Water-flooding degree and saturation change determination method and device, electronic equipment and storage medium

Abstract

The application discloses a water flooding degree and saturation change determination method and device, electronic equipment and a storage medium. The method determines at least one first geological model based on a preset number of dry rock samples; generates a second geological model corresponding to each first geological model based on the at least one first geological model; performs forward modeling on the first geological model and the second geological model for each first geological model and the second geological model corresponding to each first geological model, to obtain first seismic data and second seismic data; determines seismic difference data; extracts data from the seismic difference data to obtain seismic attribute data of at least three seismic attributes; and inputs each seismic attribute data into a pre-trained prediction model to obtain a target water flooding degree and a target saturation change corresponding to each seismic attribute data, thereby realizing rapid determination of the water flooding degree and the saturation change by using a deep learning network.

Description

Technical Field

[0001] This invention relates to the field of oil reservoir development technology, and in particular to a method, apparatus, electronic device, and storage medium for determining the degree of water flooding and saturation changes. Background Technology

[0002] For oilfields developed through water injection, understanding the degree of water flooding helps to understand the distribution of remaining oil in the study area, thereby optimizing development plans and improving the oilfield's recovery rate.

[0003] However, existing methods rely too heavily on well logging data to determine the degree of flooding, making them difficult to adapt to areas without secondary well logging. Furthermore, when well logging data is insufficient, the prediction results are too biased. Moreover, they can only obtain either fluid saturation changes or the degree of flooding, making it difficult to obtain both simultaneously. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, and storage medium for determining the degree of flooding and changes in saturation, in order to solve the problem of over-reliance on well logging data when determining the degree of flooding and changes in saturation.

[0005] According to one aspect of the present invention, a method for determining the degree of flooding and changes in saturation is provided, the method comprising:

[0006] Based on a predetermined number of dry rock samples, at least one first geological model is determined; the first geological model is a geological model simulating the target reservoir before exploitation.

[0007] Based on the at least one first geological model, a second geological model is generated corresponding to each first geological model; the second geological model is a water flooding model simulating the target oil reservoir after extraction.

[0008] For each first geological model and its corresponding second geological model, forward modeling is performed on the first geological model and the second geological model to obtain the first earthquake data and the second earthquake data.

[0009] Determine earthquake difference data, wherein the earthquake difference data is the difference between a first earthquake data and a corresponding second earthquake data;

[0010] Data extraction is performed on the seismic difference data to obtain seismic attribute data for at least three preset seismic attributes;

[0011] By inputting the various earthquake attribute data into a pre-trained prediction model, the target flooding degree and target saturation change corresponding to each earthquake attribute data are obtained.

[0012] The prediction model is a deep learning network model, which includes an input layer, an output layer and at least three hidden layers. The prediction model is trained based on at least one sample seismic attribute data set and the sample flooding degree and sample saturation set corresponding to each sample seismic attribute set.

[0013] According to another aspect of the present invention, an apparatus for determining the degree of flooding and saturation change is provided. The apparatus includes: a model determination module for determining at least one first geological model based on a preset number of dry rock samples; wherein the first geological model is a geological model simulating the target oil reservoir before extraction.

[0014] The second model determination module is used to generate a second geological model corresponding to each of the first geological models based on the at least one first geological model; the second geological model is a water flooding model simulating the oil reservoir after the target layer is exploited.

[0015] The earthquake data determination module is used to perform forward modeling on each first geological model and the corresponding second geological model to obtain first earthquake data and second earthquake data.

[0016] The difference data determination module is used to determine earthquake difference data, wherein the earthquake difference data is the difference between a first earthquake data and a corresponding second earthquake data.

[0017] The data extraction module is used to extract earthquake differential data to obtain earthquake attribute data of at least three preset earthquake attributes;

[0018] The data prediction module is used to input various earthquake attribute data into a pre-trained prediction model to obtain the target flooding degree and target saturation change corresponding to each earthquake attribute data.

[0019] The prediction model is a deep learning network model, which includes an input layer, an output layer, and at least three hidden layers. The prediction model is trained based on at least one set of sample seismic attribute data and the corresponding flooding and saturation levels for each set of sample seismic attribute data.

[0020] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0021] At least one processor; and

[0022] A memory that is communicatively connected to at least one processor; wherein,

[0023] The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to perform the method for determining the degree of flooding and saturation changes according to any embodiment of the present invention.

[0024] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to execute the method for determining the degree of flooding and saturation changes according to any embodiment of the present invention.

[0025] The technical solution of this invention involves determining at least one first geological model based on a preset number of dry rock samples; generating second geological models corresponding to each first geological model based on the at least one first geological model; performing forward modeling on each first geological model and its corresponding second geological model to obtain first and second seismic data; determining seismic difference data, which is the difference between the first seismic data and the second seismic data corresponding to the first seismic data; extracting data from the seismic difference data to obtain seismic attribute data for at least three preset seismic attributes; and finally inputting each seismic attribute data into a pre-trained prediction model to obtain the target flooding degree and target saturation change corresponding to each seismic attribute data. This method enables rapid determination of flooding degree and saturation change, reduces reliance on well logging data, and thus adapts to areas without secondary well logging while ensuring the accuracy of the calculation results.

[0026] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a flowchart of a method for determining the degree of flooding and saturation changes according to Embodiment 1 of the present invention;

[0029] Figure 2 This is a schematic diagram of the first geological model provided according to Embodiment 1 of the present invention;

[0030] Figure 3This is a schematic diagram of the second geological model provided in Embodiment 1 of the present invention;

[0031] Figure 4 This is a schematic diagram of a device for determining the degree of flooding and saturation changes according to Embodiment 2 of the present invention;

[0032] Figure 5 This is a schematic diagram of the structure of an electronic device that implements the method for determining the degree of flooding and saturation changes according to embodiments of the present invention. Detailed Implementation

[0033] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0034] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0035] Example 1

[0036] Figure 1 This invention provides a flowchart of a method for determining the degree of flooding and changes in saturation according to Embodiment 1. This embodiment is applicable to situations where the degree of flooding and changes in saturation of a target layer need to be determined. This method can be executed by a device for determining the degree of flooding and changes in saturation, which can be implemented in hardware and / or software and can be configured in an electronic device with data processing capabilities. Figure 1 As shown, the method includes:

[0037] S110. Based on a preset number of dry rock samples, determine at least one first geological model.

[0038] The dry rock samples are rock samples within the target layer; the first geological model is a geological model simulating the target layer reservoir before extraction.

[0039] The target layer is a rock formation containing oil and gas with reserves exceeding the preset reserves. See also... Figure 2 Based on the actual situation of the target layer, a first geological model corresponding to the target layer can be constructed. The first geological model is a geological model that has not been flooded.

[0040] In one alternative approach, determining at least one first geological model based on a predetermined number of dry rock samples may include steps A1-A3:

[0041] Step A1: For each dry rock sample, perform rock physical measurements on the dry rock sample to obtain rock physical measurement data.

[0042] Step A2: Based on the comprehensive cross-plot analysis method and the nonlinear least squares method, determine the first relationship and the second relationship according to the rock physics measurement data. The first relationship is the correspondence between the bulk modulus of the dry rock sample and the porosity, density and effective pressure of the dry rock sample. The second relationship is the correspondence between the shear modulus of the dry rock sample and the porosity, density and effective pressure of the dry rock sample.

[0043] Step A3: Generate the first geological model based on the first and second relationships.

[0044] Rock samples can be rocks from the target layer. Rock physics measurements are the process of quantitatively determining the physical properties of rocks using various experimental techniques and instruments. These physical properties include, but are not limited to, rock density, porosity, permeability, elastic modulus, wave velocity, and resistivity. These measurements allow for a better understanding of the internal structure, composition, and behavior of rocks under different geological conditions. Cross-plot analysis is a widely used data analysis method in fields such as petroleum exploration and geological analysis. It primarily involves graphically plotting two or more related parameters (such as rock physics parameters, well logging data, etc.) on a coordinate system to visually analyze the relationships, variation patterns, and data distribution characteristics between these parameters. Porosity refers to the ratio of pore volume to the total volume of a rock. Density refers to the mass of a unit volume of rock. Effective pressure refers to the pressure acting on the rock skeleton; it is the difference between the total pressure and the pore fluid pressure.

[0045] Obtain n dried rock samples from the target layer. Perform rock physical measurements on each dried rock sample to determine its data, including porosity, density, bulk modulus, and shear modulus. Using the rock physical measurement data from the n dried rock samples, construct the first relationship between the bulk modulus and porosity, density, and effective pressure of the dried rock samples using cross-plot analysis and nonlinear least squares method; and construct the second relationship between the shear modulus and porosity, density, and effective pressure.

[0046] For ease of understanding, let k be used. dry The bulk modulus of a dried rock sample is expressed in μ. dry Indicates the shear modulus of a dried rock sample; The porosity of a dried rock sample is expressed in ρ. k The density of a dried rock sample is expressed in terms of P. e This indicates the effective pressure of the dried rock sample.

[0047] At this point, the first relation can be represented as The second relation can be represented as

[0048] At this point, by using the first and second relations, the first geological model is generated.

[0049] Optionally, after performing rock physical measurements on each dry rock sample to obtain rock physical measurement data, the method further includes:

[0050] Based on a preset range of rock physical measurement data, each rock physical measurement data is filtered to obtain at least one filtering result.

[0051] Since one or more rock physical measurement data points may be abnormal in the acquired data, direct use of these data points could lead to inaccurate results. Therefore, to improve the accuracy of the calculations, a preset range of rock physical measurement data is used to filter the data. Data points outside this range are identified and extracted, resulting in the final filtered results.

[0052] For example, after obtaining rock physical measurement data corresponding to n numbers of dry rock samples, these data are filtered to obtain a filtering result, which contains m results. The filtering result is the set of rock physical measurement data that falls within the preset range of rock physical measurement data.

[0053] In one alternative approach, a first geological model is generated based on the first and second relationships, including steps B1-B5:

[0054] Step B1: Based on the Gassmann equation, the first relation, and the second relation, generate the third relation and the fourth relation. The third relation is the relationship between the bulk modulus of saturated fluid rock and water saturation, oil saturation, gas saturation, porosity, dry rock density, and effective pressure. The fourth relation is the relationship between the shear modulus of saturated fluid rock and water saturation, oil saturation, gas saturation, porosity, dry rock density, and effective pressure.

[0055] Step B2: Based on classical rock physics theory, determine the fifth relationship, which is the relationship between the first longitudinal wave velocity and the bulk modulus, shear modulus, and density of saturated fluid rock.

[0056] Step B3: Determine the second longitudinal wave velocity and the density of the overlying layer corresponding to the target layer.

[0057] Step B4: Determine the third P-wave velocity and the density of the underlying layer corresponding to the target layer.

[0058] Step B5: Generate the first geological model based on the second P-wave velocity, overlying layer density, third P-wave velocity, underlying layer density, third relation, fourth relation, and fifth relation.

[0059] After obtaining the first relationship and the second relationship Subsequently, by deriving the Gassmann equation for saturated fluid rocks, a third relationship can be established between the bulk modulus of saturated fluid rocks and water saturation, oil saturation, gas saturation, porosity, density, and effective pressure. A fourth relationship can then be established between the shear modulus of saturated fluid rocks and these same factors.

[0060] Correspondingly, the third relation can be represented by the following formula:

[0061]

[0062] In the formula, S w Indicates water saturation; S o Indicates oil saturation; S g Indicates gas saturation.

[0063] The fourth relation can be represented by the following formula:

[0064]

[0065] Classical rock physics theory can be used to establish the relationship between the first longitudinal wave velocity and the bulk modulus, shear modulus, and density of saturated fluid rocks. This relationship can be expressed by the following formula:

[0066]

[0067] In the formula, v p This represents the first longitudinal wave velocity, which is also the longitudinal wave velocity of saturated fluid in rock; ρ sat This indicates the density of rock in saturated fluid.

[0068] For saturated fluid rock density ρ sat It can be obtained using the arithmetic mean formula.

[0069] Based on well logging data or rock physics measurements, the second P-wave velocity and density of the overlying layer corresponding to the target layer can be obtained, as well as the third P-wave velocity and density of the underlying layer corresponding to the target layer.

[0070] The target layer corresponds to the overlying layer, which consists of all strata above it, including multiple layers of rock, soil, and other geological materials, whose weight exerts pressure on the underlying strata. The target layer corresponds to the underlying layer, which is the strata below it.

[0071] Among them, the second longitudinal wave velocity can be used Indicated; the velocity of the third longitudinal wave can be expressed as... Indicate; the density of the overburden can be expressed as... Indicates; the density of the underlying layer can be expressed as... express.

[0072] After obtaining the second P-wave velocity, overlying layer density, third P-wave velocity, underlying layer density, third relation, fourth relation, and fifth relation, the properties of the target layer, the overlying layer of the target layer, and the underlying layer of the target layer can be determined, thereby enabling the construction of the first geological model.

[0073] S120. Based on at least one first geological model, generate a second geological model corresponding to each first geological model.

[0074] S130. For each first geological model and the corresponding second geological model, perform forward modeling on the first geological model and the second geological model to obtain the first earthquake data and the second earthquake data.

[0075] See Figure 3 The second geological model is a water flooding model simulating the oil reservoir after extraction.

[0076] For different primary geological models, after obtaining the primary geological model, k secondary geological models with different degrees of flooding and fluid saturation can be generated based on the primary geological model. The secondary geological model is obtained by flooding the primary geological model. Here, k is greater than or equal to 1. The secondary geological model can be used... To express.

[0077] For example, This can be understood as the first second geological model corresponding to the second first geological model.

[0078] The first seismic data can be obtained by performing forward modeling on the first geological model using existing forward modeling techniques. The first seismic data is denoted by D... ba x e The representation is as follows. In the forward modeling process, a zero-phase Ricker wavelet with the same dominant frequency as the original seismic data is used.

[0079] Similarly, by performing forward modeling on the second geological model, the second seismic data can be obtained. The second seismic data is presented in the format of... To express.

[0080] S140, Determine earthquake difference data.

[0081] The earthquake difference data is the difference between the first earthquake data and the corresponding second earthquake data.

[0082] The earthquake difference data is obtained by subtracting the first earthquake data from the second earthquake data.

[0083] By obtaining the first and second earthquake data, the corresponding earthquake difference data can be determined, which is obtained by subtracting the first and second earthquake data.

[0084] Among them, earthquake difference data can be used To express.

[0085] S150. Extract earthquake attribute data from the earthquake difference data to obtain earthquake attribute data of at least three earthquake attributes.

[0086] In one alternative approach, seismic attribute data corresponding to the first geological model are determined based on various seismic difference data, including steps C1-C6:

[0087] For each of the aforementioned seismic difference data, a preset angle phase shift is applied to the seismic difference data to obtain phase-shifted seismic difference data;

[0088] The first seismic data is interpreted at the top of the target layer to obtain the first interpretation layer data;

[0089] The phase shift seismic difference data is interpreted using peak interpretation to obtain second interpretation layer data;

[0090] Based on the first interpreted stratigraphic data, the reflected seismic amplitude value at the top of the first target layer is obtained;

[0091] Draw an intersection diagram of the reflection amplitude value at the top of the first target layer and the reservoir thickness of the target layer, and determine the stable reflection amplitude based on the intersection diagram;

[0092] Based on the second interpretation horizon data and the steady reflection amplitude, seismic attribute data of at least three preset seismic attributes are obtained.

[0093] The preset angle can be 90°.

[0094] After obtaining the various seismic difference data, to improve observation efficiency, the seismic difference data can be phase-shifted by 90°. After phase shifting, interpretation is performed along the top of the first seismic data to obtain the top interpretation horizon data of the target layer, i.e., the first interpretation horizon data. To express.

[0095] Interpreting the relatively strong peaks in the phase-shifted seismic difference data yields the peak interpretation data, also known as the second interpretation layer data. To express.

[0096] Along the first interpretation layer data Determine the reflection amplitude at the top of the target layer, in order to Representation is performed. The reflection amplitude value at the top of the plotted layer is shown. A graph showing the relationship between the target layer thickness and the target layer thickness is used to identify areas where the target layer thickness is greater than a certain value. Then a steady reflection amplitude appears, and this amplitude is determined to be

[0097] Referring to the zero-phase Ricker wavelet used in forward modeling, the attribute extraction window is set to W. a Then, based on the phase shift seismic difference data along Open the window to the center as W a Extract at least three seismic attribute data points, including amplitude, frequency, and phase. The time window W for extracting the seismic attribute data should be specified. a The time length corresponding to the wave crest of the zero-phase Ricker wavelet in forward modeling is used as a reference to explain T. Diff Centered on the stratigraphic level, the time window for extracting seismic attribute data is W. a Suppose that among the extracted seismic attribute data, the amplitude attribute is A. i ,i=1,2,...,n (n≥1).

[0098] The extracted amplitude-type attributes are standardized, and the standardized attribute values ​​are set as follows:

[0099]

[0100] S160. Input the various earthquake attribute data into the pre-trained prediction model to obtain the target flooding degree and target saturation change corresponding to each earthquake attribute data.

[0101] The prediction model is a deep learning network model, which includes an input layer, an output layer and at least three hidden layers. The prediction model is trained based on at least one second seismic attribute data set and the second flooding degree and second saturation degree sets corresponding to each second seismic attribute set.

[0102] Optionally, the prediction model is built on the TensorFlow framework and consists of an input layer with at least one input neuron and one bias neuron, at least three hidden layers, and an output layer with two neurons. During training, the learning rate of the gradient descent algorithm is between [0.01, 0.001]. The loss function of the prediction model is the squared error loss function.

[0103] For training the prediction model, iterative training is performed by pre-acquiring sample earthquake attribute data sets and the corresponding sample flooding degree and sample saturation degree sets for each sample earthquake attribute set. The correspondence between the sample earthquake attribute data sets and the corresponding sample flooding degree and sample saturation degree sets for each sample earthquake attribute set is found, and the correspondence is used during prediction to determine the specific values ​​of each saturation degree in the flooding degree and saturation degree sets.

[0104] According to the technical solution of the present invention, at least one first geological model is determined based on a preset number of dry rock samples; based on the at least one first geological model, a second geological model is generated corresponding to each first geological model; for each first geological model and the second geological model corresponding to each first geological model, forward modeling is performed on the first geological model and the second geological model to obtain first seismic data and second seismic data; seismic difference data is determined, which is the difference between the first seismic data and the second seismic data corresponding to the first seismic data; data extraction is performed on the seismic difference data to obtain seismic attribute data of at least three seismic attributes; finally, each seismic attribute data is input into a pre-trained prediction model to obtain the target flooding degree and target saturation change corresponding to each seismic attribute data. This enables rapid determination of flooding degree and saturation change, and reduces dependence on well logging data, thereby adapting to areas without secondary well logging while ensuring the accuracy of the calculation results.

[0105] Example 2

[0106] Figure 4This invention provides a structural block diagram of a device for determining the degree of flooding and changes in saturation, applicable to situations requiring the determination of the degree of flooding and changes in saturation at a target layer. This device can be implemented in hardware and / or software and can be configured in an electronic device with data processing capabilities. Figure 4 As shown, the device for determining the degree of flooding and saturation changes in this embodiment may include: a model determination module 310, a second model determination module 320, a seismic data determination module 330, a difference data determination module 340, a data extraction module 350, and a data prediction module 360. Wherein:

[0107] The model determination module 310 is used to determine at least one first geological model based on a preset number of dry rock samples, wherein the dry rock samples are rock samples within the target layer range; and the first geological model is a geological model simulating the target layer reservoir before exploitation.

[0108] The second model determination module 320 is used to determine at least one first geological model based on a preset number of dry rock samples; the first geological model is a geological model simulating the target reservoir before exploitation;

[0109] The earthquake data determination module 330 is used to perform forward modeling on each first geological model and the second geological model corresponding to each first geological model to obtain first earthquake data and second earthquake data.

[0110] The difference data determination module 340 is used to determine earthquake difference data, which is the difference between the first earthquake data and the corresponding second earthquake data.

[0111] The data extraction module 350 is used to extract data from earthquake difference data to obtain earthquake attribute data with at least three earthquake attributes.

[0112] The data prediction module 360 ​​is used to input various earthquake attribute data into a pre-trained prediction model to obtain the target flooding degree and target saturation change corresponding to each earthquake attribute data.

[0113] The prediction model is a deep learning network model, which includes an input layer, an output layer and at least three hidden layers. The prediction model is trained based on at least one sample seismic attribute data set and the sample flooding degree and sample saturation set corresponding to each sample seismic attribute set.

[0114] Based on the above embodiments, optionally, the model determination module 310 includes:

[0115] For each dry rock sample, rock physical measurements were performed to obtain rock physical measurement data;

[0116] Based on the comprehensive cross-plot analysis method and the nonlinear least squares method, and according to the rock physics measurement data, the first relationship and the second relationship are determined. The first relationship is the correspondence between the bulk modulus of the dry rock sample and the porosity, density and effective pressure of the dry rock sample. The second relationship is the correspondence between the shear modulus of the dry rock sample and the porosity, density and effective pressure of the dry rock sample.

[0117] Based on the first and second relationships, the first geological model is generated.

[0118] Based on the above embodiments, optionally, a first geological model is generated based on the first relationship and the second relationship, including:

[0119] Based on the Gassmann equation, the first relation, and the second relation, the third relation and the fourth relation are generated. The third relation is the relationship between the bulk modulus of saturated fluid rock and water saturation, oil saturation, gas saturation, porosity, dry rock density, and effective pressure. The fourth relation is the relationship between the shear modulus of saturated fluid rock and water saturation, oil saturation, gas saturation, porosity, dry rock density, and effective pressure.

[0120] Based on classical rock physics theory, a fifth relation is determined, which is the relationship between the first longitudinal wave velocity and the bulk modulus, shear modulus, and density of saturated fluid rocks.

[0121] Determine the second longitudinal wave velocity and the density of the overlying layer corresponding to the target layer;

[0122] Determine the third P-wave velocity and the density of the underlying layer corresponding to the target layer;

[0123] The first geological model is generated based on the second P-wave velocity, overlying layer density, third P-wave velocity, underlying layer density, third relation, fourth relation, and fifth relation.

[0124] Based on the above embodiments, optionally, the data extraction module 350 includes:

[0125] For each of the aforementioned seismic difference data, a preset angle phase shift is applied to the seismic difference data to obtain phase-shifted seismic difference data;

[0126] The first seismic data is interpreted at the top of the target layer to obtain the first interpretation layer data;

[0127] The phase shift seismic difference data is interpreted using peak interpretation to obtain second interpretation layer data;

[0128] Based on the first interpreted stratigraphic data, the reflected seismic amplitude value at the top of the first target layer is obtained;

[0129] Draw an intersection diagram of the reflection amplitude value at the top of the first target layer and the reservoir thickness of the target layer, and determine the stable reflection amplitude based on the intersection diagram;

[0130] Based on the second interpreted horizon data and the steady-state reflection amplitude, seismic attribute data for at least three preset seismic attributes are obtained.

[0131] Based on the above embodiments, optionally, after performing rock physical measurements on each of the dry rock samples to obtain rock physical measurement data, the method further includes:

[0132] Based on a preset range of rock physical measurement data, each rock physical measurement data is filtered to obtain at least one filtering result;

[0133] Accordingly, based on the comprehensive cross-plot analysis method and the nonlinear least squares method, and according to the rock physical measurement data, the first relationship and the second relationship are determined, including:

[0134] Based on the comprehensive intersection graph analysis method and the nonlinear least squares method, the first relationship and the second relationship are determined according to the screening results.

[0135] Based on the above embodiments, optionally, the loss function of the prediction model is the squared error loss function.

[0136] The device for determining the degree of flooding and saturation change provided in this embodiment of the invention can execute the method for determining the degree of flooding and saturation change provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0137] Example 3

[0138] Figure 5 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. 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 can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0139] like Figure 5As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0140] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0141] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as methods for determining the degree of flooding and saturation changes.

[0142] In some embodiments, the method for determining the degree of flooding and saturation change can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for determining the degree of flooding and saturation change described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the method for determining the degree of flooding and saturation change by any other suitable means (e.g., by means of firmware).

[0143] Various embodiments 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-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0144] Computer programs used to implement 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, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0145] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0146] To provide interaction with a user, the systems and techniques described herein can 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 pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0147] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0148] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0149] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0150] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for determining the degree of flooding and changes in saturation, characterized in that, include: Based on a predetermined number of dry rock samples, at least one first geological model is determined; The first geological model is a geological model simulating the target reservoir before extraction; Based on the at least one first geological model, a second geological model is generated corresponding to each first geological model; the second geological model is a water flooding model simulating the target oil reservoir after extraction. For each first geological model and its corresponding second geological model, forward modeling is performed on the first geological model and the second geological model to obtain the first earthquake data and the second earthquake data. Determine earthquake difference data, wherein the earthquake difference data is the difference between a first earthquake data and a second earthquake data corresponding to the first earthquake data; Data extraction is performed on the seismic difference data to obtain seismic attribute data for at least three preset seismic attributes; By inputting the various earthquake attribute data into a pre-trained prediction model, the target flooding degree and target saturation change corresponding to each earthquake attribute data are obtained. The prediction model is a deep learning network model, which includes an input layer, an output layer and at least three hidden layers. The prediction model is trained based on at least one sample seismic attribute data set and the sample flooding degree and sample saturation set corresponding to each sample seismic attribute set. This involves extracting seismic difference data to obtain seismic attribute data for at least three preset seismic attributes, including: For each of the aforementioned seismic difference data, a 90-degree phase shift is performed on the seismic difference data to obtain phase-shifted seismic difference data; The first seismic data is interpreted at the top of the target layer to obtain the first interpretation layer data; The phase shift seismic difference data is interpreted using peak interpretation to obtain second interpretation layer data; Based on the first interpreted layer data, the reflection amplitude value at the top of the first target layer is obtained; Draw an intersection diagram of the reflection amplitude value at the top of the first target layer and the reservoir thickness of the target layer, and determine the stable reflection amplitude based on the intersection diagram; Based on the second interpretation horizon data and the steady reflection amplitude, seismic attribute data of at least three preset seismic attributes are obtained; Based on a predetermined number of dry rock samples, at least one first geological model is determined, including: For each of the aforementioned dry rock samples, rock physical measurements are performed on the dry rock samples to obtain rock physical measurement data; Based on the comprehensive cross-plot analysis method and the nonlinear least squares method, a first relationship and a second relationship are determined according to the rock physics measurement data. The first relationship is the correspondence between the bulk modulus of the dry rock sample and the porosity, density and effective pressure of the dry rock sample. The second relationship is the correspondence between the shear modulus of the dry rock sample and the porosity, density and effective pressure of the dry rock sample. Based on the Gassmann equation, the first relation, and the second relation, a third relation and a fourth relation are generated. The third relation is the relationship between the bulk modulus of saturated fluid rock and water saturation, oil saturation, gas saturation, porosity, dry rock density, and effective pressure. The fourth relation is the relationship between the shear modulus of saturated fluid rock and water saturation, oil saturation, gas saturation, porosity, dry rock density, and effective pressure. Based on classical rock physics theory, a fifth relationship is determined, which is the relationship between the first longitudinal wave velocity and the bulk modulus, shear modulus, and density of saturated fluid rock. Determine the second longitudinal wave velocity and the density of the overlying layer corresponding to the target layer; Determine the third P-wave velocity and the density of the underlying layer corresponding to the target layer; The first geological model is generated based on the second P-wave velocity, overlying layer density, third P-wave velocity, underlying layer density, third relation, fourth relation, and fifth relation. The density of saturated fluid rock is determined based on an arithmetic mean formula. The method further includes, after performing rock physical measurements on each of the dry rock samples to obtain rock physical measurement data, the following steps: Based on a preset range of rock physical measurement data, each rock physical measurement data is filtered to obtain at least one filtering result; Accordingly, based on the comprehensive cross-plot analysis method and the nonlinear least squares method, and according to the rock physical measurement data, the first relationship and the second relationship are determined, including: Based on the comprehensive intersection graph analysis method and the nonlinear least squares method, the first relationship and the second relationship are determined according to the screening results.

2. The method according to claim 1, characterized in that, The loss function of the prediction model is the squared error loss function.

3. A device for determining the degree of flooding and changes in saturation, characterized in that, include: The model determination module is used to determine at least one first geological model based on a preset number of dry rock samples, wherein the dry rock samples are rock samples within the target layer range; The first geological model is a geological model simulating the target reservoir before extraction; The second model determination module is used to generate a second geological model corresponding to each of the first geological models based on the at least one first geological model; the second geological model is a water flooding model simulating the oil reservoir after the target layer is exploited. The earthquake data determination module is used to perform forward modeling on each first geological model and the corresponding second geological model to obtain first earthquake data and second earthquake data. The difference data determination module is used to determine earthquake difference data, wherein the earthquake difference data is the difference between a first earthquake data and a second earthquake data corresponding to the first earthquake data; The data extraction module is used to extract earthquake differential data to obtain earthquake attribute data of at least three preset earthquake attributes; The data prediction module is used to input various earthquake attribute data into a pre-trained prediction model to obtain the target flooding degree and target saturation change corresponding to each earthquake attribute data. The prediction model is a deep learning network model, which includes an input layer, an output layer and at least three hidden layers. The prediction model is trained based on at least one sample seismic attribute data set and the sample flooding degree and sample saturation set corresponding to each sample seismic attribute set. The data extraction module includes: For each of the aforementioned seismic difference data, a 90-degree phase shift is performed on the seismic difference data to obtain phase-shifted seismic difference data; The first seismic data is interpreted at the top of the target layer to obtain the first interpretation layer data; The phase shift seismic difference data is interpreted using peak interpretation to obtain second interpretation layer data; Based on the first interpreted layer data, the reflection amplitude value at the top of the first target layer is obtained; Draw an intersection diagram of the reflection amplitude value at the top of the first target layer and the reservoir thickness of the target layer, and determine the stable reflection amplitude based on the intersection diagram; Based on the second interpretation horizon data and the steady reflection amplitude, seismic attribute data of at least three preset seismic attributes are obtained; Based on a predetermined number of dry rock samples, at least one first geological model is determined, including: For each of the aforementioned dry rock samples, rock physical measurements are performed on the dry rock samples to obtain rock physical measurement data; Based on the comprehensive cross-plot analysis method and the nonlinear least squares method, a first relationship and a second relationship are determined according to the rock physics measurement data. The first relationship is the correspondence between the bulk modulus of the dry rock sample and the porosity, density and effective pressure of the dry rock sample. The second relationship is the correspondence between the shear modulus of the dry rock sample and the porosity, density and effective pressure of the dry rock sample. Based on the Gassmann equation, the first relation, and the second relation, a third relation and a fourth relation are generated. The third relation is the relationship between the bulk modulus of saturated fluid rock and water saturation, oil saturation, gas saturation, porosity, dry rock density, and effective pressure. The fourth relation is the relationship between the shear modulus of saturated fluid rock and water saturation, oil saturation, gas saturation, porosity, dry rock density, and effective pressure. Based on classical rock physics theory, a fifth relationship is determined, which is the relationship between the first longitudinal wave velocity and the bulk modulus, shear modulus, and density of saturated fluid rock. Determine the second longitudinal wave velocity and the density of the overlying layer corresponding to the target layer; Determine the third P-wave velocity and the density of the underlying layer corresponding to the target layer; The first geological model is generated based on the second P-wave velocity, overlying layer density, third P-wave velocity, underlying layer density, third relation, fourth relation, and fifth relation. The density of saturated fluid rock is determined based on an arithmetic mean formula. The method further includes, after performing rock physical measurements on each of the dry rock samples to obtain rock physical measurement data, the following steps: Based on a preset range of rock physical measurement data, each rock physical measurement data is filtered to obtain at least one filtering result; Accordingly, based on the comprehensive cross-plot analysis method and the nonlinear least squares method, and according to the rock physical measurement data, the first relationship and the second relationship are determined, including: Based on the comprehensive intersection graph analysis method and the nonlinear least squares method, the first relationship and the second relationship are determined according to the screening results.

4. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method for determining the degree of flooding and saturation change as described in any one of claims 1-2.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method for determining the degree of flooding and saturation change as described in any one of claims 1-2.

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