A pressure coefficient prediction method based on comprehensive index and movable fluid index classification

Abstract

The present invention discloses a method for predicting pressure coefficient based on comprehensive index and movable fluid index classification, which specifically relates to the field of oil and gas field exploration and development. The present invention obtains the three porosities of each sample well in the study area, calculates the ratio of the three porosities of each formation in each sample well, constructs a comprehensive index calculation model and a movable fluid index calculation model to calculate the comprehensive index and the movable fluid index of each sample well, constructs a formation pressure type discriminant formula based on the comprehensive index and the movable fluid index of each formation in the sample well to discriminate the pressure type of the formation to be predicted, and then constructs a pressure coefficient prediction model applicable to each pressure type, and uses the pressure coefficient prediction model to accurately predict the pressure coefficient of the formation to be predicted. The present invention solves the problem that it is difficult to predict the pressure coefficient of non-continuous sedimentary formations, comprehensively considers the influence of formation burial depth, three porosities, resistivity and pore fluid on the formation pressure coefficient, and provides a new method for accurately predicting formation pressure.

Description

Technical Field

[0001] The present invention relates to the field of oil and gas field exploration and development, and particularly to a method for predicting pressure coefficient based on comprehensive index and movable fluid index classification. Background Art

[0002] As the main factor affecting the drilling engineering operation, the accurate acquisition of formation pore pressure is directly related to the safety of the drilling process. If the accurate detection of formation pore pressure cannot be guaranteed, problems such as lost circulation, blowout, and stuck pipe will occur during the drilling process. Accurately predicting the magnitude of formation pore pressure has become an urgent technical problem to be solved in current oil exploration and production.

[0003] As the most commonly used method for calculating formation pore pressure, the Eaton method is a method for calculating pressure coefficient based on the formation pressure trend line, which makes it unable to accurately evaluate the pressure coefficient of sandstone, mudstone, and limestone formations with discontinuous deposition. And the formation pressure coefficient is greatly affected by formation burial depth, porosity, and resistivity. Therefore, it is urgent to comprehensively consider the relationship between the formation comprehensive index (including formation burial depth, porosity, and resistivity), movable fluid index, and formation pressure coefficient, and propose a method for predicting pressure coefficient based on comprehensive index and movable fluid index classification to accurately evaluate the pressure coefficient of the formation. Summary of the Invention

[0004] The present invention aims to solve the deficiencies of the prior art, and proposes a method for predicting pressure coefficient based on comprehensive index and movable fluid index classification. By introducing the comprehensive index and movable fluid index, the pressure types of the formation are divided into ultra-low pressure, normal pressure, and overpressure, and then a pressure coefficient prediction model suitable for formations of various pressure types is constructed. Using the pressure coefficient prediction model to accurately predict the formation pressure coefficient is beneficial to the accurate evaluation of formation pressure.

[0005] The present invention adopts the following technical solutions:

[0006] A method for predicting pressure coefficient based on comprehensive index and movable fluid index classification specifically includes the following steps:

[0007] Step 1, obtain the acoustic porosity, density porosity, and neutron porosity of each sample well;

[0008] Select multiple sample wells in the study area where the formation to be predicted develops, obtain the logging data and test data of each sample well, and respectively for each sample well, determine the acoustic porosity curve, density porosity curve, and neutron porosity curve according to the logging data, and obtain the acoustic porosity, density porosity, and neutron porosity of the formation at different depths of each sample well;

[0009] Step 2, obtain the triple porosity ratio of each sample well;

[0010] For each sample well, according to the acoustic porosity curve, density porosity curve, and neutron porosity curve of the sample well, calculate the triple-porosity ratio of the formation at different depths of the sample well, as shown in Equation (1):

[0011] (1)

[0012] In the formula, is the triple-porosity ratio of the formation, with the unit of %; is the acoustic porosity of the formation, with the unit of %; is the density porosity of the formation, with the unit of %; is the neutron porosity of the formation, with the unit of %;

[0013] Step 3: Construct a comprehensive index calculation model to calculate the comprehensive index of each sample well;

[0014] According to the resistivity curve and total porosity curve in the well logging data of the sample well, combined with the triple-porosity ratio of the formation at different well logging depths of the sample well, construct a comprehensive index calculation model, and use the comprehensive index calculation model to calculate the comprehensive index of the formation at different depths of each sample well respectively, as shown in Equation (2):

[0015] (2)

[0016] In the formula, is the calculated value of the comprehensive index, dimensionless; is the well logging measurement depth, with the unit of ; is the deep lateral resistivity of the formation, with the unit of ; is the triple-porosity ratio of the formation, with the unit of %; is the total porosity of the formation, with the unit of %;

[0017] Step 4: Construct a movable fluid index calculation model to calculate the movable fluid index of each sample well;

[0018] Combine the well logging measurement depth of the sample well with the movable fluid saturation curve in the well logging data to construct a movable fluid index calculation model, and then use the movable fluid index calculation model to calculate the movable fluid index of the formation at different depths of each sample well respectively, as shown in Equation (3):

[0019] (3)

[0020] In the formula, is the calculated value of the movable fluid index, dimensionless; is the well logging measurement depth, with the unit of ; $S_{mf}$ is the movable fluid saturation of the formation, in %;

[0021] Step 5: Construct a formation pressure type discriminant based on the comprehensive index and movable fluid index of the formation at different depths in each sample well;

[0022] Plot a crossplot based on the comprehensive index and logging depth measurement of the formation at different depths in each sample well, and obtain an ultra-low pressure discriminant as shown in Equation (4):

[0023] (4)

[0024] In the formula, $\hat{I}$ is the discriminant value of the comprehensive index of the formation to be predicted; $H$ is the logging depth measurement, in ; , are both comprehensive index discriminant coefficients;

[0025] Then plot a crossplot based on the movable fluid index and logging depth measurement of the formation at different depths in each sample well, and obtain a normal pressure and overpressure discriminant as shown in Equation (5):

[0026] (5)

[0027] In the formula, $\hat{J}$ is the discriminant value of the movable fluid index of the formation to be predicted; $H$ is the logging depth measurement, in ; , are both movable fluid index discriminant coefficients;

[0028] Step 6: Use the formation pressure type discriminant to determine the pressure type of the formation to be predicted;

[0029] According to the logging depth measurement of the formation to be predicted, use the ultra-low pressure discriminant to calculate the discriminant value $\hat{I}$ of the comprehensive index of the formation to be predicted. Combine with the logging data of the formation to be predicted, use the comprehensive index calculation model to calculate the comprehensive index of the formation to be predicted, and obtain the calculated value $I$ of the comprehensive index of the formation to be predicted. If the calculated value of the comprehensive index of the formation to be predicted is less than the discriminant value, it is determined that the pressure type of the formation to be predicted is ultra-low pressure;

[0030] If the calculated value of the comprehensive index of the formation to be predicted is not less than the discriminant value, then use the normal pressure and overpressure discriminant to calculate the discriminant value , combining with the logging data of the formation to be predicted, use the movable fluid index calculation model to calculate the movable fluid index of the formation to be predicted, and obtain the calculated value of the movable fluid index of the formation to be predicted , if the calculated value of the movable fluid index of the formation to be predicted is less than the discrimination value, it is determined that the pressure type of the formation to be predicted is normal pressure; if the calculated value of the movable fluid index of the formation to be predicted is not less than the discrimination value, it is determined that the pressure type of the formation to be predicted is overpressure;

[0031] Step 7, construct a pressure coefficient prediction model applicable to each formation pressure type. According to the pressure type of the formation to be predicted, select the corresponding pressure coefficient prediction model and calculate the predicted value of the pressure coefficient of the formation to be predicted, which specifically includes the following sub-steps:

[0032] Step 7.1, according to the formation pressure coefficients in the test data of each sample well, divide the pressure types of the formations at different depths of each sample well. Among them, the formations with a pressure coefficient less than 0.8 in each sample well are divided into ultra-low pressure formations, the formations with a pressure coefficient of 0.8 - 1 in each sample well are divided into normal pressure formations, and the formations with a pressure coefficient greater than 1 in each sample well are divided into overpressure formations;

[0033] Step 7.2, based on the logging data and test data of each sample well, determine the total porosity, movable fluid saturation, resistivity, triple porosity ratio, and vertical depth of each ultra-low pressure formation. Through multiple regression analysis of the total porosity, movable fluid saturation, resistivity, triple porosity ratio, and vertical depth of all ultra-low pressure formations, establish a pressure coefficient prediction model applicable to ultra-low pressure formations, as shown in Equation (6):

[0034] (6)

[0035] In the formula, is the predicted value of the pressure coefficient of the ultra-low pressure formation; is the movable fluid saturation of the formation, in %; the deep lateral resistivity of the formation, in ; is the triple porosity ratio of the formation, in %; is the vertical depth of the formation, determined according to the logging measurement depth and the formation dip angle in the test data; 、 、 、 、 are all pressure coefficient prediction parameters for ultra-low pressure formations;

[0036] Step 7.3, based on the logging data and testing data of each sample well, determine the total porosity, movable fluid saturation, resistivity, triple porosity ratio, and vertical depth of each normal pressure formation. Through multiple regression analysis of the total porosity, movable fluid saturation, resistivity, triple porosity ratio, and vertical depth of all normal pressure formations, establish a pressure coefficient prediction model applicable to normal pressure formations, as shown in Equation (7):

[0037] (7)

[0038] In the formula, is the predicted value of the pressure coefficient of the normal pressure formation; , , , , are all prediction parameters of the pressure coefficient of the normal pressure formation;

[0039] Step 7.4, based on the logging data and testing data of each sample well, determine the total porosity, movable fluid saturation, resistivity, triple porosity ratio, and vertical depth of each overpressure formation. Through multiple regression analysis of the total porosity, movable fluid saturation, resistivity, triple porosity ratio, and vertical depth of all overpressure formations, establish a pressure coefficient prediction model applicable to overpressure formations, as shown in Equation (8):

[0040] (8)

[0041] In the formula, is the predicted value of the pressure coefficient of the overpressure formation; , , , , are all prediction parameters of the pressure coefficient of the overpressure formation;

[0042] Step 7.5, according to the pressure type of the formation to be predicted, select the corresponding pressure coefficient prediction model, and calculate the predicted value of the pressure coefficient of the formation to be predicted;

[0043] Step 8, verify the accuracy of the pressure coefficient prediction model;

[0044] Select verification wells in the study area. After using the formation pressure coefficient type discriminant formula to discriminate the pressure types of each formation in the verification wells, then use the pressure coefficient prediction model to calculate the predicted values of the pressure coefficients of each formation in the verification wells. Combine the testing data of the verification wells to obtain the measured values of the pressure coefficients of each formation. By comparing the predicted values and the measured values of the pressure coefficients of each formation, verify the accuracy of the pressure coefficient prediction model.

[0045] Preferably, the well logging data includes acoustic travel time curve, density curve, neutron logging curve, total porosity curve, movable fluid saturation curve and resistivity curve, and the test data includes formation pressure coefficient, formation pressure and formation dip angle.

[0046] Preferably, in step 1, the acoustic travel time value of the formation at each depth is determined according to the acoustic travel time curve of the sample well, and the acoustic porosity of the formation at each depth of the sample well is calculated as shown in Equation (9):

[0047] (9)

[0048] In the formula, is the acoustic porosity of the formation, in %; is the acoustic travel time value of the formation, in ; is the acoustic travel time value of the rock skeleton, in ; is the acoustic travel time value of the rock fluid, in ;

[0049] The density logging value of the formation at each depth is determined according to the density curve of the sample well, and the density porosity of the formation at each depth of the sample well is calculated as shown in Equation (10):

[0050] (10)

[0051] In the formula, is the density porosity of the formation, in %; is the density logging value of the formation, in ; is the density value of the rock skeleton, in ; is the density value of the rock fluid, in ;

[0052] The neutron logging value of the formation at each depth is determined according to the neutron logging curve of the sample well, and the neutron porosity of the formation at each depth of the sample well is calculated as shown in Equation (11):

[0053] (11)

[0054] In the formula, is the neutron porosity of the formation, in %; is the neutron logging value of the formation, in %; is the neutron logging value of the rock skeleton, in %; is the neutron logging value of the rock fluid, in %.

[0055] Preferably, in the step 5, the formation pressure type discriminant includes an ultra-low pressure discriminant, an atmospheric pressure and overpressure discriminant.

[0056] Preferably, in the step 7.5, when the pressure type of the formation to be predicted is ultra-low pressure, a pressure coefficient prediction model applicable to ultra-low pressure formations is selected to calculate the pressure coefficient of the formation to be predicted; when the pressure type of the formation to be predicted is applicable to atmospheric pressure, a pressure coefficient prediction model applicable to atmospheric pressure formations is selected to calculate the pressure coefficient of the formation to be predicted; when the pressure type of the formation to be predicted is applicable to overpressure, a pressure coefficient prediction model applicable to overpressure formations is selected to calculate the pressure coefficient of the formation to be predicted.

[0057] The present invention has the following beneficial effects:

[0058] The present invention proposes a pressure coefficient prediction method based on the classification of comprehensive index and movable fluid index. First, the formation pressure type is determined by using the comprehensive index of comprehensive formation resistivity, porosity, triple porosity ratio and the movable fluid index of comprehensive logging measurement depth and movable fluid saturation. The formation pressure type is divided into ultra-low pressure, atmospheric pressure and overpressure by using the formation pressure type discriminant. Then, by constructing a pressure coefficient prediction model applicable to formations of different pressure types, the pressure coefficients of formations of different pressure types are accurately predicted.

[0059] The present invention fully considers the influence of formation burial depth, triple porosity, resistivity and pore fluid on the formation pressure coefficient, overcomes the deficiency that the existing formation pressure prediction methods are difficult to accurately predict the pressure coefficient of discontinuous sedimentary formations, has a wider application range and accurate prediction results, and provides a new method for the accurate prediction of formation pressure. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a flow chart of a pressure coefficient prediction method based on the classification of comprehensive index and movable fluid index of the present invention.

[0061] Figure 2 It is a cross plot drawn by using the formation comprehensive index and logging measurement depth at different depths cross plot.

[0062] Figure 3 It is a cross plot drawn by using the formation movable fluid index and logging measurement depth at different depths cross plot.

[0063] Figure 4 It is an accuracy verification diagram of the pressure coefficient prediction model of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0064] The following further illustrates the detailed implementation manners of the present invention with reference to the drawings and a certain research area:

[0065] Taking a certain research area as an example, a method for predicting pressure coefficient based on comprehensive index and movable fluid index classification proposed by the present invention is adopted. As Figure 1 shown, the specific steps are as follows:

[0066] A method for predicting pressure coefficient based on comprehensive index and movable fluid index classification specifically includes the following steps:

[0067] Step 1, obtain the acoustic porosity, density porosity, and neutron porosity of each sample well

[0068] In the research area where the formation to be predicted develops, multiple sample wells are selected, and the logging data and test data of each sample well are obtained. Among them, the logging data includes acoustic time difference curve, density curve, neutron logging curve, total porosity curve, movable fluid saturation curve, and resistivity curve, and the test data includes formation pressure coefficient, formation pressure, and formation dip.

[0069] For each sample well respectively, determine the acoustic time difference value of the formation at each depth according to the acoustic time difference curve of the sample well, calculate the acoustic porosity of the formation at different depths in the sample well using formula (9) to obtain the acoustic porosity curve of the sample well, then determine the density logging value of the formation at each depth according to the density curve of the sample well, calculate the density porosity of the formation at different depths in the sample well using formula (10) to obtain the density porosity curve of the sample well, and finally determine the neutron logging value of the formation at each depth according to the neutron logging curve of the sample well, calculate the neutron porosity of the formation at different depths in the sample well using formula (11) to obtain the neutron porosity curve of the sample well.

[0070] Step 2, obtain the triple porosity ratio of each sample well

[0071] For each sample well respectively, calculate the triple porosity ratio of the formation at different depths in the sample well according to the acoustic porosity curve, density porosity curve, and neutron porosity curve of the sample well, as shown in formula (1):

[0072] (1)

[0073] In the formula, is the triple porosity ratio of the formation, with the unit of %; is the acoustic porosity of the formation, with the unit of %; is the density porosity of the formation, with the unit of %; is the neutron porosity of the formation, with the unit of %.

[0074] Step 3, construct a comprehensive index calculation model to calculate the comprehensive index of each sample well

[0075] Since the formation pressure coefficient is positively correlated with the formation burial depth, resistivity, and three porosities (acoustic porosity, density porosity, and neutron porosity), and negatively correlated with the total porosity of the formation, the resistivity curve, total porosity curve, and the ratio of the three porosities of the formation are combined in the present invention to construct a comprehensive index calculation model. The comprehensive index of the formation at different depths of each sample well is calculated using the comprehensive index calculation model, as shown in Equation (2):

[0076] (2)

[0077] In the formula, is the calculated value of the comprehensive index, dimensionless; is the logging measurement depth, with the unit of ; is the deep lateral resistivity of the formation, with the unit of ; is the ratio of the three porosities of the formation, with the unit of %; is the total porosity of the formation, with the unit of %.

[0078] Step 4: Construct a movable fluid index calculation model to calculate the movable fluid index of each sample well

[0079] The logging measurement depth of the sample well is combined with the movable fluid saturation curve to construct a movable fluid index calculation model. Then, using the movable fluid index calculation model, the movable fluid index of the formation at different depths of each sample well is calculated, as shown in Equation (3):

[0080] (3)

[0081] In the formula, is the calculated value of the movable fluid index, dimensionless; is the logging measurement depth, with the unit of ; is the movable fluid saturation of the formation, with the unit of %;

[0082] Step 5: Construct a formation pressure type discriminant based on the comprehensive index and movable fluid index of the formation

[0083] Based on the comprehensive index of the formation at different depths of each sample well and the logging measurement depth, a crossplot is drawn, as Figure 2 shown. According to the crossplot drawn in this embodiment, the ultra-low pressure discriminant is obtained as:

[0084] (12)

[0085] In the formula, is the discriminant value of the comprehensive index of the formation to be predicted; For the logging measurement depth, the unit is .

[0086] Then, plot the cross plot according to the movable fluid index of the formation at different depths of each sample well and the logging measurement depth, such as Figure 3 shown. According to the cross plot drawn in this embodiment, obtain the discriminant formula for normal pressure and overpressure, as shown in Equation (5):

[0087] (13)

[0088] In the formula, is the discriminant value of the movable fluid index of the formation to be predicted; is the logging measurement depth, and the unit is .

[0089] Step 6: Use the formation pressure type discriminant formula to discriminate the pressure type of the formation to be predicted.

[0090] According to the logging measurement depth of the formation to be predicted, use the ultra-low pressure discriminant formula to calculate the discriminant value of the comprehensive index of the formation to be predicted. , combined with the logging data of the formation to be predicted, use the comprehensive index calculation model to calculate the comprehensive index of the formation to be predicted, and obtain the calculated value of the comprehensive index of the formation to be predicted. , if the calculated value of the comprehensive index of the formation to be predicted is less than the discriminant value, that is, , then it is determined that the pressure type of the formation to be predicted is ultra-low pressure (formation pressure coefficient less than 0.8);

[0091] If the calculated value of the comprehensive index of the formation to be predicted is not less than the discriminant value, that is, , then use the discriminant formula for normal pressure and overpressure to calculate the discriminant value of the movable fluid index of the formation to be predicted. , combined with the logging data of the formation to be predicted, use the movable fluid index calculation model to calculate the movable fluid index of the formation to be predicted, and obtain the calculated value of the movable fluid index of the formation to be predicted. , if the calculated value of the movable fluid index of the formation to be predicted is less than the discriminant value, that is, , then it is determined that the pressure type of the formation to be predicted is normal pressure (formation pressure coefficient is 0.8 - 1), and if the calculated value of the movable fluid index of the formation to be predicted is not less than the discriminant value, that is, , then it is determined that the pressure type of the formation to be predicted is overpressure (formation pressure coefficient greater than 1).

[0092] Step 7: Construct a pressure coefficient prediction model applicable to each formation pressure type. According to the pressure type of the formation to be predicted, select the corresponding pressure coefficient prediction model and calculate the predicted value of the pressure coefficient of the formation to be predicted, which specifically includes the following sub-steps:

[0093] Step 7.1: According to the formation pressure coefficients in the test data of each sample well, classify the pressure types of the formations at different depths in each sample well. Among them, the formations with a pressure coefficient less than 0.8 in each sample well are classified as ultra-low pressure formations, the formations with a pressure coefficient of 0.8 - 1 in each sample well are classified as normal pressure formations, and the formations with a pressure coefficient greater than 1 in each sample well are classified as overpressure formations.

[0094] Step 7.2: Based on the logging data and test data of each sample well, determine the total porosity, movable fluid saturation, resistivity, triple porosity ratio, and vertical depth of each ultra-low pressure formation. By performing multiple regression analysis on the total porosity, movable fluid saturation, resistivity, triple porosity ratio, and vertical depth of all ultra-low pressure formations, establish a pressure coefficient prediction model applicable to ultra-low pressure formations. In this embodiment, the pressure coefficient prediction model applicable to ultra-low pressure formations is:

[0095] (14)

[0096] In the formula, is the predicted value of the ultra-low pressure formation pressure coefficient; is the movable fluid saturation of the formation, in %; the deep lateral resistivity of the formation, in ; is the triple porosity ratio of the formation, in %; is the vertical depth of the formation. Determining the vertical depth of the formation based on the logging measurement depth and formation dip angle is prior art in this field.

[0097] Step 7.3: Based on the logging data and test data of each sample well, determine the total porosity, movable fluid saturation, resistivity, triple porosity ratio, and vertical depth of each normal pressure formation. By performing multiple regression analysis on the total porosity, movable fluid saturation, resistivity, triple porosity ratio, and vertical depth of all normal pressure formations, establish a pressure coefficient prediction model applicable to normal pressure formations. In this embodiment, the pressure coefficient prediction model applicable to normal pressure formations is:

[0098] (15)

[0099] In the formula, is the predicted value of the normal pressure formation pressure coefficient.

[0100] Step 7.4, based on the logging data and testing data of each sample well, determine the total porosity, movable fluid saturation, resistivity, triple porosity ratio, and vertical depth of each overpressure formation. By performing multiple regression analysis on the total porosity, movable fluid saturation, resistivity, triple porosity ratio, and vertical depth of all overpressure formations, establish a pressure coefficient prediction model applicable to overpressure formations. In this embodiment, the pressure coefficient prediction model applicable to overpressure formations is as follows:

[0101] (16)

[0102] In the formula, is the predicted value of the overpressure formation pressure coefficient.

[0103] Step 7.5, according to the pressure type of the formation to be predicted, select the corresponding pressure coefficient prediction model, and calculate the predicted value of the pressure coefficient of the formation to be predicted.

[0104] Step 8, verify the accuracy of the pressure coefficient prediction model

[0105] Select verification wells in the study area. After using the formation pressure coefficient type discriminant formula to discriminate the pressure types of each formation in the verification wells respectively, then use the pressure coefficient prediction model to calculate the predicted values of the pressure coefficients of each formation in the verification wells respectively. Combine the testing data of the verification wells to obtain the measured values of the pressure coefficients of each formation. By comparing the predicted values and the measured values of the pressure coefficients of each formation, as Figure 4 shown, it is found after comparison that the values of the formation pressure coefficients calculated by using the pressure coefficient prediction model have quite high consistency with the measured pressure coefficient values, verifying the accuracy of the method for predicting formation pressure coefficients of the present invention, which is a pressure coefficient prediction method based on comprehensive index and movable fluid index classification.

[0106] Certainly, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples either. Changes, modifications, additions, or substitutions made by those skilled in the art within the essence of the present invention should also fall within the protection scope of the present invention.

Claims

1. A method for predicting pressure coefficient based on comprehensive index and movable fluid index classification, characterized in that, it specifically includes the following steps: Step 1, obtain the acoustic porosity, density porosity and neutron porosity of each sample well; Select multiple sample wells in the study area where the formation to be predicted is developed, obtain the logging data and test data of each sample well, respectively for each sample well, determine the acoustic porosity curve, density porosity curve and neutron porosity curve according to the logging data, and obtain the acoustic porosity, density porosity and neutron porosity of the formation at different depths of each sample well; Step 2, obtain the triple porosity ratio of each sample well; Respectively for each sample well, calculate the triple porosity ratio of the formation at different depths of the sample well according to the acoustic porosity curve, density porosity curve and neutron porosity curve of the sample well, as shown in formula (1): （1）； In the formula, is the triple porosity ratio of the formation, in %; is the acoustic porosity of the formation, in %; is the density porosity of the formation, in %; is the neutron porosity of the formation, in %. Step 3, construct a comprehensive index calculation model to calculate the comprehensive index of each sample well; According to the resistivity curve and total porosity curve in the logging data of the sample well, combined with the triple porosity ratio of the formation at different logging depths of the sample well, construct a comprehensive index calculation model, and use the comprehensive index calculation model to calculate the comprehensive index of the formation at different depths of each sample well respectively, as shown in formula (2): （2）； In the formula, is the calculated value of the comprehensive index, dimensionless; is the logging measurement depth, with the unit of ; is the deep lateral resistivity of the formation, with the unit of ; is the ratio of three porosities of the formation, with the unit of %; is the total porosity of the formation, with the unit of %. Step 4, construct a movable fluid index calculation model to calculate the movable fluid index of each sample well; Combine the logging measurement depth of the sample well with the movable fluid saturation curve in the logging data to construct a movable fluid index calculation model, and then use the movable fluid index calculation model to calculate the movable fluid index of the formation at different depths of each sample well respectively, as shown in formula (3): （3）； In the formula, is the calculated value of the movable fluid index, dimensionless; is the logging measurement depth, with the unit of ; is the movable fluid saturation of the formation, with the unit of %; Step 5, construct a formation pressure type discriminant formula according to the comprehensive index and movable fluid index of the formation at different depths of each sample well; Plot the crossplot based on the comprehensive index of the formation at different depths of each sample well and the logging measurement depth to obtain the ultra-low pressure discriminant formula as shown in Equation (4): （4）； In the formula, is the discrimination value of the comprehensive index of the formation to be predicted; is the logging measurement depth, with the unit of ; , are both comprehensive index discrimination coefficients; Then, based on the movable fluid indices of the formations at different depths of each sample well and the logging measurement depths, plot a crossplot to obtain a discriminant for normal pressure and overpressure as shown in Equation (5): （5）； In the formula, is the discriminant value of the movable fluid index of the formation to be predicted; is the logging measurement depth, with the unit of ; , are both discriminant coefficients of the movable fluid index; Step 6, use the formation pressure type discriminant formula to discriminate the pressure type of the formation to be predicted; According to the logging measurement depth of the formation to be predicted, the discriminant value of the comprehensive index of the formation to be predicted is calculated using the ultra-low pressure discriminant formula. Combined with the logging data of the formation to be predicted, the comprehensive index of the formation to be predicted is calculated using the comprehensive index calculation model to obtain the calculated value of the comprehensive index of the formation to be predicted. If the calculated value of the comprehensive index of the formation to be predicted is less than the discriminant value, it is determined that the pressure type of the formation to be predicted is ultra-low pressure. If the calculated value of the comprehensive index of the formation to be predicted is not less than the discrimination value, then use the atmospheric pressure and overpressure discrimination formula to calculate the discrimination value of the movable fluid index of the formation to be predicted , combined with the logging data of the formation to be predicted, use the movable fluid index calculation model to calculate the movable fluid index of the formation to be predicted, and obtain the calculated value of the movable fluid index of the formation to be predicted , if the calculated value of the movable fluid index of the formation to be predicted is less than the discrimination value, then determine that the pressure type of the formation to be predicted is atmospheric pressure; if the calculated value of the movable fluid index of the formation to be predicted is not less than the discrimination value, then determine that the pressure type of the formation to be predicted is overpressure; Step 7, construct a pressure coefficient prediction model applicable to each formation pressure type, according to the pressure type of the formation to be predicted, select the corresponding pressure coefficient prediction model, and calculate the predicted value of the pressure coefficient of the formation to be predicted. Specifically, it includes the following sub-steps: Step 7.1, according to the formation pressure coefficient in the test data of each sample well, divide the pressure type of the formation at different depths of each sample well. Among them, the formation with a pressure coefficient less than 0.8 in each sample well is divided into ultra-low pressure formation, the formation with a pressure coefficient of 0.8 - 1 in each sample well is divided into normal pressure formation, and the formation with a pressure coefficient greater than 1 in each sample well is divided into overpressure formation; Step 7.2, based on the logging data and test data of each sample well, determine the total porosity, movable fluid saturation, resistivity, triple porosity ratio and vertical depth of each ultra-low pressure formation. Through multiple regression analysis of the total porosity, movable fluid saturation, resistivity, triple porosity ratio and vertical depth of all ultra-low pressure formations, establish a pressure coefficient prediction model applicable to ultra-low pressure formations, as shown in formula (6): （6）； In the formula, is the predicted value of the ultra-low pressure formation pressure coefficient; is the movable fluid saturation of the formation, in %; the deep lateral resistivity of the formation, in ; is the ratio of three porosities of the formation, in %; is the vertical depth of the formation, determined according to the logging measurement depth and the formation dip angle in the test data; , , , , are all prediction parameters of the ultra-low pressure formation pressure coefficient; Step 7.3, based on the logging data and test data of each sample well, determine the total porosity, movable fluid saturation, resistivity, triple porosity ratio, and vertical depth of each normal pressure formation. Through multiple regression analysis of the total porosity, movable fluid saturation, resistivity, triple porosity ratio, and vertical depth of all normal pressure formations, establish a pressure coefficient prediction model applicable to normal pressure formations, as shown in Equation (7): （7）； In the formula, is the predicted value of the normal formation pressure coefficient; , , , , are all prediction parameters of the normal formation pressure coefficient; Step 7.4, based on the logging data and test data of each sample well, determine the total porosity, movable fluid saturation, resistivity, triple porosity ratio, and vertical depth of each overpressure formation. Through multiple regression analysis of the total porosity, movable fluid saturation, resistivity, triple porosity ratio, and vertical depth of all overpressure formations, establish a pressure coefficient prediction model applicable to overpressure formations, as shown in Equation (8): （8）； In the formula, is the predicted value of the overpressure formation pressure coefficient; , , , , are all overpressure formation pressure coefficient prediction parameters; Step 7.5, according to the pressure type of the formation to be predicted, select the corresponding pressure coefficient prediction model, and calculate the predicted value of the pressure coefficient of the formation to be predicted; Step 8, verify the accuracy of the pressure coefficient prediction model; Select verification wells in the study area. After using the formation pressure coefficient type discriminant formula to discriminate the pressure types of each formation in the verification wells, then use the pressure coefficient prediction model to calculate the predicted values of the pressure coefficients of each formation in the verification wells. Combine the test data of the verification wells to obtain the measured values of the pressure coefficients of each formation. By comparing the predicted values and the measured values of the pressure coefficients of each formation, verify the accuracy of the pressure coefficient prediction model.

2. A pressure coefficient prediction method based on classification of comprehensive index and movable fluid index according to claim 1, characterized in that, the logging data includes acoustic travel time curve, density curve, neutron logging curve, total porosity curve, movable fluid saturation curve, and resistivity curve, and the test data includes formation pressure coefficient, formation pressure, and formation dip angle.

3. A pressure coefficient prediction method based on classification of comprehensive index and movable fluid index according to claim 1, characterized in that, in Step 1, according to the acoustic travel time curve of the sample well, determine the acoustic travel time values of each depth formation, and calculate the acoustic porosity of the formation at each depth of the sample well, as shown in Equation (9): （9）； Wherein, is the acoustic porosity of the formation, in %; is the acoustic time difference of the formation, in ; is the acoustic time difference of the rock skeleton, in ; is the acoustic time difference of the rock fluid, in ; According to the density curve of the sample well, determine the density logging values of each depth formation, and calculate the density porosity of the formation at each depth of the sample well, as shown in Equation (10): （10）； Wherein, is the density porosity of the formation, in %; is the density logging value of the formation, in ; is the density value of the rock matrix, in ; is the density value of the rock fluid, in ; According to the neutron logging curve of the sample well, determine the neutron logging values of each depth formation, and calculate the neutron porosity of the formation at each depth of the sample well, as shown in Equation (11): （11）； Wherein, is the neutron porosity of the formation, in %; is the neutron log value of the formation, in %; is the neutron log value of the rock matrix, in %; is the neutron log value of the rock fluid, in %.

4. A pressure coefficient prediction method based on classification of comprehensive index and movable fluid index according to claim 1, characterized in that, in Step 5, the formation pressure type discriminant formula includes an ultra-low pressure discriminant formula, a normal pressure and overpressure discriminant formula.

5. A pressure coefficient prediction method based on classification of comprehensive index and movable fluid index according to claim 1, characterized in that, In step 7.5, when the pressure type of the formation to be predicted is ultra-low pressure, select a pressure coefficient prediction model applicable to ultra-low pressure formations to calculate the pressure coefficient of the formation to be predicted; when the pressure type of the formation to be predicted is applicable to normal pressure, select a pressure coefficient prediction model applicable to normal pressure formations to calculate the pressure coefficient of the formation to be predicted; when the pressure type of the formation to be predicted is applicable to overpressure, select a pressure coefficient prediction model applicable to overpressure formations to calculate the pressure coefficient of the formation to be predicted.

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