Method and device for predicting abnormally high pressure formations in sandstone and mudstone formations

Abstract

The present application provides a method and device for predicting abnormally high pressure formations in sand-mudstone formations, the method comprising: in response to an abnormally high pressure prediction instruction, obtaining acoustic wave time difference data at multiple different vertical depth points of a well to be measured in the sand-mudstone formation, constructing a nonlinear compaction trend line based on the acoustic wave time difference data at at least three different vertical depth points of the compacted layer in the well to be measured, determining the abnormally high pressure formation in the sand-mudstone formation based on the nonlinear compaction trend line, the acoustic wave time difference data at multiple different vertical depth points, and the overlying formation stress at multiple different vertical depth points, and the abnormally high pressure formation is used to guide the development of oil and gas fields. Therefore, the present application can accurately predict abnormally high pressure formations in sand-mudstone formations.

Description

Technical Field

[0001] The present application relates to the technical field of oil and gas exploration and development, and in particular to a method and device for predicting abnormally high-pressure sandstone and mudstone formations. Background Art

[0002] In the process of oil and gas field exploration and development, formation pressure refers to the pressure existing in the pore fluid of the formation. When the formation pore pressure gradient exceeds the normal pressure gradient, it is called abnormally high pressure. The formation where the oil and gas layer is located is often accompanied by abnormally high pressure. Abnormally high pressure is an important driving force for oil and gas migration, which can provide power for the initial migration of hydrocarbons. Abnormally high pressure also slows down the compaction of mudstone, making the mudstone have relatively large porosity and permeability, thereby accelerating the expulsion of hydrocarbons. At the same time, abnormally high pressure reduces the porosity loss caused by compaction, which is beneficial to the preservation of reservoir pores. In the drilling process of oil and gas fields, the prediction of abnormally high pressure formations can provide an important basis for the optimization of well control equipment, drilling fluid density and wellbore structure design, which is of great significance to the entire process of oil and gas field exploration and development.

[0003] At present, the compaction trend line method is often used to predict abnormally high pressure in sandstone and mudstone formations, that is, two points are randomly selected in the mudstone (no porosity) formation to measure the acoustic wave time difference to establish a compaction trend line, which is a straight line. Then, the deviation of the measured acoustic wave curve relative to the compaction trend line is used to predict abnormally high pressure formations.

[0004] However, there may be multiple abnormally high-pressure systems in the sandstone-mudstone formation, and the above-mentioned compaction trend line based on a straight line cannot approach the actual change of the formation pressure. Therefore, the above-mentioned method cannot accurately predict the abnormally high-pressure formation in the sandstone-mudstone formation. Summary of the invention

[0005] The present application provides a method and device for predicting abnormally high-pressure formations in sandstone and mudstone formations, so as to accurately predict abnormally high-pressure formations in sandstone and mudstone formations.

[0006] In a first aspect, the present application provides a method for predicting abnormally high pressure sandstone strata, comprising:

[0007] In response to the abnormally high pressure prediction instruction, the acoustic wave time difference data of a plurality of different vertical depth points of the well to be measured in the sandstone-mudstone formation are obtained.

[0008] A nonlinear compaction trend line is constructed based on the acoustic time difference data of at least three different vertical depth points of the compacted layer in the well to be tested.

[0009] Based on the nonlinear compaction trend line, the acoustic time difference data at multiple different vertical depths and the overlying stratum stress at multiple different vertical depths, the abnormally high-pressure strata in the sandstone and mudstone strata are determined, and the abnormally high-pressure strata are used to guide the development of oil and gas fields.

[0010] Optionally, a nonlinear compaction trend line is constructed based on acoustic time difference data of at least three different vertical depth points of the compacted layer in the well to be tested, including:

[0011] Determine the normal compaction trend line formula; construct a nonlinear compaction trend line based on the acoustic time difference data of at least three different vertical depth points of the compaction layer in the well to be measured and the normal compaction trend line formula.

[0012] Optionally, a nonlinear compaction trend line is constructed based on the acoustic time difference data of at least three different vertical depth points of the compaction layer in the well to be tested and a normal compaction trend line formula, including:

[0013] According to the acoustic time difference data of at least three different vertical depth points of the compacted layer in the well to be measured, the trend parameter in the normal compaction trend line formula is determined; according to the trend parameter, the nonlinear compaction trend line is determined.

[0014] Optionally, the trend parameters in the normal compaction trend line formula are determined based on the acoustic time difference data of at least three different vertical depth points of the compaction layer in the well to be tested, including:

[0015] The acoustic time difference data of at least three different vertical depth points are converted into a logarithmic function form, and a trend parameter is obtained according to each converted logarithmic function form.

[0016] Optionally, the trend parameter includes a first trend parameter and a second trend parameter.

[0017] The normal compaction trend line formula is as follows:

[0018]

[0019] Among them, DT5 represents the normal compaction trend line, A and A0 are the first trend parameter and the second trend parameter respectively, and H is the vertical depth.

[0020] Optionally, the at least three different vertical depth points are four different vertical depth points, and the acoustic time difference data of the at least three different vertical depth points are converted into a logarithmic function form, including:

[0021] The acoustic time difference data at four different vertical depth points are converted into a logarithmic function form, where the logarithmic function form includes X and Y, wherein X is the coefficient of the logarithmic function and Y represents the logarithmic function value;

[0022] in,

[0023] Among them, SD1, SD2, SD3, and SD4 respectively represent the depths of four different vertical depth points, and X1, X2, X3, and X4 respectively represent the coefficients of the logarithmic functions corresponding to the four different vertical depth points.

[0024] Among them, Y1=log(DT1), Y2=log(DT2), Y3=log(DT3), Y4=log(DT4).

[0025] Among them, DT1, DT2, DT3, and DT4 respectively represent the acoustic time difference data at four different vertical depth points, and Y1, Y2, Y3, and Y4 respectively represent the logarithmic function values ​​corresponding to the four different vertical depth points.

[0026] The trend parameters are obtained according to the logarithmic function form after each transformation, including:

[0027] According to the logarithmic function forms X1, X2, X3, X4, Y1, Y2, Y3, and Y4 at four different vertical depth points, the first parameter A1, the second parameter B, the third parameter C, the fourth parameter D, and the fifth parameter A2 are obtained respectively, where the first parameter The second parameter B = X1 + X2 + X3 + X4, the third parameter C = X1 * Y1 + X2 * Y2 + X3 * Y3 + X4 * Y, the fourth parameter D = Y1 + Y2 + Y3 + Y4, the fifth parameter

[0028] According to the first parameter A1, the second parameter B, the third parameter C, the fourth parameter D, the fifth parameter A2 and the following formula 2 and formula 3, the first trend parameter A and the second trend parameter A0 are obtained respectively.

[0029]

[0030]

[0031] In a second aspect, the present application provides a prediction device for abnormally high pressure sandstone formations, comprising:

[0032] The acquisition module is used to respond to the abnormal high pressure prediction instruction and acquire the acoustic wave time difference data of multiple different vertical depth points of the well to be tested in the sandstone mudstone formation.

[0033] The construction module is used to construct a nonlinear compaction trend line according to the acoustic time difference data of at least three different vertical depth points of the compaction layer in the well to be measured.

[0034] The determination module is used to determine the abnormally high-pressure formation in the sandstone mudstone formation based on the nonlinear compaction trend line, the acoustic time difference data at multiple different vertical depth points and the overlying formation stress at multiple different vertical depth points. The abnormally high-pressure formation is used to guide the development of oil and gas fields.

[0035] Optional, building blocks for:

[0036] Determine the normal compaction trend line formula; construct a nonlinear compaction trend line based on the acoustic time difference data of at least three different vertical depth points of the compaction layer in the well to be measured and the normal compaction trend line formula.

[0037] Optional, building blocks for:

[0038] According to the acoustic time difference data of at least three different vertical depth points of the compacted layer in the well to be measured, the trend parameter in the normal compaction trend line formula is determined; according to the trend parameter, the nonlinear compaction trend line is determined.

[0039] Optional, building blocks for:

[0040] The acoustic time difference data of at least three different vertical depth points are converted into a logarithmic function form, and a trend parameter is obtained according to each converted logarithmic function form.

[0041] Optionally, the trend parameter includes a first trend parameter and a second trend parameter;

[0042] The normal compaction trend line formula is as follows:

[0043]

[0044] Among them, DT5 represents the normal compaction trend line, A and A0 are the first trend parameter and the second trend parameter respectively, and H is the vertical depth.

[0045] Optionally, the at least three different vertical depth points are four different vertical depth points, and the building module is specifically used for:

[0046] The acoustic time difference data at four different vertical depth points are converted into a logarithmic function form, which includes X and Y, where X is the coefficient of the logarithmic function and Y represents the logarithmic function value.

[0047] in,

[0048] Among them, SD1, SD2, SD3, and SD4 respectively represent the depths of four different vertical depth points, and X1, X2, X3, and X4 respectively represent the coefficients of the logarithmic functions corresponding to the four different vertical depth points.

[0049] Among them, Y1=log(DT1), Y2=log(DT2), Y3=log(DT3), Y4=log(DT4).

[0050] Among them, DT1, DT2, DT3, and DT4 respectively represent the acoustic time difference data at four different vertical depth points, and Y1, Y2, Y3, and Y4 respectively represent the logarithmic function values ​​corresponding to the four different vertical depth points.

[0051] According to the logarithmic function forms X1, X2, X3, X4, Y1, Y2, Y3, and Y4 at four different vertical depth points, the first parameter A1, the second parameter B, the third parameter C, the fourth parameter D, and the fifth parameter A2 are obtained respectively, where the first parameter The second parameter B = X1 + X2 + X3 + X4, the third parameter C = X1 * Y1 + X2 * Y2 + X3 * Y3 + X4 * Y, the fourth parameter D = Y1 + Y2 + Y3 + Y4, the fifth parameter

[0052] According to the first parameter A1, the second parameter B, the third parameter C, the fourth parameter D, the fifth parameter A2 and the following formula 2 and formula 3, the first trend parameter A and the second trend parameter A0 are obtained respectively.

[0053]

[0054]

[0055] In a third aspect, the present application provides a prediction device for abnormally high pressure sandstone formations, comprising: a memory and a processor;

[0056] The memory is used to store program instructions;

[0057] The processor is used to call the program instructions in the memory to execute the method for predicting abnormally high-pressure sandstone and mudstone formations as described in the first aspect of the present application.

[0058] In a fourth aspect, the present application provides a computer-readable storage medium, in which computer program instructions are stored. When the computer program instructions are executed, the method for predicting abnormally high-pressure sandstone and mudstone formations as described in the first aspect of the present application is implemented.

[0059] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the method for predicting abnormally high-pressure sandstone and mudstone formations as described in the first aspect of the present application.

[0060] The prediction method and device of abnormally high pressure formation in sand-mudstone formation provided by the present application obtains the acoustic time difference data of multiple different vertical depth points of the well to be measured in the sand-mudstone formation in response to the abnormally high pressure prediction instruction, constructs a nonlinear compaction trend line according to the acoustic time difference data of at least three different vertical depth points of the compacted layer in the well to be measured, and determines the abnormally high pressure formation in the sand-mudstone formation according to the nonlinear compaction trend line, the acoustic time difference data of multiple different vertical depth points and the overlying formation stress at multiple different vertical depth points. The abnormally high pressure formation is used to guide the development of oil and gas fields. Since the present application constructs a nonlinear compaction trend line according to the acoustic time difference data of at least three different vertical depth points of the compacted layer in the well to be measured, the nonlinear compaction trend line can approach the actual change of formation pressure, and therefore, can accurately predict the abnormally high pressure formation of the sand-mudstone formation containing multiple abnormally high pressure systems, thereby providing an important basis for the optimization of well control equipment, drilling fluid density and wellbore structure design, and can guide the exploration and development of oil and gas fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0062] Figure 1 A schematic diagram of an application scenario provided for an embodiment of the present application;

[0063] Figure 2 A flow chart of a method for predicting abnormally high pressure formations in sandstone and mudstone formations provided in one embodiment of the present application;

[0064] Figure 3 A flow chart of a method for predicting abnormally high pressure formations in sandstone and mudstone formations provided in another embodiment of the present application;

[0065] Figure 4 A schematic diagram showing a comparison between a nonlinear compaction trend line and a conventional linear compaction trend line provided in an embodiment of the present application;

[0066] Figure 5 A schematic diagram of formation pressure prediction of Well XX in Tarim Oilfield provided in one embodiment of the present application;

[0067] Figure 6 A schematic diagram of the structure of a device for predicting abnormally high-pressure formations in sandstone and mudstone formations provided in one embodiment of the present application;

[0068] Figure 7 A schematic diagram of the structure of a device for predicting abnormally high-pressure formations in sandstone and mudstone formations provided in another embodiment of the present application;

[0069] Figure 8 A schematic diagram of the structure of a device for predicting abnormally high-pressure sandstone and mudstone formations provided in another embodiment of the present application. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0071] During the exploration and development of oil and gas fields, abnormally high pressure often exists in the strata where the oil and gas layers are located. Abnormally high pressure is an important driving force for oil and gas migration, which can provide power for the initial migration of hydrocarbons. Abnormally high pressure also slows down the compaction of mudstone, making the mudstone have relatively large porosity and permeability, thereby accelerating the expulsion of hydrocarbons. At the same time, abnormally high pressure reduces the porosity loss caused by compaction, which is conducive to the preservation of reservoir pores. For the ultra-deep sandstone natural gas reservoir in the Kuche Depression of the Tarim Basin, the prediction of abnormally high pressure and its vertical distribution trend is more realistic for drilling engineering. It is an important basis for the optimization of well control equipment, drilling fluid density and wellbore structure design. Inaccurate pressure prediction often brings a series of problems to drilling work, such as well kick, well wall instability, drill bit sticking, wellbore scrapping, etc. In severe cases, it may even lead to fire and blowout, causing safety accidents. Therefore, accurate prediction of abnormally high pressure formations is very important for guiding drilling construction, and is of great significance to the entire process of oil and gas field exploration and development.

[0072] At present, the compaction trend line method is often used to predict abnormally high pressure in sandstone and mudstone formations, that is, the acoustic wave time difference value is measured at any two points in the mudstone (no porosity) formation to establish a compaction trend line, which is a straight line, and then the deviation of the measured acoustic wave curve from the compaction trend line is used to predict abnormally high pressure formations. However, there may be multiple abnormally high pressure systems in the sandstone and mudstone formations, and the above-mentioned compaction trend line based on a straight line cannot approach the actual change of formation pressure. Therefore, the above-mentioned method cannot accurately predict abnormally high pressure formations in sandstone and mudstone formations.

[0073] Therefore, the present application provides a method and device for predicting abnormally high-pressure formations in sandstone and mudstone formations. A nonlinear compaction trend line is constructed based on the acoustic wave time difference data of at least three different vertical depth points of the compacted layer in the well to be measured. The nonlinear compaction trend line can approach the actual change of formation pressure. Therefore, it is possible to accurately predict abnormally high-pressure formations in sandstone and mudstone formations containing multiple abnormally high-pressure systems.

[0074] Figure 1 A schematic diagram of an application scenario provided in an embodiment of the present application, such as Figure 1 As shown, this application scenario includes an acoustic time difference data collection device 110 and a computer device 120. Among them, the acoustic time difference data collection device 110 is used to collect acoustic time difference data at multiple different vertical depth points of a well to be measured in a sandstone mudstone formation. The acoustic time difference data collection device 110 is, for example, a logging instrument, used to measure wellbore data, and the wellbore data includes acoustic time difference data at multiple different vertical depth points of a well to be measured in a sandstone mudstone formation. The acoustic time difference data collection device 110 is, for example, a detector, used to collect three-dimensional acoustic wave data during blasting, and the three-dimensional acoustic wave data includes acoustic time difference data at multiple different vertical depth points of a well to be measured in a sandstone mudstone formation. The computer device 120 is used to obtain the acoustic time difference data at multiple different vertical depth points of a well to be measured in a sandstone mudstone formation from the acoustic time difference data collection device 110.

[0075] Figure 2 This is a flow chart of a method for predicting abnormally high pressure sandstone formations provided in an embodiment of the present application. The method of this embodiment can be applied to electronic devices, which can be terminal devices, servers, etc. The terminal devices can be, for example, mobile phones, tablet computers, laptop computers, desktop computers, etc. Figure 2 As shown, the method of this embodiment may include:

[0076] S201, in response to an abnormally high pressure prediction instruction, obtaining acoustic wave time difference data of a plurality of different vertical depth points of a well to be measured in a sandstone-mudstone formation.

[0077] In this embodiment, the abnormally high pressure prediction instruction may be input by a user to the electronic device executing the embodiment of the present method, or may be sent by other devices to the electronic device executing the embodiment of the present method. The acquired acoustic time difference data includes: the acoustic time difference value and the vertical depth value of each depth point. The acoustic time difference data may be obtained from the wellbore data of the well to be measured in the sandstone mudstone formation at multiple different vertical depth points, or the layer velocity data may be obtained from the seismic data of the well to be measured in the sandstone mudstone formation, and the layer velocity may be converted into acoustic time difference data at multiple different vertical depth points. The present application does not limit the method of obtaining the acoustic time difference data. Among them, the acoustic time difference data of multiple different vertical depth points of the well to be measured in the above-mentioned sandstone mudstone formation may be input by a user to the electronic device executing the embodiment of the present method, or may be sent by other devices to the electronic device executing the embodiment of the present method.

[0078] S202: construct a nonlinear compaction trend line based on the acoustic wave time difference data of at least three different vertical depth points of the compaction layer in the well to be measured.

[0079] In this embodiment, the well to be measured in the sandstone mudstone formation contains a compacted layer. After obtaining the acoustic wave time difference data of multiple different vertical depth points of the well to be measured in the sandstone mudstone formation, a nonlinear compaction trend line is constructed based on the acoustic wave time difference data of at least three different vertical depth points of the compacted layer in the well to be measured.

[0080] S203, determining abnormally high-pressure formations in sandstone and mudstone formations based on the nonlinear compaction trend line, acoustic wave time difference data at multiple different vertical depths, and overlying formation stress at multiple different vertical depths, wherein the abnormally high-pressure formations are used to guide the development of oil and gas fields.

[0081] In this embodiment, the overburden formation stress is the vertical stress at different vertical depth points in the well to be measured, which can be obtained according to the depth of each depth point and the corresponding formation density. After the nonlinear compaction trend line of the well to be measured is constructed, and the acoustic wave time difference data at multiple different vertical depth points and the overburden formation stress at multiple different vertical depth points are obtained, the abnormally high pressure formation in the sandstone mudstone formation can be determined, and the abnormally high pressure formation is used to guide the development of oil and gas fields.

[0082] The prediction method of abnormally high pressure formation in sand-mudstone formation provided in the present embodiment obtains acoustic wave time difference data of multiple different vertical depth points of the well to be measured in the sand-mudstone formation in response to the abnormally high pressure prediction instruction, constructs a nonlinear compaction trend line according to the acoustic wave time difference data of at least three different vertical depth points of the compacted layer in the well to be measured, and determines the abnormally high pressure formation in the sand-mudstone formation according to the nonlinear compaction trend line, the acoustic wave time difference data of multiple different vertical depth points and the overlying formation stress at multiple different vertical depth points. The abnormally high pressure formation is used to guide the development of oil and gas fields. Since the present application constructs a nonlinear compaction trend line according to the acoustic wave time difference data of at least three different vertical depth points of the compacted layer in the well to be measured, the nonlinear compaction trend line can approach the actual change of formation pressure, and therefore, can accurately predict the abnormally high pressure formation of the sand-mudstone formation containing multiple abnormally high pressure systems, thereby providing an important basis for the optimization of well control equipment, drilling fluid density and wellbore structure design, and can guide the exploration and development of oil and gas fields.

[0083] exist Figure 2 Based on the embodiments shown, in some embodiments, Figure 3 A flowchart of a method for predicting abnormally high pressure formations in sandstone and mudstone formations provided in another embodiment of the present application is shown in FIG. Figure 3 As shown, the method of this embodiment may include:

[0084] S301, in response to an abnormally high pressure prediction instruction, obtaining acoustic wave time difference data of a plurality of different vertical depth points of a well to be measured in a sandstone-mudstone formation.

[0085] In this embodiment, the specific implementation process of S301 can be found in Figure 2 The relevant description of the illustrated embodiment will not be repeated here.

[0086] S302, determine the normal compaction trend line formula.

[0087] In this embodiment, optionally, the normal compaction trend line formula is the following formula 1:

[0088]

[0089] Among them, DT5 represents the normal compaction trend line, A and A0 are the first trend parameter and the second trend parameter respectively, and H is the vertical depth. DT represents the acoustic time difference value at different vertical depths, and the compaction measurement standard, that is, the compaction degree f = (DT / DT5) expn , where expn is the empirical exponent, for example, 1.5.

[0090] The first trend parameter A and the second trend parameter A0 in Formula 1 are key parameters for constructing a normal compaction trend line. For different wellbores or rock formations, A and A0 may be different.

[0091] S303, determining trend parameters in the normal compaction trend line formula according to the acoustic wave time difference data of at least three different vertical depth points of the compacted layer in the well to be measured.

[0092] In this embodiment, optionally, the trend parameter includes a first trend parameter and a second trend parameter, such as the first trend parameter A and the second trend parameter A0 in the above formula 1. In the compacted layer of the well to be measured, the acoustic time difference data of at least three different vertical depth points are arbitrarily selected, and the acoustic time difference data includes: the acoustic time difference value and the vertical depth value of each depth point. According to the acoustic time difference data of at least three different vertical depth points selected, the trend parameter in the normal compaction trend line formula is determined.

[0093] Optionally, the acoustic wave time difference data of at least three different vertical depth points are converted into a logarithmic function form, and a trend parameter is obtained according to each converted logarithmic function form.

[0094] Optionally, the at least three different vertical depth points are 4 different vertical depth points. The acoustic wave time difference data of the 4 different vertical depth points are converted into a logarithmic function form, the logarithmic function form includes X and Y, wherein X is a coefficient of the logarithmic function, and Y represents a logarithmic function value;

[0095] in,

[0096] Among them, SD1, SD2, SD3, and SD4 represent the depths of four different vertical depth points, and X1, X2, X3, and X4 represent the coefficients of the logarithmic functions corresponding to the four different vertical depth points;

[0097] Among them, Y1=log(DT1), Y2=log(DT2), Y3=log(DT3), Y4=log(DT4);

[0098] Among them, DT1, DT2, DT3, and DT4 represent the acoustic time difference data at four different vertical depth points, and Y1, Y2, Y3, and Y4 represent the logarithmic function values ​​corresponding to the four different vertical depth points;

[0099] The trend parameters are obtained according to the logarithmic function form after each transformation, including:

[0100] According to the logarithmic function forms X1, X2, X3, X4, Y1, Y2, Y3, and Y4 at four different vertical depth points, the first parameter A1, the second parameter B, the third parameter C, the fourth parameter D, and the fifth parameter A2 are obtained respectively, wherein the first parameter The second parameter B = X1 + X2 + X3 + X4, the third parameter C = X1 * Y1 + X2 * Y2 + X3 * Y3 + X4 * Y, the fourth parameter D = Y1 + Y2 + Y3 + Y4, the fifth parameter

[0101] According to the first parameter A1, the second parameter B, the third parameter C, the fourth parameter D, the fifth parameter A2 and the following formula 2 and formula 3, the first trend parameter A and the second trend parameter A0 are obtained respectively;

[0102]

[0103]

[0104] In this embodiment, the first trend parameter A and the second trend parameter A0 are determined according to Formula 2 and Formula 3, respectively, that is, the trend parameters in the normal compaction trend line formula are determined.

[0105] S304. Determine a nonlinear compaction trend line according to the trend parameter.

[0106] In this embodiment, after determining the trend parameters A and A0 in the normal compaction trend line formula, A and A0 are substituted into Formula 1, and the corresponding nonlinear compaction trend line is determined according to Formula 1. For example: Figure 4 Schematic diagram of a comparison between a nonlinear compaction trend line and a conventional linear compaction trend line provided in an embodiment of the present application. Figure 4As shown in the figure, both the nonlinear compaction trend line and the conventional linear compaction trend line are established in the normal compaction layer A. In the normal compaction layer B, the measured acoustic wave time difference curve deviates from the conventional linear compaction trend line (straight line), showing abnormal high pressure characteristics; while the measured acoustic wave time difference curve is close to (does not deviate from) the nonlinear compaction trend line (parabola), showing normal compaction characteristics. Therefore, the nonlinear compaction trend line can more accurately predict the formation pressure, determine the abnormally high pressure formation, and predict multiple sets of abnormally high pressure layers.

[0107] S305. Determine abnormally high-pressure formations in sandstone and mudstone formations based on the nonlinear compaction trend line, acoustic wave time difference data at multiple different vertical depths, and overlying formation stress at multiple different vertical depths. The abnormally high-pressure formations are used to guide the development of oil and gas fields.

[0108] In this embodiment, optionally, the overlying formation stress is obtained according to the following formula 4:

[0109]

[0110] Among them, σ z represents the overlying stratum stress, g represents the gravitational acceleration, ρ represents the stratum density, and H represents the vertical depth.

[0111] For example, the formation density at multiple different vertical depths of the well to be tested in the sandstone-shale formation can be obtained through the wellbore data, and then the overlying formation stress at different vertical depths can be obtained according to Formula 4.

[0112] After obtaining the nonlinear compaction trend line, the acoustic time difference data at multiple different vertical depths, and the overlying formation stress at multiple different vertical depths, the formation pore pressure at multiple different vertical depths is obtained according to the following formula 5:

[0113] P f =σ z *(1-f)+a*f Formula 5

[0114] Among them, P f represents the formation pore pressure, σ z represents the stress of the overlying strata, f represents the degree of compaction, and a represents the correction coefficient of normal pore pressure (for example, the value is 1).

[0115] After obtaining the formation pore pressures at multiple different vertical depths, each formation pore pressure is converted to the corresponding formation pore pressure gradient according to the following formula 6:

[0116]

[0117] Among them, P p represents the formation pore pressure gradient, P frepresents the formation pore pressure, H represents the vertical depth, and g represents the gravitational acceleration.

[0118] According to the formation pore pressure gradients obtained at multiple different vertical depth points, when the pore pressure gradient value exceeds a threshold value (such as 1.2), the formation is determined to be an abnormally high-pressure formation. The abnormally high-pressure formation is used to guide the development of oil and gas fields.

[0119] Figure 5 This is a schematic diagram of the formation pressure prediction of Well XX in the Tarim Oilfield provided in one embodiment of the present application. Figure 5 As shown, the formation pressure of the vertical depth of XX well in Tarim Oilfield is predicted from 1600 meters to 7200 meters. The measured formation pore pressure gradient of XX well in Tarim Oilfield at 4832 meters is 1.12g / cm3. The formation pore pressure gradient (conventional) obtained by using the conventional linear compaction trend line prediction is 1.60g / cm3, which is significantly different from the measured value of 1.12g / cm3. The formation pore pressure gradient (four-point method) obtained by using the nonlinear compaction trend line prediction of the present application is 1.12g / cm3, which is consistent with the measured value of 1.12g / cm3. Therefore, the nonlinear compaction trend line of the present application can more accurately predict the formation pressure, determine the abnormally high-pressure formation, and predict multiple sets of abnormally high-pressure layers, thereby effectively guiding the development of oil and gas fields.

[0120] The prediction method of abnormally high pressure formation in sand-mudstone formation provided in the present embodiment obtains acoustic wave time difference data of multiple different vertical depth points of the well to be measured in the sand-mudstone formation in response to the abnormally high pressure prediction instruction, determines the normal compaction trend line formula, determines the trend parameter in the normal compaction trend line formula according to the acoustic wave time difference data of at least three different vertical depth points of the compacted layer in the well to be measured, determines the nonlinear compaction trend line according to the trend parameter, determines the abnormally high pressure formation in the sand-mudstone formation according to the nonlinear compaction trend line, the acoustic wave time difference data of multiple different vertical depth points and the overlying formation stress at multiple different vertical depth points, and the abnormally high pressure formation is used to guide the development of oil and gas fields. Since the present application constructs a nonlinear compaction trend line according to the acoustic wave time difference data of at least three different vertical depth points of the compacted layer in the well to be measured, the nonlinear compaction trend line can approach the actual change of formation pressure, and therefore, can accurately predict the abnormally high pressure formation of the sand-mudstone formation containing multiple abnormally high pressure systems, thereby providing an important basis for the optimization of well control equipment, drilling fluid density and wellbore structure design, and can guide the exploration and development of oil and gas fields.

[0121] Figure 6 A schematic diagram of a device for predicting abnormally high pressure formations in sandstone and mudstone formations according to an embodiment of the present application is shown in FIG. Figure 6As shown, the prediction device 600 for abnormally high pressure sandstone and mudstone formations in this embodiment includes: an acquisition module 601 , a construction module 602 , and a determination module 603 .

[0122] The acquisition module 601 is used to acquire the acoustic wave time difference data of a plurality of different vertical depth points of the well to be measured in the sandstone-mudstone formation in response to the abnormally high pressure prediction instruction.

[0123] The construction module 602 is used to construct a nonlinear compaction trend line according to the acoustic wave time difference data of at least three different vertical depth points of the compaction layer in the well to be measured.

[0124] Determination module 603 is used to determine abnormally high-pressure formations in sandstone and mudstone formations based on nonlinear compaction trend lines, acoustic wave time difference data at multiple different vertical depths, and overlying formation stresses at multiple different vertical depths. The abnormally high-pressure formations are used to guide the development of oil and gas fields.

[0125] Based on any of the above embodiments, a module 602 is constructed, specifically for:

[0126] Determine the normal compaction trend line formula;

[0127] A nonlinear compaction trend line is constructed based on the acoustic time difference data of at least three different vertical depth points of the compaction layer in the well to be tested and the normal compaction trend line formula.

[0128] Based on any of the above embodiments, a module 602 is constructed, specifically for:

[0129] Determine the trend parameter in the normal compaction trend line formula based on the acoustic time difference data of at least three different vertical depth points of the compacted layer in the well to be tested;

[0130] Based on the trend parameters, the nonlinear compaction trend line is determined.

[0131] Based on any of the above embodiments, a module 602 is constructed, specifically for:

[0132] The acoustic time difference data of at least three different vertical depth points are converted into a logarithmic function form, and a trend parameter is obtained according to each converted logarithmic function form.

[0133] Based on any of the above-mentioned embodiments, the trend parameter includes a first trend parameter and a second trend parameter;

[0134] The normal compaction trend line formula is as follows:

[0135]

[0136] Among them, DT5 represents the normal compaction trend line, A and A0 are the first trend parameter and the second trend parameter respectively, and H is the vertical depth.

[0137] Based on any of the above embodiments, the at least three different vertical depth points are four different vertical depth points, and the construction module 602 is specifically used for:

[0138] The acoustic time difference data at four different vertical depth points are converted into a logarithmic function form, where the logarithmic function form includes X and Y, wherein X is the coefficient of the logarithmic function and Y represents the logarithmic function value;

[0139] in,

[0140] Among them, SD1, SD2, SD3, and SD4 represent the depths of four different vertical depth points, and X1, X2, X3, and X4 represent the coefficients of the logarithmic functions corresponding to the four different vertical depth points;

[0141] Among them, Y1=log(DT1), Y2=log(DT2), Y3=log(DT3), Y4=log(DT4);

[0142] Among them, DT1, DT2, DT3, and DT4 represent the acoustic time difference data at four different vertical depth points, and Y1, Y2, Y3, and Y4 represent the logarithmic function values ​​corresponding to the four different vertical depth points;

[0143] According to the logarithmic function forms X1, X2, X3, X4, Y1, Y2, Y3, and Y4 at four different vertical depth points, the first parameter A1, the second parameter B, the third parameter C, the fourth parameter D, and the fifth parameter A2 are obtained respectively, wherein the first parameter The second parameter B = X1 + X2 + X3 + X4, the third parameter C = X1 * Y1 + X2 * Y2 + X3 * Y3 + X4 * Y, the fourth parameter D = Y1 + Y2 + Y3 + Y4, the fifth parameter

[0144] According to the first parameter A1, the second parameter B, the third parameter C, the fourth parameter D, the fifth parameter A2 and the following formula 2 and formula 3, the first trend parameter A and the second trend parameter A0 are obtained respectively;

[0145]

[0146]

[0147] The device of this embodiment can be used to execute the technical solution of any of the above-mentioned method embodiments. Its implementation principle and technical effects are similar and will not be repeated here.

[0148] Figure 7 A schematic diagram of a device for predicting abnormally high pressure formations in sandstone and mudstone formations according to another embodiment of the present application is shown in FIG. Figure 7 As shown, the prediction device 700 for abnormally high pressure sandstone formation in this embodiment includes: a memory 701 and a processor 702. The memory 701 and the processor 702 are connected via a bus.

[0149] The memory 701 is used to store program instructions.

[0150] The processor 702 is used to call the program instructions in the memory to execute:

[0151] In response to an abnormally high pressure prediction instruction, the acoustic wave time difference data of a plurality of different vertical depth points of a well to be measured in a sandstone-mudstone formation is obtained; a nonlinear compaction trend line is constructed based on the acoustic wave time difference data of at least three different vertical depth points of a compacted layer in the well to be measured; the abnormally high pressure formation in the sandstone-mudstone formation is determined based on the nonlinear compaction trend line, the acoustic wave time difference data of a plurality of different vertical depth points and the overlying formation stress at a plurality of different vertical depth points, and the abnormally high pressure formation is used to guide the development of oil and gas fields.

[0152] Based on any of the above embodiments, the processor 702 is specifically configured to:

[0153] Determine the normal compaction trend line formula;

[0154] A nonlinear compaction trend line is constructed based on the acoustic time difference data of at least three different vertical depth points of the compaction layer in the well to be tested and the normal compaction trend line formula.

[0155] Based on any of the above embodiments, the processor 702 is specifically configured to:

[0156] Determine the trend parameter in the normal compaction trend line formula based on the acoustic time difference data of at least three different vertical depth points of the compacted layer in the well to be tested;

[0157] Based on the trend parameters, the nonlinear compaction trend line is determined.

[0158] Based on any of the above embodiments, the processor 702 is specifically configured to:

[0159] The acoustic time difference data of at least three different vertical depth points are converted into a logarithmic function form, and a trend parameter is obtained according to each converted logarithmic function form.

[0160] Based on any of the above-mentioned embodiments, the trend parameter includes a first trend parameter and a second trend parameter;

[0161] The normal compaction trend line formula is as follows:

[0162]

[0163] Among them, DT5 represents the normal compaction trend line, A and A0 are the first trend parameter and the second trend parameter respectively, and H is the vertical depth.

[0164] Based on any of the above embodiments, the at least three different vertical depth points are four different vertical depth points, and the processor 702 is specifically configured to:

[0165] The acoustic time difference data at four different vertical depth points are converted into a logarithmic function form, where the logarithmic function form includes X and Y, wherein X is the coefficient of the logarithmic function and Y represents the logarithmic function value;

[0166] in,

[0167] Among them, SD1, SD2, SD3, and SD4 represent the depths of four different vertical depth points, and X1, X2, X3, and X4 represent the coefficients of the logarithmic functions corresponding to the four different vertical depth points;

[0168] Among them, Y1=log(DT1), Y2=log(DT2), Y3=log(DT3), Y4=log(DT4);

[0169] Among them, DT1, DT2, DT3, and DT4 represent the acoustic time difference data at four different vertical depth points, and Y1, Y2, Y3, and Y4 represent the logarithmic function values ​​corresponding to the four different vertical depth points;

[0170] According to the logarithmic function forms X1, X2, X3, X4, Y1, Y2, Y3, and Y4 at four different vertical depth points, the first parameter A1, the second parameter B, the third parameter C, the fourth parameter D, and the fifth parameter A2 are obtained respectively, wherein the first parameter The second parameter B = X1 + X2 + X3 + X4, the third parameter C = X1 * Y1 + X2 * Y2 + X3 * Y3 + X4 * Y, the fourth parameter D = Y1 + Y2 + Y3 + Y4, the fifth parameter

[0171] According to the first parameter A1, the second parameter B, the third parameter C, the fourth parameter D, the fifth parameter A2 and the following formula 2 and formula 3, the first trend parameter A and the second trend parameter A0 are obtained respectively;

[0172]

[0173]

[0174] The device of this embodiment can be used to execute the technical solution of any of the above-mentioned method embodiments. Its implementation principle and technical effects are similar and will not be repeated here.

[0175] Figure 8 A schematic diagram of a device for predicting abnormally high pressure formations in sandstone and mudstone formations according to another embodiment of the present application is shown in FIG. Figure 8 As shown, for example, the prediction device 800 for abnormally high pressure formations in sandstone and mudstone formations can be provided as a server or a computer. Figure 8 The prediction device 800 for abnormally high pressure formations in sandstone and mudstone formations includes a processing component 801, which further includes one or more processors, and a memory resource represented by a memory 802, for storing instructions executable by the processing component 801, such as an application. The application stored in the memory 802 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 801 is configured to execute instructions to perform any of the above method embodiments.

[0176] The prediction device 800 for abnormally high pressure formations in sand-mudstone formations may further include a power supply component 803 configured to perform power management of the prediction device 800 for abnormally high pressure formations in sand-mudstone formations, a wired or wireless network interface 804 configured to connect the prediction device 800 for abnormally high pressure formations in sand-mudstone formations to a network, and an input / output (I / O) interface 805. The prediction device 800 for abnormally high pressure formations in sand-mudstone formations may operate based on an operating system stored in the memory 802, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM or the like.

[0177] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned method for predicting abnormally high-pressure sandstone and mudstone formations.

[0178] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above-mentioned method for predicting abnormally high-pressure sandstone and mudstone formations is implemented.

[0179] The computer-readable storage medium mentioned above can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0180] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist as discrete components in the prediction device for abnormally high pressure formations in sandstone and mudstone formations.

[0181] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0182] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for predicting abnormally high pressure formations in sandstone and mudstone formations, characterized in that: include: In response to the abnormally high pressure prediction instruction, acquiring acoustic wave time difference data of a plurality of different vertical depth points of the well to be tested in the sandstone-mudstone formation; Constructing a nonlinear compaction trend line based on acoustic wave time difference data of at least three different vertical depth points of the compacted layer in the well to be measured; Determine an abnormally high-pressure formation in the sandstone-mudstone formation according to the nonlinear compaction trend line, the acoustic wave time difference data at the multiple different vertical depth points, and the overlying formation stress at the multiple different vertical depth points, wherein the abnormally high-pressure formation is used to guide the development of oil and gas fields; The step of constructing a nonlinear compaction trend line based on the acoustic time difference data of at least three different vertical depth points of the compacted layer in the well to be measured comprises: Determine the normal compaction trendline formula; The constructing of the nonlinear compaction trend line according to the acoustic time difference data of at least three different vertical depth points of the compaction layer in the well to be measured and the normal compaction trend line formula comprises: Determine the trend parameter in the normal compaction trend line formula according to the acoustic wave time difference data of at least three different vertical depth points of the compacted layer in the well to be tested; Determining the nonlinear compaction trend line according to the trend parameter; Determining the trend parameter in the normal compaction trend line formula according to the acoustic wave time difference data of at least three different vertical depth points of the compacted layer in the well to be measured includes: Converting the acoustic wave time difference data of the at least three different vertical depth points into a logarithmic function form, and obtaining the trend parameter according to each converted logarithmic function form; The at least three different vertical depth points are four different vertical depth points, and the step of converting the acoustic wave time difference data of the at least three different vertical depth points into a logarithmic function form includes: Converting the acoustic wave time difference data at the four different vertical depth points into a logarithmic function form, wherein the logarithmic function form includes X and Y, wherein X is a coefficient of the logarithmic function and Y represents a logarithmic function value; in, Wherein, SD1, SD2, SD3, and SD4 respectively represent the depths of the four different vertical depth points, and X1, X2, X3, and X4 respectively represent the coefficients of the logarithmic functions corresponding to the four different vertical depth points; Among them, Y1=log(DT1), Y2=log(DT2), Y3=log(DT3), Y4=log(DT4); Among them, DT1, DT2, DT3, and DT4 respectively represent the acoustic wave time difference data at the four different vertical depth points, and Y1, Y2, Y3, and Y4 respectively represent the logarithmic function values ​​corresponding to the four different vertical depth points.

2. The method according to claim 1, characterized in that The trend parameters include a first trend parameter and a second trend parameter; The normal compaction trend line formula is as follows: Among them, DT5 represents the normal compaction trend line, A and A0 are the first trend parameter and the second trend parameter respectively, and H is the vertical depth.

3. The method according to claim 2, characterized in that The step of obtaining the trend parameter according to each transformed logarithmic function form comprises: According to the logarithmic function forms X1, X2, X3, X4, Y1, Y2, Y3, and Y4 at the four different vertical depth points, the first parameter A1, the second parameter B, the third parameter C, the fourth parameter D, and the fifth parameter A2 are obtained respectively, wherein the first parameter The second parameter B = X1 + X2 + X3 + X4, the third parameter C = X1 * Y1 + X2 * Y2 + X3 * Y3 + X4 * Y4, the fourth parameter D = Y1 + Y2 + Y3 + Y4, the fifth parameter According to the first parameter A1, the second parameter B, the third parameter C, the fourth parameter D, the fifth parameter A2 and the following formula 2 and formula 3, the first trend parameter A and the second trend parameter A0 are obtained respectively; 4. A prediction device for abnormally high pressure sandstone formations, characterized in that: include: An acquisition module, for acquiring acoustic wave time difference data of a plurality of different vertical depth points of a well to be tested in a sandstone-mudstone formation in response to an abnormally high pressure prediction instruction; A construction module, for constructing a nonlinear compaction trend line according to acoustic wave time difference data of at least three different vertical depth points of the compaction layer in the well to be tested; A determination module, for determining an abnormally high-pressure formation in the sandstone-mudstone formation according to the nonlinear compaction trend line, the acoustic wave time difference data at the plurality of different vertical depth points, and the overlying formation stress at the plurality of different vertical depth points, wherein the abnormally high-pressure formation is used to guide the development of an oil and gas field; The construction module is specifically used to determine the normal compaction trend line formula; construct the nonlinear compaction trend line according to the acoustic wave time difference data of at least three different vertical depth points of the compaction layer in the well to be measured and the normal compaction trend line formula; The construction module is specifically used to determine the trend parameter in the normal compaction trend line formula according to the acoustic wave time difference data of at least three different vertical depth points of the compaction layer in the well to be measured; and determine the nonlinear compaction trend line according to the trend parameter; The construction module is specifically used to convert the acoustic wave time difference data of the at least three different vertical depth points into a logarithmic function form, and obtain the trend parameter according to each converted logarithmic function form; The at least three different vertical depth points are four different vertical depth points, and the construction module is specifically used to convert the acoustic wave time difference data of the four different vertical depth points into a logarithmic function form, wherein the logarithmic function form includes X and Y, wherein X is a coefficient of the logarithmic function, and Y represents a logarithmic function value; in, Wherein, SD1, SD2, SD3, and SD4 respectively represent the depths of the four different vertical depth points, and X1, X2, X3, and X4 respectively represent the coefficients of the logarithmic functions corresponding to the four different vertical depth points; Among them, Y1=log(DT1), Y2=log(DT2), Y3=log(DT3), Y4=log(DT4); Among them, DT1, DT2, DT3, and DT4 respectively represent the acoustic wave time difference data at the four different vertical depth points, and Y1, Y2, Y3, and Y4 respectively represent the logarithmic function values ​​corresponding to the four different vertical depth points.

5. A prediction device for abnormally high pressure sandstone formations, characterized in that: include: Memory and processor; The memory is used to store program instructions; The processor is used to call the program instructions in the memory to execute the method for predicting abnormally high-pressure sandstone and mudstone formations as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed, the method for predicting abnormally high-pressure sandstone and mudstone formations according to any one of claims 1 to 3 is implemented.

7. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for predicting abnormally high-pressure sandstone and mudstone formations according to any one of claims 1 to 3 is implemented.

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