Formation pressure determination method and device based on particle stress

Abstract

The invention provides a formation pressure determination method and device based on particle stress. Based on the target relation graph and a pre-constructed compaction trend line, a first compaction section and a second compaction section of the target well are identified and divided; determining a first particle stress corresponding to the first compaction section based on the stratum depth of the first compaction section, and determining a second particle stress corresponding to the second compaction section based on the stratum depth of the second compaction section and the longitudinal wave velocity corresponding to the interval transit time; determining a first formation pore pressure corresponding to the first compaction section on the basis of the overburden load, the formation porosity and the first particle stress of the target well; according to the overburden load, the formation porosity and the second particle stress of the target well, second formation pore pressure corresponding to the second compaction section is determined; and based on the first formation pore pressure and the second formation pore pressure, target formation pore pressure of the target well in different depth intervals is determined. Therefore, the pressure prediction precision under the complex compaction condition is effectively improved.

Description

Technical Field

[0001] This specification belongs to the field of oil and gas exploration technology, and in particular relates to a method and device for determining formation pressure based on particle stress. Background Art

[0002] Traditional formation pore pressure prediction methods have limited applicability, reliance on idealized assumptions, and single model parameter settings, making it difficult to accurately reflect the true pore pressure distribution of formations under different compaction states. Especially under complex geological conditions, there is a problem of low prediction accuracy.

[0003] To address the above issues, no effective solutions have been proposed so far. Summary of the Invention

[0004] This specification provides a method and apparatus for determining formation pressure based on grain stress. First, by constructing a target relationship diagram between acoustic transit time and formation depth, and combining it with a pre-established compaction trend line, the compaction state of the target well is identified, dividing the target well into a first compaction segment located at the top and a second compaction segment located at the bottom. This achieves segmented processing of formations with different compaction characteristics, helping to improve the targetedness of pore pressure prediction. Subsequently, a first grain stress is constructed based on the formation depth of the first compaction segment, and a second grain stress is constructed based on the formation depth and the longitudinal wave velocity corresponding to the acoustic transit time of the second compaction segment. This ensures that the grain stress calculation is more consistent with the rock stress characteristics under different compaction conditions. Furthermore, the formation pore pressure corresponding to the two segments is calculated separately, combining the overburden load and formation porosity, avoiding the errors caused by the unified model and single parameter in traditional methods. Finally, the pore pressure results of the two segments are combined to determine the target formation pore pressure at different depth intervals of the target well, effectively improving the pressure prediction accuracy under complex compaction conditions and providing a more reliable pressure basis for formation pressure risk prevention and control strategies.

[0005] This specification provides a method for determining formation pressure based on particle stress, comprising:

[0006] Obtaining well logging data of target wells in the target area;

[0007] constructing a target relationship diagram between the acoustic wave time difference and the formation depth based on the acoustic wave time difference in the logging data;

[0008] Based on the target relationship graph and the pre-constructed compaction trend line, identifying and dividing the first compaction section and the second compaction section of the target well; wherein the first compaction section is a shallow normally compacted section, and the second compaction section is a deep under-compacted section;

[0009] determining a first particle stress corresponding to the first compaction section based on the formation depth of the first compaction section, and determining a second particle stress corresponding to the second compaction section based on the formation depth of the second compaction section and the longitudinal wave velocity corresponding to the acoustic wave moveout;

[0010] determining a first formation pore pressure corresponding to the first compaction section of the target well based on an overburden load corresponding to the first compaction section, a formation porosity corresponding to the first compaction section, and the first particle stress;

[0011] determining a second formation pore pressure corresponding to the second compaction section of the target well based on the overburden load corresponding to the second compaction section, the formation porosity corresponding to the second compaction section, and the second particle stress;

[0012] Based on the first formation pore pressure and the second formation pore pressure, target formation pore pressures at different depth intervals of the target well are determined; wherein the target formation pore pressures are used to guide a formation pressure risk prevention and control strategy for the target well.

[0013] In one embodiment, determining the first particle stress corresponding to the first compaction section based on the formation depth of the first compaction section includes:

[0014] determining a first intermediate value according to a first preset linear coefficient and a formation depth corresponding to the first compaction section;

[0015] A first particle stress corresponding to the first compaction section is determined according to the first intermediate value and a preset intercept.

[0016] In one embodiment, determining the second particle stress corresponding to the second compaction section based on the formation depth of the second compaction section and the longitudinal wave velocity corresponding to the acoustic wave time difference includes:

[0017] determining a second intermediate value according to a second preset linear coefficient and a formation depth corresponding to the second compaction section;

[0018] determining a third intermediate value based on the second intermediate value and a preset reference value;

[0019] determining a fourth intermediate value according to the longitudinal wave velocity and a preset influence coefficient;

[0020] A second particle stress corresponding to the second compaction section is determined according to the second intermediate value, the third intermediate value, and the fourth intermediate value.

[0021] In one embodiment, the overburden load is determined by calculating the average density of the formation, the corresponding formation depth, and the gravitational acceleration based on the density logging data in the logging data; the formation porosity is obtained by calculating the saturated rock acoustic wave time difference, the pore fluid acoustic wave time difference, and the rock matrix acoustic wave time difference of the target well.

[0022] In one embodiment, determining the first formation pore pressure corresponding to the first compaction section of the target well based on the overburden load corresponding to the first compaction section, the formation porosity corresponding to the first compaction section, and the first particle stress includes:

[0023] The pore pressure of the first formation is determined according to the following formula:

[0024]

[0025] Among them, P f1 is the pore pressure of the first formation, Δt is the acoustic time difference of saturated rock, Δt f is the acoustic time difference of the pore fluid, Δt m is the rock matrix acoustic time difference, is the average density of the formation, h is the depth of the formation, and g is the gravitational acceleration.

[0026] In one embodiment, determining the second formation pore pressure corresponding to the second compaction section of the target well based on the overburden load corresponding to the second compaction section, the formation porosity corresponding to the second compaction section, and the second particle stress includes:

[0027] The pore pressure of the second formation is determined according to the following formula:

[0028]

[0029] Among them, P f2 is the second formation pore pressure.

[0030] In one embodiment, the identifying and dividing the first compaction section and the second compaction section of the target well based on the target relationship graph and the pre-constructed compaction trend line includes:

[0031] comparing the target relationship graph with the pre-constructed compaction trend line point by point to identify deviations of the acoustic wave moveout relative to the pre-constructed compaction trend line in different depth intervals;

[0032] When the deviation value of the acoustic time difference from the pre-established compaction trend line is not greater than a preset threshold, the formation in the corresponding formation depth interval is determined to be in a normal compaction state and divided into a first compaction section;

[0033] When the deviation value of the acoustic time difference from the pre-constructed compaction trend line is greater than a preset threshold, the formation in the corresponding formation depth interval is determined to be under-compacted and divided into a second compaction section.

[0034] This specification provides a device for determining formation pressure based on particle stress, comprising:

[0035] A data acquisition module, used to acquire logging data of a target well in a target area;

[0036] a relationship graph determination module, configured to construct a target relationship graph between acoustic wave time difference and formation depth based on the acoustic wave time difference in the well logging data;

[0037] a compaction section determination module, configured to identify and divide the target well into a first compaction section and a second compaction section based on the target relationship graph and a pre-constructed compaction trend line; wherein the first compaction section is a shallow normally compacted section, and the second compaction section is a deep under-compacted section;

[0038] a particle stress determination module, configured to determine a first particle stress corresponding to the first compaction section based on the formation depth of the first compaction section, and to determine a second particle stress corresponding to the second compaction section based on the formation depth of the second compaction section and a longitudinal wave velocity corresponding to the acoustic wave moveout;

[0039] a first pressure determination module, configured to determine a first formation pore pressure corresponding to the first compaction section of the target well based on an overburden load corresponding to the first compaction section, a formation porosity corresponding to the first compaction section, and the first particle stress;

[0040] a second pressure determination module, configured to determine a second formation pore pressure corresponding to the second compaction section of the target well based on the overburden load corresponding to the second compaction section, the formation porosity corresponding to the second compaction section, and the second particle stress;

[0041] A target pressure determination module is used to determine the target formation pore pressure in different depth intervals of the target well based on the first formation pore pressure and the second formation pore pressure; wherein the target formation pore pressure is used to guide the formation pressure risk prevention and control strategy of the target well.

[0042] This specification also provides an electronic device, including a processor and a memory for storing processor-executable instructions, wherein when the processor executes the instructions, a method for determining formation pressure based on particle stress is implemented.

[0043] This specification also provides a computer-readable storage medium having computer instructions stored thereon, which, when executed, implement a method for determining formation pressure based on particle stress.

[0044] Based on a particle stress-based formation pressure determination method provided in this specification, well logging data of a target well in a target area is obtained; based on the acoustic wave time difference in the well logging data, a target relationship diagram between the acoustic wave time difference and the formation depth is constructed; based on the target relationship diagram and a pre-constructed compaction trend line, a first compaction section and a second compaction section of the target well are identified and divided; wherein the first compaction section is a shallow normally compacted section, and the second compaction section is a deep under-compacted section; based on the formation depth of the first compaction section, a first particle stress corresponding to the first compaction section is determined, and based on the formation depth of the second compaction section and the longitudinal wave velocity corresponding to the acoustic wave time difference, the second compaction section is determined The first formation pore pressure corresponding to the first compaction section of the target well is determined based on the overburden load corresponding to the first compaction section, the formation porosity corresponding to the first compaction section, and the first particle stress; the second formation pore pressure corresponding to the second compaction section of the target well is determined based on the overburden load corresponding to the second compaction section, the formation porosity corresponding to the second compaction section, and the second particle stress; the target formation pore pressure for different depth intervals of the target well is determined based on the first formation pore pressure and the second formation pore pressure; wherein the target formation pore pressure is used to guide the formation pressure risk prevention and control strategy of the target well. In this way, the compaction state of the target well is first identified by constructing a target relationship diagram between the acoustic wave time difference and the formation depth, and combining it with the pre-constructed compaction trend line, dividing the first compaction section located at the top and the second compaction section located at the bottom, thereby achieving segmented processing of formations with different compaction characteristics, which helps to improve the targetedness of pore pressure prediction. Subsequently, the first particle stress was constructed based on the formation depth of the first compaction section, and the second particle stress was constructed based on the formation depth of the second compaction section and the longitudinal wave velocity corresponding to the acoustic time difference, so that the calculation of particle stress is more consistent with the rock stress characteristics under different compaction conditions. On this basis, the formation pore pressure corresponding to the two sections was calculated separately, combining the overburden load and formation porosity, avoiding the errors caused by the unified model and single parameters in traditional methods. Finally, the pore pressure results of the two sections were combined to determine the target formation pore pressure at different depth intervals of the target well, effectively improving the pressure prediction accuracy under complex compaction conditions and providing a more reliable pressure basis for formation pressure risk prevention and control strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of this specification, the following is a brief introduction to the drawings required for use in the embodiments. The drawings described below are only some of the embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0046] Figure 1 is a flow chart of a method for determining formation pressure based on particle stress provided by an embodiment of this specification;

[0047] Figure 2 This is a schematic diagram of the structure of an electronic device provided by an embodiment of this specification;

[0048] Figure 3 This is a schematic diagram of the structure of a device for determining formation pressure based on particle stress provided by an embodiment of this specification;

[0049] Figure 4 This is a schematic diagram of a comparison between formation pore pressure prediction and actual measurement provided by an embodiment of this specification. DETAILED DESCRIPTION

[0050] To help those skilled in the art better understand the technical solutions in this specification, the following will provide a clear and complete description of the technical solutions in the embodiments of this specification, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. All other embodiments derived by those skilled in the art based on the embodiments in this specification without creative effort shall fall within the scope of protection of this specification.

[0051] The existing formation pore pressure prediction technology has the following main problems: First, it generally relies on empirical statistical models, which has a limited scope of application, and the prediction accuracy is highly sensitive to the parameter measurement accuracy and the quality of trend line construction; second, theoretical models based on effective stress are widely used, but their assumptions are idealized and it is difficult to accurately reflect the complex skeleton stress state and pore characteristics of underground rocks, especially in undercompacted or overpressured formations. The applicability is poor; third, traditional models fail to distinguish between the differences between shallow normal compaction and deep undercompaction mechanisms, and ignore the changing laws of rock skeleton stress evolution with depth, resulting in insufficient accuracy of the overall prediction results under complex geological conditions.

[0052] To address the root cause of the above problems, this manual first constructs a target relationship diagram between the acoustic time difference and the formation depth, and combines it with the pre-constructed compaction trend line to identify the compaction state of the target well, dividing it into the first compaction section at the top and the second compaction section at the bottom, thus achieving segmented processing of strata with different compaction characteristics, which helps to improve the pertinence of pore pressure prediction. Subsequently, the first particle stress is constructed based on the formation depth of the first compaction section, and the second particle stress is constructed based on the formation depth of the second compaction section and the longitudinal wave velocity corresponding to the acoustic time difference, so that the calculation of particle stress is more consistent with the rock stress characteristics under different compaction conditions. On this basis, the formation pore pressure corresponding to the two sections is calculated separately in combination with the overburden load and formation porosity, avoiding the errors caused by the unified model and single parameter in the traditional method. Finally, the pore pressure results of the two sections are combined to determine the target formation pore pressure in different depth intervals of the target well, effectively improving the pressure prediction accuracy under complex compaction conditions and providing a more reliable pressure basis for the formation pressure risk prevention and control strategy.

[0053] See Figure 1 As shown, the embodiment of this specification provides a method for determining formation pressure based on particle stress, wherein the method is specifically applied to the server side. In specific implementation, the method may include the following:

[0054] S101: Acquire logging data of a target well in a target area;

[0055] S102: constructing a target relationship diagram between the acoustic wave time difference and the formation depth according to the acoustic wave time difference in the logging data;

[0056] S103: Based on the target relationship graph and the pre-constructed compaction trend line, identifying and dividing a first compaction section and a second compaction section of the target well; wherein the first compaction section is a shallow normally compacted section, and the second compaction section is a deep under-compacted section;

[0057] S104: determining a first grain stress corresponding to the first compaction section based on the formation depth of the first compaction section, and determining a second grain stress corresponding to the second compaction section based on the formation depth of the second compaction section and a longitudinal wave velocity corresponding to the acoustic wave moveout;

[0058] S105: determining a first formation pore pressure corresponding to the first compaction section of the target well based on the overburden load corresponding to the first compaction section, the formation porosity corresponding to the first compaction section, and the first particle stress;

[0059] S106: determining a second formation pore pressure corresponding to the second compaction section of the target well based on the overburden load corresponding to the second compaction section, the formation porosity corresponding to the second compaction section, and the second particle stress;

[0060] S107: Determine target formation pore pressures at different depth intervals of the target well based on the first formation pore pressure and the second formation pore pressure; wherein the target formation pore pressure is used to guide a formation pressure risk prevention and control strategy for the target well.

[0061] The above-mentioned well logging data includes sonic logging data and density logging data. The sonic logging data is obtained through downhole measurements using a sonic logging tool. During drilling, the tool is lowered into the wellbore. Using its built-in sonic transmitter and receiver, it emits longitudinal wave signals to the wellbore wall and surrounding formations, and receives the response as they propagate through the formation. By measuring the propagation time difference of the sonic waves over a certain distance, the propagation time difference in the formation is calculated, representing the sonic logging data, which reflects the elastic properties and compaction state of the formation. The density logging data is obtained through downhole measurements using a density logging tool. The tool carries a gamma-ray source and detector. As the tool moves along the wellbore, the source emits gamma rays into the formation. Some of these rays are scattered by electrons in the rock and received by the detector. The electron density of the formation is inferred from the attenuation of the gamma rays and converted into a volume density value, generating density logging data that reflects the compactness of the formation and the load characteristics of the overlying rock.

[0062] In some embodiments, constructing a target relationship diagram between acoustic wave time difference and formation depth based on the acoustic wave time difference in the logging data may include:

[0063] With formation depth (h) as the horizontal axis and acoustic transit time (Δt) as the vertical axis, the acoustic logging data corresponding to each depth point of the target well are extracted, and the acoustic transit time value corresponding to each depth point is plotted point by point in a two-dimensional coordinate graph to form a target relationship graph corresponding to depth-acoustic transit time. Furthermore, the target relationship graph can be curve fitted or data smoothed for subsequent comparison with a pre-constructed compaction trend line to identify the compaction status of different depth intervals.

[0064] In some embodiments, the pre-constructed compaction trend line can be based on the acoustic logging data of multiple normally compacted formation sample wells in the area. By statistically analyzing the changing relationship between the acoustic time difference and the formation depth, the formation interval data that is not affected by abnormal pressure and has a continuous and stable compaction process is selected, and a standard reference curve is established using a regression fitting method (such as exponential or linear fitting) to characterize the evolution trend of the acoustic time difference with depth under normal compaction conditions.

[0065] Specifically, first, in the target area or adjacent reference wells with similar geological conditions, a sandstone layer section with no overpressure and good compaction state is selected as a normal compaction sample, and the acoustic wave time difference (Δt) and formation depth (h) data at the corresponding depth are extracted; secondly, the selected sample data are preprocessed, including removing noise points, outliers and necessary depth correction to ensure data reliability; then, an empirical formula (such as an exponential decay function or a logarithmic function) is used to perform least squares fitting on the relationship between the acoustic wave time difference and the depth to obtain a fitting curve and its parameters; in the fitting process, different lithologic sections can be fitted separately to consider the influence of lithology on the compaction characteristics; finally, the obtained fitting function is used as the normal compaction trend line of the area, which is used to subsequently determine whether the acoustic wave time difference data of the target well deviates, thereby identifying the formation compaction state.

[0066] In some embodiments, determining the target formation pore pressures at different depth intervals of the target well based on the first formation pore pressure and the second formation pore pressure may include:

[0067] Determining, based on the first formation pore pressure and the second formation pore pressure, an intermediate pore pressure of a connecting region of the first compaction section and the second compaction section according to a preset weighted difference rule;

[0068] The target formation pore pressures at different depth intervals of the target well are determined according to the first formation pore pressure, the second formation pore pressure, and the intermediate pore pressure.

[0069] Specifically, for the transition area between the first compaction section and the second compaction section, interpolation or transition fitting is performed according to a preset weighted difference rule based on the first formation pore pressure and the second formation pore pressure corresponding to the upper and lower boundaries, respectively, to construct an intermediate pore pressure value in the connection area, which is used to smoothly transition the pressure changes between the two compaction sections; subsequently, the pressure value calculated according to the particle stress linear model in the first compaction section, the pressure value calculated according to the wave velocity correction model in the second compaction section, and the pressure value obtained by transition interpolation in the intermediate area are combined to comprehensively form a continuous pore pressure distribution curve covering the entire depth range of the target well, thereby realizing a complete prediction of the formation pore pressure in multiple depth sections of the target well.

[0070] In this way, by introducing the intermediate pore pressure transition calculation between the first compaction stage and the second compaction stage, the smooth connection of the formation pore pressure under different compaction states can be achieved.

[0071] In some embodiments, the method is based on the physical differences in the formation compaction process: in the first compaction stage, the formation is an open system, the pore fluid can be discharged freely, the compaction is mainly driven by gravity, and the particle stress increases linearly with depth; while in the second compaction stage, the formation gradually turns into a closed system, the pore drainage is blocked, and the stress state of the rock skeleton is jointly affected by the pore retention pressure and elastic wave characteristics. Therefore, it is necessary to introduce parameters such as wave velocity that reflect the stiffness of the medium to establish a nonlinear particle stress model to ensure that it is consistent with the actual geological process.

[0072] In this way, dividing the formation into the first compaction stage (normal compaction) and the second compaction stage (undercompaction), and using different models for particle stress calculation in each stage, helps accurately reflect the true stress state of the formation under different compaction environments. The normal compaction stage can be simplified to a linear model dominated by depth control, while the undercompacted stage introduces parameters such as P-wave velocity to characterize complex compaction behavior. This improves the targetedness of particle stress estimation and overall prediction accuracy, avoiding the error accumulation caused by using a unified model.

[0073] Based on the above embodiment, first, by constructing a target relationship diagram between the acoustic time difference and the formation depth, and combining it with the pre-constructed compaction trend line, the compaction state of the target well is identified, and the first compaction section located at the top and the second compaction section located at the bottom are divided, thereby realizing segmented processing of formations with different compaction characteristics, which helps to improve the pertinence of pore pressure prediction. Subsequently, the first particle stress is constructed based on the formation depth of the first compaction section, and the second particle stress is constructed based on the formation depth of the second compaction section and the longitudinal wave velocity corresponding to the acoustic time difference, so that the calculation of particle stress is more consistent with the rock stress characteristics under different compaction conditions. On this basis, the formation pore pressure corresponding to the two sections is calculated separately in combination with the overburden load and formation porosity, avoiding the errors caused by the unified model and single parameter in the traditional method. Finally, the pore pressure results of the two sections are combined to determine the target formation pore pressure of different depth intervals of the target well, effectively improving the pressure prediction accuracy under complex compaction conditions, and providing a more reliable pressure basis for the formation pressure risk prevention and control strategy.

[0074] In some embodiments, the method for determining the first particle stress corresponding to the first compaction section based on the formation depth of the first compaction section may further include the following steps when implemented:

[0075] S1: determining a first intermediate value according to a first preset linear coefficient and a formation depth corresponding to the first compaction section;

[0076] S2: Determine a first particle stress corresponding to the first compaction section according to the first intermediate value and a preset intercept.

[0077] In some embodiments, first, for the first compaction section in a normal compaction state, a first preset linear coefficient a1 is selected, and the formation depth h of each measuring point in the section is multiplied to obtain a first intermediate value; then, the first intermediate value is added to the preset intercept a2 to calculate the first particle stress.

[0078] Specifically, the first particle stress is determined according to the following formula:

[0079] σ g1 =a1h+a2

[0080] Among them, σ g1 is the first particle stress, a1 is the first preset linear coefficient, h is the formation depth, and a2 is the preset intercept.

[0081] Furthermore, the first preset linear coefficient a1 and the preset intercept a2 were obtained through regression analysis of measured well data from multiple normally compacted formations within the region. Specifically, the method used known pore pressure, porosity, and overburden load to infer grain stress. This was then linearly fitted to the grain stress and the corresponding formation depth, and the optimal a1 and a2 values ​​were calculated using the least squares method.

[0082] By estimating the particle stress in the first compaction section by constructing a linear relationship based on depth, the stress change trend of the rock skeleton under normal compaction conditions can be accurately reflected.

[0083] In some embodiments, the method for determining the second particle stress corresponding to the second compaction section based on the formation depth of the second compaction section and the longitudinal wave velocity corresponding to the acoustic wave time difference may further include the following steps when implemented:

[0084] S1: determining a second intermediate value according to a second preset linear coefficient and a formation depth corresponding to the second compaction section;

[0085] S2: determining a third intermediate value according to the second intermediate value and a preset reference value;

[0086] S3: determining a fourth intermediate value according to the longitudinal wave velocity and a preset influence coefficient;

[0087] S4: Determine a second particle stress corresponding to the second compaction section according to the second intermediate value, the third intermediate value, and the fourth intermediate value.

[0088] In some embodiments, first, the formation depth of each measuring point in the second compaction section is multiplied by a second preset linear coefficient to obtain a second intermediate value, which reflects the basic contribution of depth to particle stress; then, the second intermediate value is combined with a preset reference value (such as a regional experience correction term) to obtain a third intermediate value, which is used to correct the nonlinear deviation caused by changes in formation structure; then, the longitudinal wave velocity is multiplied by a preset influence coefficient set according to the mechanical properties of the formation to obtain a fourth intermediate value, which is used to characterize the influence of velocity anomalies on stress changes; finally, based on the above three intermediate values, a combined calculation is performed according to the preset function model to obtain the second particle stress of the second compaction section.

[0089] Specifically, the second particle stress is determined according to the following formula:

[0090] σ g2 =b1h+b2-b3Vp 2.25

[0091] Among them, σ g2 is the second particle stress, b1 is the second preset linear coefficient, h is the depth of the formation, b2 is the preset reference value, b3 is the preset influence coefficient, V p is the longitudinal wave velocity.

[0092] By introducing longitudinal wave velocity information and multiple intermediate values ​​to construct a multi-factor stress estimation model, the sensitivity and adaptability of particle stress in the second compaction stage are effectively enhanced. This model is particularly suitable for the complex changes in stress state in undercompacted closed systems and helps to more accurately reflect the actual stress borne by the rock skeleton in abnormally overpressured formations.

[0093] In some embodiments, the method may further include the following when implemented:

[0094] The overburden load is determined by calculating the average density of the formation, the corresponding formation depth, and the gravitational acceleration based on the density logging data in the logging data; the formation porosity is calculated based on the saturated rock acoustic wave delay, the pore fluid acoustic wave delay, and the rock matrix acoustic wave delay of the target well.

[0095] In some embodiments, the density values ​​measured at each depth of the wellbore are integrated or weighted averaged to calculate the average density value of the corresponding formation segment. The overburden load is then calculated using the following formula in combination with the burial depth information of the formation segment and the standard gravity acceleration constant:

[0096]

[0097] Among them, P ov is the overlying load; is the average density of the formation; h is the depth of the formation; g is the acceleration due to gravity.

[0098] In addition, the overburden load can be obtained by integrating the density curve with depth:

[0099]

[0100] Where ρ represents the density value of the formation at each depth point.

[0101] In some embodiments, the formation porosity is obtained by acoustic logging based on the Willey time-averaged equation, that is, the formation porosity is obtained by using the acoustic time difference of saturated rock (Δt), the acoustic time difference of pore fluid (Δt f ) and rock matrix acoustic time difference (Δt m ), the formation porosity is obtained according to the following formula:

[0102]

[0103] in, is the formation porosity, Δt is the acoustic time difference of saturated rock, Δt f is the acoustic time difference of the pore fluid, Δt m is the rock matrix acoustic time difference.

[0104] In some embodiments, the method of determining the first formation pore pressure corresponding to the first compaction section of the target well based on the overburden load corresponding to the first compaction section, the formation porosity corresponding to the first compaction section, and the first particle stress may further include the following steps when implemented:

[0105] The pore pressure of the first formation is determined according to the following formula:

[0106]

[0107] Among them, P f1 is the pore pressure of the first formation, Δt is the acoustic time difference of saturated rock, Δt f is the acoustic time difference of the pore fluid, Δt m is the rock matrix acoustic time difference, is the average density of the formation, h is the depth of the formation, and g is the gravitational acceleration.

[0108] In this way, based on the combination of the Willy time-averaged model and the static equilibrium principle, the influence of different media in the formation on the wave velocity and the actual contribution of the skeleton stress are comprehensively considered, which effectively improves the accuracy of the pore pressure calculation ability of the shallow normal compaction section and avoids the errors caused by ignoring the changes in particle stress in the traditional model.

[0109] In some embodiments, the method of determining the second formation pore pressure corresponding to the second compaction section of the target well based on the overburden load corresponding to the second compaction section, the formation porosity corresponding to the second compaction section, and the second particle stress may further include the following steps when implemented:

[0110] The pore pressure of the second formation is determined according to the following formula:

[0111]

[0112] Among them, P f2 is the second formation pore pressure.

[0113] In this way, by introducing the wave velocity control term in the pore pressure calculation and combining it with the multi-factor particle stress expression method of the undercompacted section, it is possible to more accurately reflect the impact of insufficient compaction on the pore pressure evolution in a closed system, improve the identification and early warning capabilities of deep abnormal pressure areas, and have stronger adaptability and practical engineering guidance value.

[0114] In some embodiments, the method for identifying and dividing the first compaction section and the second compaction section of the target well based on the target relationship graph and the pre-constructed compaction trend line may further include the following steps when implemented:

[0115] comparing the target relationship graph with the pre-constructed compaction trend line point by point to identify deviations of the acoustic wave moveout relative to the pre-constructed compaction trend line in different depth intervals;

[0116] When the deviation value of the acoustic time difference from the pre-established compaction trend line is not greater than a preset threshold, the formation in the corresponding formation depth interval is determined to be in a normal compaction state and divided into a first compaction section;

[0117] When the deviation value of the acoustic time difference from the pre-constructed compaction trend line is greater than a preset threshold, the formation in the corresponding formation depth interval is determined to be under-compacted and divided into a second compaction section.

[0118] For example, a sonic transit time-depth target relationship diagram constructed from sonic logging data from a target well shows that above 1000 meters, the transit time fits well with the pre-established compaction trend line, with deviations consistently below the set 5% threshold. Therefore, this section is judged to be in a normally compacted state and classified as the first compaction section. However, below 1000 meters, the transit time begins to deviate consistently from the trend line by more than 10%, and the deviation increases with depth, indicating restricted fluid drainage and insufficient compaction. This section is identified as undercompacted and classified as the second compaction section. This deviation-based segmentation approach enables automatic identification of formation compaction states, facilitating the adoption of more realistic stratification strategies in subsequent particle stress modeling and pore pressure calculations.

[0119] The above-mentioned segmented identification strategy can automatically distinguish the compaction state of the formation based on logging data, avoiding the uncertainty caused by human subjective judgment. At the same time, the particle stress and pore pressure models can be more specifically applied to formation areas with different compaction conditions, thereby significantly improving the accuracy and reliability of pore pressure prediction. This is especially suitable for deep formations with complex compaction behavior or abnormal pressure evolution.

[0120] In some embodiments, acoustic logging data can be obtained from multiple comparison wells in the vicinity of the target well, and a plot of their acoustic travel time versus formation depth can be constructed. Compaction trends across these multiple wells can then be compared and analyzed, with the well with the most stable changes and intact overburden being selected as the reference well. The original compaction trend line of the target well can then be fitted and corrected based on the compaction trend line of this well. This method creates a compaction trend line that better reflects the local geological background and sedimentary characteristics, thereby improving the rationality and consistency of compaction segment divisions.

[0121] In some embodiments, to improve the smoothness and anti-interference capabilities of the target relationship graph between acoustic transit time and depth, a time window sliding average algorithm can be used to locally smooth the logging data. Specifically, a sliding average of the acoustic transit time values ​​within a specific depth range is calculated to produce a smoothed acoustic transit time curve, thereby constructing a more stable target relationship graph. This approach effectively reduces the interference of local anomalies on compaction trend assessment and improves the robustness of the pore pressure prediction model in high-noise environments.

[0122] In some embodiments, a machine learning model that integrates multiple well logging data and their derivative features can automatically identify and classify the compaction state of the formation. By constructing training samples and introducing classification algorithms such as random forests or support vector machines, the multidimensional response characteristics of each depth point in the well logging data are utilized to automatically classify the first and second compaction stages.

[0123] Specifically, a variety of logging curve data from the target well or adjacent wells are selected as model inputs, such as original logging data such as acoustic time difference (Δt), density (ρ), resistivity (Rt), and natural gamma ray (GR). At the same time, their first-order or second-order derivatives, moving averages, normalized values ​​and other derivative features can be further calculated to enhance the model's ability to identify formation change trends.

[0124] During the modeling phase, these eigenvalues ​​corresponding to each depth point are used as input vectors, and their actual compaction status labels (e.g., "0" for a normally compacted section, "1" for an undercompacted section) are annotated as the target output for supervised learning. Learning is then performed using a trained classification model (such as a random forest, support vector machine, or multilayer perceptron). Once the model is trained, logging data from new wells can be fed into the model, which automatically outputs the compaction status classification results for each depth point, accurately demarcating the first and second compaction sections in the target well.

[0125] Based on the above embodiment, the automation and accuracy of compaction section division can be effectively improved, and the uncertainty caused by human subjective judgment can be reduced. It is particularly suitable for well sections with complex geological structures or large fluctuations in logging data.

[0126] In some embodiments, in order to further improve the calculation accuracy of the first formation pore pressure and the second formation pore pressure, the linear coefficients in the particle stress calculation model can be dynamically adjusted in combination with the actual formation lithologic characteristics. Specifically, the main lithologic types (such as mudstone, sandstone, siltstone, etc.) at different depths in the target well are first identified based on the natural gamma (GR) and density (RHOB) information in the logging data, and a particle stress linear coefficient library corresponding to each lithologic type is established based on existing measured or experimental data. When calculating the first particle stress and the second particle stress, the most matching coefficient combination is dynamically selected from the coefficient library based on the lithologic combination of each compaction section, and the particle stress values ​​at different depths are calculated respectively, and then substituted into the static equilibrium equation to calculate the formation pore pressure at the corresponding depth.

[0127] This parameter adaptive adjustment method based on lithologic classification can significantly improve the rationality of particle stress estimation and the geological adaptability of formation pore pressure prediction, and is particularly suitable for geological areas with frequent lithologic changes or complex stratigraphic development.

[0128] As can be seen from the above, the embodiment of this specification provides a method for determining formation pressure based on particle stress, which obtains the logging data of a target well in a target area; constructs a target relationship diagram between the acoustic time difference and the formation depth based on the acoustic time difference in the logging data; identifies and divides the first compaction section and the second compaction section of the target well based on the target relationship diagram and the pre-constructed compaction trend line; wherein, the first compaction section is a shallow normal compaction section, and the second compaction section is a deep under-compacted section; determines the first particle stress corresponding to the first compaction section based on the formation depth of the first compaction section, and determines the second compaction section based on the formation depth of the second compaction section and the longitudinal wave velocity corresponding to the acoustic time difference. The second particle stress corresponding to the compaction section; determining the first formation pore pressure corresponding to the first compaction section of the target well based on the overburden load corresponding to the first compaction section, the formation porosity corresponding to the first compaction section, and the first particle stress; determining the second formation pore pressure corresponding to the second compaction section of the target well based on the overburden load corresponding to the second compaction section, the formation porosity corresponding to the second compaction section, and the second particle stress; determining the target formation pore pressures for different depth intervals of the target well based on the first formation pore pressure and the second formation pore pressure; wherein the target formation pore pressures are used to guide the formation pressure risk prevention and control strategy of the target well. In this way, first, by constructing a target relationship diagram between the acoustic wave time difference and the formation depth, and combining it with the pre-constructed compaction trend line, the compaction state of the target well is identified, and the first compaction section located at the upper part and the second compaction section located at the lower part are divided. This achieves segmented processing of formations with different compaction characteristics, which helps to improve the targetedness of pore pressure prediction. Subsequently, the first particle stress was constructed based on the formation depth of the first compaction section, and the second particle stress was constructed based on the formation depth of the second compaction section and the longitudinal wave velocity corresponding to the acoustic time difference, so that the calculation of particle stress is more consistent with the rock stress characteristics under different compaction conditions. On this basis, the formation pore pressure corresponding to the two sections was calculated separately, combining the overburden load and formation porosity, avoiding the errors caused by the unified model and single parameters in traditional methods. Finally, the pore pressure results of the two sections were combined to determine the target formation pore pressure at different depth intervals of the target well, effectively improving the pressure prediction accuracy under complex compaction conditions and providing a more reliable pressure basis for formation pressure risk prevention and control strategies.

[0129] See Figure 2 As shown, an embodiment of this specification also provides a specific electronic device, wherein the electronic device includes a network communication port 201, a processor 202 and a memory 203, and the above structures are connected through internal cables so that each structure can perform specific data interaction.

[0130] The network communication port 201 can be used to obtain well logging data of a target well in a target area.

[0131] The processor 202 can be specifically used to construct a target relationship diagram between the acoustic time difference and the formation depth based on the acoustic time difference in the logging data; identify and divide the first compaction section and the second compaction section of the target well based on the target relationship diagram and the pre-constructed compaction trend line; wherein the first compaction section is a shallow normal compaction section, and the second compaction section is a deep under-compacted section; determine the first particle stress corresponding to the first compaction section based on the formation depth of the first compaction section, and determine the second particle stress corresponding to the second compaction section based on the formation depth of the second compaction section and the longitudinal wave velocity corresponding to the acoustic time difference; according to the The first formation pore pressure corresponding to the first compaction section of the target well is determined based on the overburden load corresponding to the first compaction section, the formation porosity corresponding to the first compaction section, and the first particle stress; the second formation pore pressure corresponding to the second compaction section of the target well is determined based on the overburden load corresponding to the second compaction section, the formation porosity corresponding to the second compaction section, and the second particle stress; the target formation pore pressures of different depth intervals of the target well are determined based on the first formation pore pressure and the second formation pore pressure; wherein the target formation pore pressures are used to guide the formation pressure risk prevention and control strategy of the target well.

[0132] The memory 203 may be specifically used to store corresponding instruction programs.

[0133] Based on the above method, the relevant structural performance of electronic equipment can be effectively utilized, the data processing speed of electronic equipment can be improved, and a method for determining formation pressure based on particle stress can be efficiently implemented.

[0134] In this embodiment, the network communication port 201 can be a virtual port that is bound to different communication protocols, thereby being capable of sending or receiving different data. For example, the network communication port can be a port responsible for web data communication, a port responsible for FTP data communication, or a port responsible for email data communication. Furthermore, the network communication port can also be a physical communication interface or communication chip. For example, it can be a wireless mobile network communication chip, such as GSM or CDMA; it can also be a Wi-Fi chip; or it can be a Bluetooth chip.

[0135] In this embodiment, the processor 202 may be implemented in any suitable manner. For example, the processor may take the form of a microprocessor or a processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, an embedded microcontroller, etc. This specification is not intended to limit this.

[0136] In this embodiment, the memory 203 may include layers. In a digital system, anything that can store binary data can be a memory. In an integrated circuit, a circuit with a storage function that has no physical form is also called a memory, such as RAM, FIFO, etc. In a system, a storage device with a physical form is also called a memory, such as a memory stick, TF card, etc.

[0137] The embodiment of this specification also provides a computer-readable storage medium based on the above-mentioned method for determining formation pressure based on particle stress, which obtains logging data of a target well in a target area; constructs a target relationship diagram between the acoustic time difference and the formation depth based on the acoustic time difference in the logging data; identifies and divides the first compaction section and the second compaction section of the target well based on the target relationship diagram and a pre-constructed compaction trend line; wherein the first compaction section is a shallow normal compaction section, and the second compaction section is a deep under-compacted section; determines the first particle stress corresponding to the first compaction section based on the formation depth of the first compaction section, and determines the first particle stress corresponding to the first compaction section based on the formation depth of the second compaction section and the longitudinal wave velocity corresponding to the acoustic time difference. Determine the second grain stress corresponding to the second compaction section; determine the first formation pore pressure corresponding to the first compaction section of the target well according to the overburden load corresponding to the first compaction section, the formation porosity corresponding to the first compaction section, and the first grain stress; determine the second formation pore pressure corresponding to the second compaction section of the target well according to the overburden load corresponding to the second compaction section of the target well, the formation porosity corresponding to the second compaction section, and the second grain stress; determine the target formation pore pressures of different depth intervals of the target well based on the first formation pore pressure and the second formation pore pressure; wherein the target formation pore pressure is used to guide the formation pressure risk prevention and control strategy of the target well.

[0138] In this embodiment, the storage medium includes, but is not limited to, random access memory (RAM), read-only memory (ROM), cache, hard disk drive (HDD), or memory card. The memory can be used to store computer program instructions. The network communication unit can be an interface configured in accordance with the standards specified by the communication protocol for network connection communication.

[0139] In this embodiment, the functions and effects specifically implemented by the program instructions stored in the computer-readable storage medium can be explained in comparison with other implementations and will not be repeated here.

[0140] See Figure 3 At the software level, the embodiments of this specification also provide a device for determining formation pressure based on particle stress, which may specifically include the following structural modules:

[0141] The data acquisition module 301 is used to acquire the logging data of the target well in the target area;

[0142] A relationship diagram determining module 302 is configured to construct a target relationship diagram between acoustic wave time difference and formation depth based on the acoustic wave time difference in the well logging data;

[0143] The compaction section determination module 303 is configured to identify and divide the target well into a first compaction section and a second compaction section based on the target relationship diagram and the pre-established compaction trend line; wherein the first compaction section is a shallow normally compacted section, and the second compaction section is a deep under-compacted section;

[0144] a grain stress determination module 304 for determining a first grain stress corresponding to the first compaction section based on the formation depth of the first compaction section, and determining a second grain stress corresponding to the second compaction section based on the formation depth of the second compaction section and the longitudinal wave velocity corresponding to the acoustic wave moveout;

[0145] a first pressure determination module 305 for determining a first formation pore pressure corresponding to the first compaction section of the target well based on the overburden load corresponding to the first compaction section, the formation porosity corresponding to the first compaction section, and the first particle stress;

[0146] a second pressure determination module 306 for determining a second formation pore pressure corresponding to the second compaction section of the target well based on the overburden load corresponding to the second compaction section, the formation porosity corresponding to the second compaction section, and the second particle stress;

[0147] The target pressure determination module 307 is used to determine the target formation pore pressure in different depth intervals of the target well based on the first formation pore pressure and the second formation pore pressure; wherein the target formation pore pressure is used to guide the formation pressure risk prevention and control strategy of the target well.

[0148] In some embodiments, the first grain stress determination module in the above-mentioned grain stress determination module 304, when specifically implemented, determines a first intermediate value based on a first preset linear coefficient and the formation depth corresponding to the first compaction segment; and determines a first grain stress corresponding to the first compaction segment based on the first intermediate value and a preset intercept.

[0149] In some embodiments, the second grain stress determination module in the above-mentioned grain stress determination module 304, when specifically implemented, determines a second intermediate value based on a second preset linear coefficient and the formation depth corresponding to the second compaction segment; determines a third intermediate value based on the second intermediate value and a preset reference value; determines a fourth intermediate value based on the longitudinal wave velocity and a preset influence coefficient; and determines the second grain stress corresponding to the second compaction segment based on the second intermediate value, the third intermediate value, and the fourth intermediate value.

[0150] In some embodiments, the device further includes: in specific implementation, the overburden load is determined based on the density logging data in the logging data by calculating the average density of the formation, the corresponding formation depth and the gravitational acceleration; the formation porosity is obtained by calculation based on the saturated rock acoustic wave time difference, the pore fluid acoustic wave time difference and the rock matrix acoustic wave time difference of the target well.

[0151] In some embodiments, the first pressure determination module 305 may determine the first formation pore pressure according to the following formula:

[0152]

[0153] Among them, P f1 is the pore pressure of the first formation, Δt is the acoustic time difference of saturated rock, Δt f is the acoustic time difference of the pore fluid, Δt m is the rock matrix acoustic time difference, is the average density of the formation, h is the depth of the formation, and g is the gravitational acceleration.

[0154] In some embodiments, the second pressure determination module 306 may determine the second formation pore pressure according to the following formula:

[0155]

[0156] Among them, P f2is the second formation pore pressure.

[0157] In some embodiments, the above-mentioned compaction segment determination module 303, when specifically implemented, compares the target relationship diagram with the pre-constructed compaction trend line point by point to identify the deviation value of the acoustic wave time difference relative to the pre-constructed compaction trend line in different depth intervals; when the deviation value of the acoustic wave time difference relative to the pre-constructed compaction trend line is not greater than a preset threshold, the formation in the corresponding formation depth interval is determined to be in a normal compaction state and is divided into the first compaction segment; when the deviation value of the acoustic wave time difference relative to the pre-constructed compaction trend line is greater than a preset threshold, the formation in the corresponding formation depth interval is determined to be in an under-compacted state and is divided into the second compaction segment.

[0158] It should be noted that the units, devices or modules described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. For the convenience of description, the above devices are described in terms of functions and are divided into various modules and described separately. Of course, when implementing this specification, the functions of each module can be implemented in the same software and / or hardware, or the modules that implement the same function can be implemented by a combination of sub-modules or sub-units. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods, such as units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0159] As can be seen from the above, a formation pressure determination device based on particle stress provided by the embodiment of this specification first identifies the compaction state of the target well by constructing a target relationship diagram between the acoustic time difference and the formation depth, and combining it with the pre-constructed compaction trend line, dividing the first compaction section located at the top and the second compaction section located at the bottom, thereby realizing segmented processing of formations with different compaction characteristics, which helps to improve the pertinence of pore pressure prediction. Subsequently, the first particle stress is constructed based on the formation depth of the first compaction section, and the second particle stress is constructed based on the formation depth of the second compaction section and the longitudinal wave velocity corresponding to the acoustic time difference, so that the calculation of particle stress is more consistent with the rock stress characteristics under different compaction conditions. On this basis, combined with the overburden load and formation porosity, the formation pore pressure corresponding to the two sections is calculated respectively, avoiding the errors caused by the unified model and single parameter in the traditional method. Finally, the pore pressure results of the two sections are combined to determine the target formation pore pressure of different depth intervals of the target well, effectively improving the pressure prediction accuracy under complex compaction conditions, and providing a more reliable pressure basis for the formation pressure risk prevention and control strategy.

[0160] In a specific scenario example, the method and apparatus for determining formation pressure based on grain stress provided in this specification can be applied. First, by constructing a target relationship diagram between acoustic transit time and formation depth, combined with a pre-established compaction trend line, the compaction state of the target well is identified, dividing the target well into a first compaction segment located at the top and a second compaction segment located at the bottom. This achieves segmented processing of formations with different compaction characteristics, helping to improve the targetedness of pore pressure prediction. Subsequently, a first grain stress is constructed based on the formation depth of the first compaction segment, and a second grain stress is constructed based on the formation depth and the longitudinal wave velocity corresponding to the acoustic transit time of the second compaction segment. This makes the grain stress calculation more consistent with the rock stress characteristics under different compaction conditions. On this basis, the formation pore pressure corresponding to the two segments is calculated separately, combining the overburden load and formation porosity, avoiding the errors caused by the unified model and single parameters in traditional methods. Finally, the pore pressure results of the two segments are combined to determine the target formation pore pressure at different depth intervals of the target well, effectively improving the pressure prediction accuracy under complex compaction conditions and providing a more reliable pressure basis for formation pressure risk prevention and control strategies. The specific implementation process may include the following contents.

[0161] In some implementations, by incorporating the principle of porous rock static equilibrium, a novel equilibrium equation was established that decomposes total stress into pore pressure and grain stress according to porosity, overcoming the limitation of traditional effective stress models that ignore the influence of porosity. This approach, based on the combined forces acting on rock skeleton particles and pore space, clarifies that pore pressure is a function of overburden load, porosity, and grain stress. This theoretically achieves a more accurate depiction of the true stress state of the formation and significantly improves the adaptability and accuracy of pore pressure prediction through segmented compaction modeling.

[0162] See Figure 4 The figure shows the distribution characteristics of formation pressure as it changes with depth, distinguishing between normally compacted sections, undercompacted sections, and measured pore pressures using different markers. The figure uses black dots to represent the formation pressure in normally compacted sections, gray diamonds to represent the pressure data in undercompacted sections, and hollow circles to represent the measured pressure values. It can be seen that in shallow areas (above approximately 1000 meters), the predicted pressure values ​​are basically distributed along the reference pressure gradient line, indicating that the formation is in a normally compacted state; while in deep areas (below 1000 meters), the predicted values ​​deviate significantly from the reference line and increase significantly, indicating the presence of overpressure. The measured pressure points in the figure are in good agreement with the predicted results, verifying the effectiveness and applicability of the proposed method.

[0163] Based on the above examples, by introducing particle stress and porosity distribution to construct equilibrium equations, a formation pore pressure prediction model was established that does not rely on empirical assumptions. This allows for segmented calculations of normally compacted and undercompacted formations, significantly improving prediction accuracy and adaptability. In practical applications in the Junggar Basin, this method has demonstrated superior prediction accuracy compared to the traditional equivalent depth method, providing more reliable technical support for oil and gas exploration and geological engineering, and promoting the development of formation pore dynamics theory towards a more rigorous and sophisticated approach.

[0164] Although this specification provides the method operation steps as described in the embodiments or flow charts, more or fewer operation steps may be included based on conventional or non-creative means. The order of steps listed in the embodiments is only one way of executing the order of many steps and does not represent the only execution order. When the device or client product in practice is executed, it can be executed in sequence or in parallel according to the method shown in the embodiments or the drawings (for example, a parallel processor or a multi-threaded processing environment, or even a distributed data processing environment). The term "comprise", "include" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, product or device including a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements inherent to such process, method, product or device. In the absence of more restrictions, it is not excluded that there are other identical or equivalent elements in the process, method, product or device including the elements. Words such as first and second are used to represent names and do not represent any particular order.

[0165] Those skilled in the art will also appreciate that, in addition to implementing the controller in pure computer-readable program code, it is entirely possible to implement the same functionality by logically programming the method steps in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, embedded microcontrollers, and the like. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be considered structures within the hardware component. Alternatively, the devices for implementing various functions can be considered both software modules implementing the method and structures within the hardware component.

[0166] Through the description of the above embodiments, it can be seen that those skilled in the art can clearly understand that this specification can be implemented by means of software plus the necessary general hardware platform. Based on this understanding, the technical solution of this specification can essentially be embodied in the form of a software product. This computer software product can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a mobile terminal, a server, or a network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments of this specification.

[0167] Although the present specification has been described through embodiments, those skilled in the art will appreciate that there are many modifications and variations to the present specification without departing from the spirit of the present specification. It is intended that the appended claims include these modifications and variations without departing from the spirit of the present specification.

Claims

1. A method for determining formation pressure based on particle stress, characterized in that: include: Obtaining well logging data of target wells in the target area; constructing a target relationship diagram between the acoustic wave time difference and the formation depth based on the acoustic wave time difference in the logging data; Based on the target relationship graph and the pre-constructed compaction trend line, identifying and dividing the first compaction section and the second compaction section of the target well; wherein the first compaction section is a shallow normally compacted section, and the second compaction section is a deep under-compacted section; determining a first particle stress corresponding to the first compaction section based on the formation depth of the first compaction section, and determining a second particle stress corresponding to the second compaction section based on the formation depth of the second compaction section and the longitudinal wave velocity corresponding to the acoustic wave moveout; determining a first formation pore pressure corresponding to the first compaction section of the target well based on an overburden load corresponding to the first compaction section, a formation porosity corresponding to the first compaction section, and the first particle stress; determining a second formation pore pressure corresponding to the second compaction section of the target well based on the overburden load corresponding to the second compaction section, the formation porosity corresponding to the second compaction section, and the second particle stress; Based on the first formation pore pressure and the second formation pore pressure, target formation pore pressures at different depth intervals of the target well are determined; wherein the target formation pore pressures are used to guide a formation pressure risk prevention and control strategy for the target well.

2. The method according to claim 1, characterized in that The determining of the first particle stress corresponding to the first compaction section based on the formation depth of the first compaction section includes: determining a first intermediate value according to a first preset linear coefficient and a formation depth corresponding to the first compaction section; A first particle stress corresponding to the first compaction section is determined according to the first intermediate value and a preset intercept.

3. The method according to claim 2, characterized in that The determining of the second particle stress corresponding to the second compaction section based on the formation depth of the second compaction section and the longitudinal wave velocity corresponding to the acoustic wave time difference includes: determining a second intermediate value according to a second preset linear coefficient and a formation depth corresponding to the second compaction section; determining a third intermediate value based on the second intermediate value and a preset reference value; determining a fourth intermediate value according to the longitudinal wave velocity and a preset influence coefficient; A second particle stress corresponding to the second compaction section is determined according to the second intermediate value, the third intermediate value, and the fourth intermediate value.

4. The method according to claim 3, characterized in that The overburden load is determined by calculating the average density of the formation, the corresponding formation depth, and the gravitational acceleration based on the density logging data in the logging data; the formation porosity is calculated based on the saturated rock acoustic wave delay, the pore fluid acoustic wave delay, and the rock matrix acoustic wave delay of the target well.

5. The method according to claim 4, characterized in that The determining, based on the overburden load corresponding to the first compaction section of the target well, the formation porosity corresponding to the first compaction section, and the first particle stress, of the first formation pore pressure corresponding to the first compaction section includes: The pore pressure of the first formation is determined according to the following formula: Among them, P f1 is the pore pressure of the first formation, Δt is the acoustic time difference of saturated rock, Δt f is the acoustic time difference of the pore fluid, Δt m is the rock matrix acoustic time difference, is the average density of the formation, h is the depth of the formation, and g is the gravitational acceleration.

6. The method according to claim 5, characterized in that The determining, based on the overburden load corresponding to the second compaction section of the target well, the formation porosity corresponding to the second compaction section, and the second particle stress, a second formation pore pressure corresponding to the second compaction section includes: The pore pressure of the second formation is determined according to the following formula: Among them, P f2 is the second formation pore pressure.

7. The method according to claim 6, characterized in that The identifying and dividing the first compaction section and the second compaction section of the target well based on the target relationship graph and the pre-constructed compaction trend line includes: comparing the target relationship graph with the pre-constructed compaction trend line point by point to identify deviations of the acoustic wave moveout relative to the pre-constructed compaction trend line in different depth intervals; When the deviation value of the acoustic time difference from the pre-established compaction trend line is not greater than a preset threshold, the formation in the corresponding formation depth interval is determined to be in a normal compaction state and divided into a first compaction section; When the deviation value of the acoustic time difference from the pre-constructed compaction trend line is greater than a preset threshold, the formation in the corresponding formation depth interval is determined to be under-compacted and divided into a second compaction section.

8. A device for determining formation pressure based on particle stress, characterized in that: include: A data acquisition module, used to acquire logging data of a target well in a target area; a relationship graph determination module, configured to construct a target relationship graph between acoustic wave time difference and formation depth based on the acoustic wave time difference in the well logging data; a compaction section determination module, configured to identify and divide the target well into a first compaction section and a second compaction section based on the target relationship graph and a pre-constructed compaction trend line; wherein the first compaction section is a shallow normally compacted section, and the second compaction section is a deep under-compacted section; a particle stress determination module, configured to determine a first particle stress corresponding to the first compaction section based on the formation depth of the first compaction section, and to determine a second particle stress corresponding to the second compaction section based on the formation depth of the second compaction section and a longitudinal wave velocity corresponding to the acoustic wave moveout; a first pressure determination module, configured to determine a first formation pore pressure corresponding to the first compaction section of the target well based on an overburden load corresponding to the first compaction section, a formation porosity corresponding to the first compaction section, and the first particle stress; a second pressure determination module, configured to determine a second formation pore pressure corresponding to the second compaction section of the target well based on the overburden load corresponding to the second compaction section, the formation porosity corresponding to the second compaction section, and the second particle stress; A target pressure determination module is used to determine the target formation pore pressure in different depth intervals of the target well based on the first formation pore pressure and the second formation pore pressure; wherein the target formation pore pressure is used to guide the formation pressure risk prevention and control strategy of the target well.

9. An electronic device, characterized in that: The invention comprises a processor and a memory for storing instructions executable by the processor, wherein when the processor executes the instructions, the steps of the method for determining formation pressure based on particle stress as claimed in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that Computer instructions are stored thereon, and when the instructions are executed by a processor, the steps of the method for determining formation pressure based on particle stress as claimed in any one of claims 1 to 7 are implemented.

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