Method and device for obtaining mean permeability valuation curve

Abstract

The invention discloses a method for obtaining a mean permeability valuation curve. The method comprises the following steps: drawing a scatter cross plot of permeability and target parameters; determining a group number corresponding to the target parameter according to the target parameter; determining a data pair corresponding to each group of target parameters, and adding the data pair corresponding to each group of target parameters to the scatter cross plot; respectively connecting the permeability low-order value Li, the permeability median value Mi and the permeability high-order value Hi in each group to obtain a low permeability valuation curve, a medium permeability valuation curve and a high permeability valuation curve; and averaging the low-order permeability valuation curve, the middle-order permeability valuation curve and the high-order permeability valuation curve to obtain a mean permeability valuation curve. According to the method, the problem of severe underestimation of the predicted permeability mean value in a regression method can be solved, the permeability extreme value problem can be stably processed, the permeability space change characteristics can be finely described, the characteristic that the predicted permeability is consistent with the original data mean value can be kept, and the permeability prediction precision can be greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration and development, and in particular to a method and device for obtaining a mean permeability estimation curve. Background Art

[0002] Permeability is an extremely important parameter in geological models and numerical simulations of oil and gas reservoirs. It characterizes the difficulty of fluid flow in rock pores and throats, measures the permeability of reservoirs and predicts the production capacity of reservoirs. Its numerical value is mainly controlled by the pore structure. In general, controlled by the sedimentary environment and diagenesis, oil and gas reservoirs often develop multiple types of pores. There are huge differences in the development degree, connectivity, and three-dimensional spatial configuration and combination of different types of pores with throats. This complex reservoir pore structure makes it difficult to quantitatively describe the intrinsic relationship between permeability and parameters such as lithology, lithofacies, grain size, sorting degree, pore throat radius, porosity, clay minerals, and connectivity.

[0003] At present, the main method for obtaining permeability is regression method. Regression method is to classify reservoir types according to lithology, lithofacies or flow units, and establish the correlation (univariate or multivariate fitting) between the permeability of a single reservoir type and other reservoir parameters (porosity, burial depth, pressure, mud content, resistivity, etc.) through core testing and well test analysis. This method is simple and practical, but in actual applications, the reservoir parameters and permeability scatter plots often show a very wide distribution band, that is, the permeability values ​​corresponding to the same reservoir parameters vary greatly, and some extremely large or extremely small permeability singular values ​​that significantly affect oil and gas flow are often intentionally or unintentionally ignored, smoothed or eliminated, resulting in large errors in the regression model. At the same time, the permeability predicted by the regression formula is far from the original permeability mean, which seriously underestimates the spatial distribution characteristics of the permeability and makes it difficult to obtain satisfactory prediction results. Summary of the invention

[0004] The purpose of the present invention is to provide a method and device for obtaining a mean permeability estimation curve to solve the above-mentioned technical problems.

[0005] To achieve the above object, the present invention provides a method for obtaining a mean permeability estimation curve, the method comprising:

[0006] Draw scatter plots of permeability and target parameters;

[0007] According to the target parameter, determining the number of groups corresponding to the target parameter;

[0008] Determine the data pairs corresponding to each group of target parameters, and add the data pairs corresponding to each group of target parameters to the scatter point intersection diagram;

[0009] Connect the low permeability values ​​L in each group respectively i , median permeability Mi and the high value of permeability H i , obtain three penetration rate valuation curves at low, medium and high levels;

[0010] The three permeability valuation curves at low, medium and high levels are averaged to obtain the mean permeability valuation curve;

[0011] Wherein, the target parameter is a reservoir parameter related to permeability; the data pair is the median value X of the target parameter in each group. i , low permeability value L i , median permeability M i and the high value of permeability H i related.

[0012] Optionally, the target parameters include at least one of the following: porosity, sonic wave, resistivity, depth, mud content and gamma.

[0013] Optionally, determining the number of groups corresponding to the target parameters according to the target parameters includes: determining the number of groups of the target parameters according to the number of target parameters using Formula 1;

[0014] m=1+log 10 A / log 10 2 Formula 1

[0015] Where m is the number of target parameter groups and A is the number of target parameters.

[0016] Optionally, the method further includes: determining a group distance corresponding to the target parameter based on the target parameter.

[0017] Optionally, determining a group distance corresponding to the target parameter according to the target parameter includes:

[0018] Determine the range of the target parameter based on the maximum and minimum values ​​of the target parameter;

[0019] The group interval of the target parameter is determined based on the ratio of the range of the target parameter to the number of groups of the target parameter.

[0020] Optionally, determining data pairs corresponding to each group of target parameters includes:

[0021] Get the sorted permeability in the ith group and calculate the low permeability value L in the ith group i , median permeability M i and the high value of permeability H i ;

[0022] According to the maximum and minimum values ​​of the i-th group interval, calculate the median value X of the target parameter in the i-th group i ;

[0023] According to the low permeability value L i , median permeability M i , high permeability value H i and the intermediate value X i , determine the data pair (X i , L i )、(X i , M i ) and (X i , H i );

[0024] Repeat the above operation to obtain the data pairs corresponding to each group.

[0025] Optionally, obtain the sorted permeability in the i-th group and calculate the low permeability value L in the i-th group i , median permeability M i and the high value of permeability H i ,include:

[0026] Sort the permeability in the i-th group from small to large, and obtain the sorted permeability, denoted as K i [1] K i [2]…K i [n], n is the number of permeabilities in the i-th group;

[0027] According to the sorted permeability, the low permeability value L in the i-th group is calculated using formula 2-4. i , median permeability M i and the high value of permeability H i ;

[0028] L i =K i [INT(1+(n-1)*0.1)]+(K i [INT(1+(n-1)*0.1)+1]-K i [INT(1+(n-1)*0.1)])

[0029] *(1+(n-1)*0.1-INT(1+(n-1)*0.1)) Formula 2

[0030] M i =K i [INT(1+(n-1)*0.5)]+(K i [INT(1+(n-1)*0.5)+1]-K i [INT(1+(n-1)*0.5)]

[0031] )*(1+(n-1)*0.5-INT(1+(n-1)*0.5))Formula 3

[0032] H i =K i [INT(1+(n-1)*0.9)]+(K i [INT(1+(n-1)*0.9)+1]-K i [INT(1+(n-1)*0.9)])

[0033] *(1+(n-1)*0.9-INT(1+(n-1)*0.9)) Formula 4

[0034] The INT function is a floor function that returns an integer that is not greater than the given parameter and is closest to the parameter.

[0035] Optionally, the permeability is the ordinate of the scatter intersection diagram, and the target parameter is the abscissa of the scatter intersection diagram.

[0036] The present invention also provides a device for obtaining a mean permeability estimation curve, the device comprising:

[0037] A drawing unit for drawing a scatter plot of permeability and target parameters;

[0038] A determination unit, configured to determine the number of groups corresponding to the target parameter according to the target parameter;

[0039] An adding unit, used for determining data pairs corresponding to each group of target parameters, and adding the data pairs corresponding to each group of target parameters to the scatter point intersection diagram;

[0040] Connection unit, used to connect the low permeability value L in each group i , median permeability M i and the high value of permeability H i , obtain three penetration rate valuation curves at low, medium and high levels;

[0041] An acquisition unit is used to average the three permeability estimation curves of low, medium and high positions to obtain a mean permeability estimation curve;

[0042] Wherein, the target parameter is a reservoir parameter related to permeability; the data pair is the median value X of the target parameter in each group. i , low permeability value L i , median permeability M i and the high value of permeability H i related.

[0043] Optionally, the target parameters include at least one of the following: porosity, sonic wave, resistivity, depth, mud content and gamma.

[0044] Technical effects and advantages of the present invention:

[0045] The present invention provides a method for obtaining a mean permeability estimation curve, the method comprising: drawing a scatter intersection diagram of permeability and target parameters; determining the number of groups corresponding to the target parameters according to the target parameters; determining the data pairs corresponding to each group of target parameters, and adding the data pairs corresponding to each group of target parameters to the scatter intersection diagram; respectively connecting the low permeability values ​​L in each group i , median permeability M i and the high value of permeability H i , obtain three low, medium and high permeability estimation curves; average the three low, medium and high permeability estimation curves to obtain the mean permeability estimation curve; wherein the target parameter is a reservoir parameter related to permeability; the data is the median value X of the target parameter in each group i , low permeability value L i , median permeability M i and the high value of permeability H i related.

[0046] The present invention can solve the problem of serious underestimation of the predicted permeability mean in the regression method, that is, it can robustly handle the permeability extreme value problem, accurately characterize the spatial variation characteristics of the permeability, and maintain the property that the predicted permeability is consistent with the original data mean, thereby greatly improving the permeability prediction accuracy.

[0047] Other features and advantages of the present invention will be described in the following description, and partly become obvious from the description, or be understood by implementing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A flow chart of a method for obtaining a mean permeability estimation curve;

[0049] Figure 2 is the relationship diagram between permeability and porosity;

[0050] Figure 3 is the relationship diagram between permeability and burial depth;

[0051] Figure 4 This is the relationship diagram between permeability and capacity using the mean permeability estimation method;

[0052] Figure 5 This is the relationship diagram between penetration rate and capacity predicted by conventional exponential regression. DETAILED DESCRIPTION

[0053] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings provided by the present invention, and the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0054] It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the limiting conditions for the implementation of the present invention, so they have no substantial technical significance. Any modification of the structure, change of the proportional relationship or adjustment of the size should still fall within the scope of the technical content disclosed by the present invention without affecting the effects and purposes that can be achieved by the present invention. At the same time, the terms such as "upper", "lower", "left", "right", "middle" and "one" quoted in this specification are only for the convenience of description, and are not used to limit the scope of the implementation of the present invention. The change or adjustment of their relative relationship should also be regarded as the scope of the implementation of the present invention without substantially changing the technical content.

[0055] In order to solve the shortcomings of the prior art, the present invention discloses a method for obtaining a mean permeability estimation curve. Figure 1 For detailed explanation:

[0056] 1. Draw a scatter plot of permeability and target parameters.

[0057] Among them, the target parameters refer to reservoir parameters that have an intrinsic relationship with permeability, such as porosity, acoustic wave, resistivity, depth, etc. Generally, the ordinate is permeability and the abscissa is other parameters.

[0058] 2. According to the target parameters, determine the number of groups corresponding to the target parameters.

[0059] 2.1. Determine the range based on the maximum and minimum values ​​of the horizontal axis data;

[0060] 2.2. Determine the number of groups and group intervals.

[0061] Determine the number of groups and group intervals of the horizontal axis of the intersection diagram according to the amount of data, density, etc. Divide the horizontal axis into several parts, each part is a group, the number of groups is the number of groups, and the distance between the two endpoints of each group is called the group interval. Generally speaking, the number of groups and the group interval are inversely proportional. When grouping, the smaller the number of groups, the larger the group interval; the larger the number of groups, the smaller the group interval. If there are too many groups and the group interval is too small, the grouping data will be cumbersome and complicated, and it will be difficult to show the characteristics and distribution laws within the population; if there are too few groups and the group interval is too large, the meaning of grouping may be lost, and the purpose of correctly reflecting objective facts cannot be achieved. In actual research, the determination of the number of groups and group intervals is the result of balancing the stability of the low, medium and high value estimates of permeability and reflecting the changes in permeability details.

[0062] The number of groups m is generally determined by the square root of the number of research data A. Preferably, the formula m=1+log 10 A / log 10 2 is calculated (m is the number of target parameter groups, A is the number of target parameters). The class interval is generally the ratio of the range to the number of groups. It should be noted that the starting point of the first group is generally the minimum value of the horizontal axis data - class interval / 2, and the limit of each class interval is increased by one class interval. In the actual calculation process, in order to obtain better stability and smoothness of the data, 30%-50% of the data overlap is retained between adjacent groups, so the starting limit should be adjusted according to the actual situation.

[0063] 2.3. According to the grouping in step 2.2, sort the permeability in the i-th group from small to large, denoted as K i [1] K i [2]…K i [n], n is the number of permeabilities in the i-th group.

[0064] 2.4. Calculate the low value L of the penetration rate in the i-th group according to the following formula: i , median permeability M i and the high value of permeability H i :

[0065] L i =K i [INT(1+(n-1)*0.1)]+(K i [INT(1+(n-1)*0.1)+1]-K i [INT(1+(n-1)*0.1)])

[0066] *(1+(n-1)*0.1-INT(1+(n-1)*0.1))

[0067] M i =K i [INT(1+(n-1)*0.5)]+(Ki [INT(1+(n-1)*0.5)+1]-K i [INT(1+(n-1)*0.5)]

[0068] )*(1+(n-1)*0.5-INT(1+(n-1)*0.5))

[0069] H i =K i [INT(1+(n-1)*0.9)]+(K i [INT(1+(n-1)*0.9)+1]-K i [INT(1+(n-1)*0.9)])

[0070] *(1+(n-1)*0.9-INT(1+(n-1)*0.9))

[0071] In the formula, the INT function is a floor function that returns an integer that is not greater than the given parameter and is closest to the parameter. The syntax format is INT(a), where a is the real number to be rounded. For example, the return value of INT(8.6) is 8, the return value of INT(8) is 8, and the return value of INT(-8.6) is -9.

[0072] 2.5. Calculate the middle value Xi of the horizontal axis in the i-th group. This value corresponds to the median value of the horizontal axis parameter value (porosity, acoustic wave, resistivity, depth, etc.) in the intersection diagram of the i-th group boundary. Finally, determine the data pairs (Xi, Li), (Xi, Mi) and (Xi, Hi) of the i-th group.

[0073] 3. Determine the data pairs corresponding to each set of target parameters, and add the data pairs corresponding to each set of target parameters to the scatter intersection diagram.

[0074] Specifically, repeat 2.3-2.5 to obtain Xi, Li, Mi and Hi for each group, i = 1, 2, 3, ..., m, where m is the number of groups. In the actual calculation process, 30%-50% of the data overlap is retained between adjacent groups to obtain relatively stable data. The data pairs (Xi, Li), (Xi, Mi) and (Xi, Hi) calculated in all groups, i = 1, 2, 3...m, are added to the intersection diagram.

[0075] 4. Connect the low permeability value L in each group respectively i , median permeability M i and the high value of permeability H i , and obtain three penetration rate valuation curves at low, medium and high levels.

[0076] 5. The three low, medium and high permeability valuation curves are averaged to obtain the mean permeability valuation curve Kp. The average refers to the calculation of the mean of the data corresponding to the three low, medium and high permeability valuation curves.

[0077] According to this mean permeability estimation curve Kp, for any horizontal coordinate data, the corresponding permeability value can be directly interpolated and extrapolated; the mean permeability estimation curve can also be fitted to obtain the regression formula of the horizontal coordinate and permeability to make permeability predictions.

[0078] In order to better explain the present invention, examples are also provided below.

[0079] Example 1

[0080] The data in this embodiment are from the public core library of the United States Geological Survey. The permeability data are low-energy bioturbated feldspathic sandstones of the Bryne Formation of the Jurassic Vestland Group in the Gyda oil field in the North Sea, UK. Figure 2 This is the correlation analysis diagram of the porosity and permeability of the sand body of the same reservoir type of sandstone. There are 96 data sample points in total, with a depth of 3300-4300m, a porosity of 7%-28%, and a permeability of (0.1-2620)×10 -3 μm 2 , the variation range exceeds 4 orders of magnitude, and generally the permeability increases with the increase of porosity.

[0081] The conventional method is to select the exponential trend line for fitting based on the least squares method to obtain the exponential regression formula:

[0082]

[0083] Where: Kr is the exponential regression permeability, is the porosity. The regression formula determines the coefficient R 2 is 0.0855, and the correlation coefficient is 0.29. Analyzing the permeability before and after exponential regression, the average value of the core permeability K is 314.72×10 -3 μm 2 The average value of the permeability Kr of the exponential regression is 148.99×10 -3 μm 2 , the two are very different, the former is about 2.11 times the latter. Therefore, even if the core error correction is performed later, the permeability model mean obtained by regression will still be too small when the formula obtained by exponential regression is used to calculate the permeability, which affects the later reservoir development effect. The root cause of this underestimation of permeability is that the best regression fit is performed on the logarithm of the permeability rather than the permeability value during the fitting process. Therefore, according to the exponential regression formula obtained from the logarithm of the permeability, the permeability estimated by the reverse nonlinear transformation is generally low.

[0084] Using the mean permeability estimation of the present invention, firstly, a permeability and porosity intersection diagram is established; according to the data distribution characteristics in the intersection diagram, the horizontal axis is divided into

[0085] There are 8 groups in total, [min,12.5%],[10%,15%],[12.5%,17.5%],[15%,20%],[17.5%,22.5%],[20%,25%],[22.5%,27.5%],[25%,max], with a group interval of 5%; the permeability within each group is arranged from small to large; the low, medium and high values ​​of the permeability Li, Mi and Hi of each group in the 8 groups are calculated respectively, where i=1, 2, 3…8, to form low, medium and high data pairs within each group.

[0086] The first group of low, medium and high data pairs: (10, 0.164), (10, 0.59), (10, 1.42)

[0087] The second set of low, medium and high data pairs: (12.5, 0.167), (12.5, 0.71), (12.5, 7.85)

[0088] The third set of low, medium and high data pairs: (15, 0.222), (15, 1.1), (15, 26.68)

[0089] The fourth group of low, medium and high data pairs: (17.5, 0.464), (17.5, 5.55), (17.5, 143.7)

[0090] The fifth set of low, medium and high data pairs: (20,5.46), (20,49.4), (20,1589)

[0091] The sixth set of low, medium and high data pairs: (22.5, 21.4), (22.5, 164), (22.5, 1705.6)

[0092] The seventh group of low, medium and high data pairs: (25,39.84), (25,310), (25,1474.8)

[0093] The 8th set of low, medium and high data pairs: (27.5,319), (27.5,643.5), (27.5,1070.1)

[0094] The above low, medium and high data pairs are superimposed on the intersection diagram and connected respectively to obtain the low, medium and high permeability valuation curves L, M and H; the low, medium and high permeability valuation curves L, M and H are averaged to obtain the mean permeability valuation curve Kp.

[0095] according to Figure 2It can be seen that the M valuation line is close to the exponential regression trend line Kr fitted by the least squares method, and shows certain fluctuations around the Kr trend line. The H and L valuation lines correspond almost to the maximum and minimum envelopes of the permeability, reflecting the more extreme changes in the permeability. When the porosity is less than 20%, the percentile mean line Kp of the permeability increases rapidly with the increase of porosity, just like the exponential regression trend line Kr; after the porosity is greater than 20%, it is obviously different from the exponential regression trend line Kr, which continues to increase. Kp tends to change steadily, indicating that the seepage capacity increases limitedly after the porosity is greater than 20%. The arithmetic mean of the permeability corresponding to the core porosity calculated by Kp is 366.56×10 -3 μm 2 , and the arithmetic mean value of core permeability is 314.72×10 -3 μm 2 The arithmetic mean of the permeability obtained by conventional exponential regression is 148.99×10 -3 μm 2 , which is significantly lower than the mean permeability of the core. Compared with the conventional exponential regression trend line Kr, the mean permeability estimate Kp eliminates the deviation of the exponential regression, not only reflecting the overall trend of permeability, but also taking into account the changes in local details of permeability.

[0096] Example 2

[0097] This embodiment is an example analysis of the permeability of an unconventional coalbed methane block in the Bowen Basin of Australia. The main coalbed methane producing layer in this block is the Moranbah coal group of the Permian Blackwater Group, which is a set of alluvial plain sediments on the shallow marine continental shelf. Its lithology is mainly sandstone, siltstone, carbonaceous mudstone, mudstone and coal. The GM coal seam is the most important coal seam in this coal group, with a thickness of about 2-8m, an average of 5m, a vitrinite reflectance of 1.3%-2.4%, and a density of 1.3-1.8g / cm 3 , with an average of 1.5 g / cm 3 , dry ash-free gas content is 6-14m 3 / t, average 12m 3 / t, with an average ash content of 17% and an average gas saturation of 70%. The coalbed methane production of the production wells in this block varies greatly, with the maximum peak gas production of 1,000-40,000m 3 / D, average 7,500m 3 / D. The tested permeability of 51 GM coal seams in the block is (0.1-300)×10 -3 μm 2 , the research and analysis show that the permeability of GM coal seams is closely related to the burial depth (MD), and the overall relationship is exponential ( Figure 3 ). Based on the least squares method, the relationship between permeability and burial depth (MD) is obtained as follows:

[0098] Kr=133.618e (-0.009×MD)

[0099] Coefficient of determination R 2 is 0.4077, and the correlation coefficient is 0.64, which shows a relatively good fitting effect. However, the average values ​​of the test permeability K and the exponential regression permeability Kr are 21.76×10 -3 μm 2 and 7.25×10 -3 μm 2 , the ratio of the two is about 3 times, indicating that the permeability Kr calculated by regression is seriously underestimated compared with the test permeability.

[0100] Using the mean permeability estimation of the present invention, firstly, a permeability and porosity intersection diagram is established; according to the data distribution characteristics in the intersection diagram, the horizontal axis is divided into 11 groups, [min, 200], [150, 250], [200, 300], [250, 350], [300, 400], [350, 450], [400, 500], [450, 550], [500, 600], [550, 650], [600, max], with a group interval of 100; the permeabilities in each group are arranged from small to large; the low, medium and high values ​​of the permeability Li, Mi and Hi of each group in the 11 groups are calculated respectively, i = 1, 2, 3, ..., 11, to form low, medium and high data pairs in each group.

[0101] The first group of low, medium and high data pairs: (150,16.21), (150,60), (150,232.8)

[0102] The second set of low, medium and high data pairs: (200,6.32), (200,113), (200,154.8)

[0103] The third set of low, medium and high data pairs: (250,3.66), (250,13.65), (250,93.85)

[0104] The fourth group of low, medium and high data pairs: (300,1.25), (300,9.34), (300,70.25)

[0105] The fifth set of low, medium and high data pairs: (350, 0.65), (350, 4.3), (350, 44.1)

[0106] The sixth set of low, medium and high data pairs: (400, 0.55), (400, 2.1), (400, 16.04)

[0107] The seventh group of low, medium and high data pairs: (450, 0.24), (450, 0.57), (450, 6.06)

[0108] The 8th set of low, medium and high data pairs: (500, 0.15), (500, 0.57), (500, 14.14)

[0109] The 9th group of low, medium and high data pairs: (550, 0.18), (550, 1.48), (550, 25.44)

[0110] The 10th set of low, medium and high data pairs: (600, 0.38), (600, 1.5), (600, 2.14)

[0111] The 11th set of low, medium and high data pairs: (650, 0.52), (650, 1.55), (650, 2.09)

[0112] The above low, medium and high data pairs are superimposed on the intersection diagram and connected respectively to obtain the low, medium and high permeability valuation curves L, M and H; the low, medium and high permeability valuation curves L, M and H are averaged to obtain the mean permeability valuation curve Kp.

[0113] The average permeability Kp curve is obtained by the mean permeability estimation method. The predicted average permeability Kp is 20.59×10 -3 μm 2 , which is very close to the average permeability of 21.76×10 -3 μm 2 . Figure 3 The mean permeability Kp not only retains the overall decreasing trend of permeability with increasing depth, but also accurately depicts abnormal details such as low permeability at a burial depth of 400-500m and high permeability at a burial depth of 500-550m.

[0114] The permeability of the GM layer of 93 production wells in the work area was predicted using the mean permeability valuation curve. Based on the permeability prediction results, a permeability and average daily gas production intersection diagram was drawn, as shown in Figure 4 and 5As shown. The ordinate in the figure is the average daily gas production per unit coal thickness in the stable production stage. The correlation coefficient between the mean permeability Kp and the average daily gas production is 0.57 (determination coefficient 0.32), while the correlation coefficient between the conventional exponential regression prediction permeability Kr and the average daily gas production is 0.37 (determination coefficient 0.14), and the former is 1.5 times the latter. Compared with the exponential prediction of permeability, the mean permeability prediction method greatly improves the correlation between the two, and better reflects the close relationship between permeability and gas well production capacity. The numerical simulation of gas reservoirs also shows that compared with the exponential regression permeability, the numerical simulation calculation using the mean permeability estimation method is easier to converge, and less manual parameter adjustment is required during the historical fitting process.

[0115] The results of the above two embodiments show that compared with conventional methods, the mean permeability method has higher permeability prediction accuracy. It can not only predict the permeability distribution unbiasedly and ensure the mean statistical characteristics consistent with the original permeability, but also has three types of permeability information: high, medium and low. It can finely characterize the subtle changes between permeability and other parameters, and provides a fast, practical and effective method for using core test data to carry out spatial permeability prediction.

[0116] The present invention also provides a device for obtaining a mean permeability estimation curve, the device comprising: a drawing unit for drawing a scatter intersection diagram of permeability and target parameters; a determining unit for determining the number of groups corresponding to the target parameters according to the target parameters; an adding unit for determining the data pairs corresponding to each group of target parameters, and adding the data pairs corresponding to each group of target parameters to the scatter intersection diagram; a connecting unit for connecting the low permeability values ​​L in each group respectively. i , median permeability M i and the high value of permeability H i , obtaining three low, medium and high permeability valuation curves; obtaining a unit for averaging the three low, medium and high permeability valuation curves to obtain a mean permeability valuation curve;

[0117] Wherein, the target parameter is a reservoir parameter related to permeability; the data pair is the median value X of the target parameter in each group. i , low permeability value L i , median permeability M i and the high value of permeability H i related

[0118] Since the content protected by this device is similar to that protected by the above method, no further introduction will be given here. Please refer to the discussion section of the above method for details.

[0119] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for obtaining a mean permeability estimation curve, characterized in that: The method comprises: Draw scatter plots of permeability and target parameters; According to the target parameter, determining the number of groups corresponding to the target parameter; Determine the data pairs corresponding to each group of target parameters, and add the data pairs corresponding to each group of target parameters to the scatter point intersection diagram; Connect the low permeability values ​​L in each group respectively i , median permeability M i and the high permeability value H i , obtain three penetration rate valuation curves at low, medium and high levels; The three permeability valuation curves at low, medium and high levels are averaged to obtain the mean permeability valuation curve; Wherein, the target parameter is a reservoir parameter related to permeability; the data pair is the median value X of the target parameter in each group. i , low value of permeability L i , median permeability M i and the high permeability value H i related.

2. The method according to claim 1, characterized in that The target parameter includes at least one of: porosity, sonic, resistivity, depth, shale content, and gamma.

3. The method according to claim 1, characterized in that: Determining the number of groups corresponding to the target parameters according to the target parameters includes: determining the number of groups of the target parameters using Formula 1 according to the number of the target parameters; m=1+log 10 A / log 10 2 Formula 1 Where m is the number of target parameter groups and A is the number of target parameters.

4. The method according to claim 3, characterized in that The method further includes: determining a group distance corresponding to the target parameter according to the target parameter.

5. The method according to claim 4, characterized in that Determining the group distance corresponding to the target parameter according to the target parameter includes: Determine the range of the target parameter based on the maximum and minimum values ​​of the target parameter; The group interval of the target parameter is determined based on the ratio of the range of the target parameter to the number of groups of the target parameter.

6. The method according to claim 5, characterized in that Determine the data pairs corresponding to each set of target parameters, including: Get the sorted permeability in the ith group and calculate the low permeability value L in the ith group i , median permeability M i and the high permeability value H i ; According to the maximum and minimum values ​​of the i-th group interval, calculate the median value X of the target parameter in the i-th group i ; According to the low value of permeability L i , median permeability M i , high permeability value H i and the intermediate value X i , determine the data pair (X i , L i )、(X i , M i ) and (X i , H i ); Repeat the above operation to obtain the data pairs corresponding to each group.

7. The method according to claim 6, characterized in that Get the sorted permeability in the ith group and calculate the low permeability value L in the ith group i , median permeability M i and the high permeability value H i ,include: Sort the permeability in the i-th group from small to large, and obtain the sorted permeability, denoted as K i [1] K i [2]…K i [n], n is the number of permeabilities in the i-th group; According to the sorted permeability, the low permeability value L in the i-th group is calculated using formula 2-4. i , median permeability M i and the high permeability value H i ; L i = K i [INT(1 + (n - 1)*0.1)] + (K i [INT(1 + (n - 1)*0.1)+1] - K i [INT(1 + (n - 1)*0.1)])*(1 + (n - 1)*0.1 - INT(1 + (n - 1)*0.1)) Formula 2 M i = K i [INT(1 + (n - 1)*0.5)] + (K i [INT(1 + (n - 1)*0.5)+1] - K i [INT(1 + (n - 1)*0.5)])*(1 + (n - 1)*0.5 - INT(1 + (n - 1)*0.5)) Formula 3 H i = K i [INT(1 + (n - 1)*0.9)] + (K i [INT(1 + (n - 1)*0.9) + 1] - K i [INT(1 + (n - 1)*0.9)])*(1 + (n - 1)*0.9 - INT(1 + (n - 1)*0.9)) Formula 4 The INT function is a floor function that returns an integer that is not greater than the given parameter and is closest to the parameter.

8. The method according to claim 1, characterized in that The permeability is the ordinate of the scatter point intersection diagram, and the target parameter is the abscissa of the scatter point intersection diagram.

9. A device for obtaining a mean permeability estimation curve, characterized in that: The device comprises: A drawing unit for drawing a scatter plot of permeability and target parameters; A determination unit, configured to determine the number of groups corresponding to the target parameter according to the target parameter; An adding unit, used for determining data pairs corresponding to each group of target parameters, and adding the data pairs corresponding to each group of target parameters to the scatter point intersection diagram; Connection unit, used to connect the low permeability value L in each group i , median permeability M i and the high permeability value H i , obtain three penetration rate valuation curves at low, medium and high levels; An acquisition unit is used to average the three permeability estimation curves of low, medium and high positions to obtain a mean permeability estimation curve; Wherein, the target parameter is a reservoir parameter related to permeability; the data pair is the median value X of the target parameter in each group. i , low value of permeability L i , median permeability M i and the high permeability value H i related.

10. The device according to claim 9, characterized in that The target parameter includes at least one of: porosity, sonic, resistivity, depth, shale content, and gamma.

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