Horizontal well productivity prediction method based on logging reservoir classification and box drilling rate

Abstract

The invention relates to a horizontal well productivity prediction method based on logging reservoir classification and a box drilling rate. The problem that an existing shale gas horizontal well productivity prediction method is low in precision is mainly solved. The method is characterized by comprising the following steps: calculating the daily gas production rate of a plurality of tested single wells in each hundred-meter fractured well section and the drilling rate of a single well box body of a tested gas horizontal well; classifying the shale gas reservoir of the pilot well of the tested gas well, and counting the maximum continuous thickness of the class I shale gas reservoir of the pilot well; establishing a binary linear regression model about the daily gas production rate of each hundred-meter fractured well section; calculating the daily gas production rate of each hundred-meter fractured well section of the single well to be predicted; and calculating the single-well daily gas production of the to-be-predicted well. According to the method, the productivity of the single well of the shale gas horizontal well is predicted by combining the maximum continuous thickness of the pilot well class I shale gas reservoir comprehensively classified by the logging reservoir and the drilling rate of the single well box of the horizontal well, so that the productivity prediction accuracy of the shale gas horizontal well is effectively improved, and a reference basis is provided for formulating a shale gas reservoir development scheme.

Description

Technical Field

[0001] The invention relates to the technical field of oil and gas well logging evaluation, in particular to a method for predicting the productivity of a horizontal well based on well logging reservoir classification and box drilling rate. Background Art

[0002] The evaluation and prediction of shale gas reservoir productivity is an important part of the preparation of oilfield development plans. Many scholars have explored this field, which can be summarized into two categories: one is to establish the statistical relationship between various logging parameters and reservoir productivity based on a large amount of oil test and production data; the other is to use machine learning methods such as neural networks, decision trees, and support vector machines to establish a productivity evaluation model using known samples (multiple logging parameters and corresponding productivity data), and then use the model to predict unknown samples. For example, there is currently a prediction method for the initial maximum productivity of shale gas horizontal wells based on linear models. Specifically, the initial maximum stable production of each shale gas well that has been tested is obtained, and the sum of the porosity of each Class I and II shale gas layer section of the horizontal section of the tested well and the length of the gas layer section of the tested well is used to establish a linear regression model based on the least squares method. In addition, the daily production of shale gas in a single well is calculated based on the brittle mineral content, formation porosity, formation permeability, and formation gas saturation. Currently, these methods only consider the influence of a certain parameter index. Although they have certain applicability, their accuracy is not high and the effect is not ideal. Summary of the invention

[0003] In order to overcome the shortcoming of low accuracy of existing shale gas horizontal well productivity prediction methods, the present invention provides a horizontal well productivity prediction method based on well logging reservoir classification and box drilling rate. This method predicts the productivity of shale gas horizontal wells by combining the maximum continuous thickness of Class I shale gas reservoirs in the pilot well of the comprehensive classification of well logging reservoirs and the box drilling rate of single horizontal well box, which effectively improves the accuracy of shale gas horizontal well productivity prediction and provides a reference for the formulation of shale gas reservoir development plans.

[0004] The technical solution of the present invention is: a method for predicting the productivity of a horizontal well based on well logging reservoir classification and box drilling rate, comprising:

[0005] S1. Calculate the daily gas production per 100-meter fracturing section QCI of several tested gas wells and the box drilling rate ZI0 of several tested gas horizontal wells;

[0006] S2. Classify the shale gas reservoirs in the pilot wells of the tested gas wells, and count the maximum continuous thickness HI0 of the Class I shale gas reservoirs in the pilot wells;

[0007] S3. Based on the results of steps S1 and S2, a binary linear regression model is established using the least squares method.

[0008] QCI=A+B1*ZI0+B2*HI0

[0009] Where: A, B1, B2 are constants;

[0010] S4, according to the model in step S3, calculate the daily gas production QCIY of the single well per 100 meters of the fracturing section to be predicted,

[0011] QCIY=A+B1*ZI+B2*HI;

[0012] Where: ZI is the case drilling rate of the single well to be predicted, HI is the maximum continuous thickness of the Class I shale gas reservoir in the pilot well of the single well to be predicted, in meters;

[0013] S5. Calculate the daily gas production QCY of the well to be predicted.

[0014] QCY=(A+B1*ZI+B2*HI)*YL / 100

[0015] Where: YL is the designed fracturing section of the well to be predicted, in meters.

[0016] Furthermore, in step S1, the daily gas production QCI of the tested gas well per 100 meters of the fracturing section is calculated according to the daily gas production QC of the tested gas well and the length YL of the fracturing section of the tested gas well.

[0017] QCI=(QC / YL0)*100

[0018] In the formula: QC unit is ten thousand cubic meters per day; YL0 unit is meter; QCI unit is ten thousand cubic meters per day.

[0019] Furthermore, in step S1, the case drilling rate ZI0 of the tested gas well is calculated according to the case drilling section length ZL0 of the tested gas well and the actual horizontal section length SL0 of the tested gas well.

[0020] ZI0=(ZL0 / SL0)*100

[0021] Where: ZL, SL are in meters.

[0022] Furthermore, in step S2, the total organic carbon content TOC, the total gas content QALL, the porosity POR and the brittle mineral content BRMC4 are first obtained, and the shale gas reservoir of the pilot well is classified according to the reservoir classification evaluation standard.

[0023] Further, in step S5, due to

[0024] QCY=QCIY*YL / 100

[0025] The daily gas production of a single well QCY is to be predicted.

[0026] QCY=(A+B1*ZI+B2*HI)*YL / 100

[0027] Where: YL is the designed fracturing section length of the single well to be predicted, in meters.

[0028] Furthermore, in order to verify the accuracy of the single well daily gas production QCY formula for the predicted well, the single well daily gas production QCI calculated according to the formula is compared with the actual daily gas production QC, and the comparison difference meets the preset threshold requirement.

[0029] Furthermore, the tested gas well and the predicted well are horizontal wells in the same formation.

[0030] The present invention has the following beneficial effects: Due to the adoption of the above scheme, the present application classifies the pilot well reservoir based on the analysis of the factors affecting the productivity of shale gas horizontal wells, applies the total organic carbon content, total gas content, porosity, and brittle mineral content in the well logging processing results data, and statistically calculates the maximum continuous thickness of the pilot well Class I shale gas reservoir. Combined with the known horizontal well drilling rate and gas test data, a binary linear equation is used to construct a shale gas horizontal well productivity prediction formula, which solves the problem of low accuracy in shale gas horizontal well productivity prediction. The correlation between the predicted productivity and actual productivity of a single shale gas horizontal well using this method reached an average of 97.2%. This is the first time in China that the maximum continuous thickness of the pilot well Class I shale gas reservoir and the horizontal well box drilling rate are comprehensively considered to predict the productivity of a single shale gas horizontal well, and the accuracy rate is at the leading level among similar technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 is a flow chart of the present invention;

[0032] Figure 2 It is a cross-plot of the predicted daily gas production per 100-meter fractured section QCIY of the 7 wells in the embodiment and the actual daily gas production per 100-meter fractured section QCI of the single well;

[0033] Figure 3 It is a cross-plot of the predicted daily gas production QCY of the 7 wells in the embodiment and the actual daily gas production data QC of a single well. DETAILED DESCRIPTION

[0034] The present invention will be further described below in conjunction with the accompanying drawings:

[0035] Depend on Figure 1 As shown, a method for predicting the productivity of a horizontal well based on well logging reservoir classification and box drilling rate includes:

[0036] S1. Obtain the daily gas production data QC of each tested gas well and the length of its fracturing section YL0 from the known database, and calculate the daily gas production QCI of each tested gas well per 100 meters of fracturing section, which is expressed as:

[0037] QCI=(QC / YL0)*100 (1)

[0038] In the formula: QC unit is ten thousand cubic meters per day; YL0 unit is meter; QCI unit is ten thousand cubic meters per day.

[0039] Furthermore, the length of the box drilled section ZL0 of each tested gas well and its actual horizontal section length data SL0 are obtained from the known database, and the box drilled rate ZI0 of the tested gas well is calculated, and its expression is:

[0040] ZI0=(ZL0 / SL0)*100 (2)

[0041] Where: ZL, SL are in meters.

[0042] S2. Obtain the total organic carbon content TOC, total gas content QALL, porosity POR and brittle mineral content BRMC4 from the logging interpretation processing result file. The units of TOC, QALL, POR and BRMC4 are %, m3 / t, %, and % respectively. Classify the shale gas reservoirs of the pilot well according to the reservoir classification evaluation standard, and calculate the maximum continuous thickness HI0 of the Class I shale gas reservoirs of the pilot well, and the unit of HI0 is meter.

[0043] S3. Based on the results of steps S1 and S2, a binary linear regression model is established according to the least squares method, and the regression curve of the model passes through the origin. The linear model is:

[0044] QCI=A+B1*ZI0+B2*HI0 (3)

[0045] Where: QCI is the daily gas production of the tested gas well per 100 meters of the fracturing section, in units of ten thousand cubic meters per day; ZI0 is the box drilling rate of the tested gas well, in units of %; HI0 is the maximum continuous thickness of the Class I shale gas reservoir in the pilot well of the tested gas well, in meters.

[0046] S4, according to the model in step S3, the daily gas production QCIY of the single well per 100 meters of the fracturing section is calculated, and the expression is:

[0047] QCIY=A+B1*ZI+B2*HI (4)

[0048] Where: QCIY is the daily gas production of the fractured well section per 100 meters of the single well to be predicted, in units of ten thousand cubic meters per day; ZI is the box drilling rate of the single well to be predicted, in units of %; HI is the maximum continuous thickness of the Class I shale gas reservoir in the pilot well of the single well to be predicted, in units of meters.

[0049] S5. Calculate the daily gas production QCY of the single well to be predicted based on the daily gas production QCIY of the single well to be predicted per 100 meters of the fracturing section and its designed fracturing section YL. The expression is:

[0050] QCY=QCIY*YL / 100 (5)

[0051] Wherein: QCY is in ten thousand cubic meters per day, QCIY is in ten thousand cubic meters per day, and YL is the length of the designed fracturing section of the well to be predicted, in meters.

[0052] The formula for daily gas production of a single well to be predicted is further obtained:

[0053] QCY=(A+B1*ZI+B2*HI)*YL / 100 (6)

[0054] Where: QCY is the daily gas production of the well to be predicted, in units of ten thousand cubic meters per day; ZI is the drilling rate of the single well box of the horizontal well to be predicted, in units of %.

[0055] It can be seen from this that the productivity of shale gas horizontal wells is related to reservoir classification and box drilling rate. The greater the maximum continuous thickness of Class I shale gas reservoirs in shale gas pilot wells, the higher the horizontal well box drilling rate, and the higher the single well productivity of shale gas horizontal wells.

[0056] To verify the accuracy of formula (6), the predicted daily gas production QCY of a single well calculated according to the formula is compared with the actual daily gas production QC of the well. The difference meets the preset threshold requirement, indicating that the formula is accurate. The preset threshold can be determined according to the reservoir conditions and well conditions.

[0057] The above-mentioned tested gas wells and the wells to be predicted are required to be horizontal wells in the same formation.

[0058] Example:

[0059] Taking the prediction of Well Z301 and Well Z305 in a block of the Zigong low-fold structural belt in the southern Sichuan low-steep structural belt of the Sichuan Basin as an example, the following steps are included:

[0060] Step S1, obtain the single well daily gas production data QC and the length of the fracturing well section YL0 of the 7 tested horizontal wells from the known data, and calculate the daily gas production QCI of the single well per 100 meters of the fracturing well section according to formula (1); according to the length of the casing drilling section ZL0 of the 8 wells and their actual horizontal section length data SL0; according to formula (2), calculate the casing drilling rate ZI0 of the horizontal wells, see Table 2 for details.

[0061] Step S2: Obtain the total organic carbon content TOC, total gas content QALL, porosity POR and brittle mineral content BRMC4 from the pilot well logging interpretation processing result file provided by the logging company. The pilot well shale gas reservoir is classified according to the reservoir classification evaluation standard. The classification standard is shown in Table 1.

[0062] Table 1

[0063]

[0064] The maximum continuous thickness HI0 of Class I shale gas reservoirs in the pilot wells of these tested gas wells was counted.

[0065] Table 2

[0066]

[0067]

[0068] Step S3: Based on the case encounter rate ZI of each well calculated in steps S1 and S2 and the maximum continuous thickness HI0 of the Class I shale gas reservoir in the pilot well, a binary linear regression model is established according to the least squares method to obtain the daily gas production QCIY of the single well per 100 meters of the fracturing section.

[0069] QCIY=-0.093+0.015*ZI+0.059*HI (7)

[0070] Then the daily gas production formula of a single well to be predicted is obtained:

[0071] QCY=(-0.093+0.015 *ZI+0.059*HI)*YL / 100 (8)

[0072] Figure 2 The cross plot of the predicted daily gas production per 100m of fractured section QCIY and the actual daily gas production per 100m of fractured section QCI of the 7 wells in Table 2 is shown in the regression analysis correlation coefficient R 2 Greater than 0.9.

[0073] According to formula (7) and formula (8), the daily gas production QCIY per 100 meters of the fractured well section and the daily gas production QCY of a single well are calculated. The predicted values ​​are shown in Table 3.

[0074] Table 3

[0075]

[0076] The predicted daily gas production QCY of each well is compared with the daily gas production QC of the test gas in Table 2. After verification, the error meets the requirements, proving that formula (8) is accurate. Figure 3 It is the intersection diagram of the predicted daily gas production QCY of the seven wells in Table 2 and the actual daily gas production QC of the seven wells, with a correlation of 97.2%.

[0077] Step S4: predict the daily gas production QCY of well Z305 and well Z301 respectively according to the formula obtained in step S3, where the daily gas production QCY of well Z305 is:

[0078] S4.11. The horizontal well casing drilling section length ZL of Well Z305 is obtained to be 1374.4 meters, and the actual horizontal section length data SL is 1730 meters. The horizontal well casing drilling rate ZI of Well Z305 is calculated to be 79.45 according to formula (2).

[0079] S4.12, the maximum continuous thickness HI of the Class I shale gas reservoir in the pilot well of Well Z305 is 11.4 meters;

[0080] S4.13. Calculate the daily gas production QCIY per 100m of the fractured section of Well Z305 according to formula (7):

[0081] QCIY=-0.093+0.015*ZI+0.059*HI

[0082] =-0.093+0.015*79.45+0.059*11.4=1.77135

[0083] S4.14. The designed fracturing section length YL of Well Z305 is obtained as 1670 meters. The daily gas production QCY of Well Z305 is calculated according to formula (8).

[0084] QCY=(-0.093+0.015*ZI+0.059*HI)*YL / 100

[0085] =(-0.093+0.015*79.45+0.059*11.4)*1670 / 100=29.58,

[0086] Therefore, the predicted daily gas production of Well Z305 is 295,800 cubic meters per day.

[0087] Well Z301 is:

[0088] S4.21, the horizontal well casing drilling section length ZL of Well Z301 is obtained to be 1635.84 meters, the actual horizontal section length data SL is 1800 meters, and the horizontal well casing drilling rate ZI of Well Z301 is calculated to be 90.88 according to formula (2);

[0089] S4.22, the maximum continuous thickness HI of the Class I shale gas reservoir in the pilot well of Well Z301 is 9 meters;

[0090] S4.23. According to formula (7), the daily gas production QCIY of the Z301 well per 100 meters of fractured well section is calculated to be 18,000 cubic meters per day;

[0091] S4.24. The designed fracturing section length YL of Well Z301 is obtained as 1800 m. According to formula (8), the daily gas production QCY of Well Z301 is calculated as 324,400 cubic meters per day.

[0092] In actual production, the actual daily gas production of Z301 well is 310,000 cubic meters / day, and the predicted daily gas production of a single well is 324,400 cubic meters / day. The absolute error between the two is 14,400 cubic meters / day, and the relative error is 4.44%, which meets the requirements.

[0093] The present invention uses logging reservoir classification technology to count the maximum continuous thickness of Class I shale gas reservoirs in shale gas pilot wells, and combines the horizontal well box drilling rate to predict the production capacity of shale gas horizontal wells. Compared with the existing technology, the accuracy of shale gas horizontal well production capacity prediction is effectively improved, and a reference basis is provided for the formulation of shale gas reservoir development plans.

[0094] Those skilled in the art will appreciate that, in the above method of specific implementation, the order in which the steps are written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of the steps should be determined by their functions and possible internal logic.

[0095] The above-mentioned embodiments are only embodiments of the present disclosure, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present disclosure. It should be pointed out that, for ordinary technicians in this field, several variations, equivalent substitutions, improvements, etc. can be made without departing from the concept of the present disclosure, and these all belong to the protection scope of the present disclosure.

Claims

1. A method for predicting horizontal well productivity based on well logging reservoir classification and box drilling rate, characterized in that include: S1. Calculate the daily gas production per 100-meter fracturing section QCI of several tested gas wells and the box drilling rate ZI0 of several tested gas horizontal wells; S2. Classify the shale gas reservoirs of the pilot wells of the tested gas wells, and count the maximum continuous thickness HI0 of the Class I shale gas reservoirs of the pilot wells; S3. Based on the results of steps S1 and S2, a binary linear regression model is established using the least squares method. QCI=A+B1*ZI0+B2*HI0 Where: A, B1, B2 are constants; S4, according to the model in step S3, calculate the daily gas production QCIY of the single well per 100 meters of the fracturing section to be predicted, QCIY=A+B1*ZI+B2*HI; Where: ZI is the case drilling rate of the single well to be predicted, HI is the maximum continuous thickness of the Class I shale gas reservoir in the pilot well of the single well to be predicted, in meters; S5. Calculate the daily gas production QCY of the well to be predicted. QCY=(A+B1*ZI+B2*HI)*YL / 100 Where: YL is the designed fracturing section of the well to be predicted, in meters.

2. The method for predicting horizontal well productivity based on well logging reservoir classification and box drilling rate according to claim 1 is characterized by: In step S1, the daily gas production QCI of the tested gas well per 100 meters of the fracturing section is calculated according to the daily gas production QC of the tested gas well and the length YL0 of the fracturing section of the tested gas well. QCI=(QC / YL0)*100 In the formula: QC unit is ten thousand cubic meters per day; YL0 unit is meter; QCI unit is ten thousand cubic meters per day.

3. The method for predicting horizontal well productivity based on well logging reservoir classification and box drilling rate according to claim 2 is characterized in that: In step S1, the case drilling rate ZI0 of the tested gas well is calculated based on the case drilling section length ZL0 of the tested gas well and the actual horizontal section length SL0 of the tested gas well. ZI0=(ZL0 / SL0)*100 Where: ZL, SL are in meters.

4. The method for predicting horizontal well productivity based on well logging reservoir classification and box drilling rate according to claim 1, characterized in that: In step S2, firstly, the total organic carbon content TOC, the total gas content QALL, the porosity POR and the brittle mineral content BRMC4 are obtained, and the shale gas reservoir of the pilot well is classified according to the reservoir classification evaluation standard.

5. The method for predicting horizontal well productivity based on well logging reservoir classification and box drilling rate according to claim 1, characterized in that: In step S5, due to QCY=QCIY*YL / 100 The daily gas production of a single well QCY is to be predicted. QCY=(A+B1*ZI+B2*HI)*YL / 100 Where: YL is the designed fracturing section length of the single well to be predicted, in meters.

6. The method for predicting horizontal well productivity based on well logging reservoir classification and box drilling rate according to claim 1, characterized in that: In order to verify the accuracy of the single well daily gas production QCY formula for the predicted well, the single well daily gas production QCY calculated according to the formula is compared with the actual daily gas production QC, and the comparison difference meets the preset threshold requirement.

7. The method for predicting horizontal well productivity based on well logging reservoir classification and box drilling rate according to any one of claims 1 to 6, characterized in that: The tested gas well and the predicted well are horizontal wells in the same formation.

Images