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

Abstract

The invention discloses a flooding degree and saturation change determination method and device, electronic equipment and a storage medium. The method comprises the following steps: determining at least one first geologic model based on a preset number of dry rock samples; based on the at least one first geologic model, generating a second geologic model corresponding to each first geologic model; for each first geologic model and a second geologic model corresponding to each first geologic model, performing forward modeling on the first geologic model and the second geologic model to obtain first seismic data and second seismic data; determining seismic difference data; performing data extraction on the seismic difference data to obtain seismic attribute data of at least three seismic attributes; and inputting each piece of seismic attribute data into a pre-trained prediction model to obtain a target flooding degree and a target saturation change corresponding to each piece of seismic attribute data, thereby realizing rapid determination of the flooding degree and the saturation change by using a deep learning network.

Description

Technical Field

[0001] The present invention relates to the technical field of reservoir development, and in particular, to a method, device, electronic device, and storage medium for determining water flooding degree and saturation change. Background Art

[0002] For a waterflooding developed oilfield, by understanding the water flooding degree, the remaining oil distribution in the study area can be understood, so as to optimize the development plan and improve the oil recovery rate of the oilfield.

[0003] However, when determining the water flooding degree by the existing methods, they rely too much on logging data and it is difficult to adapt to areas without secondary logging. In addition, when the logging data is insufficient, the prediction results have too large deviations. And only the fluid saturation change or the water flooding degree can be obtained, and it is difficult to obtain both at the same time. Summary of the Invention

[0004] The present invention provides a method, device, electronic device, and storage medium for determining water flooding degree and saturation change to solve the problem of over-reliance on logging data when determining the water flooding degree and saturation change.

[0005] According to an aspect of the present invention, a method for determining water flooding degree and saturation change is provided. The method includes:

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

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

[0008] 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;

[0009] Determine seismic difference data, where the seismic difference data is the difference between the first seismic data and the second seismic data corresponding to the first seismic data;

[0010] Extract data from the seismic difference data to obtain seismic attribute data of at least three preset seismic attributes;

[0011] Input each seismic attribute data into a pre-trained prediction model to obtain the target water flooding degree and target saturation change corresponding to each seismic attribute data;

[0012] Among them, 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 according to at least one sample seismic attribute data group, the sample water flooding degree corresponding to each sample seismic attribute group, and the sample saturation group.

[0013] According to another aspect of the present invention, there is provided a device for determining water flooding degree and saturation change, the device includes: a model determination module, configured 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 reservoir before the exploitation of the target layer;

[0014] A second model determination module, configured to generate a second geological model corresponding to each first geological model based on the at least one first geological model; the second geological model is a water flooding model simulating the reservoir after the exploitation of the target layer;

[0015] A seismic data determination module, configured to perform forward modeling on the first geological model and the second geological model corresponding to each first geological model, to obtain first seismic data and second seismic data;

[0016] A difference data determination module, configured to determine seismic difference data, where the seismic difference data is the difference between the first seismic data and the second seismic data corresponding to the first seismic data;

[0017] A data extraction module, configured to extract data from the seismic difference data to obtain seismic attribute data of at least three preset seismic attributes;

[0018] A data prediction module, configured to input each seismic attribute data into a pre-trained prediction model to obtain the target water flooding degree and the target saturation change corresponding to each seismic attribute data;

[0019] Among them, 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 according to at least one sample seismic attribute data group, the sample water flooding degree corresponding to each sample seismic attribute group, and the sample saturation group.

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

[0021] At least one processor; and

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

[0023] The memory stores a computer program that can be executed by at least one processor. The computer program is executed by at least one processor to enable the at least one processor to execute the method for determining the water flooding degree and saturation change according to any embodiment of the present invention.

[0024] According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the method for determining the water flooding degree and saturation change according to any embodiment of the present invention when executed.

[0025] The technical solution of the embodiment of the present invention is based on a preset number of dry rock samples to determine at least one first geological model; based on the at least one first geological model, generate second geological models corresponding to each first geological model; for each first geological model and the second geological models respectively corresponding to each first geological model, perform forward modeling on the first geological model and the second geological model to obtain first seismic data and second seismic data; determine seismic difference data, where the seismic difference data is the difference between the first seismic data and the second seismic data corresponding to the first seismic data; perform data extraction on the seismic difference data to obtain seismic attribute data of at least three preset seismic attributes; finally, input each seismic attribute data into a pre-trained prediction model to obtain the target water flooding degree and target saturation change corresponding to each seismic attribute data, which can realize the rapid determination of the water flooding degree and saturation change, and reduce the dependence on logging data, so as to adapt to areas without secondary logging while ensuring the accuracy of the calculation results.

[0026] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used 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

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings without creative efforts based on these drawings.

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

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

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

[0031] Figure 4 It is a schematic structural diagram of a device for determining the water flooding degree and saturation change provided by Embodiment 2 of the present invention;

[0032] Figure 5 It is a schematic structural diagram of an electronic device for implementing the method for determining the water flooding degree and saturation change of the embodiments of the present invention. Detailed implementation manners

[0033] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0034] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances 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 "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0035] Embodiment 1

[0036] Figure 1 The present invention provides a flowchart of a method for determining the water flooding degree and saturation change in Embodiment 1. This embodiment is applicable to the situation of determining the water flooding degree of the target layer and different saturation changes. This method can be executed by a device for determining the water flooding degree and saturation change. The device for determining the water flooding degree and saturation change can be implemented in the form of hardware and / or software, and the device for determining the water flooding degree and saturation change can be configured in an electronic device with data processing capabilities. As Figure 1 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 range; the first geological model is the geological model simulating the reservoir before oil and gas production in the target layer.

[0039] The target layer is a rock formation with oil and gas and an oil and gas reserve greater than the preset reserve. Refer to Figure 2 , according to the actual situation of the target layer, the first geological model corresponding to the target layer can be constructed, where the first geological model is a geological model without water flooding.

[0040] In an alternative solution, based on a preset number of dry rock samples, at least one first geological model is determined, which 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 non - linear least - squares method, according to the rock physical measurement data, determine the first relationship and the second relationship. The first relationship is the corresponding relationship 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 corresponding relationship 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 relationship and the second relationship.

[0044] The rock samples can be rocks within the target layer. Rock physical measurement can be a process of quantitatively measuring the physical properties of rocks using various experimental techniques and instrumentation. These physical properties include but are not limited to the density, porosity, permeability, elastic modulus, wave velocity, resistivity, etc. of the rock. Through these measurements, the internal structure, composition, and behavior of the rock under different geological conditions can be better understood. The comprehensive cross - plot analysis method can be a data analysis method widely used in the fields of oil exploration, geological analysis, etc. It mainly cross - plots two or more related parameters (such as rock physical parameters, logging data, etc.) graphically in a coordinate system to intuitively analyze the mutual relationships, variation laws, and data distribution characteristics among these parameters. Porosity refers to the ratio of the pore volume in the rock to the total volume of the rock. Density refers to the mass of the rock per unit volume. Effective pressure refers to the pressure acting on the rock skeleton, which is the difference between the total pressure and the pore fluid pressure.

[0045] Obtain n dry rock samples of the target layer. For each dry rock sample, conduct petrophysical measurements to determine the petrophysical measurement data of each dry rock sample, including porosity, density, bulk modulus, shear modulus, etc. Utilize the petrophysical measurement data corresponding to the n dry rock samples, and construct the first relationship between the bulk modulus of the dry rock samples and porosity, density, and effective pressure, as well as construct the second relationship between the shear modulus and porosity, density, and effective pressure by means of crossplot analysis and nonlinear least squares method.

[0046] For ease of understanding, let k dry represent the bulk modulus of the dry rock samples; let μ dry represent the shear modulus of the dry rock samples; let represent the porosity of the dry rock samples; let ρ k represent the density of the dry rock samples; let P e represent the effective pressure of the dry rock samples.

[0047] At this time, the first relationship can be expressed as The second relationship can be expressed as

[0048] At this time, with the help of the first relationship and the second relationship, a first geological model is generated.

[0049] Optionally, after conducting petrophysical measurements on each dry rock sample to obtain petrophysical measurement data for each dry rock sample, it further includes:

[0050] Screen each petrophysical measurement data based on a preset petrophysical measurement data range to obtain at least one screening result.

[0051] Since among the at least one petrophysical measurement data obtained, there may be one or more petrophysical measurement data that are abnormal results, and if directly used, it may lead to inaccurate final results. Therefore, to improve the accuracy of the calculation results, each petrophysical measurement data is screened through the preset petrophysical measurement data range, and the petrophysical measurement data that is not within the preset petrophysical measurement data range is identified and removed, thereby obtaining the final screening result.

[0052] Exemplarily, after obtaining the petrophysical measurement data corresponding to n dry rock samples, screening them to obtain a screening result, and there are m in the screening result. The screening result is the set of petrophysical measurement data within the preset petrophysical measurement data range.

[0053] In an alternative solution, generating the first geological model based on the first relationship and the second relationship includes steps B1 - B5:

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

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

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

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

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

[0059] After obtaining the first relationship and the second relationship it is possible to establish the third relationship between the bulk modulus of the saturated fluid rock and the water saturation, oil saturation, gas saturation, porosity, density, and effective pressure by obtaining the Gassmann equation (Gassmann equation) of the saturated fluid rock. And establish the fourth relationship between the shear modulus of the saturated fluid rock and the water saturation, oil saturation, gas saturation, porosity, density, and effective pressure.

[0060] Correspondingly, the third relationship can be expressed by the following formula:

[0061]

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

[0063] The fourth relationship can be expressed by the following formula:

[0064]

[0065] Through the classical rock physics theory, the relationship between the first P-wave velocity and the bulk modulus, shear modulus, and density of the saturated fluid rock can be established. It can be expressed by the following formula:

[0066]

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

[0068] For the density ρ of the saturation fluid rock sat it can be obtained by using the arithmetic mean formula.

[0069] Based on well logging data or rock physics measurements, etc., the second longitudinal wave velocity and the overlying layer density corresponding to the target layer's overlying layer can be obtained, and at the same time, the third longitudinal wave velocity and the underlying layer density corresponding to the target layer's underlying layer can be obtained.

[0070] Among them, the overlying layer corresponding to the target layer is all the strata above the target layer, including multiple layers of rock, soil, and other geological materials, and their weights exert pressure on the underlying strata. The underlying layer corresponding to the target layer is the strata below the target layer.

[0071] Among them, the second longitudinal wave velocity can be represented by ; the third longitudinal wave velocity can be represented by ; the overlying layer density can be represented by ; the underlying layer density can be represented by .

[0072] After obtaining the second longitudinal wave velocity, the overlying layer density, the third longitudinal wave velocity, the underlying layer density, the third relationship, the fourth relationship, and the fifth relationship, the attributes of the corresponding structures of the target layer, the overlying layer of the target layer, and the underlying layer of the target layer can be determined, and then the construction of the first geological model can be realized.

[0073] S120. Generate second geological models corresponding to each of the at least one first geological model.

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

[0075] Refer to Figure 3 , the second geological model is the water flooding model after simulating the oil reservoir exploitation of the target layer.

[0076] For different first geological models, after obtaining the first geological model, k second geological models with different water flooding degrees and fluid saturations can be generated based on the first geological model. Among them, the second geological model is obtained by water flooding the first geological model. Among them, k is greater than or equal to 1. The second geological model can be represented by is represented.

[0077] Exemplarily, it can be understood as the first second geological model corresponding to the second first geological model.

[0078] By performing forward modeling on the first geological model using existing forward modeling means, the first seismic data can be obtained. Among them, the first seismic data is represented by D ba x e is represented. Among them, during the forward modeling process, a zero-phase Ricker wavelet consistent with the main frequency of the original seismic data is used.

[0079] Similarly, by performing forward modeling on the second geological model, the second seismic data can be obtained. Among them, the second seismic data is represented by is represented.

[0080] S140. Determine the seismic difference data.

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

[0082] The seismic difference data is obtained by taking the difference between the first seismic data and the second seismic data.

[0083] Through the obtained first seismic data and second seismic data, the corresponding seismic difference data can be determined, that is, by taking the difference between the first seismic data and the second seismic data.

[0084] Among them, the seismic difference data can be represented by is represented.

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

[0086] In an alternative solution, based on each seismic difference data, determining the seismic attribute data corresponding to the first geological model includes steps C1 - C6:

[0087] For each of the seismic difference data, perform a preset angular phase shift on the seismic difference data to obtain phase-shifted seismic difference data;

[0088] Perform top-of-target-layer interpretation on the first seismic data to obtain first interpreted horizon data;

[0089] Perform wave-crest interpretation on the phase-shifted seismic difference data to obtain second interpreted horizon data;

[0090] Based on the first interpreted horizon data, obtain the first top-of-target-layer reflection seismic amplitude;

[0091] Plot the cross-plot of the top reflection seismic amplitude of the first target layer and the reservoir thickness of the target layer, and determine the stable reflection amplitude according to the cross-plot.

[0092] Based on the second interpreted horizon data and the stable reflection amplitude, obtain the seismic attribute data of at least three preset seismic attributes.

[0093] The preset angle can be 90°.

[0094] After obtaining each seismic difference data, to improve the observation efficiency, the seismic difference data can be phase-shifted by 90°. After the phase shift, interpret along the top of the data of the first seismic data to obtain the interpreted horizon data at the top of the target layer, that is, the first interpreted horizon data, which is represented by For presentation.

[0095] Interpret the relatively strong wave peaks in the phase-shifted seismic difference data to obtain the wave peak interpretation data in the phase-shifted seismic difference data, that is, the second interpreted horizon data, which is represented by For presentation.

[0096] Along the first interpreted horizon data Determine the top reflection seismic amplitude of the target layer, which is represented by For presentation. Plot the relationship diagram between the top reflection seismic amplitude and the thickness of the target layer, find the amplitude of the stable reflection that appears after the thickness of the target layer is greater than in the diagram, and determine this amplitude as

[0097] When extracting attributes, set the extraction attribute time window as W a with reference to the zero-phase Ricker wavelet used in the forward modeling, and then, based on the phase-shifted seismic difference data, open a time window of W centered on a to extract more than 3 seismic attribute data including amplitude, frequency, phase, etc. The time window W a for extracting seismic attribute data is set with reference to the time length occupied by the corresponding wave peak of the zero-phase Ricker wavelet in the forward modeling. With the interpreted T Diff horizon as the center, open a time window of W a for extracting seismic attribute data. Assume that among the extracted seismic attribute data, the amplitude-type attribute is A i where i = 1, 2,..., n (n≥1).

[0098] Standardize the extracted amplitude-type attributes. Let the standardized attribute value be:

[0099]

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

[0101] Among them, 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 according to at least one group of second seismic attribute data and the corresponding second water flooding degree and the second saturation group of each second seismic attribute group.

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

[0103] For the training of the prediction model, iterative training is carried out by pre-obtaining a sample seismic attribute data group and the corresponding sample water flooding degree and sample saturation group of each sample seismic attribute group, finding the corresponding relationship between the sample seismic attribute data group and the corresponding sample water flooding degree and sample saturation group of each sample seismic attribute group, and using the corresponding relationship during prediction to determine the specific values of each saturation in the water flooding degree and saturation group.

[0104] According to the technical solution of the embodiment of the present invention, based on a preset number of dry rock samples, at least one first geological model is determined; based on at least one first geological model, each first geological model generates a corresponding second geological model; for each first geological model and the corresponding second geological model of 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, and the seismic difference data 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 water flooding degree and the target saturation change corresponding to each seismic attribute data, which can realize the rapid determination of the water flooding degree and the saturation change, and reduce the dependence on logging data, so as to adapt to areas without secondary logging while ensuring the accuracy of the calculation results.

[0105] Embodiment 2

[0106] Figure 4The embodiment of the present invention provides a structural block diagram of a device for determining the water flooding degree and saturation change. This embodiment is applicable to the situation of determining the water flooding degree of the target layer and different saturation changes. The device for determining the water flooding degree and saturation change can be implemented in the form of hardware and / or software, and can be configured in an electronic device with data processing capabilities. As Figure 4 shown, the device for determining the water flooding degree and saturation change 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. Among them:

[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, and the dry rock samples are rock samples within the range of the target layer; the first geological model is a geological model simulating the pre-production of the oil reservoir in the target layer;

[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 pre-production of the oil reservoir in the target layer;

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

[0110] The difference data determination module 340 is used to determine seismic difference data, and the seismic difference data is the difference between the first seismic data and the corresponding second seismic data of the first seismic data;

[0111] The data extraction module 350 is used to extract data from the seismic difference data to obtain seismic attribute data of at least three seismic attributes;

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

[0113] Among them, the prediction model is a deep learning network model, and the prediction model includes an input layer, an output layer, and at least three hidden layers; the prediction model is trained according to at least one sample seismic attribute data group and the corresponding sample water flooding degree and sample saturation group of each sample seismic attribute group.

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

[0115] For each dry rock sample, perform rock physics measurements on the dry rock sample to obtain rock physics measurement data;

[0116] Based on the comprehensive crossplot analysis method and the non-linear least squares method, determine a first relationship and a second relationship according to the rock physics measurement data. The first relationship is the corresponding relationship between the bulk modulus of the dry rock sample, the porosity, density, and effective pressure of the dry rock sample. The second relationship is the corresponding relationship between the shear modulus of the dry rock sample, the porosity, density, and effective pressure of the dry rock sample;

[0117] Generate a first geological model based on the first relationship and the second relationship.

[0118] Optionally, on the basis of the above embodiments, generating a first geological model based on the first relationship and the second relationship includes:

[0119] Generate a third relationship and a fourth relationship based on Gassmann's equation, the first relationship, and the second relationship. The third relationship is the relationship between the bulk modulus of the fluid-saturated rock, water saturation, oil saturation, gas saturation, porosity, dry rock density, and effective pressure. The fourth relationship is the relationship between the shear modulus of the fluid-saturated rock, water saturation, oil saturation, gas saturation, porosity, dry rock density, and effective pressure;

[0120] Determine a fifth relationship based on classical rock physics theory. The fifth relationship is the relationship between the first P-wave velocity and the bulk modulus, shear modulus, and density of the fluid-saturated rock;

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

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

[0123] Generate a first geological model based on the second P-wave velocity, the density of the overlying layer, the third P-wave velocity, the density of the underlying layer, the third relationship, the fourth relationship, and the fifth relationship.

[0124] Optionally, on the basis of the above embodiments, the data extraction module 350 includes:

[0125] For each of the seismic difference data, perform a preset angular phase shift on the seismic difference data to obtain phase-shifted seismic difference data;

[0126] Perform top interpretation of the target layer on the first seismic data to obtain first interpreted horizon data;

[0127] Perform peak interpretation on the phase-shifted seismic difference data to obtain second interpreted horizon data;

[0128] Based on the first interpreted horizon data, obtain the top reflection seismic amplitude of the first target layer;

[0129] Plot the crossplot of the top reflection seismic amplitude of the first target layer and the reservoir thickness of the target layer, and determine the stable reflection amplitude according to the crossplot;

[0130] Based on the second interpreted horizon data and the stable reflection amplitude, obtain the seismic attribute data of at least three preset seismic attributes..

[0131] On the basis of the above embodiments, optionally, after performing rock physics measurements on each of the dry rock samples to obtain rock physics measurement data, it further includes:

[0132] Screen each rock physics measurement data based on a preset rock physics measurement data range to obtain at least one screening result;

[0133] Correspondingly, based on the comprehensive crossplot analysis method and the nonlinear least squares method, determine the first relationship and the second relationship according to the rock physics measurement data, including:

[0134] Based on the comprehensive crossplot analysis method and the nonlinear least squares method, determine the first relationship and the second relationship according to the screening results.

[0135] On the basis of the above embodiments, optionally, the loss function of the prediction model is the mean squared error loss function.

[0136] The water flooding degree and saturation change determination device provided by the embodiments of the present invention can execute the water flooding degree and saturation change determination method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0137] Embodiment III

[0138] Figure 5 FIG. shows a schematic structural diagram of an electronic device 10 that can be used to implement the embodiments 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 can also represent various forms of mobile devices, such as, personal digital processors, 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 only examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0139] As Figure 5As shown, the electronic device 10 includes at least one processor 11 and a memory communicatively connected to the at least one processor 11, such as read-only memory (ROM) 12, random access memory (RAM) 13, etc. Among them, the memory stores a computer program executable by the at least one processor. The processor 11 can execute 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 into 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 via a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0140] Multiple 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 disc, 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 via a computer network such as the Internet and / or various telecommunication networks.

[0141] The processor 11 can be various general-purpose and / or special-purpose 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 dedicated 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 the method for determining the waterlogging degree and saturation change.

[0142] In some embodiments, the method for determining the waterlogging degree and saturation change can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto 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 the waterlogging degree and saturation change described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute the method for determining the waterlogging degree and saturation change by any other appropriate means (for example, by means of firmware).

[0143] The various embodiments of the systems and techniques described above in this specification can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from, and transmits data and instructions to, a storage system, at least one input device, and at least one output device.

[0144] The computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The computer programs can be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

[0145] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[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 a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the 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 acoustic input, voice input, or tactile input).

[0147] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0148] A computing system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. 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 a cloud host, which is a host product in the cloud computing service system and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[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 recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is made herein.

[0150] The above specific embodiments do not constitute a limitation on the protection scope of the present 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 principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for determining flooding degree and saturation change, characterized in that: include: Determining 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 layer oil reservoir before production; Based on the at least one first geological model, generating second geological models corresponding to the first geological models respectively; the second geological model is a water flooding model simulating the target layer oil reservoir after exploitation; For each first geological model and each second geological model corresponding to each first geological model, forward simulation is performed on the first geological model and the second geological model to obtain first seismic data and second seismic data; Determine seismic difference data, where the seismic difference data is a difference between first seismic data and second seismic data corresponding to the first seismic data; Extracting data from the seismic difference data to obtain seismic attribute data of at least three preset seismic attributes; Input each 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; Among them, 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 group and the sample flooding degree and sample saturation group corresponding to each sample seismic attribute group.

2. The method according to claim 1, characterized in that Determining at least one first geological model based on a preset number of dry rock samples includes: For each of the dry rock samples, performing rock physical measurements on the dry rock samples to obtain rock physical measurement data; Based on the comprehensive cross plot analysis method and the nonlinear least square method, a first relationship and a second relationship are determined according to the rock physical measurement data, wherein the first relationship is the corresponding relationship between the bulk modulus of the dry rock sample and the porosity, density and effective pressure of the dry rock sample, and the second relationship is the corresponding relationship between the shear modulus of the dry rock sample and the porosity, density and effective pressure of the dry rock sample; A first geological model is generated based on the first relationship and the second relationship.

3. The method according to claim 2, characterized in that Generating a first geological model based on the first relationship and the second relationship includes: Based on the Gassman equation, the first relationship and the second relationship, a third relationship and a fourth relationship are generated, wherein the third relationship is the relationship between the bulk modulus of the saturated fluid rock and the water saturation, oil saturation, gas saturation, porosity, dry rock density and effective pressure, and the fourth relationship is the relationship between the shear modulus of the saturated fluid rock and the water saturation, oil saturation, gas saturation, porosity, dry rock density and effective pressure; Based on classical rock physics theory, a fifth relationship is determined, wherein the fifth relationship is a relationship between the first P-wave velocity and the bulk modulus, shear modulus and density of the fluid-saturated rock; Determine the second P-wave velocity and density of the overburden corresponding to the target layer; Determine the third P-wave velocity of the target layer and the density of the underlying layer; A first geological model is generated based on the second P-wave velocity, the overburden density, the third P-wave velocity, the underburden density, the third relationship, the fourth relationship, and the fifth relationship.

4. The method according to claim 1, characterized in that: Extract the seismic difference data to obtain seismic attribute data of at least three preset seismic attributes, including: For each of the seismic difference data, performing a phase shift at a preset angle on the seismic difference data to obtain phase-shifted seismic difference data; Performing target layer top interpretation on the first seismic data to obtain first interpreted layer data; Performing wave crest interpretation on the phase-shift seismic difference data to obtain second interpretation horizon data; Based on the first interpreted horizon data, obtaining the reflection amplitude value of the top of the first target layer; 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 interpreted horizon data and the steady reflection amplitude, seismic attribute data of at least three preset seismic attributes are obtained.

5. The method according to claim 2, characterized in that: After performing rock physical measurements on each of the dry rock samples to obtain rock physical measurement data, the method further includes: Screening each rock physical measurement data based on a preset rock physical measurement data range to obtain at least one screening result; Accordingly, based on the comprehensive crossplot analysis method and the nonlinear least squares method, the first relationship and the second relationship are determined according to the rock physical measurement data, including: Based on the comprehensive cross-plot analysis method and the nonlinear least squares method, the first relationship and the second relationship are determined according to the screening results.

6. The method according to claim 1, characterized in that The loss function of the prediction model is a square error loss function.

7. A device for determining flooding degree and saturation change, characterized in that: include: A model determination module, 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; the first geological model is a geological model simulating the target layer oil reservoir before exploitation; A second model determination module is used to generate second geological models corresponding to each first geological model based on the at least one first geological model; the second geological model is a water flooding model simulating the target layer oil reservoir after exploitation; A seismic data determination module, for each first geological model and each second geological model corresponding to each first geological model, performs forward simulation on the first geological model and the second geological model to obtain first seismic data and second seismic data; A difference data determination module, used to determine earthquake difference data, wherein the earthquake difference data is a difference between first earthquake data and second earthquake data corresponding to the first earthquake data; A data extraction module, used for extracting data from the seismic difference data to obtain seismic attribute data of at least three preset seismic attributes; A data prediction module is used to input various earthquake attribute data into a pre-trained prediction model to obtain target flooding degree and target saturation change corresponding to each earthquake attribute data; Among them, 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 group and the sample flooding degree and sample saturation group corresponding to each sample seismic attribute group.

8. The device according to claim 7, characterized in that Determining at least one first geological model based on a preset number of dry rock samples includes: For each of the dry rock samples, performing rock physical measurements on the dry rock samples to obtain rock physical measurement data; Based on the comprehensive cross plot analysis method and the nonlinear least square method, a first relationship and a second relationship are determined according to the rock physical measurement data, wherein the first relationship is the corresponding relationship between the bulk modulus of the dry rock sample and the porosity, density and effective pressure of the dry rock sample, and the second relationship is the corresponding relationship between the shear modulus of the dry rock sample and the porosity, density and effective pressure of the dry rock sample; A first geological model is generated based on the first relationship and the second relationship.

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 so that the at least one processor can perform the method for determining flooding degree and saturation change according to any one of claims 1 to 6.

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 the flooding degree and saturation change according to any one of claims 1 to 6 when executed.

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