CN113685162A - Fracturing parameter determination method, device, equipment and storage medium - Google Patents
Fracturing parameter determination method, device, equipment and storage medium Download PDFInfo
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Abstract
The application provides a method, a device, equipment and a storage medium for determining fracturing parameters, wherein the method comprises the following steps: acquiring geological data and initial fracturing parameters of a well to be fractured in a target block; determining the target category of the well to be fractured according to the geological data and the initial fracturing parameters through a clustering algorithm; determining main control fracturing factors influencing the estimated final recoverable reserve of the well to be fractured according to the target category of the well to be fractured; adjusting the initial fracturing parameters according to the main control fracturing factors to generate multiple groups of alternative fracturing parameters of the well to be fractured, determining the estimated final recoverable reserve corresponding to each group of alternative fracturing parameters based on the regression model corresponding to the target category, and determining the final fracturing parameters according to the determined estimated final recoverable reserve.
Description
Technical Field
The application relates to the technical field of oil and gas reservoir exploration, in particular to a method, a device, equipment and a storage medium for determining fracturing parameters.
Background
Hydraulic fracturing is a key technology for the development of low-permeability and compact oil and gas reservoirs. The estimated final recoverable reserves after fracturing of oil and gas wells are affected by fracturing parameters in addition to the geological data inherent to horizontal wells.
At present, the fracturing parameters of horizontal wells under different reservoir conditions are generally designed on the oil field design site through experience of designers, for example, the strength of reinforced sand in a certain region is generally about 1.0, but the fracturing design parameters of different horizontal wells are not greatly different. However, heterogeneous reservoirs have the characteristics of complex lithology and strong heterogeneity, complex geological conditions determine that fracturing schemes of different horizontal wells have large differences, the existing fracturing parameter design method cannot reflect the differences of fracturing designs under different reservoir conditions, and the target property is lacked, so that the final recoverable reserve of the horizontal wells is estimated to be low.
Disclosure of Invention
The application provides a method, a device, equipment and a storage medium for determining fracturing parameters, which are used for solving the problems that the existing fracturing parameter design method cannot reflect different reservoir conditions and lacks pertinence.
In a first aspect, the present application provides a method for determining a fracture parameter, the method comprising:
acquiring geological data and initial fracturing parameters of a well to be fractured in a target block;
determining the target category of the well to be fractured according to the geological data and the initial fracturing parameters through a clustering algorithm; wherein the target category is one of a plurality of preset categories; the preset classification is determined according to the influence factors of a plurality of groups of fractured wells in the target block through a clustering algorithm; the influencing factors include: geological data and fracturing parameters;
determining main control fracturing factors influencing the estimated final recoverable reserve of the well to be fractured according to the target category of the well to be fractured; the master control fracturing factor is at least one of fracturing parameters;
adjusting the initial fracturing parameters according to the main control fracturing factors to generate multiple groups of alternative fracturing parameters of the well to be fractured, determining estimated final recoverable reserves corresponding to each group of alternative fracturing parameters based on a regression model corresponding to the target category, and determining final fracturing parameters according to the determined estimated final recoverable reserves; and aiming at each preset category, the regression model represents the corresponding relation between the estimated final recoverable reserve and each influence factor.
Optionally, the method further includes:
obtaining geological data, fracturing parameters and estimated final recoverable reserves of a plurality of groups of fractured wells of the target block, carrying out PCA (principal component analysis) dimension reduction processing on the geological data and the fracturing parameters of the plurality of groups of fractured wells, and dividing the target block into a plurality of preset categories according to the data after the PCA dimension reduction processing by a K-means central clustering algorithm;
and aiming at each preset category, taking geological data and fracturing parameters of each group of fractured wells belonging to the preset category as input of a neural network, taking the corresponding estimated final recoverable reserve as output of the neural network, training the neural network until the precision of the neural network meets the requirement, and determining the trained neural network as a regression model corresponding to the preset category.
Optionally, the method further includes:
determining main control fracturing factors influencing the estimated final recoverable reserves by a random forest algorithm aiming at each preset category;
correspondingly, determining main control fracturing factors influencing the estimated final recoverable reserve of the well to be fractured according to the target category of the well to be fractured comprises the following steps:
and determining the main control fracturing factors corresponding to the target category of the well to be fractured as the main control fracturing factors influencing the estimated final recoverable reserves of the well to be fractured.
Optionally, the initial fracturing parameters are adjusted according to the master fracturing factor to generate multiple groups of candidate fracturing parameters of the well to be fractured, including:
and controlling the parameters corresponding to the non-master control fracturing factors in the initial fracturing parameters of the well to be fractured to be unchanged, and simultaneously adjusting the parameters corresponding to the master control fracturing factors to generate multiple groups of alternative fracturing parameters.
Optionally, determining final fracture parameters based on the determined estimated final recoverable reserves comprises:
respectively determining the fracturing investment corresponding to each group of alternative fracturing parameters;
and determining final fracturing parameters from the multiple groups of alternative fracturing parameters according to the estimated final recoverable reserves corresponding to each group of alternative fracturing parameters and the fracturing investment.
Optionally, the method further includes:
judging whether the obtained geological data and initial fracturing parameters of the well to be fractured have missing values and abnormal values or not, and/or judging whether the obtained geological data and fracturing parameters of a plurality of groups of fractured wells have missing values and abnormal values or not;
when a missing value exists, filling by using a median or a numerical value determined by a Lagrange interpolation method;
and when the abnormal value exists, receiving the numerical value input again by the user or filling the numerical value by adopting a median or a numerical value determined by a Lagrange interpolation method.
In a second aspect, the present application provides a fracture parameter determining apparatus, including:
the acquisition module is used for acquiring geological data and initial fracturing parameters of a well to be fractured in a target block;
the target category determining module is used for determining the target category of the well to be fractured according to the geological data and the initial fracturing parameters through a clustering algorithm; wherein the target category is one of a plurality of preset categories; the preset classification is determined according to the influence factors of a plurality of groups of fractured wells in the target block through a clustering algorithm; the influencing factors include: geological data and fracturing parameters;
the main control fracturing factor determination module is also used for determining main control fracturing factors influencing the estimated final recoverable reserve of the well to be fractured according to the target category of the well to be fractured; the master control fracturing factor is at least one of fracturing parameters;
the fracturing parameter determination module is used for adjusting the initial fracturing parameters according to the main control fracturing factors to generate multiple groups of alternative fracturing parameters of the well to be fractured, determining estimated final recoverable reserves corresponding to each group of the alternative fracturing parameters based on the regression model corresponding to the target category, and determining final fracturing parameters according to the determined estimated final recoverable reserves; and aiming at each preset category, the regression model represents the corresponding relation between the estimated final recoverable reserve and each influence factor.
In a third aspect, the present application provides a fracturing parameter determining apparatus, including:
a memory for storing program instructions;
a processor for calling and executing program instructions in said memory to perform a method according to any of the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the method according to any one of the first aspect.
In a fifth aspect, the present application provides a computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of the first aspect.
The application provides a method, a device, equipment and a storage medium for determining fracturing parameters, wherein the method comprises the following steps: acquiring geological data and initial fracturing parameters of a well to be fractured in a target block; determining the target category of the well to be fractured according to the geological data and the initial fracturing parameters through a clustering algorithm; determining main control fracturing factors influencing the estimated final recoverable reserve of the well to be fractured according to the target category of the well to be fractured; according to the main control fracturing factor, the initial fracturing parameters are adjusted, multiple groups of alternative fracturing parameters of the well to be fractured are generated, the estimated final recoverable reserve corresponding to each group of alternative fracturing parameters is determined based on the regression model corresponding to the target category, the final fracturing parameters are determined according to the determined estimated final recoverable reserve, the determined initial fracturing parameters of the well to be fractured are adjusted according to the main control fracturing factor of the category of the well to be fractured by judging the category of the well to be fractured, the heterogeneous characteristic of a compact reservoir can be fully considered, the determined fracturing parameters have pertinence, and the estimated final recoverable reserve of the horizontal well to be fractured is improved.
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In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic view of an application scenario provided in an embodiment of the present invention;
fig. 2 is a schematic flow chart of a method for determining fracture parameters according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a dividing result of a horizontal well of a target block according to an embodiment of the present invention;
FIG. 4 is an importance coefficient corresponding to each influence factor on the EUR of the class A horizontal well, provided by the embodiment of the invention;
FIG. 5 is an importance coefficient corresponding to each influencing factor of a type B horizontal well EUR provided by the embodiment of the invention;
FIG. 6 is a schematic diagram of predicted final recoverable reserves and fracturing project investment under different alternative fracturing parameters provided by an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a fracture parameter determination apparatus provided in an embodiment of the present invention;
fig. 8 is a schematic structural diagram of a fracturing parameter determination device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Fig. 1 is a schematic view of an application scenario provided by an embodiment of the present invention, and as shown in fig. 1, an execution subject of the present invention may be a fracturing parameter determination device, which is disposed on a fracturing parameter determination device, and the device may be implemented in a software manner. As shown in fig. 1, the input data obtained by the fracture parameter determining device 101 includes geological data, fracture data, and EUR (Estimated Ultimate recoverable reserve) of a plurality of fractured wells, and according to the data of the fractured wells, a target block in which the fractured wells are located may be divided into a plurality of preset categories, and a regression model of each preset category is obtained, where the regression model represents a relationship between the EUR and the input geological data and fracture parameters. Then, for one well to be fractured, when the fracturing parameters need to be determined, the category of the well to be fractured can be judged first, so that the main control fracturing factors affecting the EUR are determined, the alternative fracturing parameters are obtained based on the main control fracturing factors, and finally, one alternative fracturing parameter is determined from the alternative fracturing parameters based on the EUR value corresponding to each alternative fracturing parameter.
For a compact reservoir, the low-porosity and low-permeability characteristics and the development of cracks are realized, and the compact reservoir needs to be effectively developed through fracturing modification which usually adopts a hydraulic fracturing mode. The final recoverable reserves of the oil well after hydraulic fracturing are influenced by the fracturing parameters in addition to the geological data. The fracturing parameters, also referred to as engineering parameters, are used to indicate how the horizontal well is to be constructed. In the prior art, topace well testing software is generally adopted to perform dynamic analysis and well testing interpretation on a fractured horizontal well when determining fracturing parameters. But for a compact reservoir, the fracturing parameters obtained by topace well testing software have no pertinence, and the differences of fracturing designs under different reservoir conditions cannot be reflected, so that the EUR value of the horizontal well is low.
Based on the problems, the fractured wells are analyzed, the conglomerates are divided, and then main control fracturing factors influencing the EUR value can be determined, so that fracturing parameters can be determined in a targeted mode, fracturing optimization can be targeted, and the EUR value of the horizontal well to be fractured is improved.
The technical solution of the present invention will be described in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
Fig. 2 is a schematic flow chart of a method for determining a fracture parameter according to an embodiment of the present invention, as shown in fig. 2, the method according to the embodiment may include:
step S201, obtaining geological data and initial fracturing parameters of a well to be fractured in a target block.
The target block refers to a field, a plurality of drilled wells exist in the field, and the drilled wells comprise fractured wells and wells to be fractured. When determining fracturing parameters of a well to be fractured, acquiring geological data and initial fracturing data of the well to be fractured, wherein the geological data comprises formation porosity, drilling rate, oil saturation, vertical depth, oil layer thickness of the same type, Poisson ratio, minimum horizontal principal stress, Young modulus, pressure coefficient and the like; the fracturing parameters include fracture spacing, total liquid amount, total sand amount, construction discharge capacity, average sand ratio, pad liquid ratio, horizontal section length and the like. The geological data and the fracturing parameters (also known as engineering data) are common terms in the art and will not be explained in detail here. Wherein the initial fracture parameters are parameters determined based on topace well testing software.
S202, determining the target category of the well to be fractured according to the geological data and the initial fracturing parameters through a clustering algorithm; wherein the target category is one of a plurality of preset categories; the preset classification is determined according to the influence factors of a plurality of groups of fractured wells in the target block through a clustering algorithm; the influencing factors include: geological data and fracture parameters.
The horizontal well can be divided into a plurality of preset categories according to the data information of the fractured wells in the target block, and the wells to be fractured belong to one of the preset categories. The method comprises the steps of dividing a plurality of preset categories into a plurality of categories, wherein each of the plurality of divided preset categories has a clustering center, the categories can be judged by a clustering method, specifically, the distances between the geological data and initial fracturing parameters of a well to be fractured and each clustering center are obtained according to the geological data and initial fracturing parameters of the well to be fractured, and the corresponding preset category when the distance is closest is determined as the target category of the well to be fractured.
S203, determining main control fracturing factors influencing the estimated final recoverable reserve of the well to be fractured according to the target category of the well to be fractured; the master fracturing factor is at least one of the fracturing parameters.
For a fractured well, after the fractured well is divided into a plurality of preset categories, the master control factor of each preset category can be determined based on a data mining algorithm. For example, when a fractured horizontal well is divided into a type A horizontal well and a type B horizontal well, the data mining algorithm can determine that the estimated final recoverable reserves of the type A horizontal well are influenced by more geological factors, and the estimated final recoverable reserves of the type B horizontal well are influenced by more fracturing factors.
Therefore, after the target category of the well to be fractured is determined, the main control factors influencing the estimated final recoverable reserve of the well to be fractured can be further determined, and the main control fracturing factors influencing the estimated final recoverable reserve of the well to be fractured can be further determined. The main control fracturing factor is at least one of fracturing parameters, namely the construction parameters have a large influence on the estimated final recoverable reserve of the horizontal well.
Step S204, adjusting the initial fracturing parameters according to the main control fracturing factors to generate multiple groups of alternative fracturing parameters of the well to be fractured, determining estimated final recoverable reserves corresponding to each group of the alternative fracturing parameters based on a regression model corresponding to the target category, and determining final fracturing parameters according to the determined estimated final recoverable reserves; and aiming at each preset category, the regression model represents the corresponding relation between the estimated final recoverable reserve and each influence factor.
After the main control fracturing factor is determined, the initial fracturing parameters can be adjusted based on an orthogonal test method, the estimated final recoverable reserves can be obtained according to each group of alternative fracturing parameters and the corresponding regression model, and then the corresponding alternative fracturing parameters when the estimated final recoverable reserves are large can be selected as the fracturing parameters.
The regression model herein is determined based on geological data, fracture parameters, and corresponding estimated final recoverable reserves of multiple fractured wells of the same predetermined category. Thus, for an unfractured well, the corresponding estimated final recoverable reserve may be predicted from the regression model while geological data and fracture parameters are acquired.
Wherein, for a fractured well, although hydraulic fracturing is performed, the actual recoverable reserves can be known through statistical analysis after several years. Recoverable reserves are also referred to as production data and for problems with insufficient production data, production history can be fitted and the estimated final recoverable reserves for each well predicted by topize well testing software. Although actual production data of the fractured well cannot be obtained, the fractured well is basically in a quasi steady flow state, and the final recoverable reserves can be accurately estimated by adopting Topaze well testing software. The production history fitting means that a yield curve is fitted on the basis of established numerical analysis, and the fitting curve is ensured to have the same trend with an actual curve and have high conformity. When the pressure and pressure derivative curves have a straight line segment with a slope value of 1, indicating that a quasi-steady flow stage is entered, prediction of the estimated final recoverable reserve can be performed.
In the embodiment of the invention, the target category of the well to be fractured is determined firstly, then the main control fracturing factor influencing the well to be fractured is determined, a plurality of groups of alternative fracturing parameters are generated by an orthogonal test method (namely, the initial fracturing parameters are adjusted), the recoverable reserves of each group of the alternative fracturing parameters are obtained according to a regression model, and the selected alternative fracturing parameters are finally determined, so that the characteristic of heterogeneity of the well to be fractured can be fully considered, the determined alternative fracturing parameters have certain pertinence, and further the estimated final recoverable reserves of the horizontal well are improved.
Optionally, the method further includes:
obtaining geological data, fracturing parameters and estimated final recoverable reserves of a plurality of groups of fractured wells of the target block, carrying out PCA (principal component analysis) dimension reduction processing on the geological data and the fracturing parameters of the plurality of groups of fractured wells, and dividing the target block into a plurality of preset categories according to the data after the PCA dimension reduction processing by a K-means central clustering algorithm;
and aiming at each preset category, taking geological data and fracturing parameters of each group of fractured wells belonging to the preset category as input of a neural network, taking the corresponding estimated final recoverable reserve as output of the neural network, training the neural network until the precision of the neural network meets the requirement, and determining the trained neural network as a regression model corresponding to the preset category.
In this embodiment, before processing the data of the well to be fractured, the preset category of the fractured wells in the target block needs to be determined according to the data of the fractured wells. The method can acquire the address data and the fracturing parameters of the fractured well and establish the SQL fracturing database of the target block, and the database can be used for facing all people and is convenient to check and extract the required data at any time.
In particular, a clustering algorithm, such as a K-means center clustering algorithm, may be used. When a plurality of preset categories are divided, 16 characteristic parameters exist due to more types of geological data and fracturing parameters, the calculated amount is large if the geological data and the fracturing parameters are directly clustered, and meanwhile, due to the fact that some data in the geological data are linearly related, only one parameter needs to be considered for a plurality of data with the linearly related data. Therefore, the PCA dimension reduction can be performed on the acquired data, and compared with the data without dimension reduction, the dimension of the data after the dimension reduction is reduced, so that the data volume is reduced.
Fig. 3 is a schematic diagram of a division result of a horizontal well of a target block according to an embodiment of the present invention, for example, a plurality of fractured wells are divided into two types, i.e., an a-type horizontal well and a B-type horizontal well, by using a K-means central clustering algorithm, and in the division process, the interval between the plurality of fractured wells respectively belonging to the a-type horizontal well and the B-type horizontal well needs to be small, so that the interval between a cluster formed by the a-type horizontal well and a cluster formed by the B-type horizontal well is large.
The first well to be fractured belongs to the A-type horizontal well due to the fact that the first well to be fractured is close to the centroid of the A-type horizontal well, and the second well to be fractured belongs to the B-type horizontal well due to the fact that the second well to be fractured is close to the centroid of the B-type horizontal well.
In addition, after the preset categories are obtained, a regression model corresponding to each preset category can be determined, for example, geological data and fracturing parameters of all fractured wells belonging to a class a horizontal well are used as input of a BP neural network, the corresponding estimated final recoverable reserve is used as output of the BP neural network, and training of the BP neural network is performed until after geological data and fracturing parameters of a test sample are input into the BP neural network, and when the similarity between the predicted recoverable reserve and the actual estimated final recoverable reserve of the test sample meets requirements, the regression model training is completed.
According to the method, the preset categories of the horizontal well of the target block are obtained according to the geological data, the fracturing parameters and the estimated final recoverable reserves of the fractured wells, and the regression model corresponding to each preset category is used, so that the fracturing parameters of the non-fractured wells can be conveniently determined according to the characteristics of each preset category.
Optionally, the method further includes:
determining main control fracturing factors influencing the estimated final recoverable reserves by a random forest algorithm aiming at each preset category;
correspondingly, determining main control fracturing factors influencing the estimated final recoverable reserve of the well to be fractured according to the target category of the well to be fractured comprises the following steps:
and determining the main control fracturing factors corresponding to the target category of the well to be fractured as the main control fracturing factors influencing the estimated final recoverable reserves of the well to be fractured.
In this embodiment, after determining the preset categories of the target block, the master factors of each preset category need to be determined, where the master factors represent factors that have a large influence on estimating the final recoverable storage capacity. Specifically, a random forest algorithm can be adopted for implementation, a random forest model is established for a plurality of groups of data belonging to the A-type horizontal well, and a random forest model is established for a plurality of groups of data belonging to the B-type horizontal well, so that the main control factors of the A-type horizontal well and the B-type horizontal well are obtained.
Fig. 4 is an importance coefficient corresponding to each influence factor on the class a horizontal well EUR provided by the embodiment of the present invention; FIG. 5 is an importance coefficient corresponding to each influencing factor of a type B horizontal well EUR provided by the embodiment of the invention; it can be seen from fig. 4 and 5 that the class a horizontal well is greatly affected by geological factors, and the class B horizontal well is greatly affected by fracturing parameters. When the number of the main control fracturing factors is 3, the main control fracturing factors of the A-type horizontal well comprise: fracture spacing, total sand volume, and total liquor volume; the main control fracturing factors of the type B horizontal well comprise: construction displacement, crack spacing and total liquid volume.
Correspondingly, for the well to be fractured, after the preset category of the well to be fractured is determined, the corresponding main control fracturing factor can be determined, namely the main control fracturing factor of the preset category corresponding to the well to be fractured is determined as the main control fracturing factor of the well to be fractured. For example, for the first well to be fractured, which belongs to a class a horizontal well, the primary control fracturing factors of the first well to be fractured are fracture spacing, total sand amount and total liquid amount.
By the method, the main control fracturing factor of the well to be fractured can be determined, and further the fracturing parameters can be adjusted in a targeted manner.
Optionally, the initial fracturing parameters are adjusted according to the master fracturing factor to generate multiple groups of candidate fracturing parameters of the well to be fractured, including:
and controlling the parameters corresponding to the non-master control fracturing factors in the initial fracturing parameters of the well to be fractured to be unchanged, and simultaneously adjusting the parameters corresponding to the master control fracturing factors to generate multiple groups of alternative fracturing parameters.
In this embodiment, after determining the master fracturing factor, a method of an orthogonal test may be used to generate multiple sets of candidate fracturing parameters, and the specific method is as follows: and (3) keeping the parameters corresponding to the non-fracturing factors in the fracturing parameters unchanged, and controlling the parameters corresponding to the fracturing factors to change.
For example, the number of the fracturing parameters is 7, the determined main control fracturing factors are 3, and the main control fracturing factors are the fracture spacing, the total sand amount and the total liquid amount respectively; the construction discharge capacity, the average sand ratio, the pre-liquid ratio and the horizontal section length can be controlled to be unchanged, and the crack spacing, the total sand amount and the total liquid amount are controlled to be changed within a certain range. Sets of alternative fracturing parameters were obtained as shown in table 1. It should be noted that only 3 changed parameters are shown in the table, and the rest of the parameters are not shown.
TABLE 1
By the method, multiple groups of alternative fracturing parameters can be obtained, and the method has the characteristics of simplicity and effectiveness in adjusting the main control fracturing factors.
Optionally, determining final fracture parameters based on the determined estimated final recoverable reserves comprises:
respectively determining the fracturing investment corresponding to each group of alternative fracturing parameters;
and determining final fracturing parameters from the multiple groups of alternative fracturing parameters according to the estimated final recoverable reserves corresponding to each group of alternative fracturing parameters and the fracturing investment.
In this embodiment, when determining the final fracturing parameters, the fracturing investment needs to be considered comprehensively and the final recoverable reserves need to be estimated to obtain the optimal fracturing parameters. Specifically, after the fracturing parameters are determined, the fracturing investment corresponding to each set of candidate fracturing parameters can be determined by a fracturing investment calculation method. And inputting geological data of the well to be fractured and a group of alternative fracturing parameters into the regression model to obtain the predicted final recoverable reserves. And determining the final fracturing parameters based on the predicted final recoverable reserves and the fracturing investment corresponding to each group of the alternative fracturing parameters. The fracturing investment herein may be a fracturing engineering investment.
Fig. 6 is a schematic diagram of predicted final recoverable reserves and fracturing engineering investment under different alternative fracturing parameters, which is provided by an embodiment of the present invention, and as shown in fig. 6, the final recoverable reserves and the fracturing engineering investment are comprehensively considered, and the alternative fracturing parameters corresponding to the option 6, that is, the fracture spacing is 20m, and the total liquid volume is 20000m3About, total sand amount is 1000m3The left and right are the optimal construction parameter combination, the predicted final recoverable reserves are 64225t, the fracturing investment under the fracturing scale is 1213.5 ten thousand yuan, and the requirement of investment limit is met.
In addition, for the purpose of illustrating the beneficial effects of the present application, topace well testing software may also be used to predict the final recoverable reserve of the first well to be fractured, wherein the predicted final recoverable reserve is 58758 t. It can be seen that the final recoverable capacity under this scheme is improved by 9.3% compared to the original predicted final recoverable capacity.
By comprehensively considering the fracturing investment and the predicted final recoverable reserves, the yield and the cost can be simultaneously considered, so that the determined alternative fracturing parameters can generate the maximum economic benefit.
Optionally, the method further includes:
judging whether the obtained geological data and initial fracturing parameters of the well to be fractured have missing values and abnormal values or not, and/or judging whether the obtained geological data and fracturing parameters of a plurality of groups of fractured wells have missing values and abnormal values or not; when a missing value exists, filling by using a median or a numerical value determined by a Lagrange interpolation method; and when the abnormal value exists, receiving the numerical value input again by the user or filling the numerical value by adopting a median or a numerical value determined by a Lagrange interpolation method.
In this embodiment, in order to improve the accuracy of determining the fracturing parameters, it is necessary to perform preprocessing on the acquired geological data and initial fracturing parameters of the well to be fractured, and preprocessing on the acquired geological data and fracturing parameters of multiple groups of fractured wells, where the preprocessing manners of different data are the same, and the following description takes the geological data of the well to be fractured as an example.
After geological data of a well to be fractured is obtained, whether missing values exist or not can be judged, and whether abnormal values exist or not can be judged in a box type diagram mode. And when the missing value exists, further determining the missing rate, and if the missing rate is smaller than the preset missing rate, processing by adopting a median filling mode, wherein the median is the data which is arranged in the middle of the existing multiple geological data in sequence. And when the deletion rate is greater than the preset deletion rate, filling the data by using a value determined by a Lagrange interpolation method, wherein the Lagrange interpolation method is to generate a polynomial based on non-deleted data and then determine data corresponding to the position of the deleted value based on the polynomial.
When the abnormal value exists, the reason of the abnormal value can be judged, and when the abnormal value is caused by data input error, the data input by the user can be received again; and other abnormal data can be modified by filling missing values.
By judging and processing the missing value and the abnormal value, the accuracy of the acquired data can be improved, the division of the preset category of the horizontal well and the determination of the target category of the horizontal well to be fractured are further improved, and the accuracy of the determined fracturing parameters is further improved.
Fig. 7 is a schematic structural diagram of a fracture parameter determination apparatus according to an embodiment of the present invention. As shown in fig. 7, the fracture parameter determination device 70 of the present embodiment may include: an acquisition module 701, a target category determination module 702, a master fracturing factor determination module 703, and a fracturing parameter determination module 704.
The acquiring module 701 is used for acquiring geological data and initial fracturing parameters of a well to be fractured in a target block;
a target category determining module 702, configured to determine, according to the geological data and the initial fracturing parameters, a target category of the well to be fractured through a clustering algorithm; wherein the target category is one of a plurality of preset categories; the preset classification is determined according to the influence factors of a plurality of groups of fractured wells in the target block through a clustering algorithm; the influencing factors include: geological data and fracturing parameters;
a main control fracturing factor determining module 703, configured to determine, according to the target category of the well to be fractured, a main control fracturing factor that affects the estimated final recoverable reserve of the well to be fractured; the master control fracturing factor is at least one of fracturing parameters;
a fracturing parameter determining module 704, configured to adjust the initial fracturing parameters according to the master fracturing factor, generate multiple sets of candidate fracturing parameters of the well to be fractured, determine an estimated final recoverable reserve corresponding to each set of candidate fracturing parameters based on the regression model corresponding to the target category, and determine final fracturing parameters according to the determined estimated final recoverable reserve; and aiming at each preset category, the regression model represents the corresponding relation between the estimated final recoverable reserve and each influence factor.
Optionally, the apparatus further includes a category classification module and a training module;
the classification module is used for acquiring geological data, fracturing parameters and estimated final recoverable reserves of a plurality of groups of fractured wells of the target block, carrying out PCA (principal component analysis) dimension reduction processing on the geological data and the fracturing parameters of the plurality of groups of fractured wells, and classifying the target block into a plurality of preset classifications according to the data subjected to the PCA dimension reduction processing by a K-means central clustering algorithm;
and the training module is used for taking geological data and fracturing parameters of each group of fractured wells belonging to each preset category as input of a neural network, taking the corresponding estimated final recoverable reserve as output of the neural network, training the neural network until the precision of the neural network meets the requirement, and determining the trained neural network as a regression model corresponding to the preset category.
Optionally, the master control fracturing factor determining module 703 is further configured to determine, for each preset category, a master control fracturing factor that affects the estimated final recoverable reserves through a random forest algorithm;
the master control fracturing factor determining module 703 is specifically configured to determine the master control fracturing factor corresponding to the target category of the well to be fractured as the master control fracturing factor affecting the estimated final recoverable reserves of the well to be fractured when determining the master control fracturing factor affecting the estimated final recoverable reserves of the well to be fractured according to the target category of the well to be fractured.
Optionally, the fracturing parameter determining module 704 is configured to, when adjusting the initial fracturing parameter according to the master fracturing factors and generating multiple sets of candidate fracturing parameters of the well to be fractured, specifically control parameters corresponding to non-master fracturing factors in the initial fracturing parameters of the well to be fractured to be unchanged, and adjust parameters corresponding to each master fracturing factor to generate multiple sets of candidate fracturing parameters.
Optionally, the fracture parameter determining module 704, when determining the final fracture parameter according to the determined estimated final recoverable reserve, is specifically configured to:
respectively determining the fracturing investment corresponding to each group of alternative fracturing parameters;
and determining final fracturing parameters from the multiple groups of alternative fracturing parameters according to the estimated final recoverable reserves corresponding to each group of alternative fracturing parameters and the fracturing investment.
Optionally, the apparatus further includes a preprocessing module, configured to determine whether the obtained geological data and initial fracturing parameters of the well to be fractured have missing values and abnormal values, and/or determine whether the obtained geological data and fracturing parameters of multiple groups of fractured wells have missing values and abnormal values; when a missing value exists, filling by using a median or a numerical value determined by a Lagrange interpolation method; and when the abnormal value exists, receiving the numerical value input again by the user or filling the numerical value by adopting a median or a numerical value determined by a Lagrange interpolation method.
The device for determining the fracturing parameters provided by the embodiment of the invention can realize the method for determining the fracturing parameters of the embodiment shown in fig. 2 to 6, and the realization principle and the technical effect are similar, and are not described again here.
Fig. 8 is a schematic hardware structure diagram of a fracturing parameter determination device according to an embodiment of the present invention. As shown in fig. 8, the fracturing parameter determination device 80 provided in the present embodiment includes: at least one processor 801 and a memory 802. The processor 801 and the memory 802 are connected by a bus 803.
In a specific implementation, the at least one processor 801 executes the computer-executable instructions stored in the memory 802, so that the at least one processor 801 executes the method for determining the fracture parameters in the above method embodiments.
For a specific implementation process of the processor 801, reference may be made to the above method embodiments, which have similar implementation principles and technical effects, and details of this embodiment are not described herein again.
In the embodiment shown in fig. 8, it should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose processors, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.
The memory may comprise high speed RAM memory and may also include non-volatile storage NVM, such as at least one disk memory.
The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, the buses in the figures of the present application are not limited to only one bus or one type of bus.
The embodiment of the present invention further provides a computer-readable storage medium, where a computer executing instruction is stored in the computer-readable storage medium, and when a processor executes the computer executing instruction, the method for determining the fracture parameter in the above method embodiment is implemented.
The computer-readable storage medium may be implemented by any type of volatile or non-volatile memory device or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk. Readable storage media can be any available media that can be accessed by a general purpose or special purpose computer.
An exemplary readable storage medium is coupled to the processor such the processor can read information from, and write information to, the readable storage medium. Of course, the readable storage medium may also be an integral part of the processor. The processor and the readable storage medium may reside in an Application Specific Integrated Circuits (ASIC). Of course, the processor and the readable storage medium may also reside as discrete components in the apparatus.
An embodiment of the present application provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the method for determining the fracture parameters is implemented as provided in any embodiment of the present application corresponding to fig. 2 to 6.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. A method of determining fracture parameters, the method comprising:
acquiring geological data and initial fracturing parameters of a well to be fractured in a target block;
determining the target category of the well to be fractured according to the geological data and the initial fracturing parameters through a clustering algorithm; wherein the target category is one of a plurality of preset categories; the preset classification is determined according to the influence factors of a plurality of groups of fractured wells in the target block through a clustering algorithm; the influencing factors include: geological data and fracturing parameters;
determining main control fracturing factors influencing the estimated final recoverable reserve of the well to be fractured according to the target category of the well to be fractured; the master control fracturing factor is at least one of fracturing parameters;
adjusting the initial fracturing parameters according to the main control fracturing factors to generate multiple groups of alternative fracturing parameters of the well to be fractured, determining estimated final recoverable reserves corresponding to each group of alternative fracturing parameters based on a regression model corresponding to the target category, and determining final fracturing parameters according to the determined estimated final recoverable reserves; and aiming at each preset category, the regression model represents the corresponding relation between the estimated final recoverable reserve and each influence factor.
2. The method of claim 1, further comprising:
obtaining geological data, fracturing parameters and estimated final recoverable reserves of a plurality of groups of fractured wells of the target block, carrying out PCA (principal component analysis) dimension reduction processing on the geological data and the fracturing parameters of the plurality of groups of fractured wells, and dividing the target block into a plurality of preset categories according to the data after the PCA dimension reduction processing by a K-means central clustering algorithm;
and aiming at each preset category, taking geological data and fracturing parameters of each group of fractured wells belonging to the preset category as input of a neural network, taking the corresponding estimated final recoverable reserve as output of the neural network, training the neural network until the precision of the neural network meets the requirement, and determining the trained neural network as a regression model corresponding to the preset category.
3. The method of claim 1, further comprising:
determining main control fracturing factors influencing the estimated final recoverable reserves by a random forest algorithm aiming at each preset category;
correspondingly, determining main control fracturing factors influencing the estimated final recoverable reserve of the well to be fractured according to the target category of the well to be fractured comprises the following steps:
and determining the main control fracturing factors corresponding to the target category of the well to be fractured as the main control fracturing factors influencing the estimated final recoverable reserves of the well to be fractured.
4. The method of claim 3, wherein adjusting the initial fracturing parameters according to the master fracturing factor to generate multiple sets of candidate fracturing parameters for the well to be fractured comprises:
and controlling the parameters corresponding to the non-master control fracturing factors in the initial fracturing parameters of the well to be fractured to be unchanged, and simultaneously adjusting the parameters corresponding to the master control fracturing factors to generate multiple groups of alternative fracturing parameters.
5. The method of any one of claims 1-4, wherein determining final fracture parameters based on the determined estimated final recoverable reserve comprises:
respectively determining the fracturing investment corresponding to each group of alternative fracturing parameters;
and determining final fracturing parameters from the multiple groups of alternative fracturing parameters according to the estimated final recoverable reserves corresponding to each group of alternative fracturing parameters and the fracturing investment.
6. The method of claim 2, further comprising:
judging whether the obtained geological data and initial fracturing parameters of the well to be fractured have missing values and abnormal values or not, and/or judging whether the obtained geological data and fracturing parameters of a plurality of groups of fractured wells have missing values and abnormal values or not;
when a missing value exists, filling by using a median or a numerical value determined by a Lagrange interpolation method;
and when the abnormal value exists, receiving the numerical value input again by the user or filling the numerical value by adopting a median or a numerical value determined by a Lagrange interpolation method.
7. An apparatus for determining fracture parameters, comprising:
the acquisition module is used for acquiring geological data and initial fracturing parameters of a well to be fractured in a target block;
the target category determining module is used for determining the target category of the well to be fractured according to the geological data and the initial fracturing parameters through a clustering algorithm; wherein the target category is one of a plurality of preset categories; the preset classification is determined according to the influence factors of a plurality of groups of fractured wells in the target block through a clustering algorithm; the influencing factors include: geological data and fracturing parameters;
the main control fracturing factor determination module is also used for determining main control fracturing factors influencing the estimated final recoverable reserve of the well to be fractured according to the target category of the well to be fractured; the master control fracturing factor is at least one of fracturing parameters;
the fracturing parameter determination module is used for adjusting the initial fracturing parameters according to the main control fracturing factors to generate multiple groups of alternative fracturing parameters of the well to be fractured, determining estimated final recoverable reserves corresponding to each group of the alternative fracturing parameters based on the regression model corresponding to the target category, and determining final fracturing parameters according to the determined estimated final recoverable reserves; and aiming at each preset category, the regression model represents the corresponding relation between the estimated final recoverable reserve and each influence factor.
8. A fracturing parameter determination device, comprising:
a memory for storing program instructions;
a processor for calling and executing program instructions in said memory, performing the method of any of claims 1-6.
9. A computer-readable storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method according to any one of claims 1-6.
10. A computer program product, comprising a computer program, characterized in that the computer program, when executed by a processor, implements the method according to any of claims 1-6.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114130525A (en) * | 2021-11-29 | 2022-03-04 | 湖南柿竹园有色金属有限责任公司 | Control method, device, equipment and medium for mineral processing equipment |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104832166A (en) * | 2015-03-20 | 2015-08-12 | 中国石油化工股份有限公司江汉油田分公司勘探开发研究院 | Initial productivity prediction method of shale gas horizontal well |
| CN105201479A (en) * | 2015-10-09 | 2015-12-30 | 西南石油大学 | Optimum design method for staged fracturing perforation cluster parameters of horizontal well of shale reservoir |
| CN105354639A (en) * | 2015-11-10 | 2016-02-24 | 中国石油天然气股份有限公司 | Complete-period capacity prediction method and device of tight oil multiple-media coupling seepage |
| US20160139588A1 (en) * | 2014-11-18 | 2016-05-19 | Weatherford Technology Holdings, Llc | Systems and methods for optimizing formation fracturing operations |
| CN105952427A (en) * | 2016-05-04 | 2016-09-21 | 中国石油大学(北京) | Method for predicting and evaluating low permeability oil reservoir water-injection induced cracks |
| CN106703776A (en) * | 2016-12-23 | 2017-05-24 | 西南石油大学 | Method for optimizing fracturing parameters |
| CN110134984A (en) * | 2019-03-06 | 2019-08-16 | 中国石油化工股份有限公司江汉油田分公司石油工程技术研究院 | The analysis method of complex fracture extension influence factor in a kind of shale fracturing process |
| CN110242289A (en) * | 2019-06-06 | 2019-09-17 | 中国石油化工股份有限公司 | Normal pressure shale gas based on three factor control gas is enriched with high yield target preferred method |
| WO2020048028A1 (en) * | 2018-09-03 | 2020-03-12 | 中国石油大学(华东) | Fracturing potential-based fracturing design method and apparatus for horizontal well to be fractured |
| CN112253103A (en) * | 2020-11-27 | 2021-01-22 | 西南石油大学 | Shale gas reservoir fracturing horizontal well yield prediction method based on random fracture model |
| CN112561144A (en) * | 2020-12-07 | 2021-03-26 | 西安石油大学 | Method for evaluating main control factor of productivity of tight oil fracturing horizontal well and predicting productivity |
-
2021
- 2021-07-22 CN CN202110829018.2A patent/CN113685162B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160139588A1 (en) * | 2014-11-18 | 2016-05-19 | Weatherford Technology Holdings, Llc | Systems and methods for optimizing formation fracturing operations |
| CN104832166A (en) * | 2015-03-20 | 2015-08-12 | 中国石油化工股份有限公司江汉油田分公司勘探开发研究院 | Initial productivity prediction method of shale gas horizontal well |
| CN105201479A (en) * | 2015-10-09 | 2015-12-30 | 西南石油大学 | Optimum design method for staged fracturing perforation cluster parameters of horizontal well of shale reservoir |
| CN105354639A (en) * | 2015-11-10 | 2016-02-24 | 中国石油天然气股份有限公司 | Complete-period capacity prediction method and device of tight oil multiple-media coupling seepage |
| CN105952427A (en) * | 2016-05-04 | 2016-09-21 | 中国石油大学(北京) | Method for predicting and evaluating low permeability oil reservoir water-injection induced cracks |
| CN106703776A (en) * | 2016-12-23 | 2017-05-24 | 西南石油大学 | Method for optimizing fracturing parameters |
| WO2020048028A1 (en) * | 2018-09-03 | 2020-03-12 | 中国石油大学(华东) | Fracturing potential-based fracturing design method and apparatus for horizontal well to be fractured |
| CN110134984A (en) * | 2019-03-06 | 2019-08-16 | 中国石油化工股份有限公司江汉油田分公司石油工程技术研究院 | The analysis method of complex fracture extension influence factor in a kind of shale fracturing process |
| CN110242289A (en) * | 2019-06-06 | 2019-09-17 | 中国石油化工股份有限公司 | Normal pressure shale gas based on three factor control gas is enriched with high yield target preferred method |
| CN112253103A (en) * | 2020-11-27 | 2021-01-22 | 西南石油大学 | Shale gas reservoir fracturing horizontal well yield prediction method based on random fracture model |
| CN112561144A (en) * | 2020-12-07 | 2021-03-26 | 西安石油大学 | Method for evaluating main control factor of productivity of tight oil fracturing horizontal well and predicting productivity |
Non-Patent Citations (5)
| Title |
|---|
| 周大镯: "《距离多变量时间序列研究》", 30 December 2012, 石家庄：河北人民出版社 * |
| 李亚林: "基于机器学习方法研究煤层气单井产量主控因素及产量预测", 《工程科技I辑》 * |
| 郭菊娥: "基于GMDH-SVR致密气开发过程参数优化", 《运筹与管理》 * |
| 陶亮: "致密油藏水平井重复压裂多级选井方法研究", 《特种油气藏》 * |
| 马俊修: "基于机器学习的玛湖地区水平井压裂设计优化", 《深圳大学学报(理工版)》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114130525A (en) * | 2021-11-29 | 2022-03-04 | 湖南柿竹园有色金属有限责任公司 | Control method, device, equipment and medium for mineral processing equipment |
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