CN114723155B - Transverse wave curve prediction method, device, computing equipment and storage medium - Google Patents
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Abstract
The invention discloses a transverse wave curve prediction method, a device, a computing device and a storage medium, wherein the method comprises the following steps: acquiring curve data of multiple dimensions of a region to be detected and stratum property interpretation data; interpreting the data according to the stratum property, and grouping the curve data with multiple dimensions according to the stratum property; inputting curve data of multiple dimensions in any group to be detected into a transverse wave curve prediction model of corresponding stratum property for calculation to obtain predicted transverse wave curve data corresponding to the group to be detected; the transverse wave curve prediction model is obtained through pre-training; and splicing the predicted transverse wave curve data corresponding to each group to be detected to obtain the predicted transverse wave curve data of the region to be detected. By means of the method, only basic logging curve and stratum property interpretation data are needed for predicting the transverse wave curve, influences of stratum properties on a prediction result are fully considered, prediction is carried out on stratum properties, the transverse wave curve can be efficiently predicted, and accuracy of the prediction result can be improved.
Description
Technical Field
The invention relates to the technical field of petroleum geophysical exploration, in particular to a transverse wave curve prediction method, a device, computing equipment and a storage medium.
Background
The transverse wave well logging curve is critical in the works of reservoir prediction, oil gas detection and the like, and the situation that the transverse wave well logging curve is absent from a research area or the quality of the transverse wave well logging curve is unreliable is frequently encountered in practical work. The currently commonly used transverse wave logging curve prediction method comprises an empirical formula method, a multi-attribute fitting method and a petrophysical modeling method. However, the empirical formula method is greatly influenced by the region, and the parameters of different basins and different work areas are different, so that proper empirical parameters are difficult to determine; the multi-attribute fitting method adopts other logging curves to linearly fit the transverse wave curve, so that the problem that the standard interval is not well selected exists, and the results obtained by fitting different intervals have larger difference; the petrophysical modeling method requires more parameters, the prediction process is complicated, and part of petrophysical parameters are difficult to obtain. In a word, the defects of difficult parameter acquisition, difficult monitoring of a predicted result and the like in the prior art cause certain difficulty in the task of predicting the transverse wave logging curve.
Disclosure of Invention
The present invention has been made in view of the above-mentioned problems, and it is an object of the present invention to provide a shear wave curve prediction method, apparatus, computing device and storage medium that overcomes or at least partially solves the above-mentioned problems.
According to one aspect of the present invention, there is provided a transverse wave curve prediction method, the method comprising:
Acquiring curve data of multiple dimensions of a region to be detected and stratum property interpretation data;
according to stratum property interpretation data, grouping curve data of multiple dimensions according to stratum properties to obtain multiple groups to be detected;
Inputting curve data of multiple dimensions in any group to be detected into a transverse wave curve prediction model of corresponding stratum property for calculation to obtain predicted transverse wave curve data corresponding to the group to be detected;
the transverse wave curve prediction model is obtained by training according to sample data in advance;
And splicing the predicted transverse wave curve data corresponding to each group to be detected to obtain the predicted transverse wave curve data of the region to be detected.
Optionally, the method further comprises:
Acquiring curve data, transverse wave curve data and stratum property interpretation data of multiple dimensions of a sample well section;
according to stratum property interpretation data of the sample well section, grouping curve data of multiple dimensions of the sample well section according to stratum properties to obtain multiple sample groups;
Forming a sample data set according to curve data of multiple dimensions in any sample group and corresponding transverse wave curve data;
And training to obtain a transverse wave curve prediction model of the stratum property corresponding to the sample group according to the sample data set.
Optionally, the formation property interpretation data comprises: lithology interpretation data and hydrocarbon interpretation data.
Optionally, training to obtain a shear wave curve prediction model of the stratum property corresponding to the sample group according to the sample data set further includes:
dividing the sample data set into a training data set and a validation data set;
Training the initial transverse wave curve prediction model according to the training data set, carrying out error evaluation on the initial transverse wave curve prediction model according to the verification data set, and carrying out iterative training until an error evaluation result meets an iteration stop condition, so as to obtain the transverse wave curve prediction model of the stratum property corresponding to the sample group.
Optionally, the iteration stop condition is determined according to any one of the following factors: regression error change, error distribution pattern, predicted value and measured value curve intersection pattern.
Optionally, before the method is performed, further comprising:
screening according to the screening information to obtain a sample well section;
wherein the screening information includes one or more of the following: borehole environment information, borehole diameter curve smoothness information, and shear wave recording reliability information.
Optionally, the curve data for the plurality of dimensions includes one or more of: acoustic log data, density log data, gamma log data, neutron log data, resistivity log data, natural potential log data, porosity interpretation log data, water saturation interpretation log data.
According to another aspect of the present invention, there is provided a shear wave curve prediction apparatus, the apparatus comprising:
the first data acquisition module is suitable for acquiring curve data of multiple dimensions of the region to be detected and stratum property interpretation data;
the first grouping module is suitable for explaining data according to stratum properties, grouping curve data of multiple dimensions according to stratum properties to obtain multiple groups to be detected;
the prediction module is suitable for inputting curve data of a plurality of dimensions in any group to be detected into a transverse wave curve prediction model of corresponding stratum property for calculation to obtain predicted transverse wave curve data corresponding to the group to be detected; the transverse wave curve prediction model is obtained by training according to sample data in advance;
And the splicing module is suitable for splicing the predicted transverse wave curve data corresponding to each group to be detected to obtain the predicted transverse wave curve data of the region to be detected.
Optionally, the apparatus further comprises:
the second data acquisition module is suitable for acquiring curve data, transverse wave curve data and stratum property interpretation data of a plurality of dimensions of the sample well section;
The second grouping module is suitable for explaining data according to stratum properties of the sample well section, grouping curve data of multiple dimensions of the sample well section according to the stratum properties to obtain multiple sample groups;
the model training module is suitable for forming a sample data set according to curve data of a plurality of dimensions in any sample group and corresponding transverse wave curve data; and training to obtain a transverse wave curve prediction model of the stratum property corresponding to the sample group according to the sample data set.
Optionally, the formation property interpretation data comprises: lithology interpretation data and hydrocarbon interpretation data.
Optionally, the model training module is further adapted to: dividing the sample data set into a training data set and a validation data set; training the initial transverse wave curve prediction model according to the training data set, carrying out error evaluation on the initial transverse wave curve prediction model according to the verification data set, and carrying out iterative training until an error evaluation result meets an iteration stop condition, so as to obtain the transverse wave curve prediction model of the stratum property corresponding to the sample group.
Optionally, the iteration stop condition is determined according to any one of the following factors: regression error change, error distribution pattern, predicted value and measured value curve intersection pattern.
Optionally, the apparatus further comprises: the screening module is suitable for screening according to the screening information to obtain a sample well section;
wherein the screening information includes one or more of the following: borehole environment information, borehole diameter curve smoothness information, and shear wave recording reliability information.
Optionally, the curve data for the plurality of dimensions includes one or more of: acoustic log data, density log data, gamma log data, neutron log data, resistivity log data, natural potential log data, porosity interpretation log data, water saturation interpretation log data.
According to yet another aspect of the present invention, there is provided a computing device comprising: the device comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete communication with each other through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the transverse wave curve prediction method.
According to still another aspect of the present invention, there is provided a computer storage medium having stored therein at least one executable instruction for causing a processor to perform operations corresponding to the above-described shear wave curve prediction method.
According to the transverse wave curve prediction method, the transverse wave curve prediction device, the computing equipment and the storage medium, the transverse wave curve prediction method comprises the following steps: acquiring curve data of multiple dimensions of a region to be detected and stratum property interpretation data; interpreting the data according to the stratum property, and grouping the curve data with multiple dimensions according to the stratum property; inputting curve data of multiple dimensions in any group to be detected into a transverse wave curve prediction model of corresponding stratum property for calculation to obtain predicted transverse wave curve data corresponding to the group to be detected; the transverse wave curve prediction model is obtained through pre-training; and splicing the predicted transverse wave curve data corresponding to each group to be detected to obtain the predicted transverse wave curve data of the region to be detected. By means of the method, only basic logging curve and stratum property interpretation data are needed for predicting the transverse wave curve, influences of stratum properties on a prediction result are fully considered, prediction is carried out on stratum properties, the transverse wave curve can be efficiently predicted, and accuracy of the prediction result can be improved.
The foregoing description is only an overview of the present invention, and is intended to be implemented in accordance with the teachings of the present invention in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present invention more readily apparent.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
FIG. 1 shows a flow chart of a transverse wave curve prediction method provided by an embodiment of the invention;
FIG. 2 is a flowchart of a method for predicting a transverse wave curve according to another embodiment of the present invention;
FIG. 3 is a schematic diagram showing the structure of a neural network according to another embodiment of the present invention;
FIG. 4a shows a schematic diagram of an iteration stop condition in another embodiment of the present invention;
FIG. 4b shows a schematic diagram of an iteration stop condition in another embodiment of the present invention;
FIG. 4c shows a schematic diagram of an iteration stop condition in another embodiment of the present invention;
Fig. 5 shows a schematic structural diagram of a transverse wave curve prediction apparatus according to an embodiment of the present invention;
FIG. 6 illustrates a schematic diagram of a computing device provided by an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Fig. 1 shows a flowchart of a transverse wave curve prediction method according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
Step S110, curve data of multiple dimensions of the area to be detected and formation property interpretation data are obtained.
The area to be detected is the area needing to predict the shear wave curve, the data needed for predicting the shear wave curve comprises curve data with multiple dimensions and stratum property interpretation data, the curve data with multiple dimensions are curve data related to well logging, the curve data with multiple dimensions specifically comprise conventional well logging curve data and well logging interpretation data, the stratum property interpretation data comprise lithology interpretation data and oil-gas property interpretation data, and the curve data with multiple dimensions are grouped according to stratum properties by using the stratum property interpretation data.
Step S120, according to the stratum property interpretation data, grouping the curve data with multiple dimensions according to the stratum property to obtain multiple groups to be detected.
Because the functional relationship between the shear wave curve and the curve related to other logging may have a difference due to different formation properties, in order to improve the prediction accuracy, the curve data of multiple dimensions are grouped according to the formation properties, for example, the curve data of multiple dimensions of a well section where the mudstone is located is divided into a group to be detected, the curve data of multiple dimensions of a well section where the oil layer is located is divided into a group to be detected, that is, one group to be detected corresponds to one formation property, and the group to be detected includes the curve data of multiple dimensions of the well section corresponding to the formation property.
Step S130, inputting curve data of multiple dimensions in any group to be detected into a transverse wave curve prediction model of corresponding stratum property for calculation to obtain predicted transverse wave curve data corresponding to the group to be detected.
The transverse wave curve prediction model is obtained through sample data training in advance, and for each stratum property, a function relation between curve data of multiple dimensions and transverse wave curve data is continuously learned through a deep learning algorithm in advance, so that the transverse wave curve prediction model of the stratum property is constructed. And when formally predicting the transverse wave curve, grouping curve data of a plurality of dimensions of the region to be detected according to stratum properties, inputting the curve data contained in each group into a transverse wave curve prediction model of corresponding stratum properties for calculation, and outputting predicted transverse wave curve data corresponding to the group to be detected by the transverse wave curve prediction model.
And step S140, splicing the predicted transverse wave curve data corresponding to each group to be detected to obtain the predicted transverse wave curve data of the region to be detected.
And splicing the predicted transverse wave curve data corresponding to each group to be detected according to the stratum depth to obtain the predicted transverse wave curve data of the region to be detected.
According to the transverse wave curve prediction method provided by the embodiment, curve data of multiple dimensions of a region to be detected and stratum property interpretation data are obtained, so that basic data for transverse wave curve prediction are obtained, and the basic data are measured data and are easy to obtain; according to the formation property interpretation data, grouping the curve data of multiple dimensions according to the formation property to obtain multiple groups to be detected, so as to distinguish the curve data of the intervals of different formation properties; inputting curve data of multiple dimensions in any group to be detected into a transverse wave curve prediction model of corresponding stratum property for calculation, wherein the transverse wave curve prediction model is obtained by training according to sample data in advance, and predicted transverse wave curve data corresponding to the group to be detected is obtained, so that the effect of predicting transverse wave curves of stratum property is achieved; and splicing the predicted transverse wave curve data corresponding to each group to be detected to obtain the predicted transverse wave curve data of the region to be detected. By the mode, only basic logging curves and stratum property interpretation data are needed for predicting the transverse wave curve, the data needed by prediction are easy to obtain, the data volume is small, and the prediction method is more convenient, rapid and efficient; in addition, the influence of stratum properties on the prediction result is fully considered, and the stratum properties are predicted, so that the accuracy of the prediction result can be improved.
Fig. 2 shows a flowchart of a transverse wave curve prediction method according to another embodiment of the present invention, as shown in fig. 2, the method includes the following steps:
step S210, curve data, transverse wave curve data and formation property interpretation data of a plurality of dimensions of a sample well section are obtained.
Wherein the curve data of the plurality of dimensions specifically includes one or more of the following: acoustic log data, density log data, gamma log data, neutron log data, resistivity log data, natural potential log data, porosity interpretation log data, water saturation interpretation log data. Preferably, the multi-dimensional curve data includes all of the curve data listed above. Formation property interpretation data includes: lithology interpretation data and oil gas interpretation data, and also obtain transverse wave curve data of a sample well section, wherein the data obtained in the step can be measured values.
In an alternative mode, screening according to screening information to obtain a sample well section; wherein the screening information includes one or more of the following: borehole environment information, borehole diameter curve smoothness information, and shear wave recording reliability information. For example, a well section of a well logging with good well bore environment, smooth well bore curve and reliable transverse wave record is selected as a sample, and the accuracy of a transverse wave curve prediction model is improved by screening the sample according to screening information.
In an alternative mode, after screening out the sample well sections of the sample logging, correcting or directly eliminating the layer sections recording the abnormality, so as to avoid the influence of the introduction of abnormal data on the accuracy of model prediction.
Step S220, according to the stratum property interpretation data of the sample well section, grouping the curve data of multiple dimensions of the sample well section according to the stratum property to obtain multiple sample groups.
Wherein the formation properties include lithology and oil and gas properties, for an interval, if the interval is a pure rock formation, the formation properties include only lithology; if the interval is a simple oil layer, the stratum property only comprises oil gas property; if the interval is a mixed interval of rock and oil and gas, its formation properties include both lithology and oil and gas properties.
Determining formation properties of formations at various depths of a sample well section according to lithology interpretation data and hydrocarbon interpretation data of the sample well section, grouping curve data of multiple dimensions of the sample well section according to the formation properties, and dividing logging curve data of multiple dimensions of a layer section corresponding to the same formation properties into one sample group, namely, one sample group corresponds to one formation property and comprises curve data of multiple dimensions of the layer section corresponding to the formation property. For example, if an interval of 500 meters to 600 meters has the same formation property, curve data of multiple dimensions corresponding to 500 meters to 600 meters is divided into one sample packet.
Step S230, a sample data set is formed according to curve data of a plurality of dimensions and corresponding transverse wave curve data in any sample group, and a transverse wave curve prediction model of stratum properties corresponding to the sample group is obtained through training according to the sample data set.
For any sample group, a sample data set is formed according to the curve data of a plurality of dimensions and the corresponding transverse wave curve data, a neural network is adopted to construct a transverse wave curve prediction model, the input of the neural network model is the curve data of the plurality of dimensions, and the input of the neural network model is the corresponding transverse wave curve data.
Fig. 3 shows a schematic structural diagram of a neural network in another embodiment of the present invention, where the neural network is a sequential model, and as shown in fig. 3, the neural network integrally includes an input layer, a hidden layer, and an output layer, the number of ganglion points of the input layer is the type number of curve data with multiple dimensions, the number of ganglion points of the output layer is one, and the number of layers and nodes of the hidden layer can be set according to actual service requirements.
In an optional manner, training to obtain a shear wave curve prediction model of the stratum property corresponding to the sample group according to the sample data set further includes: dividing the sample data set into a training data set and a validation data set; training the initial transverse wave curve prediction model according to the training data set, carrying out error evaluation on the initial transverse wave curve prediction model according to the verification data set, and carrying out iterative training until an error evaluation result meets an iteration stop condition, so as to obtain the transverse wave curve prediction model of the stratum property corresponding to the sample group.
For example, 80% of the data is used as the training data set, and the remaining 20% is used as the validation data set. The method comprises the steps of continuously training an initial transverse wave curve prediction model by using a training data set, continuously updating parameters of the initial transverse wave curve prediction model, specifically inputting a group of curve data with multiple dimensions in a sample data set into the model to obtain a transverse wave curve data prediction value, differencing the transverse wave curve data prediction value and a corresponding transverse wave curve data actual measurement value, feeding back the difference to the initial transverse wave curve prediction model for parameter adjustment, simultaneously carrying out error evaluation on the initial transverse wave curve prediction model by using a verification data set, and continuously iterating the training until an error evaluation result meets an iteration stop condition, so that the trained transverse wave curve prediction model with stratum properties corresponding to the sample group is obtained.
Specifically, the iteration stop condition can be determined according to the regression error change condition, and specifically, the iteration stop condition is that the training error and the verification error are not changed any more or the fitting begins to occur; or the iteration stop condition is determined according to the distribution form of the error (the difference between the predicted value and the measured value), specifically, the iteration stop condition is that the error distribution is close to the normal distribution; or the iteration stop condition is determined according to the intersection graph of the predicted value and the measured value, and specifically, the iteration stop condition is that the intersection graph of the predicted value and the measured value is close to a straight line.
FIG. 4a is a schematic diagram showing an iteration stop condition according to another embodiment of the present invention, wherein the abscissa represents the epoch times and the ordinate represents the average absolute error, and the iteration stop condition is determined according to the regression error change, that is, the iteration stop condition is satisfied when the training error and the verification error are no longer changed; FIG. 4b is a schematic diagram showing an iteration stop condition in another embodiment of the present invention, wherein an abscissa represents a Prediction error (Prediction error) and an ordinate represents a number (count), and the iteration stop condition is determined according to a Prediction error distribution form, that is, the Prediction error distribution form is normally distributed, and the iteration stop condition is satisfied; fig. 4c is a schematic diagram of an iteration stop condition according to another embodiment of the present invention, in which an abscissa represents an actual value (True values), and an ordinate represents a predicted value (Predictions), and when the actual value and the predicted value curve intersect the graph form to be close to a straight line, it is determined that the iteration stop condition is satisfied.
Step S240, acquiring curve data of multiple dimensions of the region to be detected and formation property interpretation data.
When the transverse wave curve of the area to be detected needs to be predicted, curve data of multiple dimensions of the area to be detected and stratum property interpretation data are acquired, wherein the curve data of the multiple dimensions specifically comprise one or more of the following: acoustic log data, density log data, gamma log data, neutron log data, resistivity log data, natural potential log data, porosity interpretation log data, and water saturation interpretation log data. Preferably, the multi-dimensional curve data includes all of the curve data listed above, and the formation property interpretation data includes: lithology interpretation data and hydrocarbon interpretation data.
Step S250, according to stratum property interpretation data, grouping curve data of multiple dimensions according to stratum properties to obtain multiple groups to be detected.
Similarly, the stratum property comprises lithology and oil gas property, the stratum property of the stratum at each depth of the region to be detected is determined according to the lithology interpretation data and the oil gas interpretation data of the region to be detected, the curve data of the plurality of dimensions of the region to be detected are grouped according to the stratum property, the curve data of the plurality of dimensions of the layer section corresponding to the same stratum property are divided into one group to be detected, namely one group to be detected corresponds to one stratum property and comprises the curve data of the plurality of dimensions of the layer section corresponding to the stratum property.
Step S260, inputting curve data of multiple dimensions in any group to be detected into a transverse wave curve prediction model of corresponding stratum property for calculation, and obtaining predicted transverse wave curve data corresponding to the group to be detected.
The transverse wave curve prediction model of the stratum property is obtained through training in the previous steps. For each group to be detected, curve data of multiple dimensions contained in the group to be detected are input into a transverse wave curve prediction model of corresponding stratum property for calculation, wherein the number of input nerve nodes of the transverse wave curve prediction model is consistent with the number of types of curve data for prediction, the curve data of each dimension are input into the transverse wave curve prediction model through corresponding input nerve nodes, and the transverse wave curve prediction model outputs predicted transverse wave curve data corresponding to the group to be detected.
And step S270, splicing the predicted transverse wave curve data corresponding to each group to be detected to obtain the predicted transverse wave curve data of the region to be detected.
And splicing the predicted transverse wave curves corresponding to the groups to be detected according to the depth to obtain predicted transverse wave curve data of the region to be detected.
According to the transverse wave curve prediction method provided by the embodiment, the transverse wave curve prediction can be realized only by the basic logging curve and logging interpretation data, the required data volume is small, the data is easy to obtain, and the prediction method is convenient, quick and efficient; modeling is carried out by adopting artificial neural network deep learning, lithology and hydrocarbon reservoir prediction are carried out, and curve relationships different due to lithology and hydrocarbon reservoir are fully considered, so that a transverse wave curve prediction result is more accurate; and monitoring errors in the model training process, setting proper iteration termination conditions, and ensuring the controllable quality of the prediction result.
Fig. 5 shows a schematic structural diagram of a transverse wave curve prediction apparatus according to an embodiment of the present invention, where, as shown in fig. 5, the apparatus includes:
The first data acquisition module 51 is adapted to acquire curve data of multiple dimensions of the region to be detected and formation property interpretation data;
The first grouping module 52 is adapted to interpret data according to formation properties, and group curve data of multiple dimensions according to formation properties to obtain multiple groups to be detected;
The prediction module 53 is adapted to input curve data of multiple dimensions in any group to be detected into a transverse wave curve prediction model of corresponding stratum property for calculation, so as to obtain predicted transverse wave curve data corresponding to the group to be detected; the transverse wave curve prediction model is obtained by training according to sample data in advance;
and the splicing module 54 is suitable for splicing the predicted transverse wave curve data corresponding to each group to be detected to obtain the predicted transverse wave curve data of the region to be detected.
Optionally, the apparatus further comprises:
the second data acquisition module is suitable for acquiring curve data, transverse wave curve data and stratum property interpretation data of a plurality of dimensions of the sample well section;
The second grouping module is suitable for explaining data according to stratum properties of the sample well section, grouping curve data of multiple dimensions of the sample well section according to the stratum properties to obtain multiple sample groups;
the model training module is suitable for forming a sample data set according to curve data of a plurality of dimensions in any sample group and corresponding transverse wave curve data; and training to obtain a transverse wave curve prediction model of the stratum property corresponding to the sample group according to the sample data set.
Optionally, the formation property interpretation data comprises: lithology interpretation data and hydrocarbon interpretation data.
Optionally, the model training module is further adapted to: dividing the sample data set into a training data set and a validation data set; training the initial transverse wave curve prediction model according to the training data set, carrying out error evaluation on the initial transverse wave curve prediction model according to the verification data set, and carrying out iterative training until an error evaluation result meets an iteration stop condition, so as to obtain the transverse wave curve prediction model of the stratum property corresponding to the sample group.
Optionally, the iteration stop condition is determined according to any one of the following factors: regression error change, error distribution pattern, predicted value and measured value curve intersection pattern.
Optionally, the apparatus further comprises: the screening module is suitable for screening according to the screening information to obtain a sample well section;
wherein the screening information includes one or more of the following: borehole environment information, borehole diameter curve smoothness information, and shear wave recording reliability information.
Optionally, the curve data for the plurality of dimensions includes one or more of: acoustic log data, density log data, gamma log data, neutron log data, resistivity log data, natural potential log data, porosity interpretation log data, water saturation interpretation log data.
The embodiment of the invention provides a non-volatile computer storage medium, which stores at least one executable instruction, and the computer executable instruction can execute the transverse wave curve prediction method in any of the method embodiments.
FIG. 6 illustrates a schematic diagram of an embodiment of a computing device of the present invention, and the embodiments of the present invention are not limited to a particular implementation of the computing device.
As shown in fig. 6, the computing device may include: a processor 602, a communication interface Communications Interface, a memory 606, and a communication bus 608.
Wherein: processor 602, communication interface 604, and memory 606 perform communication with each other via communication bus 608. Communication interface 604 is used to communicate with network elements of other devices, such as clients or other servers. The processor 602 is configured to execute the program 610, and may specifically perform relevant steps in the above-described embodiment of a method for predicting a transverse wave curve for a computing device.
In particular, program 610 may include program code including computer-operating instructions.
The processor 602 may be a central processing unit CPU, or an Application-specific integrated Circuit ASIC (Application SPECIFIC INTEGRATED Circuit), or one or more integrated circuits configured to implement embodiments of the present invention. The one or more processors included by the computing device may be the same type of processor, such as one or more CPUs; but may also be different types of processors such as one or more CPUs and one or more ASICs.
A memory 606 for storing a program 610. The memory 606 may comprise high-speed RAM memory or may further comprise non-volatile memory (non-volatile memory), such as at least one disk memory.
The algorithms or displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general-purpose systems may also be used with the teachings herein. The required structure for a construction of such a system is apparent from the description above. In addition, embodiments of the present invention are not directed to any particular programming language. It will be appreciated that the teachings of the present invention described herein may be implemented in a variety of programming languages, and the above description of specific languages is provided for disclosure of enablement and best mode of the present invention.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the above description of exemplary embodiments of the invention, various features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed as reflecting the intention that: i.e., the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. The modules or units or components of the embodiments may be combined into one module or unit or component and, furthermore, they may be divided into a plurality of sub-modules or sub-units or sub-components. Any combination of all features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be used in combination, except insofar as at least some of such features and/or processes or units are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.
Various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components according to embodiments of the present invention may be implemented in practice using a microprocessor or Digital Signal Processor (DSP). The present invention can also be implemented as an apparatus or device program (e.g., a computer program and a computer program product) for performing a portion or all of the methods described herein. Such a program embodying the present invention may be stored on a computer readable medium, or may have the form of one or more signals. Such signals may be downloaded from an internet website, provided on a carrier signal, or provided in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names. The steps in the above embodiments should not be construed as limiting the order of execution unless specifically stated.
Claims (9)
1. A method for predicting a shear wave curve, the method comprising:
Acquiring curve data, transverse wave curve data and stratum property interpretation data of multiple dimensions of a sample well section;
according to the stratum property interpretation data of the sample well section, grouping the curve data of multiple dimensions of the sample well section according to the stratum property to obtain multiple sample groups;
Forming a sample data set according to curve data of multiple dimensions in any sample group and corresponding transverse wave curve data;
According to the sample data set, training to obtain a transverse wave curve prediction model of stratum properties corresponding to the sample group;
Acquiring curve data of multiple dimensions of a region to be detected and stratum property interpretation data;
According to the stratum property interpretation data of the region to be detected, grouping the curve data of multiple dimensions of the region to be detected according to the stratum property to obtain multiple groups to be detected;
Inputting curve data of multiple dimensions in any group to be detected into a transverse wave curve prediction model of corresponding stratum property for calculation to obtain predicted transverse wave curve data corresponding to the group to be detected;
And splicing the predicted transverse wave curve data corresponding to each group to be detected to obtain the predicted transverse wave curve data of the region to be detected.
2. The method of claim 1, wherein the formation property interpretation data comprises: lithology interpretation data and hydrocarbon interpretation data.
3. The method of claim 1, wherein training a shear wave curve prediction model of formation properties corresponding to the sample group from the sample data set further comprises:
Dividing the sample dataset into a training dataset and a validation dataset;
Training the initial transverse wave curve prediction model according to the training data set, carrying out error evaluation on the initial transverse wave curve prediction model according to the verification data set, and carrying out iterative training until an error evaluation result meets an iteration stop condition, so as to obtain the transverse wave curve prediction model of the stratum property corresponding to the sample group.
4. A method according to claim 3, wherein the iteration stop condition is determined according to any one of the following factors: regression error change, error distribution pattern, predicted value and measured value curve intersection pattern.
5. The method of claim 1, wherein prior to performing the method, further comprises:
screening according to the screening information to obtain the sample well section;
Wherein the screening information includes one or more of the following: borehole environment information, borehole diameter curve smoothness information, and shear wave recording reliability information.
6. The method of claim 1, wherein the curve data for the plurality of dimensions comprises a plurality of: acoustic log data, density log data, gamma log data, neutron log data, resistivity log data, natural potential log data, porosity interpretation log data, water saturation interpretation log data.
7. A shear wave curve prediction apparatus, the apparatus comprising:
the second data acquisition module is suitable for acquiring curve data, transverse wave curve data and stratum property interpretation data of a plurality of dimensions of the sample well section;
The second grouping module is suitable for explaining data according to stratum properties of the sample well section, grouping curve data of multiple dimensions of the sample well section according to the stratum properties to obtain multiple sample groups;
The model training module is suitable for forming a sample data set according to curve data of a plurality of dimensions in any sample group and corresponding transverse wave curve data; training to obtain a transverse wave curve prediction model of stratum properties corresponding to the sample group according to the sample data set;
the first data acquisition module is suitable for acquiring curve data of multiple dimensions of the region to be detected and stratum property interpretation data;
The first grouping module is suitable for grouping curve data of multiple dimensions of the region to be detected according to stratum properties according to stratum property interpretation data of the region to be detected to obtain multiple groups to be detected;
The prediction module is suitable for inputting curve data of a plurality of dimensions in any group to be detected into a transverse wave curve prediction model of corresponding stratum property for calculation to obtain predicted transverse wave curve data corresponding to the group to be detected;
And the splicing module is suitable for splicing the predicted transverse wave curve data corresponding to each group to be detected to obtain the predicted transverse wave curve data of the region to be detected.
8. A computing device, comprising: the device comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete communication with each other through the communication bus;
the memory is configured to store at least one executable instruction that causes the processor to perform operations corresponding to the shear wave curve prediction method according to any one of claims 1-6.
9. A computer storage medium having stored therein at least one executable instruction for causing a processor to perform operations corresponding to the shear wave curve prediction method of any one of claims 1-6.
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| CN109667573A (en) * | 2018-12-12 | 2019-04-23 | 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 | Three-dimensional shale reservoir pore pressure prediction method, apparatus and electronic equipment |
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