Shale gas resource evaluation parameter characterization method based on trend surface model calculation

Abstract

The invention discloses a shale gas resource evaluation parameter characterization method based on trend surface model calculation, and relates to the technical field of shale gas resource evaluation, in particular to the shale gas resource evaluation parameter characterization method based on trend surface model calculation. Comprising the following steps: collecting well point geological data and resource evaluation parameter actual values in a shale gas evaluation block, defining a parameter trend surface, establishing a parameter trend surface model, solving the parameter trend surface model, and determining an evaluation area parameter trend surface model. By applying the method, the trend surface model of the shale gas resource evaluation parameters is established in the evaluation block, so that effective characterization of the shale gas resource evaluation parameters in the evaluation block can be quantitatively realized, and the distribution trend of the shale gas resource evaluation parameters in the evaluation block can be mastered; the distribution range of the high-value area and the low-value area of the shale gas evaluation parameters in the evaluation area is determined, and a reliable basis is provided for work such as delineation of the range of the shale gas resource evaluation favorable area and optimization of the exploration development deployment favorable area.

Description

Technical Field

[0001] The present invention relates to the technical field of shale gas resource evaluation, and in particular to a shale gas resource evaluation parameter characterization method based on trend surface model calculation. Background Art

[0002] Shale gas resource evaluation is an important part of my country's oil and gas resource evaluation, and has important guiding significance for major decisions such as shale gas resource potential evaluation, reserve and production prediction, and exploration and development deployment. With the continuous deepening of shale gas exploration and development in my country, the results of shale gas resource evaluation in China are becoming more and more abundant. The evaluation objects cover a variety of types such as marine phase, marine-continental transition phase and continental phase. The evaluation layers include Silurian, Cambrian, Permian, Triassic and other layers. The evaluation area covers most of the domestic basins or regions such as Sichuan Basin, Ordos Basin, Tarim Basin, North China Basin, etc. In general, the evaluation of shale gas resources in my country is relatively complicated. In order to carry out shale gas resource evaluation more efficiently, domestic scholars have comprehensively considered the geological characteristics and exploration and development levels of shale gas in major basins in my country, and developed a series of shale gas resource evaluation methods, including the genetic method, analogy method, volumetric method, etc. Among them, the volumetric method is the most widely used and most efficient shale gas resource evaluation method in China, and can be applied to exploration blocks with low exploration levels and mature blocks with high exploration and development levels.

[0003] This method evaluates the shale gas resources by multiplying the total weight of shale in the evaluation block by the gas content per unit weight of shale. The calculation formula is as follows: Q=0.01Ahρc(1) Where: Q: Shale gas resources in the evaluation block, 10 8 m 3 ; A: Shale gas-bearing area in the evaluation block, km 2 ; h: effective thickness of shale in the evaluation block, m; ρ: shale density in the evaluation area, t / m 3 ; C: Total gas content of shale in the evaluation block, m 3 / t.

[0004] The reasonable determination of evaluation parameters such as shale effective thickness, shale density, and shale gas content in the evaluation block is the most important evaluation step in the shale gas resource evaluation process. As can be seen from Formula 1, the shale gas resource volume is obtained by multiplying the various resource evaluation parameters, and any evaluation parameter in the evaluation block often has hundreds or even thousands, and the distribution characteristics of the evaluation parameters show a certain degree of heterogeneity. Therefore, the reasonable characterization of the evaluation parameters in the block has always been a difficult problem in the shale gas resource evaluation process. If the shale gas evaluation parameter value deviates during the evaluation process and cannot characterize the distribution characteristics of the evaluation parameter in the block, it is likely to cause a very large deviation in the final shale gas resource evaluation result. Especially for shale gas evaluation blocks with a resource volume of trillions of cubic meters, the deviation value of the evaluation result will also be more than 100 million cubic meters, which will seriously affect the later exploration and development of shale gas blocks.

[0005] Therefore, how to accurately carry out reasonable calculation of shale gas resource evaluation parameters in shale gas evaluation blocks and improve the block representativeness of shale gas resource evaluation parameter calculation results has become an urgent problem to be solved in shale gas resource evaluation work.

[0006] The solution of prior art 1 Characterization methods of shale gas resource evaluation parameters: First, the conditional probability method, which mainly uses probability distribution functions to characterize resource evaluation parameters, including constant distribution, triangular distribution, normal distribution, etc., among which the most commonly used is the normal distribution function. According to mathematical statistics analysis, as the amount of data increases, the distribution of discrete data tends to be more normal. Therefore, as the exploration level of shale gas evaluation blocks continues to increase, the distribution of shale gas resource evaluation parameters will gradually tend to be normal: Where: x—evaluation parameter value; f(x)—probability distribution corresponding to the evaluation parameter; μ—mean of the evaluation parameter distribution; δ—variance of the evaluation parameter distribution; a, b—the upper and lower limits of the evaluation parameter distribution.

[0007] The conditional probability method describes the normal distribution probability of all values ​​of each evaluation parameter in the shale gas evaluation block, calculates the corresponding probability density function, and assigns probability values ​​of different conditions to the function integral (Formula 2) to obtain the geological parameter values ​​of the corresponding conditions.

[0008] According to the abundance of the shale gas evaluation parameter in the evaluation block, the corresponding probability value is assigned, and the shale gas evaluation parameter value is back-calculated as the parameter representative value for calculating shale gas resources by the volume method.

[0009] Table 1 Shale gas conditional probability assignment reference Probability Parameter meaning Degree of control Assignment reference P5 Very unfavorable, small chance Basically unsure optimism P25 Unfavorable, but possible Low level of confidence Loose P50 Generally, shale gas accumulates or does not accumulate Be confident Median P75 Favorable, but still with great uncertainty High degree of control strict P95 Very favorable, but small probability events are still not ruled out Very confident harsh Disadvantages of the prior art 1 In-depth analysis found that the conditional probability method has certain defects in the application process, mainly manifested as follows: (1) The conditional probability method assigns corresponding probabilities according to the degree of understanding of the evaluation parameters in the evaluation area, and finally homogenizes the distribution characteristics of the evaluation parameters in an evaluation area with only one value. However, it is difficult to fully and accurately characterize the distribution characteristics of the shale gas evaluation parameters in the entire evaluation area with only one value, resulting in low reliability of the calculated shale gas resources in the evaluation area, which is particularly obvious when the heterogeneity of the shale gas evaluation parameters in the evaluation block is strong.

[0010] (2) The probability assignment of shale gas evaluation parameters is completely dependent on the evaluator's understanding of the shale gas evaluation parameters in the evaluation block. The operation is too subjective. Different evaluators assign different probability values, and the corresponding shale gas evaluation parameter values ​​calculated are also different. Especially when the evaluator is not familiar with the evaluation block, the probability assignment may be biased, which has a great impact on the shale gas resource evaluation results. Therefore, this method has high requirements on the professional level of shale gas evaluation workers.

[0011] Technical solution of prior art 2 The second method for characterizing shale gas resource evaluation parameters is the small facet method. This method mainly divides the shale gas evaluation block into several grids, each of which is called a small facet. By directly obtaining the representative values ​​of resource evaluation parameters such as shale gas gas-bearing area, effective thickness, and gas content corresponding to each small facet, the shale gas resource of the small facet is directly calculated using the volume method. The shale gas resource calculation results of all small facets in the evaluation block are summed up to finally obtain the shale gas resource of the evaluation block.

[0012] Q cell-i =0.01A cell-i h cell-i ρ cell-i c cell-i (3) Where: Q: To evaluate the shale gas resources in the block, 10 8 m 3 ; Q cell-i : The shale gas resources of the ith sub-surface in the evaluation block, 10 8 m 3 ; A cell-i : The distribution area of ​​gas-bearing shale in the ith small area in the evaluation block, km 2 ; hcell-i : effective shale thickness of the ith small area in the evaluation block, m; ρ cell-i : Shale density of the ith small area in the evaluation block, t / m 3 ; c cell-i : Total gas content of the ith small area in the evaluation block, m 3 / t.

[0013] The small facet method divides the evaluation block into several small facets by gridding the evaluation block, and the sum of the calculated shale resources of the small facets represents the shale gas resources of the entire block. This method fully considers the differences in shale gas evaluation parameters in different areas of the evaluation block through small facets during the evaluation process, especially for evaluation blocks with strong heterogeneity, the accuracy and reliability of the evaluation results are relatively higher.

[0014] Disadvantages of the second prior art The key to the small-surface method is the division of small-surface elements and the acquisition of evaluation parameter values ​​within small-surface elements. In-depth analysis shows that there are certain defects in the application of the small-surface method to calculate shale gas resources, mainly manifested in the difficulty in grasping the degree of division of small-surface elements.

[0015] The area of ​​a single small bin is large, and some small bins may contain multiple shale gas wells, resulting in a certain degree of heterogeneity in the shale gas evaluation parameters in the small bin. Directly determining the representative values ​​of the shale gas evaluation parameters in the small bin will result in a certain deviation. Some small bins even need to use the conditional probability method to represent the representative values ​​of shale gas resource evaluation parameters, which greatly increases the workload of the shale gas evaluation block and reduces the reliability of the evaluation results. See the attached Figure 1 .

[0016] When the division of small facets is fine, the control area of ​​a single small facet is small, and the representative value of each evaluation parameter in the small facet can be directly determined. However, for shale gas blocks with relatively low exploration levels, the density of shale gas drilling is low, and it is difficult to ensure that each small facet is covered by shale gas wells. There are multiple blank small facets without shale gas well control in the evaluation block. The evaluation parameters of these blank small facets cannot be directly obtained based on the actual shale gas wells in the block, and can only be estimated through interpolation. The greater the distance between the well points, the more blank small facets there are between the two wells, and the worse the accuracy of the estimated evaluation parameter values. In addition, the finer the division of small facets, the greater the overall evaluation workload, and the lower the efficiency of the evaluation work. See attached. Figure 2 .

[0017] By analyzing the defects of existing methods in the actual application of shale gas resource evaluation parameter characterization, it is found that both the conditional probability method used for the entire rating block and the small facet method of refined summation rely on the geological data of existing shale gas wells within the evaluation range to obtain shale gas resource evaluation parameter values. The larger the area that cannot be controlled by the drilled shale gas wells within the evaluation range, the greater the deviation of the shale gas evaluation parameter values ​​obtained by the two methods, and the lower the accuracy of the resource evaluation results. Only by increasing the drilling density of shale gas wells within the evaluation range and increasing the degree of control per unit area of ​​shale gas wells can the accuracy of the resource evaluation results of the two methods be improved; The survey found that, except for the Sichuan Basin, the remaining shale gas blocks in China are in the middle-low shale gas exploration level, the drilling density is insufficient, and there are even large shale gas blank areas in some local blocks. It is difficult to obtain accurate and reliable shale gas resource evaluation results by directly applying the above two methods. Therefore, how to use the existing data in the shale gas evaluation block to achieve the reasonable characterization of each shale gas evaluation parameter in the entire block, especially to improve the accuracy of the characterization of blank areas that cannot be controlled by existing shale gas wells, has become a difficult problem to be solved in the current shale gas evaluation work. Summary of the invention

[0018] The present invention introduces a parameter trend surface analysis method, uses existing data in the shale gas evaluation block to establish a trend surface representation model of the evaluation parameters, and realizes the quantitative representation of the shale gas evaluation parameters in the evaluation block. The trend surface model can not only characterize the distribution law of the shale gas evaluation parameters in the entire evaluation block, but more importantly, it can realize the reasonable prediction of the evaluation parameter values ​​in the blank area that cannot be controlled by the existing wells, overcomes the defects of the existing methods in the shale gas resource evaluation process, and improves the accuracy and reliability of shale gas resource evaluation.

[0019] The present invention provides a shale gas resource evaluation parameter characterization method based on trend surface model calculation, comprising the following steps: (1) Collect geological data of well points and actual values ​​of resource evaluation parameters in the shale gas evaluation block; (2) Definition of parameter trend surface; A shale gas evaluation block is regarded as a three-dimensional space. The plane where all shale gas wells in the evaluation block are located is the x-axis-y-axis plane of the three-dimensional space. The horizontal and vertical coordinate points of each well point are an observation point (x i ,y i ), the resource evaluation parameter value obtained by applying the geological data of the well point is used as the value z in the z-axis direction in the three-dimensional space i , then the values ​​z of all well points in the evaluation block in the z-axis direction i Distributed on a real surface; According to the values ​​of all known points on the actual surface, a mathematical surface model is fitted, and the mathematical surface model is applied to form a fitting surface near the actual surface, which is the parameter trend surface G; (3) Establishment of parameter trend surface model Assuming that there are n shale gas wells in the evaluation block, there are n sets of coordinate values ​​(x i ,y i ,z i )(i=1,2,..n), a polynomial parameter trend surface model is constructed by these n sets of coordinate values. The specific expression of the parameter trend surface model is: z'=b0+b1x+b2y+b3x 2 +b4xy+b5y 2 +…(5) Among them: z'—parameter trend value; x, y—horizontal and vertical coordinates of the well points in the evaluation area; b0, b1, b2, b3…—polynomial coefficients of the trend surface model; (4) Parameter trend surface model solution Solve the parameter trend surface model, that is, determine a set of polynomial coefficients b0, b1, b2, b3…, so that the sum of the overall squares of the deviations between the resource evaluation parameter trend value z' corresponding to the n shale gas wells in the evaluation block calculated by the parameter trend surface model and the actual value z of the resource evaluation parameter collected for each well in the evaluation block is minimized, that is, Minimum; make According to the polynomial minimum principle, let the derivative of D(b0,b1,b2,…) with respect to the polynomial coefficients b0,b1,b2,b3… be equal to 0, and obtain a set of derivative polynomial equations: The polynomial equations are sorted out to obtain a set of polynomial equations in a conventional format. The highest degree of the polynomial parameter trend surface model is set to m, then It is expressed in the form of a matrix equation system as A·B=C (9) in: B=(b0 b1 b2 b3 b4 b5……)’(11) By iteratively solving the matrix equation group, all polynomial coefficients b0, b1, b2, b3… of the parameter trend surface model are obtained, and finally a parameter trend surface model for shale gas resource evaluation in the evaluation block is established; (5) Determination of parameter trend surface model of the evaluation area Apply the parameter trend surface model established in step (4) and combine the actual values ​​of all shale gas wells in the evaluation block (x i ,y i ,z i ), and use formula (5) to calculate the parameter trend surface model fitting value z', and further obtain the overall deviation square sum R of the evaluation block m ,make: Where R m —The sum of squares of the overall deviations calculated by the trend surface model with the highest order being m; Starting from the establishment of a first-order polynomial parameter trend surface model, the highest order m of the parameter trend surface is gradually increased to establish a shale gas resource evaluation parameter trend surface model for the evaluation block, and the corresponding overall deviation square sum R is calculated. m , and the total sum of squares of deviations R corresponding to the trend surface model with the highest order m-1 m-1 Compare and set the error value η according to the actual situation. The parameter trend surface model with the highest order being m-1 is determined as the shale gas resource evaluation parameter trend surface model of the evaluation block, and is used to characterize the shale gas resource evaluation parameters in the evaluation block.

[0020] Compared with the prior art, the present invention has the following beneficial effects: By using the present invention to establish a trend surface model of shale gas resource evaluation parameters in the evaluation block, the effective characterization of the shale gas resource evaluation parameters in the evaluation block can be quantitatively realized, the distribution trend of the shale gas resource evaluation parameters in the evaluation block can be grasped, and the distribution range of high-value areas and low-value areas of the shale gas evaluation parameters in the evaluation area can be clarified, providing a reliable basis for the delineation of the scope of favorable areas for shale gas resource evaluation and the optimization of favorable areas for exploration and development deployment.

[0021] For shale gas evaluation blocks with low shale gas exploration degree and low shale gas well drilling density, the parameter trend surface model in the present invention is used to predict the relevant evaluation parameter values ​​in the uncontrollable blank areas between the existing shale gas wells in the block, so that the resource evaluation parameter prediction value of the blank area can be quickly obtained. The predicted value is more reliable than the result obtained by blind interpolation. It can be used as a parameter reference value for carrying out shale gas resource evaluation work at this point in the evaluation block to improve the perfection of the shale gas resource evaluation parameters in the evaluation block, and greatly improve the accuracy of shale gas resource evaluation in shale gas evaluation blocks with low exploration degree.

[0022] The shale gas resource evaluation parameter trend surface model in the present invention can be combined with existing methods (conditional probability method and small facet method). In the conditional probability method, the application of the parameter trend surface model can further increase the shale gas evaluation parameter data participating in the conditional probability volume method in the evaluation block. The more data there are, the closer the distribution characteristics of the evaluation parameters in the evaluation block are to the normal distribution, and the higher the reliability of the application of the conditional probability method. The more data there are, the more the evaluation workers understand the distribution characteristics of the evaluation parameters, and the more confident they are in assigning probability values. In the small facet method, the application of the parameter trend surface model can calculate the shale gas resource evaluation parameter values ​​at any position in the evaluation block, and can quickly and accurately determine the parameter values ​​of the blank small facet area between shale gas wells during the small facet division process. Therefore, in the actual evaluation process, the accuracy of the shale gas evaluation results in the evaluation block can be improved by increasing the degree of refinement of the grid division of the small facet method. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 Schematic diagram of the situation where the small surface element is divided too coarsely.

[0024] Figure 2 Schematic diagram of the situation where small facets are divided too finely.

[0025] Figure 3 It is a schematic diagram of the trend surface of resource evaluation parameters in the present invention.

[0026] Figure 4 It is a flow chart of the present invention.

[0027] Figure 5 This is a fitting effect diagram of the effective thickness trend surface of the highest order 1 in the embodiment.

[0028] Figure 6 This is a fitting effect diagram of the effective thickness trend surface of the highest 2 times in the embodiment.

[0029] Figure 7 It is a fitting effect diagram of the effective thickness trend surface of the highest three times in the embodiment.

[0030] Figure 8 This is a fitting effect diagram of the effective thickness trend surface of the highest 4 orders in the embodiment.

[0031] Fig. 9 This is a fitting effect diagram of the effective thickness trend surface of the highest 5 times in the embodiment.

[0032] Fig.10 This is a diagram showing the prediction effect of the effective thickness of shale gas at the blank point in the study block in the embodiment. DETAILED DESCRIPTION

[0033] The present invention provides a shale gas resource evaluation parameter characterization method based on trend surface model calculation, comprising the following steps: (1) Collect geological data of well points and actual values ​​of resource evaluation parameters in the shale gas evaluation block; (2) Definition of parameter trend surface; A shale gas evaluation block is regarded as a three-dimensional space. The plane where all shale gas wells in the evaluation block are located is the x-axis-y-axis plane of the three-dimensional space. The horizontal and vertical coordinate points of each well point are an observation point (x i ,y i ), the resource evaluation parameter value obtained by applying the geological data of the well point is used as the value z in the z-axis direction in the three-dimensional space i , then the values ​​z of all well points in the evaluation block in the z-axis direction i Distributed on a real surface; According to the values ​​of all known points on the actual surface, a mathematical surface model is fitted, and the mathematical surface model is applied to form a fitting surface near the actual surface, which is the parameter trend surface G; (3) Establishment of parameter trend surface model Assuming that there are n shale gas wells in the evaluation block, there are n sets of coordinate values ​​(x i ,y i ,z i )(i=1,2,..n), a polynomial parameter trend surface model is constructed by these n sets of coordinate values. The specific expression of the parameter trend surface model is: z'=b0+b1x+b2y+b3x 2 +b4xy+b5y 2 +…(5) Among them: z'—parameter trend value; x, y—horizontal and vertical coordinates of the well points in the evaluation area; b0, b1, b2, b3…—polynomial coefficients of the trend surface model; (4) Parameter trend surface model solution Solve the parameter trend surface model, that is, determine a set of polynomial coefficients b0, b1, b2, b3…, so that the sum of the overall squares of the deviations between the resource evaluation parameter trend value z' corresponding to the n shale gas wells in the evaluation block calculated by the parameter trend surface model and the actual value z of the resource evaluation parameter collected for each well in the evaluation block is minimized, that is, Minimum; make According to the polynomial minimum principle, let the derivative of D(b0,b1,b2,…) with respect to the polynomial coefficients b0,b1,b2,b3… be equal to 0, and obtain a set of derivative polynomial equations: The polynomial equations are sorted out to obtain a set of polynomial equations in a conventional format. The highest degree of the polynomial parameter trend surface model is set to m, then It is expressed in the form of a matrix equation system as A·B=C (9) in: B=(b0 b1 b2 b3 b4 b5……)’(11) By iteratively solving the matrix equation group, all polynomial coefficients b0, b1, b2, b3… of the parameter trend surface model are obtained, and finally a parameter trend surface model for shale gas resource evaluation in the evaluation block is established; (5) Determination of parameter trend surface model of the evaluation area The parameter trend surface model established in step (4) is applied, combined with the actual values ​​of all shale gas wells in the evaluation block (x i ,y i ,z i ), and use formula (5) to calculate the parameter trend surface model fitting value z', and further obtain the overall deviation square sum R of the evaluation block m ,make: Where R m —The sum of squares of the overall deviations calculated by the trend surface model with the highest order being m; As the highest order m of the trend surface model increases, the trend surface fit is closer to the actual surface where the actual well point is located, and R m The corresponding trend surface model is also decreasing, but the complexity of the trend surface model is also increasing. The overly complex trend surface model not only increases the difficulty of calculation in the actual application of the model, which is not conducive to actual operation, but also may cause the trend surface fitting effect to be overfitted, so that the established trend surface model excessively pursues the fitting of local outliers, and loses the grasp of the overall distribution characteristics of the evaluation parameter in the evaluation area, which is not conducive to the accurate prediction of the evaluation parameter value of the blank area. Therefore, it is necessary to determine a trend surface model that meets the trend surface fitting requirements and has a simple structure as the parameter trend surface model of the actual block. The specific process is as follows: Starting from the establishment of a first-order polynomial parameter trend surface model, the highest order m of the parameter trend surface is gradually increased to establish a shale gas resource evaluation parameter trend surface model for the evaluation block, and the corresponding overall deviation square sum R is calculated. m , and the total sum of squares of deviations R corresponding to the trend surface model with the highest order m-1 m-1Compare and set the error value η according to the actual situation. This means that the fitting degree of the trend surface model is very close, and there is no need to continue to increase the number of trend surface models. Based on the requirements of simple model structure and easy operation, the parameter trend surface model with the highest order of m-1 is determined as the shale gas resource evaluation parameter trend surface model of the evaluation block, which is used to characterize the shale gas resource evaluation parameters in the evaluation block. Example

[0034] In specific use, the present invention is applied by taking the shale gas resource evaluation parameters of a shale gas exploration block in the Ordos Basin in China as an example.

[0035] There are 30 shale gas wells in this block. Combined with the effective thickness judgment standard for shale gas resource evaluation, the effective thickness data of shale gas resource evaluation in the 30 wells in the area are statistically obtained, as shown in Table 2. Table 1 Statistics of effective thickness of shale gas research area The parameter trend surface model was applied to establish the effective thickness model of shale gas resource evaluation parameters in the region. The trend surface model was optimized and the error value η was set to 5%. The trend surface model with the highest order of 3 was selected as the optimal model by calculating the sum of squares of the overall deviation.

[0036] Table 2 Statistics of the total deviation and square error of effective thickness in the shale gas study area Through the analysis of the fitting graph, it is also found that when the highest order is small, such as Figure 5 , Figure 6 , cannot reflect the distribution trend of effective shale gas thickness in the study area, and when the highest order is too large, such as Figure 8 , Fig. 9 , then overfitting begins to occur, which has a greater impact on the prediction of the blank area in the later stage. Figure 7 The trend surface model is more consistent with the geological characteristics of shale gas and is more reasonable. Finally, the trend surface model with the highest order of 3 is determined as the trend surface model of effective thickness, a shale gas resource evaluation parameter in this study area: z'=b0+b1x+b2y+b3x 2 +b4xy+b5y 2 +b6x 3 +b7x 2 y+b8xy 2 +b9y 3 Where b0 = -9.044e+09; b1=1405; b2 = -37.74; b3 = -7.412e-05; b4=1.665e-05; b5 = -3.07e-05; b6=1.316e-12; b7 = -6.226e-13; b8 = 9.318e-13; b9=1.04e-12; By using the present invention to establish a trend surface model of shale gas resource evaluation parameters in the evaluation block, the effective characterization of the shale gas resource evaluation parameters in the evaluation block can be quantitatively realized, the distribution trend of the shale gas resource evaluation parameters in the evaluation block can be grasped, and the distribution range of high-value areas and low-value areas of the shale gas evaluation parameters in the evaluation area can be clarified, providing a reliable basis for the delineation of the scope of favorable areas for shale gas resource evaluation and the optimization of favorable areas for exploration and development deployment.

Claims

1. A method for characterizing shale gas resource evaluation parameters based on trend surface model calculation, characterized in that: The following steps are involved: (1) Collect geological data of well points and actual values ​​of resource evaluation parameters in the shale gas evaluation block; (2) Definition of parameter trend surface; A shale gas evaluation block is regarded as a three-dimensional space. The plane where all shale gas wells in the evaluation block are located is the x-axis-y-axis plane of the three-dimensional space. The horizontal and vertical coordinate points of each well point are a trend surface observation point (x i ,y i ), the resource evaluation parameter value obtained by applying the geological data of the well point is used as the value z in the z-axis direction in the three-dimensional space i , then the values ​​z of all well points in the evaluation block in the z-axis direction i Distributed on a real surface; According to the values ​​of all known points on the actual surface, a mathematical surface model is fitted, and the mathematical surface model is applied to form a fitting surface near the actual surface, which is the parameter trend surface G; (3) Establishment of parameter trend surface model Assuming that there are n shale gas wells in the evaluation block, there are n sets of coordinate values ​​(x i ,y i ,z i )(i=1,2,..n), a polynomial parameter trend surface model is constructed by these n sets of coordinate values. The specific expression of the parameter trend surface model is: <h2 style=";text-align:left;direction:ltr">z' = b0 + b1x + b2y + b3x<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> +b4xy+b5y<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> +…(5) Among them: z'—parameter trend value; x, y—horizontal and vertical coordinates of the well points in the evaluation area; b0, b1, b2, b3…—polynomial coefficients of the trend surface model; (4) Parameter trend surface model solution Solve the parameter trend surface model, that is, determine a set of polynomial coefficients b0, b1, b2, b3…, so that the sum of the overall squares of the deviations between the resource evaluation parameter trend value z' corresponding to the n shale gas wells in the evaluation block calculated by the parameter trend surface model and the actual value z of the resource evaluation parameter collected for each well in the evaluation block is minimized, that is, Minimum; make According to the polynomial minimum principle, Let the derivative of D(b0,b1,b2,…) with respect to the polynomial coefficients b0,b1,b2,b3… be equal to 0, and obtain a set of derivative polynomial equations: The polynomial equations are sorted out to obtain a set of polynomial equations in a conventional format. The highest degree of the polynomial parameter trend surface model is set to m, then It is expressed in the form of a matrix equation system as A·B=C(9) in: B=(b0 b1 b2 b3 b4 b5……)’(11) By iteratively solving the matrix equation group, all polynomial coefficients b0, b1, b2, b3… of the parameter trend surface model are obtained, and finally a parameter trend surface model for shale gas resource evaluation in the evaluation block is established; (5) Determination of parameter trend surface model of the evaluation area The parameter trend surface model established in step (4) is applied, combined with the actual values ​​of all shale gas wells in the evaluation block (x i ,y i ,z i ), and use formula (5) to calculate the parameter trend surface model fitting value z', and further obtain the overall deviation square sum R of the evaluation block m ,make: Among them, R m —The sum of squares of the overall deviations calculated by the trend surface model with the highest order being m; Starting from the establishment of a first-order polynomial parameter trend surface model, the highest order m of the parameter trend surface is gradually increased to establish a shale gas resource evaluation parameter trend surface model for the evaluation block, and the corresponding overall deviation square sum R is calculated. m , and the total sum of squares of deviations R corresponding to the trend surface model with the highest order m-1 m-1 Compare and set the error value η according to the actual situation. The parameter trend surface model with the highest order being m-1 is determined as the shale gas resource evaluation parameter trend surface model of the evaluation block, and is used to characterize the shale gas resource evaluation parameters in the evaluation block.

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