Three-dimensional visualization method, system, equipment and medium for evaluating leakage risk of fractured formation

Abstract

The invention relates to a three-dimensional visualization method, system, equipment and medium for evaluating the leakage risk of a fractured formation, and the method comprises the following steps: obtaining various data of a target oil field in a selected target oil field region; constructing a three-dimensional characteristic parameter attribute model required by leakage risk prediction by using a Kriging interpolation method according to the acquired data, and performing normalization processing on the three-dimensional characteristic parameter attribute model; carrying out importance sorting on the feature parameters required by the loss risk prediction, and determining the relative importance between every two key feature parameters to obtain a complementary judgment table; calculating a weight coefficient of each characteristic parameter by using a fuzzy analytic hierarchy process based on the complementary judgment table; and based on the weight coefficient of each characteristic parameter, constructing a three-dimensional leakage risk index of the oil field area, and evaluating the leakage risk of the fractured formation. The method can be widely applied to the field of drilling fluid leakage prediction in petroleum and natural gas industries.

Description

Technical Field

[0001] The present invention belongs to the field of drilling fluid loss prediction in the oil and gas industry, and specifically relates to a three-dimensional visualization method, system, equipment and medium for evaluating the risk of leakage in fractured formations. Background Art

[0002] Fractured oil and gas reservoirs occupy a very important position in the global oil and gas reserves and production, and are the key areas of oil and gas resource exploration and development. Most of these formations have natural fractures. Although natural fractures can significantly improve the permeability of the reservoir and even provide a certain storage space for oil and gas accumulation, on the other hand, due to the development of fractures in the formation, the formation pressure bearing capacity is low and the mud safety density window is narrow, which is very easy to cause complex leakage events. Loss of the well will increase the non-productive time, the loss of drilling fluid, the loss of plugging materials and reservoir damage, and may also induce a series of complex situations and accidents such as well collapse, stuck drill and blowout, and even cause the wellbore to be scrapped, resulting in additional increase in drilling costs, which seriously restricts the efficient exploration and development of oil and gas resources in fractured formations. Field practice shows that by predicting the risk of downhole leakage in advance, the complex leakage events that occur during the drilling process can be avoided or reduced to a great extent, thereby reducing the huge losses caused by leakage. How to more accurately predict the risk of downhole leakage has become one of the current research trends.

[0003] However, the current prediction of well leakage risk is mainly based on single data, with limited prediction range and accuracy, and lacks a system, without comprehensive consideration of the impact of various factors on leakage. 3D geological modeling technology has the advantage of multi-source data fusion, and has been initially applied in fracture and leakage pressure prediction. How to achieve leakage risk prediction and improve the accuracy of well leakage risk prediction through multi-source data fusion technology is crucial. Summary of the invention

[0004] In response to the above problems, the purpose of the present invention is to provide a three-dimensional visualization method, system, equipment and medium for evaluating the risk of leakage in fractured formations. By fully integrating multi-source data and comprehensively considering multiple factors, the degree of formation leakage risk is quantitatively characterized, thereby providing technical support for the accurate prediction of leakage risk in fractured formations.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a three-dimensional visualization method for assessing the risk of leakage in a fractured formation, comprising the following steps:

[0007] Obtain various data on the target oil field area;

[0008] Based on the acquired data, the three-dimensional characteristic parameter attribute model required for leakage risk prediction is constructed using the Kriging interpolation method and normalized;

[0009] The characteristic parameters required for leak risk prediction are ranked in order of importance, and the relative importance between every two characteristic parameters is determined to obtain a complementary judgment table;

[0010] Based on the complementary judgment table, the weight coefficient of each characteristic parameter is calculated;

[0011] Based on the weight coefficients of various characteristic parameters, a three-dimensional leakage risk index model for the target oilfield area is constructed to evaluate the leakage risk of fractured formations.

[0012] Furthermore, the target oilfield area data include: well logging data, seismic data, daily drilling reports, adjacent well data, geological interpretation data, geological models and core test data;

[0013] The logging data include: imaging logging, resistivity logging, acoustic time difference, neutron porosity, density logging and gamma logging;

[0014] The seismic data include seismic wave reflection characteristics, seismic spectrum amplitude change rate and related seismic interpretation data;

[0015] The adjacent well data include statistics on leakage in adjacent wells, lithology distribution in adjacent wells and engineering geological data in adjacent wells;

[0016] The geological interpretation data include geostress field, formation pressure, rock mechanics parameters, fracture scale and lithology distribution;

[0017] The geological model includes stratigraphic information, velocity model, structural model and sedimentary facies model;

[0018] The core experimental data include core single-axis and triaxial experimental data, electron microscope scanning experimental data and longitudinal and transverse wave velocity experimental data.

[0019] Furthermore, the three-dimensional characteristic parameter attribute model required for leakage risk prediction is constructed and normalized based on the acquired data using the Kriging interpolation method, including:

[0020] Determine the characteristic parameters required for leakage risk prediction;

[0021] Based on the existing characteristic parameter values ​​and geological model, the Kriging interpolation method is used to perform three-dimensional spatial interpolation to obtain a three-dimensional characteristic parameter attribute model;

[0022] According to the different effects of characteristic parameters on lost circulation, the 3D characteristic parameter attribute model is normalized.

[0023] Furthermore, the characteristic parameters required for the loss risk prediction are ranked in order of importance, and the relative importance between every two characteristic parameters is determined to obtain a complementary judgment table, including:

[0024] According to the influence of each characteristic parameter on the leakage risk, the importance of each characteristic parameter is ranked from large to small;

[0025] On the basis of the numerical scaling method, the relative importance between every two characteristic parameters is determined to obtain a complementary judgment table.

[0026] Further, the weight coefficient of each characteristic parameter is calculated based on the complementary judgment table, including:

[0027] The obtained complementary judgment table is converted into a complementary judgment matrix;

[0028] Based on the complementary judgment matrix, the weight of each feature parameter is calculated.

[0029] Furthermore, the weight of each characteristic parameter is calculated based on the complementary judgment matrix, including:

[0030] Normalize each column of the judgment matrix;

[0031] Each column of the normalized judgment matrix is ​​added row by row;

[0032] The vector obtained To formalize;

[0033] Calculate the maximum eigenvalue λ of the judgment matrix max ;

[0034] Determine whether the judgment matrix meets the preset conditions. If so, obtain the weights of each characteristic parameter based on the maximum characteristic root.

[0035] Furthermore, the three-dimensional leakage risk index model of the target oilfield area is:

[0036] LCRI=x1×A+x2×B+...+x i X

[0037] Where LCRI is the leakage risk index; x i is the weight coefficient, and i∈(1,...,n); A, B, X are different feature parameters.

[0038] In a second aspect, the present invention provides a three-dimensional visualization system for assessing the risk of leakage in fractured formations, comprising:

[0039] Data acquisition module, used to obtain various data of the target oil field area;

[0040] The data preprocessing module is used to construct the three-dimensional characteristic parameter attribute model required for leakage risk prediction based on the acquired data using the Kriging interpolation method and perform normalization processing;

[0041] The characteristic parameter sorting module is used to sort the characteristic parameters required for the loss risk prediction by importance, and determine the relative importance between every two characteristic parameters to obtain a complementary judgment table;

[0042] A weight coefficient calculation module, used to calculate the weight coefficient of each characteristic parameter based on the complementary judgment table;

[0043] The risk assessment module is used to construct a three-dimensional leakage risk index model for the target oilfield area based on the weight coefficients of various characteristic parameters, and to assess the leakage risk of fractured formations.

[0044] In a third aspect, the present invention provides a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform any method.

[0045] In a fourth aspect, the present invention provides a computing device, comprising: one or more processors and a memory, wherein the memory stores one or more programs and is configured to be executed by the one or more processors, and the one or more programs include instructions for executing any method.

[0046] The present invention adopts the above technical solution, which has the following advantages:

[0047] (1) The present invention expands the leakage risk prediction area from the traditional single vertical position of the drilled well to the entire study area including the undrilled wells, realizing the visualization of three-dimensional leakage risk. This comprehensive method can provide a more macro risk assessment and help to more accurately identify potential leakage areas;

[0048] (2) The present invention considers the comprehensive influence of multiple factors in the prediction of leakage risk, and significantly improves the accuracy of leakage risk prediction by coupling multiple characteristic parameters related to leakage, compared with the method of considering only a single factor;

[0049] (3) The present invention can give full play to the concept of geological engineering integration by integrating all the leakage-related data of the target oil field, reduce human judgment errors, realize pre-drilling risk prediction, ensure the safety of drilling operations and reduce drilling costs;

[0050] (4) The present invention can optimize the wellbore trajectory or take corresponding preventive measures by predicting the leakage risk of the study area in advance, thereby reducing the occurrence rate of leakage accidents or reducing the degree of harm caused by leakage accidents and improving drilling time efficiency.

[0051] Therefore, the present invention can be widely applied to the field of drilling fluid loss prediction in the oil and gas industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Throughout the accompanying drawings, the same reference numerals are used to represent the same components. In the accompanying drawings:

[0053] Figure 1 A schematic diagram of a three-dimensional visualization method for assessing the risk of leakage in fractured formations according to an embodiment of the present invention;

[0054] Figure 2 A three-dimensional leakage risk index attribute body according to an embodiment of the present invention;

[0055] Figure 3 This is a well-connected profile of a leakage risk index according to an embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0057] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0058] In some embodiments of the present invention, a three-dimensional visualization method for evaluating the leakage risk of fractured formations is provided, which includes: obtaining various data of the target oil field in the selected target oil field area; constructing a three-dimensional characteristic parameter attribute model required for leakage risk prediction using the Kriging interpolation method based on the various data obtained and normalizing it; ranking the characteristic parameters required for leakage risk prediction by importance using expert decision making, and determining the relative importance between every two key characteristic parameters to obtain a complementary judgment table; based on the complementary judgment table, calculating the weight coefficient of each characteristic parameter using the fuzzy hierarchical analysis method; based on the weight coefficient of each characteristic parameter, constructing a three-dimensional leakage risk index for the oil field area to evaluate the leakage risk of fractured formations. The present invention considers the comprehensive influence of multiple factors in the leakage risk prediction, and by coupling multiple characteristic parameters related to leakage, significantly improves the accuracy of leakage risk prediction compared to the method that only considers a single factor.

[0059] Correspondingly, in some other embodiments of the present invention, a three-dimensional visualization system, equipment and medium for evaluating the risk of leakage in fractured formations are provided.

[0060] Example 1

[0061] like Figure 1 As shown, the present invention provides a three-dimensional visualization method for evaluating the risk of leakage in fractured formations, which includes the following steps:

[0062] 1) Obtain various data of the target oil field in the selected target oil field area;

[0063] 2) Based on the acquired data, the three-dimensional characteristic parameter attribute model required for leakage risk prediction is constructed using the Kriging interpolation method and normalized;

[0064] 3) Use expert decision making to rank the importance of the characteristic parameters required for leakage risk prediction, and determine the relative importance between every two key characteristic parameters to obtain a complementary judgment table;

[0065] 4) Based on the complementary judgment table, calculate the weight coefficient of each characteristic parameter;

[0066] 5) Based on the weight coefficients of each characteristic parameter, a three-dimensional leakage risk index for the oilfield area is constructed to evaluate the leakage risk of fractured formations.

[0067] Furthermore, in the above step 1), various data of the target oil field obtained include: well logging data, seismic data, daily drilling reports, adjacent well data, geological interpretation data, geological models and core experiment data.

[0068] Among them, logging data include: imaging logging, resistivity logging, acoustic time difference, neutron porosity, density logging and gamma logging; seismic data include seismic wave reflection characteristics, seismic spectrum amplitude change rate and related seismic interpretation data; neighboring well data include neighboring well leakage statistics, neighboring well lithology distribution and neighboring well engineering geological data; geological interpretation data include geostress field, formation pressure, rock mechanics parameters, fracture scale and lithology distribution; geological models include stratigraphic information, velocity model, structural model and sedimentary phase model; core experimental data include core single triaxial experimental data, electron microscope scanning experimental data and longitudinal and transverse wave velocity experimental data.

[0069] Furthermore, the above step 2) includes the following steps:

[0070] 2.1) Determine the characteristic parameters required for leakage risk prediction.

[0071] When selecting the characteristic parameters required for leakage risk prediction, it is necessary to comprehensively consider the development of natural fractures, ground stress environment and rock strength. Among them, rock strength includes but is not limited to the use of tensile strength, brittleness index and other methods. In this embodiment, rock strength is expressed by brittleness index, and the sum of the normalized elastic modulus and the normalized Poisson's ratio is used to represent the brittleness index. The expression method of brittleness index includes but is not limited to this, and can also be expressed by the proportion of brittle minerals in the total minerals, residual stress calculation method and other methods, which are not limited to the present invention.

[0072] Therefore, in this embodiment, the characteristic parameters required for leakage risk prediction include but are not limited to fracture strength, minimum horizontal ground stress, elastic modulus, Poisson's ratio, compressive strength, tensile strength, ground stress, pore pressure, etc. Among them, the characteristic parameters are calculated from the various data obtained in step 1, and the calculation method is a well-known technology for those skilled in the art, and the present invention will not be repeated here.

[0073] 2.2) Based on the existing characteristic parameter values ​​and geological model, the Kriging interpolation method is used to perform three-dimensional spatial interpolation to obtain a three-dimensional characteristic parameter attribute model.

[0074] Based on the determined characteristic parameters, the present invention adopts the Kriging interpolation method to obtain a three-dimensional fracture strength model, a three-dimensional minimum horizontal stress model, and a three-dimensional brittle index model. Among them, the three-dimensional fracture strength model needs to use the one-dimensional fracture strength data of some wells that have been drilled interpreted by imaging logging as the main variable, and the seismic interpretation data body is interpolated as a secondary variable. The seismic interpretation data body refers to the data body obtained by median filtering, chaotic body processing, and multiple ant body tracking of the original seismic data body.

[0075] Specifically, when the Kriging interpolation method is used to perform three-dimensional spatial interpolation on the characteristic parameters required for loss risk prediction, the existing characteristic parameters are used as variables, and the spatial structure information provided by the variogram model is used to solve the Kriging equations to calculate the weighted coefficients. Then, a weighted linear estimation is performed to obtain the three-dimensional attribute volume of the required characteristic parameters, namely, the Kriging estimate The specific algorithm principle is as follows:

[0076] The trend control model is:

[0077] E[Z(u)]=a0+a1y(u)

[0078] Where u is the coordinate of the data point; y(u) is the secondary variable, reflecting the spatial trend of the Z variable (corresponding to the two parameters a0 and a1).

[0079] Kriging estimates:

[0080]

[0081] Where: is the estimated value at position u; is the weighting coefficient; Z(u α ) is u α The measured value at the location; n is the number of measurements used in the estimation process.

[0082] Kriging equations:

[0083]

[0084] Where: μ() is the Lagrangian parameter; C R () is the residual covariance function; μ0(), μ1() are Lagrangian parameters; u α 、u β is the data coordinate point; is the weighting coefficient.

[0085] 2.3) According to the different effects of characteristic parameters on well leakage, the three-dimensional characteristic parameter attribute model is normalized.

[0086] When the characteristic parameters are normalized, the greater the fracture strength, the smaller the minimum ground stress, and the greater the brittleness index, the easier the formation is to leak. Therefore, in this embodiment, the characteristic parameters are divided into positive characteristic parameters and negative characteristic parameters according to their impact on well leakage, and are normalized separately.

[0087] Specifically, for the positive characteristic parameters that affect lost circulation, the normalized formula is:

[0088]

[0089] For the negative characteristic parameters that affect lost circulation, the normalized formula is:

[0090]

[0091] Where: FI is the crack strength; FI n is the normalized crack intensity; FI min is the minimum crack strength; FI max is the maximum crack strength; BI is the brittleness index; BI n is the normalized brittleness index; BI min is the minimum value of the brittleness index; BI max is the maximum value of the brittleness index; Sh is the minimum horizontal ground stress; Sh n is the normalized minimum horizontal in-situ stress; Sh min is the minimum value of the minimum horizontal ground stress; Sh max is the maximum value of the minimum horizontal stress.

[0092] Furthermore, the above step 3) includes the following steps:

[0093] 3.1) According to the influence of each characteristic parameter on the leakage risk, the importance of each characteristic parameter is ranked from large to small.

[0094] In this embodiment, expert decision-making is used to rank the importance of each characteristic parameter. Specifically, an expert evaluation scoring table is established to determine the order of importance of characteristic parameters from large to small: crack strength, minimum ground stress, and brittleness index.

[0095] 3.2) Based on the numerical scaling method (as shown in Table 1), the relative importance between every two characteristic parameters is determined, and a complementary judgment table is obtained.

[0096] In this embodiment, the relative importance of crack strength, minimum ground stress and brittleness index is shown in Table 2.

[0097] Table 1 Numerical scaling method

[0098]

[0099]

[0100] Note: Characteristic parameter A and characteristic parameter B represent any two characteristic parameters.

[0101] Table 2 Complementary judgment table

[0102] Decision-making goals Crack Strength Minimum ground stress Brittleness Index Crack Strength 1 3 4 Minimum ground stress 1 / 3 1 2 Brittleness Index 1 / 4 1 / 2 1

[0103] Furthermore, the above step 4) includes the following steps:

[0104] 4.1) The complementary judgment table obtained in step 3) is converted into a complementary judgment matrix S, as shown below:

[0105]

[0106] 4.2) Based on the complementary judgment matrix S, calculate the weight of each feature parameter.

[0107] The weights of the characteristic parameters are calculated by using, but not limited to, the sum-product method, the square root method, the characteristic root method, the logarithmic least squares method, and the least squares method. The sum-product method is used in this embodiment. Specifically, the calculation method is:

[0108] ① Normalize each column of the judgment matrix. That is:

[0109]

[0110] Where: is the normalized judgment matrix; bij is the value corresponding to row i and column j in the complementary judgment matrix S; b kj is the value corresponding to the kth row and jth column in the complementary judgment matrix S.

[0111] Calculate the normalized judgment matrix

[0112] ② Each column of the normalized judgment matrix is ​​added row by row. That is:

[0113]

[0114] Where: The value obtained by adding rows of the normalized judgment matrix for a row; for Vector composed of.

[0115] According to the formula, adding up the rows, we get:

[0116]

[0117] ③ Vector Normalization. That is:

[0118]

[0119] The obtained W=[W1,W2,…,W n ] T is the desired eigenvector, that is, the weight vector.

[0120]

[0121] Then the required weight vector W = [0.623, 0.240, 0.137] T

[0122] ④ Calculate the maximum eigenvalue λ of the judgment matrix max .Right now:

[0123]

[0124] Where: SW is the product of the complementary judgment matrix S and the weight vector W; (SW) i Represents the i-th component of the vector SW.

[0125]

[0126] In order to check the consistency of the judgment matrix, it is necessary to calculate its consistency index CI, defined as

[0127]

[0128] Where n is the number of characteristic parameters.

[0129] Obviously, when the judgment matrix has complete consistency, CI = 0. In order to check whether the judgment matrix has satisfactory consistency, it is necessary to compare CI with the average random consistency index RI (as shown in Table 3), which is recorded as the consistency ratio CR.

[0130]

[0131] When the consistency ratio CR is less than 0.1, it indicates that the judgment matrix is ​​adoptable, otherwise the judgment matrix needs to be adjusted.

[0132] Table 3 RI random consistency index query table

[0133] m-level 3 4 5 6 7 8 9 RI 0.52 0.89 1.12 1.26 1.36 1.41 1.46

[0134] The weight distribution of each characteristic parameter is obtained as shown in Table 4 below.

[0135] Table 4 Weight distribution table of each feature parameter

[0136]

[0137] Furthermore, in the above step 5), after determining the specific weight coefficient of each characteristic parameter, a linear weighted method is used to fuse the multi-attribute body to obtain a three-dimensional leakage risk index model of the target oilfield area, which is expressed as:

[0138] LCRI=x1×A+x2×B+...+x i X

[0139] Where LCRI is the leakage risk index; x i is the weight coefficient, and i∈(1,...,n); A, B, X are different feature parameters.

[0140] Specifically in this embodiment, the formula is as follows:

[0141] LCRI = 0.623 × crack strength + 0.240 × minimum ground stress + 0.137 × brittleness index

[0142] like Figure 2 , Figure 3 As shown, the three-dimensional leakage risk index attribute body and the leakage risk index well-connected profile obtained in this embodiment.

[0143] Example 2

[0144] The above-mentioned embodiment 1 provides a three-dimensional visualization method for evaluating the risk of leakage in fractured formations. Correspondingly, this embodiment provides a three-dimensional visualization system for evaluating the risk of leakage in fractured formations. The system provided in this embodiment can implement the three-dimensional visualization method for evaluating the risk of leakage in fractured formations in embodiment 1, and the system can be implemented by software, hardware, or a combination of software and hardware. For example, the system may include integrated or separate functional modules or functional units to execute the corresponding steps in each method of embodiment 1. Since the system of this embodiment is basically similar to the method embodiment, the process described in this embodiment is relatively simple, and the relevant parts can refer to the partial description of embodiment 1. The embodiment of the system provided in this embodiment is only illustrative.

[0145] The three-dimensional visualization system for assessing the risk of leakage in fractured formations provided in this embodiment includes:

[0146] The data acquisition module is used to acquire various data of the target oil field in the selected target oil field area;

[0147] The data preprocessing module is used to construct the three-dimensional characteristic parameter attribute model required for leakage risk prediction based on the acquired data using the Kriging interpolation method and perform normalization processing;

[0148] The characteristic parameter ranking module is used to use expert decision making to rank the characteristic parameters required for leak risk prediction, and determine the relative importance between every two key characteristic parameters to obtain a complementary judgment table;

[0149] A weight coefficient calculation module, used to calculate the weight coefficient of each characteristic parameter based on the complementary judgment table;

[0150] The risk assessment module is used to construct a three-dimensional leakage risk index for the oilfield area based on the weight coefficients of various characteristic parameters and to assess the leakage risk of fractured formations.

[0151] Example 3

[0152] This embodiment provides a processing device corresponding to the three-dimensional visualization method for assessing the risk of leakage in fractured formations provided in this embodiment 1. The processing device can be a processing device for a client, such as a mobile phone, a laptop computer, a tablet computer, a desktop computer, etc., to execute the method of embodiment 1.

[0153] The processing device includes a processor, a memory, a communication interface and a bus, and the processor, the memory and the communication interface are connected through the bus to complete mutual communication. The memory stores a computer program that can be run on the processor, and the processor executes the three-dimensional visualization method for assessing the risk of leakage in fractured formations provided in Example 1 when running the computer program.

[0154] Preferably, the memory may be a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory.

[0155] Preferably, the processor may be a central processing unit (CPU), a digital signal processor (DSP) or other general-purpose processors of various types, which are not limited here.

[0156] Example 4

[0157] The three-dimensional visualization method for assessing the risk of leakage in fractured formations of this embodiment 1 can be specifically implemented as a computer program product, which may include a computer-readable storage medium carrying computer-readable program instructions for executing the three-dimensional visualization method for assessing the risk of leakage in fractured formations described in this embodiment 1.

[0158] Computer readable storage media can be tangible devices that hold and store instructions used by instruction execution devices. Computer readable storage media can be, for example, but not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any combination thereof.

[0159] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0160] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0161] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0162] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0163] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A three-dimensional visualization method for assessing the risk of leakage in fractured formations, characterized in that: The following steps are involved: Obtain various data on the target oil field area; Based on the acquired data, the three-dimensional characteristic parameter attribute model required for leakage risk prediction is constructed using the Kriging interpolation method and normalized; The characteristic parameters required for leak risk prediction are ranked in order of importance, and the relative importance between every two characteristic parameters is determined to obtain a complementary judgment table; Based on the complementary judgment table, the weight coefficient of each characteristic parameter is calculated; Based on the weight coefficients of various characteristic parameters, a three-dimensional leakage risk index model for the target oilfield area is constructed to evaluate the leakage risk of fractured formations.

2. A three-dimensional visualization method for assessing the risk of leakage in fractured formations according to claim 1, characterized in that: Various data of the target oilfield area, including: well logging data, seismic data, daily drilling reports, adjacent well data, geological interpretation data, geological models and core test data; The logging data include: imaging logging, resistivity logging, acoustic time difference, neutron porosity, density logging and gamma logging; The seismic data include seismic wave reflection characteristics, seismic spectrum amplitude change rate and related seismic interpretation data; The adjacent well data include statistics on leakage in adjacent wells, lithology distribution in adjacent wells and engineering geological data in adjacent wells; The geological interpretation data include geostress field, formation pressure, rock mechanics parameters, fracture scale and lithology distribution; The geological model includes stratigraphic information, velocity model, structural model and sedimentary facies model; The core experimental data include core single-axis and triaxial experimental data, electron microscope scanning experimental data and longitudinal and transverse wave velocity experimental data.

3. A three-dimensional visualization method for assessing the risk of leakage in fractured formations according to claim 2, characterized in that: The three-dimensional characteristic parameter attribute model required for leakage risk prediction is constructed and normalized based on the acquired data using the Kriging interpolation method, including: Determine the characteristic parameters required for leakage risk prediction; Based on the existing characteristic parameter values ​​and geological model, the Kriging interpolation method is used to perform three-dimensional spatial interpolation to obtain a three-dimensional characteristic parameter attribute model; According to the different effects of characteristic parameters on lost circulation, the three-dimensional characteristic parameter attribute model is normalized.

4. A three-dimensional visualization method for assessing the risk of leakage in fractured formations according to claim 1, characterized in that: The characteristic parameters required for the loss risk prediction are sorted by importance, and the relative importance between every two characteristic parameters is determined to obtain a complementary judgment table, including: According to the influence of each characteristic parameter on the leakage risk, the importance of each characteristic parameter is ranked from large to small; On the basis of the numerical scaling method, the relative importance between every two characteristic parameters is determined to obtain a complementary judgment table.

5. A three-dimensional visualization method for assessing the risk of leakage in fractured formations according to claim 1, characterized in that: The weight coefficient of each characteristic parameter is calculated based on the complementary judgment table, including: The obtained complementary judgment table is converted into a complementary judgment matrix; Based on the complementary judgment matrix, the weight of each feature parameter is calculated.

6. A three-dimensional visualization method for assessing the risk of leakage in fractured formations according to claim 5, characterized in that: The weight of each characteristic parameter is calculated based on the complementary judgment matrix, including: Normalize each column of the judgment matrix; Each column of the normalized judgment matrix is ​​added row by row; The vector obtained To formalize; Calculate the maximum eigenvalue λ of the judgment matrix max ; Determine whether the judgment matrix meets the preset conditions. If so, obtain the weights of each characteristic parameter based on the maximum characteristic root.

7. A three-dimensional visualization method for assessing the risk of leakage in fractured formations according to claim 1, characterized in that: The three-dimensional leakage risk index model of the target oilfield area is: ICRI=x1×A+x2×B+...+x i X Where LCRI is the leakage risk index; x i is the weight coefficient, and i∈(1,...,n); A, B, X are different feature parameters.

8. A three-dimensional visualization system for assessing the risk of leakage in fractured formations, comprising: Data acquisition module, used to obtain various data of the target oil field area; The data preprocessing module is used to construct the three-dimensional characteristic parameter attribute model required for leakage risk prediction based on the acquired data using the Kriging interpolation method and perform normalization processing; The characteristic parameter sorting module is used to sort the characteristic parameters required for the loss risk prediction by importance, and determine the relative importance between every two characteristic parameters to obtain a complementary judgment table; A weight coefficient calculation module, used to calculate the weight coefficient of each characteristic parameter based on the complementary judgment table; The risk assessment module is used to construct a three-dimensional leakage risk index model for the target oilfield area based on the weight coefficients of various characteristic parameters, and to assess the leakage risk of fractured formations.

9. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform any one of the methods of claims 1 to 7.

10. A computing device, characterized in that include: One or more processors and a memory, wherein the memory stores one or more programs and is configured to be executed by the one or more processors, wherein the one or more programs include instructions for executing any one of the methods described in claims 1 to 7.

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