CN117421549A - Shale gas horizontal well fracture pressure prediction method and system - Google Patents
Shale gas horizontal well fracture pressure prediction method and system Download PDFInfo
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Abstract
The invention discloses a shale gas horizontal well fracture pressure prediction method and a shale gas horizontal well fracture pressure prediction system, wherein the method comprises the following steps: determining a horizontal well section with logging data and element logging data in a current target area to be researched, and acquiring the element logging data and the ground stress data of a designated well section; optimizing element types meeting geological characteristics of a target area from element logging data, and determining optimal element combination data of a corresponding well section according to the element logging data of a specified well section; establishing a first model for predicting the ground stress data according to the ground stress data and the optimal element combination data; and predicting the ground stress data of the well section to be predicted by using the first model according to the optimal element combination data corresponding to the element logging data of the well section to be predicted in the target area, and calculating the corresponding fracture pressure. The method and the device realize the prediction of the fracture pressure of the shale gas horizontal well without logging data.
Description
Technical Field
The invention belongs to the technical field of petroleum engineering, and particularly relates to a shale gas horizontal well fracture pressure prediction method and system.
Background
Shale gas is commonly obtained today in a manner that develops shale gas blobs in a horizontal well pattern of a well factory. Among these, the main development method is large-scale hydraulic fracturing. When shale gas is obtained in a shale gas horizontal well by utilizing a hydraulic fracturing development method, the fracturing pressure of the shale gas horizontal well is an important basis for selecting shale gas fracturing construction equipment and designing shale gas fracturing construction parameters. Thus, the predicted fracture pressure is the basis for designing shale gas horizontal well fracturing schemes.
In the process of realizing the invention, the inventor finds that the horizontal well section of part of shale gas platform wells has no logging data, or that part of shale gas platforms (one platform of 6-8 horizontal wells) have only 1-2 horizontal wells with logging data. In addition, the three horizontal sections of the partial horizontal well with log data are free of neutron data, density data, etc., of log data related to the radioactive source, which is the necessary data for the log to predict the fracture pressure.
Disclosure of Invention
In order to solve the above problems, the embodiment of the invention provides a shale gas horizontal well fracture pressure prediction method, which comprises the following steps: determining a horizontal well section with logging data and element logging data in a current target area to be researched, and acquiring the element logging data and the ground stress data of a designated well section; optimizing element types meeting geological characteristics of a target area from element logging data, and determining optimal element combination data of a corresponding well section according to the element logging data of the appointed well section; establishing a first model for predicting the ground stress data according to the ground stress data and the optimal element combination data; and predicting the ground stress data of the well section to be predicted by using the first model according to the optimal element combination data corresponding to the element logging data of the well section to be predicted in the target area, and calculating the corresponding fracture pressure.
Preferably, the method for predicting the fracture pressure of the shale gas horizontal well provided by the embodiment of the invention further comprises the following steps: and sequentially carrying out well depth scale unified processing and well depth calibration processing on the logging data and the element logging data of the same well section.
Preferably, in the step of well depth scale unified processing, the method comprises: respectively configuring sampling intervals for scale unified processing for the logging data and the element logging data; extracting the ground stress information of the appointed well section from well logging data acquired in the well logging construction process, and sampling the ground stress information according to a first sampling interval to obtain the ground stress data; extracting original element logging data of the appointed well section from element logging data obtained in the element logging construction process, and sampling the original element logging data according to a second sampling interval to obtain the element logging data; and integrating the ground stress data and the element logging data according to the well depth so as to be unified under the same depth scale.
Preferably, the range of the first sampling interval is preferably 0.1-0.125 m; the second sampling interval is preferably in the range of 1 to 2m.
Preferably, in the deep homing processing step, it includes: and carrying out natural GR radioactivity total amount measurement on the quantitative rock debris while drilling of the appointed well section, and carrying out comparison analysis on the measurement result and GR while drilling data in the logging while drilling data, so that the well depths of the logging data and the element logging data which are subjected to depth unified processing are restored according to the comparison analysis result, and the well depths after the restoration are consistent with the actual geological conditions.
Preferably, in the step of optimizing the element type from the element log data to conform to the geological properties of the target region, the step of: calculating the correlation among the element types in the element logging data by using a correlation analysis method, screening out a plurality of groups of element type combinations with correlation calculation results exceeding a preset first threshold value, and recording the element type combinations as first type element types; according to the correlation calculation result, aiming at each group of element type combinations, analyzing the similarity between each element in the combination and the whole group of element type combinations, and further screening the element type corresponding to the highest similarity data in each group of element type combinations, and marking the element type as a second type element type; and analyzing and processing the main components of the second class element types to determine corresponding optimal element combination data.
Preferably, the method employs grey correlation analysis and/or cluster analysis methods to analyze the similarity.
Preferably, in the step of establishing the first model, the method includes: and performing data fitting on a preset multiple regression model by using the ground stress data and the optimal element combination data, so that a fitting result is used as the first model.
Preferably, in the step of calculating the fracture pressure of the current wellbore section to be predicted, it comprises: according to the ground stress data of the well section to be predicted, obtaining the well periphery three-way main stress of the current well section to be predicted; and determining the fracture pressure of the current well section to be predicted by using a tensile strength theory according to the main stress of the well Zhou Sanxiang of the well section to be predicted.
In another aspect, the present invention also provides a shale gas horizontal well fracture pressure prediction system, including: the data acquisition module is used for determining a horizontal well section with logging data and element logging data in a current target area to be researched, and acquiring the element logging data and the ground stress data of a designated well section; the logging data screening module is used for optimizing element types meeting geological characteristics of a target area from element logging data and determining optimal element combination data of a corresponding well section according to the element logging data of the appointed well section; the model building module is used for building a first model for predicting the ground stress data according to the ground stress data and the optimal element combination data; and the fracture pressure prediction module is used for predicting the ground stress data of the well section to be predicted by using the first model according to the optimal element combination data corresponding to the element logging data of the well section to be predicted in the target area and calculating the corresponding fracture pressure.
One or more embodiments of the above-described solution may have the following advantages or benefits compared to the prior art:
the invention provides a shale gas horizontal well fracture pressure prediction method. According to the method, the ground stress data of the well section to be predicted is obtained by combining the related data of the logging while drilling element with the GR data while drilling, so that the fracture pressure of the current well section to be predicted is predicted through calculation by utilizing the obtained ground stress data of the well section to be predicted. The method realizes the prediction of the fracture pressure of the shale gas horizontal well without logging data, provides a basis for selecting shale gas fracturing construction equipment and designing shale gas fracturing construction parameters, and optimizes the targeted fracturing schemes of 'one well one strategy' and 'one section one strategy'.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention, without limitation to the invention. In the drawings:
fig. 1 is a step diagram of a first example of a shale gas horizontal well fracture pressure prediction method according to an embodiment of the present application.
FIG. 2 is a block diagram of a first example of a shale gas horizontal well fracture pressure prediction system in accordance with an embodiment of the present application.
Fig. 3 is a step diagram of a second example of a shale gas horizontal well fracture pressure prediction method according to an embodiment of the present application.
Detailed Description
The following will describe embodiments of the present invention in detail with reference to the drawings and examples, thereby solving the technical problems by applying technical means to the present invention, and realizing the technical effects can be fully understood and implemented accordingly. It should be noted that, as long as no conflict is formed, each embodiment of the present invention and each feature of each embodiment may be combined with each other, and the formed technical solutions are all within the protection scope of the present invention.
Additionally, the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that herein.
Shale gas is commonly obtained today in a manner that develops shale gas blobs in a horizontal well pattern of a well factory. Among these, the main development method is large-scale hydraulic fracturing. When shale gas is obtained in a shale gas horizontal well by utilizing a hydraulic fracturing development method, the fracturing pressure of the shale gas horizontal well is an important basis for selecting shale gas fracturing construction equipment and designing shale gas fracturing construction parameters. Thus, the predicted fracture pressure is the basis for designing shale gas horizontal well fracturing schemes.
In the process of realizing the invention, the inventor finds that the horizontal well section of part of shale gas platform wells has no logging data, or that part of shale gas platforms (one platform of 6-8 horizontal wells) have only 1-2 horizontal wells with logging data. In addition, the three horizontal sections of the partial horizontal well with log data are free of neutron data, density data, etc., of log data related to the radioactive source, which is the necessary data for the log to predict the fracture pressure.
Therefore, in order to solve the problem that the fracture pressure of the shale gas horizontal well without logging data is difficult to predict, the invention provides a fracture pressure prediction method for the shale gas horizontal well. According to the method, the ground stress data of the well section to be predicted is obtained by combining the related data of the logging while drilling element with the GR data while drilling, so that the fracture pressure of the current well section to be predicted is predicted through calculation by utilizing the obtained ground stress data of the well section to be predicted. The method realizes the prediction of the fracture pressure of the shale gas horizontal well without logging data, provides a basis for selecting shale gas fracturing construction equipment and designing shale gas fracturing construction parameters, and optimizes the targeted fracturing schemes of 'one well one strategy' and 'one section one strategy'.
Example 1
Fig. 1 is a step diagram of a first example of a shale gas horizontal well fracture pressure prediction method according to an embodiment of the present application. A first example of the cracking pressure prediction method according to the embodiment of the invention will be described in detail with reference to fig. 1.
As shown in fig. 1, in step S110, a horizontal well section having both logging data and element logging data in a target area to be studied is determined, and element logging data and ground stress data of a specified well section are acquired. Firstly, a current target area to be researched is defined in a construction area for exploiting shale gas, and a horizontal well section with logging data and element logging data is determined in the current target area to be researched. Thereafter, log data and elemental log data are acquired for a horizontal interval having both log data and elemental log data, and further the earth stress data is extracted from the log data. And finally, obtaining the element logging data and the ground stress data of the appointed well section. In the embodiment of the application, the element logging data and the logging data of the appointed well section are acquired by determining the appointed well section in the horizontal well section with the logging data and the element logging data at the same time, wherein the appointed well section is located on the same shale gas platform or on adjacent shale gas platforms, in other words, the appointed well section is taken from the same stratum area or from different stratum areas with similar geological characteristics.
The element logging data generated in the element logging construction process generally relate to more than 20 element types, the element logging data covering all the element types is directly utilized to obtain the fracture pressure of the shale gas horizontal well, and the data calculation workload is large. In addition, in practical application, part of elements in element types related in the element logging construction process are irrelevant to or have weak relevance to the ground stress, and data corresponding to the part of elements can influence the accuracy of the fracture pressure calculation result of the shale gas horizontal well. Therefore, the method reduces the calculated amount by preprocessing the obtained element logging data, and improves the accuracy of the fracture pressure calculation result of the shale gas horizontal well.
Next, a detailed description will be given of a preprocessing process of element logging data in the embodiment of the present application.
After the element log data of the specified well section is obtained in step S110, step S120 preferably selects an element type conforming to the geological characteristics of the target area from the element log data, and determines the optimal element combination data of the corresponding well section according to the element log data of the specified well section. Specifically, the method performs preprocessing on the element logging data (original element logging data) of the designated well section obtained in the step S110, so as to optimize the element type conforming to the geological characteristics of the current target area among the element types related to the original element logging data for the geological characteristics of the current target area, and determines the optimal element combination data of the corresponding well section in the designated well section according to the element logging data corresponding to the optimized element type after optimizing the element type.
Further, a correlation analysis method is utilized to calculate the correlation among the element types in the element logging data, a plurality of groups of element type combinations with correlation calculation results exceeding a preset first threshold value are screened out, and the element type combinations are recorded as first type element types. Specifically, a correlation analysis method is adopted to perform correlation analysis on several elements involved in the element logging data (original element logging data) of the specified well section obtained in step S110, and element types with strong correlation are screened out. In the embodiment of the application, the correlation between any two elements in a plurality of elements related in the original element logging data is calculated respectively, and a correlation calculation result (namely, a correlation coefficient) representing the correlation degree among the element types under different element combinations is obtained. And then, comparing and analyzing the correlation coefficients under different element combinations with a threshold value (namely a first threshold value) with strong correlation between preset characterization element types, determining a plurality of groups of element type combinations with correlation calculation results exceeding the preset first threshold value, and marking the current plurality of groups of element type combinations as a first type element type (set). Accordingly, the first class (set) of element types includes a high degree of correlation between the individual elements within each of the plurality of sets of element type combinations.
And then, according to a correlation calculation result, analyzing the similarity between each element in the combination and the whole group of element type combinations according to each group of element type combinations, and screening the element type corresponding to the highest similarity data in each group of element type combinations, and marking the element type as a second type element type. After a correlation calculation result (correlation coefficient) is obtained, the invention calculates the similarity between the element and the whole group of element type combinations to which the element belongs by using the correlation coefficient according to each element in each group of element type combinations, and screens the element type corresponding to the highest similarity data in each group of element type combinations. Next, the element types screened in the current each group of element type combinations are marked as second type element types (sets). The second type element category (collection) is formed by extracting one element from each group of element combination, and the obtained second type element category (collection) has low similarity among various elements, in other words, the various elements in the second type element category (collection) can be regarded as individuals which are independent and cannot influence each other, so that the problem of collinearity among element logging data is avoided.
In this way, the primary screening of the element types is completed by determining the element types with strong correlation and low similarity involved in the original logging data, so that the optimized screening of the element types meeting the geological characteristics of the current target area to be researched can be further developed according to the element types with strong correlation and low similarity.
Next, the respective elements in the second class element species (set) are subjected to principal component analysis processing, and the corresponding optimum element combination data is determined. According to the characteristic that the principal component analysis method can keep original information by using as few new variables as possible and can reduce the operation complexity, the invention performs further optimization screening treatment on the second class element type (set) on the basis of the second class element type (set). In other words, after the second element type (set) is obtained, the invention also optimizes the element type (namely, optimized element type) which accords with the geological characteristics of the current target area to be researched from the second element type (set), so that the optimized element combination data of the corresponding well section is obtained by utilizing the optimized element type and combining the related data (such as content data) corresponding to each optimized element type in the element logging construction process.
Next, a detailed description will be given of an acquisition process of the optimal element combination data in the embodiment of the present application. Firstly, constructing all element types contained in a second type element type (set) as a candidate set, then randomly extracting element combinations of different numbers and different element types from the constructed candidate set by increasing or replacing elements or element ratios to form a plurality of groups of new element combinations, then respectively combining content data of each element in each group of new element combinations with ground stress data, calculating similarity between each element in each group of new element combinations based on the ground stress data (namely, obtaining the similarity between each element by utilizing conversion relations between the ground stress data and data corresponding to each type of element), thereby determining corresponding correlation coefficients for each group of new element combinations, and finally determining the new element combination with the highest correlation number as the preferred element type. And meanwhile, determining content data corresponding to each element in the preferred element types or content ratio data among the elements as optimal element combination data.
The invention fully utilizes more than 20 elements related to the element logging data generated in the element logging construction process, adopts a gray correlation analysis method and/or a cluster analysis method to obtain the similarity between each element in the combination and the element type combination of the element aiming at a plurality of groups of element type combinations in the first type element type (set). In the embodiment of the application, the element types corresponding to the highest similarity data in each group of element type combinations are screened from the original element logging data by using a cluster analysis method, so that each element constituting the second type element type (set) has low similarity, the second type element type (namely, the preferred sensitive element) is formed according to the low similarity, the cracking pressure is calculated based on the element logging data corresponding to the second type element type, the calculation error can be reduced, and the calculation workload can be reduced. According to the method, the characteristics of similarity among different data sources can be measured by using a cluster analysis method, the similarity or the proximity degree between the element and the element type combination to which the element belongs is calculated by using a systematic cluster algorithm, the element type (second type element type) with low similarity for carrying out principal component analysis is screened, the basis is provided for the optimization of a later model, and the problem of collinearity between partial rock components and the element is eliminated.
Further, a first model for predicting the ground stress data is established according to the ground stress data and the optimal element combination data in step S130. In the embodiment of the application, a first model for predicting the ground stress data is established according to the ground stress data in the logging data determined in the step S110 and the optimal element combination data determined in the step S120 in combination with a multiple linear regression method.
In the practical application process, the correlation analysis method can only perform preliminary screening on element types with strong correlation in the original logging data, and due to the problem of collinearity between the horizontal well section rock components and logging elements, the corresponding logging element types with the problem of collinearity with the rock components cannot be determined, and the degree of collinearity between the rock components and the corresponding logging elements cannot be determined. Meanwhile, the influence caused by the collinearity between the rock components of the horizontal well section and the corresponding logging elements cannot be completely eliminated by utilizing a correlation analysis method. If the first model for predicting the ground stress data is directly built by using the correlation data corresponding to the element types obtained through the correlation analysis, serious deviation exists in the built first model, and thus reliable ground stress data cannot be obtained by using the first model. Therefore, the invention also utilizes a multiple linear stepwise regression method to develop a preference for the data base on which the first model is constructed, thereby determining the best first model.
Further, data fitting is carried out on a preset multiple regression model by utilizing the ground stress data and the optimal element combination data, so that a fitting result is used as a first model. In the embodiment of the application, the mathematical model is preferably developed by using a multiple linear stepwise regression method to obtain the first model. Determining element types for carrying out data fitting on a preset multiple regression model from the preferred element types obtained through correlation analysis step by step, firstly substituting content data representing different elements or content ratio data among different elements and corresponding stress data corresponding to all the preferred element types into the preset multiple regression model respectively to calculate a determination coefficient of the model (the determination coefficient reflects the fitting goodness of the model), then removing any one element type from all the preferred element types, obtaining the determination coefficient of the model in a similar way to the method, continuing removing any element type from the remaining preferred element types, obtaining the determination coefficient of the corresponding model, and then analogizing until no remaining preferred element type exists, finally determining an optimal regression model according to the fitting goodness, and substituting the content data of each element in the optimal element combination data or the content ratio data among the elements into the optimal regression model to obtain the first model for predicting the stress. Wherein, the preset multiple regression model is represented by the following expression:
Y＝α 0 +α 1 X 1 +α 2 X 2 +…+α i X i (1)
wherein Y represents ground stress data, alpha 0 、α 1 、α 2 、…、α k Respectively represent regression coefficients, X 1 、X 2 、…、X i Respectively representing content data of different elements or content ratio data among different elements.
Further, in step S140, according to the optimal element combination data corresponding to the element logging data of the well section to be predicted in the target area, the first model is utilized to predict the ground stress data of the well section to be predicted, and the corresponding fracture pressure is calculated. In the embodiment of the application, firstly, a well section to be predicted (a well section which is free of logging data or cannot be predicted in ground stress due to the lack of relevant data in the logging data) in a target area is determined, namely, a well section which is imperfect in relevant data for predicting ground stress in the logging data, and optimal element combination data corresponding to element logging data of the well section to be predicted are obtained, then the optimal element combination data of a horizontal well to be predicted are substituted into an optimal first model in the step S130 to obtain ground stress data of the well section to be predicted, after the ground stress data of the well section to be predicted are obtained, the ground stress data of the well section to be predicted are utilized to obtain the stress distribution situation of the current well section to be predicted, then the stress distribution situation of the circumference of the current well section to be predicted is obtained according to the stress distribution situation of the circumference of the well, and finally the fracture pressure of the current well section to be predicted is calculated.
Further, according to the ground stress data of the well section to be predicted, the well periphery three-way main stress of the current well section to be predicted is obtained; then, the fracture pressure of the current well section to be predicted is determined by using the tensile strength theory according to the main stress of the well Zhou Sanxiang of the well section to be predicted. In the embodiment of the application, basic parameters (such as rock density, static poisson ratio, static Young modulus and the like) of stratum rock are obtained based on basic logging data of a current target area to be researched, a well peripheral stress field of a current well section to be predicted is restored, and various main stress parameters (such as vertical stress, horizontal maximum main stress, horizontal minimum main stress and the like) of the well periphery are determined according to the well peripheral stress field to obtain well Zhou Zhu stress of the current well section to be predicted. Finally, according to the well cycle three-way main stress of the current well section to be predicted, the analysis result obtained after the analysis of the fracture condition of the current well section to be predicted by utilizing the tensile strength theory is combined, so that the prediction of the fracture pressure is completed.
Example two
Based on the method for predicting the fracture pressure of the shale gas horizontal well according to the first embodiment, the embodiment of the invention further provides a fracture pressure prediction system (hereinafter referred to as a fracture pressure prediction system) for the shale gas horizontal well. FIG. 2 is a block diagram of a first example of a shale gas horizontal well fracture pressure prediction system in accordance with an embodiment of the present application.
As shown in fig. 2, the fracture pressure prediction system in the embodiment of the present invention includes: a data acquisition module 21, a logging data screening module 22, a model building module 23 and a fracture pressure prediction module 24. Specifically, the data acquisition module 21 is implemented according to the method described in the above step S110, and is configured to determine a horizontal well section having logging data and element logging data in the target area to be studied at the same time, and acquire the element logging data and the ground stress data of the specified well section; the logging data screening module 22 is implemented according to the method described in the above step S120, and is configured to preferably select an element type conforming to the geological characteristics of the target area from the element logging data acquired by the data acquisition module 21, and determine the optimal element combination data corresponding to the well section according to the element logging data of the specified well section; the model building module 23 is implemented according to the method described in the above step S130, and is configured to build a first model for predicting the ground stress data according to the ground stress data acquired by the data acquisition module 21 and the optimal element combination data screened by the logging data screening module 22; the fracture pressure prediction module 24 is implemented according to the method described in the above step S140, and is configured to predict the ground stress data of the well section to be predicted by using the first model established by the model establishment module 23 according to the optimal element combination data corresponding to the element logging data of the well section to be predicted in the target area, and calculate the corresponding fracture pressure.
Example III
Based on the first embodiment, in order to ensure the accuracy of the ground stress prediction result, the fracture pressure prediction method provided by the embodiment of the invention further includes the step of sequentially performing well depth scale unified processing and well depth calibration processing on the logging data and the element logging data of the same well section. Fig. 3 is a step diagram of a second example of a shale gas horizontal well fracture pressure prediction method according to an embodiment of the present application. A second example of the cracking pressure prediction method according to the embodiment of the invention will be described in detail with reference to fig. 3.
As shown in fig. 2, step S310 determines a horizontal well section having both logging data and elemental logging data within a current target area to be studied, and obtains elemental logging data and earth stress data for a specified well section. Meanwhile, step S320 sequentially performs a well depth scale unification process and a well depth calibration process on the logging data and the element logging data of the same well section obtained in step S310. And step S330 is to select element types conforming to the geological characteristics of the target area from the element logging data according to the element logging data subjected to the well depth scale unified processing and the well depth calibration processing obtained in step S320, and determine the optimal element combination data of the corresponding well section according to the element logging data of the appointed well section. Next, step S340 builds a first model for predicting the ground stress data from the ground stress data obtained through the well depth scale unification process and the well depth calibration process obtained in step S320 and the optimal element combination data obtained in step S330. Finally, step S350 predicts the earth stress data of the well section to be predicted according to the optimal element combination data corresponding to the element logging data of the well section to be predicted in the target area by using the first model established in step S340, and calculates the corresponding fracture pressure.
It should be noted that, in the present embodiment, step S310 is similar to the method described in step S110, step S330 is similar to the method described in step S120, step S340 is similar to the method described in step S130, and step S350 is similar to the method described in step S140, so the description of step S310, step S330, step S340 and step S350 will not be repeated here.
In the embodiment of the present application, step S320 sequentially performs a unified well depth scale processing and a well depth calibration processing on the logging data and the element logging data of the same well section according to the ground stress data and the element logging data in the logging data obtained in step S310. Firstly, according to the logging data of the horizontal well section where the ground stress data is located and the element logging data of the same well section obtained in the step S310, the logging data and the logging data are sorted by using a method for uniformly processing the well depth scale, and then the well depth of the corresponding horizontal well section is calibrated by using the logging data and the element logging data which are uniformly processed by the well depth scale, so that the ground stress data and the element logging data which are uniformly processed under the same depth scale are obtained.
Further, in the process of performing the well depth scale unification, first, sampling intervals for the scale unification are respectively configured for the well logging data and the element well logging data, the sampling intervals for the scale unification of the well logging data are noted as first sampling intervals, and the sampling intervals for the scale unification of the element well logging data are noted as second sampling intervals.
And then, extracting the ground stress information of the appointed well section from the well logging data acquired in the well logging construction process, and sampling the ground stress information according to a first sampling interval to obtain the ground stress data. And extracting relevant construction data in the logging construction process from the horizontal well section with logging data and element logging data in the current target area to be researched. And then, acquiring the ground stress information in the logging construction data, sampling data points in the ground stress information according to a first sampling interval, and integrating the data of each sampling data point of the corresponding horizontal well section into a ground stress distribution data set.
And extracting original element logging data of a designated well section from the element logging data acquired in the element logging construction process, and sampling the original element logging data according to a second sampling interval to obtain the element logging data. And extracting relevant construction data (namely original element logging data) in the element logging construction process from a horizontal well section with logging data and element logging data in the current target area to be researched. And then, sampling data points in the original element logging data according to a second sampling interval, integrating the data of each sampled data point of the corresponding horizontal well section into an original element logging data set, and obtaining the ground stress data according to the original logging data set which is integrated currently.
And finally, according to the well depth data, unifying the ground stress data set of the corresponding horizontal well section for determining the optimal element combination data and the original element logging data set to the same well depth scale. Accordingly, the purpose of unifying logging data intervals and element logging intervals to the same depth scale is achieved, and the logging data arrangement result and logging data arrangement result of the same well section after arrangement can reflect information of the same horizon of the horizontal well section.
Further, the range of the first sampling interval is preferably 0.1 to 0.125m, and the range of the second sampling interval is preferably 1 to 2m. In other words, in the embodiment of the present application, the distance between the data sampling points of the logging data is 0.1-0.125 m, and the distance between the data sampling points of the logging data of the original element is 1-2 m. It should be noted that, in the embodiment of the present invention, the sizes of the specified first sampling interval and the specified second sampling interval are not specifically limited, and may be set by those skilled in the art according to actual situations.
In practical application, the logging depth is related to the cable depth, the element logging data is related to the drilling tool depth, and the depth data obtained by using the logging construction method and the element logging construction method are not uniform due to a certain systematic error between the cable depth and the drilling tool depth. In order to improve the precision of the ground stress prediction result, the invention adopts a depth homing method to process logging data and element logging data so as to calibrate the actual well depth.
In the process of deep homing treatment, the invention carries out natural GR radioactivity total amount measurement on quantitative rock debris while drilling of a designated well section, and carries out comparative analysis on measurement results and GR while drilling data in logging while drilling data, thereby homing the well depths of logging data and element logging data which are subjected to depth unified treatment according to comparative analysis results, and leading the well depths after homing to be consistent with actual geological conditions. In the embodiment of the present application, firstly, quantitative rock cuttings while drilling of a horizontal well section where the ground stress data is located in step S310 are obtained, natural GR radioactivity total amount of the quantitative rock cuttings while drilling is measured by using a rock cuttings natural gamma detector, natural GR data of a corresponding horizontal well section is obtained, then, the natural GR data and the GR while drilling data in the GR while drilling logging data are subjected to comparative analysis, similar natural GR data and GR while drilling data are determined, then, the actual well depth corresponding to the natural GR data is calibrated below the well depth corresponding to the GR while drilling data similar to the current natural GR data, so that actual depth homing processing of the logging data and the element logging data is completed, and well depth data consistent with actual geological conditions, logging data and element logging data matched with accurate well depth data are obtained.
The invention discloses a shale gas horizontal well fracture pressure prediction method. According to the method, the ground stress data of the well section to be predicted is obtained by combining the related data of the logging while drilling element with the GR data while drilling, so that the fracture pressure of the current well section to be predicted is predicted through calculation by utilizing the obtained ground stress data of the well section to be predicted. The method and the system realize the prediction of the fracture pressure of the shale gas horizontal well without logging data, and provide data support for the optimization of hydraulic fracturing construction equipment and hydraulic fracturing construction parameters of the shale gas horizontal well. Meanwhile, the method also provides guidance for the design of the hydraulic fracturing scheme of the shale gas horizontal well.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the scope of the present invention are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Of course, the present invention is capable of other various embodiments and its several details are capable of modification and variation in light of the present invention, as will be apparent to those skilled in the art, without departing from the spirit and scope of the invention as defined in the appended claims.
It will be appreciated by those skilled in the art that the modules or steps of the invention described above may be implemented in a general purpose computing device, they may be centralized on a single computing device, or distributed over a network of computing devices, or they may alternatively be implemented in program code executable by computing devices, such that they may be stored in a memory device and executed by computing devices, or they may be separately fabricated as individual integrated circuit modules, or multiple modules or steps within them may be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
Although the embodiments of the present invention are described above, the embodiments are only used for facilitating understanding of the present invention, and are not intended to limit the present invention. Any person skilled in the art can make any modification and variation in form and detail without departing from the spirit and scope of the present disclosure, but the scope of the present disclosure is still subject to the scope of the appended claims.
Claims (10)
1. A shale gas horizontal well fracture pressure prediction method comprising:
determining a horizontal well section with logging data and element logging data in a current target area to be researched, and acquiring the element logging data and the ground stress data of a designated well section;
optimizing element types meeting geological characteristics of a target area from element logging data, and determining optimal element combination data of a corresponding well section according to the element logging data of the appointed well section;
establishing a first model for predicting the ground stress data according to the ground stress data and the optimal element combination data;
and predicting the ground stress data of the well section to be predicted by using the first model according to the optimal element combination data corresponding to the element logging data of the well section to be predicted in the target area, and calculating the corresponding fracture pressure.
2. The method according to claim 1, wherein the method further comprises:
and sequentially carrying out well depth scale unified processing and well depth calibration processing on the logging data and the element logging data of the same well section.
3. The method of claim 2, wherein in the well depth scale unification step, comprising:
respectively configuring sampling intervals for scale unified processing for the logging data and the element logging data;
extracting the ground stress information of the appointed well section from well logging data acquired in the well logging construction process, and sampling the ground stress information according to a first sampling interval to obtain the ground stress data;
extracting original element logging data of the appointed well section from element logging data obtained in the element logging construction process, and sampling the original element logging data according to a second sampling interval to obtain the element logging data;
and integrating the ground stress data and the element logging data according to the well depth so as to be unified under the same depth scale.
4. The method of claim 3, wherein the step of,
the range of the first sampling interval is preferably 0.1-0.125 m;
the second sampling interval is preferably in the range of 1 to 2m.
5. The method according to any one of claims 2 to 4, characterized in that in the depth homing processing step, it comprises:
and carrying out natural GR radioactivity total amount measurement on the quantitative rock debris while drilling of the appointed well section, and carrying out comparison analysis on the measurement result and GR while drilling data in the logging while drilling data, so that the well depths of the logging data and the element logging data which are subjected to depth unified processing are restored according to the comparison analysis result, and the well depths after the restoration are consistent with the actual geological conditions.
6. The method according to any one of claims 1 to 5, wherein in the step of optimizing the element type from the element log data that meets the geological properties of the target zone, it comprises:
calculating the correlation among the element types in the element logging data by using a correlation analysis method, screening out a plurality of groups of element type combinations with correlation calculation results exceeding a preset first threshold value, and recording the element type combinations as first type element types;
according to the correlation calculation result, aiming at each group of element type combinations, analyzing the similarity between each element in the combination and the whole group of element type combinations, and further screening the element type corresponding to the highest similarity data in each group of element type combinations, and marking the element type as a second type element type;
and analyzing and processing the main components of the second class element types to determine corresponding optimal element combination data.
7. The method of claim 6, wherein the method employs gray correlation analysis and/or cluster analysis to analyze the similarity.
8. The method according to any one of claims 1 to 7, characterized in that in the step of establishing the first model, it comprises:
and performing data fitting on a preset multiple regression model by using the ground stress data and the optimal element combination data, so that a fitting result is used as the first model.
9. The method according to any one of claims 1 to 8, wherein in the step of calculating the fracture pressure of the current wellbore section to be predicted, comprising:
according to the ground stress data of the well section to be predicted, obtaining the well periphery three-way main stress of the current well section to be predicted;
and determining the fracture pressure of the current well section to be predicted by using a tensile strength theory according to the main stress of the well Zhou Sanxiang of the well section to be predicted.
10. A shale gas horizontal well fracture pressure prediction system, the system comprising:
the data acquisition module is used for determining a horizontal well section with logging data and element logging data in a current target area to be researched, and acquiring the element logging data and the ground stress data of a designated well section;
the logging data screening module is used for optimizing element types meeting geological characteristics of a target area from element logging data and determining optimal element combination data of a corresponding well section according to the element logging data of the appointed well section;
the model building module is used for building a first model for predicting the ground stress data according to the ground stress data and the optimal element combination data;
and the fracture pressure prediction module is used for predicting the ground stress data of the well section to be predicted by using the first model according to the optimal element combination data corresponding to the element logging data of the well section to be predicted in the target area and calculating the corresponding fracture pressure.
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