CN114398828A - Drilling rate intelligent prediction and optimization method, system, equipment and medium - Google Patents
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Abstract
The invention relates to a drilling rate intelligent prediction and optimization method, a system, equipment and a medium, which comprises the following steps: preprocessing the acquired original drilling data to obtain a sample set; training the built drilling speed prediction model based on the sample set to obtain a trained drilling speed prediction model; and inputting the real-time drilling data of the block to be predicted into the drilling speed prediction model for prediction to obtain the mechanical drilling speed. According to the invention, on one hand, real-time data and results of well logging and well logging are selected from the data, on the other hand, a BP neural network with strong nonlinear fitting capability and a genetic algorithm with strong nonlinear optimization capability are selected from the data processing method, so that the data and algorithm processes are reduced. Therefore, the invention can be widely applied to the field of petroleum exploration and development.
Description
Technical Field
The invention relates to the field of petroleum exploration and development, in particular to a drilling speed intelligent prediction and optimization method, a drilling speed intelligent prediction and optimization system, drilling speed intelligent prediction and optimization equipment and a drilling speed intelligent prediction and optimization medium based on real-time drilling data in drilling engineering.
Background
In recent years, with the increasing demand and the increasing exploration and development of oil and gas resources, the oil and gas development field in China is expanding from conventional oil and gas resources to unconventional oil and gas resources such as low-permeability, deep-layer, ultra-deep-layer, deep-water, shale oil and gas and the like. This also presents new challenges for drilling: the geological conditions are more complex, the environment is more severe, and the operating conditions are more severe, so that the mining cost becomes higher. Therefore, further development of oil exploration techniques is urgently required.
In order to better perform exploration and development, improve the drilling efficiency, reduce the drilling cost and reduce the drilling risk, one of the most effective methods is to improve the mechanical drilling speed. The optimization of the mechanical drilling speed is one of the problems to be solved urgently in the current drilling engineering, and is also a key overcoming project in the current drilling engineering. In the drilling process, the drilling rate of the drilling machine can be accurately predicted, so that on one hand, the drilling accident can be monitored and prevented in advance, the drilling risk is reduced, and the drilling safety is improved; on the other hand, powerful support can be provided for the drilling optimization method based on real-time prediction, so that the drilling period is reduced, and the cost is reduced.
From different angles, a plurality of scholars explore a method for improving the mechanical drilling speed by combining multiple means. However, in the current research, there are still many problems:
(1) with the continuous and complete informatization degree of each large petroleum company and the rapid development of underground measuring tools for many years, a large amount of historical and real-time data are accumulated on a system of a drilling technology, the types of the data are dozens, the data volume is extremely large, and a flow, a standard and standardized method for effectively processing and utilizing the real-time data is lacked at present;
(2) the underground condition in the well drilling is complex, the factors influencing ROP (rate of penetration) are too many, various data are not completely independent, and complex internal connection exists between the various data, so that the model construction is difficult, and a well drilling optimization method based on real-time data prediction is further lacked.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a drilling rate intelligent prediction and optimization method, a system, equipment and a medium, wherein the influence of drilling parameters on the drilling rate is analyzed based on real drilling data, an ROP model is constructed by using a BP neural network, a large amount of related data is used for training and verifying, then an optimal solution is obtained globally through a genetic algorithm, namely, the maximum average drilling rate value is searched, and the corresponding parameter value is obtained.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the invention provides a drilling rate intelligent prediction and optimization method, which comprises the following steps:
preprocessing the acquired original drilling data to obtain a sample set;
training the built drilling speed prediction model based on the sample set to obtain a trained drilling speed prediction model;
and inputting the real-time drilling data of the block to be predicted into the drilling speed prediction model for prediction to obtain the mechanical drilling speed.
Further, the method for preprocessing the acquired raw drilling data to obtain a sample set comprises:
acquiring original drilling data; performing data cleaning on original drilling data; and carrying out standardization processing on the cleaned drilling data to obtain a sample set.
Further, the method for performing data cleaning on the original drilling data comprises the following steps: missing value processing, abnormal value processing, repeated value processing, and sampling value processing.
Further, the method for training the established drilling rate prediction model based on the sample set to obtain the trained drilling rate prediction model comprises the following steps:
establishing a drilling speed prediction model; and obtaining a training set, a verification set and a test set based on the sample set, training the drilling speed prediction model by using the training set, and verifying the training result by using the verification set and the test set.
Further, the drilling rate prediction model is a three-layer BP neural network and comprises an input layer, a middle layer and an output layer;
the input layer is used for receiving drilling data in a training set;
the middle layer is used for processing drilling data to obtain the optimized mechanical drilling speed;
the output layer is used for outputting the mechanical drilling speed.
Further, the method for training the drilling speed prediction model comprises the following steps: and training the drilling speed prediction model by adopting a training set, and testing and verifying the trained model by adopting a verification set and a test set until the model verification requirement is met.
Further, the model verification requirement refers to: correlation R of penetration rate obtained by model prediction and actual penetration rate in training set, test set and verification set2Satisfies the set model minimum R2And (4) requiring.
In a second aspect, the present invention provides an intelligent drilling rate prediction and optimization system, which is characterized in that the system comprises:
the sample set acquisition module is used for preprocessing the acquired original drilling data to obtain a sample set;
the model training module is used for training the built drilling speed prediction model based on the sample set to obtain a trained drilling speed prediction model;
and the drilling rate prediction module is used for inputting the real-time drilling data of the block to be predicted into the drilling rate prediction model for prediction to obtain the mechanical drilling rate.
In a third aspect, the present invention provides a processing apparatus, comprising at least a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the intelligent drilling rate prediction and optimization method.
In a fourth aspect, the present invention provides a computer storage medium having computer readable instructions stored thereon which are executable by a processor to perform the steps of the method for intelligent prediction and optimization of rate of penetration.
Due to the adoption of the technical scheme, the invention has the following advantages:
(1) aiming at the problems of various data types, huge data size and complex internal relation, the invention adopts a machine learning method (such as a deep neural network model), does not independently research the influence of one factor, but comprehensively learns and analyzes all data, and reduces the influence of human experience;
(2) in order to ensure the real-time performance of the model in practical application, on one hand, real-time data and results of well logging and well logging are selected from the data, and on the other hand, a BP neural network with strong nonlinear fitting capability and a genetic algorithm with strong nonlinear optimization capability are selected from the data processing method, so that the data and algorithm processes are reduced;
(3) in order to ensure the practicability of a machine learning model, the invention aims to find out the maximum average drilling speed aiming at a certain stratum and obtain corresponding parameters thereof to guide well drilling;
therefore, the invention can be widely applied to the field of petroleum exploration and development.
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. Like reference numerals refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a flow chart of a method for intelligently predicting and optimizing drilling rate according to an embodiment of the present invention;
FIG. 2 is a training set correlation of a ROP prediction model in an embodiment of the present invention;
FIG. 3 is a validation set correlation of a ROP prediction model in an embodiment of the present invention;
FIG. 4 is a test set correlation of a ROP prediction model in an embodiment of the present invention;
FIG. 5 is the correlation R of all data of the predicted ROP and the actual ROP in the embodiment of the present invention2；
FIG. 6 is a comparison of predicted and actual ROPs based on depth in an embodiment of the present invention;
FIG. 7 is a comparison of actual and optimized ROPs based on depth in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings of the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention, are within the scope of the invention.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
In some embodiments of the present invention, an intelligent drilling rate prediction and optimization method is provided, which includes the following steps: preprocessing the acquired original drilling data to obtain a sample set; training the built drilling speed prediction model based on the sample set to obtain a trained drilling speed prediction model; and inputting the real-time drilling data of the block to be predicted into the drilling speed prediction model for prediction to obtain the mechanical drilling speed. Aiming at the problems of various data types, huge data size and complex internal relation, the invention adopts a machine learning method (such as a deep neural network model) to comprehensively learn and analyze all data, thereby reducing the influence of human experience. At the same time, the user can select the desired position,
correspondingly, in other embodiments of the present invention, an intelligent drilling rate prediction and optimization system, apparatus and medium are also provided.
Example 1
As shown in fig. 1, the present embodiment provides an intelligent drilling rate prediction and optimization method, which includes the following steps:
step 1: and preprocessing the acquired original drilling data to obtain a sample set.
Specifically, the step 1 can be implemented by the following steps: acquiring original drilling data; performing data cleaning on original drilling data; and carrying out standardization processing on the cleaned drilling data to obtain a sample set.
In a preferred embodiment, the method of acquiring raw well data is: and selecting a block, and uniformly arranging all drilling data including the whole logging data, the logging data and the like into an Excel table to serve as original drilling data.
In a preferred embodiment, a method of data cleansing raw drilling data comprises: missing value processing, abnormal value processing, repeated value processing, and sampling value processing.
Wherein, missing value processing means: and compensating or deleting the missing part data generated in the collection process according to the actual situation.
The abnormal value processing means: and carrying out re-compensation calculation or deletion on abnormal data with errors caused by various reasons according to actual conditions.
The repeated value processing means: duplicate detection and deletion are performed on the data that is repeatedly recorded.
The sampling value processing means: and processing different sampling frequencies of each parameter to reach the same sampling depth to obtain a processed data set.
In a preferred embodiment, a method of normalizing cleaned well data, comprising: the well drilling data are zoomed according to a preset proportion, so that the phenomenon that the magnitude between parameters or the magnitude of the data is too large to cause that the magnitude is smaller than the magnitude is avoided, and the small data cannot act or acts less.
Step 2: and training the built drilling speed prediction model based on the sample set to obtain the trained drilling speed prediction model.
Specifically, the step 2 can be implemented by the following steps: establishing a drilling speed prediction model; and obtaining a training set, a verification set and a test set based on the sample set, wherein the training set, the verification set and the test set are respectively used for training, verifying and testing the drilling speed prediction model.
In a preferred embodiment, the rate of penetration prediction model employs a three-layer BP neural network, which includes an input layer, an intermediate layer, and an output layer. Wherein the input layer is used for receiving the drilling data in the training set; the middle layer is used for processing the drilling data to obtain the optimized mechanical drilling speed, and the middle layer is provided with proper layers and nodes, so that the depth of a neural network can be increased, and the characterization capability of a nonlinear model is improved; and the output layer is used for outputting the obtained drilling rate.
In a preferred embodiment, a method for obtaining a training set, a validation set, and a test set based on a sample set includes: and sampling from the sample set by adopting a random sampling method according to a preset proportion to obtain a training set, a verification set and a test set. Preferably, the preset proportions of the training set, validation set and test set are 70%, 15% and 15%, respectively.
In a preferred embodiment, a method of training a rate of penetration prediction model includes: and training the drilling speed prediction model by adopting a training set, and testing and verifying the trained model by adopting a verification set and a test set until the model verification requirement is met.
Preferably, the model validation requirements refer to: correlation R of penetration rate obtained by model prediction and actual penetration rate in training set, test set and verification set2Satisfies the set model minimum R2And (4) requiring.
Optimizing drilling parameters, utilizing the characteristics of self organization, self adaptation and intelligence of a genetic algorithm, updating the parameters by adjusting and setting controllable drilling parameters, namely the drilling pressure WOB and the rotating speed RPM, utilizing the genetic algorithm to update the parameters, finding out the maximum value of a complex function trained by a BP neural network, namely finding out the maximum average mechanical drilling speed value, and obtaining the corresponding drilling pressure WOB and the corresponding rotating speed RPM.
And step 3: and inputting the real-time drilling data of the block to be predicted into the drilling speed prediction model for prediction to obtain the mechanical drilling speed.
Example 2
The present embodiment takes real-time data in a drilling process of a certain block as an example, and describes the method of embodiment 1 in detail, including the following steps:
step 1: selecting a block, and uniformly arranging all available drilling data, namely whole logging data, logging data and the like, into an Excel table to serve as original drilling data;
step 2: performing data preprocessing on original drilling data;
and step 3: the preprocessed well drilling data are subjected to standardization processing, the data are scaled to be within a (0, 1) range according to a proportion, the phenomenon that the magnitude of the parameter or the magnitude of the data are too large to cause the phenomenon that the magnitude of the data is too small to cause the effect of the small data to be incapable or less, and the partially processed data are shown in the following table 1.
Table 1 partial data after processing
And 4, step 4: establishing a drilling rate prediction model, determining the input layer vector of the three-layer BP neural network as the drilling data after standard processing, setting the number of middle layer as one layer and 30 neurons, and setting the output layer as the actual mechanical drilling rate;
and 5: the training number set adopts 70% of randomly sampled preprocessed data to train the model, and the rest 30% of data is used as a test verification set to test and verify the trained model;
as shown in FIGS. 2-4, the correlations R of the training set of predicted and actual rates of penetration, respectively2(FIG. 2), verify the relevance of the set (FIG. 3), test the relevance of the set (FIG. 4); selecting the lowest R of the model verification set285%, so the model meets the precision requirement;
step 6: finally establishing a prediction model by using all data of training and verification tests, predicting the correlation R of the ROP and the actual ROP2As shown in fig. 5, the ROP based on depth comparison is shown in fig. 6;
and 7: the drilling parameters are optimized, the characteristics of self organization, self adaptation and intelligence of a genetic algorithm are utilized, the maximum value of a complex function trained by a BP neural network can be found based on simple binary genetic codes by adjusting and setting controllable drilling parameters (the bit pressure WOB and the rotating speed RPM), namely the maximum average drilling rate value is found, corresponding parameter values are obtained, and the depth-based comparison of the actual drilling rate and the optimized drilling rate is shown in figure 7, so that the average ROP is improved by 35.7%.
The drilling speed intelligent prediction and optimization method based on the drilling real-time data provided by the embodiment of the invention adopts the whole real-time logging data of the wells in the same block to train the model, so that the universality of the model in the block is strong; meanwhile, the embodiment of the invention also carries out prediction optimization based on real-time drilling data, so that the model can be effectively utilized in the actual drilling work and can be guided to work. In addition, the ROP and relevant parameters have strong nonlinearity and regularity which are difficult to characterize by using a conventional method. The machine learning model is good at processing nonlinear problems, parameter optimization can be further carried out after real-time modeling, and the machine learning model has important significance for guiding field operation. And training and verifying side well data, logging data and logging data by using a neural network model in machine learning, and performing global optimal solution on the ROP by using a genetic algorithm to obtain the highest average ROP and corresponding control parameter values thereof.
Example 3
The embodiment 1 provides an intelligent drilling rate prediction and optimization method, and correspondingly, the embodiment provides an intelligent drilling rate prediction and optimization system. The identification system provided in this embodiment may implement the intelligent drilling rate prediction and optimization method in embodiment 1, and the system may be implemented by software, hardware, or a combination of software and hardware. For example, the system may comprise integrated or separate functional modules or functional units to perform the corresponding steps in the methods of embodiment 1. Since the system of this embodiment is basically similar to the method embodiment, the description process of this embodiment is relatively simple, and reference may be made to part of the description of embodiment 1 for relevant points.
The intelligent drilling rate predicting and optimizing system provided by the embodiment comprises:
the sample set acquisition module is used for preprocessing the acquired original drilling data to obtain a sample set;
the model training module is used for training the built drilling speed prediction model based on the sample set to obtain a trained drilling speed prediction model;
and the drilling rate prediction module is used for inputting the real-time drilling data of the block to be predicted into the drilling rate prediction model for prediction to obtain the mechanical drilling rate.
Example 4
This embodiment provides a processing device corresponding to the drilling rate intelligent prediction and optimization method provided in embodiment 1, where the processing device may be a processing device for a client, such as a mobile phone, a notebook computer, a tablet computer, a desktop computer, and the like, to execute the method of embodiment 1.
The processing equipment comprises a processor, a memory, a communication interface and a bus, wherein the processor, the memory and the communication interface are connected through the bus so as to complete mutual communication. The memory stores a computer program that can be run on the processor, and the processor executes the intelligent drilling rate prediction and optimization method provided by embodiment 1 when running the computer program.
In some embodiments, the Memory may be a high-speed Random Access Memory (RAM) and may also include a non-volatile Memory, such as at least one disk Memory.
In other embodiments, the processor may be various general-purpose processors such as a Central Processing Unit (CPU), a Digital Signal Processor (DSP), and the like, and is not limited herein.
Example 5
An intelligent drilling rate prediction and optimization method of embodiment 1 may be embodied as a computer program product, which may include a computer readable storage medium having computer readable program instructions for executing the intelligent drilling rate prediction and optimization method of embodiment 1.
The computer readable storage medium may be a tangible device that retains and stores instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any combination of the foregoing.
It should be noted that the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s).
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
The above embodiments are only used for illustrating the present invention, and the structure, connection mode, manufacturing process, etc. of the components may be changed, and all equivalent changes and modifications performed on the basis of the technical solution of the present invention should not be excluded from the protection scope of the present invention.
Claims (10)
1. An intelligent drilling rate prediction and optimization method is characterized by comprising the following steps:
preprocessing the acquired original drilling data to obtain a sample set;
training the built drilling speed prediction model based on the sample set to obtain a trained drilling speed prediction model;
and inputting the real-time drilling data of the block to be predicted into the drilling speed prediction model for prediction to obtain the mechanical drilling speed.
2. The intelligent drilling rate prediction and optimization method of claim 1, wherein: the method for preprocessing the acquired raw drilling data to obtain the sample set comprises the following steps:
acquiring original drilling data; performing data cleaning on original drilling data; and carrying out standardization processing on the cleaned drilling data to obtain a sample set.
3. The intelligent drilling rate prediction and optimization method of claim 2, wherein: the method for performing data cleaning on the original drilling data comprises the following steps: missing value processing, abnormal value processing, repeated value processing, and sampling value processing.
4. The intelligent drilling rate prediction and optimization method of claim 1, wherein: the method for training the established drilling rate prediction model based on the sample set to obtain the trained drilling rate prediction model comprises the following steps:
establishing a drilling speed prediction model; and obtaining a training set, a verification set and a test set based on the sample set, training the drilling speed prediction model by using the training set, and verifying the training result by using the verification set and the test set.
5. The intelligent drilling rate prediction and optimization method of claim 4, wherein: the drilling speed prediction model is a three-layer BP neural network and comprises an input layer, a middle layer and an output layer;
the input layer is used for receiving drilling data in a training set;
the middle layer is used for processing drilling data to obtain the optimized mechanical drilling speed;
the output layer is used for outputting the mechanical drilling speed.
6. The intelligent drilling rate prediction and optimization method of claim 4, wherein: the method for training the drilling speed prediction model comprises the following steps: and training the drilling speed prediction model by adopting a training set, and testing and verifying the trained model by adopting a verification set and a test set until the model verification requirement is met.
7. The intelligent drilling rate prediction and optimization method of claim 6, wherein: the model verification requirements are as follows: correlation R of penetration rate obtained by model prediction and actual penetration rate in training set, test set and verification set2Satisfies the set model minimum R2And (4) requiring.
8. An intelligent drilling rate prediction and optimization system, comprising:
the sample set acquisition module is used for preprocessing the acquired original drilling data to obtain a sample set;
the model training module is used for training the built drilling speed prediction model based on the sample set to obtain a trained drilling speed prediction model;
and the drilling rate prediction module is used for inputting the real-time drilling data of the block to be predicted into the drilling rate prediction model for prediction to obtain the mechanical drilling rate.
9. A processing apparatus comprising at least a processor and a memory, the memory having stored thereon a computer program, wherein the steps of the method for intelligent prediction and optimization of drilling rate as claimed in any one of claims 1 to 7 are performed by the processor when executing the computer program.
10. A computer storage medium having computer readable instructions stored thereon which are executable by a processor to perform the steps of a method for intelligent prediction and optimization of rate of penetration according to any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114856540A (en) * | 2022-05-11 | 2022-08-05 | 西南石油大学 | Horizontal well mechanical drilling speed while drilling prediction method based on online learning |
| CN115094193A (en) * | 2022-06-27 | 2022-09-23 | 中冶华天南京工程技术有限公司 | Intelligent molten iron pretreatment desulfurization system based on data mining |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114856540A (en) * | 2022-05-11 | 2022-08-05 | 西南石油大学 | Horizontal well mechanical drilling speed while drilling prediction method based on online learning |
| CN114856540B (en) * | 2022-05-11 | 2024-05-28 | 西南石油大学 | Horizontal well mechanical drilling speed while drilling prediction method based on online learning |
| CN115094193A (en) * | 2022-06-27 | 2022-09-23 | 中冶华天南京工程技术有限公司 | Intelligent molten iron pretreatment desulfurization system based on data mining |
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