Buried hill formation pressure calculation method and system based on logging binary data coupling

Abstract

The invention relates to a buried hill formation pressure calculation method based on logging binary data coupling. The buried hill formation pressure calculation method mainly comprises the following steps that S1, logging parameters and logging parameters are collected; s2, simulating actual formation pressure based on a Dc index, drilling efficiency-mechanical specific energy monitoring, gas logging auxiliary judgment, actually measured pressure measurement data and the like; s3, drawing a cross plot between the logging data and the formation pressure value by using a unary linear regression method to obtain a unary fitting equation between the formation pressure value and the logging parameters; s4, drawing a cross plot between the logging-logging binary data and the formation pressure value by using a binary linear regression method to obtain a binary fitting equation between the formation pressure value and the logging-logging parameters; and S5, integrating the binary fitting equation between the formation pressure value and the logging-logging parameters, and establishing a buried hill formation pressure calculation model based on logging-logging binary coupling data.

Description

Technical Field

[0001] The invention belongs to the field of rock mechanics, and particularly relates to a buried hill formation pressure calculation method based on well logging binary data coupling, which establishes an effective calculation model system and can realize the quantitative evaluation of buried hill overpressure. Background Art

[0002] The basement of the Qiongdongnan Basin is mainly composed of granite buried hills of the Indosinian and Yanshanian periods (age: 65Ma). The formation and evolution of the granite buried hills in the Qiongdongnan Basin were affected by multi-period tectonic movements in the Indosinian, Yanshanian and Himalayan periods, which provided good conditions for the formation of weathering crusts and internal fracture reservoirs in the buried hills. The Qiongdongnan buried hills are divided into three buried hill belts: the southern buried hill belt, the central buried hill belt and the northern buried hill belt. In general, from the southern buried hill belt to the northern buried hill belt, the burial depth, burial time and current seawater depth are decreasing, and the location of the buried hill development is rising.

[0003] At present, the research on overpressure in sandstone and mudstone formations has gradually matured at home and abroad. In recent years, the formation pressure monitoring implemented in the Yingqiong Basin has already had preliminary means of formation pressure monitoring based on the causes of pressure, and the pressure monitoring accuracy is high in formations with conventional lithology and common causes of pressure (undercompaction, fluid expansion, pressure conduction, etc.). However, in recent years, bedrock buried hills have gradually become one of the hot areas of exploration and development. The evaluation technology for the special lithology formation pressure of bedrock buried hills in the Qiongdongnan Basin is relatively limited, which seriously restricts the evaluation accuracy of formation pressure.

[0004] Conventional formation pressure evaluation models mainly include equivalent depth method, empirical coefficient method, Eaton method and Bowers method, etc., and the evaluation parameters used are mainly logging parameters such as acoustic time difference, resistivity, density and Dc index. There is no obvious response to traditional logging data such as the wave velocity in the overpressure section of special lithology formations such as carbonate rocks, and conventional formation pressure evaluation methods have poor adaptability in special lithology formations such as carbonate rocks and basement granite. Therefore, the present invention proposes a new formation pressure calculation method based on the coupling of logging binary data, combining logging with logging, solving the problem that conventional formation pressure evaluation models are difficult to apply to special lithology formations of bedrock buried hills, and providing a theoretical basis for the accurate evaluation of formation pressure in the Yingqiong Basin and buried hills. Summary of the invention

[0005] The present invention provides a method for calculating buried hill formation pressure based on logging binary data coupling, which comprises the following steps:

[0006] S1: Collect well logging data and mud logging data;

[0007] S2: Simulate the actual formation pressure based on Dc index, drilling efficiency-mechanical specific energy monitoring, gas measurement auxiliary judgment, measured pressure data, etc.;

[0008] S3: draw an intersection diagram between the well logging data and the formation pressure value, and use a univariate linear regression method to obtain a univariate fitting equation between the formation pressure value and the well logging data;

[0009] S4: draw an intersection diagram between logging data, mud logging data and formation pressure values, and use a binary linear regression method to obtain a binary fitting equation between the formation pressure value and the logging-mud logging parameters;

[0010] S5: A binary fitting equation between the formation pressure value and the logging parameters is integrated to establish a formation pressure calculation model based on the logging-logging binary coupling data.

[0011] Specifically, in the S1, the logging data include the acoustic time difference VP and the density DEN, and the logging data mainly include the Dc index, the SIGA index, the lateral work, the longitudinal work, and the mechanical specific energy MSE.

[0012] Preferably, in the above S2, the actual formation pressure is simulated based on the Dc index, drilling efficiency-mechanical specific energy monitoring, gas measurement auxiliary judgment, measured pressure data, etc. The specific steps are as follows:

[0013] 1) For the same well A, select the logging data used to calculate the formation pressure;

[0014] 2) Monitor formation pressure based on Dc index and drilling efficiency-mechanical specific energy, and calculate formation pressure at different depths;

[0015] 3) Assist in determining formation pressure based on gas logging data;

[0016] 4) Obtain the formation pressure at the test point through actual on-site testing;

[0017] 5) The mutual correction between the formation pressure results is evaluated through the Dc index, drilling efficiency-mechanical specific energy monitoring, gas measurement auxiliary judgment, and measured pressure data to obtain the final simulated actual formation pressure.

[0018] In a specific implementation, in the above S3, an intersection diagram between the well logging data and the formation pressure value is drawn, and a univariate linear regression method is used to obtain a univariate fitting equation between the formation pressure value and the well logging data. The specific steps are as follows:

[0019] 1) Based on the logging data collected in step 1 and the formation pressure data calculated in step 2, draw an intersection diagram of the logging data and the formation pressure. For example, Figure 3 and Figure 4 shown.

[0020] 2) Using the univariate linear regression method, the binary fitting equation between the formation pressure value and the logging data is obtained.

[0021] In a specific implementation, in the S4, an intersection diagram between logging parameters, logging data and formation pressure values ​​is drawn, and a binary linear regression method is used to obtain a binary fitting equation between the formation pressure value and the logging parameters. The specific steps are as follows:

[0022] 1) Based on the logging data collected in S1 and the formation pressure data calculated in S2, draw an intersection diagram of the logging data of well A and the formation pressure;

[0023] 2) The binary linear regression method is used to obtain the binary fitting equation between the formation pressure value and the logging parameters.

[0024] In another embodiment, in S5, the buried hill formation pressure calculation model based on well logging-mud logging binary data coupling is:

[0025] P p =m×VP+n×logging parameters (Dc, SIGA, MSE, longitudinal work, transverse work)+O

[0026] P p =j×DEN+k×logging parameters (Dc, SIGA, MSE, longitudinal work, transverse work)+L

[0027] Where: m, n, O, j, k, L are coefficients, which need to be determined by block.

[0028] Specifically, the changing rules of the fitting equation coefficients include size and positive and negative. The closer the coefficient is to 1, the greater the correlation between the logging parameter and the formation pressure. A positive coefficient indicates a positive correlation, and a negative coefficient indicates a negative correlation.

[0029] The present invention provides a system for calculating buried hill formation pressure based on the coupling of logging-mud binary data, which comprises a module for executing steps S1 to S5 in the method described, specifically, a data input module for inputting data from step S1; a data processing module for executing the data calculation in steps S2 to S5; and a result output module for outputting the result obtained by the data processing module.

[0030] Optionally, a result display module is also included.

[0031] Preferably, the input is realized by keyboard or scanner recognition input, and the result display template is a screen, a remote terminal or a mobile terminal.

[0032] Compared with the prior art, the present invention has the following advantages: 1) The present invention discloses a formation pressure calculation method based on the coupling of logging binary data, which makes full use of logging data, integrates the complex geological background of the buried hill reservoir, and establishes an effective buried hill formation pressure calculation model through univariate linear regression and binary linear regression methods, providing a new method and approach for quantitative evaluation of buried hill overpressure. 2) The present invention draws an intersection diagram between logging data and formation pressure values, and comprehensively considers the interaction between the drill bit and the buried hill formation based on logging data analysis. Further, a binary fitting equation between logging parameters is obtained, and a logging data body that can effectively characterize the overpressure of the buried hill formation is constructed. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a flow chart of the method for calculating buried hill formation pressure based on the coupling of binary data from well logging.

[0034] Figure 2 is the logging data collected in step 1.

[0035] Figure 3 This is the intersection fitting diagram of well logging density and formation pressure.

[0036] Figure 4 This is the intersection fitting diagram of the acoustic wave time difference and the formation pressure.

[0037] Figure 5 This is the intersection fitting diagram of Dc index and formation pressure.

[0038] Figure 6 It is the intersection fitting diagram of MSE and formation pressure.

[0039] Figure 7 This is the logging data of Well B.

[0040] Figure 8 This is the dc index-depth semi-logarithmic curve relationship diagram of Well B.

[0041] Fig. 9 Comparison chart of formation pressure obtained by traditional method and new method. DETAILED DESCRIPTION

[0042] In combination with the accompanying drawings and examples, a new method for calculating the buried hill formation pressure based on the coupling of well logging binary data is described below. The calculation method flow chart is as follows: Figure 1 As shown, a more specific description is given, and the specific implementation process is as follows:

[0043] Example 1: Establishment of a formation pressure calculation model system

[0044] Step 1: Select an overpressure well A in the buried hill formation and query the corresponding VP, DEN logging data and Dc index, SIGA index, lateral work, vertical work, and MSE logging parameters. The specific collection is as follows: Figure 2 .

[0045] Step 2: simulate the actual formation pressure based on Dc index, drilling efficiency-mechanical specific energy monitoring, gas measurement auxiliary judgment, measured pressure data, etc. The specific steps are as follows:

[0046] 1) For the same well A, select the logging data used to calculate the formation pressure;

[0047] 2) Monitor formation pressure based on Dc index and drilling efficiency-mechanical specific energy, and calculate formation pressure at different depths;

[0048] 3) Assist in determining formation pressure based on gas logging data;

[0049] 4) Obtain the formation pressure at the test point through actual on-site testing;

[0050] 5) The mutual correction between the formation pressure results is evaluated through the Dc index, drilling efficiency-mechanical specific energy monitoring, gas measurement auxiliary judgment, and measured pressure data to obtain the final simulated actual formation pressure.

[0051] Step 3: draw an intersection diagram between the well logging data and the formation pressure value, and use the univariate linear regression method to obtain a univariate fitting equation between the formation pressure value and the well logging data. The specific steps are as follows:

[0052] 1) Based on the logging data collected in step 1 and the formation pressure data calculated in step 2, draw an intersection diagram of the logging data and the formation pressure, such as Figure 3 and Figure 4 shown.

[0053] 2) Using the univariate linear regression method, the binary fitting equation between the formation pressure value and the logging data is obtained.

[0054] Step 4: Draw an intersection diagram between logging parameters, logging data and formation pressure values, and use a binary linear regression method to obtain a binary fitting equation between the formation pressure value and the logging parameters. The specific steps are as follows:

[0055] 1) Based on the logging data collected in step 1 and the formation pressure data calculated in step 2, draw an intersection diagram of the logging data and formation pressure of well A, such as Figure 5 and Figure 6 shown.

[0056] 2) The binary linear regression method is used to obtain the binary fitting equation between the formation pressure value and the logging parameters, as shown in the following table:

[0057] Table 1. Binary fitting equation between formation pressure value and logging parameters of Well A

[0058]

[0059] Step 5: A binary fitting equation between the formation pressure value and the logging-mud logging parameters is integrated to establish a buried hill formation pressure calculation model based on the logging-mud logging binary coupling data.

[0060] P p =m×VP+n×logging parameters (Dc, SIGA, MSE, longitudinal work, transverse work)+O

[0061] P p =j×DEN+k×logging parameters (Dc, SIGA, MSE, longitudinal work, transverse work)+L

[0062] Embodiment 2: Application example of the formation pressure calculation model system of the present invention

[0063] Taking a buried hill formation in Yingqiong Basin, my country as an example, the application of the present invention in formation pressure calculation in the exploration and development of buried hill reservoirs is systematically explained.

[0064] Step 1: In this embodiment, the system selects the buried hill reservoir B well as an example, collects the corresponding VP, DEN logging data and Dc index, SIGA index, lateral work, vertical work, MSE logging parameters, such as Figure 7 shown.

[0065] Step 2, based on Example 1, a formation pressure calculation model for Well B is established, and a binary fitting equation between the formation pressure value and the logging parameters can be obtained, as shown in the following table:

[0066] Table 2. Binary fitting equation between formation pressure value and logging parameters of Well B

[0067]

[0068] Step 3, based on the traditional formation pressure calculation method, taking the DC index method as an example, calculate the reservoir overpressure size. The specific steps are as follows:

[0069] 1) For the same well B, select the logging data used to calculate the formation pressure;

[0070] 2) Establish the normal trend line equation of the dc index of the well. Draw a semi-logarithmic curve relationship between the dc index and depth of Well B, such as Figure 8 As shown, the normal trend line equation of the well is obtained as follows:

[0071] h=388.314lgd cn +4020.35

[0072] In the formula, h is the depth of the well, m; d is cn —Normal trend line d of depth h c value.

[0073] 3) According to the traditional formation pressure calculation method, the formation pressure at different depths is calculated. The pressure calculation formula is as follows:

[0074]

[0075] Where P P —formation pressure, MPa; Cd is the correction coefficient, which is obtained by inverse calculation from the measured formation pressure; P n —Normal formation pore pressure, MPa.

[0076] Step 4: Draw a comparison chart of the formation pressure calculated based on the traditional method and the pressure calculated based on the new method for calculating the buried hill formation pressure based on the coupling of the binary data of logging and well logging, as shown in Figure 4. Fig. 9 As shown, the overpressure of buried hill reservoir is evaluated.

[0077] This embodiment uses the traditional formation pressure calculation method and the new formation pressure calculation method based on the coupling of logging binary data to calculate the buried hill formation pressure, and compares them with the measured formation pressure. It is found that the formation pressure obtained by the new method has higher prediction accuracy than the traditional formation pressure calculation method, and the relative error is reduced from 6% to 3%, which reveals the rationality and effectiveness of the present invention.

Claims

1. A method for calculating buried hill formation pressure based on the coupling of logging binary data, characterized in that: The following steps are involved: S1: Collect well logging data and mud logging data; S2: Simulate the actual formation pressure based on Dc index, drilling efficiency-mechanical specific energy monitoring, gas measurement auxiliary judgment, measured pressure data, etc.; S3: draw an intersection diagram between the well logging data and the formation pressure value, and use a univariate linear regression method to obtain a univariate fitting equation between the formation pressure value and the well logging data; S4: draw an intersection diagram between logging data, mud logging data and formation pressure values, and use a binary linear regression method to obtain a binary fitting equation between the formation pressure value and the logging-mud logging parameters; S5: A binary fitting equation between the formation pressure value and the logging parameters is integrated to establish a formation pressure calculation model based on the logging-logging binary coupling data.

2. The calculation method according to claim 1, characterized in that: In the S1, the logging data include the acoustic time difference VP and the density DEN, and the logging data mainly include the Dc index, the SIGA index, the lateral work, the longitudinal work, and the mechanical specific energy MSE.

3. The calculation method according to claim 1, characterized in that: In the above S2, the actual formation pressure is simulated based on the Dc index, drilling efficiency-mechanical specific energy monitoring, gas measurement auxiliary judgment, measured pressure data, etc. The specific steps are as follows: 1) For the same well A, select the logging data used to calculate the formation pressure; 2) Monitor formation pressure based on Dc index and drilling efficiency-mechanical specific energy, and calculate formation pressure at different depths; 3) Assist in determining formation pressure based on gas logging data; 4) Obtain the formation pressure at the test point through actual on-site testing; 5) The mutual correction between the formation pressure results is evaluated through the Dc index, drilling efficiency-mechanical specific energy monitoring, gas measurement auxiliary judgment, and measured pressure data to obtain the final simulated actual formation pressure.

4. The calculation method according to claim 1, characterized in that: In the above S3, an intersection diagram between the well logging data and the formation pressure value is drawn, and a univariate linear regression method is used to obtain a univariate fitting equation between the formation pressure value and the well logging data. The specific steps are as follows: 1) based on the well logging data collected in step 1 and the formation pressure data calculated in step 2, draw an intersection diagram of the well logging data and the formation pressure; 2) Using the univariate linear regression method, the binary fitting equation between the formation pressure value and the logging data is obtained.

5. The calculation method according to claim 1, characterized in that: In the above S4, an intersection diagram between logging parameters, logging data and formation pressure values ​​is drawn, and a binary fitting equation between formation pressure values ​​and logging parameters is obtained by using a binary linear regression method. The specific steps are as follows: 1) Based on the logging data collected in S1 and the formation pressure data calculated in S2, draw an intersection diagram of the logging data of well A and the formation pressure; 2) The binary linear regression method is used to obtain the binary fitting equation between the formation pressure value and the logging parameters.

6. The calculation method according to claim 1, characterized in that: In the above S5, the buried hill formation pressure calculation model based on the well logging-mud logging binary data coupling is: P p =m×VP+n×logging parameters (Dc, SIGA, MSE, longitudinal work, transverse work)+O P p =j×DEN+k×logging parameters (Dc, SIGA, MSE, longitudinal work, transverse work)+L Where: m, n, O, j, k, L are coefficients, which need to be determined by block.

7. The calculation method according to claim 3, characterized in that: The changing rules of the fitting equation coefficients include size and positive and negative. The closer the coefficient is to 1, the greater the correlation between the logging parameter and the formation pressure. A positive coefficient indicates a positive correlation, and a negative coefficient indicates a negative correlation.

8. A system for calculating buried hill formation pressure based on the coupling of logging binary data, characterized in that: It includes a module for executing steps S1 to S5 in the method according to any one of claims 1 to 7, and specifically includes a data entry module for entering data from step S1; A data processing module, used to perform data calculations in steps S2 to S5; And a result output module is used to output the results obtained by the data processing module.

9. The system according to claim 9, characterized in that Also includes a result display module.

10. The system according to claim 9, characterized in that The input is realized through a keyboard and a scanner, and the result display template is a screen, a remote terminal, or a mobile terminal.

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