CN112016766A - Oil and gas well drilling overflow and leakage early warning method based on long-term and short-term memory network - Google Patents

Oil and gas well drilling overflow and leakage early warning method based on long-term and short-term memory network Download PDF

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CN112016766A
CN112016766A CN202010979086.2A CN202010979086A CN112016766A CN 112016766 A CN112016766 A CN 112016766A CN 202010979086 A CN202010979086 A CN 202010979086A CN 112016766 A CN112016766 A CN 112016766A
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李玉飞
王留洋
刘俊男
罗园
胡旭光
卿玉
罗卫华
赖向东
胡光辉
曾国玺
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CNPC Chuanqing Drilling Engineering Co Ltd
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Abstract

The invention provides an oil and gas well drilling overflow and leakage early warning method based on a long-term and short-term memory network, which comprises the following steps: constructing an overflow and leakage risk early warning model based on a long and short term memory network, wherein the overflow and leakage risk early warning model comprises an input layer, a hidden layer and an output layer, and the hidden layer comprises at least two layers of long and short term memory network structures; training an overflow and leakage risk early warning model by using the existing drilling risk data; and (4) performing drilling and leakage early warning on the oil and gas well by using the trained leakage risk early warning model. The beneficial effects of the invention can include: the method can timely and accurately give out the identification result, can reduce the dependence of risk identification on prior knowledge and expert experience, enables the early warning of the drilling risk to be more intelligent and efficient, and has good field application prospect.

Description

Oil and gas well drilling overflow and leakage early warning method based on long-term and short-term memory network
Technical Field
The invention relates to the field of well control safety in petroleum and natural gas drilling engineering, in particular to an oil and gas well drilling overflow and leakage early warning method based on a long-term and short-term memory network.
Background
Overflow and well leakage are two risks which are easy to occur in the drilling process, and the overflow and leakage dangerous situation is easy to occur particularly in oil and gas exploration blocks with the characteristics of deep wells, ultra-deep wells, fault development, carbonate rocks and the like and newly opened blocks with some underground pressure systems which are not very clear. The overflow and the lost circulation not only can cause serious damage to a reservoir stratum, increase the development cost and reduce the development efficiency, but also can induce the drilling accidents such as drilling sticking, well collapse, blowout and the like once the control is not successful, thereby causing serious casualties and economic loss. Therefore, the real-time monitoring and early warning of early overflow and lost circulation in the drilling process is of great significance to safe and efficient drilling and drilling cost saving.
The drilling process is a complex nonlinear dynamic process, uncertainty factors are numerous, random interference on various drilling parameters is large, the parameters are correlated and coupled, an accurate overflow and leakage risk identification model is difficult to establish, and the accuracy of overflow and leakage early warning is limited.
Disclosure of Invention
In view of the deficiencies in the prior art, it is an object of the present invention to address one or more of the problems in the prior art as set forth above. For example, one of the objectives of the present invention is to improve the accuracy of the overflow monitoring and early warning.
In order to achieve the purpose, the invention provides an oil and gas well drilling overflow and leakage early warning method based on a long-term and short-term memory network. The early warning method can comprise the following steps: constructing an overflow and leakage risk early warning model based on a long and short term memory network, wherein the overflow and leakage risk early warning model comprises an input layer, a hidden layer and an output layer, and the hidden layer comprises at least two layers of long and short term memory network structures; training an overflow and leakage risk early warning model by using the existing drilling risk data; and (4) performing drilling and leakage early warning on the oil and gas well by using the trained leakage risk early warning model.
According to an exemplary embodiment of the present invention, the early warning method may include the steps of: constructing an overflow and leakage risk early warning model based on a long and short term memory network, wherein the overflow and leakage risk early warning model comprises an input layer, a hidden layer and an output layer, and the hidden layer comprises at least two layers of long and short term memory network structures; training an overflow and leakage risk early warning model by using the existing drilling risk data; and (4) performing drilling and leakage early warning on the oil and gas well by using the trained leakage risk early warning model.
According to an exemplary embodiment of the present invention, the long-short term memory network structure of the output layer and the hidden layer can be connected by using a softmax activation function.
According to an exemplary embodiment of the invention, the output layer is capable of outputting a probability of occurrence of at least one of a flooding risk, a lost circulation risk and a normal operating condition.
According to an exemplary embodiment of the present invention, the step of training the early warning model of overflow risk includes: forward propagation of data and backward propagation of errors.
According to an exemplary embodiment of the present invention, the step of training the early warning model of overflow risk includes: step A: setting training parameters of the constructed overflow and leakage risk early warning model; and B: initializing the weight and bias of the constructed overflow and leakage risk early warning model; and C: inputting the drilling risk data, and outputting the actual working condition by the overflow and leakage risk early warning model under the current weight and bias; step D: comparing the actual working condition with the expected output, and reversely propagating layer by layer under the condition that the actual working condition does not meet the expected output, and distributing the error to each layer of the overflow and leakage risk early warning model; step E: and C, adjusting the weight and the bias, and repeating the steps B to D until the output actual working condition meets the expected output.
According to an exemplary embodiment of the present invention, the step of training the early warning model of overflow risk includes: screening drilling risk data from the drilling data, the drilling risk data comprising: the volume of a mud pit on the ground, the pressure of a riser, the outlet flow of drilling fluid at a wellhead, the annular pressure at the bottom of a well and the annular temperature at the bottom of the well; carrying out normalization processing on the drilling risk data, and removing outlier points in the data obtained after the normalization processing; expanding the drilling risk data in an overlapped sampling mode; then, the above steps A to E are carried out.
According to an exemplary embodiment of the present invention, the steps of screening drilling risk data, normalizing, removing outliers, and expanding data may be implemented by an input layer of the overflow risk early warning model.
According to an exemplary embodiment of the invention, the drilling risk data may comprise: the volume of a mud pit on the ground, the pressure of a riser, the outlet flow rate of drilling fluid at a wellhead, the annular pressure at the bottom of a well and the annular temperature at the bottom of the well.
According to an exemplary embodiment of the present invention, before the training of the early warning model of risk of overflow, the method may further include the steps of: and carrying out normalization processing on the drilling risk data.
According to an exemplary embodiment of the present invention, the drilling risk data may be normalized using a maximum method.
According to an exemplary embodiment of the present invention, the method may further comprise the steps of: and removing outlier points in the data obtained after the normalization processing.
According to an exemplary embodiment of the present invention, the well risk data may be augmented in an overlapping sampling manner before the training of the spill risk early warning model.
Compared with the prior art, the beneficial effects of the invention can comprise at least one of the following:
(1) when the overflow and leakage risk monitoring is carried out, the identification result can be timely and accurately given.
(2) The method can avoid the characteristic extraction process of the current mainstream overflow and leakage early warning method, and reduces the dependence of risk identification on prior knowledge and expert experience, so that the early warning of the drilling risk is more intelligent and efficient.
(3) The input and output data of the invention are established according to the artificial intelligence model which does not depend on the theoretical mechanism model seriously, and the requirement on expert experience can be reduced.
(4) With the promotion of digital oil field construction and the establishment of a drilling risk database, the drilling risk early warning method has good field application prospect.
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The above and other objects and features of the present invention will become more apparent from the following description taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a schematic diagram of a long short term memory network-based early warning model of risk of overflow of the present invention;
FIG. 2 shows a schematic diagram of the present invention for performing an overlap acquisition;
fig. 3 shows a training flow chart of the early warning model of overflow risk of the present invention.
Detailed Description
Hereinafter, the oil and gas well drilling overflow early warning method based on the long-short term memory network will be described in detail with reference to the accompanying drawings and exemplary embodiments.
The current overflow leakage monitoring method adopted in the drilling site mainly takes manual post-setting monitoring of the liquid level of a mud tank, and is influenced by the sensitivity of a liquid level sensor and the fluctuation of the liquid level of the mud tank, so that the monitoring accuracy is not high. Aiming at the problems of limited real-time performance and accuracy and low intelligent degree of the existing overflow, well leakage and other drilling risk monitoring, the invention combines the characteristics that three monitoring parameters of underground, well head and ground and a Long short-term memory network (LSTM) are suitable for processing strongly coupled and strongly time-dependent data, and provides an intelligent overflow risk early warning method based on the LSTM network, which is used for improving the accuracy of overflow monitoring and early warning.
In an exemplary embodiment of the invention, the oil and gas well drilling overflow early warning method based on the long-short term memory network can comprise the following steps:
s10: an overflow and leakage risk early warning model based on a long-short term memory network is constructed and comprises an input layer, a hidden layer and an output layer, wherein the overflow and leakage risk early warning model is shown in figure 1.
S20: and training the overflow and leakage risk early warning model by using the existing drilling risk data.
S30: and (4) performing drilling and leakage early warning on the oil and gas well by using the trained leakage risk early warning model.
In this embodiment, the input layer may have functions of monitoring parameter selection and data preprocessing.
(1) Monitoring parameter selection
In order to accurately monitor the risk of overflow and lost circulation, appropriate monitoring parameters need to be selected. The invention combines the change characteristics of relevant parameters when the overflow and leakage risk occurs, and can preferably select five parameters of the volume of a mud pit on the ground, the pressure of a stand pipe, the outlet flow of drilling fluid at a wellhead, the annular pressure at the bottom of a well and the annular temperature as parameters for monitoring the overflow and leakage risk.
(2) Data pre-processing
In the actual drilling process, the types of sensors are numerous, the dimensions and the magnitude of different sensors are different, and in order to accelerate the training speed of the network, improve the classification precision and enhance the applicability of the model, before the training of the network model, a data sequence acquired by the sensors is normalized by adopting a most value method, as shown in formula (1).
Figure BDA0002686910910000041
Wherein the content of the first and second substances,
Figure BDA0002686910910000042
is the value after normalization, xiIs a current value, xminAnd xmaxRespectively representing the minimum and maximum values of the acquired data sequence.
Meanwhile, the influence of noise and random interference on the data acquired on site in the acquisition and transmission processes inevitably causes outliers, and the accuracy of the identification model is influenced. Is provided with
Figure BDA0002686910910000043
Is the mean, σ, of n sampled data sequences2When the nth sample point satisfies the variance
Figure BDA0002686910910000044
And in time, the point is considered as a outlier point, and the outlier point is replaced by the neighborhood mean value.
In addition, the missing data possibly existing in the monitoring data is supplemented by adopting a nearest neighbor interpolation mode.
(3) Data expansion
Further, the input layer may have a function of data expansion. In consideration of relatively less risk data such as overflow, well leakage and the like, in order to avoid network overfitting, the invention provides that a data set is expanded by adopting an overlapped sampling mode. A schematic diagram of overlapping samples is shown in fig. 2. When the model is trained, the data expansion function (namely the link) can be adopted to expand the sample data, and when the model is applied to overflow monitoring, the data expansion function is not needed.
In this embodiment, the hidden layer may be formed by two layers of LSTM structures, and the number, output form, and parameters of neuron nodes included in each layer may be as shown in table 1.
TABLE 1 hidden layer information
Layer unit Output Shape Param
LSTM1 32 (None,5,32) 6784
LSTM2 16 (None,16) 3136
Wherein None indicates that the dimension cannot be determined, and is related to the number of samples of batch data during neural network training.
In this embodiment, the output layer may be pre-warned for both overflow and lost circulation risks. Each drilling risk may be associated with a type of label, which may be represented by "one-hot vector", for example, in a specific form shown in table 2.
TABLE 2 Risk types and methods of representation
Figure BDA0002686910910000051
And the output layer and the LSTM unit of the hidden layer can be connected by adopting a softmax activation function, and finally, the overflow risk, the well leakage risk and the probability of the occurrence of normal working conditions are respectively output.
In this embodiment, in step S20, that is, during the training process, 32 groups of data may be randomly extracted from the training samples to form a Batch (Batch), one Batch of data is trained for each iteration, and the training loss value and the test accuracy are recorded. Considering that the number of samples in this experiment is relatively small, in order to prevent the neural network from being overfit, L can be introduced in two LSTM layers2The regularization strategy constrains.
The specific training process includes two parts, namely forward propagation of data and backward propagation of errors, as shown in fig. 3. Firstly, setting training parameters of a network, initializing weight and bias of the network, inputting monitoring data, calculating actual output under the current weight and bias sequentially through an LSTM layer and a full connection layer, calculating error between the actual output and expected output, reversely propagating layer by layer, distributing the error to each layer, and adjusting the weight and the bias of the network by using an Adam algorithm until training conditions are met so as to realize supervised training of the network.
The technical scheme of the invention introduces an advanced deep learning method into the field of drilling risk monitoring of oil and gas wells, and provides a drilling overflow and leakage risk early warning method based on an LSTM network, namely, the LSTM network learns the existing drilling risk data to obtain an overflow and leakage risk early warning model, the acquired original drilling data is preprocessed and then directly input into the trained LSTM network model for overflow and leakage risk monitoring, and the identification result can be timely and accurately given.
Although the present invention has been described above in connection with exemplary embodiments, it will be apparent to those skilled in the art that various modifications and changes may be made to the exemplary embodiments of the present invention without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (10)

1. An oil and gas well drilling overflow and leakage early warning method based on a long-term and short-term memory network is characterized by comprising the following steps:
constructing an overflow and leakage risk early warning model based on a long and short term memory network, wherein the overflow and leakage risk early warning model comprises an input layer, a hidden layer and an output layer, and the hidden layer comprises at least two layers of long and short term memory network structures;
training an overflow and leakage risk early warning model by using the existing drilling risk data;
and (4) performing drilling and leakage early warning on the oil and gas well by using the trained leakage risk early warning model.
2. The oil and gas well drilling overflow and leakage early warning method based on the long-short term memory network is characterized in that the output layer is connected with the long-short term memory network structure of the hidden layer by adopting a softmax activation function.
3. The oil and gas well drilling overflow and leakage early warning method based on the long-short term memory network as claimed in claim 1, wherein the output layer can output the probability of at least one of overflow risk, well leakage risk and normal working condition.
4. The oil and gas well drilling overflow and leakage early warning method based on the long-short term memory network according to claim 1, wherein the step of training the overflow and leakage risk early warning model comprises the following steps: forward propagation of data and backward propagation of errors.
5. The oil and gas well drilling overflow and leakage early warning method based on the long-short term memory network as claimed in claim 1, wherein the step of training the overflow and leakage risk early warning model comprises:
step A: setting training parameters of the constructed overflow and leakage risk early warning model;
and B: initializing the weight and bias of the constructed overflow and leakage risk early warning model;
and C: inputting the drilling risk data, and outputting the actual working condition by the overflow and leakage risk early warning model under the current weight and bias;
step D: comparing the actual working condition with the expected output, and reversely propagating layer by layer under the condition that the actual working condition does not meet the expected output, and distributing the error to each layer of the overflow and leakage risk early warning model;
step E: and C, adjusting the weight and the bias, and repeating the steps B to D until the output actual working condition meets the expected output.
6. The oil and gas well drilling overflow and leakage early warning method based on the long-short term memory network as claimed in claim 1, wherein the drilling risk data comprises:
the volume of a mud pit on the ground, the pressure of a riser, the outlet flow rate of drilling fluid at a wellhead, the annular pressure at the bottom of a well and the annular temperature at the bottom of the well.
7. The oil and gas well drilling spillage early warning method based on the long and short term memory network as claimed in claim 1, wherein before the training of the spillage risk early warning model, the method further comprises the steps of: and carrying out normalization processing on the drilling risk data.
8. The oil and gas well drilling overflow and leakage early warning method based on the long-short term memory network as claimed in claim 7, wherein the drilling risk data is normalized by adopting a most value method.
9. The method for warning of oil and gas well drilling spillage and leakage of long and short term memory network according to claim 7, wherein the method further comprises the steps of: and removing outlier points in the data obtained after the normalization processing.
10. The oil and gas well drilling overflow and leakage early warning method based on the long-short term memory network as claimed in claim 1, wherein the drilling risk data is expanded in an overlapping sampling mode before the overflow and leakage risk early warning model is trained.
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