CN107630679B - Shale gas horizontal well initial-stage maximum productivity prediction method based on index model - Google Patents
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Abstract
The invention relates to a prediction method of shale gas horizontal well initial highest productivity based on an index model, which comprises the steps of obtaining the porosity and length of each class I and II shale gas layer section of a tested horizontal section of a work area and the corresponding single well initial highest stable productivity data, establishing a linear regression model according to the least square method for the porosity and length of each class I and II shale gas layer section of the horizontal section of a well to be predicted, calculating the highest stable productivity Qgmax of the shale gas horizontal well to be predicted, and outputting a prediction result, wherein the average error of the prediction and the average error obtained by actual field production are not more than 20% when a 173-mouth well is applied to an F L shale gas field, and the prediction method accords with the requirement of rapidly predicting the highest stable productivity of the single well horizontal section shale gas layer on the field.
Description
Technical Field
The invention belongs to the field of shale gas exploration and development, relates to a shale gas horizontal well single well productivity prediction method, and particularly relates to a shale gas horizontal well initial-stage highest productivity prediction method based on an index model.
Background
The exploration and development of shale gas become a hot spot in the energy field at the present stage. Shale gas is mainly present in dark shale, mainly in adsorbed or free state. The shale gas reservoir has the characteristics of ultralow porosity and permeability, and can be effectively developed with certain economic value only by drilling a horizontal well and modifying large-scale water conservancy fracturing. Due to the particularity of development, the capacity prediction of the shale gas reservoir is more complex than that of the conventional gas reservoir.
With the commercial development of shale gas fields, mysterious veil of shale gas reservoirs is gradually uncovered, and meanwhile, a lot of detailed research data and some practical experience are provided for shale gas workers. Numerous studies have also found that the north american shale gas development model, especially the initial complete flowback, short term high production, long term medium low production, is not scientific relying on multiple-well-trip, multiple fracturing models.
How to simply, quickly and cheaply predict the highest stable capacity of the shale gas horizontal well at the initial stage of a single well is a key technology which is always concerned by a plurality of shale gas exploration and development field workers in China. At present, in a production site, after large-scale hydraulic fracturing is carried out on a plurality of shale gas horizontal wells in the same block, the highest stable productivity in the initial stage of a single well is rapidly estimated by adopting a single parameter analogy method for the length of the horizontal section of the shale gas horizontal well, although the method is simple, convenient and rapid and has low cost, the relative error is more than 30 percent on average, the prediction error is large, the application range is small, and great improvement and improvement spaces are left.
On-site example research finds that staged fracturing is carried out on a shale gas horizontal well, the length of each staged is close to the vertical thickness of a shale layer, namely, equal-thickness staged operation, a plurality of 3000-horsepower fracturing trucks are adopted for saturated fracturing operation, the sand-liquid ratio is not lower than 4%, fracturing of the shale gas layer is fully improved, the seepage characteristic of the shale gas layer can be fully enhanced, the shale gas horizontal well can obtain better industrial airflow, and the porosity POR and the length L of gas layers in the horizontal sections I and II (high yield and medium yield) of the shale gas horizontal well are main control factors influencing the gas well productivity.
Disclosure of Invention
The invention aims to provide a prediction method for predicting the initial highest productivity of a single shale gas horizontal well, which is simple, convenient, rapid, efficient, low in cost and has certain universality, aiming at the technical current situation.
The invention aims to realize a prediction method of the initial highest productivity of a shale gas horizontal well based on an index model, which comprises the following specific steps:
1) obtaining data of tested well and well to be predicted in work area
(1) The tested well data comprises a well location report book of the shale gas well testing task, a logging geological well completion summary report, a well logging interpretation data table and a well completion gas testing report, and specifically comprises the porosity POR n-1 of each I and II type shale gas layer section of the tested well horizontal section, the length L n-1 of the tested well gas layer section, the single well initial-stage highest stable productivity Qgmax m-1 of the tested well corresponding to the well to be predicted,
wherein m-1 represents the serial number of a tested well, and n-1 represents the serial numbers of I and II shale gas layer sections of an m-1 well;
POR n-1 unit of measurement, length L n-1 unit of measurement is 100m, and Qgmaxm-1 unit of measurement of the highest stable production capacity at the initial stage of a single wellIs 104m3/d；
(2) The well data to be predicted comprises a well position report of the shale gas well to be predicted, a logging geological well completion summary report, a logging interpretation result data table and a well completion gas test report, and specifically comprises the porosity POR n of each I and II type shale gas layer sections of the horizontal section of the well to be predicted and the length L n of the gas layer section of the predicted well,
wherein n represents the serial numbers of the shale gas layer sections I and II to be pre-logged;
POR n is measured in units and length L n is measured in units of 100 m;
2) verification and optimization model using well-tested data
Establishing an index model according to least square regression by using the highest stable yield Qgmax m-1 of each tested shale gas well in the single-well initial stage obtained in the step 1) (1), and the sum ∑ (POR n-1, L n-1) of the porosity POR n-1 of each type I and II shale gas layer section of the tested well horizontal section and the length L n-1 data of the tested gas layer section, wherein the regression curve of the model passes through the origin, and the regression analysis correlation coefficient is R;
the exponential model formula Qgmax ═ CeB(POR·L)C and B are index model coefficients, when a tested well has n-1 sections of shale gas intervals (POR. L) ═ ∑ (POR n-1. L n-1), Qgmax m-1 data in the step 1) (1) are substituted into an index model formula to obtain the index model coefficients C and B;
correlation coefficient R of regression analysis2Greater than 0.7, the exponential model Qgmax ═ Ce is consideredB(POR·L)The application is carried out;
3) ∑ (POR n L n) of shale gas intervals of I and II types of well horizontal sections to be predicted are obtained;
4) using the calculation model Qgmax ═ Ce in step 2)B∑(POR n·L n)Calculating the highest stable productivity Qgmax m of the shale gas horizontal well to be predicted by using (POR n L n) in the step 3) and the model coefficient B, C obtained in the step 2);
the initial highest stable productivity Qgmax refers to the highest stable productivity of a single well within 3 months after the shale gas well testing task is completed, the data is from a well completion gas testing report, the data of the porosity POR n of each I and II type shale gas layer section of the horizontal section of the well to be predicted and the length L n of the gas layer section of the predicted well are from a logging interpretation result data table;
5) and outputting a prediction result.
The method solves the problem that the accuracy for rapidly estimating the highest stable productivity in the initial stage of the single well is not high by adopting a single parameter analogy method for the length of the horizontal section of the shale gas horizontal well on site, and has a wider application range.
The method disclosed by the invention is applied to the F L shale gas field by 173 wells, the predicted highest stable yield of the single shale gas horizontal well at the initial stage is close to the highest stable yield obtained by actual production on site, the average error is 18% and is not more than 20%, the requirement for rapidly predicting the highest stable yield of the shale gas layer at the horizontal section of the single well on site is met, the high-efficiency development of the gas field is facilitated, and the prediction level of the single shale gas horizontal well yield in China is improved.
Drawings
FIG. 1 is a block diagram of the workflow of the present invention;
FIG. 2 is a plan for forecasting the productivity of a shale gas horizontal well of the J work area index model.
Detailed Description
Referring to FIG. 1, the method includes the steps of obtaining porosity and length of each class I and II shale gas layer section of a horizontal section of a tested well in a work area and data of initial highest stable productivity Qgmax m-1 of a single well of the tested well corresponding to the porosity and length of each class I and II shale gas layer section of the horizontal section of the tested well to be tested, summing the obtained initial highest stable yield Qgmax m-1 of each shale gas tested well of the tested well, porosity PORn-1 and length L n-1 data of each class I and II shale gas layer section of the horizontal section of the corresponding well with the obtained porosity and length of each class I and II shale gas layer section of the tested well with ∑ (POR n-1 and L n-1), establishing a linear regression model according to a least square method, enabling a regression curve of the model to pass through an origin, obtaining model coefficients B, C, and utilizing a calculation model Qgmax to be CeB∑(POR n·L n)Calculating the highest stable productivity Qgmax of the shale gas horizontal well to be predicted according to the model coefficients B and C; and outputting a prediction result.
The shale gas layer sections I and II have high yield and can achieve commercial development value.
The present invention is described in detail below with reference to specific examples.
Example 1: R6-bHF well in J work area of certain shale gas field
1) Obtaining data of tested well and well to be predicted in work area
(1) Acquiring the porosity PORn-1 and the length L n-1 of each I and II type shale gas interval of the horizontal section of 12 tested wells in a J work area and data such as a well logging geological completion summary report, a well logging explanation data table, a well completion gas testing report and the like, wherein one of the wells in the tested wells is an engineering accident well and does not participate in building a model, and therefore the tested wells comprise W1H wells, W1-2H wells, W1-3HF wells, W2H wells, W4H wells, W7-2HF wells, W8-2HF wells, W9-2HF wells, W10-2HF wells, W11-2HF wells, W12-3HF, and 11 wells in total, and m is from 1 to 11;
(2) acquiring the porosity POR n and the length L n of each class I and II shale gas interval of the horizontal section of the shale gas well to be predicted according to a well position report, a logging geological completion summary report, a logging interpretation result data sheet, a well completion gas test report and the like of the shale gas well to be predicted R6-bHF;
2) verification and optimization model using well-tested data
(1) The initial highest stable yield Qgmax m-1 of shale gas obtained from the 11 tested wells, the porosity POR n-1 of each I and II type shale gas layer sections of the horizontal section of the corresponding well and the sum ∑ (POR n-1, L n-1) of the length L n-1 data (table 1) are used for establishing a linear regression model (see figure 2) according to a least square method, and the regression curve of the model passes through the origin.
∑ (POR n-1. L n-1) and Qgmaxm-1 values for shale gas intervals of class I and II in horizontal sections of shale gas horizontal wells have been tested and are shown in Table 1.
TABLE 1
| Number of well | ∑(POR n-1·L n-1) | Qgmax m-1 |
| W1H | 42.042 | 17.2 |
| W1-2H | 68.985 | 33.0 |
| W1-3HF | 48.384 | 20.2 |
| W2H | 86.022 | 34.0 |
| W4H | 69.484 | 26.0 |
| W7-2HF | 30.8 | 13.3 |
| W8-2HF | 86.942 | 54.7 |
| W9-2HF | 8.96 | 5.9 |
| W10-2HF | 77.77 | 37.7 |
| W11-2HF | 76.572 | 41.5 |
| W12-3HF | 91.77 | 41.1 |
(2) According to an exponential model, Qgmax m-1 ═ CeB∑(POR n-1·L n-1)When n-1 shale gas interval sections of each shale gas horizontal well exist (POR L) ═ ∑ (POR n-1 · L n-1), the data in the table 1 are brought into an index model Qgmax m-1 ═ CeB∑(POR n-1·L n-1)Performing medium regression to obtain index model coefficients B of 0.023 and C of 5.866;
(3) correlation coefficient R of regression analysis20.93, greater than 0.7, and the exponential model Qgmax ═ CeB∑(POR n·L n)The method is applicable.
3) The ∑ (POR n L n) of the horizontal section I and II shale gas interval of the R6-bHF shale gas horizontal well to be predicted is 75.327 hm.
TABLE 2 POR and L values of class I and class II shale gas intervals in horizontal section of R6-bHF shale gas horizontal well
4) Using the calculation index model Qgmax ═ Ce in step 2)B∑(POR n·L n)Calculating the maximum stable productivity Qgmax m of the shale gas horizontal well to be predicted to be 33.17 × 10 by utilizing ∑ (PORn L n) in the step 3) and the model coefficient B, C obtained in the step 2)4m3/d。
5) The result of the prediction is output and,the predicted calculated maximum stable production Qgmax of the R6-bHF shale gas horizontal well is 33.17 × 104m3The highest stable yield of the actual test of the well shale gas development is 36.3 × 104m3And d, the error is 8.6 percent and less than 15.0 percent, and the prediction requirement of the highest stable capacity on site is met.
Example 2: R90-bHF well in J work area of certain shale gas field
1) Obtaining data of tested well and well to be predicted in work area
(1) Acquiring the porosity PORn-1 and the length L n-1 of each I and II type shale gas layer section of the horizontal section of 12 tested wells in the J work area and the highest stable productivity Qgmax m-1 of the tested wells at the initial stage according to the data such as a logging geological well completion summary report, a logging interpretation data sheet, a well completion gas test report and the like, wherein the measurement units are respectively% hm (100m) and 10 m4m3D, m and n are natural numbers such as 1, 2 and 3 … …, m represents a well serial number, n represents serial numbers of I and II shale gas layer sections of the mth well, one of 12 tested wells is an engineering accident well and does not participate in building a model, so the tested wells comprise 11 wells such as W1H well, W1-2H well, W1-3HF well, W2H well, W4H well, W7-2HF well, W8-2HF well, W9-2HF well, W10-2HF well, W11-2HF well and W12-3HF, and the value of m is from 1 to 11;
(2) acquiring the porosity POR n and the length L n of each class I and II shale gas interval of the horizontal section of the well to be predicted according to a well position report, a logging geological completion summary report, a logging interpretation result data sheet, a well completion gas testing report and the like of the R90-bHF shale gas well to be predicted, wherein the measurement units are respectively% hm (100m), n is a natural number such as 1, 2, 3 … … and the like, and represents the nth shale gas interval;
(3) the initial highest stable productivity Qgmax refers to the highest stable yield of a single well within 3 months after the shale gas well testing task is completed, and data are from a well completion gas testing report, wherein the data of the porosity POR and the length L of the shale gas layer sections I and II of the horizontal section are from a logging interpretation result data table;
2) verification and optimization model using well-tested data
(1) Establishing a linear regression model (figure 2) according to a least square method by summing ∑ (POR n-1, L n-1) data of initial highest stable yield Qgmax m-1 of shale gas obtained from the 11 tested wells, porosity POR n-1 and length L n-1 of each class I and II shale gas layer section of the horizontal section of the corresponding well (table 1), wherein the regression curve of the model passes through an origin;
(2) the exponential model is Qgmax ═ CeB∑(POR n·L n)In the formula, B, C is an exponential model coefficient, when n-1 shale gas interval exists (POR L) ═ ∑ (POR n-1 · L n-1), the data in (1) is brought into the model to obtain a model coefficient B of 0.023 and a model coefficient C of 5.866;
(3) correlation coefficient R of regression analysis20.93, greater than 0.7, this exponential model is considered suitable;
3) the ∑ (POR n L n) of the shale gas interval of the R90-bHF horizontal well section I and II to be predicted is calculated to be 50.13 hm% (table 3);
TABLE 3 POR and L values of class I and II shale gas intervals in horizontal section of R90-bHF shale gas horizontal well
4) Using the exponential model Qgmax ═ Ce calculated in step 2)B∑(POR n·L n)Calculating the maximum stable productivity Qgmax of the shale gas horizontal well to be predicted to be 18.58 × 10 by utilizing ∑ (PORn L n) in the step 3) and the model coefficient B, C obtained in the step 2)4m3/d。
5) Outputting a prediction result, and calculating the highest stable yield Qgmax m of the well R90-bHF to be predicted to be 18.58 × 104m3The actual test of the well shale gas development has the highest stable yield of 20.65 × 104m3And d, the error is 10 percent and is less than 15.0 percent, and the prediction requirement of the highest stable productivity on site is met.
Claims (2)
1. The shale gas horizontal well initial-stage maximum productivity prediction method based on the index model is characterized by comprising the following steps of: the method comprises the following specific steps:
1) obtaining data of tested well and well to be predicted in work area
(1) The tested well data comprises a well location report book of the completed shale gas well testing task, a logging geological well completion summary report, a well logging interpretation data table and a well completion gas testing report, and specifically comprises the porosity PORn-1 of each I and II type shale gas layer section of the tested well horizontal section, the length L n-1 of the tested well gas layer section, the highest stable productivity Qgmaxm-1 of the tested well in the initial stage corresponding to the well to be predicted,
wherein m-1 represents the serial number of a tested well, and n-1 represents the serial numbers of I and II shale gas layer sections of an m-1 well;
PORn-1 is measured in units, length L n-1 is measured in units of 100m, and the maximum stable production capacity Qgmax m-1 at the initial stage of a single well is measured in units of 104m3/d；
(2) The well data to be predicted comprises a well position report of the shale gas well to be predicted, a logging geological well completion summary report, a logging interpretation result data table and a well completion gas test report, and specifically comprises the porosity PORn of each class I and II shale gas layer section of the horizontal section of the well to be predicted, the gas layer section length L n of the predicted well,
wherein n represents the serial numbers of the shale gas layer sections I and II to be pre-logged;
PORn is measured in percentage and length L n is measured in 100 m;
2) verification and optimization model using well-tested data
Establishing an index model according to least square regression by using the highest stable yield Qgmax m-1 of each tested shale gas in the single-well initial stage of each tested shale gas well obtained in the step 1) (1), and the sum ∑ (PORn-1, L n-1) of the porosity PORn-1 of each type I and II shale gas layer section of the tested well horizontal section and the length L n-1 data of the tested gas layer section, wherein the regression curve of the model passes through the origin, and the correlation coefficient of regression analysis is R;
the exponential model formula Qgmax ═ CeB(POR·L)C and B are index model coefficients, when a tested well has n-1 sections of shale gas intervals (POR. L) ═ ∑ (PORn-1. L n-1), Qgmax m-1 data in the step 1) (1) are substituted into an index model formula to obtain the index model coefficients C and B;
correlation coefficient R of regression analysis2Greater than 0.7, the exponential model Qgmax ═ Ce is consideredB(POR·L)The application is carried out;
3) ∑ (PORn L n) of shale gas intervals of I and II types of well horizontal sections to be predicted are obtained;
4) using the calculation model Qgmax ═ Ce in step 2)B∑(PORn·Ln)Calculating the highest stable productivity Qgmax m of the shale gas horizontal well to be predicted by using the (PORn L n) in the step 3) and the model coefficient B, C obtained in the step 2);
the initial highest stable productivity Qgmax refers to the highest stable productivity of a single well within 3 months after the shale gas well testing task is completed, the data is from a well completion gas testing report, the porosity PORn of each I and II type shale gas layer section of a well horizontal section to be predicted and the length L n of a gas layer section of a predicted well are from a logging interpretation result data table;
5) and outputting a prediction result.
2. The method for predicting initial maximum productivity of a shale gas horizontal well based on an exponential model as claimed in claim 1, wherein: the shale gas layer sections I and II have high yield and can achieve commercial development value.
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