Carbonate rock fault control fracture-cavity type high-quality reservoir prediction method and system and electronic equipment

Abstract

The invention discloses a carbonate rock fault control fracture-vug type high-quality reservoir prediction method and system and electronic equipment, and belongs to the technical field of fracture-vug type carbonate rock oil and gas reservoir development. The method comprises the following steps: obtaining the number of finished drilling wells, the number of wells with mud leakage or drilling tool emptying, the number of wells without mud leakage or drilling tool emptying, corresponding amplitude values and maximum likelihood attribute values in a seismic data work area, and introducing a binary Logistic regression model according to obtained original data, and establishing a relation model of whether emptying leakage occurs, an amplitude value and a maximum likelihood attribute value. On the basis of the model, the probability of emptying leakage at any position in the same set of seismic data work area can be judged, and prediction of a high-quality reservoir is achieved. According to the method, a mathematical probability model is introduced, and quantitative characterization and prediction of the fault control fracture-cavity type high-quality reservoir are realized through combination of a geophysical prospecting technology and mathematical analysis; the process is simple and rapid, and accurate prediction of the cave type fracture-vug type high-quality reservoir is achieved for the first time.

Description

Technical Field

[0001] The invention belongs to the technical field of fracture-cavity carbonate oil and gas reservoir development, and specifically relates to a method, system and electronic equipment for predicting carbonate fault-controlled fracture-cavity high-quality reservoirs. Background Art

[0002] The fault-controlled fracture-cave reservoir of carbonate rock is a kind of fracture-cave reservoir aggregate formed by karstification along the fault. According to the size of the fractures and caves, carbonate reservoirs can be divided into three categories: cave type, hole type, and fracture type. The diameter is greater than 100mm for cave type reservoirs, and wells that encounter this type of reservoir often have engineering anomalies such as mud leakage and drilling tool emptying. The hole-cave reservoir refers to the generally visible dissolution holes with a diameter of 2 to 100mm, which appear as irregular dark spots on the imaging logging image. The genesis of fracture-type reservoirs is relatively complex, and there are mainly three types from the genesis: structural fractures, dissolution fractures and diagenetic fractures. Among these three types of reservoirs, the cave-type reservoir has the best reservoir properties and the largest scale, and is the highest quality reservoir. Therefore, the cave-type reservoir is the preferred target for drilling fault-controlled fracture-cave oil and gas reservoirs.

[0003] At present, structural tensor technology, coherence technology, seismic inversion technology, attribute carving and fusion technology are effective means to predict fault-controlled fracture-cavity reservoirs (Liao Maohui, 2020). However, due to the complex internal structure of fault-controlled fracture-cavity reservoirs, which is a collective reflection of matrix and cave-type, pore-type, and fracture-type reservoirs, the above technologies cannot quantitatively characterize and predict fault-controlled fracture-cavity high-quality reservoirs, and it is difficult to accurately predict the development location of cave-type high-quality reservoirs. Based on seismic and well data, the present invention can predict whether any spatial position in the same seismic data work area is a high-quality reservoir. Summary of the invention

[0004] In order to overcome the shortcomings of the above-mentioned prior art, the purpose of the present invention is to provide a prediction method for carbonate fault-controlled fracture-vuggy high-quality reservoirs, so as to solve the problem that the prior art cannot quantitatively characterize and predict fault-controlled fracture-vuggy high-quality reservoirs, and it is difficult to accurately predict the development location of cave-type high-quality reservoirs.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A prediction method for carbonate fault-controlled fracture-vuggy high-quality reservoirs, comprising:

[0007] S1: The number of completed wells, wells with mud loss or drilling tool emptying, and wells without mud loss or drilling tool emptying in the work area where seismic data was obtained;

[0008] S2: Obtain the amplitude value and maximum likelihood attribute value of each well where mud loss or drilling tool emptying occurs at the emptying and loss point; obtain the amplitude value and maximum likelihood attribute value of each well where mud loss or drilling tool emptying does not occur at the "beaded" reflection section position;

[0009] S3: Substitute the obtained amplitude value and maximum likelihood attribute value at the point of draining and losing of each well with mud loss or drilling tool draining and the amplitude value and maximum likelihood attribute value at the point of draining and losing of each well without mud loss or drilling tool draining into the relationship model between draining and losing of each well and the amplitude value and maximum likelihood attribute value to obtain the probability value of the point;

[0010] S4: According to the obtained probability value, determine whether the point is a cave reservoir.

[0011] Preferably, the relationship model between whether emptying loss occurs and the amplitude value and the maximum likelihood attribute value is based on the data obtained in S2, and a binary logistic regression model is introduced to establish a relationship between the probability value of the dependent variable taking the value 1 and the independent variable; the dependent variable is whether emptying loss occurs, and the independent variables are the amplitude value and the maximum likelihood attribute value.

[0012] Preferably, the relationship model between whether emptying leakage occurs and the amplitude value and the maximum likelihood attribute value is specifically:

[0013]

[0014] Among them, P is the probability value of the dependent variable taking the value 1; X is the amplitude value; Y is the maximum likelihood attribute value; a, b, c are constant coefficients.

[0015] Preferably, based on the sample data obtained in S2, the SPSS software is used to establish a relationship between the dependent variable, which is the probability value of the occurrence of emptying leakage, and the independent variable.

[0016] Preferably, in S1, the sum of the number of wells with mud loss or drilling tool emptying and the number of wells without mud loss or drilling tool emptying is equal to the number of completed wells in the seismic data work area.

[0017] Preferably, in S1, the number of wells with mud loss or drilling tool emptying and the number of wells without mud loss or drilling tool emptying are both greater than zero.

[0018] Preferably, in S2, the amplitude value and maximum likelihood attribute value of each well where mud loss or drilling tool emptying occurs at the emptying and loss point are read on the seismic data; as well as the amplitude value and maximum likelihood attribute value of each well where mud loss or drilling tool emptying does not occur at the "beaded" reflection section position.

[0019] Preferably, in S4, if the obtained probability value is greater than 0.5, it is a cave type reservoir, and if the obtained probability value is less than 0.5, it is not a cave type reservoir.

[0020] The present invention also discloses a prediction system for carbonate fault-controlled fracture-vuggy high-quality reservoirs, comprising:

[0021] The first acquisition module is used to obtain the number of completed wells, the number of wells with mud loss or drilling tool emptying, and the number of wells without mud loss or drilling tool emptying in the seismic data work area;

[0022] The second acquisition module is used to obtain the amplitude value and maximum likelihood attribute value of each well where mud loss or drilling tool emptying occurs at the emptying and loss point; obtain the amplitude value and maximum likelihood attribute value of each well where mud loss or drilling tool emptying does not occur at the "beaded" reflection section position;

[0023] A calculation module is used to substitute the acquired amplitude value and maximum likelihood attribute value at the point of draining and losing of each well where mud loss or drilling tool draining occurs and the amplitude value and maximum likelihood attribute value at the point of draining and losing of each well where mud loss or drilling tool draining does not occur into the relationship model between draining and losing of the well and the amplitude value and maximum likelihood attribute value, so as to obtain the probability value of the point;

[0024] The judgment module is used to judge whether the point is a cave reservoir according to the size of the obtained probability value.

[0025] The present invention also discloses an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for predicting high-quality carbonate fault-controlled fracture-vuggy reservoirs described in any one of the above items are implemented.

[0026] The present invention also discloses a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the method for predicting high-quality carbonate fault-controlled fracture-vuggy reservoirs described in any one of the above items are implemented.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] When drilling into a high-quality reservoir, mud loss, drilling tool emptying and other conditions may occur. The amplitude value and maximum likelihood attribute value of the location where the emptying and loss occurred can be read on the seismic data; if the "beaded" reflection is drilled through and no emptying and loss occurs, an amplitude value and maximum likelihood attribute value can also be obtained at the "beaded" reflection section. Based on the original data obtained, a binary Logstic regression model is introduced to establish a relationship model between emptying and loss and the amplitude value and maximum likelihood attribute value. Based on this model, the probability of emptying and loss at any location in the same set of seismic data work area can be identified to achieve the prediction of high-quality reservoirs. The present invention introduces a mathematical probability model, and through the combination of geophysical exploration technology and mathematical analysis, it realizes the quantitative characterization and prediction of fault-controlled fracture-cavity type high-quality reservoirs; the process is simple and fast, and for the first time, it has achieved the accurate prediction of such fracture-cavity type high-quality reservoirs as cave-type. This method can be used to accurately predict cavernous reservoirs, guide target determination before drilling, optimize trajectory adjustment during drilling, increase the drilling rate of high-quality reservoirs in horizontal wells, and thus increase the drilling success rate and the proportion of high-yield and high-efficiency wells. This method can be used to predict complex working conditions such as mud loss and drilling tool emptying in advance to ensure well control safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a flow chart of the method of the present invention;

[0030] Figure 2 This is a flow chart of a method for predicting high-quality carbonate rock fault-controlled fracture-vuggy reservoirs according to Example 2 of the present invention;

[0031] Figure 3 It is a schematic diagram of the prediction system of the carbonate rock fault-controlled fracture-vuggy high-quality reservoir of the present invention. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0034] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0035] The prediction of the development location of high-quality carbonate fault-controlled fracture-cavity reservoirs can guide the design of drilling trajectories and the optimization of target adjustments, improve the drilling rate of cave-type reservoirs, and at the same time, predict complex working conditions such as mud loss and drilling tool emptying in advance to ensure well control safety.

[0036] The location of high-quality carbonate fault-controlled fracture-vuggy reservoirs is mainly reflected in two aspects: amplitude intensity and fault activity intensity. Through forward modeling and numerical simulation, it is recognized that carbonate fault-controlled fracture-vuggy high-quality reservoirs show "beaded, strong amplitude" reflection characteristics on seismic profiles, and the amplitude value can be used to characterize the amplitude intensity; the greater the intensity of fault activity, the more favorable it is for the development of fault-controlled fracture-vuggy reservoirs, and the maximum likelihood attribute value can be used to characterize the intensity of fault activity.

[0037] When drilling into a high-quality reservoir, mud loss and drilling tool emptying may occur. The amplitude value and maximum likelihood attribute value of the emptying and loss position can be read on the seismic data; if the "beaded" reflection is drilled through and no emptying and loss occurs, an amplitude value and maximum likelihood attribute value can also be obtained at the "beaded" reflection section. Based on the original data obtained, the binary Logstic regression model is introduced to establish a relationship model between emptying and loss and the amplitude value and maximum likelihood attribute value. Based on this model, the probability of emptying and loss at any position in the same set of seismic data work area can be identified to achieve the prediction of high-quality reservoirs.

[0038] See also Figure 1 The present invention discloses a method for predicting high-quality carbonate fault-controlled fracture-vuggy reservoirs, comprising:

[0039] S1: The number of completed wells, wells with mud loss or drilling tool emptying, and wells without mud loss or drilling tool emptying in the work area where seismic data was obtained;

[0040] S2: Obtain the amplitude value and maximum likelihood attribute value of each well where mud loss or drilling tool emptying occurs at the emptying and loss point; obtain the amplitude value and maximum likelihood attribute value of each well where mud loss or drilling tool emptying does not occur at the "beaded" reflection section position;

[0041] S3: Substitute the obtained amplitude value and maximum likelihood attribute value at the point of draining and losing of each well with mud loss or drilling tool draining and the amplitude value and maximum likelihood attribute value at the point of draining and losing of each well without mud loss or drilling tool draining into the relationship model between draining and losing of each well and the amplitude value and maximum likelihood attribute value to obtain the probability value of the point;

[0042] S4: According to the obtained probability value, determine whether the point is a cave reservoir.

[0043] When drilling into a high-quality reservoir, mud loss, drilling tool emptying and other conditions may occur. The amplitude value and maximum likelihood attribute value of the location where the emptying and loss occurred can be read on the seismic data; if the "beaded" reflection is drilled through and no emptying and loss occurs, an amplitude value and maximum likelihood attribute value can also be obtained at the "beaded" reflection section. Based on the original data obtained, a binary Logstic regression model is introduced to establish a relationship model between emptying and loss and the amplitude value and maximum likelihood attribute value. Based on this model, the probability of emptying and loss at any location in the same set of seismic data work area can be identified to achieve the prediction of high-quality reservoirs. The present invention introduces a mathematical probability model, and through the combination of geophysical exploration technology and mathematical analysis, it realizes the quantitative characterization and prediction of fault-controlled fracture-cavity type high-quality reservoirs; the process is simple and fast, and for the first time, it has achieved the accurate prediction of such fracture-cavity type high-quality reservoirs as cave-type. This method can be used to accurately predict cavernous reservoirs, guide target determination before drilling, optimize trajectory adjustment during drilling, increase the drilling rate of high-quality reservoirs in horizontal wells, and thus increase the drilling success rate and the proportion of high-yield and high-efficiency wells. This method can be used to predict complex working conditions such as mud loss and drilling tool emptying in advance to ensure well control safety.

[0044] In some embodiments, the relationship model between whether emptying and missing occurs and the amplitude value and the maximum likelihood attribute value is based on the data obtained in S2, and a binary logistic regression model is introduced to establish a relationship between the probability value of the dependent variable taking the value 1 and the independent variable; the dependent variable is whether emptying and missing occurs, and the independent variables are the amplitude value and the maximum likelihood attribute value. Specifically:

[0045]

[0046] Among them, P is the probability value of the dependent variable taking the value 1; X is the amplitude value; Y is the maximum likelihood attribute value; a, b, and c are constant coefficients, which are generated by substituting the sample points into the binary logistic regression model.

[0047] In some embodiments,

[0048]

[0049] In the definition, 1 means that emptying leakage occurs, and 0 means that emptying leakage does not occur. Based on the sample data obtained from S2, the relationship between the probability value of the dependent variable taking the value 1 and the independent variable is established using SPSS software.

[0050] In some embodiments, in S1, the sum of the number of wells with mud loss or drilling tool emptying and the number of wells without mud loss or drilling tool emptying is equal to the number of completed wells in the seismic data work area. The number of wells with mud loss or drilling tool emptying and the number of wells without mud loss or drilling tool emptying are both greater than zero.

[0051] In some embodiments, the amplitude value and maximum likelihood attribute value of each well where mud loss or drilling tool emptying occurs at the emptying and loss point are read on the seismic data; as well as the amplitude value and maximum likelihood attribute value of each well where mud loss or drilling tool emptying does not occur at the "beaded" reflection section position.

[0052] [Example 1]

[0053] S1: The number of completed wells, wells with mud loss or drilling tool emptying, and wells without mud loss or drilling tool emptying in the work area where seismic data was obtained;

[0054] S2: Obtain the amplitude value and maximum likelihood attribute value of each well where mud loss or drilling tool emptying occurs at the emptying and loss point; obtain the amplitude value and maximum likelihood attribute value of each well where mud loss or drilling tool emptying does not occur at the "beaded" reflection section position;

[0055] S3: Substitute the obtained amplitude value and maximum likelihood attribute value at the point of draining and losing of each well with mud loss or drilling tool draining and the amplitude value and maximum likelihood attribute value at the point of draining and losing of each well without mud loss or drilling tool draining into the relationship model between draining and losing of each well and the amplitude value and maximum likelihood attribute value to obtain the probability value of the point;

[0056] S4: According to the obtained probability value, determine whether the point is a cave reservoir.

[0057] The present invention introduces a mathematical probability model, and realizes quantitative characterization and prediction of fault-controlled fracture-cavity high-quality reservoirs by combining geophysical exploration technology with mathematical analysis; the process is simple and fast, and it realizes the accurate prediction of such fracture-cavity high-quality reservoirs for the first time. The accurate prediction of cave-type reservoirs can be achieved by using this method, which can guide the determination of targets before drilling, the adjustment and optimization of trajectories during drilling, and the increase of the drilling rate of high-quality reservoirs in horizontal wells, thereby increasing the drilling success rate and the proportion of high-yield and high-efficiency wells. The method can be used to predict complex working conditions such as mud loss and drilling tool emptying in advance to ensure well control safety.

[0058] [Example 2]

[0059] See also Figure 2 , which is a flow chart of the method for predicting high-quality carbonate fault-controlled fracture-vuggy reservoirs of the present invention. The method is applicable to a certain number of wells that have been drilled within the same set of seismic data work area, some of which have experienced mud loss and drilling tool emptying in the target layer, while the other wells have not experienced mud loss and drilling tool emptying in the target layer.

[0060] Step 1: Assume that N wells are drilled within a set of seismic data work area, among which well N1 has mud loss and drilling tool emptying in the target layer, and well N2 has no mud loss and drilling tool emptying (N=N1+N2, N1>0, N2>0).

[0061] Step 2: Read the amplitude value X and the maximum likelihood attribute value Y of the N1 well where the discharge and leakage occurred on the seismic data; read the amplitude value X and the maximum likelihood attribute value Y of the N2 well where the discharge and leakage did not occur at the "beaded" reflection section.

[0062] Step 3: Introduce a binary logistic regression model, define 1 to indicate that emptying and leakage occurs, and 0 to indicate that emptying and leakage does not occur. Based on the sample data obtained in step 2, use SPSS software to establish the probability value P of the dependent variable (whether emptying and leakage occurs) taking the value 1 and the mathematical formula of the independent variables (amplitude value X, maximum likelihood attribute value Y):

[0063]

[0064] Among them, P is the probability value of the dependent variable taking the value 1; X is the amplitude value; Y is the maximum likelihood attribute value; a, b, and c are constant coefficients, which are generated by substituting the sample points into the binary logistic regression model.

[0065] Step 4: Read the amplitude value X and the maximum likelihood attribute value Y of a certain point within the range of the seismic data. According to the mathematical formula established in step 3, the probability of emptying and leakage at this location can be obtained, that is, the probability that this location is a cave-type high-quality reservoir. If P>0.5, it is determined to be a cave-type reservoir; if P<0.5, it is determined not to be a cave-type reservoir.

[0066] Through the above steps, the prediction of high-quality carbonate fault-controlled fracture-vuggy reservoirs can be completed.

[0067] The present invention introduces a mathematical probability model, and realizes quantitative characterization and prediction of fault-controlled fracture-cavity high-quality reservoirs by combining geophysical exploration technology with mathematical analysis; the process is simple and fast, and it realizes the accurate prediction of such fracture-cavity high-quality reservoirs for the first time. The accurate prediction of cave-type reservoirs can be achieved by using this method, which can guide the determination of targets before drilling, the adjustment and optimization of trajectories during drilling, and the increase of the drilling rate of high-quality reservoirs in horizontal wells, thereby increasing the drilling success rate and the proportion of high-yield and high-efficiency wells. The method can be used to predict complex working conditions such as mud loss and drilling tool emptying in advance to ensure well control safety.

[0068] [Example 3]

[0069] The specific embodiments of the present invention are further described in detail. The illustrative examples and descriptions of the present invention are used to explain the present invention, but are not intended to limit the present invention.

[0070] Step 1: Eleven wells (N=11) were drilled in a certain earthquake work area, of which eight wells (N1=8) had mud loss and drilling tools were emptied, and three wells (N2=3) had no mud loss and drilling tools were emptied, meeting the applicable conditions of the technical invention.

[0071] Step 2: Read the amplitude values ​​and maximum likelihood attribute values ​​at the leakage points of 8 wells with mud loss and drilling tool drain; read the amplitude values ​​and maximum likelihood attribute values ​​at the "beaded" reflection section position of 3 wells without drainage and drain.

[0072] Step 3: Establish the relationship between the probability P of the dependent variable (whether emptying leakage occurs) taking the value 1 and the independent variables (amplitude value X, maximum likelihood attribute value Y):

[0073]

[0074] The P value indicates the probability of emptying and leakage. When P is greater than 0.5, it is judged that emptying and leakage have occurred. When P is less than 0.5, it is judged that emptying and leakage have not occurred. According to the established relationship, the accuracy of the identification result reaches 100%, which confirms the reliability of this formula in this earthquake work area (Table 1).

[0075] Table 1 Amplitude value, maximum likelihood value and prediction of single well emptying and leakage point emptying and leakage probability

[0076]

[0077] Step 4: Read the amplitude value X and the maximum likelihood attribute value Y of a certain point within the range of the seismic data. According to the mathematical formula established in step 3, the probability of emptying and leakage at this location can be obtained, that is, the probability that this location is a cave-type high-quality reservoir. If P>0.5, it is determined to be a cave-type reservoir; if P<0.5, it is determined not to be a cave-type reservoir.

[0078] See also Figure 3 The present invention also discloses a prediction system for carbonate fault-controlled fracture-vuggy high-quality reservoirs, comprising:

[0079] The first acquisition module is used to obtain the number of completed wells, the number of wells with mud loss or drilling tool emptying, and the number of wells without mud loss or drilling tool emptying in the seismic data work area;

[0080] The second acquisition module is used to obtain the amplitude value and maximum likelihood attribute value of each well where mud loss or drilling tool emptying occurs at the emptying and loss point; obtain the amplitude value and maximum likelihood attribute value of each well where mud loss or drilling tool emptying does not occur at the "beaded" reflection section position;

[0081] A calculation module is used to substitute the acquired amplitude value and maximum likelihood attribute value at the point of draining and losing of each well where mud loss or drilling tool draining occurs and the amplitude value and maximum likelihood attribute value at the point of draining and losing of each well where mud loss or drilling tool draining does not occur into the relationship model between draining and losing of the well and the amplitude value and maximum likelihood attribute value, so as to obtain the probability value of the point;

[0082] The judgment module is used to judge whether the point is a cave reservoir according to the size of the obtained probability value.

[0083] The present invention also discloses an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for predicting high-quality carbonate fault-controlled fracture-vuggy reservoirs as described in any one of the claims above are implemented.

[0084] The present invention also discloses a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the method for predicting high-quality carbonate fault-controlled fracture-vuggy reservoirs described in any one of the above items are implemented.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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 method for predicting high-quality carbonate reservoirs with fault-controlled fractures and caves, characterized in that: include: The number of completed wells, the number of wells with mud loss or drilling tool emptying, and the number of wells without mud loss or drilling tool emptying in the work area where seismic data is obtained; Obtain the amplitude value and maximum likelihood attribute value at the point of mud loss or drilling tool emptying of each well; obtain the amplitude value and maximum likelihood attribute value at the position of the "beaded" reflection section of each well that has not experienced mud loss or drilling tool emptying; Substitute the obtained amplitude value and maximum likelihood attribute value at the point of draining and losing of each well with mud loss or drilling tool draining and the amplitude value and maximum likelihood attribute value at the point of draining and losing of each well without mud loss or drilling tool draining into the relationship model between draining and losing of the well and the amplitude value and maximum likelihood attribute value to obtain the probability value of the point; According to the obtained probability value, it is determined whether the point is a cave reservoir.

2. The prediction method of a carbonate fault-controlled fracture-vuggy high-quality reservoir according to claim 1, characterized in that: The relationship model between whether emptying loss occurs and the amplitude value and the maximum likelihood attribute value is based on the data obtained in S2, and a binary logistic regression model is introduced to establish a relationship between the probability value of the dependent variable taking the value 1 and the independent variable; the dependent variable is whether emptying loss occurs, and the independent variables are the amplitude value and the maximum likelihood attribute value.

3. The prediction method of a carbonate fault-controlled fracture-vuggy high-quality reservoir according to claim 2, characterized in that: The relationship model between whether emptying leakage occurs and the amplitude value and the maximum likelihood attribute value is specifically: Among them, P is the probability value of the dependent variable taking the value 1; X is the amplitude value; Y is the maximum likelihood attribute value; a, b, c are constant coefficients.

4. The prediction method of a carbonate fault-controlled fracture-vuggy high-quality reservoir according to claim 2, characterized in that: Based on the sample data obtained from S2, the SPSS software was used to establish a relationship between the probability value of the dependent variable, which is the occurrence of emptying leakage, and the independent variable.

5. The prediction method of a carbonate fault-controlled fracture-vuggy high-quality reservoir according to claim 1, characterized in that: In S1, the sum of the number of wells with mud loss or drilling tool release and the number of wells without mud loss or drilling tool release is equal to the number of completed wells in the seismic data work area; the number of wells with mud loss or drilling tool release and the number of wells without mud loss or drilling tool release are both greater than zero.

6. The prediction method of a carbonate fault-controlled fracture-vuggy high-quality reservoir according to claim 1, characterized in that: In S2, the amplitude value and maximum likelihood attribute value of each well with mud loss or drilling tool emptying at the emptying and loss point are read from the seismic data; as well as the amplitude value and maximum likelihood attribute value of each well without mud loss or drilling tool emptying at the "beaded" reflection section position.

7. The prediction method of a carbonate fault-controlled fracture-vuggy high-quality reservoir according to claim 1, characterized in that: In S4, if the obtained probability value is greater than 0.5, it is a cave-type reservoir, and if the obtained probability value is less than 0.5, it is not a cave-type reservoir.

8. A prediction system for carbonate fault-controlled fracture-cavity high-quality reservoirs, characterized in that: include: The first acquisition module is used to obtain the number of completed wells, the number of wells with mud loss or drilling tool emptying, and the number of wells without mud loss or drilling tool emptying in the seismic data work area; The second acquisition module is used to obtain the amplitude value and maximum likelihood attribute value of each well where mud loss or drilling tool emptying occurs at the emptying and loss point; obtain the amplitude value and maximum likelihood attribute value of each well where mud loss or drilling tool emptying does not occur at the "beaded" reflection section position; A calculation module is used to substitute the acquired amplitude value and maximum likelihood attribute value at the point of draining and losing of each well where mud loss or drilling tool draining occurs and the amplitude value and maximum likelihood attribute value at the point of draining and losing of each well where mud loss or drilling tool draining does not occur into the relationship model between draining and losing of the well and the amplitude value and maximum likelihood attribute value, so as to obtain the probability value of the point; The judgment module is used to judge whether the point is a cave reservoir according to the size of the obtained probability value.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for predicting high-quality carbonate fault-controlled fracture-vuggy reservoirs as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the method for predicting high-quality carbonate fault-controlled fracture-vuggy reservoirs as described in any one of claims 1 to 7.

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