CN114000862A - Geological drilling process drilling speed intelligent control system based on dynamic optimization - Google Patents
Geological drilling process drilling speed intelligent control system based on dynamic optimization Download PDFInfo
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- E21B44/00—Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
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Abstract
The invention provides a drilling rate intelligent control system based on dynamic optimization in a geological drilling process, which mainly solves the problem that the drilling rate optimization efficiency is low because most of the conventional geological drilling process drilling rate optimization methods adopt an off-line optimization mode and are difficult to recommend proper operating parameters (a drilling pressure set value and a rotating speed set value) to a driller under the condition of variable stratum environments. The drilling rate optimization framework is divided into two layers (intelligent optimization layer and basic automation layer). In an intelligent optimization layer, drilling rate modeling and drilling rate optimization are carried out alternately in sequence along with the development of a drilling process, and a dynamic relation model between the drilling rate and the well depth, the drilling pressure and the rotating speed is established by applying a plurality of technologies such as a sliding window, an extreme learning machine, ten-turn cross validation and the like; then the hybrid bat algorithm is applied to increase the drilling rate by optimizing the drilling operation parameters (weight on bit, rotational speed). The invention has the beneficial effects that: can adapt to different stratum conditions, and effectively improves the efficiency of the drilling process.
Description
Technical Field
The invention relates to the field of intelligent control of a complex geological drilling process, in particular to a drilling speed intelligent control system based on dynamic optimization in a geological drilling process.
Background
The resource and energy safety is an important component of national safety and is also one of the keys of national economic sustainable development. Ensuring the safety of resources and energy resources should be based on the nation. With the increasing exhaustion of shallow mineral resources, the development of deep mineralization theory and the exploration of a large number of deep mineral resources at home and abroad, deep geological exploration and development become inevitable. However, in the deep geological drilling process, a complex geomechanical environment with three-high-one disturbance exists, and the characteristics of nonlinearity, strong coupling, strong interference and the like are outstanding, so that the geological drilling process is low in efficiency and poor in safety. Therefore, intelligent control of the drilling rate is an important basis for realizing the safe and efficient target of the drilling process of complex geology.
Disclosure of Invention
In order to solve the problems, the invention provides a drilling speed intelligent control system based on dynamic optimization in a geological drilling process, which can adapt to different stratum conditions, intelligently optimize drilling process parameter parameters (drilling pressure and rotating speed) on line, and send the drilling process parameter parameters to a basic automation layer to perform closed-loop control respectively, thereby effectively improving the drilling process efficiency. This geology creeps into process drilling rate intelligent control system mainly includes: two layers: the intelligent optimization layer is an upper layer, and the basic automation layer is a lower layer;
the intelligent optimization layer is used for establishing a drilling rate dynamic relation model between the drilling rate and the drilling process parameters according to the drilling process data, optimizing the drilling rate and the drilling pressure, further realizing intelligent drilling rate optimization calculation, and sending the optimized drilling pressure and the optimized drilling rate to a lower layer as set values;
and the basic automation layer is used for respectively carrying out closed-loop control on the bit pressure and the rotating speed and sending the collected various drilling process data to the upper layer.
Further, the drilling process data comprises: drilling rate, weight on bit and depth at the current moment.
Further, the intelligent optimization layer includes a drilling rate modeling phase and a drilling rate optimization phase.
Further, in the drilling rate modeling stage, a sliding window, an extreme learning machine method and a ten-fold cross validation method are adopted, a dynamic relation model between the drilling rate and the drilling process parameters is established by using the drilling process data, the input of the dynamic relation model is the current drilling rate, the current drilling pressure and the current depth, and the output is the drilling rate at the next moment.
Furthermore, in the drilling rate optimization stage, firstly, the data of the drilling process is preprocessed, and then, the optimized rotating speed and the optimized drilling pressure are obtained by utilizing a mixed bat algorithm.
Further, establishing the dynamic relation model of the drilling rate comprises the following processes:
1) establishing a relation model among the drilling pressure at the current moment, the rotating speed at the current moment, the depth at the current moment and the drilling speed at the next moment by using an extreme learning machine method, wherein the relation model formula is as follows:
β＝H+T
where H is the hidden layer matrix, H+Moore-Penrose inverse of H, T is the output vector, β is the weight from the hidden layer to the output layer, g () is the activation function, xiIs an input parameter, k is the number of hidden layer nodes, ωiIs the weight from the input layer to the hidden layer, biIs the hidden layer bias, i ═ 1, 2.., n;
2) introducing a sliding window and a ten-fold cross validation method into the relation model to establish a drilling rate dynamic relation model;
3) when a sample data set is used for training a drilling speed dynamic relation model, the sample data set needs to be subjected to normalization processing;
wherein x isnormIs a normalized data set, x is the true data set, xminIs the smallest data in the data set, xmaxIs the largest data in the data set;
4) the verification indexes when the trained drilling speed dynamic relation model is tested by using the test set are as follows:
where RMSE is the root mean square error, NRMSE is the normalized root mean square error, yiIs the measurement data that is to be measured,
is predictive data.
Further, the procedure for optimizing the rate of penetration and weight on bit is as follows:
1) preprocessing drilling process data, and then establishing a drilling speed dynamic optimization model based on a drilling speed dynamic relation model and a drilling constraint condition;
where y is the drilling rate at the next time, H is the hidden layer matrix, g () is the activation function, XnIs a set of input data, xiIs an input parameter, k is the number of hidden layer nodes, H+Is the Moore-Penrose inverse of H, Tt,ωt,btIs the weight, offset and output vector at time t, x1min,x1max,x2min,x2maxThe lower and upper numerical values of the bit pressure and the rotating speed are respectively;
2) and optimizing drilling operation parameters by utilizing a mixed bat algorithm based on the drilling speed dynamic optimization model, wherein the drilling operation parameters comprise the drilling pressure and the rotating speed.
The technical scheme provided by the invention has the beneficial effects that:
(1) according to the geological drilling process drilling rate intelligent control system and system based on dynamic optimization, a drilling rate intelligent control framework is established in two layers (an intelligent optimization layer and a basic automation layer), and a good foundation can be laid for the following drilling rate intelligent control work;
(2) according to the geological drilling process drilling rate intelligent control system and system based on dynamic optimization, drilling operation parameters (drilling pressure and rotating speed) are optimized by using a mixed bat algorithm, local optimization can be achieved to a great extent, and the drilling process efficiency is effectively improved.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a block diagram of an intelligent control system for drilling rate in an embodiment of the present invention.
FIG. 2 is a graph showing the comparison of the intelligent control of drilling rate in the embodiment of the present invention.
FIG. 3 is a graph of comparison results of iterative runs of an algorithm in an embodiment of the present invention.
Detailed Description
For a more clear understanding of the technical features, objects and effects of the present invention, embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
The embodiment of the invention provides a drilling rate intelligent control system based on dynamic optimization in a geological drilling process, which mainly solves the problem that the drilling rate optimization efficiency is low because most of the conventional geological drilling process drilling rate optimization methods adopt an off-line optimization mode and are difficult to recommend proper operating parameters (a drilling pressure set value and a rotating speed set value) to a driller under the condition of variable stratum environments.
Referring to fig. 1, fig. 1 is a structural diagram of an intelligent drilling rate control system according to an embodiment of the present invention, where a frame of the intelligent drilling rate control system is divided into two layers: an intelligent optimization layer and a basic automation layer. In the intelligent optimization layer, the drilling rate modeling stage and the drilling rate optimization stage (or the drilling rate implementation stage) are included, and in the drilling rate optimization stage, firstly, the drilling process data are preprocessed, and then, the optimized rotating speed and the optimized drilling pressure are obtained by utilizing a mixed bat algorithm. The drilling rate modeling stage and the drilling rate optimizing stage are carried out alternately in sequence along with the development of the drilling process, a plurality of technologies such as a sliding window, an extreme learning machine, a ten-turn cross validation and the like are applied to the drilling rate modeling stage and the drilling rate optimizing stage, a drilling rate dynamic relation model between the drilling rate and the well depth, the drilling pressure and the rotating speed is established, the input of the dynamic relation model is the current drilling rate, the current drilling pressure and the current depth, and the output is the drilling rate at the next moment.
The establishing of the drilling rate dynamic relation model comprises the following processes:
1) establishing a relation model among the drilling pressure at the current moment, the rotating speed at the current moment, the depth at the current moment and the drilling speed at the next moment by using an extreme learning machine method, wherein the relation model formula is as follows:
β＝H+T
where H is the hidden layer matrix, H+Moore-Penrose inverse of H, T is the output vector, β is the weight from the hidden layer to the output layer, g () is the activation function, xiIs an input parameter, k is the number of hidden layer nodes, ωiIs the weight from the input layer to the hidden layer, biIs the hidden layer bias, i ═ 1, 2.., n;
2) introducing a sliding window and a ten-fold cross validation method into the relation model to establish a drilling rate dynamic relation model;
3) when a sample data set is used for training a drilling speed dynamic relation model, the sample data set needs to be subjected to normalization processing;
wherein x isnormIs a normalized data set, x is the true data set, xminIs the smallest data in the data set, xmaxIs the largest data in the data set;
4) the verification indexes when the trained drilling speed dynamic relation model is tested by using the test set are as follows:
where RMSE is the root mean square error, NRMSE is the normalized root mean square error, yiIs the measurement data that is to be measured,
is predictive data.
The procedure for optimizing the rate of penetration and weight on bit is as follows:
1) the drilling process data is pre-processed and then based on a dynamic relationship model of rate of penetration and drilling constraints (i.e., the following weight-on-bit x)10And a rotational speed x20The value range) of the drilling rate, and establishing a drilling rate dynamic optimization model;
where y is the drilling rate at the next time, H is the hidden layer matrix, g () is the activation function, XnIs a set of input data, xiIs an input parameter, k is the number of hidden layer nodes, H+Is the Moore-Penrose inverse of H, Tt,ωt,btIs the weight, offset and output vector at time t, x1min,x1max,x2min,x2maxRespectively, weight on bit x10And a rotational speed x20Lower and upper bound values of;
2) based on the drilling speed dynamic optimization model, the drilling speed is improved by optimizing drilling operation parameters (drilling pressure and rotating speed) by using a mixed bat algorithm. The optimized bit pressure and the optimized rotating speed can be used as set values to be sent to a basic automation layer for closed-loop control.
The optimized drilling rate and the optimized drilling pressure are respectively input into a rotating speed control system and a drilling pressure control system of a lower basic automation layer, then corresponding execution mechanisms execute the optimized drilling rate and the optimized drilling pressure, various process information, namely various drilling process data, is detected through various detection instruments, and the various drilling process data are transmitted to an upper intelligent optimization layer.
The drilling rate modeling and drilling rate optimization/implementation method in the intelligent optimization layer in fig. 1 is used for calculation, and drilling operation parameters (drilling pressure and rotating speed) are sent to the basic automation layer, so that the drilling rate intelligent control comparison result shown in fig. 2 is obtained, and the drilling rate is increased by 28.37% by the provided method, and is the highest in the comparison method. The seven comparison methods comprise: a sliding window-extreme learning machine-bat algorithm (MW-ELM-BA), a sliding window-extreme learning machine-particle swarm algorithm (MW-ELM-PSO), a support vector regression-mixed bat algorithm (SVR-HBA), a support vector regression-simplex algorithm (SVR-NM), a support vector regression-particle swarm algorithm (SVR-PSO), a support vector regression-simulated annealing algorithm (SVR-SA), and a support vector regression-mixed frog-leap algorithm (SVR-SFLA). Compared with the results of 7 well-known drilling rate optimization methods, the effectiveness of the method is verified.
The comparison result of the iterative operation of the algorithm is shown in fig. 3, and it can be known that the proposed method can rapidly jump out of the local optimum and converge.
The invention has the beneficial effects that:
(1) according to the geological drilling process drilling rate intelligent control system and system based on dynamic optimization, a drilling rate intelligent control framework is established in two layers (an intelligent optimization layer and a basic automation layer), and a good foundation can be laid for the following drilling rate intelligent control work;
(2) according to the geological drilling process drilling rate intelligent control system and system based on dynamic optimization, drilling operation parameters (drilling pressure and rotating speed) are optimized by using a mixed bat algorithm, local optimization can be achieved to a great extent, and the drilling process efficiency is effectively improved.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (7)
1. The utility model provides a geology creeps into process drilling rate intelligence control system based on dynamic optimization which characterized in that: this geology creeps into process drilling rate intelligence control system includes two-layer: the intelligent optimization layer is an upper layer, and the basic automation layer is a lower layer;
the intelligent optimization layer is used for establishing a drilling rate dynamic relation model between the drilling rate and the drilling process parameters according to the drilling process data, optimizing the drilling rate and the drilling pressure, further realizing intelligent drilling rate optimization calculation, and sending the optimized drilling pressure and the optimized drilling rate to a lower layer as set values;
and the basic automation layer is used for respectively carrying out closed-loop control on the bit pressure and the rotating speed and sending the collected various drilling process data to the upper layer.
2. The intelligent control system for the drilling rate of the geological drilling process based on dynamic optimization as claimed in claim 1, wherein: the drilling process data comprises: drilling rate, weight on bit and depth at the current moment.
3. The intelligent control system for the drilling rate of the geological drilling process based on dynamic optimization as claimed in claim 1, wherein: the intelligent optimization layer comprises a drilling rate modeling stage and a drilling rate optimization stage.
4. The intelligent control system for the drilling rate of the geological drilling process based on dynamic optimization as claimed in claim 3, wherein: in the drilling rate modeling stage, a sliding window, an extreme learning machine method and a ten-fold cross validation method are adopted, a dynamic relation model between the drilling rate and the drilling process parameters is established by using the drilling process data, the input of the dynamic relation model is the drilling rate at the current moment, the drilling pressure at the current moment and the depth at the current moment, and the output is the drilling rate at the next moment.
5. The intelligent control system for the drilling rate of the geological drilling process based on dynamic optimization as claimed in claim 3, wherein: in the drilling rate optimization stage, firstly, the data of the drilling process is preprocessed, then, a drilling rate dynamic optimization model is established based on a drilling rate dynamic relation model and a drilling constraint condition, and finally, an optimized rotating speed and drilling pressure are obtained by utilizing a mixed bat algorithm.
6. The intelligent control system for the drilling rate of the geological drilling process based on dynamic optimization as claimed in claim 1, wherein: the establishing of the drilling rate dynamic relation model comprises the following processes:
1) establishing a relation model among the drilling pressure at the current moment, the rotating speed at the current moment, the depth at the current moment and the drilling speed at the next moment by using an extreme learning machine method, wherein the relation model formula is as follows:
β＝H+T
where H is the hidden layer matrix, H+Moore-Penrose inverse of H, T is the output vector, β is the weight from the hidden layer to the output layer, g () is the activation function, xiIs an input parameter, k is the number of hidden layer nodes, ωiIs the weight from the input layer to the hidden layer, biIs the hidden layer bias, i ═ 1, 2.., n;
2) introducing a sliding window and a ten-fold cross validation method into the relation model to establish a drilling rate dynamic relation model;
3) when a sample data set is used for training a drilling speed dynamic relation model, the sample data set needs to be subjected to normalization processing;
wherein x isnormIs a normalized data set, x is the true data set, xminIs the smallest data in the data set, xmaxIs the largest data in the data set;
4) the verification indexes when the trained drilling speed dynamic relation model is tested by using the test set are as follows:
where RMSE is the root mean square error, NRMSE is the normalized root mean square error, yiIs the measurement data that is to be measured,
is predictive data.
7. The intelligent control system for the drilling rate of the geological drilling process based on dynamic optimization as claimed in claim 1, wherein: the procedure for optimizing the rate of penetration and weight on bit is as follows:
1) preprocessing drilling process data, and then establishing a drilling speed dynamic optimization model based on a drilling speed dynamic relation model and a drilling constraint condition;
wherein y is the drilling rate at the next moment,h is the hidden layer matrix, g () is the activation function, XnIs a set of input data, xiIs an input parameter, k is the number of hidden layer nodes, H+Is the Moore-Penrose inverse of H, Tt，ωt，btIs the weight, offset and output vector at time t, x1min，x1max，x2min，x2maxThe lower and upper numerical values of the bit pressure and the rotating speed are respectively;
2) and optimizing drilling operation parameters by utilizing a mixed bat algorithm based on the drilling speed dynamic optimization model, wherein the drilling operation parameters comprise the drilling pressure and the rotating speed.
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| US20040256152A1 (en) * | 2003-03-31 | 2004-12-23 | Baker Hughes Incorporated | Real-time drilling optimization based on MWD dynamic measurements |
| CN111434886A (en) * | 2019-01-15 | 2020-07-21 | 中国石油化工股份有限公司 | Mechanical drilling speed calculation method and device for drilling process |
| CN113268803A (en) * | 2021-06-08 | 2021-08-17 | 中国石油大学(北京) | Method for generating drilling overflow diagnosis model, drilling overflow diagnosis method and device |
| CN113338894A (en) * | 2021-07-15 | 2021-09-03 | 西安石油大学 | Control method of small intelligent drilling machine |
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