CN117422000A - Water injection zone pressure control drilling prediction method, device, equipment and medium - Google Patents
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Abstract
The invention relates to the technical field of oilfield exploitation, in particular to a method, a device, equipment and a medium for predicting pressure-controlled drilling of a water injection zone, wherein the method comprises the following steps: acquiring historical drilling fluid injection data of a target oil field at a historical moment; based on the historical drilling fluid injection data and the long-short-term memory neural network model, a drilling fluid injection prediction model is constructed, and the drilling fluid injection prediction model is used for predicting the drilling fluid injection quantity at the future moment; acquiring drilling fluid injection data at the current moment; based on the drilling fluid injection data at the current moment and the drilling fluid injection prediction model, predicting the drilling fluid injection quantity of the target oil field at the future time, and performing fluid injection exploitation according to the predicted drilling fluid injection quantity at the future time, so that the drilling efficiency of the drilling fluid injection area of the oil field can be effectively improved.
Description
Technical Field
The invention relates to the technical field of oilfield exploitation, in particular to a water injection zone pressure control drilling prediction method, a device, equipment and a medium.
Background
The oil field drilling fluid is injected and developed to the middle and later stages, an encryption well is often deployed to improve the development of residual oil of underground crude oil, and the oil field drilling fluid is one of measures for increasing the yield of the oil field. Advanced drilling fluid injection methods of injection-before-production are also commonly used to develop low permeability fields, thereby ensuring efficient development and stable production of the well. However, long-term drilling fluid injection development causes pressure anomalies in portions of the formation, and the original pressure distribution of the formation changes. The methods can cause the phenomenon of abnormal high pressure of an underground drilling fluid injection layer, and the phenomena of associated gas, oil and water discharge and the like in the drilling process. If the well body has a simple structure or no corresponding intervention equipment, well control accidents such as lost circulation, collapse, blowout and the like are very easy to occur, so that the drilling speed of the oil well is reduced, the rotating speed of a drill bit is reduced, the drilling cost is increased, and the development process of the oil field is seriously influenced. Therefore, the liquid injection amount in the drilling process needs to be regulated and controlled in time according to the actual condition of the stratum, the pressure-controlled drilling in the drilling process is ensured, and the requirements of no pressure release and no leakage of drilling when the drilling is stopped are met.
Aiming at the drilling fluid injection development and the advanced drilling fluid injection area, the drilling is carried out in the area which is easy to encounter abnormal high pressure, and the formation pressure is balanced by improving the density of the drilling fluid. If the well depth structure is simple, the solid phase content of the drilling fluid becomes larger due to the simple improvement of the density of the drilling fluid, the rheological property of the drilling fluid becomes worse, and the drilling fluid is easy to flow into the stratum to pollute the stratum due to lost circulation.
Therefore, how to effectively control the pressure drilling so as to improve the drilling efficiency of the drilling fluid injection area of the oil field is a technical problem to be solved.
Disclosure of Invention
In view of the above, the present invention provides a method, apparatus, device and medium for pressure-controlled drilling prediction in a water injection zone that overcomes or at least partially solves the above-mentioned problems.
In a first aspect, the present invention provides a method for predicting pressure-controlled drilling in a water injection zone, including:
acquiring historical drilling fluid injection data of a target oil field at a historical moment, wherein the historical drilling fluid injection data comprises: drilling fluid injection speed at each historical moment, drilling fluid injection amount at each historical moment and influence factors of drilling fluid injection;
constructing a drilling fluid injection prediction model based on the historical drilling fluid injection data and the long-short-term memory neural network model, wherein the drilling fluid injection prediction model is used for predicting the drilling fluid injection quantity at the future moment;
acquiring drilling fluid injection data at the current moment;
and predicting the drilling fluid injection quantity of the target oil field at the future moment based on the drilling fluid injection data at the current moment and the drilling fluid injection prediction model.
Preferably, after acquiring the historical drilling fluid injection data of the target oil field at the historical moment, the method further comprises:
and carrying out normalization processing on the historical drilling fluid injection data so as to convert the historical drilling fluid injection data into standard normal distribution data.
Preferably, the building a drilling fluid injection prediction model based on the historical drilling fluid injection data and the long-short-term memory neural network model, where the drilling fluid injection prediction model is used for predicting the drilling fluid injection amount at a future time, includes:
determining input data and output data based on the historical drilling fluid injection data;
and inputting the input data and the output data into the long-short-period memory neural network model for training, and constructing a drilling fluid injection prediction model, wherein the drilling fluid injection prediction model is used for predicting the drilling fluid injection quantity at the future moment.
Preferably, the determining input data and output data based on the historical drilling fluid injection data includes:
and determining the drilling injection speed at the previous moment and the drilling injection quantity at the previous moment and the influence factors of the drilling fluid injection as input data and determining the drilling injection quantity at the next moment as output data based on the drilling fluid injection speed at each historical moment, the drilling fluid injection quantity at each historical moment and the influence factors of the drilling fluid injection, wherein the previous moment and the next moment are adjacent moments.
Preferably, after constructing a drilling fluid injection prediction model based on the historical drilling fluid injection data and the long-short-term memory neural network model, the drilling fluid injection prediction model is used for predicting the drilling fluid injection amount at the future time, the method further comprises:
and evaluating the accuracy of the drilling fluid injection prediction model.
Preferably, the predicting the drilling fluid injection amount of the target oil field at the future time based on the drilling fluid injection data at the current time and the drilling fluid injection prediction model includes:
and inputting the drilling fluid injection data at the current moment into the drilling fluid injection prediction model, so that the drilling fluid injection prediction model outputs drilling fluid injection quantity at a future moment, wherein the future moment is the moment next to the current moment, and predicting the drilling fluid injection quantity of the target oil field at the future moment.
In a second aspect, the present invention further provides a water injection zone pressure control drilling prediction apparatus, including:
the first acquisition module is used for acquiring historical drilling fluid injection data of the target oil field at historical moments;
the construction module is used for constructing a drilling fluid injection prediction model based on the historical drilling fluid injection data and the long-short-term memory neural network model, and the drilling fluid injection prediction model is used for predicting the drilling fluid injection quantity at the future moment;
the second acquisition module is used for acquiring drilling fluid injection data at the current moment;
and the prediction module is used for predicting the drilling fluid injection quantity of the target oil field at the future moment based on the drilling fluid injection data at the current moment and the drilling fluid injection prediction model.
In a third aspect, the present invention also provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method steps described in the first aspect when the program is executed.
In a fourth aspect, the present invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method steps described in the first aspect.
One or more technical solutions in the embodiments of the present invention at least have the following technical effects or advantages:
the invention provides a water injection zone pressure control drilling prediction method, which comprises the following steps: acquiring historical drilling fluid injection data of a target oil field at a historical moment; based on the historical drilling fluid injection data and the long-short-term memory neural network model, a drilling fluid injection prediction model is constructed, and the drilling fluid injection prediction model is used for predicting the drilling fluid injection quantity at the future moment; acquiring drilling fluid injection data at the current moment; based on the drilling fluid injection data at the current moment and the drilling fluid injection prediction model, predicting the drilling fluid injection quantity of the target oil field at the future time, and performing fluid injection exploitation according to the predicted drilling fluid injection quantity at the future time, so that the drilling efficiency of the drilling fluid injection area of the oil field can be effectively improved.
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 throughout the drawings, like reference numerals are used to designate like parts. In the drawings:
FIG. 1 is a schematic flow chart showing the steps of a method for predicting pressure-controlled drilling in a water injection area according to an embodiment of the invention;
FIG. 2 shows a schematic diagram of an LSTM model in an embodiment of the invention;
FIG. 3 is a schematic diagram showing a loss function calculation result of a drilling fluid injection prediction model according to an embodiment of the present invention;
FIG. 4 is a graph showing a comparison of actual, predicted, and predicted values of drilling fluid injection in an embodiment of the present invention;
FIG. 5 shows an overall scheme flow diagram in an embodiment of the invention;
FIG. 6 shows a schematic structural diagram of a pressure-controlled drilling prediction device in a water injection zone in an embodiment of the invention;
fig. 7 shows a schematic structural diagram of a computer device for implementing a method for predicting pressure-controlled drilling in a water injection area according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure 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 disclosure to those skilled in the art.
Example 1
The embodiment of the invention provides a water injection zone pressure control drilling prediction method, which is shown in fig. 1 and comprises the following steps:
s101, acquiring historical drilling fluid injection data of a target oil field at a historical moment, wherein the historical drilling fluid injection data comprises: drilling fluid injection speed at each historical moment, drilling fluid injection amount at each historical moment and influence factors of drilling fluid injection;
s102, constructing a drilling fluid injection prediction model based on historical drilling fluid injection data and a long-short-term memory neural network model, wherein the drilling fluid injection prediction model is used for predicting drilling fluid injection quantity at a future moment;
s103, acquiring drilling fluid injection data at the current moment;
s104, predicting the drilling fluid injection quantity of the target oil field at the future moment based on the drilling fluid injection data at the current moment and the drilling fluid injection prediction model.
In a specific embodiment, the historical drilling fluid injection data in S101 includes: drilling fluid injection speed at each historical moment, drilling fluid injection amount at each historical moment, and drilling fluid injection influencing factors.
Among the influencing factors of drilling fluid injection are narrow pressure formation depth, narrow pressure formation layer thickness, narrow formation pressure, and formation connectivity. These influence factor data are collected during long-term drilling to provide basis for prediction of later drilling fluid injection.
Next, after S101, further including: and carrying out normalization processing on the historical drilling fluid injection data so as to convert the historical drilling fluid injection data into standard normal distribution data.
Because there may be a large range of values, it is necessary to avoid the influence of the input of too large characteristic values of the data on the prediction result, so it is necessary to perform pretreatment, specifically normalization, on the data to convert the historical drilling fluid injection data into standard n-ethernet distribution data, so as to balance the numerical difference between the characteristics, thereby improving the speed and stability of model training.
After processing the data, a data set is formed, and the data set is divided according to a preset proportion, so that a 62% training set and a 38% testing set are obtained.
Next, S102 is performed to construct a drilling fluid injection prediction model for predicting a drilling fluid injection amount at a future time based on the historical drilling fluid injection data and the long-short-term memory neural network model.
In a specific embodiment, input data and output data are determined based on historical drilling fluid injection data; and inputting the input data and the output data into a long-short-period memory neural network model for training, and constructing a drilling fluid injection prediction model which is used for predicting the drilling fluid injection amount in a future time period.
Specifically, among the drilling fluid injection speed at each historical time, the drilling fluid injection amount at each historical time and the influence factors of the drilling fluid injection, the drilling fluid injection speed at the previous time, the drilling fluid injection amount at the previous time and the influence factors of the drilling fluid injection are determined as input data, and the drilling fluid injection amount at the next time is determined as output data.
For example, the drilling injection rate at the first time, the drilling injection amount at the first time, and the influence factor of the drilling fluid injection are used as input data, and the drilling fluid injection amount at the second time is used as output data. Wherein the first time and the second time are adjacent front and rear times.
The adopted network model is a Long-Short-Term Memory neural network model (LSTM), and can capture the historical information of the sequence in consideration of the time correlation of the data and apply the historical information to the calculation of the current output, so that the problem that other neural network models cannot adapt to time sequence data is effectively solved, the possible steps of the RNN disappear and gradient explode defects are overcome, and the Memory deficiency problem exists in the Long-time sequence processing. The long-term memory neural network model can effectively solve the problem of large injection prediction error of drilling fluid in the drilling process, and ensures that the drilling fluid injection process can stabilize the well wall and does not fracture the stratum.
In network parameters of the initial long-short-term memory neural network model, the number of neurons of a hidden layer is 50, one neuron of an output layer is used, the training frequency of the model is 60, and the data volume of a training set for training is 72 when the training set is grabbed each time. The network model optimizes and selects an Adam optimization function to calculate the self-adaptive learning rate of each parameter of the neural network.
The data set adopted in the training process is a training set. Specific training procedures are described in detail below:
based on the LSTM model, the output of the model is the predicted flow:the historical flow at the time t before the predicted period is: />Wherein the sampling interval is 5s, +.>=369，/>=408. The drilling fluid injection influencing factors are as follows: />Wherein->Is the oil pressure influencing factor>Is a jacket pressure, wherein->Other influencing factors are also included, and in the embodiment of the invention, the oil pressure and the casing pressure are mainly includedTwo influencing factors. The input data at the t-th moment is jointly formed by the influence factors of the historical injection flow and drilling fluid injection, and can be expressed as follows:。
as shown in fig. 2, a schematic structural diagram of the LSTM model is shown.
During training, the input data at the t timeFirstly, through an input gate, the value of an input layer of the memory unit at the t moment and the candidate value of the hidden layer state are respectively as follows: />，/>
Wherein,for hyperbolic tangent excitation function +.>Is->Time->Output of->As a function of the Sigmoid excitation,is->Time->Output of function->For the weight vector of the input gate, +.>Is the weight vector of the memory cell, +.>Is the bias vector of the input gate, +.>Is the bias vector of the memory cell, +.>Is the output at time t-1.
Information to be discarded is determined by forgetting a gate, which excites the output value of the function by Sigmoid ([ 0, 1)]Values within the interval), and the value of the forgetting layer of the memory unit at the t-th moment is calculated, and the forgetting gate calculation formula is expressed as follows:
wherein,for Sigmoid excitation function,/->For the output of the Sigmoid excitation function at time t,/->Weight vector for forgetting gate, +.>Is the output at time t-1.
Updating the memory unit to obtain a memory unit update value at the t moment, wherein the memory unit update value is as follows:
wherein,for long-term memory at time t +.>Is the memory at time t-1.
The information is determined to be output through the output gate, and the output values of the output layer of the memory unit at the t moment and the final memory unit are respectively as follows:，，
wherein,for time t->Output of->For the prediction of time t +.>，/>A weight vector for the output gate;is the bias vector of the output gate.
The long-term and short-term memory network model is trained through the training process, then a test set is adopted to test the model obtained through training, and the drilling fluid injection prediction model is obtained through continuously adjusting parameters of the model.
After obtaining the drilling fluid injection prediction model, the method further comprises the following steps:
and evaluating the accuracy of the drilling fluid injection prediction model. In particular, root Mean Square Error (RMSE), mean absolute error (Absolute error)(MAE) and determination coefficient (R) 2 ) As an index for evaluating the accuracy of the drilling fluid injection prediction model.
The following table shows:
the index clearly shows that it is more accurate than using other network models.
Moreover, the calculation result of the loss function of the drilling fluid injection prediction model is shown in fig. 3, the loss function is greatly reduced after the previous 28 times of iteration, and the loss function gradually becomes stable in the 28 th to 60 th iteration stages, which shows that the finally obtained parameter is the optimal parameter result under the structure.
As shown in fig. 4, which is a graph comparing the actual value with the predicted value of the injection amount of the drilling fluid and the predicted value using the RNN model, it can be seen that the predicted value of the injection amount of the drilling fluid obtained using the LSTM model is more similar to the actual value of the injection amount of the drilling fluid than the injection amount of the drilling fluid obtained using the RNN model, and thus, the accuracy of the injection prediction model of the drilling fluid obtained using the LSTM model is higher.
Next, S103 is executed to acquire drilling fluid injection data at the current time; finally, S104, based on the drilling fluid injection data at the current moment and the drilling fluid injection prediction model, predicting the drilling fluid injection quantity of the target oil field at the future moment.
The drilling fluid injection data at the current moment is input into a drilling fluid injection prediction model, so that the drilling fluid injection prediction model outputs drilling fluid injection quantity at the future moment, the future moment is the moment next to the current moment, and the drilling fluid injection quantity of a target oil field at the future moment is predicted.
S103-S104 are specifically application of a model, namely the drilling fluid injection prediction model obtained in S101-S102 is utilized, and the collected drilling fluid injection data at the current moment are input into the drilling fluid injection prediction model, so that the drilling fluid injection quantity at the future moment is predicted.
FIG. 5 shows an overall scheme flow chart, wherein the overall scheme flow chart comprises the steps of acquiring historical drilling fluid injection data, preprocessing the data, dividing a data set into a test set and a training set according to a preset proportion, training by adopting an LSTM model, predicting according to actual input data to obtain predicted data, and finally evaluating the model.
One or more technical solutions in the embodiments of the present invention at least have the following technical effects or advantages:
the invention provides a water injection zone pressure control drilling prediction method, which comprises the following steps: acquiring historical drilling fluid injection data of a target oil field in a historical moment; based on the historical drilling fluid injection data and the long-short-term memory neural network model, a drilling fluid injection prediction model is constructed, and the drilling fluid injection prediction model is used for predicting the drilling fluid injection quantity at the future moment; acquiring drilling fluid injection data at the current moment; based on the drilling fluid injection data at the current moment and the drilling fluid injection prediction model, predicting the drilling fluid injection quantity of the target oil field at the future time, and performing fluid injection exploitation according to the predicted drilling fluid injection quantity at the future time, so that the drilling efficiency of the drilling fluid injection area of the oil field can be effectively improved.
Example two
Based on the same inventive concept, the embodiment of the invention also provides a water injection zone pressure control drilling prediction device, as shown in fig. 6, comprising:
a first obtaining module 601, configured to obtain historical drilling fluid injection data of a target oilfield in a historical moment, where the historical drilling fluid injection data includes: drilling fluid injection speed at each historical moment, drilling fluid injection amount at each historical moment and influence factors of drilling fluid injection;
the construction module 602 is configured to construct a drilling fluid injection prediction model based on the historical drilling fluid injection data and the long-short-term memory neural network model, where the drilling fluid injection prediction model is used for predicting drilling fluid injection amount at a future time;
a second obtaining module 603, configured to obtain drilling fluid injection data at a current moment;
and the prediction module 604 is configured to predict the drilling fluid injection amount of the target oil field at a future time based on the drilling fluid injection data at the current time and the drilling fluid injection prediction model.
In an alternative embodiment, the historical drilling fluid injection data comprises:
drilling fluid injection speed at each historical moment, drilling fluid injection amount at each historical moment, and drilling fluid injection influencing factors.
In an alternative embodiment, the method further comprises: the preprocessing module is used for:
and carrying out normalization processing on the historical drilling fluid injection data so as to convert the historical drilling fluid injection data into standard normal distribution data.
In an alternative embodiment, a building block is configured to:
determining input data and output data based on the historical drilling fluid injection data;
and inputting the input data and the output data into the long-short-period memory neural network model for training, and constructing a drilling fluid injection prediction model, wherein the drilling fluid injection prediction model is used for predicting the drilling fluid injection quantity at the future moment.
In an alternative embodiment, the building block is specifically configured to:
and determining the drilling injection speed at the previous moment and the drilling injection quantity at the previous moment and the influence factors of the drilling fluid injection as input data and determining the drilling injection quantity at the later moment as output data based on the drilling fluid injection speed at each historical moment, the drilling fluid injection quantity at each historical moment and the influence factors of the drilling fluid injection.
In an alternative embodiment, the method further comprises: an evaluation module for:
and evaluating the accuracy of the drilling fluid injection prediction model.
In an alternative embodiment, the prediction module 604 is configured to:
and inputting the drilling fluid injection data at the current moment into the drilling fluid injection prediction model, so that the drilling fluid injection prediction model outputs drilling fluid injection quantity at a future moment, wherein the future moment is the moment next to the current moment, and predicting the drilling fluid injection quantity of the target oil field at the future moment.
Example III
Based on the same inventive concept, an embodiment of the present invention provides a computer device, as shown in fig. 7, including a memory 704, a processor 702, and a computer program stored in the memory 704 and capable of running on the processor 702, where the processor 702 implements the steps of the water injection zone pressure control drilling prediction method described above when executing the program.
Where in FIG. 7 a bus architecture (represented by bus 700), bus 700 may comprise any number of interconnected buses and bridges, with bus 700 linking together various circuits, including one or more processors, as represented by processor 702, and memory, as represented by memory 504. Bus 700 may also link together various other circuits such as peripheral devices, voltage regulators, power management circuits, etc., as are well known in the art and, therefore, will not be described further herein. Bus interface 706 provides an interface between bus 700 and receiver 701 and transmitter 703. The receiver 701 and the transmitter 703 may be the same element, i.e. a transceiver, providing a unit for communicating with various other apparatus over a transmission medium. The processor 702 is responsible for managing the bus 700 and general processing, while the memory 704 may be used to store data used by the processor 702 in performing operations.
Example IV
Based on the same inventive concept, an embodiment of the present invention provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of the above-described water injection zone pressure control drilling prediction method.
The algorithms and 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, the present invention is 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 foregoing description of exemplary embodiments of the invention, various features 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 embodiment. Rather, as each embodiment reflects, 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 a specific implementation, any of the claimed embodiments may 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 functions of some or all of the components of a water injection zone pressure controlled drilling prediction apparatus, a computer device, 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.
Claims (9)
1. The method for predicting the pressure-controlled drilling of the water injection area is characterized by comprising the following steps of:
acquiring historical drilling fluid injection data of a target oil field at a historical moment, wherein the historical drilling fluid injection data comprises: drilling fluid injection speed at each historical moment, drilling fluid injection amount at each historical moment and influence factors of drilling fluid injection;
constructing a drilling fluid injection prediction model based on the historical drilling fluid injection data and the long-short-term memory neural network model, wherein the drilling fluid injection prediction model is used for predicting the drilling fluid injection quantity at the future moment;
acquiring drilling fluid injection data at the current moment;
and predicting the drilling fluid injection quantity of the target oil field at the future moment based on the drilling fluid injection data at the current moment and the drilling fluid injection prediction model.
2. The method of claim 1, wherein after obtaining historical drilling fluid injection data for the target oilfield at the historical time, further comprising:
and carrying out normalization processing on the historical drilling fluid injection data so as to convert the historical drilling fluid injection data into standard normal distribution data.
3. The method of claim 1, wherein the constructing a drilling fluid injection prediction model based on the historical drilling fluid injection data and a long-short term memory neural network model, the drilling fluid injection prediction model for predicting a drilling fluid injection amount at a future time, comprises:
determining input data and output data based on the historical drilling fluid injection data;
and inputting the input data and the output data into the long-short-period memory neural network model for training, and constructing a drilling fluid injection prediction model, wherein the drilling fluid injection prediction model is used for predicting the drilling fluid injection quantity at the future moment.
4. The method of claim 3, wherein the determining input data and output data based on the historical drilling fluid injection data comprises:
and determining the drilling injection speed at the previous moment and the drilling injection quantity at the previous moment and the influence factors of the drilling fluid injection as input data and determining the drilling injection quantity at the next moment as output data based on the drilling fluid injection speed at each historical moment, the drilling fluid injection quantity at each historical moment and the influence factors of the drilling fluid injection, wherein the previous moment and the next moment are adjacent moments.
5. The method of claim 1, wherein after constructing a drilling fluid injection prediction model based on the historical drilling fluid injection data and the long-short term memory neural network model, the drilling fluid injection prediction model is used to predict a drilling fluid injection amount at a future time, further comprising:
and evaluating the accuracy of the drilling fluid injection prediction model.
6. The method of claim 1, wherein predicting the drilling fluid injection amount of the target oilfield at a future time based on the drilling fluid injection data at the current time and the drilling fluid injection prediction model comprises:
and inputting the drilling fluid injection data at the current moment into the drilling fluid injection prediction model, so that the drilling fluid injection prediction model outputs drilling fluid injection quantity at a future moment, wherein the future moment is the moment next to the current moment, and predicting the drilling fluid injection quantity of the target oil field at the future moment.
7. A water injection zone pressure control drilling prediction apparatus, comprising:
the first acquisition module is used for acquiring historical drilling fluid injection data of a target oil field at historical moments, wherein the historical drilling fluid injection data comprises: drilling fluid injection speed at each historical moment, drilling fluid injection amount at each historical moment and influence factors of drilling fluid injection;
the construction module is used for constructing a drilling fluid injection prediction model based on the historical drilling fluid injection data and the long-short-term memory neural network model, and the drilling fluid injection prediction model is used for predicting the drilling fluid injection quantity at the future moment;
the second acquisition module is used for acquiring drilling fluid injection data at the current moment;
and the prediction module is used for predicting the drilling fluid injection quantity of the target oil field at the future moment based on the drilling fluid injection data at the current moment and the drilling fluid injection prediction model.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1-6 when the program is executed by the processor.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-6.
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