Method, device, equipment and medium for determining saturation of hydrate in stratum

Abstract

The invention discloses a method, a device, equipment and a medium for determining saturation of hydrates in a stratum. The method comprises the following steps: acquiring actually measured missile-electricity data of a target stratum, and determining initial hydrate saturation; processing the hydrate saturation and the actually measured elastoelectric data based on a pre-constructed multi-component elastoelectric prediction model to obtain predicted elastoelectric data of the target stratum; determining an error value based on the predicted missile-electricity data and the actually measured missile-electricity data; and updating the hydrate saturation according to the size of the error value until the error value is smaller than a given error limit, and determining the hydrate saturation of the target based on the adjusted hydrate saturation. The problem that the hydrate saturation prediction precision is low due to the fact that the same rock physical model cannot be used for calculating the elastic and electrical data at the same time in the prior art is solved, the elastic and electrical data synchronous combined interpretation of the hydrate saturation is achieved, and the effect of improving the hydrate saturation prediction accuracy is achieved.

Description

Technical Field

[0001] The present invention relates to the field of physical exploration technology, and in particular to a method, device, equipment and medium for determining hydrate saturation in a formation. Background Art

[0002] At present, natural gas hydrates are widely distributed in the strata of deep water areas and permafrost areas. The prediction of hydrate saturation and distribution has important scientific significance and practical value. Hydrate saturation can reflect the content of hydrates.

[0003] The existing method of predicting hydrate saturation is usually to measure or invert rock physical parameters (such as P- and S-wave velocities, elastic modulus, etc.) using technologies such as well logging, seismic and marine controlled source electromagnetic, and further invert hydrate saturation through rock physical parameters. This method relies on a unified elastic and electrical rock physics model. Not only is rock physics modeling difficult, but there is also the problem of low hydrate saturation prediction accuracy. Summary of the invention

[0004] The present invention provides a method, device, equipment and medium for determining hydrate saturation in a formation, so as to realize synchronous joint interpretation of elastic and electrical data of hydrate saturation, thereby achieving the technical effect of improving the accuracy of hydrate saturation prediction.

[0005] According to one aspect of the present invention, there is provided a method for determining hydrate saturation in a formation, the method comprising:

[0006] Acquire measured elastic and electrical data of the target formation and determine the initial hydrate saturation; wherein the target formation includes solid minerals and pore fluids, the solid minerals are composed of quartz and clay, and the pore fluids include hydrates and water;

[0007] Processing the hydrate saturation and the measured elastic and electrical data based on a pre-built multi-component elastic and electrical prediction model to obtain predicted elastic and electrical data of the target formation;

[0008] Determining an error value based on the predicted elastic-electric data and the measured elastic-electric data;

[0009] The hydrate saturation is updated according to the size of the error value, so as to re-execute the operations of determining the predicted elastic and electrical data of the target formation and determining the error value based on the updated hydrate saturation until the error value is less than a given error limit, and the hydrate saturation of the target is determined based on the hydrate saturation.

[0010] According to another aspect of the present invention, there is provided a device for determining hydrate saturation in a formation, the device comprising:

[0011] A measured elastic and electrical data acquisition module is used to acquire measured elastic and electrical data of a target formation and determine an initial hydrate saturation; wherein the target formation includes solid minerals and pore fluids, the solid minerals are composed of quartz and clay, etc., and the pore fluids include hydrates and water;

[0012] A predicted elastic-electric data determination module is used to process the hydrate saturation and the measured elastic-electric data based on a pre-built multi-component elastic-electric prediction model to obtain predicted elastic-electric data of the target formation;

[0013] An error value determination module, used to determine an error value based on the predicted elastic-electric data and the measured elastic-electric data;

[0014] A target hydrate saturation determination module is used to update the hydrate saturation according to the size of the error value, so as to re-execute the operations of determining the predicted elastic and electrical data of the target formation and determining the error value based on the updated hydrate saturation until the error value is less than a given error limit, and determine the target hydrate saturation based on the hydrate saturation.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for determining the hydrate saturation in a formation as described in any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions for enabling a processor to implement the method for determining hydrate saturation in a formation as described in any embodiment of the present invention when the computer instructions are executed.

[0020] The technical solution of the embodiment of the present invention is to obtain the measured elastic and electrical data of the target formation and determine the initial hydrate saturation; the target formation includes solid minerals and porous fluids, the solid minerals are composed of quartz and clay, and the porous fluids include hydrates and water; the hydrate saturation and the measured elastic and electrical data are processed based on a pre-constructed multi-component elastic and electrical prediction model to obtain the predicted elastic and electrical data of the target formation; the error value is determined based on the predicted elastic and electrical data and the measured elastic and electrical data; the hydrate saturation is updated according to the size of the error value, and the operations of determining the predicted elastic and electrical data of the target formation and determining the error value are re-executed based on the updated hydrate saturation until the error value is less than a given error limit, and the target is determined based on the hydrate saturation. The hydrate saturation solves the problem that the elastic and electrical data cannot be calculated simultaneously using the same rock physics model in the prior art, resulting in the problem of low hydrate saturation prediction accuracy. It realizes that in the process of determining the predicted hydrate data of the formation by processing the measured hydrate data and the hydrate saturation in a multi-component hydrate prediction model, the hydrate saturation is adjusted according to the error between the predicted hydrate data and the measured hydrate data until the predicted hydrate data is consistent with the measured hydrate data, and the adjusted optimal hydrate saturation is obtained, so that the hydrate saturation is close to the actual hydrate saturation, and the synchronous joint interpretation of the hydrate saturation and hydrate data is realized, so as to achieve the technical effect of improving the accuracy of hydrate saturation prediction.

[0021] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 is a flow chart of a method for determining hydrate saturation in a formation provided according to Embodiment 1 of the present invention;

[0024] Figure 2 is a schematic diagram of a method for determining hydrate saturation provided in accordance with Embodiment 1 of the present invention;

[0025] Figure 3 is a schematic diagram of a method for determining hydrate saturation provided in accordance with Embodiment 1 of the present invention;

[0026] Figure 4This is a practical application effect diagram of the method for verifying and determining the hydrate saturation in a formation provided in the first embodiment of the present invention;

[0027] Figure 5 is a schematic structural diagram of a device for determining hydrate saturation in a formation according to a second embodiment of the present invention;

[0028] Figure 6 It is a schematic diagram of the structure of an electronic device for implementing the method for determining hydrate saturation in a formation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] Embodiment 1

[0032] Figure 1 This is a flow chart of a method for determining hydrate saturation in a formation according to Embodiment 1 of the present invention. This embodiment is applicable to the case of determining natural gas hydrate saturation. The method can be executed by a device for determining hydrate saturation in a formation. The device for determining hydrate saturation in a formation can be implemented in the form of hardware and / or software. The device for determining hydrate saturation in a formation can be configured in a computing device. Figure 1 As shown, the method includes:

[0033] S110, obtaining measured elastic and electrical data of the target formation, and determining the initial hydrate saturation.

[0034] Among them, the target formation can be a rock or formation with porosity and permeability and fluid storage and circulation space conditions. The target formation includes solid minerals and porous fluids. The solid minerals are composed of quartz and clay, etc., and the porous fluids include hydrates and water. Hydrates can refer to natural gas hydrates, specifically ice-like crystalline substances formed by natural gas and water under high pressure and low temperature conditions. The measured elastic and electrical data include elastic parameters and electrical parameters, which can specifically include elastic parameters and electrical parameters of various substances in the target formation. Elastic parameters may include but are not limited to elastic modulus (including bulk modulus and shear modulus), longitudinal wave velocity, shear wave velocity, rock density, longitudinal wave (shear wave) impedance, Poisson's ratio, longitudinal and shear wave velocity ratio, etc. Electrical parameters may include but are not limited to conductivity and resistivity, etc.

[0035] In this embodiment, for different target formations in different work areas, corresponding measurement tools can be selected to collect formation geophysical data and obtain real measured formation elastic and electrical data. Before determining the hydrate saturation of the target formation, a hydrate saturation to be used can also be given so that the hydrate saturation can be predicted based on the given hydrate saturation and measured elastic and electrical data. For example, the hydrate saturation is less than the critical porosity corresponding to the target formation, and 0.05 is selected.

[0036] S120. Processing the hydrate saturation and measured elastic and electrical data based on a pre-constructed multi-component elastic and electrical prediction model to obtain predicted elastic and electrical data of the target formation.

[0037] Among them, the multi-component elastic-electric prediction model can be used to predict the formation hydrate saturation. The predicted elastic-electric data include elastic parameters and electrical parameters.

[0038] See also Figure 2 The measured elastic and electrical data and the given hydrate saturation can be input into the multi-component elastic and electrical prediction model. By processing the measured elastic and electrical data and the hydrate saturation, the elastic parameter data and electrical parameter data of the target formation are output as the predicted elastic and electrical data.

[0039] In this embodiment, the measured elastic and electrical data include the porosity and mineral component content interpreted by well logging; the hydrate saturation and the measured elastic and electrical data are processed based on a pre-constructed multi-component elastic and electrical prediction model to obtain the predicted elastic and electrical data of the target formation, including: processing the hydrate saturation and the porosity and mineral component content interpreted by well logging based on the multi-component elastic and electrical prediction model to determine the predicted elastic and electrical data of the target formation.

[0040] Specifically, the first elastic-electric prediction function in the multi-component elastic-electric prediction model can be used to calculate the elastic parameters (e.g., shear modulus and bulk modulus) and electrical parameters (e.g., conductivity) of the target formation containing multiple materials. The first elastic-electric prediction function can be expressed as:

[0041]

[0042]

[0043]

[0044] Among them, f i , K i , μ i and σ i are the hydrate saturation (the initial hydrate saturation is given), bulk modulus, shear modulus and conductivity corresponding to the i-th substance respectively; P i , Q i and R i is the polarization factor; K SCA , μ SCA and σ SCA are the bulk modulus, shear modulus and conductivity of the target formation respectively; m is the total number of material types.

[0045] S130, determining an error value based on the predicted elastic-electric data and the measured elastic-electric data.

[0046] In this embodiment, the error between the model output result (i.e., predicted elastic-electric data) and the measured data can be calculated as the error value. The specific implementation method can be: based on the target loss function, the predicted elastic-electric data and the measured elastic-electric data are processed to determine the error value.

[0047] Among them, the objective loss function is used to evaluate the error between the actual measured data and the forward simulation response of the multi-component electroelastic prediction model.

[0048] In this embodiment, the objective loss function (J) can be expressed as:

[0049]

[0050] in: and are the measured P-wave velocity and measured resistivity, respectively; and To predict the P-wave velocity and resistivity. In the case of joint elastic-electric inversion, α is 0.5, J(S h ) is represented as the error value.

[0051] Specifically, the predicted P-wave velocity is determined by predicting the bulk modulus in the elastic-electric data and the rock density of the target formation. For example, the quotient of the bulk modulus and the rock density is used as the predicted P-wave velocity. The predicted resistivity is determined by predicting the conductivity in the elastic-electric data. For example, the inverse of the conductivity is used as the predicted resistivity. Furthermore, the predicted P-wave velocity, the predicted resistivity, the hydrate saturation, the P-wave velocity and the resistivity in the measured elastic-electric data can be input into the target loss function. After the target loss function is solved, the error value J(S h ).

[0052] S140, updating the hydrate saturation according to the size of the error value, and re-executing the operations of determining the predicted elastic and electrical data of the target formation and determining the error value based on the updated hydrate saturation until the error value is less than a given error limit, and determining the target hydrate saturation based on the hydrate saturation.

[0053] It should be noted that when predicting the hydrate saturation (reflecting the hydrate content) by the multi-component electroelastic prediction model, there is a certain error between the initially given hydrate saturation and the actual hydrate saturation. In order to obtain the most realistic hydrate saturation, the size of the error value can be used to determine whether the hydrate saturation needs to be adjusted to obtain the optimal hydrate saturation that is consistent with the actual situation.

[0054] In this embodiment, the hydrate saturation is updated according to the size of the error value, including: if the error value is greater than the error limit, the hydrate saturation is adjusted to obtain the adjusted hydrate saturation, based on the multi-component elastic-electric prediction model according to the adjusted hydrate saturation and the porosity and mineral component content interpreted by the well logging, the predicted elastic-electric data of the target formation is re-predicted, and the operation of determining the error value based on the predicted elastic-electric data and the measured elastic-electric data is re-executed; if the error value is not greater than the error limit, the adjusted hydrate saturation is used as the target hydrate saturation to be inverted. Among them, the error limit can be a pre-set error threshold, and its specific numerical value can be determined by the staff according to the actual working situation, and is not limited here.

[0055] Continue to see Figure 2, the error value and the error limit can be compared and processed to determine whether the error value is greater than the error limit. If not, it means that the error between the predicted elastic and electrical data calculated at this time and the measured elastic and electrical data is small, and the hydrate saturation at this time can be used as the hydrate saturation of the target formation, that is, the target hydrate saturation. If yes, it means that the predicted elastic and electrical data calculated at this time are quite different from the measured elastic and electrical data, and the hydrate saturation can be adjusted, such as increasing the hydrate saturation, and the predicted elastic and electrical data can be recalculated by the adjusted hydrate saturation. That is, based on the adjusted hydrate saturation, the step of determining the predicted elastic and electrical data of the target formation based on the hydrate saturation and the measured elastic and electrical data based on the multi-component elastic and electrical prediction model is re-executed, including: taking the predicted elastic and electrical data as the initial condition, and gradually adjusting the hydrate saturation using the second elastic and electrical prediction function in the multi-component elastic and electrical prediction model until the error value is not greater than the error limit, indicating that the adjusted hydrate saturation is consistent with the actual hydrate saturation, and the adjusted hydrate saturation can be used as the target saturation.

[0056] Exemplarily, the second elastic-electric prediction function can be expressed as:

[0057]

[0058]

[0059]

[0060] Among them, x j , K j , μ j and σ j are the hydrate saturation, bulk modulus, shear modulus and conductivity corresponding to the jth substance; P j , Q j and R j is the polarization factor; K DEM , μ DEM and σ DEM are the predicted bulk modulus, predicted shear modulus and predicted conductivity of the target formation respectively; dx j It is expressed as the adjustment amount of the hydrate saturation of the jth substance.

[0061] In order to improve the accuracy of hydrate saturation prediction and the prediction efficiency at the same time, the predicted elastoelectric data of the target formation are re-predicted based on the multi-component elastoelectric prediction model according to the adjusted hydrate saturation and the porosity and mineral component content interpreted by well logging, including: in the process of processing the hydrate saturation and the porosity and mineral component content interpreted by well logging based on the multi-component elastoelectric prediction model, the order of adding different solid minerals and porous fluids is adjusted to obtain the predicted elastoelectric data of the target formation.

[0062] In this embodiment, a multi-component electroelastic prediction model may be pre-constructed. The method of constructing the multi-component electroelastic prediction model may be:

[0063] See also Figure 3 First, we use the first electroelastic prediction function (i.e. Figure 3 Model A) calculates the bulk modulus, shear modulus and conductivity of the mixed fluid composed of pore-filling hydrate and water. The first elastic-electric prediction function is:

[0064]

[0065]

[0066]

[0067] Then, the predicted elastic-electric data calculated by the first elastic-electric prediction function is used as the initial condition, and the second elastic-electric prediction function (i.e. Figure 3 Model B) gradually adjusts the hydrate and water saturation (for example, adding hydrate and water to the mixed fluid) until the hydrate saturation reaches the critical porosity. The second elastic-electric prediction function is:

[0068]

[0069]

[0070]

[0071] Furthermore, assuming that the pore aspect ratio of the mixed fluid is the same as the particle aspect ratio of the clay, model A is used to calculate the bulk modulus, shear modulus, and conductivity of the three-component mixture consisting of hydrate-water-clay, and model B is then used to gradually adjust the hydrate saturation of hydrate, water, and clay (for example, adding hydrate, water, and clay to the mixed fluid) until the hydrate saturation reaches the critical porosity.

[0072] Furthermore, assuming that the aspect ratio of quartz particles is 1, model A is used to calculate the bulk modulus, shear modulus and conductivity of the four-component mixture consisting of hydrate-water-clay-quartz, and then model B is used to gradually adjust the hydrate saturation of hydrate, water, clay and quartz (for example, adding hydrate, water, clay and quartz to the mixed fluid) until the hydrate saturation reaches the critical porosity. At this time, the four-component mixture is a pore-filling hydrate reservoir.

[0073] In practical applications, the mixture can be a two-component mixture consisting of hydrate and water, a three-component mixture consisting of hydrate, water and clay, or a four-component mixture consisting of hydrate, water, clay and quartz. If the mixture is a four-component mixture, the first elastic-electric prediction function can be used to calculate the elastic-electric parameters of the two-component mixture consisting of hydrate and water; then the second elastic-electric prediction function can be used to adjust the saturation of hydrate and water according to the actual content ratio of hydrate and water until the saturation of hydrate and water reaches the critical porosity; further, the first elastic-electric prediction function and the second elastic-electric prediction function in the multi-component elastic-electric prediction model are used to calculate the elastic and electrical parameters of the three-component mixture consisting of the two-component mixture and clay particles; further, the multi-component elastic-electric prediction model is used to calculate the elastic and electrical parameters of the four-component mixture consisting of the three-component mixture and clay particles; the multi-component elastic-electric prediction model is used in combination with the global optimization algorithm to predict the hydrate saturation and apply it to the calculation of the pore-filling hydrate saturation.

[0074] Based on the above technical solutions, Figure 4 This is a practical application effect diagram to verify the method of determining the hydrate saturation in the formation. The hydrate saturation predicted by the multi-component elastic-electric prediction model based on single P-wave velocity, single resistivity and P-wave velocity-resistivity combined data is compared with the well logging interpretation hydrate saturation calculated by the Archie formula. As can be seen from the figure, although there are differences in the predicted hydrate saturation, the overall trend of the prediction results is relatively close. In addition, the single P-wave velocity prediction is low and the single resistivity prediction is high, which may be related to the presence of a small amount of free gas in the hydrate layer. In comparison, the hydrate saturation predicted by the multi-component elastic-electric properties is consistent with the results calculated by the Archie formula, proving that the elastic-electric properties combined prediction method can effectively improve the prediction accuracy of hydrate saturation.

[0075] The technical solution of this embodiment is to obtain the measured elastic and electrical data of the target formation and determine the initial hydrate saturation; the target formation includes solid minerals and pore fluids, the solid minerals are composed of quartz and clay, and the pore fluids include hydrates and water; the hydrate saturation and the measured elastic and electrical data are processed based on a pre-constructed multi-component elastic and electrical prediction model to obtain the predicted elastic and electrical data of the target formation; the error value is determined based on the predicted elastic and electrical data and the measured elastic and electrical data; the hydrate saturation is updated according to the size of the error value, and the operations of determining the predicted elastic and electrical data of the target formation and determining the error value are re-executed based on the updated hydrate saturation until the error value is less than a given error limit, and the target hydrate saturation is determined based on the hydrate saturation. The method solves the problem that the elastic and electrical data cannot be calculated simultaneously using the same rock physics model in the prior art, resulting in low accuracy in hydrate saturation prediction. The method realizes that in the process of determining the predicted hydrate data of the formation by processing the measured hydrate data and the hydrate saturation in a multi-component hydrate prediction model, the hydrate saturation is adjusted according to the error between the predicted hydrate data and the measured hydrate data until the predicted hydrate data is consistent with the measured hydrate data, thereby obtaining the adjusted optimal hydrate saturation, making the hydrate saturation close to the actual hydrate saturation, and realizing the synchronous joint interpretation of the hydrate saturation and hydrate data, thereby achieving the technical effect of improving the accuracy of hydrate saturation prediction.

[0076] Embodiment 2

[0077] Figure 5 FIG. 1 is a schematic diagram of a device for determining hydrate saturation in a formation according to Embodiment 2 of the present invention. Figure 5 As shown, the device includes: a measured elastic and electrical data acquisition module 210, a predicted elastic and electrical data determination module 220, an error value determination module 230 and a target hydrate saturation determination module 240.

[0078] Among them, the measured elastic and electrical data acquisition module 210 is used to obtain the measured elastic and electrical data of the target formation and determine the initial hydrate saturation; wherein the target formation includes solid minerals and porous fluids, the solid minerals are composed of quartz and clay, etc., and the porous fluids include hydrates and water; the predicted elastic and electrical data determination module 220 is used to process the hydrate saturation and the measured elastic and electrical data based on a pre-constructed multi-component elastic and electrical prediction model to obtain the predicted elastic and electrical data of the target formation; the error value determination module 230 is used to determine the error value based on the predicted elastic and electrical data and the measured elastic and electrical data; the target hydrate saturation determination module 240 is used to update the hydrate saturation according to the size of the error value, so as to re-execute the operations of determining the predicted elastic and electrical data of the target formation and determining the error value based on the updated hydrate saturation, until the error value is less than a given error limit, and the target hydrate saturation is determined based on the hydrate saturation.

[0079] The technical solution of this embodiment is to obtain the measured elastic and electrical data of the target formation and determine the initial hydrate saturation; the target formation includes solid minerals and pore fluids, the solid minerals are composed of quartz and clay, and the pore fluids include hydrates and water; the hydrate saturation and the measured elastic and electrical data are processed based on a pre-constructed multi-component elastic and electrical prediction model to obtain the predicted elastic and electrical data of the target formation; the error value is determined based on the predicted elastic and electrical data and the measured elastic and electrical data; the hydrate saturation is updated according to the size of the error value, and the operations of determining the predicted elastic and electrical data of the target formation and determining the error value are re-executed based on the updated hydrate saturation until the error value is less than a given error limit, and the target hydrate saturation is determined based on the hydrate saturation. The method solves the problem that the elastic and electrical data cannot be calculated simultaneously using the same rock physics model in the prior art, resulting in low accuracy in hydrate saturation prediction. The method realizes that in the process of determining the predicted hydrate data of the formation by processing the measured hydrate data and the hydrate saturation in a multi-component hydrate prediction model, the hydrate saturation is adjusted according to the error between the predicted hydrate data and the measured hydrate data until the predicted hydrate data is consistent with the measured hydrate data, thereby obtaining the adjusted optimal hydrate saturation, making the hydrate saturation close to the actual hydrate saturation, and realizing the synchronous joint interpretation of the hydrate saturation and hydrate data, thereby achieving the technical effect of improving the accuracy of hydrate saturation prediction.

[0080] On the basis of the above device, optionally, the measured elastic and electrical data include the porosity and mineral component content interpreted by well logging; the predicted elastic and electrical data determination module is specifically used to process the hydrate saturation and the porosity and mineral component content interpreted by well logging based on the multi-component elastic and electrical prediction model to determine the predicted elastic and electrical data of the target formation;

[0081] The predicted elastic-electric data include elastic parameters and electrical parameters.

[0082] On the basis of the above-mentioned device, optionally, the error value determination module is specifically used to process the predicted elastic and electrical data and the measured elastic and electrical data based on a target loss function to determine the error value.

[0083] Based on the above device, optionally, the target hydrate saturation determination module 240 includes a saturation adjustment unit.

[0084] The saturation adjustment unit is used to adjust the hydrate saturation if the error value is greater than the error limit to obtain the adjusted hydrate saturation, re-predict the predicted elastic and electrical data of the target formation based on the multi-component elastic and electrical prediction model according to the adjusted hydrate saturation and the porosity and mineral component content interpreted by the logging, and re-execute the operation of determining the error value based on the predicted elastic and electrical data and the measured elastic and electrical data; if the error value is not greater than the error limit, the adjusted hydrate saturation is used as the target hydrate saturation to be inverted.

[0085] Based on the above-mentioned device, optionally, the saturation adjustment unit is also used to adjust the order of adding different solid minerals and porous fluids in the process of processing the hydrate saturation and the porosity and mineral component content interpreted by the logging based on the multi-component electroelastic prediction model to obtain the predicted electroelastic data of the target formation.

[0086] The device for determining the hydrate saturation in a formation provided in an embodiment of the present invention can execute the method for determining the hydrate saturation in a formation provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0087] Embodiment 3

[0088] Figure 6 Schematic diagram of the structure of an electronic device for implementing the method of determining hydrate saturation in a formation according to an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0089] like Figure 6As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, ROM 12 and RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0090] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0091] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a method for determining hydrate saturation in a formation.

[0092] In some embodiments, the method for determining hydrate saturation in a formation may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for determining hydrate saturation in a formation described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the method for determining hydrate saturation in a formation in any other suitable manner (e.g., by means of firmware).

[0093] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0094] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0095] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0096] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0097] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0098] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0099] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0100] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for determining hydrate saturation in a formation, characterized in that: include: Acquire measured elastic and electrical data of the target formation and determine the initial hydrate saturation; wherein the target formation includes solid minerals and pore fluids, the solid minerals are composed of quartz and clay, and the pore fluids include hydrates and water; Processing the hydrate saturation and the measured elastic and electrical data based on a pre-built multi-component elastic and electrical prediction model to obtain predicted elastic and electrical data of the target formation; Determining an error value based on the predicted elastic-electric data and the measured elastic-electric data; The hydrate saturation is updated according to the size of the error value, so as to re-execute the operations of determining the predicted elastic and electrical data of the target formation and determining the error value based on the updated hydrate saturation until the error value is less than a given error limit, and the hydrate saturation of the target is determined based on the hydrate saturation.

2. The method according to claim 1, characterized in that The measured elastic and electrical data include the porosity and mineral component content interpreted by well logging; the hydrate saturation and the measured elastic and electrical data are processed based on the pre-built multi-component elastic and electrical prediction model to obtain the predicted elastic and electrical data of the target formation, including: Processing the hydrate saturation and the porosity and mineral component content interpreted by the well logging based on the multi-component elastic-electric prediction model to determine the predicted elastic-electric data of the target formation; The predicted elastic-electric data include elastic parameters and electrical parameters.

3. The method according to claim 1, characterized in that The determining of the error value based on the predicted elastic-electric data and the measured elastic-electric data comprises: The predicted elastic-electric data and the measured elastic-electric data are processed based on a target loss function to determine the error value.

4. The method according to claim 1, characterized in that: The updating of the hydrate saturation according to the error value includes: If the error value is greater than the error limit, the hydrate saturation is adjusted to obtain an adjusted hydrate saturation, and the predicted elastic and electrical data of the target formation are re-predicted based on the multi-component elastic and electrical prediction model according to the adjusted hydrate saturation and the porosity and mineral component content interpreted by the well logging, and the operation of determining the error value based on the predicted elastic and electrical data and the measured elastic and electrical data is re-executed; If the error value is not greater than the error limit, the adjusted hydrate saturation is used as the target hydrate saturation to be inverted.

5. The method according to claim 4, characterized in that The re-prediction of the predicted elastic and electrical data of the target formation based on the multi-component elastic and electrical prediction model according to the adjusted hydrate saturation and the porosity and mineral component content interpreted by the well logging includes: In the process of processing the hydrate saturation and the porosity and mineral component content interpreted by the well logging based on the multi-component electroelastic prediction model, the order of adding different solid minerals and pore fluids is adjusted to obtain the predicted electroelastic data of the target formation.

6. A device for determining hydrate saturation in a formation, characterized in that: include: A measured elastic and electrical data acquisition module is used to acquire measured elastic and electrical data of a target formation and determine an initial hydrate saturation; wherein the target formation includes solid minerals and pore fluids, the solid minerals are composed of quartz and clay, etc., and the pore fluids include hydrates and water; A predicted elastic-electric data determination module is used to process the hydrate saturation and the measured elastic-electric data based on a pre-built multi-component elastic-electric prediction model to obtain predicted elastic-electric data of the target formation; An error value determination module, used to determine an error value based on the predicted elastic-electric data and the measured elastic-electric data; A target hydrate saturation determination module is used to update the hydrate saturation according to the size of the error value, so as to re-execute the operations of determining the predicted elastic and electrical data of the target formation and determining the error value based on the updated hydrate saturation until the error value is less than a given error limit, and determine the target hydrate saturation based on the hydrate saturation.

7. The device according to claim 6, characterized in that The measured elastic and electrical data include the porosity and mineral component content interpreted by well logging; the predicted elastic and electrical data determination module is specifically used to process the hydrate saturation and the porosity and mineral component content interpreted by well logging based on the multi-component elastic and electrical prediction model to determine the predicted elastic and electrical data of the target formation; The predicted elastic-electric data include elastic parameters and electrical parameters.

8. The device according to claim 6, characterized in that The error value determination module is specifically used to process the predicted elastic-electrical data and the measured elastic-electrical data based on a target loss function to determine the error value.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the method for determining hydrate saturation in a formation according to any one of claims 1-5.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for determining hydrate saturation in a formation according to any one of claims 1 to 5 when executed.

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