threshold is: in favorable wells, I e The minimum value of the top 65%-85% of the values ​​​​arranged from large to small and the I in the unfavorable wells e Select multiple I values ​​between the first 65%-85% of the maximum values ​​arranged from small to large. 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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