Method, device and equipment for predicting formation pore pressure and storage medium
By comprehensively processing well foundation data and seismic data, a formation pressure model was established using multiple methods, which solved the problem of multiple solutions in formation pressure prediction and achieved high-precision pressure prediction, applicable to drilling fluid design under complex geological conditions.
Patent Information
- Application Number
- CN202411048684.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-01
- Publication Date
- 2026-02-03
AI Technical Summary
Existing technologies for predicting formation pore pressure, especially in abnormally high-pressure formations, suffer from multiple solutions and struggle to accurately reflect the complexity and longitudinal and lateral differences in formation pressure distribution.
By acquiring well-based data and performing standardized preprocessing, combined with seismic data, and using various methods to calculate and correct formation pressure, including Average, NCT_Bowers, Amoco, Eaton, etc., an initial model is established and iteratively corrected. Finally, the formation pressure is predicted by fitting parameters to the seismic data volume.
It achieves high-precision prediction of formation pressure, is applicable to pressure prediction under complex geological conditions, and improves the scientific nature of drilling fluid density design and drilling safety.
Smart Images
Figure CN121454645A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application belongs to the technical field of oil and gas field development, and particularly relates to a method and device for predicting formation pore pressure, equipment and a storage medium. BACKGROUND
[0002] The prediction of formation pore pressure is related to all aspects of oil and gas exploration and development, especially in the pre-drilling pressure prediction stage. Accurate control of formation pressure can well guide the configuration of surface drilling fluid density, and has very important significance for improving drilling efficiency, shortening drilling cycle, reducing drilling cost and improving the economic benefit of natural gas exploration and development. Oil and gas reservoirs generally have abnormal pressure, especially abnormal high pressure. The abnormal high pressure mechanism is very complex, mainly including mudstone undercompaction, tectonic extrusion, hydrothermal pressurization, organic matter degradation, clay mineral dehydration and gas migration. The classical formation pore pressure prediction is based on the mudstone undercompaction theory. In addition to mudstone undercompaction, other complex mechanisms do not have a general algorithm, and these mechanisms are mainly used for qualitative analysis.
[0003] However, almost all current formation pressure predictions are based on the simple "adjacent well reference principle", which selects the adjacent well closest to the deployment well point, and predicts the formation pressure combined with seismic layer velocity. The disadvantage is that the prediction result has multiple solutions. SUMMARY
[0004] Embodiments of the application provide a method, device, equipment and storage medium for predicting formation pore pressure, which breaks through the lithology restriction and solves the problem that the actual formation pressure has different formation mechanisms in different vertical sections and complex formation pressure distribution characteristics in the horizontal direction.
[0005] Other characteristics and advantages of the application will become apparent from the following detailed description, or will be learned by practice of the application.
[0006] According to a first aspect of embodiments of the application, as shown in Figure 1 A method for predicting formation pore pressure is provided, comprising:
[0007] obtaining well basic data, standardizing and preprocessing the obtained well basic data to obtain standardized acoustic curve and density curve data, filtering the acoustic curve by using the Average method to obtain a filtered acoustic curve, and fitting the density curve by using the Amoco method to obtain a fitted density trend line;
[0008] The overburden pressure is calculated by using the density trend line, the NCT_Bowers method is adopted, the filtered acoustic curve and the overburden pressure curve are combined, the normal compaction trend is calculated by adjusting parameters; and the formation pressure data is calculated by using the overburden pressure, the normal compaction trend and the filtered acoustic curve, and is compared with the known single well actual drilled formation pressure data and the drilling fluid density data, parameters are continuously adjusted, the calculation data is corrected, and the well point pressure modeling is completed through model fitting correction;
[0009] The seismic data body and the seismic horizon are loaded, the data of the filtered acoustic curve or the density curve is selected, the initial model is created by using velocity inversion, the time-depth relationship is imported, the initial average velocity field is established and is corrected, the time domain inversion layer velocity is converted to the depth domain layer velocity, the seismic work area and the depth domain velocity are set, and the Amoco method is selected to calculate the seismic density trend body;
[0010] The overburden pressure body is calculated by selecting the Survey and the density trend body, the normal compaction trend body is calculated by using the overburden pressure body and the seismic density trend body to fit parameters, the formation pressure body is calculated by using the Eaton method to select the overburden pressure body, the seismic normal compaction trend body and the depth domain layer velocity body, plane mapping is completed, finally, the well side seismic data is extracted and saved as a logging curve to compare velocities, and the prediction result is improved.
[0011] According to a second aspect of the embodiments of the present application, as Figure 2 shown, a device for predicting formation pore pressure is provided, comprising:
[0012] The acquisition module is configured to acquire well basic data, perform standardization preprocessing on the acquired well basic data, obtain standardized acoustic curve and density curve data, perform filtering processing on the acoustic curve by using the Average method to obtain a filtered acoustic curve, and perform fitting on the density curve by using the Amoco method to obtain a fitted density trend line;
[0013] The calculation module is configured to calculate overburden pressure by using the density trend line, adopt the NCT_Bowers method, combine the filtered acoustic curve and the overburden pressure curve, calculate the normal compaction trend by adjusting parameters, and calculate formation pressure data by using the overburden pressure, the normal compaction trend and the filtered acoustic curve, and compare the formation pressure data with known single well actual drilled formation pressure data and drilling fluid density data, continuously adjust parameters, and correct the calculation data;
[0014] Creation module: for creating initial model based on formation pressure data, loading seismic data volume and seismic horizon, selecting filtered acoustic curve or density curve data, using velocity inversion, importing time-depth relationship, establishing initial average velocity field and performing correction, converting time domain inversion layer velocity to depth domain layer velocity, and setting seismic work area and depth domain velocity, and selecting Amoco method to calculate seismic density trend volume;
[0015] Fitting and perfecting module: for selecting Survey and density trend volume to calculate overlying formation pressure volume, and using overlying formation pressure volume and seismic density trend volume to fit parameter and calculate normal compaction trend volume, using Eaton method to select overlying formation pressure volume, seismic normal compaction trend volume and depth domain layer velocity volume to calculate formation pressure volume, completing plane mapping, extracting well-side seismic data to save as logging curve for velocity comparison, and perfecting prediction result.
[0016] According to a third aspect of the embodiments of the present application, a device for predicting formation pore pressure is provided, comprising a processor and a memory, the memory storing computer program instructions capable of being executed by the processor, and the processor executes the computer program instructions to implement the steps of the method according to any one of the first aspect.
[0017] According to a fourth aspect of the embodiments of the present application, a computer readable storage medium is provided, the computer readable storage medium storing computer program instructions, and the computer program instructions are executed by a processor to cause the processor to implement the steps of the method according to any one of the first aspect.
[0018] In the present application, the logging, seismic and measured pressure data are superimposed to obtain formation pressure prediction data volume, and then the formation pore pressure coefficient of any deployed well point is obtained, and for different pressure systems, different parameters can be used for block prediction, and then seamless splicing is performed, which is a more accurate, more widely applicable and more advanced method.
[0019] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS
[0020] The accompanying drawings, which are incorporated into and form part of the specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the application. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor. In the drawings:
[0021] Figure 1 Fig. 1 shows a flow diagram of a method for predicting formation pore pressure according to an embodiment;
[0022] Figure 2 Fig. 2 shows a block diagram of an apparatus for predicting formation pore pressure according to an embodiment;
[0023] Figure 3 Fig. 3 shows a block diagram of an apparatus for predicting formation pore pressure according to an embodiment. DETAILED DESCRIPTION
[0024] The technical solutions in the embodiments of the present application will be clearly and completely described in connection with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative work fall within the scope of protection of the present application.
[0025] In addition, the described features, structures or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a sufficient understanding of the embodiments of the present application. However, a person of ordinary skill in the art will realize that the technical solutions of the present application can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be used. In other cases, well-known methods, devices, implementations or operations are not shown or described in detail to avoid obscuring the aspects of the present application.
[0026] The block diagrams shown in the drawings are only functional entities, which do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.
[0027] The flowcharts shown in the drawings are only exemplary illustrations, which do not necessarily include all the contents and operations / steps, and are not necessarily executed in the described order. For example, some operations / steps can be further decomposed, and some operations / steps can be combined or partially combined, so that the actual execution order can be changed according to the actual situation.
[0028] Figure 1 Fig. 1 shows a flow diagram of a method for predicting formation pore pressure according to an embodiment. As shown in Fig. 1, a method for predicting formation pore pressure is provided, which can include the following steps 100 to 400. Figure 1
[0029] S100, obtain well foundation data, standardize and pretreat the obtained well foundation data, obtain standardized acoustic curve and density curve data, filter the acoustic curve by using the Average method, obtain the filtered acoustic curve, and simultaneously fit the density curve by using the Amoco method to obtain the fitted density trend line.
[0030] It can be understood that in this step, S100 includes S101 and S102, wherein:
[0031] S101, the well foundation data, wherein the well foundation data includes well location coordinate data, deviation data, well logging interpretation result table data, layering data, lithology data, well logging curve data, measured formation pressure data, and mud density data.
[0032] S102, the method for filtering the acoustic curve by using the Average method is to define the filter length as 300-500, and the specific value is determined according to the curve sampling density.
[0033] It should be noted that in this step, the well foundation data is obtained and pretreated, wherein the well foundation data includes well location coordinate data, deviation data, well logging interpretation result table data, layering data, lithology data, well logging curve data, measured formation pressure data, and mud density data, wherein the pretreatment mainly refers to standardizing the well logging curve data of a single well. When the velocity curve filtering process is obtained, the acoustic curve is filtered by using the Average method, the filter length is defined as 300-500, and the value depends on the curve sampling density. Then, the density curve is fitted by using the Amoco method, wherein the adjustment parameters are Coef_A and Coef_B, so that the fitting line passes through the middle part of the curve segment as much as possible.
[0034] Therefore, the above S100 step obtains and pretreats the well foundation data, especially standardizes the well logging curve data, to ensure the consistency and comparability of the data. For example, the acoustic curve is filtered by using the Average method, which effectively removes noise and outliers and improves the data quality and reliability. The density curve is fitted by using the Amoco method, the parameters Coef_A and Coef_B are adjusted, and the fitting line passes through the middle part of the curve segment as much as possible, so that a more accurate density trend line is obtained. The comprehensive application of these steps improves the accuracy of formation pressure calculation and provides a reliable data basis for subsequent geological analysis and safety evaluation.
[0035] S200, calculate the overburden pressure using the density trend line, adopt the NCT_Bowers method, combine the filtered acoustic curve and the overburden pressure curve, calculate the normal compaction trend by adjusting the parameters; and calculate the formation pressure data using the overburden pressure, the normal compaction trend and the filtered acoustic curve.
[0036] It can be understood that the present S200 step includes S201 and S202.
[0037] S201, obtain the measured formation pressure points and perform quality control on the measured formation pressure points to obtain the quality-controlled measured pressure;
[0038] S202, iteratively adjust based on the difference between the predicted pressure and the quality-controlled measured pressure, return to the normal compaction window to adjust the parameters again, and calculate the formation pressure.
[0039] It should be noted that when calculating the formation pressure, the Eaton Velocity method is selected, the overburden pressure OBG_Den_Amoco calculated in the above steps, the normal compaction trend V_normal obtained, and the filtered acoustic curve Vi are used to calculate the formation pressure, and then the measured formation pressure points are used for quality control. The fitting of the predicted pressure and the measured pressure is an iterative process, which needs to be based on the difference between the current predicted result and the measured pressure, then returns to the normal compaction window to adjust the parameters, and then calculates again to complete the comprehensive result display, which displays the logging curve, measured pressure, predicted formation pressure and mud density data, and compares and analyzes the curve law.
[0040] In the implementation process, the Eaton Velocity method is used to calculate the formation pressure, and the measured formation pressure points are used for quality control, realizing high-precision fitting of the predicted pressure and the measured pressure. Through the process of iterative adjustment and parameter optimization, the prediction result can be continuously improved, ensuring the accuracy and reliability of the formation pressure prediction. At the same time, the comprehensive result displays the logging curve, the predicted formation pressure and the mud density data, which is convenient for comparative analysis of the curve law, thereby improving the comprehensiveness and accuracy of the formation pressure evaluation.
[0041] S300, load the seismic data body and seismic horizon, select the filtered acoustic curve or density curve data to create an initial model using velocity inversion, import the time-depth relationship, establish an initial average velocity field and perform correction, convert the time-domain inversion layer velocity to the depth-domain layer velocity, and set the seismic work area and depth-domain velocity, and select the Amoco method to calculate the seismic density trend body.
[0042] It can be understood that the present step S300 includes S301.
[0043] S301, input the time-depth relationship into the model, and establish an initial average velocity field using the drilled well time-depth relationship;
[0044] According to the well-seismic layering and horizon matching relationship, the initial average velocity field is corrected, and the data error is counted, wherein the Error Rate of the velocity in the average velocity field is not more than 5%.
[0045] In some embodiments, when establishing the inversion initial model, it is necessary to ensure that the horizon participating in the inversion has been interpolated, and under the premise that the up-and-down stacking relationship and the stratum contact relationship are correct, the filtered acoustic wave data or the density curve data is selected to create an initial model using velocity inversion, model interpolation parameters, and in this step, Linear is recommended, and then the seismic data volume is inverted. Subsequently, the time-depth relationship is imported, and an initial average velocity field is established using the drilled well time-depth relationship; the initial average velocity field is corrected using the well-seismic layering / horizon matching relationship, and the data error is counted, and generally the Error Rate of the velocity is not more than 5%.
[0046] The time-domain data is converted into depth-domain data, the velocity model is closer to the actual geological conditions, and the accuracy of stratum pressure and geological structure analysis is improved. The converted depth-domain velocity data can be more accurately matched and compared with other depth-domain geological data (such as drilling data and seismic data), ensuring the consistency and reliability of the comprehensive analysis results. Moreover, the depth-domain velocity data provides reliable basic data for subsequent seismic density trend volume calculation, overlying stratum pressure volume and normal compaction trend volume calculation, improving the accuracy and reliability of the overall geological model.
[0047] In the implementation process, by loading the seismic data volume and the seismic horizon, the filtered acoustic curve or the density curve data is selected to create an initial model using velocity inversion, which can more accurately reflect the underground stratum structure. The time-depth relationship is imported, and the initial average velocity field is established and corrected to ensure the accuracy of the velocity field. The time-domain inversion layer velocity is converted into depth-domain layer velocity, improving the practical application value of the seismic data. Moreover, by setting the seismic work area and the depth-domain velocity, the Amoco method is selected to calculate the seismic density trend volume, further improving the accuracy of the density data.
[0048] In this step, the time-depth relationship is input into the model, and an initial average velocity field is established using the drilled well time-depth relationship, and the initial average velocity field is corrected in combination with the well-seismic layering and horizon matching relationship, which can effectively reduce the error and ensure that the velocity error rate of the average velocity field is not more than 5%. The comprehensive application of these steps improves the accuracy and reliability of stratum pressure and geological structure analysis, and provides high-quality data support for geological exploration and resource development.
[0049] S400, selecting Survey and density trend body to calculate overburden pressure body, and using overburden pressure body and seismic density trend body to fit parameters to calculate normal compaction trend body, using Eaton method to select overburden pressure body, seismic normal compaction trend body and depth domain interval velocity body to calculate formation pressure body, completing plane mapping, finally extracting well-side seismic data to save as logging curve to compare velocity, and perfecting prediction results.
[0050] It can be understood that step S400 includes S401, wherein:
[0051] S401, using overburden pressure body and seismic density trend body to fit parameters to calculate normal compaction trend body, wherein:
[0052] Selecting Survey, density trend body and overburden pressure body, and loading NCT method saved on well to fit parameters to calculate compaction trend body.
[0053] In the implementation process, using Eaton method, selecting seismic overburden pressure body, seismic normal compaction trend body and depth domain interval velocity body, loading corresponding parameters of well pressure prediction method to fit, obtaining formation pressure body, then completing plane mapping after data body smoothing, extracting well-side seismic data and saving as logging curve, converting into velocity using well sonic curve, comparing well velocity and inversion velocity, and perfecting prediction results.
[0054] In the implementation process, by selecting Survey and density trend body to calculate overburden pressure body, and using overburden pressure body and seismic density trend body to fit parameters to calculate normal compaction trend body, comprehensive and accurate formation pressure data can be obtained, and using Eaton method to combine overburden pressure body, seismic normal compaction trend body and depth domain interval velocity body to calculate formation pressure body, ensuring high precision of formation pressure prediction.
[0055] It can be understood that after completing plane mapping, extracting well-side seismic data and saving as logging curve to compare velocity. This process can verify and optimize formation pressure prediction results, further perfecting and correcting prediction model through comparison and analysis of actual logging data, ensuring accuracy and consistency of prediction results. And then, by comprehensively applying Survey, density trend body, seismic data and depth domain interval velocity body, and combining actual logging data for analysis and display, ensuring comprehensive and visual effect of data, and improving overall accuracy and operability of geological model.
[0056] In the embodiment, an example is taken for illustration: assuming that the A well point is drilled to a depth of 5840m, and the formations encountered from top to bottom are N2d, N1t, N1s, E2-3a, E1z, K1h, K1q, J2t, a total of 8 sets of formations, the upper and middle formations are sandstone and mudstone well sections, and the lower formation is a pure sandstone well section. Based on the classic mudstone undercompaction theory, the Eaton empirical formula algorithm is used to obtain the formation pressure prediction result, and the deviation of the measured formation pressure data is large. It can be seen from the prediction profile that in the upper sandstone and mudstone well section: the acoustic time difference is highly positively correlated with the shale content, the formation pressure conforms to the Eaton model, and the formation pressure is highly positively correlated with the acoustic time difference; in the middle sandstone and mudstone well section: the acoustic time difference deviates from the shale content, the formation pressure does not conform to the Eaton model, and the formation pressure prediction value is low; in the lower sandstone well section: the formation pressure does not conform to the Eaton model, and the formation pressure prediction value is low.
[0057] In order to obtain a formation pressure prediction result with high precision, the method steps of the present application are used. First, a well point pressure model is established, the same parameters as the well model are set, three-dimensional velocity and density inversion is performed by comprehensively using seismic velocity and seismic post-stack inversion results, high-quality and reliable input data are provided for three-dimensional pore pressure and overburden pressure calculation, and on the basis of the pressure model optimized in a single well, three-dimensional data body pressure calculation is completed, and the predicted pressure is consistent with the measured pressure in the whole well section.
[0058] In summary, the formation pore pressure prediction result is consistent with the drilling result after the well drilling construction in the same well area, thereby verifying the reliability and practicality of the method for predicting the formation pore pressure based on data fitting.
[0059] The method for predicting the formation pore pressure of the present application is scientific and reliable, and can be used for predicting the formation pore pressure of subsequent other complex geological conditions and mixed blocks of multiple genetic mechanisms. The method is a set of formation pore pressure prediction method which can more scientifically design drilling fluid density and reduce drilling risk, and can be widely applied to the field of pressure prediction in the future.
[0060] The device embodiment of the present application is introduced below, which can be used to execute the method for predicting the formation pore pressure in the above-mentioned embodiments of the present application. For details not disclosed in the device embodiment of the present application, refer to the above-mentioned embodiments of the method for predicting the formation pore pressure.
[0061] The embodiment provides a device for predicting formation pore pressure, as shown in Figure 2 The device comprises:
[0062] The acquisition module is configured to acquire well foundation data, perform standardization preprocessing on the acquired well foundation data, obtain standardized acoustic curve and density curve data, perform filtering processing on the acoustic curve by using the Average method, obtain the filtered acoustic curve, perform fitting on the density curve by using the Amoco method, and obtain the fitted density trend line.
[0063] The calculation module is configured to calculate the overburden pressure by using the density trend line, calculate the normal compaction trend by adjusting parameters in combination with the filtered acoustic curve and the overburden pressure curve by using the NCT_Bowers method, and calculate the formation pressure data by using the overburden pressure, the normal compaction trend, and the filtered acoustic curve, compare the calculated data with known single-well actual drilled formation pressure data and drilling fluid density data, and continuously adjust parameters to correct the calculated data.
[0064] The creation module is configured to load a seismic data body and a seismic horizon based on the formation pressure data, select data of the filtered acoustic curve or the density curve to create an initial model by using velocity inversion, import a time-depth relationship, establish an initial average velocity field and perform correction, convert the time-domain inversion layer velocity to the depth-domain layer velocity, and set a seismic work area and a depth-domain velocity, and calculate a seismic density trend body by using the Amoco method.
[0065] The fitting and perfecting module is configured to calculate an overburden pressure body by using the Survey and the density trend body, calculate a normal compaction trend body by using fitting parameters of the overburden pressure body and the seismic density trend body, calculate a formation pressure body by using the Eaton method, select the overburden pressure body, the seismic normal compaction trend body, and the depth-domain layer velocity body, complete planar mapping, finally extract well-side seismic data to save as a logging curve for velocity comparison, and perfect the prediction result.
[0066] It should be noted that, as to the apparatus in the above-described embodiments, the specific manners in which various modules perform operations have been described in detail in the embodiments of the method, and thus will not be described here in detail.
[0067] Corresponding to the above method embodiments, the present embodiment also provides an apparatus for predicting formation pore pressure, and the apparatus for predicting formation pore pressure described below can be mutually corresponding to the method for predicting formation pore pressure described above.
[0068] Figure 3 is a block diagram of an apparatus 800 for predicting formation pore pressure according to an example embodiment. As shown in FIG. 8, the apparatus 800 includes an acquisition module 810, a calculation module 820, a creation module 830, and a fitting and perfecting module 840. Figure 3As shown, the device 800 for predicting formation pore pressure includes a processor 801 and a memory 802. The device 800 for predicting formation pore pressure also includes one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.
[0069] The processor 801 is configured to control overall operation of the device 800 for predicting formation pore pressure to accomplish at least or more steps in the method for predicting formation pore pressure described above. The memory 802 is configured to store various types of data to support operations of the device 800 for predicting formation pore pressure. The data can include, for example, instructions for any application or method operating on the device 800 for predicting formation pore pressure, and application-related data, such as contact data, transmitted and received messages, pictures, audio, video, and the like. The memory 802 can be implemented by any type of volatile or non-volatile memory devices 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 multimedia component 803 can include a screen, such as a touch screen, and an audio component for outputting and / or inputting audio signals. For example, the audio component can include a microphone for receiving external audio signals. The received audio signals can be further stored in the memory 802 or transmitted through the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, such as a keyboard, a mouse, or buttons. The buttons can be virtual buttons or physical buttons. The communication component 805 is configured to enable wired or wireless communication between the device 800 for predicting formation pore pressure and other devices. The wireless communication, such as Wi-Fi, Bluetooth, near field communication (NFC), 2G, 3G, or 4G, or a combination of one or more of them, so the corresponding communication component 805 can include a Wi-Fi module, a Bluetooth module, or an NFC module.
[0070] In an example embodiment, the device 800 for predicting formation pore pressure can be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic elements for performing the method for predicting formation pore pressure as described above.
[0071] In another example embodiment, a computer-readable storage medium including program instructions that, when executed by a processor, implement the steps of the method for predicting formation pore pressure as described above is also provided. For example, the computer-readable storage medium can be the memory 802 as described above including program instructions executable by the processor 801 of the device 800 for predicting formation pore pressure to perform the method for predicting formation pore pressure as described above.
[0072] Corresponding to the method embodiments above, in this embodiment, a computer-readable storage medium is also provided, which can be referred to below in conjunction with the method for predicting formation pore pressure as described above.
[0073] The computer program stored on the computer-readable storage medium implements the steps of the method for predicting formation pore pressure of the method embodiments as described above when executed by a processor.
[0074] The computer-readable storage medium can be specifically a U disk, a mobile hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, etc.
[0075] The above only describes the embodiments of the present application and is not intended to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the scope of the claims of the present application.
Claims
1. A method of predicting formation pore pressure, characterized by, The method comprises the following steps: obtaining well foundation data, standardizing and preprocessing the obtained well foundation data to obtain standardized acoustic curve and density curve data, filtering the acoustic curve by using an Average method to obtain a filtered acoustic curve, and fitting the density curve by using an Amoco method to obtain a fitted density trend line; calculating overburden pressure by using the density trend line, calculating normal compaction trend by using the filtered acoustic curve and the overburden pressure curve in combination with an NCT_Bowers method through adjustment of parameters, and calculating formation pressure data by using the overburden pressure, the normal compaction trend and the filtered acoustic curve, comparing the calculated data with known single-well actual drilling formation pressure data and drilling fluid density data, continuously adjusting parameters and correcting the calculated data, and completing well point pressure modeling through model fitting correction; loading a seismic data body and a seismic horizon, selecting data of the filtered acoustic curve or the density curve to create an initial model by using velocity inversion, importing a time-depth relationship, establishing an initial average velocity field and correcting it, converting time-domain inversion layer velocity to depth-domain layer velocity, setting a seismic work area and a depth-domain velocity, and calculating a seismic density trend body by using an Amoco method; calculating an overburden pressure body by using the Survey and the density trend body, calculating a normal compaction trend body by using the overburden pressure body and the seismic density trend body to fit parameters, and calculating a formation pressure body by using the Eaton method to select the overburden pressure body, the seismic normal compaction trend body and the depth-domain layer velocity body, completing planar mapping, finally extracting well-side seismic data to save as a logging curve for velocity comparison, and improving the prediction result.
2. The method of predicting formation pore pressure of claim 1, wherein, The well foundation data, wherein the well foundation data comprises well location coordinate data, well deviation data, logging interpretation result table data, stratification data, lithology data, logging curve data, actual formation pressure data and mud density data.
3. The method of predicting formation pore pressure of claim 1, wherein, The filtering method of the acoustic curve by using the Average method is to customize a filter length of 300-500, and the specific value is determined according to the curve sampling density.
4. The method of predicting formation pore pressure of claim 1, wherein, The normal compaction trend is calculated by adjusting parameters, which comprises: parameters Coef_A, Coef_B and Vel_mudline are fitted, and the normal compaction trend is calculated, wherein the parameter adjustment idea of Coef_A and Coef_B is consistent with the Amoco parameter in the density prediction, and the other parameter Vel_mudline is a surface rock velocity.
5. The method of predicting formation pore pressure of claim 1, wherein, The formation pressure data is calculated by using the overburden pressure, the normal compaction trend and the filtered acoustic curve, and then comprises: actual formation pressure points are obtained, and the actual formation pressure points are quality controlled to obtain quality-controlled actual pressure; differences between predicted pressure and quality-controlled actual pressure are iteratively adjusted, parameters are repeatedly adjusted in the normal compaction window for multiple times, formation pressure is calculated, rules are analyzed, and finally predicted formation pressure and mud density data and actual formation pressure data are matched.
6. The method of predicting formation pore pressure of claim 1, wherein, The time-depth relationship is imported into the model, and the initial average velocity field is established by using the time-depth relationship of the drilled well. The time-depth relationship is imported into the model, and the initial average velocity field is established by using the time-depth relationship of the drilled well. According to the well-seismic layering and the horizon matching relationship, the initial average velocity field is corrected, and the data error is counted, wherein the Error Rate of the velocity in the average velocity field is not more than 5%.
7. The method of predicting formation pore pressure of claim 1, wherein, The normal compaction trend body is calculated by using the overlying strata pressure body and the seismic density trend body fitting parameters, wherein the method comprises the following steps: The Survey, the density trend body, and the overlying pressure body are selected, and the NCT method fitting parameters saved in the Empirical Formula are loaded to calculate the compaction trend body.
8. An apparatus for predicting formation pore pressure, characterized by, The method comprises the following steps: The acquisition module is used to acquire the well basic data, standardize and pretreat the acquired well basic data, obtain the standardized acoustic curve and density curve data, filter the acoustic curve by using the Average method to obtain the filtered acoustic curve, and fit the density curve by using the Amoco method to obtain the fitted density trend line; The calculation module is used to calculate the overlying strata pressure by using the density trend line, calculate the normal compaction trend by using the NCT_Bowers method in combination with the filtered acoustic curve and the overlying strata pressure curve through parameter adjustment, and calculate the strata pressure data by using the overlying strata pressure, the normal compaction trend, and the filtered acoustic curve; The creation module is used to load the seismic data body and the seismic horizon, select the data of the filtered acoustic curve or the density curve to create an initial model by using velocity inversion, import the time-depth relationship, establish and correct the initial average velocity field, convert the time-domain inversion layer velocity to the depth-domain layer velocity, set the seismic work area and the depth-domain velocity, and select the Amoco method to calculate the seismic density trend body; The fitting and perfecting module is used to select the Survey and the density trend body to calculate the overlying strata pressure body, calculate the normal compaction trend body by using the overlying strata pressure body and the seismic density trend body fitting parameters, calculate the strata pressure body by using the Eaton method to select the overlying strata pressure body, the seismic normal compaction trend body, and the depth-domain layer velocity body, complete planar mapping, extract the well-side seismic data to save as a logging curve for velocity comparison, and perfect the prediction result.
9. An apparatus for predicting formation pore pressure, comprising a processor and a memory, wherein, The memory stores computer program instructions capable of being executed by the processor, and the processor executes the computer program instructions to realize the steps of the method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer program instructions, and the computer program instructions are executed by the processor to cause the processor to realize the steps of the method according to any one of claims 1 to 7.