Pressure prediction method and device, electronic equipment, storage medium and program product

Abstract

This disclosure provides a pressure prediction method, apparatus, electronic device, storage medium, and program product. The disclosure involves: acquiring the cause of pressure formation from current state data; determining a pressure prediction model for different types of pressure formation causes; determining target sensitive parameters; determining the target sensitive parameters based on seismic data inversion; and inputting the target sensitive parameters into the pressure prediction model to obtain the pressure prediction result. In summary, this disclosure fully considers the differences in the causes of different overpressures and constructs pressure prediction models adapted to various pressure formation causes. Simultaneously, by refining the seismic data, high-precision target sensitive parameters that better meet the needs of pre-drilling pressure prediction can be obtained. Therefore, this disclosure can determine different pressure prediction models for different overpressure formation causes and simultaneously obtain high-precision target velocities for prediction, resulting in more accurate prediction results that can well meet practical needs.

Description

Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to a pressure prediction method, apparatus, electronic device, storage medium, and program product. Background Technology

[0002] Currently, formation pressure prediction models are typically built based solely on well logging data, and three-dimensional formation pressure prediction is performed using velocity volumes obtained from three-dimensional seismic data.

[0003] Thus, on the one hand, the lack of sufficient consideration of the differences in the causes of different overpressures makes the interpretability of the pressure prediction model poor; on the other hand, the failure to select the best method to obtain the three-dimensional formation velocity volume (i.e., sensitive parameters) makes the accuracy of the velocity volume unable to fully meet the needs of pre-drilling pressure prediction, resulting in insufficient reliability of the three-dimensional formation pressure prediction results and failing to meet the actual objective needs. Summary of the Invention

[0004] This disclosure provides a pressure prediction method, apparatus, electronic device, storage medium, and program product.

[0005] According to one aspect of this disclosure, a pressure prediction method is provided, the method comprising: acquiring the causes of pressure formation from current state data; determining a pressure prediction model for different types of pressure formation causes; determining target sensitive parameters; determining the target sensitive parameters based on seismic data inversion; and inputting the target sensitive parameters into the pressure prediction model to obtain pressure prediction results.

[0006] According to one aspect of the method of this disclosure, obtaining the current cause of pressure formation includes at least one of the following: obtaining features of current state data and comparing them to determine the cause of pressure formation; the features include at least one of the following: acoustic transit time, resistance, density; obtaining the loading or unloading curve to which the current state data belongs to obtain the cause of pressure formation; obtaining the relationship curve between the current state data and normal state data to obtain the cause of pressure formation; the relationship curve includes at least one of the following: effective vertical stress of velocity, effective vertical stress of density.

[0007] According to one aspect of the method of this disclosure, the causes of pressure formation include at least one of the following: uneven compaction, fluid expansion, diagenesis, and pressure transmission.

[0008] According to one aspect of the method of this disclosure, a pressure prediction model is determined for different types of pressure formation causes, including: obtaining sensitive parameters corresponding to the pressure formation causes; the sensitive parameters include at least one of the following: sound wave velocity, acoustic impedance; and obtaining a pressure prediction model based on a first relationship and normal state data; the first relationship is the correspondence between the sensitive parameters and the pressure.

[0009] According to one aspect of the method of this disclosure, determining target sensitive parameters includes: acquiring seismic data; and determining target sensitive parameters by inversion based on the seismic data and current state data.

[0010] According to one aspect of the method of this disclosure, the inversion includes at least one of the following: pre-stack inversion and post-stack inversion; the pre-stack inversion is based on elastic impedance determination.

[0011] According to another aspect of this disclosure, a pressure prediction device is provided, comprising: an acquisition unit for acquiring pressure formation causes from current state data; a first determination unit for determining a pressure prediction model for different types of pressure formation causes; a second determination unit for determining target sensitive parameters; the target sensitive parameters being determined based on seismic data inversion; and a processing unit for inputting the target sensitive parameters into the pressure prediction model to obtain pressure prediction results.

[0012] According to another aspect of this disclosure, an electronic device is provided, comprising: a memory for storing computer-readable instructions; and a processor for executing the computer-readable instructions, causing the electronic device to perform the method as described in any embodiment of one aspect.

[0013] According to another aspect of this disclosure, a non-transitory computer-readable storage medium is provided for storing computer-readable instructions that, when executed by a processor, cause the processor to perform the method as described in any embodiment of one aspect.

[0014] According to another aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the method as described in any embodiment of one aspect.

[0015] This disclosure provides a pressure prediction method, apparatus, electronic device, storage medium, and program product. This disclosure obtains the causes of pressure formation from current state data and determines different pressure prediction models for different types of pressure formation causes. It also determines target sensitive parameters based on seismic data inversion. The target sensitive parameters are input into the pressure prediction model to obtain the pressure prediction result. In summary, the technical solution provided by this disclosure can fully consider the differences in the causes of different overpressures and construct pressure prediction models adapted to various pressure formation causes, making the models more interpretable. Simultaneously, by inverting seismic data, the geological information contained in the seismic data can be fully extracted, and the seismic data can be refined to obtain high-precision target sensitive parameters (i.e., target acoustic velocity) that better meet the needs of pre-drilling pressure prediction. Therefore, this disclosure can determine different pressure prediction models for different overpressure formation causes and obtain high-precision target velocities for prediction, resulting in more accurate prediction results that can well meet practical needs.

[0016] It should be understood that both the foregoing general description and the following detailed description are exemplary and intended to provide further illustration of the claimed technology. Attached Figure Description

[0017] The above and other objects, features, and advantages of this disclosure will become more apparent from the more detailed description of the embodiments thereof in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this disclosure and form part of the specification. They are used together with the embodiments of this disclosure to explain the disclosure and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0018] Figure 1 A schematic flowchart of a pressure prediction method provided in an embodiment of this disclosure;

[0019] Figure 2 This is the overpressure logging response of well W1 provided in this disclosure;

[0020] Figure 3 Loading and unloading curves for well W1 provided in this embodiment of the disclosure;

[0021] Figure 4 Well logging pressure prediction for well W2 provided in this embodiment of the disclosure;

[0022] Figure 5 A predicted pressure profile of well W3 provided in an embodiment of this disclosure;

[0023] Figure 6 Wellside path for pre-drilling pressure prediction of W3 well provided in this embodiment of the disclosure;

[0024] Figure 7 The complete stress prediction process provided in this disclosure;

[0025] Figure 8 A structural block diagram of a pressure prediction device provided in an embodiment of this disclosure;

[0026] Figure 9 A hardware block diagram of an electronic device provided in an embodiment of this disclosure;

[0027] Figure 10 This is a schematic diagram of a computer-readable storage medium provided in an embodiment of this disclosure. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this disclosure more apparent, exemplary embodiments according to this disclosure will now be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this disclosure, and not all embodiments of this disclosure. It should be understood that this disclosure is not limited to the exemplary embodiments described herein.

[0029] Currently, formation pressure prediction models are typically built based solely on well logging data, and three-dimensional formation pressure prediction is performed using velocity volumes obtained from three-dimensional seismic data.

[0030] Thus, on the one hand, the lack of sufficient consideration of the differences in the causes of different overpressures makes the interpretability of the pressure prediction model poor; on the other hand, the failure to select the best method to obtain the three-dimensional formation velocity volume (i.e., sensitive parameters) makes the accuracy of the velocity volume unable to fully meet the needs of pre-drilling pressure prediction, resulting in insufficient reliability of the three-dimensional formation pressure prediction results and failing to meet the actual objective needs.

[0031] Therefore, this disclosure provides a pressure prediction method that can determine different pressure prediction models based on the different causes of different overpressures, and simultaneously obtain high-precision target velocities for prediction, resulting in more accurate prediction results. First, please refer to... Figure 1 , Figure 1 This is a schematic flowchart illustrating a pressure prediction method provided in an embodiment of this disclosure. Figure 1 As shown, the method includes:

[0032] In step S101, the cause of the pressure is obtained from the current state data.

[0033] In this disclosure, current state data can be understood as data derived from at least one source, such as geological exploration reports, core analysis data, and well logging data, that describes the current underground rock structure, fluid distribution, stress state, etc. Comprehensive analysis of these multi-source data allows for precise determination of the cause of pressure formation. This can be determined through at least one method, such as geological structural analysis, rock physical property comparison, or fluid pressure gradient analysis, without specific limitations.

[0034] In this disclosure, the cause of pressure formation can be understood as an overpressure phenomenon caused by at least one of the following: geological tectonic activity, uneven sedimentation rate, fluid migration and phase change, diagenesis, etc., resulting in the underground pore fluid pressure deviating from the normal hydrostatic pressure state. The causes of pressure formation in this disclosure include, but are not limited to, at least one of the following: uneven compaction, fluid expansion, diagenesis, and pressure transmission, which will be explained in detail below with reference to embodiments.

[0035] In step S102, a pressure prediction model is determined for different types of pressure formation causes.

[0036] In this disclosure, the pressure prediction model and the cause of pressure formation are corresponding. For different causes of pressure formation, corresponding pressure prediction models can be constructed based on their inherent physical mechanisms and related geological parameters.

[0037] Specifically, when pressure is caused by uneven compaction, this process involves the uneven pressure exerted by the weight of the overlying strata on the underlying formation, resulting in differences in the degree of compression of space at different depths and / or regions, thus causing overpressure. The corresponding pressure prediction model can be constructed by considering at least one of the following: porosity evolution, effective stress, etc. For example, it can be constructed using the effective stress formula. If the pressure is caused by fluid expansion, this is usually due to at least one of the following: the volume of fluid in the formation increases due to temperature rise, phase transformation (e.g., liquid water becomes gaseous water), or chemical reactions producing new fluid substances. The corresponding pressure prediction model can be constructed based on fluid thermodynamics, phase equilibrium theory, and rock flow theory. For example, it can be constructed using the fluid state equation. When pressure is caused by diagenesis, the core issue is that at least one of the following processes during diagenesis—mineral transformation, cementation, dissolution, etc.—alters the pore structure and physical properties of the rock, thereby affecting the pressure state of the pore fluid. The corresponding pressure prediction model can be constructed by considering at least one of the following: the sequence of diagenetic stages, changes in mineral composition, and the influence of permeability. For example, a diagenetic step index formula can be used. When the pressure formation is caused by pressure transmission, a pressure prediction model can be constructed based on at least one of the following: fluid flow mechanics, pressure diffusion equations, etc. For example, a pressure diffusion equation can be used for construction, without detailed limitations. Specific models will be described later with examples.

[0038] In step S103, the target sensitive parameters are determined; the target sensitive parameters are determined based on seismic data inversion.

[0039] In this disclosure, the target sensitive parameter can be understood as an attribute parameter obtained by inverting seismic data that can produce a significant response to changes in underground pressure and is closely related to the causes of specific pressure formation. The target sensitive parameter of this disclosure can be obtained from at least one of the following: the propagation characteristics of seismic waves (such as velocity, amplitude, and frequency) and the reflection characteristics of the formation in the seismic data. This allows for precise analysis and application of these target sensitive parameters, providing in-depth insight into subtle changes in formation pressure. Compared to conventional methods of rough estimation that are not correlated with pressure formation, this approach offers advantages such as high accuracy, strong specificity, and a better reflection of the details of pressure changes under complex geological conditions.

[0040] In step S104, the target sensitive parameters are input into the pressure prediction model to obtain the pressure prediction results.

[0041] In this disclosure, firstly, the target sensitive parameters precisely determined by seismic data inversion can be input one-to-one into the corresponding model. For example, for a pressure prediction model based on non-uniform compaction, if the required input parameters are P-wave velocity, porosity, and formation thickness, then the P-wave velocity values ​​at each depth obtained from seismic data inversion, the calculated porosity data, and the known formation thickness values ​​need to be accurately filled into the corresponding parameter input positions of the model. Next, after inputting the parameters, the pressure prediction model is started for calculation. The pressure prediction model can perform calculations based on at least one of the preset mathematical algorithms, physical mechanisms, and logical relationships. Finally, the output pressure prediction results can be presented in different forms as needed. For example, it can be a specific, tabular list of precise pressure values ​​at different depths and locations; or it can be an intuitive pressure contour map, pressure distribution profile, etc., with no specific limitations.

[0042] In summary, the technical solution provided in this disclosure fully considers the differences in the causes of various overpressures and constructs pressure prediction models adapted to different pressure formation causes, making the models more interpretable. Simultaneously, by inverting seismic data, the geological information contained in the seismic data can be fully extracted, and the seismic data can be refined to obtain high-precision target sensitive parameters (i.e., target acoustic velocity) that better meet the needs of pre-drilling pressure prediction. Therefore, this disclosure can determine different pressure prediction models for different overpressure causes and obtain high-precision target velocities for prediction, resulting in more accurate prediction results that well meet practical needs.

[0043] As mentioned above, the causes of pressure formation disclosed herein may include, but are not limited to, at least one of the following: uneven compaction, fluid expansion, diagenesis, and pressure transmission. Specifically, uneven compaction refers to a situation where the sediment deposition rate is too fast or the rock permeability decreases, causing the deposition rate to exceed the fluid outflow rate. Pore fluids then bear part of the overlying formation pressure on the rock skeleton, forming abnormally high pressure. Fluid expansion refers to the expansion of pore fluids under the influence of factors such as oil and gas generation, oil cracking into gas, and water heating. Simultaneously, the expansion is constrained by the surrounding permeable strata, leading to an increase in formation pressure. Diagenesis refers to the alteration of the pore structure and physical properties of rocks during rock formation and evolution through processes such as mineral precipitation, cementation, dissolution, and recrystallization, thereby affecting formation pressure. Pressure transmission refers to the flow of fluid from high-pressure areas to low-pressure areas when a pressure difference exists within the formation. In this process, pressure is transmitted, increasing the pressure in areas that were originally lower. In summary, different causes of pressure formation have been specifically described. Whether for resource development or engineering construction, a comprehensive analysis and understanding of these causes of pressure formation is a prerequisite for subsequent accurate pressure prediction.

[0044] As mentioned above, the cause of the current pressure can be determined. The cause of pressure can be determined by considering multiple factors. The following details different methods for determining the cause of pressure provided in this disclosure. These methods may include, but are not limited to, at least one of the following:

[0045] The characteristics of the current state data are acquired and compared to determine the cause of pressure formation; the characteristics include at least one of the following: sound wave transit time, resistance, and density;

[0046] Obtain the load or unload curve to which the current status data belongs to determine the cause of the pressure.

[0047] Obtain the relationship curve between the current state data and the normal state data to determine the cause of the pressure formation; the relationship curve includes at least one of the following: effective vertical stress at velocity and effective vertical stress at density.

[0048] In one embodiment of this disclosure, at least one characteristic of the current data, such as acoustic transit time, resistivity, and density, can be obtained. This can be achieved through at least one method, such as analyzing well logging data or measuring core samples. Furthermore, by comparing the changes in these characteristics with burial depth, the causes of different pressure formations can be determined. Specifically, if the acoustic transit time increases or the velocity decreases, the resistivity decreases, and the density decreases significantly with increasing burial depth in the overpressure zone, then this overpressure zone is caused by uneven compaction. If the acoustic transit time increases or the velocity decreases, the resistivity increases, and the density remains unchanged or slightly decreases with increasing burial depth in the overpressure zone, then this overpressure zone may be due to hydrocarbon generation expansion (i.e., fluid expansion), without limitation.

[0049] In another embodiment of this disclosure, when there are sufficient measured pressure data points to support the current state data, it can also be determined whether the current state data is located on a loading or unloading curve. The loading curve is a curve reflecting the rock's stress-deformation process obtained from a core sample collected from the target stratum during an axial loading experiment. The unloading curve is a curve plotted by gradually reducing the axial stress after it has reached a certain value, while simultaneously recording relevant parameters such as axial strain and volumetric strain of the core sample. Both curves are standard reference curves that can reflect the response characteristics of the rock at different stress stages. The position of the current state data on the curve can be determined by comparing and analyzing the actual current state data with these standard loading and unloading curves. If it matches the characteristics of the loading curve, it indicates that the rock is undergoing a process of continuously increasing stress, which may be caused by uneven compaction. If it matches the characteristics of the unloading curve, it indicates that the rock is in a stage of stress release or stress adjustment, which may be caused by fluid expansion, and the specific cause is not limited.

[0050] In another embodiment of this disclosure, when there are not enough measured pressure data points to support the current state data, the cause of pressure formation can be determined by acquiring normal state data and establishing a correlation analysis with the current state data. Specifically, firstly, normal state data can be acquired based on at least one of the pressure data of the normal segment and existing geological knowledge. Then, a relationship curve of at least one of velocity-vertical effective stress and density-vertical effective stress can be established with the current state data. The cause of pressure formation can then be determined based on the shape characteristics, trend of change, and deviation from the normal state curve. The velocity-vertical effective stress curve is a graph plotted with vertical effective stress as the abscissa and the propagation velocity of seismic waves in the rock (such as P-wave velocity, S-wave velocity, etc., depending on the research focus and actual measurement conditions) as the ordinate. The density-vertical effective stress curve is a graph plotted with vertical effective stress as the abscissa and rock density as the ordinate. When the slope of the velocity-vertical effective stress curve suddenly decreases or even becomes negative, the possible cause is fluid expansion. When the velocity-vertical effective stress curve shows localized abnormal fluctuations, the possible cause is diagenesis. When the density-vertical effective stress curve shows a decrease in density value within a certain depth range instead of an increase, the possible cause is uneven compaction. If the density-vertical effective stress curve shows abnormal jumps in a certain depth segment, the possible cause is pressure transmission, but no specific limitation is made.

[0051] It should be noted that any of the above embodiments can be used in combination as needed, without limitation.

[0052] In an exemplary embodiment, Figure 2 The overpressure logging response of well W1 provided in this disclosure. Figure 3 Loading and unloading curves for well W1 provided in this embodiment of the disclosure. Figure 2 The diagram displays multiple curves of different colors, representing different logging parameters (i.e., characteristics of the current state data disclosed herein). Blue represents the velocity curve; the graph shows a decreasing trend in velocity values ​​during the overpressured section (grid segment) of well W1 in the study area. Red represents the density curve, which remains essentially horizontal during this overpressured section, indicating that the density is essentially constant. The characteristic of decreasing velocity and essentially constant density during the overpressured section suggests that the overpressure is not caused by uneven compaction, but rather by fluid expansion, montmorillonite-illite transformation, pressure transmission, or tectonic loading. A more precise assessment can be made by combining... Figure 3 . Figure 3The data shows a significant number of overpressure zones located in the loading zone, consistent with the characteristics of overpressure caused by hydrocarbon generation expansion (i.e., fluid expansion). Furthermore, considering the well's evolution history, this strongly suggests a hydrocarbon generation expansion (i.e., fluid expansion) cause for overpressure. Comprehensive analysis confirms that the primary cause of overpressure in this study area is hydrocarbon generation expansion.

[0053] As mentioned earlier, different pressure prediction models can be determined based on the causes of different types of pressure. In this case, the methods include:

[0054] Acquire the sensitive parameters corresponding to the causes of pressure formation; the sensitive parameters include at least one of the following: sound velocity, acoustic impedance;

[0055] Based on the first relation and normal state data, a pressure prediction model is obtained; the first relation is the correspondence between sensitive parameters and pressure.

[0056] In this disclosure, the sensitive parameters can reflect the characteristics of formation pressure changes. The sensitive parameters differ depending on the cause of overpressure: because unbalanced compaction overpressure exhibits significantly low values ​​in acoustic velocity and density, acoustic velocity and density can be selected as sensitive parameters for unbalanced compaction overpressure. Similarly, because fluid expansion overpressure shows a decrease in acoustic velocity with increasing overpressure, while density remains essentially constant, acoustic velocity can be selected as a sensitive parameter for fluid expansion overpressure.

[0057] In this disclosure, the first relationship is the correspondence between sensitive parameters and pressure. This can be understood as a relatively stable quantitative relationship, with a causal link, between changes in the value of sensitive parameters and changes in formation pressure under ideal conditions that conform to conventional geological evolution laws. For example, the sensitive parameter for uneven compaction is acoustic velocity. As the degree of uneven compaction intensifies, that is, as formation pressure increases due to uneven compaction, acoustic velocity will gradually decrease. Therefore, the first relationship can be that acoustic velocity and formation pressure are roughly negatively correlated.

[0058] In this disclosure, after determining the sensitive parameters and primary relationships for different pressure formation causes, it is also necessary to obtain data under normal conditions. Using the offset values ​​of the sensitive parameters from their actual observed values ​​under normal conditions, a pressure prediction model is constructed through appropriate mathematical methods. Specifically, using sensitive parameters (such as sonic velocity and density during unbalanced compaction overpressure, or sonic velocity during fluid expansion overpressure) as independent variables and formation pressure as the dependent variable, at least one data fitting method, such as linear regression analysis, polynomial fitting, or neural network algorithms, is employed to construct a mathematical model that accurately reflects the quantitative relationship between the sensitive parameters and formation pressure—that is, a pressure prediction model. This provides important reference for subsequent geological engineering practices.

[0059] It is important to note that if sufficient measured overpressure data of different origins are available, pressure prediction models for overpressure segments of different origins can be directly established using specific data of sensitive parameters obtained under different overpressure conditions. Then, the location of the current state data within the overpressure segment prediction model can be used to accurately predict the specific magnitude and spatial distribution of overpressure in the target formation.

[0060] After determining the pressure prediction model, it can be applied to other wells in the study area to determine whether they conform to the overpressure characteristics of the geological strata in the study area. Figure 4 The logging pressure prediction for well W2 provided in this embodiment of the disclosure. Figure 4 The first two green areas represent the measured pressure values ​​of well W2, while the last four curves represent the predicted results. It can be seen that the predicted formation pressure results from the well logging are in good agreement with the measured pressure values, indicating that the predicted pressure model is suitable for the overpressure characteristics of the target formation in the study area.

[0061] In one exemplary embodiment, the target formation pressure prediction model can satisfy the following formula:

[0062] PP=S V -(S V -PHY)(V / V normol ) n

[0063] Where PP is the predicted formation pressure, and S V The overlying formation pressure is V0, PHY is normal hydrostatic pressure, and V is the formation velocity of the target layer. normol denoted as the formation velocity of the target layer during normal compaction, and 'n' as an adjustment coefficient, representing the degree of response of the velocity to the effective stress.

[0064] When specifically calculating PP, the accurate normal state S is determined. V PHY, V normol Next, we need to determine n. The following describes the method of determining the optimal n using the least squares method, and its derivation process is as follows:

[0065]

[0066] So,

[0067]

[0068] Construct the following equation:

[0069]

[0070] set up The equation can be simplified to:

[0071] For problems involving determining model parameters *n* using measured pressure points (i.e., current state data), since only one parameter needs to be determined, while there are usually multiple measured pressure points, this is a typical constant-determined or overdetermined problem in the inversion field. That is, the number of data points is equal to or greater than the number of model parameters, and the data provides sufficient constraints on the model parameters. Solving this type of inversion problem using least squares involves finding the model parameters that minimize the error between the predicted and observed data. Solving the above equation using least squares yields the model parameters *n*, specifically expressed as:

[0072]

[0073] in, PP(i), PHY(i), S V (i) represent the formation pressure, hydrostatic pressure, and overlying formation pressure at the overpressure point, respectively, where i represents the serial number of the measured pressure point, and V(i), V normol (i) represents the acoustic velocity at the overpressure point and the acoustic velocity under normal compaction conditions, respectively. The optimal pressure prediction model parameters n are obtained directly through a data-driven method using least squares inversion.

[0074] In summary, given a sufficient number of measured pressure points (i.e., current state data) and with sound wave velocity as the sensitive parameter, the above formula can be used to determine the predicted pressure value. In other words, whether it is uneven compaction (sensitive parameters are sound wave velocity and density) or fluid expansion (sensitive parameter is sound wave velocity), given a sufficient number of measured pressure points, the above model can be used to calculate and obtain the predicted pressure.

[0075] In another exemplary embodiment, the target formation pressure prediction model can satisfy the following formula:

[0076]

[0077] Where PP1 is the effective stress under loading conditions, S V 'V' represents the speed of sound. P1 V represents the current or measured velocity of sound (under load). 01 A1 and B1 are the model parameters under loading conditions, representing a reference wave velocity or standard wave velocity (under loading conditions), i.e., the sound wave velocity measured in undisturbed soil.

[0078] PP2 is the effective stress under unloading conditions, σ V 'V' represents the effective stress at the start of unloading. P2 V represents the current or measured velocity of sound (under unloading conditions). 02 Reference wave velocity or standard wave velocity (under unloading conditions), A2, B2, and U are model parameters for unloading, σ max'This represents the maximum effective stress at the start of unloading.'

[0079] When there is insufficient data on measured pressure points (i.e., current state data), measured pressure data under normal conditions can be obtained. This data can be used to construct a pressure prediction model for loading conditions. This loading-type pressure prediction model can then be used to determine the predicted pressures caused by loading-type overpressure, such as unbalanced compaction. By determining the model parameters for loading-type overpressure and unloading-type overpressure, the unloading-type pressure prediction model can be used to determine the predicted pressures caused by unloading-type overpressure, such as fluid expansion.

[0080] As mentioned earlier, after determining the pressure prediction model corresponding to the cause of pressure formation, the target sensitivity parameters can be determined. The following details how to determine the target sensitivity parameters, including:

[0081] Acquire earthquake data;

[0082] Based on seismic data and current status data, the target sensitive parameters are determined by inversion.

[0083] In this disclosure, the target sensitive parameter can be at least one of acoustic velocity, acoustic impedance, etc. Acoustic impedance data, as a target sensitive parameter, can better reflect the physical properties of the formation. Acoustic impedance, as a target sensitive parameter, can better reflect various aspects of information such as lithological changes, porosity characteristics, fluid type and content, and formation pressure state.

[0084] Determining the target sensitive parameters first requires acquiring seismic data. Different types of seismic data necessitate different processing methods. Next, the degree of fit between the processed seismic data and the current state data must be assessed. Finally, based on the degree of fit, an appropriate inversion algorithm and parameter settings are selected to calculate the target sensitive parameters. If the processed seismic data matches the current state data well, a relatively simple and efficient linear inversion algorithm, such as the method based on seismic trace integrals, can be used to quickly obtain the target sensitive parameters. If the fit is complex and involves many uncertainties, a nonlinear inversion algorithm, such as full-waveform inversion or model-based inversion methods, is required. This involves iteratively optimizing and adjusting model parameters to obtain more accurate target sensitive parameter values.

[0085] Specifically, firstly, the processing methods for different types of seismic data will be explained:

[0086] When the acquired seismic data is mature post-stack seismic data (i.e., processed seismic data containing seismic wave reflection information), the acoustic impedance data of the target sensitive parameter can be directly obtained by analyzing the post-stack seismic data. This can be achieved through at least one of the following methods: seismic trace integral inversion, recursive inversion, or model inversion. In this way, acoustic impedance data can be obtained quickly and relatively accurately, providing an important data foundation for subsequent work such as formation pressure prediction.

[0087] When the acquired seismic data is pre-stack seismic data (i.e., unprocessed seismic data), each pre-stack data can be processed by angular volume analysis, and then high-precision acoustic velocity can be obtained by elastic impedance inversion, which will be elaborated in detail later.

[0088] Next, it is necessary to compare the matching degree between the processed seismic data and the current state data, including but not limited to at least one of the following: the matching degree of physical parameters and the correspondence of geological features, without any specific restrictions here.

[0089] Finally, depending on the degree of fit between the two, if the processed seismic data and the current state data have good consistency and matching in the above aspects, then the target sensitive parameters can be determined based on the inverted seismic data.

[0090] In one exemplary embodiment, Figure 5 A predicted pressure profile of well W3 provided for an embodiment of this disclosure. Figure 5 Color coding was used to represent different formation characteristics. The figure uses multiple colors, such as red, yellow, green, and blue, to represent formation attributes at different depths and locations. The curve in the middle represents the predicted pressure result, and the figure shows a good match between the curve and well-seismic data. This can also be combined with... Figure 6 , Figure 6 A well bypass path for pre-drilling pressure prediction of well W3 provided in an embodiment of this disclosure. Figure 6 The curves in the diagram represent the prediction results, showing the relationship between the predicted pre-drilling pressure and the measured pressure points (i.e., the pressure at the drilling site). Figure 6 The green squares in the figures show a good match. Combining the two figures, it can be seen that the prediction model accurately characterizes the overpressure features of the target chromatography.

[0091] As mentioned above, the earthquake data disclosed herein is divided into mature post-stack earthquake data and pre-stack earthquake data. Therefore, the corresponding inversion is also divided into pre-stack inversion and post-stack inversion.

[0092] The pre-stack inversion disclosed herein is based on elastic impedance, which can be understood as determining the angular stacking range of pre-stack seismic data, performing angular volume stacking on the pre-stack gathers to obtain near-offset, intermediate-offset, and far-offset partial stacking data, and performing inversion using the following formulas for elastic impedance, P-wave velocity, S-wave velocity, and density, which can satisfy the following formulas:

[0093]

[0094] Where EI(θ) is the elastic resistance, V P Longitudinal wave velocity, V S Let ρ be the transverse wave velocity, ρ be the density, and θ be the angle of incidence.

[0095] It is important to note that when using predictive models for 3D formation pressure prediction, the target layer in the same study area may have one or more pressure formation causes. When the target layer has only one pressure formation cause, the pressure prediction model can be directly determined based on that cause, and then the predicted pressure can be determined using the target sensitivity parameters. When the target layer has multiple pressure formation causes, a comprehensive pressure prediction model can be determined using the corresponding pressure prediction models for each cause. For different pressure formation causes in the target layer, the predicted pressure can be determined using the determined target sensitivity parameters.

[0096] In one exemplary embodiment, Figure 7 The complete stress prediction process and methodology provided in this disclosure include:

[0097] In step S701, geological, geophysical, and engineering data of the study area are collected (i.e., the current status data of this disclosure).

[0098] In step S702, well logging data, measured pressure data points, and geological knowledge are comprehensively considered to quantitatively determine the cause of overpressure (i.e., to obtain the cause of pressure formation) using multiple methods.

[0099] In step S703, the optimal overpressure sensitive parameters are selected based on the causes of overpressure and the availability of data.

[0100] In step S704, based on the overpressure characteristics, a formation pressure prediction model guided by the cause of overpressure is constructed (i.e., the pressure prediction model determined by the cause of pressure formation in this disclosure).

[0101] In step S705, the overpressure sensitive parameter volume is obtained through methods such as pre-stack and post-stack inversion (i.e., the target sensitive parameter is determined).

[0102] In step S706, a planar variable pressure prediction model is constructed to predict the formation pressure of the overpressured strata in the study area.

[0103] In step S707, pressure prediction results are analyzed to guide drilling operations and reservoir prediction.

[0104] In one exemplary embodiment, the following details how prediction is performed:

[0105] Based on the analysis of measured pressure data, a comprehensive analysis was conducted to determine the cause of overpressure in the study area. Assuming that the cause of the overpressure is uneven compaction, a formation pressure prediction model for overpressure caused by uneven compaction was constructed, and the parameters of the pressure prediction model were determined using the least squares method.

[0106] Compared to velocities obtained directly from seismic data, velocities (elastic parameters) obtained during inversion are more accurate. Furthermore, by comparing the existing depth migration velocities in the study area with the inverted velocity volumes, it was determined that the inverted velocity volumes show a high degree of agreement with well logging velocities, meeting the requirements for high-precision seismic formation pressure prediction. Therefore, based on the pressure prediction model, high-precision velocity volumes obtained from seismic inversion are used to conduct three-dimensional pre-drilling formation pressure prediction.

[0107] This disclosure also provides a pressure prediction device. Figure 8 A structural block diagram of a pressure prediction device provided in an embodiment of this disclosure is shown below. Figure 8 As shown, the pressure prediction device 800 includes:

[0108] The acquisition unit 801 is used to acquire the cause of the pressure in the current state data.

[0109] The first determining unit 802 is used to determine the pressure prediction model for different types of pressure formation causes;

[0110] The second determining unit 803 is used to determine the target sensitive parameters, which are determined based on seismic data inversion.

[0111] The processing unit 804 is used to input the target sensitive parameters into the pressure prediction model to obtain the pressure prediction results.

[0112] In one exemplary embodiment, obtaining the current cause of pressure formation includes at least one of the following: obtaining features of current state data and comparing them to determine the cause of pressure formation; the features include at least one of the following: sound wave transit time, resistance, density; obtaining the loading or unloading curve to which the current state data belongs to obtain the cause of pressure formation; obtaining the relationship curve between the current state data and the normal state data to obtain the cause of pressure formation; the relationship curve includes at least one of the following: effective vertical stress of velocity, effective vertical stress of density.

[0113] In one exemplary embodiment, the pressure formation is caused by at least one of the following: uneven compaction, fluid expansion, diagenesis, and pressure transmission.

[0114] In one exemplary embodiment, determining a pressure prediction model for different types of pressure formation causes includes: obtaining sensitive parameters corresponding to the pressure formation causes; the sensitive parameters include at least one of the following: sound wave velocity, acoustic impedance; obtaining a pressure prediction model based on a first relationship and normal state data; the first relationship is the correspondence between the sensitive parameters and the pressure.

[0115] In one exemplary embodiment, determining the target sensitive parameters includes: acquiring seismic data; and determining the target sensitive parameters by inversion based on the seismic data and current state data.

[0116] In one exemplary embodiment, the inversion includes at least one of the following: pre-stack inversion and post-stack inversion; the pre-stack inversion is determined based on elastic impedance.

[0117] Figure 9 This is a hardware block diagram of an electronic device provided according to an embodiment of the present disclosure. The electronic device 900 according to an embodiment of the present disclosure includes at least a processor; and a memory for storing computer-readable instructions. When the computer-readable instructions are loaded and executed by the processor, the processor performs the stress prediction method described in any of the preceding embodiments of the present disclosure.

[0118] Figure 9 The illustrated electronic device 900 specifically includes a central processing unit (CPU) 901, a graphics processing unit (GPU) 902, and a memory 903. These units are interconnected via a bus 904. The CPU 901 and / or GPU 902 can function as the aforementioned processor, and the memory 903 can function as the aforementioned memory storing computer-readable instructions. Furthermore, the electronic device 900 may also include a communication unit 905, a storage unit 906, an output unit 907, an input unit 908, and an external device 909, all of which are also connected to the bus 904.

[0119] Figure 10 This is a schematic diagram of a computer-readable storage medium provided in an embodiment of this disclosure. (As shown...) Figure 10 As shown, a computer-readable storage medium 1000 according to an embodiment of the present disclosure stores computer-readable instructions 1001 thereon. When the computer-readable instructions 1001 are executed by a processor, the stress prediction method described with reference to the above figures according to any embodiment of the present disclosure is performed. The computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, optical disk, magnetic disk, etc.

[0120] This disclosure further provides a computer program product, including a computer program that, when executed by a processor, implements the stress prediction method described in any of the preceding embodiments of this disclosure.

[0121] The present disclosure provides a pressure prediction method, apparatus, electronic device, storage medium, and program product. This disclosure obtains the causes of pressure formation from current state data and determines different pressure prediction models for different types of pressure formation causes. It also determines target sensitive parameters based on seismic data inversion. The target sensitive parameters are input into the pressure prediction model to obtain the pressure prediction result. In summary, the technical solution provided by this disclosure can fully consider the differences in the causes of different overpressures and construct pressure prediction models adapted to various pressure formation causes, making the models more interpretable. Simultaneously, by inverting seismic data, the geological information contained in the seismic data can be fully extracted, and the seismic data can be refined to obtain high-precision target sensitive parameters (i.e., target acoustic velocity) that better meet the needs of pre-drilling pressure prediction. Therefore, this disclosure can determine different pressure prediction models for different overpressure formation causes and obtain high-precision target velocities for prediction, resulting in more accurate prediction results that can well meet practical needs.

[0122] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0123] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0124] The block diagrams of devices, apparatuses, devices, and systems disclosed herein are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0125] Additionally, as used herein, the “or” used in a list of items beginning with “at least one” indicates a separate list, such that a list of, for example, “at least one of A, B, or C” means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word “exemplary” does not imply that the described example is preferred or better than other examples.

[0126] It should also be noted that in the systems and methods of this disclosure, the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalent solutions to this disclosure.

[0127] Various changes, substitutions, and modifications can be made to the technology described herein without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of this disclosure is not limited to the specific aspects of the processes, machines, manufactures, events, means, methods, and actions described above. Currently existing or later-developed processes, machines, manufactures, events, means, methods, or actions that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein can be utilized. Therefore, the appended claims include such processes, machines, manufactures, events, means, methods, or actions within their scope.

[0128] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0129] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A pressure prediction method characterized by, The method includes: The reasons for the pressure to obtain current status data; For each of the different types of causes of pressure, a pressure prediction model is determined; The target sensitive parameters are determined based on seismic data inversion. The target sensitive parameters are input into the pressure prediction model to obtain the pressure prediction results.

2. The method of claim 1, wherein, The determination of the cause of the current pressure includes at least one of the following: The characteristics of the current state data are acquired and compared to determine the cause of the pressure formation; the characteristics include at least one of the following: sound wave transit time, resistance, and density; Obtain the loading or unloading curve to which the current state data belongs, and thus determine the cause of the pressure formation; Obtain the relationship curve between the current state data and the normal state data to determine the cause of the pressure formation; the relationship curve includes at least one of the following: effective vertical stress at velocity and effective vertical stress at density.

3. The method of claim 1, wherein, The pressure formation is caused by at least one of the following: uneven compaction, fluid expansion, diagenesis, and pressure transmission.

4. The method of claim 1, wherein, The determination of pressure prediction models for different types of pressure formation causes includes: Acquire the sensitive parameters corresponding to the cause of the pressure formation; the sensitive parameters include at least one of the following: sound velocity and acoustic impedance; The pressure prediction model is obtained based on the first relationship and normal state data; the first relationship is the correspondence between the sensitive parameter and the pressure.

5. The method of claim 1, wherein, The determination of the target sensitive parameters includes: Acquire the earthquake data; Based on the earthquake data and the current status data, the target sensitive parameters are determined by inversion.

6. The method of claim 5, wherein, The inversion includes at least one of the following: pre-stack inversion and post-stack inversion; the pre-stack inversion is determined based on elastic impedance.

7. A pressure prediction device, characterized by, The device includes: The acquisition unit is used to acquire the current state data and the reasons for the pressure formation. The first determining unit is used to determine a pressure prediction model for different types of pressure formation causes; The second determining unit is used to determine the target sensitive parameters; the target sensitive parameters are determined based on seismic data inversion. The processing unit is used to input the target sensitive parameters into the pressure prediction model to obtain the pressure prediction result.

8. An electronic device, comprising: include: Memory, used to store computer-readable instructions; as well as A processor for executing the computer-readable instructions, causing the electronic device to perform the method as described in any one of claims 1-6.

9. A non-transitory computer-readable storage medium storing computer-readable instructions, the computer-readable instructions comprising: When the computer-readable instructions are executed by a processor, the processor performs the method as described in any one of claims 1-6.

10. A computer program product, characterised in that, Includes a computer program that, when executed by a processor, implements the method as described in any one of claims 1-6.

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