CN107630679B - Prediction method of initial maximum productivity of shale gas horizontal well based on exponential model - Google Patents

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

Prediction method of initial maximum productivity of shale gas horizontal well based on exponential model

technical field

The invention belongs to the field of shale gas exploration and development, and relates to a single well productivity prediction method of a shale gas horizontal well, in particular to a method for predicting the initial maximum productivity of a shale gas horizontal well based on an exponential model.

Background technique

The exploration and development of shale gas has become a hot spot in the energy industry at this stage. Shale gas mainly exists in dark shale, and mainly exists in adsorption or free state. Shale gas reservoirs have the characteristics of ultra-low porosity and permeability. Only through horizontal well drilling and large-scale hydraulic fracturing can they be effectively developed with certain economic value. Due to the particularity of its development, the productivity prediction of shale gas reservoirs is more complicated than that of conventional gas reservoirs.

With the commercial development of shale gas fields, the mystery of shale gas reservoirs has been gradually revealed, and at the same time, many detailed research materials and practical experience have been 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, and long-term medium and low production, relying on multiple wells and multiple fracturing models is not scientific.

How to easily, quickly and cost-effectively predict the initial maximum stable productivity of a shale gas horizontal well is a key technology that many domestic shale gas exploration and development field workers have been concerned about. At present, in the production field, after large-scale hydraulic fracturing of multiple shale gas horizontal wells in the same block, the single-parameter analogy method of horizontal section length of shale gas horizontal wells is often used to quickly estimate the initial maximum stable productivity of a single well. Although it is simple, fast and low in cost, the average relative error exceeds 30%, the prediction error is large, and the scope of application is small, and there is still much room for improvement and improvement.

The field case study found that the shale gas horizontal wells were subjected to staged fracturing, and the length of the stage was close to the vertical thickness of the shale layer, that is, "equal thickness stage". The ratio is not less than 4%, the shale gas layer is sufficiently fractured and the seepage characteristics of the shale gas layer can be fully enhanced, the shale gas horizontal well can obtain good industrial gas flow, and the horizontal sections I and II of the shale gas horizontal well The porosity POR and length L of the gas layer (high production, medium production) are the main controlling factors affecting the productivity of gas wells.

SUMMARY OF THE INVENTION

The purpose of the present invention is to provide a method for predicting the initial maximum productivity of a single well of a shale gas horizontal well, which is simple, fast, efficient, low-cost, and has certain universality.

The realization method of the object of the present invention is, the prediction method of the initial maximum productivity of shale gas horizontal wells based on the exponential model, and the specific steps are:

1) Obtain the well-tested and to-be-predicted well data in the work area

(1) The well-tested data includes the well position report, the logging geological completion summary report, the logging interpretation report, the logging interpretation data table and the completion and gas test report for the completed shale gas well testing task; the details include The porosity POR n-1 of each type I and II shale gas interval in the horizontal section of the well-tested well, and the length of the well-tested gas interval L n-1, are the highest in the initial stage of the tested well corresponding to the well-tested well Stable production capacity Qgmax m-1,

Among them, m-1 represents the serial number of the well tested well, and n-1 represents the serial number of the shale gas interval of type I and II in well m-1;

The unit of measurement of POR n-1 is %, the unit of measurement of length Ln-1 is 100m, and the unit of measurement of Qgmaxm-1 is 10 ⁴ m ³ /d at the initial stage of a single well;

(2) The well data to be predicted includes the well position report of the shale gas well to be predicted, the logging geological well completion summary report, the logging interpretation report, the logging interpretation result data table and the well completion and gas test report; it specifically includes the pending prediction report. The porosity POR n of each I and II shale gas interval in the horizontal section of the well, and the gas interval length L n of the predicted well,

where n represents the sequence number of the I and II shale gas intervals of the well to be predicted;

The measurement unit of POR n is %, and the measurement unit of length Ln is 100m;

2) Verify and optimize the model using the well-tested data

The initial maximum stable production Qgmax m-1 of each well tested shale gas obtained in step 1)(1) and the porosity POR n- 1. The sum of the data of the gas interval length L n-1 of the well-tested well ∑(POR n-1 · L n-1), according to the least squares regression, establish an exponential model, the regression curve of the model passes through the origin, and the regression analysis is related to The coefficient is R;

The exponential model formula Qgmax=Ce ^B(POR·L) , C and B are exponential model coefficients, when there is n-1 shale gas interval in the well tested (POR·L)=∑(POR n-1· L n-1), the Qgmax m-1 data in step 1) (1) is brought into the exponential model formula to obtain the exponential model coefficients C and B;

The regression analysis correlation coefficient R ² is greater than 0.7, and the exponential model Qgmax=Ce ^B(POR·L) is considered suitable;

3) Obtain the ∑(POR n·L n) of the shale gas interval of type I and II in the horizontal section of the well to be predicted;

4) Using the calculation model Qgmax=Ce ^B∑(PORn · ^Ln ) in step 2 ⁾ , using (PORn·Ln) in step 3) and the model coefficients B and C obtained in step 2), calculate The maximum stable productivity Qgmax m of the shale gas horizontal well to be predicted;

The initial maximum stable production capacity Qgmax refers to the highest stable production capacity of a single well within 3 months after the completion of the shale gas well test task; the data comes from the completion gas test report; the shale gas layers I and II in the horizontal section of the well to be predicted The porosity POR n of the interval and the gas interval length L n of the predicted well come from the data table of logging interpretation results;

5) Output the prediction result.

The invention solves the problem that the single-parameter analogy method of the horizontal section length of the shale gas horizontal well is used in the field to quickly estimate the initial maximum stable production capacity of the single well and the accuracy is not high, and the scope of application is wider.

The present invention has been applied to 173 wells in the FL shale gas field, and the predicted maximum initial stable production capacity of a single well of shale gas horizontal wells is close to the highest stable production obtained by field actual production, with an average error of 18% and no more than 20%, which is in line with the field Rapidly predicting the maximum and stable production requirements of shale gas layers in the horizontal section of a single well is conducive to the efficient development of gas fields and improves the single-well productivity prediction level of domestic shale gas horizontal wells.

Description of drawings

Fig. 1 is the work flow block diagram of the present invention;

Fig. 2 is a chart showing the productivity prediction chart of the shale gas horizontal well in the J working area of the present invention.

Detailed ways

Referring to Fig. 1, the present invention is to obtain the porosity and length of each type I and II shale gas interval in the well-tested horizontal section of the work area and the corresponding initial maximum stable productivity Qgmax m-1 of the well-tested single well. , the porosity and length of each type I and II shale gas interval in the horizontal section of the well to be predicted, the initial maximum stable production Qgmax m-1 of the tested shale gas wells that have been tested and the corresponding wells The sum of the data of porosity PORn-1 and length Ln-1 of each type I and II shale gas interval in the horizontal section of the The regression curve of the model passes through the origin; the model coefficients B and C are obtained; the highest stable productivity Qgmax of the horizontal shale gas well to be predicted is calculated by using the calculation model Qgmax=Ce ^{B∑(POR n·L n)} and the model coefficients B and C m; output the prediction result.

Type I and II shale gas intervals are shale gas intervals with high production and commercial development value.

The present invention will be described in detail below with specific examples.

Example 1: Well R6-bHF in J work area of a shale gas field

1) Obtain the well-tested and to-be-predicted well data in the work area

(1) Obtain each type I and II shale gas in the horizontal section of the 12 tested wells in the J work area according to the logging geological completion summary report, logging interpretation report, logging interpretation data table, and completion gas test report. The interval porosity PORn-1, length Ln-1 and the initial maximum stable productivity of a single well tested Qgmax m-1, one of the 12 tested wells is an engineering accident well and does not participate in the establishment of the model, so , The number of wells tested are 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, W12-3HF has a total of 11 wells, and the value of m ranges from 1 to 11;

(2) Obtain the level of the well to be predicted according to the well position report of the to-be-predicted R6-bHF shale gas well, the logging geological completion summary report, the logging interpretation report, the logging interpretation result data table, and the completion gas test report, etc. Porosity POR n and length L n of each type I and II shale gas interval;

2) Verify and optimize the model using the well-tested data

(1) Calculate the initial maximum stable production Qgmax m-1 of shale gas obtained from the above 11 well-tested wells and the porosity POR n-1, length of the horizontal sections of the corresponding wells of each type I and II shale gas intervals The sum of L n-1 data ∑ (POR n-1 · L n-1) (Table 1), according to the least squares method, establish a linear regression model (see Figure 2), the regression curve of the model passes through the origin.

Table 1 shows the tested values of ∑(POR n-1·L n-1) and Qgmaxm-1 in the horizontal sections of each shale gas horizontal well I and II shale gas intervals.

Table 1

Hashtag ∑(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 the exponential model as Qgmax m-1=Ce ^{B∑(POR n-1·L n-1)} , it has been tested that each shale gas horizontal well has n-1 shale gas interval (POR·L )=∑(POR n-1·L n-1), bring the data in Table 1 into the exponential model Qgmax m-1=Ce ^{B∑(POR n-1·L n-1)} and regress to obtain the exponential model The coefficient B is 0.023 and C is 5.866;

(3) The correlation coefficient R ² of regression analysis is 0.93, which is greater than 0.7, and the exponential model Qgmax=Ce ^{B∑(POR n·L n)} is considered suitable.

3) The ∑(POR n·L n) of the horizontal section I and II shale gas intervals of the horizontal section of the R6-bHF shale gas horizontal well to be predicted is 75.327hm·%.

Table 2. POR and L values of each type I and II shale gas interval in the horizontal section of the R6-bHF shale gas horizontal well

4) Using the calculated exponential model Qgmax=Ce ^B∑( PORn ^{· Ln} ) in step 2), using ∑(PORn·Ln) in step 3) and the model coefficients B and C obtained in step 2), It is calculated that the maximum stable productivity Qgmax m of the shale gas horizontal well to be predicted is 33.17×10 ⁴ m ³ /d.

5) Output the prediction results. The predicted maximum stable production Qgmax m of the predicted R6-bHF shale gas horizontal well is 33.17×10 ⁴ m ³ /d, and the actual maximum stable production of shale gas development in this well is 36.3×10 ⁴ m ³ /d, the error is 8.6%, which is less than 15.0%, which is in line with the demand for the highest stable production capacity prediction on site.

Example 2: Well R90-bHF in J work area of a shale gas field

1) Obtain the well-tested and to-be-predicted well data in the work area

(1) Obtain each type I and II shale gas in the horizontal section of the 12 tested wells in the J work area according to the logging geological completion summary report, logging interpretation report, logging interpretation data table, and completion gas test report. Interval porosity PORn-1, length Ln-1 and the initial maximum stable productivity ^Qgmax m- ¹ of a single well tested It is a natural number such as 1, 2, 3, etc., m represents the well serial number, n represents the serial number of the class I and II shale gas intervals in the m-th well, and one of the 12 well-tested wells is an engineering accident well and does not participate in the establishment of The model, therefore, has tested wells W1H, W1-2H, W1-3HF, W2H, W4H, W7-2HF, W8-2HF, W9-2HF, W10-2HF, W11- There are 11 wells in 2HF well and W12-3HF, and the value of m ranges from 1 to 11;

(2) Obtain the level of the well to be predicted according to the well position report of the to-be-predicted R90-bHF shale gas well, the logging geological completion summary report, the logging interpretation report, the logging interpretation result data table, and the completion gas test report, etc. The porosity POR n and length L n of each class I and II shale gas interval, the unit of measurement is %, hm (100m), n is a natural number such as 1, 2, 3, etc., representing the nth shale gas layer;

(3) The initial maximum stable production Qgmax refers to the highest stable production of a single well within 3 months after the completion of the shale gas well test task. The data comes from the completion gas test report; The data of degree POR and length L come from the data table of logging interpretation results;

2) Verify and optimize the model using the well-tested data

(1) Calculate the initial maximum stable production Qgmax m-1 of shale gas obtained from the above 11 well-tested wells and the porosity POR n-1, length of the horizontal sections of the corresponding wells of each type I and II shale gas intervals The sum of L n-1 data ∑(POR n-1 · L n-1) (Table 1), according to the least squares method, establish a linear regression model (Figure 2), the regression curve of the model passes through the origin;

(2) The exponential model is Qgmax=Ce ^{B∑(POR n·L n)} , where B and C are exponential model coefficients, when there is n-1 shale gas interval (POR·L)=∑(POR n -1·L n-1), bring the data in (1) into the model to obtain the model coefficient B of 0.023 and C of 5.866;

(3) The correlation coefficient R ² of regression analysis is 0.93, which is greater than 0.7, and this index model is considered suitable;

3) The ∑(POR n·L n) of the horizontal section of the horizontal section of the R90-bHF shale gas well to be predicted is 50.13hm·% (Table 3);

Table 3 POR and L values of each shale gas interval of type I and II in the horizontal section of R90-bHF shale gas horizontal well

4) Using the exponential model Qgmax=Ce ^{B∑(PORn · Ln} ) calculated in step 2), using ∑(PORn·Ln) in step 3) and the model coefficients B and C obtained in step 2), The maximum stable productivity Qgmax m of the shale gas horizontal well to be predicted is calculated to be 18.58×10 ⁴ m ³ /d.

5) Output the prediction results. The calculated maximum stable production Qgmax m of the horizontal well R90-bHF shale gas well to be predicted is 18.58×10 ⁴ m ³ /d, and the actual maximum stable production of shale gas development in this well is 20.65×10 ⁴ m ³ /d, the error is 10%, less than 15.0%, which meets the needs of the highest stable production capacity prediction on site.

Claims (2)

1. The method for predicting the initial maximum productivity of a shale gas horizontal well based on an exponential model is characterized in that: the specific steps are: 1) Obtain the well-tested and to-be-predicted well data in the work area (1) The well-tested data includes the well position report, the logging geological completion summary report, the logging interpretation report, the logging interpretation data table and the completion and gas test report for the completed shale gas well testing task; the details include The porosity PORn-1 of each type I and II shale gas interval in the horizontal section of the well-tested well, the length of the well-tested gas interval Ln-1, and the well-tested single well corresponding to the well-tested well has the highest stability in the initial stage Capacity Qgmax m-1, Among them, m-1 represents the serial number of the well tested well, and n-1 represents the serial number of the shale gas interval of type I and II in well m-1; The unit of measurement for PORn-1 is %, the unit of measurement for length Ln-1 is 100m, and the unit of measurement for Qgmax m-1 is 10 ⁴ m ³ /d at the initial stage of a single well; (2) The data of the wells to be predicted include the well position report of the shale gas wells to be predicted, the logging geological well completion summary report, the logging interpretation report, the logging interpretation result data table and the well completion and gas test report; The porosity PORn of each I and II shale gas interval in the horizontal section of the well, and the gas interval length Ln of the predicted well, where n represents the sequence number of the I and II shale gas intervals of the well to be predicted; The measurement unit of PORn is %, and the measurement unit of length Ln is 100m; 2) Verify and optimize the model using the well-tested data The initial maximum stable production Qgmax m-1 of each well-tested shale gas obtained in step 1)(1) and the porosity PORn-1 of each type I and II shale gas interval in the horizontal section of the well-tested well , the sum of the data of the gas interval length L n-1 of the well-tested well ∑(PORn-1·L n-1), according to the least square regression, an exponential model is established, the regression curve of the model passes through the origin, and the regression analysis correlation coefficient is R; The exponential model formula Qgmax=Ce ^B(POR·L) , C and B are exponential model coefficients, when there is n-1 shale gas interval in the well tested (POR·L)=∑(PORn-1·L n-1), the Qgmax m-1 data in step 1) (1) is brought into the exponential model formula to obtain the exponential model coefficients C and B; The regression analysis correlation coefficient R ² is greater than 0.7, and the exponential model Qgmax=Ce ^B(POR·L) is considered suitable; 3) Obtain the ∑(PORn·L n) of the shale gas interval of type I and II in the horizontal section of the well to be predicted; 4) Using the calculation model Qgmax=Ce ^{B∑(PORn·Ln)} in step 2), using (PORn·Ln) in step 3) and the model coefficients B and C obtained in step 2), calculate the to-be-predicted The highest stable productivity Qgmax m of shale gas horizontal wells; The initial maximum stable production capacity Qgmax refers to the highest stable production capacity of a single well within 3 months after the completion of the shale gas well test task; the data comes from the completion gas test report; the shale gas layers I and II in the horizontal section of the well to be predicted The porosity PORn of the interval and the gas interval length Ln of the predicted well come from the data table of logging interpretation results; 5) Output the prediction result. 2. The method for predicting the initial maximum productivity of a shale gas horizontal well based on an exponential model according to claim 1, wherein the shale gas intervals of class I and II are shale with high production and commercial development value. gas layer.

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