Multi-mechanism composite formation pressure prediction method, system and equipment and storage medium

Abstract

The invention discloses a multi-mechanism composite formation pressure prediction method, system and device and a storage medium. The method comprises the steps that pressure prediction is conducted on an artabotrys hexapetalus and a yellow flow group of a warbler-agar basin through an Eaton method; according to the shale content, the formation porosity and the effective stress, longitudinal wave velocity model parameters are inversely calculated through combination of multiple nonlinear regression and a least square method, optimal model calculation parameters of a target block are obtained, and the effective stress of the high-pressure sections of the high-pressure layers of the lower Meishan group and the three groups is calculated based on the set of parameters; calculating sand body pore pressure predicted values corresponding to the lower Meishan group and the three groups according to the effective stress of the high-pressure section and the pressure of the overlying strata; and realizing multi-mechanism composite formation pressure prediction according to the under-compaction pore pressure prediction value and the high-pressure sand body pore pressure prediction value. The artabotrys hexapetalus and yellow flow group on the upper portion and the Meishan group and the three-branch group on the lower portion are subjected to pressure prediction through the Eaton method and the multi-parameter effective stress method, and therefore the pressure prediction precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of pressure prediction, and in particular to a multi-mechanism composite formation pressure prediction method, system, equipment and storage medium. Background Art

[0002] The Ying-Qiong Basin in the South China Sea is rich in oil and gas resources, and the amount of natural gas resources is estimated to exceed 4 trillion cubic meters, accounting for two-thirds of the total resources, with huge resource potential. The South China Sea is located at the intersection of three major plates. The Ying-Qiong Basin is a typical high-temperature and high-pressure basin in the South my country Sea, and one of the three major high-temperature and high-pressure areas in the world's offshore. The reservoir temperature is as high as 249°C, and the reservoir pressure coefficient is as high as 2.38g / cm3. High-temperature and high-pressure drilling is one of the technical difficulties in drilling in the world today. The formation pressure in the Ying-Qiong Basin is extremely complex. The multiple mechanisms of undercompaction, structural compression, diapirism, hydrothermal pressurization, hydrocarbon generation, and other source pressure conduction are coupled with each other. Using a single pressure prediction model, the accuracy of pore pressure is difficult to meet engineering needs. At the same time, the limitations of the offshore operating environment have brought greater risks and challenges to high-temperature and high-pressure drilling. The actual drilling statistics of many wells in the Ying-Qiong Basin show that many wells were completed in advance due to insufficient prediction accuracy of formation pressure, resulting in not reaching the designed well depth. Therefore, how to improve the accuracy of pre-drilling pressure prediction under the pressure coupling mechanism of this block has become an urgent problem to be solved.

[0003] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of the present invention is to provide a multi-mechanism composite formation pressure prediction method, system, equipment and storage medium, aiming to solve the technical problem of how to improve the accuracy of pre-drilling pressure prediction under the pressure coupling mechanism of the block.

[0005] To achieve the above object, the present invention provides a multi-mechanism composite formation pressure prediction method, the multi-mechanism composite formation pressure prediction method comprising:

[0006] The pressure of the upper Yinggehai and Huangliu formations in the Yingqiong Basin was predicted by the Eaton method, and the pore pressure prediction values ​​under the undercompaction mechanism corresponding to the upper Yinggehai and Huangliu formations were obtained;

[0007] Determine the shale content, formation porosity and effective stress of the normal compaction section corresponding to different layers of the target well in the Yingqiong Basin;

[0008] A P-wave velocity model is constructed according to the mud content, the formation porosity and the effective stress of the normal compaction section by a multi-parameter effective stress method, and the P-wave velocity model parameters are back-calculated by combining multivariate nonlinear regression with the least square method to obtain the optimal model calculation parameters of the target block, and the effective stress of the high-pressure section of the lower Meishan Formation and Sanya Formation high-pressure layers is calculated based on the optimal model calculation parameters of the target block;

[0009] Calculate the predicted values ​​of the sand body pore pressure corresponding to the lower Meishan Formation and Sanya Formation according to the effective stress of the high-pressure section and the pressure of the overburden formation;

[0010] The multi-mechanism composite formation pressure prediction is achieved based on the predicted pore pressure values ​​under the undercompaction mechanism corresponding to the upper Yinggehai and Huangliu formations and the predicted pore pressure values ​​of the sand bodies corresponding to the lower Meishan and Sanya formations.

[0011] Optionally, the step of determining the shale content corresponding to different layers of the target well in the Yingqiong Basin includes:

[0012] According to the exploration well gamma logging data, the shale content corresponding to different layers of the target well in the Yingqiong Basin is determined by using the shale content vertical distribution profile model;

[0013] The vertical distribution profile model of mud content is:

[0014]

[0015] In the formula, SH is the relative value of natural gamma, GR is the natural gamma logging value of the target layer, and GR min is the natural gamma logging value of pure lithologic formation, GR max is the natural gamma logging value of pure mudstone formation, V sh is the mud content, and GCUR is an empirical coefficient related to the stratigraphic age.

[0016] Optionally, the step of determining the formation porosity corresponding to different layers of the target well in the Yingqiong Basin includes:

[0017] Determine the formation porosity corresponding to different layers of the target well in the Yingqiong Basin through a formation porosity vertical distribution profile model based on the exploration well density logging data;

[0018] The vertical distribution profile model of the formation porosity is:

[0019] φ=(ρ matrix -ρ bulk ) / (ρ matrx -ρ fluid )

[0020] In the formula, ρ matrix is the rock skeleton density, ρbulk is the volume density, ρ fluid is the pore flow volume density, and φ is the formation porosity.

[0021] Optionally, the step of determining the effective stress of the normal compaction section of the Yingqiong Basin comprises:

[0022] Determine whether there is upper density logging data and lower density logging data in the density logging data;

[0023] If the upper density logging data and the lower density logging data do not exist, the density scatter point data of the entire well section is determined by a density-sonic wave correlation formula or a density-layer velocity correlation formula;

[0024] The density-sound wave correlation formula is:

[0025] ρ bi =A(10 6 / DT) B

[0026] In the formula, ρ bi is the density scatter data of the whole well section, DT is the formation acoustic time difference, A and B are empirical coefficients;

[0027] The density-layer velocity correlation formula is:

[0028] ρ bi =A(V Int ) B

[0029] Where V Int is the stratigraphic velocity;

[0030] Obtaining the overburden formation pressure through an overburden formation pressure curve fitting formula according to the density scatter point data of the entire well section;

[0031] The fitting formula of the overburden pressure curve is:

[0032]

[0033] Where OBG is the overburden pressure, ρ w is the density of seawater, H w is the water depth, ΔH is the depth interval;

[0034] Determine the effective stress of the normal compaction section of the Yingqiong Basin according to the overburden stratum pressure and the hydrostatic pressure by using the effective stress formula of the normal compaction section;

[0035] The formula for the effective stress in the normal compaction stage is:

[0036] P e =OBG-pw

[0037] Where P e is the effective stress formula for the normal compaction section, and pw is the hydrostatic pressure coefficient.

[0038] Optionally, after the step of determining whether there are upper density logging data and lower density logging data in the density logging data, the method further includes:

[0039] If the upper density logging data does not exist, and the lower density logging data exists, the density scatter point data of the entire well section is determined by the lower geophysical density fitting formula;

[0040] The full well section density fitting formula is:

[0041] ρ bi =ρ 0 +A·z B

[0042] In the formula, ρ 0 is the density of the sea surface layer, z is the depth of the layer below the mud surface, and A and B are empirical coefficients.

[0043] Optionally, the step of constructing a P-wave velocity model according to the shale content, the formation porosity and the effective stress of the normal compaction section by a multi-parameter effective stress method comprises:

[0044] The longitudinal wave velocity model is:

[0045]

[0046] Where, is the effective stress in the normal compaction stage, V p is the P-wave velocity, is the formation porosity, V sh is the mud content, B 0 , B 1 , B 2 , B 3 , D is the regional experience coefficient.

[0047] Optionally, the step of calculating the predicted longitudinal pore pressure corresponding to the lower Meishan Formation and the Sanya Formation according to the effective stress of the high-pressure section and the overburden formation pressure comprises:

[0048] According to the effective stress of the high-pressure section and the pressure of the overburden formation, the predicted value of the sand body pore pressure of the lower Meishan Formation and Sanya Formation is obtained by using the effective stress theory pore pressure calculation formula;

[0049] The effective stress theory pore pressure calculation formula is:

[0050] pp=OBG-P e

[0051] Where pp is the predicted value of sand body pore pressure, OBG is the overburden pressure, and P e is the effective stress in the high pressure section.

[0052] In addition, to achieve the above-mentioned purpose, the present invention also proposes a multi-mechanism composite formation pressure prediction system, the multi-mechanism composite formation pressure prediction system comprising:

[0053] A prediction module is used to predict the pressure of the upper Yinggehai and Huangliu formations in the Yingqiong Basin by using the Eaton method, and obtain the predicted pore pressure values ​​under the undercompaction mechanism corresponding to the upper Yinggehai and Huangliu formations;

[0054] A determination module, used to determine the mud content, formation porosity and effective stress of the normal compaction section corresponding to different layers of the target well in the Yingqiong Basin;

[0055] A calculation module, for constructing a P-wave velocity model according to the shale content, the formation porosity and the effective stress of the normal compaction section by a multi-parameter effective stress method, and back-calculating the P-wave velocity model parameters by combining multivariate nonlinear regression with the least squares method to obtain the optimal model calculation parameters of the target block, and calculating the effective stress of the high-pressure section of the high-pressure layers of the lower Meishan Formation and Sanya Formation based on the optimal model calculation parameters of the target block;

[0056] The prediction module is also used to calculate the predicted values ​​of the sand body pore pressure corresponding to the lower Meishan Formation and Sanya Formation according to the effective stress of the high-pressure section and the pressure of the overburden formation;

[0057] The prediction module is also used to realize multi-mechanism composite formation pressure prediction based on the predicted pore pressure values ​​under the undercompaction mechanism corresponding to the upper Yinggehai and Huangliu formations and the predicted pore pressure values ​​of the sand bodies corresponding to the lower Meishan and Sanya formations.

[0058] In addition, to achieve the above-mentioned objectives, the present invention also proposes a multi-mechanism composite formation pressure prediction device, which includes: a memory, a processor, and a multi-mechanism composite formation pressure prediction program stored in the memory and executable on the processor, and the multi-mechanism composite formation pressure prediction program is configured to implement the steps of the multi-mechanism composite formation pressure prediction method as described above.

[0059] In addition, to achieve the above-mentioned objectives, the present invention also proposes a storage medium, on which a multi-mechanism composite formation pressure prediction program is stored, and when the multi-mechanism composite formation pressure prediction program is executed by a processor, the steps of the multi-mechanism composite formation pressure prediction method as described above are implemented.

[0060] The present invention firstly predicts the pressure of the upper Yinggehai and Huangliu formations in the Yingqiong Basin by using the Eaton method, and obtains the predicted pore pressure values ​​under the undercompaction mechanism corresponding to the upper Yinggehai and Huangliu formations. Then, the mud content, formation porosity and effective stress of the normal compaction section corresponding to different layers of the target wells in the Yingqiong Basin are determined. A P-wave velocity model is constructed according to the mud content, formation porosity and effective stress of the normal compaction section by using the multi-parameter effective stress method. The P-wave velocity model is then inverted by combining multivariate nonlinear regression with the least squares method. The wave velocity model parameters are obtained to obtain the optimal model calculation parameters of the target block, and the effective stress of the high-pressure section of the high-pressure layer of the lower Meishan Formation and Sanya Formation is calculated based on the optimal model calculation parameters of the target block. Then, the predicted values ​​of the sand body pore pressure corresponding to the lower Meishan Formation and Sanya Formation are calculated according to the effective stress of the high-pressure section and the pressure of the overburden formation. Finally, the multi-mechanism composite formation pressure prediction is realized according to the predicted values ​​of the pore pressure under the undercompaction mechanism corresponding to the upper Yinggehai and Huangliu Formation and the predicted values ​​of the sand body pore pressure corresponding to the lower Meishan Formation and Sanya Formation. Compared with the single pressure prediction model used in the prior art for the Yingqiong Basin, the pore pressure accuracy is difficult to meet the engineering needs. The present invention uses the Eaton method and the multi-parameter effective stress method to predict the pressure of the upper Yinggehai and Huangliu Formation and the lower Meishan Formation and Sanya Formation, respectively, thereby improving the pressure prediction accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a structural schematic diagram of a multi-mechanism composite formation pressure prediction device in a hardware operating environment involved in an embodiment of the present invention;

[0062] Figure 2 It is a schematic diagram of the flow chart of the first embodiment of the multi-mechanism composite formation pressure prediction method of the present invention;

[0063] Figure 3 A schematic diagram of the sedimentation rate in the Yingqiong Basin according to the first embodiment of the multi-mechanism composite formation pressure prediction method of the present invention;

[0064] Figure 4 A thickness map of the source rock of the Yacheng Formation in the first embodiment of the multi-mechanism composite formation pressure prediction method of the present invention;

[0065] Figure 5 It is a density-acoustic wave velocity cross diagram of the first embodiment of the multi-mechanism composite formation pressure prediction method of the present invention;

[0066] Figure 6 It is a flow chart of the multi-mechanism composite pressure prediction method of the first embodiment of the multi-mechanism composite formation pressure prediction method of the present invention;

[0067] Figure 7 It is a reservoir pore pressure prediction diagram of the Meishan Formation in the LD10-1-1 well of the first embodiment of the multi-mechanism composite formation pressure prediction method of the present invention;

[0068] Figure 8 It is a schematic diagram of the pressure prediction profile (Eaton index method + multi-parameter effective stress method) of the LD10-1-1 well in the first embodiment of the multi-mechanism composite formation pressure prediction method of the present invention;

[0069] Fig. 9 It is a structural block diagram of the first embodiment of the multi-mechanism composite formation pressure prediction system of the present invention.

[0070] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0071] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0072] Reference Figure 1 , Figure 1 It is a schematic diagram of the structure of a multi-mechanism composite formation pressure prediction device in the hardware operating environment involved in the embodiment of the present invention.

[0073] like Figure 1 As shown, the multi-mechanism composite formation pressure prediction device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (Wireless-Fidelity, Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM), or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk storage. The memory 1005 may also be a storage system independent of the aforementioned processor 1001.

[0074] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation on the multi-mechanism composite formation pressure prediction device, and may include more or less components than those shown in the figure, or a combination of certain components, or a different arrangement of components.

[0075] like Figure 1As shown, the memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module, and a multi-mechanism composite formation pressure prediction program.

[0076] exist Figure 1 In the multi-mechanism composite formation pressure prediction device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the multi-mechanism composite formation pressure prediction device of the present invention can be set in the multi-mechanism composite formation pressure prediction device, and the multi-mechanism composite formation pressure prediction device calls the multi-mechanism composite formation pressure prediction program stored in the memory 1005 through the processor 1001, and executes the multi-mechanism composite formation pressure prediction method provided by an embodiment of the present invention.

[0077] The embodiment of the present invention provides a multi-mechanism composite formation pressure prediction method, referring to Figure 2 , Figure 2 It is a schematic flow chart of the first embodiment of the multi-mechanism composite formation pressure prediction method of the present invention.

[0078] It should be noted that the error in the pre-drilling pressure prediction of some wells in the Yingqiong Basin is large, which leads to complex drilling accidents in the target high-pressure layer. The main reason is that the mechanism of high pressure formation is unclear. Using a single pressure prediction model, no matter how the parameters are optimized, it is difficult to ensure the accuracy of formation pressure prediction in the entire well section. The pre-drilling prediction of the target layer of the LS13-2-1 well is 1.69g / cm3, and the actual drilling formation pressure is 1.98g / cm3. The drilling fluid density is too low, and serious gas invasion occurred during drilling, which brought great well control risks. The pre-drilling prediction pressure coefficient of the Huangliu Formation in the LD10-1-1 well is 1.82-1.98g / cm3, and the actual drilling pressure is not less than 2.25g / cm3. The prediction error is as high as 0.27g / cm3, causing serious overflow. In order to improve the accuracy of pre-drilling pressure prediction, this paper conducts a comprehensive analysis from the aspects of structural evolution, reservoir formation mode, and loading and unloading mechanism to explain the formation mechanism of high pressure in different layers.

[0079] Undercompaction high pressure mechanism-tectonic evolution:

[0080] Since 5.3 Ma in the new tectonic period of the Yingqiong Basin, the tectonic activity has been characterized by rapid sedimentation and subsidence with a high sedimentation rate. The late rapid sedimentation and subsidence are important geological conditions for the formation of overpressure in the Yingqiong Basin. Figure 3 , Figure 3This is a schematic diagram of the sedimentation rate in the Yingqiong Basin of the first embodiment of the multi-mechanism composite formation pressure prediction method of the present invention. The sedimentation rates of the Yinggehai Formation and the Huangliu Formation in the Yingqiong Basin are 200-1500m / Ma, which exceeds the overpressure threshold of 150m / Ma, forming a loaded abnormally high pressure, which is manifested as an undercompaction mechanism. The Eaton index method can be used for effective pressure prediction in this layer.

[0081] Fluid filling high pressure formation mechanism-accumulation model:

[0082] The Ledong and Yinggehai formations are dominated by large sets of mudstones with good caprock preservation conditions. The Lingshui formation and above have low organic matter abundance overall and are dominated by poor to moderate source rocks. Figure 4 , Figure 4 This is a diagram of the thickness of the source rock of the Yacheng Formation in the first embodiment of the multi-mechanism composite formation pressure prediction method of the present invention. Lingshui 13-5 is located above the Lingshui sub-basin. The thickness of the source rock of the Yacheng Formation in the Lingshui sub-basin is 600m-1000m, with a large burial depth, a high degree of thermal evolution, and a hydrocarbon generation intensity of 8.5 billion cubic meters / km2. The western sub-basin / main basin supplies hydrocarbons in both directions, and the FS and F2 faults transport oil and gas, with a relay filling type (15Ma-present). The effective filling time of the Meishan-Sanya Formation is long, which is mainly manifested in the high-pressure formation mechanism of fluid filling. The traditional Eaton method has a large error in predicting the lower formation pressure.

[0083] Loading and unloading overpressure formation mechanism:

[0084] refer to Figure 5 , Figure 5 This is the density-acoustic wave velocity cross plot of the first embodiment of the multi-mechanism composite formation pressure prediction method of the present invention. By analyzing the rock density-acoustic wave velocity cross plot of the drilled wells in the Yingqiong Basin, the Yinggehai Formation and Huangliu Formation formations both show loading mechanisms, the Meishan Formation shows a loading and unloading composite mechanism, and the Sanya Formation formations preliminarily show an unloading mechanism. The abnormal high pressure of the Yinggehai and Huangliu Formations is caused by the undercompaction mechanism, and the abnormal high pressure of the Meishan and Sanya Formations is caused by the coupling mechanism of undercompaction and fluid injection.

[0085] Based on the structural evolution, reservoir formation mode and loading and unloading curves, the upper Yinggehai Formation and Huangliu Formation are mainly undercompacted, and the pressure-generating interface is located in the middle and lower sections of the Yinggehai Formation. The Huangliu Formation is a pressure transition zone, and the lower Meishan Formation and Sanya Formation are undercompacted and fluid-filled coupled genetic mechanisms, which are overpressure development zones. For the coupled genetic mechanism of the lower formation, conventional methods can no longer meet the prediction accuracy requirements. Therefore, based on the high-pressure genetic coupling mechanism, a multi-mechanism composite pressure prediction method is established to improve the pressure prediction accuracy. Reference Figure 6 , Figure 6 This is a flow chart of the multi-mechanism composite pressure prediction method of the first embodiment of the multi-mechanism composite formation pressure prediction method of the present invention.

[0086] In this embodiment, the multi-mechanism composite formation pressure prediction method includes the following steps:

[0087] Step S10: Predicting the pressure of the upper Yinggehai and Huangliu formations in the Yingqiong Basin by using the Eaton method, and obtaining the predicted pore pressure values ​​under the undercompaction mechanism corresponding to the upper Yinggehai and Huangliu formations.

[0088] It is easy to understand that the execution subject of this embodiment can be a multi-mechanism composite formation pressure prediction system with functions such as data processing, network communication and program running, or other computer equipment with similar functions, etc., and this embodiment is not limited.

[0089] It should be noted that the undercompaction of the upper Yinggehai Formation and Huangliu Formation can be accurately predicted by the Eaton method.

[0090] The Eaton method is a relationship between formation pore pressure and logging data established through experience and theoretical analysis to predict pore pressure. When used, a normal compaction trend line needs to be established. It is mainly applicable to abnormally high pressure caused by undercompaction of mudstone.

[0091]

[0092] In the formula, pp 上 is the predicted value of pore pressure under the undercompaction mechanism corresponding to the upper Yinggehai and Huangliu formations (i.e., the predicted value of pore pressure corresponding to different depths of the upper Yinggehai and Huangliu formations), MPa, OBG is the overburden pressure, g / cm 3 , PP N is the hydrostatic column pressure, g / cm 3 , V, V N They are respectively the velocity of the normally compacted mudstone layer, the velocity of the mudstone layer determined by logging or seismic, and n—Eaton index, which is determined by regional laws or actual drilling data.

[0093] Step S20: Determine the mud content, formation porosity and effective stress of the normal compaction section corresponding to different layers of the target well in the Yingqiong Basin.

[0094] Furthermore, the shale content of the Yingqiong Basin was determined based on the exploratory well gamma logging data through the shale content vertical distribution profile model;

[0095] The vertical distribution profile model of mud content is:

[0096]

[0097] Where SH is the relative value of natural gamma, GR is the natural gamma logging value of the target layer, GRmin is the natural gamma logging value of the pure lithological formation, GRmax is the natural gamma logging value of the pure mudstone formation, Vsh is the mud content (i.e., the mud content corresponding to different layers of the target well), %, and GCUR is an empirical coefficient related to the formation age, which is 3.7 for new formations and 2 for old formations.

[0098] It should also be noted that the longitudinal distribution profile of mud content can be calculated using the mud content longitudinal distribution profile model.

[0099] Furthermore, the formation porosity of the Yingqiong Basin was determined based on the well density logging data through the formation porosity vertical distribution profile model;

[0100] The vertical distribution profile model of formation porosity is:

[0101] φ=(ρ matrix -ρ bulk ) / (ρ matrx -ρ fluid )

[0102] In the formula, ρ matrix is the rock skeleton density, g / cm 3 , ρ bulk is the bulk density, g / cm 3 , ρ fluid is the pore flow volume density, g / cm 3 , φ is the formation porosity (i.e., the formation porosity corresponding to different layers of the target well).

[0103] It should also be noted that the vertical distribution profile of the formation porosity is calculated using the vertical distribution profile model of the formation porosity.

[0104] It should also be noted that the density logging data is used to obtain the overburden pressure. If the density logging data is available for the entire well section, the overburden pressure gradient profile can be obtained by cumulative integration. For offshore wells, the influence of seawater must be considered and the cumulative integration is performed in sections. If the upper density logging data is missing, the Gardner model and the Power Law model can be used to fit and supplement the density curve, and finally the density data of the entire well section are combined, and then the overburden pressure is obtained by cumulative integration.

[0105] In this embodiment, it is determined whether there are upper density logging data and lower density logging data in the density logging data; if there are no upper density logging data and lower density logging data, the density scatter data is determined by a density-sonic wave correlation formula or a density-layer velocity correlation formula;

[0106] The density-sound wave correlation formula is:

[0107] ρbi =A(10 6 / DT) B

[0108] In the formula, ρ bi is the scattered density data of the whole well section, g / cm 3 , DT is the formation acoustic time difference, us / ft, A and B are empirical coefficients, usually A = 0.25, B = 0.245, which can be fitted according to the specific block data;

[0109] The density-layer velocity correlation formula is:

[0110] ρ bi =A(V Int ) B

[0111] Where V Int is the stratigraphic velocity, m / s.

[0112] In this embodiment, if there is no upper density logging data, and there is lower density logging data, the density scatter data is determined by the lower geodensity fitting formula;

[0113] The fitting formula of the whole well section geodensity is:

[0114] ρ bi =ρ 0 +A·z B

[0115] In the formula, ρ 0 is the density of the seabed surface layer, and z is the depth of the layer below the mud surface.

[0116] Furthermore, the overburden pressure is obtained by accumulating density based on the density scatter data of the entire well section;

[0117] The calculation formula of overburden rock pressure is:

[0118]

[0119] Where OBG is the overburden pressure (i.e., the overburden pressure gradient at a certain depth), g / cm 3 , ρ w is the density of seawater, g / cm 3 , H w is the water depth, g / cm 3 , ΔH is the depth interval (m).

[0120] It should also be noted that the normal compacted formation with comprehensive logging data was selected, and gamma logging, density logging, sonic logging and other data were obtained to calculate the parameters such as shale content Vsh, formation porosity φ, and overburden pressure OBG; the effective stress formula Pe of the normal compacted section was obtained based on the overburden pressure and hydrostatic pressure through the effective stress formula. The pore pressure of the normal compacted formation is the hydrostatic pressure, and the pressure coefficient Pw = 1.03g / cm 3 , the effective stress Pe of the normal compaction section is calculated by the following formula;

[0121] P e =OBG-pw

[0122] Step S30: construct a P-wave velocity model according to the mud content, the formation porosity and the effective stress of the normal compaction section by a multi-parameter effective stress method, and inversely calculate the P-wave velocity model parameters by combining multivariate nonlinear regression with the least squares method to obtain the optimal model calculation parameters of the target block, and calculate the effective stress of the high-pressure section of the lower Meishan Formation and Sanya Formation high-pressure layers based on the optimal model calculation parameters of the target block.

[0123] In this embodiment, since there are many model parameters and it is difficult to solve, there are large errors when applied in some specific areas. In order to combine the characteristics of specific oilfield blocks to achieve accurate solution of model parameters and improve prediction accuracy, a solution idea combining multivariate nonlinear regression and least squares method is established.

[0124] The longitudinal wave velocity model is:

[0125]

[0126] Where P e is the effective stress in the normal compaction stage, V p is the longitudinal wave velocity, is the formation porosity, V sh is the mud content, B 0 , B 1 , B 2 , B 3 , D is the empirical coefficient.

[0127] Obtain model coefficient B 0 ,B 1 ,B 2 ,B 3 ,D.

[0128] Since the above formula has four parameters (Vp, Vsh, Pe), five coefficients (B 0 ,B 1 ,B 2 ,B 3,D), is a multivariate nonlinear regression problem, which is very complex. We use the stepwise forward regression method and the least squares method to iteratively find the optimal solution. First, we fix a D value (D generally varies between 1-40) and get B by regression. 0 ,B 1 ,B 2 ,B 3 , record its variance, and after all regressions are completed, determine a set of values ​​B with the smallest variance 0 ,B 1 ,B 2 ,B 3 , D is the optimal result. The process is as follows:

[0129] Choose the approximate equation as:

[0130]

[0131] Use the least squares principle to determine the coefficient B in the equation 0 ,B 1 ,B 2 ,B 3 .make

[0132]

[0133] To make φ(B 0 ,B 1 ,B 2 ,B 3 ) reaches a minimum. Apply the above formula to B 0 ,B 1 ,B 2 ,B 3 , find the partial derivative and set it equal to zero. Repeat the calculation of the variance A value. When A is less than the given error value, the optimal solution coefficient B can be obtained. 0 ,B 1 ,B 2 ,B 3 ,D.

[0134]

[0135] in:

[0136]

[0137] In this embodiment, a Matlab program is developed to implement multivariate nonlinear regression and least squares method solutions. The details are as follows:

[0138] Establish the longitudinal wave velocity model: Vp = inline(b(1)+b(2).*x(:,1)+b(3).*sqrt(x(:,2))+b(4).*(x(:,3)-exp(-b(5).*x(:,3)))','b','x'), initialize the model coefficient beta0 = [1,1,1,1,1], call beta = nlinfit(X,Y,Vp,beta0), and solve the longitudinal wave velocity model coefficient.

[0139] Inline is an inline function. The function of the inline function is to convert a line of expression into a MATLAB function. It can define a function by inputting variables and return a function handle.

[0140] The nlinfit function is a special multivariate nonlinear fitting function of Matlab. For non-robust estimation, the Levenberg-Marquardt (LM) method, also known as the damped least squares method, is used; for robust estimation, the Iteratively Reweighted Least Squares method is used, which is a method of adjusting the weight of each fitting point based on the Least Squares.

[0141] In the specific implementation, five coefficients (B 0 ,B 1 ,B 2 ,B 3 , D), and then bring it into the construction of the P-wave velocity model, and then use the logging data to obtain the lower high-pressure layer Vp, Vsh, and other parameters, calculate the corresponding high pressure section effective stress Pe.

[0142] Step S40: Calculate the predicted values ​​of the sand body pore pressure corresponding to the lower Meishan Formation and Sanya Formation according to the effective stress of the high-pressure section and the overlying rock pressure.

[0143] Furthermore, the predicted values ​​of the sand body pore pressure of the lower Meishan Formation and Sanya Formation are calculated based on the effective stress of the high-pressure section and the pressure of the overburden formation by using the pore pressure calculation formula of the effective stress theory;

[0144] The pore pressure calculation formula of effective stress theory is:

[0145] pp=OBG-P e

[0146] Where pp is the predicted value of sand body pore pressure (i.e., the pore pressure of high-pressure layers at different depths), OBG is the overburden pressure, and P e is the effective stress in the high-pressure section of the formation.

[0147] It should also be noted that the pore pressure values ​​of all the lower high-pressure layers can be obtained through the above method. Figure 7 , Figure 7 This is a reservoir pore pressure prediction diagram of the Meishan Formation in the LD10-1-1 well of the first embodiment of the multi-mechanism composite formation pressure prediction method of the present invention.

[0148] Step S50: realizing multi-mechanism composite formation pressure prediction according to the predicted values ​​of pore pressure under the undercompaction mechanism corresponding to the upper Yinggehai and Huangliu formations and the predicted values ​​of sand body pore pressure corresponding to the lower Meishan and Sanya formations.

[0149] The pore pressure profile of the entire well section is established based on the predicted pore pressure values ​​under the undercompaction mechanism corresponding to the upper Yinggehai and Huangliu formations (i.e., the pore pressure values ​​of all upper formations) and the predicted pore pressure values ​​of the sand bodies corresponding to the lower Meishan and Sanya formations (i.e., the pore pressure values ​​of all lower high-pressure layers).

[0150] In this example, based on the seismic layer velocity data, the Eaton method was used to predict the formation pressure before drilling. The well started to rise from the middle of the first section of the Yinggehai Formation, and the pressure gradually increased. The pressure coefficient of the lower Huangliu Formation and the Meishan Formation was predicted to be 1.82-1.98 g / cm before drilling. 3 The actual drilling pressure of the Meishan Formation is 2.25 g / cm 3 , the prediction error is as high as 0.27g / cm 3 , resulting in severe gas invasion, and gradually increasing the mud density to 2.26g / cm 3 , the formation pressure is basically balanced. Combined with the early high pressure genesis mechanism of the block, the Meishan Formation belongs to the coupling mechanism of undercompaction and fluid injection, and the conventional Eaton method cannot accurately predict the lower high pressure. In order to improve the prediction accuracy of the lower formation, a multi-parameter effective stress model is used to evaluate the pressure of the Meishan Formation reservoir. This method was used to conduct field verification in the LD10-1-1 well, and the model parameters were determined by the multivariate nonlinear fitting regression mathematical method. At the same time, factors such as mud content, porosity and effective stress were introduced to establish the vertical distribution profile of the pore pressure of the well. Compared with the conventional Eaton index method, the prediction error of the Meishan Formation is from 0.27g / cm 3 Reduced to 0.01g / cm 3 , providing reliable pressure data guidance for drilling in the lower overpressure layer.

[0151] Statistics of the actual drilling pressure test data of the lower reservoir of the well: 3987m@2.87g / cm 3 、4023m@2.2g / cm 3 、4098m@2.25g / cm 3 , using the multivariate nonlinear regression mathematical method, the block model parameters are fitted, and the prediction equation is finally obtained as follows:

[0152]

[0153] Where: B 0 =4.91,B 1 =2.31,B 2 =-0.48,B 3 =1.88,D=2.15

[0154] The reservoir parameters such as shale content and porosity are obtained through gamma and density logging, and the overburden pressure is calculated through density logging. Finally, a chart showing the change of pore pressure of the Meishan Formation with shale content and porosity is established, as shown in Figure 2. Figure 7 As shown in Figure 2, the reservoir pressure coefficient of the Meishan Formation is 2.24 g / cm 3 , the prediction error is 0.01g / cm3, which greatly improves the prediction accuracy compared with the Eaton index method. Refer to Table 1, which is the Meishan Formation reservoir pressure prediction and error analysis table.

[0155] Table 1 Meishan Formation reservoir pressure prediction and error analysis

[0156]

[0157] Using the multi-mechanism composite pressure prediction method, the upper Yinggehai Formation uses the Eaton index, and the lower Meishan Formation uses the multi-parameter effective stress method to predict the longitudinal profile, and finally the prediction results of the entire longitudinal profile are obtained, such as Figure 8 As shown, Figure 8 This is a schematic diagram of the pressure prediction profile of the LD10-1-1 well (Eaton index method + multi-parameter effective stress method) of the first embodiment of the multi-mechanism composite formation pressure prediction method of the present invention. It can be seen from the figure that the multi-mechanism composite pressure prediction method can better reflect the step-like mutation characteristics of the lower high pressure, has high prediction accuracy, and can provide reliable pressure data guidance for drilling in the lower overpressure layer.

[0158] In this implementation, the Eaton method was first used to predict the pressure of the upper Yinggehai and Huangliu formations in the Yingqiong Basin, and the pore pressure prediction values ​​under the undercompaction mechanism of the upper Yinggehai and Huangliu formations were obtained. Then, the shale content, formation porosity, and effective stress of the normal compaction section corresponding to different layers of the target wells in the Yingqiong Basin were determined. The P-wave velocity model was constructed according to the shale content, formation porosity, and effective stress of the normal compaction section by the multi-parameter effective stress method. The multivariate nonlinear regression was combined with the least squares method to back-calculate the P-wave velocity model. The P-wave velocity model parameters are used to obtain the optimal model calculation parameters of the target block, and the effective stress of the high-pressure section of the high-pressure layer of the lower Meishan Formation and Sanya Formation is calculated based on the optimal model calculation parameters of the target block. Then, the predicted values ​​of the sand body pore pressure corresponding to the lower Meishan Formation and Sanya Formation are calculated according to the effective stress of the high-pressure section and the pressure of the overburden formation. Finally, the multi-mechanism composite formation pressure prediction is realized according to the predicted values ​​of the pore pressure under the undercompaction mechanism corresponding to the upper Yinggehai and Huangliu Formation and the predicted values ​​of the sand body pore pressure corresponding to the lower Meishan Formation and Sanya Formation. Compared with the single pressure prediction model used in the prior art for the Yingqiong Basin, the pore pressure accuracy is difficult to meet the engineering needs. In this embodiment, the Eaton method and the multi-parameter effective stress method are used to predict the pressure of the upper Yinggehai and Huangliu Formation and the lower Meishan Formation and Sanya Formation, respectively, thereby improving the pressure prediction accuracy.

[0159] Reference Fig. 9 , Fig. 9 It is a structural block diagram of the first embodiment of the multi-mechanism composite formation pressure prediction system of the present invention.

[0160] like Fig. 9 As shown, the multi-mechanism composite formation pressure prediction system proposed in the embodiment of the present invention includes:

[0161] Prediction module 9003, used to predict the pressure of the upper Yinggehai and Huangliu formations in the Yingqiong Basin by using the Eaton method, and obtain the predicted pore pressure values ​​under the undercompaction mechanism corresponding to the upper Yinggehai and Huangliu formations;

[0162] Determination module 9001, used to determine the shale content, formation porosity and effective stress of the normal compaction section corresponding to different layers of the target well in the Yingqiong Basin;

[0163] The calculation module 9002 is used to construct a P-wave velocity model according to the shale content, the formation porosity and the effective stress of the normal compaction section by a multi-parameter effective stress method, and to inversely calculate the P-wave velocity model parameters by combining multivariate nonlinear regression with the least square method to obtain the optimal model calculation parameters of the target block, and to calculate the effective stress of the high-pressure section of the high-pressure layers of the lower Meishan Formation and Sanya Formation based on the optimal model calculation parameters of the target block;

[0164] The prediction module 9003 is also used to calculate the predicted value of the sand body pore pressure corresponding to the lower Meishan Formation and Sanya Formation according to the effective stress of the high-pressure section and the pressure of the overburden formation;

[0165] The prediction module 9003 is also used to realize multi-mechanism composite formation pressure prediction based on the predicted pore pressure values ​​under the undercompaction mechanism corresponding to the upper Yinggehai and Huangliu formations and the predicted pore pressure values ​​of the sand bodies corresponding to the lower Meishan and Sanya formations.

[0166] In this embodiment, the pressure of the upper Yinggehai and Huangliu formations in the Yingqiong Basin is first predicted by the Eaton method to obtain the predicted pore pressure under the undercompaction mechanism of the upper Yinggehai and Huangliu formations. Then, the mud content, formation porosity and effective stress of the normal compaction section corresponding to different layers of the target wells in the Yingqiong Basin are determined. A P-wave velocity model is constructed according to the mud content, formation porosity and effective stress of the normal compaction section by the multi-parameter effective stress method. The multivariate nonlinear regression is combined with the least squares method to reverse the pore pressure. Calculate the parameters of the longitudinal wave velocity model, obtain the calculation parameters of the optimal model of the target block, and calculate the effective stress of the high-pressure section of the high-pressure layer of the lower Meishan Formation and Sanya Formation based on the calculation parameters of the optimal model of the target block, and then calculate the predicted values ​​of the pore pressure of the sand bodies corresponding to the lower Meishan Formation and Sanya Formation according to the effective stress of the high-pressure section and the pressure of the overburden formation. Finally, according to the predicted values ​​of the pore pressure under the undercompaction mechanism corresponding to the upper Yinggehai and Huangliu Formation and the predicted values ​​of the pore pressure of the sand bodies corresponding to the lower Meishan Formation and Sanya Formation, the multi-mechanism composite formation pressure prediction is realized. Compared with the use of a single pressure prediction model in the Yingqiong Basin in the prior art, the pore pressure accuracy is difficult to meet the engineering needs, and this embodiment uses the Eaton method and the multi-parameter effective stress method to predict the pressure of the upper Yinggehai and Huangliu Formation and the lower Meishan Formation and Sanya Formation, respectively, thereby improving the pressure prediction accuracy.

[0167] Other embodiments or specific implementations of the multi-mechanism composite formation pressure prediction system of the present invention can refer to the above-mentioned method embodiments, which will not be described in detail here.

[0168] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0169] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0170] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory / random access memory, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0171] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A multi-mechanism composite formation pressure prediction method, characterized in that: The multi-mechanism composite formation pressure prediction method comprises the following steps: The pressure of the upper Yinggehai and Huangliu formations in the Yingqiong Basin was predicted by the Eaton method, and the pore pressure prediction values ​​under the undercompaction mechanism corresponding to the upper Yinggehai and Huangliu formations were obtained; Determine the shale content, formation porosity and effective stress of the normal compaction section corresponding to different layers of the target well in the Yingqiong Basin; A P-wave velocity model is constructed according to the mud content, the formation porosity and the effective stress of the normal compaction section by a multi-parameter effective stress method, and the P-wave velocity model parameters are back-calculated by combining multivariate nonlinear regression with the least square method to obtain the optimal model calculation parameters of the target block, and the effective stress of the high-pressure section of the lower Meishan Formation and Sanya Formation high-pressure layers is calculated based on the optimal model calculation parameters of the target block; Calculate the predicted values ​​of the sand body pore pressure corresponding to the lower Meishan Formation and Sanya Formation according to the effective stress of the high-pressure section and the pressure of the overburden formation; The multi-mechanism composite formation pressure prediction is achieved based on the predicted pore pressure values ​​under the undercompaction mechanism corresponding to the upper Yinggehai and Huangliu formations and the predicted pore pressure values ​​of the sand bodies corresponding to the lower Meishan and Sanya formations.

2. The method according to claim 1, characterized in that The step of determining the shale content corresponding to different layers of the target well in the Yingqiong Basin comprises: According to the exploration well gamma logging data, the shale content corresponding to different layers of the target well in the Yingqiong Basin is determined by using the shale content vertical distribution profile model; The vertical distribution profile model of mud content is: In the formula, SH is the relative value of natural gamma, GR is the natural gamma logging value of the target layer, and GR min is the natural gamma logging value of pure lithologic formation, GR max is the natural gamma logging value of pure mudstone formation, V sh is the mud content, and GCUR is an empirical coefficient related to the stratigraphic age.

3. The method according to claim 1, characterized in that The step of determining the formation porosity corresponding to different layers of the target well in the Yingqiong Basin includes: Determine the formation porosity corresponding to different layers of the target well in the Yingqiong Basin through a formation porosity vertical distribution profile model based on the exploration well density logging data; The vertical distribution profile model of the formation porosity is: φ=(ρ matrix -r bulk ) / (ρ matrx -r fluid ) In the formula, ρ matrix is the rock skeleton density, ρ bulk is the volume density, ρ fluid is the pore flow volume density, and φ is the formation porosity.

4. The method according to any one of claims 1 to 3, characterized in that: The step of determining the effective stress of the normal compaction section of the Yingqiong Basin comprises: Determine whether there is upper density logging data and lower density logging data in the density logging data; If the upper density logging data and the lower density logging data do not exist, the density scatter point data of the entire well section is determined by a density-sonic wave correlation formula or a density-layer velocity correlation formula; The density-sound wave correlation formula is: ρ bi =A(10 6 / DT) B In the formula, ρ bi is the density scatter data of the whole well section, DT is the formation acoustic time difference, A and B are empirical coefficients; the density-layer velocity correlation formula is: r bi =A(V Int ) B Where V Int is the stratigraphic velocity; Obtaining the overburden formation pressure through an overburden formation pressure curve fitting formula according to the density scatter point data of the entire well section; The fitting formula of the overburden pressure curve is: Where OBG is the overburden pressure, ρ w is the density of seawater, H w is the water depth, ΔH is the depth interval; Determine the effective stress of the normal compaction section of the Yingqiong Basin according to the overburden stratum pressure and the hydrostatic pressure by using the effective stress formula of the normal compaction section; The effective stress formula of the normal compaction stage is: P e =OBG-pw Where P e is the effective stress formula for the normal compaction section, and pw is the hydrostatic pressure coefficient.

5. The method according to claim 4, characterized in that After the step of determining whether there are upper density logging data and lower density logging data in the density logging data, the method further includes: If the upper density logging data does not exist, and the lower density logging data exists, the density scatter point data of the entire well section is determined by the lower geophysical density fitting formula; The full well section density fitting formula is: r bi =ρ0+A·z B Where ρ0 is the density of the sea surface layer, z is the depth of the layer below the mud surface, and A and B are empirical coefficients.

6. The method according to claim 4, characterized in that The step of constructing a P-wave velocity model according to the shale content, the formation porosity and the effective stress of the normal compaction section by using a multi-parameter effective stress method comprises: The longitudinal wave velocity model is: Where P e is the effective stress in the normal compaction stage, V p is the longitudinal wave velocity, is the formation porosity, V sh is the mud content, B0, B1, B2, B3 and D are regional empirical coefficients.

7. The method according to claim 6, characterized in that The step of calculating the predicted vertical pore pressure values ​​corresponding to the lower Meishan Formation and the Sanya Formation according to the effective stress of the high-pressure section and the overburden formation pressure comprises: According to the effective stress of the high-pressure section and the pressure of the overburden formation, the predicted value of the sand body pore pressure of the lower Meishan Formation and Sanya Formation is obtained by using the effective stress theory pore pressure calculation formula; The effective stress theory pore pressure calculation formula is: pp=OBG-P e Where pp is the predicted value of sand body pore pressure, OBG is the overburden pressure, and P e is the effective stress in the high pressure section.

8. A multi-mechanism composite formation pressure prediction system, characterized in that: The multi-mechanism composite formation pressure prediction system comprises: A prediction module is used to predict the pressure of the upper Yinggehai and Huangliu formations in the Yingqiong Basin by using the Eaton method, and obtain the predicted pore pressure values ​​under the undercompaction mechanism corresponding to the upper Yinggehai and Huangliu formations; A determination module, used to determine the mud content, formation porosity and effective stress of the normal compaction section corresponding to different layers of the target well in the Yingqiong Basin; A calculation module, for constructing a P-wave velocity model according to the shale content, the formation porosity and the effective stress of the normal compaction section by a multi-parameter effective stress method, and back-calculating the P-wave velocity model parameters by combining multivariate nonlinear regression with the least squares method to obtain the optimal model calculation parameters of the target block, and calculating the effective stress of the high-pressure section of the high-pressure layers of the lower Meishan Formation and Sanya Formation based on the optimal model calculation parameters of the target block; The prediction module is also used to calculate the predicted values ​​of the sand body pore pressure corresponding to the lower Meishan Formation and Sanya Formation according to the effective stress of the high-pressure section and the pressure of the overburden formation; The prediction module is also used to realize multi-mechanism composite formation pressure prediction based on the predicted pore pressure values ​​under the undercompaction mechanism corresponding to the upper Yinggehai and Huangliu formations and the predicted pore pressure values ​​of the sand bodies corresponding to the lower Meishan and Sanya formations.

9. A multi-mechanism composite formation pressure prediction device, characterized in that: The device includes: a memory, a processor, and a multi-mechanism composite formation pressure prediction program stored in the memory and executable on the processor, wherein the multi-mechanism composite formation pressure prediction program is configured to implement the steps of the multi-mechanism composite formation pressure prediction method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores a multi-mechanism composite formation pressure prediction program, which, when executed by a processor, implements the steps of the multi-mechanism composite formation pressure prediction method according to any one of claims 1 to 7.

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