Formation pressure prediction method and device for high water injection rate working condition

Abstract

The invention provides a formation pressure prediction method and device for a high water injection rate working condition, and is suitable for the technical field of data processing, and the method comprises the steps: obtaining water injection speed information, water injection time information and suspended particle concentration information; according to the water injection speed information, the water injection time information and the suspended particle concentration information, the water injection rate and the stratum permeability are determined; according to the water injection rate and the formation permeability, multi-dimensional calculation is conducted on formation pressure, and initial formation pressure values of multiple dimensions are obtained; generating a plurality of formation pressure matrixes according to the initial formation pressure values of the multiple dimensions; and based on a preset weight vector, a nonlinear mapping function and a gating vector, performing prediction calculation on the formation pressure matrix to obtain a target formation pressure value. Under the condition that the formation permeability changes, the formation pressure is predicted so as to evaluate the formation stability in the water injection process, adverse effects of water injection work on well drilling are avoided, and the safety of oil collection work is improved.

Description

Technical Field

[0001] The present application belongs to the field of data processing technology, and in particular, relates to a formation pressure prediction method and device for high water injection conditions. Background Art

[0002] In the process of offshore oil development, the injection of produced water into the formation is becoming a popular engineering project. In the operation of a 10,000 cubic meter water injection well with a single well water injection volume of 15,000 cubic meters to 30,000 cubic meters, the production water will be injected while drilling and development operations are carried out. As the injection time and injection volume increase, the pressure of the production water injection well continues to rise, which will bring great high-pressure safety hazards to the water injection pipeline, wellbore and formation; whether long-term large-volume injection of produced water will make the injection layer an abnormally high-pressure layer, thereby causing subsequent drilling development risks, blowouts and other accidents, it is urgent to clarify; long-term injection causes the underground diffusion range to continue to expand, and the dynamic range of produced water needs to be clarified; long-term injection causes drastic changes in the formation pressure system, ground stress, etc., which is prone to formation instability and other problems, and it is urgent to provide an effective safety assessment through formation pressure prediction.

[0003] In the prior art, the method for predicting formation pressure is mainly used in the application scenario of detecting formation pressure before drilling. According to the timing characteristics of the detection data and the sedimentary characteristics of the formation, a formation pressure prediction model is constructed, and the formation characteristics at different locations are analyzed to obtain the cause of the formation pressure.

[0004] However, the existing technology is only used for formation survey before drilling when the formation characteristics are stable, and cannot be used to predict the formation pressure after the drilling operation begins and during the drilling development process when the formation characteristics change over time. Summary of the invention

[0005] In view of this, an embodiment of the present application provides a method and device for predicting formation pressure under high water injection conditions, which can perform multi-dimensional prediction of formation pressure during the drilling process by considering changes in formation permeability, so as to solve the problem in the prior art that formation pressure can only be predicted when the formation characteristics are stable, but cannot be predicted for high water injection conditions when the formation characteristics change, thereby ensuring that during the operation of the Wanfang water injection well, the reinjection of produced water will not cause safety hazards to the drilling and development operations.

[0006] A first aspect of an embodiment of the present application provides a formation pressure prediction method for a high water injection rate condition, comprising:

[0007] Obtain water injection speed information, water injection time information and suspended particle concentration information;

[0008] Determine the water injection volume and formation permeability according to the water injection speed information, water injection time information and suspended particle concentration information;

[0009] According to the water injection volume and the formation permeability, the formation pressure is calculated in multiple dimensions to obtain initial formation pressure values ​​in multiple dimensions;

[0010] generating a plurality of formation pressure matrices according to the initial formation pressure values ​​in the plurality of dimensions;

[0011] Based on the preset weight vector, nonlinear mapping function and gating vector, the formation pressure matrix is ​​predicted and calculated to obtain a target formation pressure value.

[0012] A second aspect of an embodiment of the present application provides a formation pressure prediction device for a high water injection rate working condition, comprising:

[0013] An information acquisition module is used to obtain water injection speed information, water injection time information and suspended particle concentration information;

[0014] A water injection volume and formation permeability determination module, used to determine the water injection volume and formation permeability according to the water injection speed information, water injection time information and suspended particle concentration information;

[0015] An initial formation pressure value calculation module is used to perform multi-dimensional calculation of the formation pressure according to the water injection volume and the formation permeability to obtain initial formation pressure values ​​in multiple dimensions;

[0016] A formation pressure matrix generating module, used for generating a plurality of formation pressure matrices according to the initial formation pressure values ​​of the plurality of dimensions;

[0017] The target formation pressure value calculation module is used to perform prediction calculation on the formation pressure matrix based on a preset weight vector, a nonlinear mapping function and a gating vector to obtain a target formation pressure value.

[0018] A third aspect of an embodiment of the present application provides a terminal device, which includes a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, it implements the steps of the formation pressure prediction method for high water injection conditions as described in any one of the first aspects above.

[0019] The fourth aspect of the embodiments of the present application provides a computer-readable storage medium, comprising: a computer program stored therein, which, when executed by a processor, implements the steps of the formation pressure prediction method for high water injection conditions as described in any one of the first aspects above.

[0020] The beneficial effects of the embodiments of the present application compared with the prior art are as follows: the present application calculates the formation pressure when the formation permeability changes, and then maps the calculated formation pressure value to a multidimensional nonlinear space through a preset nonlinear mapping function, and further analyzes and calculates the formation pressure value in the nonlinear space through a preset weight vector and a preset gating vector, which is used for nonlinear prediction of the formation pressure value when the formation permeability changes, thereby ensuring that the drilling operation will not be affected by the formation pressure change caused by the reinjection of produced water during the offshore oil development process, so as to improve the operation safety during the offshore oil development process. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0022] Figure 1 It is a schematic diagram of the implementation flow of the formation pressure prediction method for high water injection conditions provided in an embodiment of the present application;

[0023] Figure 2 It is a schematic diagram of the implementation flow of the formation pressure prediction method for high water injection conditions provided in an embodiment of the present application;

[0024] Figure 3 It is a schematic diagram of the implementation flow of the formation pressure prediction method for high water injection conditions provided in an embodiment of the present application;

[0025] Figure 4 It is a schematic diagram of the implementation flow of the formation pressure prediction method for high water injection conditions provided in an embodiment of the present application;

[0026] Figure 5 It is a schematic diagram of the implementation flow of the formation pressure prediction method for high water injection conditions provided in an embodiment of the present application;

[0027] Figure 6 It is a schematic diagram of the implementation flow of the formation pressure prediction method for high water injection conditions provided in an embodiment of the present application;

[0028] Figure 7 It is a schematic diagram of the implementation flow of the formation pressure prediction method for high water injection conditions provided in an embodiment of the present application;

[0029] Figure 8 It is a structural schematic diagram of a formation pressure prediction device for high water injection conditions provided in an embodiment of the present application;

[0030] Fig. 9 It is a schematic diagram of a terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0032] In order to illustrate the technical solution described in this application, a specific embodiment is provided below for illustration.

[0033] Figure 1 The following is a flowchart of the method for predicting formation pressure under high water injection conditions provided in Example 1 of the present application, which is described in detail as follows:

[0034] Step S101, obtaining water injection speed information, water injection time information and suspended particle concentration information.

[0035] In this embodiment, the water injection rate information may be a water injection rate, which refers to the amount of water injected into the formation per unit time, usually expressed in cubic meters per day (m 3 / d), cubic meters / month (m 3 / mon) or cubic meters / year (104m 3 / a); water injection time information may refer to the time taken to inject water into the formation at a certain water injection speed; suspended particle concentration information may refer to the suspended particle concentration in the reinjected water, that is, the number or mass concentration of suspended particles in the reinjected liquid; wherein, suspended particles refer to particles suspended in the reinjected liquid with a diameter between 0.1μm and 1000μm, and these particles include silt, clay, protozoa, algae, bacteria, viruses, and high molecular organic matter, etc. Among them, water injection speed information can be measured by a specific sensor and manually input into a computer for subsequent analysis and processing; water injection time information can be obtained by measuring the time during the water injection operation by a timing device; suspended particle concentration information can be obtained by collecting production water samples from the oil field, that is, water samples of reinjected water, and analyzing the production water samples using the GB / T14848-9 groundwater quality analysis standard. The production water information obtained by analysis is CaCl2 water type, with a total mineralization of 3.21%, and a total calcium and magnesium ion content of 973mg / L, which belongs to medium mineralization water quality. The pH of produced water is 6.5, which is weakly acidic, and the density is 1.0457g / cm 3, viscosity 0.4cP (40℃), mass concentration of suspended particles in water is 1800mg / L, median particle size>0.45μm, flocculated flake structure, agglomeration when standing, analysis results including suspended particle concentration, can be manually input into the computer for subsequent calculations.

[0036] Step S102, determining the water injection volume and the formation permeability according to the water injection speed information, the water injection time information and the suspended particle concentration information.

[0037] In this embodiment, the production water sample of the offshore oil field, that is, the water sample of the reinjection water, can be collected first, and the injection volume can be calculated by the water injection rate information and the water injection time information. The permeability can be measured by means of a core displacement experiment. Specifically, the indoor core displacement experiment can adopt the industry standard SY / T5358-2010 "Reservoir Sensitivity Flow Experiment Evaluation Method". In the core displacement experiment, a core with a core depth of 1003-1005m can be collected, and the core can be loaded into a core holder, kept under artificially set reservoir temperature conditions, and used to drive with production water until the pressure is stable, so that under the same temperature conditions, the reinjection water is injected into the core at different injection rates, and the formation permeability under specific injection conditions is measured, and then the measured formation permeability is artificially input into the computer for analysis and calculation.

[0038] Step S103, performing multi-dimensional calculation on the formation pressure according to the water injection volume and the formation permeability to obtain initial formation pressure values ​​in multiple dimensions.

[0039] In this embodiment, the initial formation pressure value can be calculated through the Python module of Petrel RE. Specifically, the injection volume and the injection volume are input into the Python module of Petrel RE, which can be used to calculate the corresponding formation permeability at different times, so as to obtain the time-varying law of the formation permeability, and then the Python module of Petrel RE is further used to analyze and calculate the time-varying law of the formation permeability to obtain the pressure distribution characteristics of each block of the rock, and the pressure distribution characteristics are extracted to obtain the initial formation pressure values ​​of multiple dimensions. The dimension can be the horizontal dimension, the vertical dimension, and the depth dimension. Among them, the horizontal dimension can be represented by the x-axis, the vertical dimension can be represented by the y-axis, and the depth dimension can be represented by the z-axis.

[0040] Step S104: generating a plurality of formation pressure matrices according to the initial formation pressure values ​​in the plurality of dimensions.

[0041] In this embodiment, the initial formation pressure values ​​of multiple dimensions can be first converted into a multidimensional vector map, and then the multidimensional vector map can be converted into a formation pressure matrix using One-hot encoding, TF-IDF, Word2Vec, Doc2Vec, BERT, ELMo, GPT, FastText, GloVe and other methods; or the multidimensional initial formation pressure values ​​can be converted into a one-dimensional array, and then the one-dimensional array can be converted into a matrix, thereby generating a formation pressure matrix for subsequent prediction analysis and calculation.

[0042] Step S105 , based on the preset weight vector, nonlinear mapping function and gating vector, predictive calculation is performed on the formation pressure matrix to obtain a target formation pressure value.

[0043] In this embodiment, the preset weight vector can be set manually, and is used to highlight the values ​​that need to be paid attention to in the initial formation pressure value through different weights to improve the accuracy of the prediction result; the preset nonlinear mapping function can be a Sigmoid function, or a Tanh function, or a ReLU function, or a Leaky ReLU function, or a Softmax function, which is used to introduce nonlinear factors into the prediction of the formation pressure value. It can be understood that the change law of the formation pressure value is not linear, so the initial formation pressure value needs to be mapped to the nonlinear space for analysis to realize the prediction calculation. The value obtained after the prediction calculation is the target formation pressure value, which is used to characterize whether the current water injection operation may cause formation rupture, so as to guide the staff to take corresponding measures to avoid the negative impact of continuous water injection on drilling work.

[0044] The formation pressure prediction method for high water injection conditions provided in the embodiment of the present application calculates the formation pressure when the formation permeability changes, and then maps the calculated formation pressure value to a multidimensional nonlinear space through a preset nonlinear mapping function, and further analyzes and calculates the formation pressure value in the nonlinear space through a preset weight vector and a preset gating vector, which is used for nonlinear prediction of the formation pressure value when the formation permeability changes, thereby ensuring that the drilling operation will not be affected by the formation pressure change caused by the reinjection of produced water during the offshore oil development process, so as to improve the operation safety during the offshore oil development process.

[0045] Figure 2 The flowchart of the method for predicting formation pressure under high water injection conditions provided in the second embodiment of the present application is shown. The difference between the method and the first embodiment is that the initial formation pressure value includes a formation pressure data value, a formation pressure dimension value and a formation pressure dimension axis identification number; the step S104 specifically includes:

[0046] Step S201, generating a formation pressure data value array and a formation pressure dimension value array according to the formation pressure data value and the formation pressure dimension value.

[0047] In this embodiment, it can be understood that the initial formation pressure value is a multidimensional value, which is used to characterize the pressure exerted on a certain point in the formation in various directions. The formation pressure data value may refer to the resultant force value of the pressure of the formation pressure value in various dimensions. The formation pressure dimension value may be the component force value of the pressure of the formation pressure value in various dimensions. The formation pressure dimension axis identification number is used to characterize the dimension to which the component force belongs, and can be represented by the first dimension, the second dimension, and the third dimension, and is used to splice and merge the arrays formed by multiple component force values ​​later. It can be understood that the formation pressure data value and the formation pressure dimension value are respectively formed into a one-dimensional array or a multi-dimensional array for subsequent splicing processing.

[0048] Step S202: According to the formation pressure dimension axis identification number, the formation pressure data value array and the formation pressure dimension value array are concatenated to obtain a plurality of one-dimensional arrays of formation pressure values.

[0049] In this embodiment, the formation pressure data value array and the formation pressure dimension value array are spliced ​​in the order of the formation pressure dimension axis identification number. For example, when the formation pressure dimension axis identification number is the first dimension, the array composed of the formation pressure dimension values ​​under the first dimension is spliced, and then the formation pressure data value array and the array composed of the formation pressure dimension values ​​are spliced ​​to obtain a one-dimensional array, that is, a one-dimensional array of formation pressure values.

[0050] Step S203: generating a plurality of formation pressure matrices according to the one-dimensional array of formation pressure values, a preset number of matrix rows, and a preset number of matrix columns.

[0051] In this embodiment, the reshape method in the NumPy library can be used to convert a one-dimensional array into a multidimensional array. The preset number of matrix rows and the preset number of matrix columns are both set manually.

[0052] The formation pressure prediction method for high water injection conditions provided in the embodiment of the present application reduces the dimensionality of the multi-dimensional initial formation pressure values, and regenerates the one-dimensional matrix after the dimensionality reduction into a formation pressure matrix with a fixed number of rows and columns, which is used for subsequent prediction and analysis of the formation pressure values, thereby reducing the complexity of the calculation and improving the timeliness of the prediction calculation, making it easier for staff to promptly discover abnormal formation pressure values, and thus take timely measures to avoid formation ruptures due to the reinjection of produced water, thereby ensuring the safety of drilling operations.

[0053] Figure 3The flowchart of the method for predicting formation pressure under high water injection conditions provided in the third embodiment of the present application is shown. The difference between the method and the first embodiment is that the step S105 specifically includes:

[0054] Step S301, performing a convolution operation on the formation pressure matrix, a preset weight vector and a preset nonlinear mapping function to obtain a formation pressure characteristic matrix.

[0055] In this embodiment, the formation pressure matrix and the weight vector may be first convolved, and then all numerical values ​​of the results of the convolution operation may be used as independent variables of the nonlinear mapping function to calculate the function value of the nonlinear mapping function, and then all the calculated function values ​​of the nonlinear mapping function may be used to generate a matrix, that is, to generate a formation pressure characteristic matrix, which is used to characterize the important features in the formation pressure values, thereby ensuring that the important features in the formation pressure values ​​are taken into consideration in the subsequent prediction analysis.

[0056] Step S302: generating a formation pressure global representation characteristic matrix according to the maximum value of all values ​​in the formation pressure characteristic matrix.

[0057] In this embodiment, by extracting the maximum value of the formation pressure feature matrix, a one-dimensional vector with the same scale number as the formation pressure feature matrix is ​​obtained, which is used to reduce the size of the formation pressure feature matrix, reduce the amount of calculation and memory consumption. All the extracted one-dimensional vectors at different scales are spliced ​​to generate a vector graph as the global representation feature of the formation pressure, that is, the formation pressure global representation feature matrix.

[0058] Step S303 , performing weighted sum calculation on the formation pressure global representation characteristic matrix according to a preset nonlinear mapping function and a plurality of preset gating vectors, to obtain a plurality of formation pressure state weight matrices.

[0059] In this embodiment, the gating vector may be used as the weight value for weighted summation, or the global representation characteristic matrix of formation pressure may be weighted summed according to one or more gating vectors to further enhance important numerical features in the formation pressure values, and the result after the weighted summation may be used as the independent variable of the nonlinear mapping function, and the function value of the obtained nonlinear mapping function may be used as the numerical value in the formation pressure state weight matrix, thereby generating the formation pressure state weight matrix.

[0060] Step S304: performing nonlinear calculation on the formation pressure global representation characteristic matrix according to a preset nonlinear mapping function and a preset gating vector to obtain a formation pressure migration weight matrix.

[0061] In this embodiment, the nonlinear calculation may be a hyperbolic tangent function. The gating vector may be used as a weight value for weighted summation calculation, and a weighted summation calculation is performed on the formation pressure global representation feature matrix according to one or more gating vectors, and the calculation result is used as an independent variable of the hyperbolic tangent function, and the function value is further calculated. The calculated function value is used as the value of the formation pressure offset weight matrix, thereby generating a formation pressure offset weight matrix, which is used to quantify the offset of important features in the formation pressure value relative to other features, thereby indirectly quantifying the offset of the target formation pressure value of the prediction result and the initial formation pressure value.

[0062] Step S305: Calculate a target formation pressure matrix according to the formation pressure state weight matrix and the formation pressure offset weight matrix.

[0063] In this embodiment, the formation pressure state weight matrix and the formation pressure offset weight matrix can be calculated by point multiplication to obtain the target formation pressure matrix. It can be understood that the important features in the initial formation pressure values ​​are analyzed and calculated to obtain the offset of the important features that may occur in the future during the continuous water injection process, and then the offset of the important features in the numerical features of the initial formation pressure is adjusted. The offset adjustment method of the important features in the matrix values ​​can be a point multiplication operation to obtain the important features of the predicted target formation pressure values. Therefore, the matrix composed of the important features of the calculated target formation pressure values ​​is the target formation pressure matrix.

[0064] Step S306: obtaining a target formation pressure value according to the target formation pressure matrix.

[0065] In this embodiment, the target formation pressure matrix can be converted into a one-dimensional matrix, and the one-dimensional matrix can be transformed into a one-dimensional array, so as to extract the corresponding numerical values ​​therein as the target formation pressure value. Specifically, the target formation pressure data value, the target formation pressure dimension value and the target formation pressure dimension axis identification number of the target formation pressure value can be extracted, or only the resultant force value of the target formation pressure in multiple dimensions can be extracted.

[0066] The formation pressure prediction method for high water injection conditions provided in the embodiment of the present application introduces a nonlinear function to map the initial formation pressure matrix into a nonlinear space for calculation, extracts and enhances important features in the initial formation pressure matrix through a weight vector to ensure that important features in the initial formation pressure values ​​are considered in the prediction calculation process, and calculates possible offsets of important features in the initial formation pressure matrix through a gating vector to quantify the offsets that may occur to important features of the formation pressure values ​​in the nonlinear space during the production water reinjection process, thereby obtaining the predicted target formation pressure value through the initial formation pressure matrix and the possible offsets that may occur during the water injection process.

[0067] Figure 4 The flowchart of the method for predicting formation pressure under high water injection conditions provided by the fourth embodiment of the present application is shown. The difference between the fourth embodiment and the third embodiment is that: the gating vector includes a first gating vector, a second gating vector and a third gating vector; the formation pressure state weight matrix includes a first formation pressure state weight matrix, a second formation pressure state weight matrix and a third formation pressure state weight matrix; the step S303 specifically includes:

[0068] Step S401 : performing nonlinear calculation on the formation pressure global representation characteristic matrix according to the first gating vector, a preset nonlinear mapping function and a preset time state matrix to obtain a first formation pressure state weight matrix.

[0069] In this embodiment, the formation pressure global representation characteristic matrix can be represented by X, the first gate vector can be represented by E1, and the preset nonlinear mapping function can be represented by δ(). The preset time state matrix can be set manually to introduce the time dimension into the prediction result, so that the predicted formation pressure value can present a nonlinear time-varying law in the time domain, which is convenient for the staff to observe the formation pressure changes at different times. The time state matrix can be represented by T. The process of calculating the first formation pressure state weight matrix Y1 can be expressed as:

[0070] Y1=δ(E1·X+T·X)

[0071] Step S402: performing nonlinear calculation on the formation pressure global representation characteristic matrix according to the second gating vector, a preset nonlinear mapping function and a preset time state matrix to obtain a second formation pressure state weight matrix.

[0072] In this embodiment, the second gating vector can be expressed as E2. The process of calculating the second formation pressure state weight matrix Y2 can be expressed as:

[0073] Y2=δ(E2·X+T·X)

[0074] Step S403: performing nonlinear calculation on the formation pressure global representation characteristic matrix according to the third gating vector, a preset nonlinear mapping function and a preset time state matrix to obtain a third formation pressure state weight matrix.

[0075] In this embodiment, the third gating vector can be expressed as E3, and the process of calculating the third formation pressure state weight matrix Y3 can be expressed as:

[0076] Y3=δ(E3·X+T·X)

[0077] The formation pressure prediction method for high water injection conditions provided in the embodiment of the present application calculates the global representation feature matrix of the formation pressure by introducing different gating vectors, so as to retain the features of different scales or dimensions in the initial formation pressure matrix, so as to determine which scales or dimensions of features should be retained or enhanced in subsequent analysis and calculation, so as to ensure the accuracy of the formation pressure value prediction. By introducing the time state matrix, time characteristics are introduced into the prediction of the formation pressure, so as to present the predicted values ​​in the time dimension, so as to facilitate the staff to visualize the predicted formation pressure values, so as to timely observe the changes in the formation pressure during the production water reinjection process, and take timely measures to avoid affecting the drilling operation when the formation may or is about to rupture, so as to ensure the safety and reliability of the offshore oil production process.

[0078] Figure 5 The flowchart of the method for predicting formation pressure under high water injection conditions provided in the fifth embodiment of the present application is shown. The difference between the method and the fourth embodiment is that the step S305 specifically includes:

[0079] Step S501: performing a dot multiplication operation on the first formation pressure state weight matrix and the formation pressure offset weight matrix to obtain a first formation pressure state variable matrix.

[0080] In this embodiment, the formation pressure offset weight matrix can be expressed as D, the first formation pressure state variable matrix can be expressed as Z1, and the process of calculating the first formation pressure state variable matrix Z1 can be expressed as:

[0081] Z1=Y1⊙D

[0082] Among them, ⊙ represents the dot multiplication operation of the matrix.

[0083] Step S502: performing a point multiplication operation on the second formation pressure state weight matrix and a preset time state matrix to obtain a second formation pressure state variable matrix.

[0084] In this embodiment, the second formation pressure state variable matrix Z2 can be expressed as:

[0085] Z2=Y2⊙D

[0086] Step S503: summing the first formation pressure state variable matrix and the second formation pressure state variable matrix to obtain a formation pressure state global variable matrix.

[0087] In this embodiment, the calculation process of the formation pressure state global variable matrix G can be expressed as:

[0088] G=Z1+Z2

[0089] Step S504: performing nonlinear transformation on the formation pressure state global variable matrix to obtain a formation pressure state global mapping matrix.

[0090] In this embodiment, the calculation process of the formation pressure state global mapping matrix F can be expressed as:

[0091] F = tanh(G)

[0092] Among them, tanh() represents the hyperbolic tangent function, which is used to realize nonlinear transformation.

[0093] Step S505: performing a point multiplication operation on the third formation pressure state variable matrix and the formation pressure state global mapping matrix to obtain a target formation pressure matrix.

[0094] In this embodiment, the calculation process of the target formation pressure matrix R can be expressed as:

[0095] R=Y3⊙F

[0096] The formation pressure prediction method for high water injection conditions provided in the embodiment of the present application strengthens the important features in the initial formation pressure matrix through weight vectors to ensure that the important features in the initial formation pressure values ​​can be taken into account in the prediction calculation, and weights the possible offsets in the formation pressure values ​​through the formation pressure offset weight matrix to highlight the most likely offsets, thereby improving the accuracy of formation pressure prediction for high water injection conditions and ensuring that staff can make effective judgments on the water injection situation based on the predicted formation pressure values. By introducing a time state matrix, the predicted formation pressure values ​​can be combined with different time steps, which facilitates the visualization of changes in formation pressure in the time dimension, thereby improving the efficiency of taking countermeasures when abnormal changes in formation pressure occur.

[0097] Figure 6 The flowchart of the method for predicting formation pressure under high water injection conditions provided in the sixth embodiment of the present application is shown. The difference between the method and the first embodiment is that after step S105, the method further includes:

[0098] Step S601, based on a preset geostress calculation model, calculate the geostress value according to the target formation pressure value.

[0099] In this embodiment, the preset geostress calculation model can be the Mohr-Coulomb constitutive model in the Visage simulator, which couples the formation seepage model with the geomechanics model, and calculates the constantly changing geostress value during the production water reinjection process based on the target formation pressure value by simulating the formation seepage process.

[0100] Step S602: Generate a distribution variation law of the geostress according to the geostress value and preset time step information.

[0101] In this embodiment, the preset time step may be the injection time information of the reinjected water, which may be represented by multiple moments, and the time intervals between the multiple moments may be manually set. The Mohr-Coulomb constitutive model in the Visage simulator may be used to fit the geostress curve, and the curve obtained by fitting may characterize the spatial distribution of geostress and its change with injection time. The change law of geostress distribution may be the geostress curve obtained by the fitting.

[0102] Step S603, obtaining the geostress distribution field and geostress concentration points according to the geostress distribution variation law and the preset strain boundary conditions.

[0103] In this embodiment, the Mohr-Coulomb constitutive model in the Visage simulator can be used to generate a multi-dimensional image of the curve representing the ground stress obtained by fitting. The generated multi-dimensional image is the ground stress distribution field, which is used to characterize the ground stress distribution change during the water injection process. The preset strain boundary condition can be determined by artificially adjusting the strain parameter, and the strain parameter can be the static compressive elastic modulus, and the maximum value of the static compressive elastic modulus Eh max = 0.0009 and the minimum value of static compressive elastic modulus Eh min =0.0018, which can be used to meet the minimum horizontal principal stress equivalent density of 1.5g / cm near the reinjection well. 3 , maximum principal stress equivalent density 1.9g / cm 3 , vertical stress equivalent density 2.1g / cm 3 In the interval determined by the two threshold values ​​of the static compressive elastic modulus, the point with the largest ground stress in the ground stress distribution field can be determined as the ground stress concentration point. It can be understood that at the ground stress concentration point, the formation is most likely to rupture, so it is necessary to add steel plates around the formation at the ground stress concentration point to avoid stress concentration and formation rupture that affects the drilling operation.

[0104] The formation pressure prediction method for high water injection conditions provided in the embodiment of the present application calculates the ground stress value according to the formation pressure value through the ground stress calculation model, and then generates the distribution of ground stress at different times according to the time scale of the water injection operation. By setting the strain boundary condition, the maximum ground stress value falling within the strain interval is determined, and the point with the maximum ground stress value is determined as the ground stress concentration point, so as to timely reinforce the ground stress concentration point to avoid rupture of the ground stress concentration point due to the continuous water injection operation, so as to ensure that the production water reinjection process will not cause formation rupture, and effectively ensure the safety and efficiency of the drilling operation.

[0105] Figure 7 The flowchart of the method for predicting formation pressure under high water injection conditions provided in the seventh embodiment of the present application is shown. The difference between the method and the sixth embodiment is that the step S603 specifically includes:

[0106] Step S701, calculating a plurality of principal stress equivalent densities according to the change law of the geostress distribution and the preset strain boundary conditions.

[0107] In this embodiment, the principal stress equivalent density refers to the stress state at a certain point in three-dimensional space can be expressed by a scalar, which is the stress equivalent density. The principal stress equivalent density is a characteristic value of the stress tensor, indicating the maximum shear stress magnitude of the rock in the formation at this point; the strain parameter can be the static compressive elastic modulus, and the maximum value of the static compressive elastic modulus Eh max = 0.0009 and the minimum value of static compressive elastic modulus Eh min =0.0018, the preset strain boundary condition is the interval range composed of the maximum value of the static compressive elastic modulus and the minimum value of the static compressive elastic modulus. The minimum horizontal principal stress equivalent density of 1.5 g / cm near the reinjection well can be calculated by the Mohr-Coulomb constitutive model of the Visage simulator. 3 , maximum principal stress equivalent density 1.9g / cm 3 , vertical stress equivalent density 2.1g / cm 3 .

[0108] Step S702, obtaining a geostress distribution field according to the plurality of principal stress equivalent densities.

[0109] In this embodiment, the spatial position information of multiple principal stress equivalent densities can be calibrated, and then the position can be reconstructed according to the calibrated spatial position information to obtain a geostress distribution field for characterizing the geostress distribution in three-dimensional space.

[0110] Step S703, obtaining a ground stress concentration point according to the maximum value of the principal stress equivalent density.

[0111] In this embodiment, the point with the maximum ground stress value is determined as the ground stress concentration point, so that the ground stress concentration point is reinforced in time to avoid rupture of the ground stress concentration point due to the continuous water injection operation.

[0112] The formation pressure prediction method for high water injection conditions provided in the embodiment of the present application determines the maximum value of the ground stress value falling within the strain interval, and determines the point with the maximum ground stress value as the ground stress concentration point, which is used to timely reinforce the ground stress concentration point to avoid rupture of the ground stress concentration point due to the continuous water injection operation during the production water reinjection process, thereby effectively ensuring the safety and efficiency of the drilling operation.

[0113] Corresponding to the method of the above embodiment, Figure 8 A structural block diagram of a formation pressure prediction device for high water injection conditions provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown. Figure 8 The exemplary formation pressure prediction device for high water injection conditions may be an execution body of the formation pressure prediction method for high water injection conditions provided in the aforementioned first embodiment.

[0114] Reference Figure 8 The formation pressure prediction device for high water injection conditions comprises:

[0115] The information acquisition module 810 is used to acquire water injection speed information, water injection time information and suspended particle concentration information;

[0116] The water injection volume and formation permeability determination module 820 is used to determine the water injection volume and formation permeability according to the water injection speed information, water injection time information and suspended particle concentration information;

[0117] The initial formation pressure value calculation module 830 is used to perform multi-dimensional calculation of the formation pressure according to the water injection volume and the formation permeability to obtain initial formation pressure values ​​in multiple dimensions;

[0118] A formation pressure matrix generating module 840 is used to generate multiple formation pressure matrices according to the initial formation pressure values ​​of the multiple dimensions;

[0119] The target formation pressure value calculation module 850 is used to perform prediction calculation on the formation pressure matrix based on a preset weight vector, a nonlinear mapping function and a gating vector to obtain a target formation pressure value.

[0120] The process of each module realizing its own function in the formation pressure prediction device for high water injection conditions provided in the embodiment of the present application can be specifically referred to the aforementioned Figure 1The description of the first embodiment is not repeated here.

[0121] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0122] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0123] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0124] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0125] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish descriptions, and cannot be understood as indicating or suggesting relative importance. It should also be understood that although the terms "first", "second", etc. are used to describe various elements in some embodiments of the present application in the text, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, the first table can be named as the second table, and similarly, the second table can be named as the first table without departing from the scope of the various described embodiments. The first table and the second table are both tables, but they are not the same table.

[0126] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0127] The formation pressure prediction method for high water injection conditions provided in the embodiments of the present application can be applied to terminal devices such as mobile phones, tablet computers, wearable devices, vehicle-mounted devices, augmented reality (AR) / virtual reality (VR) devices, laptops, ultra-mobile personal computers (UMPC), netbooks, personal digital assistants (PDA), etc. The embodiments of the present application do not impose any restrictions on the specific types of terminal devices.

[0128] For example, the terminal device can be a station (STAION, ST) in a WLAN, a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA) device, a handheld device with wireless communication function, a computing device or other processing device connected to a wireless modem, a vehicle-mounted device, a vehicle networking terminal, a computer, a laptop computer, a handheld communication device, a handheld computing device, a satellite wireless device, a wireless modem card, a TV set-top box (settop box, STB), customer premises equipment (customer premises equipment, CPE) and / or other devices for communicating on a wireless system and a next-generation communication system, such as a mobile terminal in a 5G network or a mobile terminal in a future evolved Public Land Mobile Network (PLMN) network, etc.

[0129] As an example but not limitation, when the terminal device is a wearable device, the wearable device can also be a general term for wearable devices that are intelligently designed and developed using wearable technology for daily wear, such as glasses, gloves, watches, clothing and shoes. A wearable device is a portable device that is worn directly on the body or integrated into the user's clothes or accessories. Wearable devices are not just hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those that are fully functional, large in size, and can achieve complete or partial functions without relying on smartphones, such as smart watches or smart glasses, as well as those that only focus on a certain type of application function and need to be used in conjunction with other devices such as smartphones, such as various smart bracelets and smart jewelry for vital sign monitoring.

[0130] Fig. 9 Schematic diagram of the structure of a terminal device provided by an embodiment of the present application. Fig. 9 As shown, the terminal device 9 of this embodiment includes: at least one processor 90 ( Fig. 9 Only one is shown in the figure), a memory 91, wherein the memory 91 stores a computer program 92 that can be run on the processor 90. When the processor 90 executes the computer program 92, the steps in the above-mentioned embodiments of the formation pressure prediction method for high water injection conditions are implemented, for example Figure 1 Alternatively, when the processor 90 executes the computer program 92, the functions of each module / unit in the above-mentioned device embodiments are realized, for example, Figure 8 Functions of modules 810 to 850 are shown.

[0131] The terminal device 9 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal device may include, but is not limited to, a processor 90 and a memory 91. Those skilled in the art will appreciate that Fig. 9 It is only an example of the terminal device 9 and does not constitute a limitation on the terminal device 9. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device may also include an input sending device, a network access device, a bus, etc.

[0132] The processor 90 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0133] In some embodiments, the memory 91 may be an internal storage unit of the terminal device 9, such as a hard disk or memory of the terminal device 9. The memory 91 may also be an external storage device of the terminal device 9, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device 9. Further, the memory 91 may also include both an internal storage unit of the terminal device 9 and an external storage device. The memory 91 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory 91 may also be used to temporarily store data that has been sent or is to be sent.

[0134] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0135] An embodiment of the present application also provides a terminal device, which includes at least one memory, at least one processor, and a computer program stored in the at least one memory and executable on the at least one processor, wherein when the processor executes the computer program, the terminal device implements the steps in any of the above-mentioned method embodiments.

[0136] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0137] An embodiment of the present application provides a computer program product. When the computer program product runs on a terminal device, the terminal device can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0138] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0139] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0140] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0141] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0142] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A formation pressure prediction method for high water injection conditions, characterized in that: include: Obtain water injection speed information, water injection time information and suspended particle concentration information; Determine the water injection volume and formation permeability according to the water injection speed information, water injection time information and suspended particle concentration information; According to the water injection volume and the formation permeability, the formation pressure is calculated in multiple dimensions to obtain initial formation pressure values ​​in multiple dimensions; generating a plurality of formation pressure matrices according to the initial formation pressure values ​​in the plurality of dimensions; Based on the preset weight vector, nonlinear mapping function and gating vector, the formation pressure matrix is ​​predicted and calculated to obtain a target formation pressure value.

2. The formation pressure prediction method for high water injection conditions according to claim 1, characterized in that: The initial formation pressure value includes a formation pressure data value, a formation pressure dimension value, and a formation pressure dimension axis identification number; The step of generating multiple formation pressure matrices according to the initial formation pressure values ​​of the multiple dimensions specifically includes: Generate a formation pressure data value array and a formation pressure dimension value array according to the formation pressure data value and the formation pressure dimension value; According to the formation pressure dimension axis identification number, the formation pressure data value array and the formation pressure dimension value array are concatenated to obtain a plurality of one-dimensional arrays of formation pressure values; A plurality of formation pressure matrices are generated according to the one-dimensional array of formation pressure values, a preset number of matrix rows, and a preset number of matrix columns.

3. The formation pressure prediction method for high water injection conditions according to claim 1, characterized in that: The step of predicting and calculating the formation pressure matrix based on the preset weight vector, nonlinear mapping function and gating vector to obtain the target formation pressure value specifically includes: Performing a convolution operation on the formation pressure matrix, a preset weight vector, and a preset nonlinear mapping function to obtain a formation pressure characteristic matrix; Generate a formation pressure global representation characteristic matrix according to the maximum value of all values ​​in the formation pressure characteristic matrix; According to a preset nonlinear mapping function and a plurality of preset gate vectors, a weighted sum calculation is performed on the formation pressure global representation characteristic matrix to obtain a plurality of formation pressure state weight matrices; According to a preset nonlinear mapping function and a preset gating vector, a nonlinear calculation is performed on the formation pressure global representation characteristic matrix to obtain a formation pressure migration weight matrix; Calculating a target formation pressure matrix according to the formation pressure state weight matrix and the formation pressure offset weight matrix; According to the target formation pressure matrix, a target formation pressure value is obtained.

4. The formation pressure prediction method for high water injection conditions according to claim 3, characterized in that: The gating vectors include a first gating vector, a second gating vector and a third gating vector; The formation pressure state weight matrix includes a first formation pressure state weight matrix, a second formation pressure state weight matrix and a third formation pressure state weight matrix; The step of performing weighted sum calculation on the formation pressure global representation characteristic matrix according to the preset nonlinear mapping function and the preset gating vector to obtain multiple formation pressure state weight matrices specifically includes: According to the first gating vector, a preset nonlinear mapping function and a preset time state matrix, a nonlinear calculation is performed on the formation pressure global representation characteristic matrix to obtain a first formation pressure state weight matrix; According to the second gating vector, a preset nonlinear mapping function and a preset time state matrix, a nonlinear calculation is performed on the formation pressure global representation characteristic matrix to obtain a second formation pressure state weight matrix; According to the third gating vector, a preset nonlinear mapping function and a preset time state matrix, a nonlinear calculation is performed on the formation pressure global representation characteristic matrix to obtain a third formation pressure state weight matrix.

5. The formation pressure prediction method for high water injection conditions according to claim 4, characterized in that: The step of calculating the target formation pressure matrix according to the formation pressure state weight matrix and the formation pressure offset weight matrix specifically includes: Performing a dot multiplication operation on the first formation pressure state weight matrix and the formation pressure offset weight matrix to obtain a first formation pressure state variable matrix; Performing a point multiplication operation on the second formation pressure state weight matrix and a preset time state matrix to obtain a second formation pressure state variable matrix; Summing the first formation pressure state variable matrix and the second formation pressure state variable matrix to obtain a formation pressure state global variable matrix; Performing nonlinear transformation on the formation pressure state global variable matrix to obtain a formation pressure state global mapping matrix; The third formation pressure state variable matrix and the formation pressure state global mapping matrix are subjected to a point multiplication operation to obtain a target formation pressure matrix.

6. The formation pressure prediction method for high water injection conditions according to claim 1, characterized in that: After the formation pressure matrix is ​​predicted and calculated based on the preset weight vector, nonlinear mapping function and gating vector to obtain the target formation pressure value, the method further includes: Based on a preset geostress calculation model, the geostress value is calculated according to the target formation pressure value; Generate a distribution change law of ground stress according to the ground stress value and preset time step information; According to the changing law of the geostress distribution and the preset strain boundary conditions, the geostress distribution field and the geostress concentration point are obtained.

7. The formation pressure prediction method for high water injection conditions according to claim 6, characterized in that: The step of obtaining the geostress distribution field and the geostress concentration point according to the geostress distribution variation law and the preset strain boundary conditions specifically includes: Calculating multiple principal stress equivalent densities according to the change law of the in-situ stress distribution and the preset strain boundary conditions; Obtaining a geostress distribution field according to the plurality of principal stress equivalent densities; According to the maximum value of the principal stress equivalent density, the ground stress concentration point is obtained.

8. A formation pressure prediction device for high water injection conditions, characterized in that: include: An information acquisition module is used to obtain water injection speed information, water injection time information and suspended particle concentration information; A water injection volume and formation permeability determination module, used to determine the water injection volume and formation permeability according to the water injection speed information, water injection time information and suspended particle concentration information; An initial formation pressure value calculation module is used to perform multi-dimensional calculation of the formation pressure according to the water injection volume and the formation permeability to obtain initial formation pressure values ​​in multiple dimensions; A formation pressure matrix generating module, used for generating a plurality of formation pressure matrices according to the initial formation pressure values ​​of the plurality of dimensions; The target formation pressure value calculation module is used to perform prediction calculation on the formation pressure matrix based on a preset weight vector, a nonlinear mapping function and a gating vector to obtain a target formation pressure value.

9. A terminal device, characterized in that: The terminal device includes a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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