Directional drilling effect evaluation prediction method, system and electronic device

Abstract

The application discloses a directional drilling effect evaluation and prediction method and system and electronic equipment, and relates to the technical field of directional drilling. The method comprises the following steps: collecting and preprocessing historical data associated with directional drilling; after the historical data is preprocessed, the preprocessed historical data is subjected to redundant data stripping, data dimension reduction and deep feature fusion in sequence, a plurality of samples are obtained, each sample comprises a feature vector for representing all drilling target parameters and a plurality of parameters associated with a wellbore trajectory; a preset network model is trained based on all the samples, and a directional wellbore trajectory prediction model is obtained; and the wellbore trajectory of directional drilling is predicted according to current data associated with directional drilling and by using the directional wellbore trajectory prediction model. The application trains the preset network model by analyzing and utilizing the historical data associated with directional drilling, and improves the scientificity and accuracy of determining the directional drilling scheme.

Description

Technical Field

[0001] This invention relates to the field of directional drilling technology, and in particular to a method, system, and electronic device for evaluating and predicting the effectiveness of directional drilling. Background Technology

[0002] Directional drilling technology, by controlling the wellbore trajectory, enables precise drilling of horizontal and extended-reach wells, significantly improving oil and gas recovery rates. However, traditional directional drilling methods rely heavily on engineers' experience and geological data analysis, inherently involving subjectivity and uncertainty. This method struggles to guarantee drilling accuracy and efficiency in complex formations and variable well conditions, necessitating more scientific and systematic predictive models to optimize drilling strategies. While domestic and international oilfield service companies have made some innovative advancements in directional drilling tools, they still face common challenges in predictive modeling methods for evaluating directional drilling effectiveness: because directional drilling effectiveness is influenced by multiple factors such as formation, drill string, rock breaking, and vibration, it is difficult to establish theoretical models that accurately reflect field conditions.

[0003] Currently, the invention patent with publication number "CN118095060A" and subject title "A Method for Predicting Rock Mechanical Parameters While Drilling Based on Dynamic Time Warping Algorithm" discloses a method that uses a sliding window-based autoencoder outlier identification method to identify outliers in rock mechanical parameters and then uses a BP neural network to correct these outliers. It integrates a pre-built CNN-LSTM model and a random forest model based on a dynamic time warping algorithm, automatically adjusting the weight distribution of the CNN-LSTM and random forest models. Based on the corrected rock mechanical parameters, it constructs elastic modulus prediction, Poisson's ratio prediction, and compressive strength prediction models using a novel fusion algorithm model to achieve rock mechanical parameter prediction while drilling. This demonstrates that existing methods lack systematic analysis and modeling of the coupling relationships between multiple factors, leading to significant uncertainty in the formulation of directional drilling plans. Especially under complex well conditions, traditional methods struggle to provide reliable predictions and guidance, impacting drilling efficiency and safety. Summary of the Invention

[0004] The technical problem to be solved by this invention is to address the shortcomings of existing technologies, specifically by providing a method, system, and electronic device for evaluating and predicting the effectiveness of directional drilling, as detailed below:

[0005] 1) In a first aspect, the present invention provides a method for evaluating and predicting the effectiveness of directional drilling, the specific technical solution of which is as follows:

[0006] Collect and preprocess historical data associated with directional drilling;

[0007] After performing redundant data stripping, data dimensionality reduction, and deep feature fusion on the preprocessed historical data, multiple samples are obtained. Each sample includes: a feature vector for characterizing all drilling target parameters and multiple parameters associated with the wellbore trajectory.

[0008] The preset network model is trained based on all samples to obtain a directional wellbore trajectory prediction model;

[0009] Based on the current data associated with directional drilling, and using a directional wellbore trajectory prediction model, the wellbore trajectory of directional drilling is predicted.

[0010] The beneficial effects of the directional drilling effect evaluation and prediction method provided by this invention are as follows:

[0011] By analyzing and utilizing historical data associated with directional drilling, a pre-set network model is trained to improve the scientific rigor and accuracy of determining directional drilling schemes.

[0012] Based on the above scheme, the directional drilling effect evaluation and prediction method of the present invention can be further improved as follows.

[0013] Furthermore, historical data associated with directional drilling is preprocessed, including:

[0014] Missing and outlier values ​​in historical data associated with directional drilling are processed.

[0015] Furthermore, the historical data associated with directional drilling includes: historical geological data, historical drilling parameters, historical measurement-while-drilling data, and historical integrated logging data.

[0016] Furthermore, all drilling target parameters include at least: drilling pressure, rotation speed, and displacement, and multiple parameters associated with the wellbore trajectory include: well inclination angle, azimuth angle, and drill bit attitude.

[0017] 2) Secondly, the present invention also provides a directional drilling effect evaluation and prediction system, the specific technical solution of which is as follows:

[0018] It includes: a data collection and preprocessing module, a data refinement module, a model training module, and a prediction module;

[0019] The data collection and preprocessing module is used to: collect and preprocess historical data associated with directional drilling;

[0020] The data fine processing module is used to: sequentially perform redundant data stripping, data dimensionality reduction and deep feature fusion on the preprocessed historical data to obtain multiple samples. Each sample includes: feature vectors used to characterize all drilling target parameters and multiple parameters associated with the wellbore trajectory;

[0021] The model training module is used to: train the preset network model based on all samples to obtain a directional wellbore trajectory prediction model;

[0022] The prediction module is used to predict the wellbore trajectory of directional drilling based on the current data associated with the directional drilling and using a directional wellbore trajectory prediction model.

[0023] Based on the above scheme, the directional drilling effect evaluation and prediction system of the present invention can be further improved as follows.

[0024] Furthermore, the data collection and preprocessing module is specifically used for:

[0025] Missing and outlier values ​​in historical data associated with directional drilling are processed.

[0026] Furthermore, the historical data associated with directional drilling includes: historical geological data, historical drilling parameters, historical measurement-while-drilling data, and historical integrated logging data.

[0027] Furthermore, all drilling target parameters include at least: drilling pressure, rotation speed, and displacement, and multiple parameters associated with the wellbore trajectory include: well inclination angle, azimuth angle, and drill bit attitude.

[0028] 3) In a third aspect, the present invention also provides an electronic device, the electronic device including a processor coupled to a memory, the memory storing at least one computer program, the at least one computer program being loaded and executed by the processor, so as to enable the electronic device to implement any of the above-mentioned directional drilling effect evaluation and prediction methods.

[0029] 4) In a fourth aspect, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any of the above-mentioned methods for evaluating and predicting the effects of directional drilling.

[0030] It should be noted that the beneficial effects of the technical solutions of the second to fourth aspects of the present invention and their corresponding possible implementations can be found in the above description of the technical effects of the first aspect and its corresponding possible implementations, and will not be repeated here. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments of the present invention will be briefly introduced below:

[0032] Figure 1 This is a flowchart illustrating a method for evaluating and predicting the effectiveness of directional drilling according to an embodiment of the present invention.

[0033] Figure 2 This is a schematic diagram of a directional drilling effect evaluation and prediction system according to an embodiment of the present invention;

[0034] Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0035] The principles and features of the present invention are described below. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0036] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0037] like Figure 1 As shown, an embodiment of the present invention provides a method for evaluating and predicting the effectiveness of directional drilling, comprising the following steps:

[0038] S1. Collect and preprocess historical data associated with directional drilling;

[0039] The historical data associated with directional drilling includes: historical geological data, historical drilling parameters, historical measurement-while-drilling data, and historical integrated logging data. Historical geological data is received through the static geological data interface, historical drilling parameters are received through the static drilling parameter interface, historical measurement-while-drilling data is received through the historical measurement-while-drilling data interface, and historical integrated logging data is received through the historical integrated logging data interface.

[0040] The specific implementation process of S1 is as follows:

[0041] The missing and outlier values ​​in the historical data associated with directional drilling are processed, specifically:

[0042] By cleaning the historical data associated with directional drilling, missing and outlier values ​​in the historical data can be processed. For missing values, interpolation or mean method is used to fill in the missing values; for outliers, threshold range is set to remove or correct them.

[0043] S2. After performing redundant data stripping, data dimensionality reduction and deep feature fusion on the preprocessed historical data, multiple samples are obtained. Each sample includes: feature vectors used to characterize all drilling target parameters and multiple parameters associated with the wellbore trajectory.

[0044] The specific implementation process for redundant data stripping is as follows:

[0045] 1) Since the historical data associated with directional drilling comes from diverse sources, it is first necessary to standardize the time format, coordinate system, and units of the historical data from different sources. Specifically, the timestamps are standardized to the international standard format, the depths are standardized to the metric system and correspond one-to-one with the vertical depth, and the coordinate system is standardized to WGS84.

[0046] 2) Then, field mapping and alignment are performed by establishing mapping rules between data source fields (for example, the depth fields of measurement while drilling data and well depth fields of logging data can be mapped and aligned).

[0047] 3) After data mapping and alignment, check for duplicate records from different data sources at the same time or depth point, and perform similarity analysis on attributes (such as formation characteristics, dip angle, etc.) to identify duplicate data.

[0048] 4) Detect logical conflicts between different data sources at the same time or depth point.

[0049] 5) For duplicate data, remove redundant data. For logical conflicts, prioritize data based on data source reliability and data integrity, using the highest priority data. For logical conflicts between two consecutive measurements taken from the same data source at the same depth, use the latest data.

[0050] The specific implementation methods for data dimensionality reduction are as follows:

[0051] This method utilizes a nonlinear solution algorithm to perform data standardization, core data extraction, and dimensionality reduction on high-dimensional acquired data while preserving data characteristics. Simultaneously, a feature matrix reconstruction method is used to fuse time-series and depth-series data during drilling, ensuring that each time step corresponds to a depth value. This approach denoises low-frequency noise and removes weakly correlated data items from the acquired data, while enhancing the influence of strongly correlated data on the model's predictive performance. This effectively improves the solution efficiency and accuracy of directional drilling performance evaluation and prediction methods.

[0052] Data standardization is achieved through "matrix standardization" in S20.

[0053] Among them, core data extraction and data dimensionality reduction refer to: based on the direction of the maximum variance of the feature vector, retaining the projection mapping of the first seven features in the following thirteen-dimensional matrix, realizing the extraction of seven main core data, and realizing data dimensionality reduction from thirteen dimensions to seven dimensions.

[0054] The specific implementation process of S2 is described as follows:

[0055] By processing data from adjacent wells at the same depth or actual drilling data from the upper section of this well, a foundation of input data is provided for evaluating and predicting the drilling performance of the lower section of this well. Among the collected input data, logging data and daily drilling reports are time-series datasets, including time (T), lithology (KR), drilling weight (WOB), rotational speed (RPM), displacement (Q), pump pressure (P), and mechanical drilling speed (ROP), forming a seven-dimensional matrix. Static formation data and measurement-while-drilling (MWD) data are depth-series datasets, including vertical depth (TVD), formation dip angle (Fa), dip direction (Fφ), strike direction (Ft), well inclination angle (INC), and azimuth angle (AZI), forming a six-dimensional matrix. Data is then fused according to the time-depth correspondence to form a thirteen-dimensional comprehensive matrix capable of reproducing and characterizing the on-site drilling process. The data sources are then finely processed using the methods described below.

[0056] Among them, the daily drilling report contains historical drilling parameters, which is the main source of historical drilling parameters.

[0057] S20, Matrix Standardization:

[0058]

[0059] S21. Calculate the covariance matrix and determine the data direction with the largest projection variance:

[0060]

[0061] Calculate the variance based on the covariance matrix:

[0062]

[0063] Solving for eigenvalues ​​and eigenvectors:

[0064]

[0065] L(w,λ)=w T Σw+λ(1-w T w)

[0066] We can obtain:

[0067]

[0068] maxD(x)=max{w T Σw}=max{w T λw}=maxλ

[0069] At this point, the maximum variance and direction have been determined. A new coordinate system is established based on the eigenvectors, and the original data is projected and transformed into the new coordinate system, achieving data dimensionality reduction while preserving the main data features. The inventors conducted tests using real-world datasets from multiple key regions in China and concluded that retaining the first seven eigenvectors to form the final input data matrix achieves optimal results in both model training efficiency and prediction accuracy.

[0070] S3. Train the preset network model based on all samples to obtain the directional wellbore trajectory prediction model;

[0071] To address the challenges of multi-dimensional data interaction, dynamic changes in time and depth, and physical constraints inherent in directional drilling, a CNN-LSTM directional drilling performance evaluation and prediction model was created as the pre-defined network model. The CNN layer extracts local features from the upper well section within 50m or the vertical depth ±25m of adjacent wells at the same drilling location, improving the model's computational efficiency and robustness. The LSTM layer captures the long-term interaction logic and influence relationships of time-series data, aiding in the simulation and prediction of dynamic changes during the drilling process.

[0072] Optionally, the CNN-LSTM directional drilling performance evaluation and prediction model is trained using historical data, and the parameters are optimized using stochastic gradient descent; cross-validation is used to evaluate the model performance to prevent overfitting; and the model learning rate and the number of hidden layer nodes are adjusted based on the validation results.

[0073] S4. Based on the current data associated with directional drilling, and using the directional wellbore trajectory prediction model, predict the wellbore trajectory of directional drilling.

[0074] Optionally, the real-time data acquisition function is responsible for continuously acquiring dynamic data during the drilling process, such as measurement-while-drilling (MWD) data and logging-while-drilling (LMD) data. The real-time optimization function updates model parameters by inputting real-time data into the model, performs online training based on new data, improves the model's regional adaptability, and enables the model to self-iterate and upgrade.

[0075] Optionally, in the above technical solution, all drilling target parameters include at least: drilling pressure, rotation speed and displacement, and multiple parameters associated with the wellbore trajectory include: well inclination angle, azimuth angle and drill bit attitude.

[0076] This invention offers a novel approach to solving this problem by utilizing a composite neural network modeling method based on multidimensional data. By leveraging historical and real-time data, combined with deep learning neural network technology, a more accurate evaluation and prediction model for directional drilling performance can be established. However, currently, data-driven modeling methods in the field of directional drilling lack systematic and scientific research and application. Therefore, this invention proposes a directional drilling performance evaluation and prediction method based on data mining and processing methods and deep learning neural network technology. This method effectively mines historical data, achieves multi-factor coupling, and provides a scientific basis for predicting directional drilling direction and formulating plans.

[0077] In the above embodiments, although the steps are numbered S1, S2, etc., they are only specific embodiments given by the present invention. Those skilled in the art can adjust the execution order of S1, S2, etc. according to the actual situation, which is also within the protection scope of the present invention. It can be understood that in some embodiments, some or all of the above embodiments may be included.

[0078] like Figure 2 As shown, a directional drilling effect evaluation and prediction system 200 according to an embodiment of the present invention includes: a data collection and preprocessing module 201, a data fine processing module 202, a model training module 203, and a prediction module 204;

[0079] The data collection and preprocessing module 201 is used to: collect and preprocess historical data associated with directional drilling;

[0080] The data fine processing module 202 is used to: sequentially perform redundant data stripping, data dimensionality reduction and deep feature fusion on the preprocessed historical data to obtain multiple samples. Each sample includes: feature vectors used to characterize all drilling target parameters and multiple parameters associated with the wellbore trajectory.

[0081] Model training module 203 is used to: train a preset network model based on all samples to obtain a directional wellbore trajectory prediction model;

[0082] The prediction module 204 is used to predict the wellbore trajectory of directional drilling based on the current data associated with directional drilling and by using a directional wellbore trajectory prediction model.

[0083] Optionally, in the above technical solution, the data collection and preprocessing module 201 is specifically used for:

[0084] Missing and outlier values ​​in historical data associated with directional drilling are processed.

[0085] Optionally, in the above technical solution, the historical data associated with directional drilling includes: historical geological data, historical drilling parameters, historical measurement-while-drilling data, and historical integrated logging data.

[0086] Optionally, in the above technical solution, all drilling target parameters include at least: drilling pressure, rotation speed and displacement, and multiple parameters associated with the wellbore trajectory include: well inclination angle, azimuth angle and drill bit attitude.

[0087] This invention establishes a method for evaluating and predicting the effectiveness of directional drilling, comprehensively addressing the subjectivity and uncertainty inherent in traditional directional drilling schemes that rely on experience and geological data analysis. Through a data collection and preprocessing module, static geological data, static drilling parameters, historical measurement-while-drilling (MWD) data, and historical comprehensive logging data are fully integrated to ensure data integrity and accuracy. A refined data processing module is responsible for redundant data removal, dimensionality reduction, and depth feature fusion. It utilizes nonlinear solving algorithms to perform data standardization, core data extraction, and dimensionality reduction operations on high-dimensional acquired data while preserving data characteristics. Simultaneously, through a feature matrix reconstruction method, the MWD data is fused in both time-series and depth-series formats, ensuring that each time step has a corresponding depth value. This method effectively removes low-frequency noise and weakly correlated data items from the acquired data, while enhancing the influence of strongly correlated data on the model's predictive performance, thus significantly improving the solution efficiency and accuracy of the directional drilling effectiveness evaluation and prediction method. The directional drilling performance evaluation and prediction model construction module utilizes the CNN-LSTM algorithm, combined with geological features and drill bit-formation interaction characteristics, to establish a multi-factor coupled directional drilling model, accurately predicting the inclination angle, azimuth angle, and drill bit attitude of the next drilling section. The model training and validation module optimizes model parameters using stochastic gradient descent and evaluates model performance using cross-validation to ensure the model's generalization ability and reliability. The real-time data application and model optimization module continuously updates and optimizes model parameters using real-time acquired measurement-while-drilling data, improving the model's adaptive performance. Ultimately, this invention not only significantly improves the accuracy and scientific rigor of directional drilling direction prediction but also enables real-time optimization of model parameters during the drilling process, providing a more efficient and reliable directional drilling solution and offering strong technical support for actual drilling operations.

[0088] It should be noted that the beneficial effects of the directional drilling effect evaluation and prediction system 200 provided in the above embodiments are the same as those of the directional drilling effect evaluation and prediction method described above, and will not be repeated here. Furthermore, the system provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to the actual situation to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process is detailed in the method embodiments, and will not be repeated here.

[0089] The directional drilling effect evaluation and prediction system of the present invention can be a computer program (including program code) running on a computer device. For example, the directional drilling effect evaluation and prediction system of the present invention is an application software that can be used to execute the corresponding steps in the directional drilling effect evaluation and prediction method of the present invention.

[0090] In some embodiments, the directional drilling effect evaluation and prediction system of the present invention can be implemented in a combination of hardware and software. As an example, the directional drilling effect evaluation and prediction system of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the directional drilling effect evaluation and prediction method of the present invention. For example, the processor in the form of a hardware decoding processor can be one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0091] The modules described in the embodiments of this invention can be implemented in software or hardware. The names of the modules are not, in some cases, limiting the scope of the module itself.

[0092] An electronic device according to an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-mentioned directional drilling effect evaluation and prediction methods. That is, an electronic device according to an embodiment of the present invention may include, but is not limited to: a processor and a memory; the memory is used to store the computer program; the processor is used to execute the directional drilling effect evaluation and prediction method shown in any embodiment of the present invention by calling the computer program.

[0093] In one alternative embodiment, an electronic device is provided, such as Figure 3 As shown, Figure 3The illustrated electronic device 4000 includes a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, for example, via a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 4004 is not limited to one type, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0094] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. Processor 4001 may also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0095] Bus 4002 may include a path for transmitting information between the aforementioned components. Bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 4002 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The bus 4002 is represented by only one thick line, but this does not mean that there is only one bus or one type of bus.

[0096] The memory 4003 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0097] The memory 4003 stores the application code (computer program) for executing the present invention, and its execution is controlled by the processor 4001. The processor 4001 executes the application code stored in the memory 4003 to implement the content shown in the foregoing method embodiments.

[0098] Among them, electronic devices can also be terminal devices. A terminal device can be any terminal device that can install applications and access web pages through applications, including at least one of smartphones, tablets, laptops, desktop computers, smart speakers, smartwatches, smart TVs, and smart in-vehicle devices.

[0099] It should be noted that, Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0100] An embodiment of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any of the above-mentioned methods for evaluating and predicting the effectiveness of directional drilling.

[0101] Alternatively, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, a floppy disk, and an optical data storage device, etc.

[0102] In an exemplary embodiment, a computer program product or computer program is also provided, which includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform any of the aforementioned directional drilling effect evaluation and prediction methods.

[0103] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0104] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0105] The computer-readable storage medium provided in this invention can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0106] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the method shown in the above embodiments.

[0107] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

[0108] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and represent a limitation on a specific order or sequence. Where appropriate, the order of use for similar objects can be interchanged so that the embodiments of this application described herein can be implemented in an order other than that shown or described.

[0109] Those skilled in the art will recognize that this invention can be implemented as a system, method, or computer program product. Therefore, this invention can be specifically implemented in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, this invention can also be implemented as a computer program product contained in one or more computer-readable media, which includes computer-readable program code.

[0110] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for evaluating and predicting the effectiveness of directional drilling, characterized in that, include: Collect and preprocess historical data associated with directional drilling; After performing redundant data stripping, data dimensionality reduction, and deep feature fusion on the preprocessed historical data, multiple samples are obtained. Each sample includes: a feature vector for characterizing all drilling target parameters and multiple parameters associated with the wellbore trajectory. The preset network model is trained based on all samples to obtain a directional wellbore trajectory prediction model; Based on the current data associated with directional drilling, and using a directional wellbore trajectory prediction model, the wellbore trajectory of directional drilling is predicted.

2. The method for evaluating and predicting the effectiveness of directional drilling according to claim 1, characterized in that, Preprocessing of historical data associated with directional drilling includes: Missing and outlier values ​​in historical data associated with directional drilling are processed.

3. The method for evaluating and predicting the effectiveness of directional drilling according to claim 1, characterized in that, Historical data associated with directional drilling includes: historical geological data, historical drilling parameters, historical measurement-while-drilling data, and historical integrated logging data.

4. The method for evaluating and predicting the effectiveness of directional drilling according to claim 1, characterized in that, All drilling target parameters include at least: drilling pressure, rotation speed, and displacement. Multiple parameters associated with the wellbore trajectory include: well inclination angle, azimuth angle, and drill bit attitude.

5. A directional drilling performance evaluation and prediction system, characterized in that, include: The system includes a data collection and preprocessing module, a data refinement module, a model training module, and a prediction module. The data collection and preprocessing module is used to: collect and preprocess historical data associated with directional drilling; The data fine processing module is used to: sequentially perform redundant data stripping, data dimensionality reduction and deep feature fusion on the preprocessed historical data to obtain multiple samples. Each sample includes: feature vectors used to characterize all drilling target parameters and multiple parameters associated with the wellbore trajectory. The model training module is used to: train a preset network model based on all samples to obtain a directional wellbore trajectory prediction model; The prediction module is used to predict the wellbore trajectory of directional drilling based on the current data associated with the directional drilling and using a directional wellbore trajectory prediction model.

6. The directional drilling effect evaluation and prediction system according to claim 5, characterized in that, The data collection and preprocessing module is specifically used for: Missing and outlier values ​​in historical data associated with directional drilling are processed.

7. The directional drilling effect evaluation and prediction system according to claim 5, characterized in that, Historical data associated with directional drilling includes: historical geological data, historical drilling parameters, historical measurement-while-drilling data, and historical integrated logging data.

8. The directional drilling effect evaluation and prediction system according to claim 5, characterized in that, All drilling target parameters include at least: drilling pressure, rotation speed, and displacement. Multiple parameters associated with the wellbore trajectory include: well inclination angle, azimuth angle, and drill bit attitude.

9. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the directional drilling effect evaluation and prediction method according to any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the directional drilling effect evaluation and prediction method according to any one of claims 1 to 4.

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