CN114856540A - Horizontal well mechanical drilling speed while drilling prediction method based on online learning - Google Patents
Horizontal well mechanical drilling speed while drilling prediction method based on online learning Download PDFInfo
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Abstract
The invention relates to a horizontal well mechanical drilling speed while drilling prediction method based on online learning, and belongs to the technical field of oil and gas drilling. In order to overcome the problems in the prior art, the invention provides a horizontal well mechanical drilling speed while drilling prediction method based on online learning, which comprises the following steps: s1, acquiring real-time drilling data; s2, preprocessing real-time data; s3, calculating the operation parameters of the drill bit in real time; s4, establishing and updating a mechanical drilling rate prediction model; s5, carrying out real-time monitoring on formation change and updating while drilling of a mechanical drilling speed prediction model; calculating a stratum drillability extreme value in real time, and judging whether the stratum changes or not; and if the stratum changes, reconstructing the weight and deviation of the connecting layer, keeping the data structure, and repeating the steps S1-S4 to establish a new stratum drilling rate prediction model. The method solves the problem that the drilling speed of the horizontal well machinery cannot be accurately predicted in real time in the prior art, can be directly applied on the basis of the conventional drilling equipment, and does not need an additional underground measuring tool.
Description
Technical Field
The invention relates to a horizontal well mechanical drilling speed while drilling prediction method based on online learning, and belongs to the technical field of oil and gas drilling.
Background
The mechanical drilling rate as an index of the drilling efficiency is always a hotspot of drilling engineering research, and numerous scholars are dedicated to establishing a proper model to predict the mechanical drilling rate. In recent years, the mechanical drilling rate prediction research has achieved a great deal of results due to machine learning and application of big data technology, but the shortcomings still exist. The main performance is as follows: at present, a prediction model of an intelligent algorithm is utilized, and adjacent well data are adopted for training and learning. Due to uncertainty of geological conditions and stratum heterogeneity, the model popularization after training is limited.
Most of the current mechanical drilling rate prediction models are researched aiming at a vertical well, and the theories need to be further perfected and popularized to a horizontal well. Because downhole measurement devices are expensive, the data currently used for modeling is typically surface log data. In a vertical well, the well track and the well body structure are relatively simple, the well depth is equal to the vertical depth, the ground bit pressure is equal to the bit pressure of a drill bit, the rotating speed of the logging data record is the real rotating speed of the drill bit, and the modeling can be directly carried out by using well head data. However, in the drilling process of the horizontal well, the difference between the ground bit pressure and the bottom hole bit pressure is large due to the friction between the drill string and the well wall, and the real rotating speed and torque of the drill bit are difficult to obtain due to the screw rod + PDC technology. Therefore, in the horizontal well, the influence of the weight-on-bit transmission efficiency and the screw drill needs to be considered, and the operation parameters of the drill bit need to be corrected.
Therefore, the invention provides a horizontal well mechanical drilling speed while drilling prediction method based on online learning, discusses how to comprehensively consider the friction torque and the influence of a screw drill to establish a mechanical drilling speed prediction model, and researches how to train by using current well data and update the mechanical drilling speed prediction model in real time.
Disclosure of Invention
In order to overcome the problems in the prior art, the invention provides a horizontal well mechanical drilling speed while drilling prediction method based on online learning.
The technical scheme provided by the invention for solving the technical problems is as follows: a horizontal well mechanical drilling speed while-drilling prediction method based on online learning comprises the following steps:
s1, acquiring real-time drilling data;
the real-time data comprises dynamic data and quasi-dynamic data;
s2, preprocessing real-time data;
preprocessing real-time data based on a time series moving average filter;
s3, calculating the operation parameters of the drill bit in real time;
the drill bit operation parameters comprise drill bit weight, drill bit torque and drill bit rotating speed;
s4, establishing and updating a mechanical drilling rate prediction model;
selecting bit weight, bit speed, bit torque, inlet discharge capacity and well depth as input characteristic variables, selecting the mechanical drilling speed as an output variable, and performing offline training on acquired real-time data by adopting a batch learning algorithm to obtain a neural network to establish a mechanical drilling speed prediction model;
then, performing online training on the acquired new data based on an online learning algorithm to obtain an online neural network, and updating a mechanical drilling speed prediction model in real time;
s5, carrying out real-time monitoring on formation change and updating while drilling of a mechanical drilling speed prediction model;
calculating a stratum drillability extreme value in real time, and judging whether the stratum changes or not; and if the stratum changes, reconstructing the weight and deviation of the mechanical drilling rate prediction model connecting layer, keeping the data structure, and repeating the steps S1-S4 to establish a new stratum mechanical drilling rate prediction model.
The further technical scheme is that the dynamic data comprises hook load, wellhead torque, wellhead rotating speed, inlet flow, mechanical drilling speed and well depth; the quasi-dynamic data comprises drilling tool combination data, well structure data, well track data and drilling fluid density data.
The further technical scheme is that abnormal points are removed and smoothed in the step S2 according to the time sequence data of hook load, wellhead torque, wellhead rotating speed, inlet flow and drilling rate.
The further technical scheme is that the calculation formula of the bit weight of the drill bit is as follows:
in the formula: f 0 Hook load, N; mu.s i The friction coefficient between the unit body of the i section and the well wall is zero dimension; alpha is alpha i 、
The well bevel angle, dog-leg angle and rad at two ends of the unit body respectively; l is i Is the length of the ith section of unit body, m; delta alpha i Rads are the well angle increments at the two ends of the unit body of the i section; q. q.s m The floating weight N/m of the unit body at the i section in the drilling fluid is obtained; q Pi 、Q P(i-1) Shearing forces N are applied to the upper end and the lower end of the i-th section of unit body on the P plane; q Ri 、Q R(i-1) Shearing forces N are applied to the upper end and the lower end of the I section of unit body on the R plane; n is a radical of i The radial supporting force N borne by the i-th section of unit body.
The further technical scheme is that the calculation formula of the bit torque is as follows:
if the screw drill is not adopted at the current moment, determining the drill bit rotating speed according to the following formula:
RPM＝RPM 0
in the formula: RPM is the rotating speed of the drill bit, r/min; RPM 0 The rotation speed of the well head is r/min.
If the screw drill is adopted at the current moment, determining the drill bit rotating speed according to the following formula:
in the formula: RPM is the rotating speed of the drill bit, r/min; RPM 0 The rotating speed of the wellhead is r/min; q is inlet flow, L/s; and q is the flow per revolution of the screw drill, L/r.
The further technical scheme is that the calculation formula of the drill bit rotating speed is as follows:
if the screw drill is not used at the current time, determining the bit torque according to the following formula:
in the formula: t is 0 Is the torque of the well head, N.m; mu.s t The coefficient of circumferential friction resistance is zero dimension; r is i Is the outer diameter of the drill stem, mm; n is a radical of i The radial supporting force N borne by the i-th section of unit body.
If a screw drill is used at the present time, determining the bit torque according to the following formula:
in the formula: t is 0 Is the torque of the well head, N.m; mu.s t The coefficient of circumferential friction resistance is zero dimension; r is i Is the outer diameter of the drill stem, mm; q is the flow per revolution of the screw drill, L/r; delta P is the pressure drop of the screw drill, MPa; n is a radical of i The radial supporting force N borne by the i-th section of unit body.
The further technical scheme is that the batch learning algorithm comprises the following steps: and (3) assuming that k-1 groups of samples exist at the current moment, performing offline training on the samples by adopting a neural network to obtain the optimal weight and deviation, and obtaining an offline neural network model.
The further technical scheme is that the online learning algorithm comprises the following steps:
(1) assuming that the k-1 group training sample learning is finished, taking the weight and the deviation of the k-1 group online neural network as the kth group training data to learn the initial weight and the deviation of the training network online;
(2) calculating the actual output of the online neural network under the conditions of the initial weight and the deviation;
(3) calculating the output error of each neuron by using the actual output and the expected output of the online neural network, and further calculating the accumulated error amount;
(4) and (3) updating the weight and the deviation according to an online gradient descent method, iterating to obtain a new weight and a new deviation, taking the obtained new weight and the obtained new deviation as the initial weight and the deviation of the k +1 group of training data, and repeating the steps (1) to (4) to complete the online learning of the k +1 group of training data.
The further technical scheme is that the calculation formula of the stratum drillability extreme value is as follows:
in the formula: RPM is the bit rotation speed, r/min; WOB is the weight on bit, kN; ROP is the mechanical drilling speed, m/h; d is the drill diameter, mm.
The further technical scheme is that the specific steps of judging whether the stratum changes in the step S5 are as follows:
obtaining a drillability extreme value time sequence from the current stratum initial drilling moment to the current moment according to the stratum drillability extreme value, and taking values of the time sequence according to the depth at equal intervals to obtain a drillability sequence according to the depth;
the depth axis is taken as a time axis, and a time sequence singular spectrum analysis method is utilized to predict a stratum drillability extreme value of a stratum from the current depth h to the depth h +1(m), and the stratum drillability extreme value is a sequence TS 1 ；
Calculating the practical value of the stratum drillability extreme value of the stratum with the depth of h to h +1(m) according to the real drilling data to be the sequence TS 2 ；
Calculating sequences TS separately 1 And a sequence TS 2 If the difference between the two means is greater than 1, the formation change is determined.
The invention has the following beneficial effects:
1. the invention can be directly applied on the basis of the existing conventional drilling equipment without an additional underground measuring tool;
2. the method can be used for predicting the drilling speed of the drilling machinery of the vertical well, the highly-deviated well and the horizontal well in real time, and has wide application range;
3. the method can be used for predicting the mechanical drilling speed while drilling in a drilling site without adjacent well data.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a graph of the results of real-time calculations of drill bit operational parameters;
FIG. 3 is a diagram illustrating a comparison between predicted and measured formation drillability extrema;
FIG. 4 is a schematic representation of a formation change.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it is to be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in FIG. 1, the horizontal well mechanical drilling rate while drilling prediction method based on online learning comprises the following steps:
s1, acquiring real-time drilling data;
the real-time data comprises dynamic data of fixed frequency acquired according to real-time logging data of a drilling site and quasi-dynamic data acquired according to a drilling log; wherein the dynamic data comprises hook load, wellhead torque, wellhead rotating speed, inlet flow, mechanical drilling speed and well depth; the quasi-dynamic data comprises drilling tool combination data, well structure data, well track data and drilling fluid density data;
s2, preprocessing real-time data;
based on a time sequence moving average filter, abnormal points are removed and smoothed from time sequence data of hook load, wellhead rotating speed, wellhead torque, inlet flow and mechanical drilling speed;
s3, calculating the operation parameters of the drill bit in real time;
the drill bit operation parameters comprise drill bit weight, drill bit torque and drill bit rotating speed;
the bit weight of the drill bit is calculated according to the following formula:
in the formula: f 0 Hook load, N; mu.s i The friction coefficient between the unit body of the i section and the well wall is zero dimension; alpha is alpha i 、
The well bevel angle, dog-leg angle and rad at two ends of the unit body respectively; l is i Is the length of the ith section of unit body, m; delta alpha i Rads are the well angle increments at the two ends of the unit body of the i section; q. q.s m The floating weight N/m of the unit body at the i section in the drilling fluid is obtained; q Pi 、Q P(i-1) Shearing forces N are applied to the upper end and the lower end of the i-th section of unit body on the P plane; q Ri 、Q R(i-1) Shearing forces N are applied to the upper end and the lower end of the I section of unit body on the R plane; n is a radical of i The radial supporting force N borne by the i section unit body;
if the screw drill is not adopted at the current moment, determining the drill bit rotating speed according to the following formula:
RPM＝RPM 0
in the formula: RPM is the rotating speed of the drill bit, r/min; RPM 0 The rotation speed of the wellhead is r/min;
if the screw drill is adopted at the current moment, determining the rotation speed of the drill bit according to the following formula:
in the formula: RPM is the rotating speed of the drill bit, r/min; RPM 0 The rotating speed of the wellhead is r/min; q is inlet flow, L/s; q is the flow per revolution of the screw drill, L/r;
if the screw drill is not used at the current time, determining the bit torque according to the following formula:
in the formula: t is 0 Is the torque of the well head, N.m; mu.s t The coefficient of circumferential friction resistance is zero dimension; r is i Is the outer diameter of the drill stem, mm; n is a radical of i Is a unit body of the i-th sectionRadial bearing force, N;
if a screw drill is used at the present time, determining the bit torque according to the following formula:
in the formula: t is 0 Is the torque of the well head, N.m; mu.s t The coefficient of circumferential friction resistance is zero dimension; r is i Is the outer diameter of the drill stem, mm; q is the flow per revolution of the screw drill, L/r; delta P is the pressure drop of the screw drill, MPa; n is a radical of i The radial supporting force N borne by the i section unit body;
s4, establishing and updating a mechanical drilling rate prediction model;
s41, selecting bit weight, bit speed, bit torque, inlet displacement and well depth as input characteristic variables, selecting the drilling rate as an output variable, and performing offline training on the acquired real-time data by adopting a batch learning algorithm to obtain a neural network to establish a drilling rate prediction model;
the batch learning algorithm is as follows: supposing that k-1 groups of samples exist at the current moment, performing offline training on the samples by adopting a neural network to obtain an optimal weight and deviation, and obtaining an offline neural network model;
s42, performing online training on the acquired new data based on an online learning algorithm to obtain an online neural network, and updating a drilling rate prediction model of the machine in real time;
the online learning algorithm is as follows: (1) assuming that batch learning of k-1 groups of training samples is finished, taking the weight and the deviation of the k-1 groups of online neural networks as kth group of training data to learn the initial weight and the deviation of the training networks on line; (2) calculating the actual output of the online neural network under the conditions of the initial weight and the deviation; (3) calculating the output error of each neuron by using the actual output and the expected output of the online neural network, and further calculating the accumulated error amount; (4) updating the weight and the deviation according to an online gradient descent method, iteratively obtaining a new weight and a new deviation, taking the obtained new weight and the obtained new deviation as the initial weight and the deviation of the k +1 group of training data, and repeating the steps (1) to (4) to complete online learning of the k +1 group of training data;
s5, carrying out real-time monitoring on formation change and updating while drilling of a mechanical drilling speed prediction model;
s51, calculating a stratum drillability extreme value in real time;
in the formula: RPM is the bit rotation speed, r/min; WOB is the weight on bit, kN; ROP is the mechanical drilling speed, m/h; d is the diameter of the drill bit, mm;
s52, obtaining a drillability extreme value time sequence from the initial drilling time of the current stratum to the current time according to the stratum drillability extreme value, and taking values of the time sequence at equal intervals according to depths to obtain a drillability sequence taken according to the depths;
s53, taking the depth axis as a time axis, and predicting a stratum drillability extreme value of a stratum from the current depth h to the depth h +1(m) by using a time sequence singular spectrum analysis method to obtain a sequence TS 1 ；
S54, calculating the practical value of the stratum drillability extreme value of the stratum with the depth of h to h +1(m) depth into a sequence TS according to the practical drilling data 2 ；
S55, respectively calculating sequences TS 1 And a sequence TS 2 If the difference value of the two mean values is more than 1, determining that the stratum changes;
s56, if the stratum changes, reconstructing weight and deviation of a mechanical drilling rate prediction model connecting layer, keeping a data structure, and repeating the steps S1-S4 to establish a new stratum mechanical drilling rate prediction model.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
The simulation data is taken from a horizontal well of a shale gas block in Sichuan and comprises dynamic data and quasi-dynamic data. The dynamic data is time-based, one per 5 seconds, and includes hook load, wellhead weight on bit, wellhead speed, wellhead torque, flow, rate of penetration, well depth, and drilling fluid density.
The basic data are as follows:
(1) data of actual wellbore trajectory
TABLE 1 actual drilling trajectory data
(2) Real drilling tool combination data
TABLE 2 actual drilling trajectory data
(3) Data of actual well body structure
TABLE 3 actual drilling trajectory data
1. Calculating the drill bit operation parameters and the stratum drillability extreme value in real time;
the results of the real-time calculation of the bit operation parameters and the formation drillability extreme values are shown in fig. 2, and the bit weight, bit speed and bit torque are calculated in real time, and as can be seen from the figure, the bit operation parameters and the wellhead operation parameters have larger differences.
2. Establishing and updating a mechanical drilling speed prediction model in real time;
and (3) carrying out normalization processing on the real-time data, taking 80% of samples to establish a training set, taking 20% of samples to establish a testing set, selecting the bit weight, the bit speed, the bit torque, the inlet discharge capacity and the well depth of the drill bit as input characteristic variables, selecting the mechanical drilling rate as an output variable, and carrying out off-line training on the acquired real-time data by adopting a batch learning training mode to obtain a neural network to establish a mechanical drilling rate prediction model. And then, carrying out online training on the acquired new data based on an online learning algorithm to obtain an online neural network, and updating the drilling rate prediction model of the machine in real time.
3. Reconstructing a stratum change monitoring and mechanical drilling speed prediction model;
the predicted value and measured value of the extreme value of the formation drillability are shown in FIG. 3, and the predicted sequence TS 1 And a measured sequence TS 2 The mean values of the two are respectively 4.42 and 6.56, the difference value of the mean values of the two is more than 1, the formation change is judged, and the mechanical drilling rate model is reconstructed. As shown in FIG. 4, the change of the stratum can be effectively judged based on the stratum drillability extreme value.
Although the present invention has been described with reference to the above embodiments, it should be understood that the present invention is not limited to the above embodiments, and those skilled in the art can make various changes and modifications without departing from the scope of the present invention.
Claims (10)
1. A horizontal well mechanical drilling speed while drilling prediction method based on online learning is characterized by comprising the following steps:
s1, acquiring real-time drilling data;
the real-time data comprises dynamic data and quasi-dynamic data;
s2, preprocessing real-time data;
preprocessing real-time data based on a time series moving average filter;
s3, calculating the operation parameters of the drill bit in real time;
the drill bit operation parameters comprise drill bit weight, drill bit torque and drill bit rotating speed;
s4, establishing and updating a mechanical drilling rate prediction model;
selecting bit weight, bit speed, bit torque, inlet discharge capacity and well depth as input characteristic variables, selecting the mechanical drilling speed as an output variable, and performing offline training on acquired real-time data by adopting a batch learning algorithm to obtain a neural network to establish a mechanical drilling speed prediction model;
then, performing online training on the acquired new data based on an online learning algorithm to obtain an online neural network, and updating a mechanical drilling speed prediction model in real time;
s5, carrying out real-time monitoring on formation change and updating while drilling of a mechanical drilling speed prediction model;
calculating a stratum drillability extreme value in real time, and judging whether the stratum changes or not; and if the stratum changes, reconstructing the weight and deviation of the mechanical drilling rate prediction model connecting layer, keeping the data structure, and repeating the steps S1-S4 to establish a new stratum mechanical drilling rate prediction model.
2. The horizontal well mechanical drilling speed while drilling prediction method based on online learning of claim 1 is characterized in that the dynamic data comprises hook load, wellhead torque, wellhead rotation speed, inlet flow, mechanical drilling speed and well depth; the quasi-dynamic data comprises drilling tool assembly data, well structure data, well track data and drilling fluid density data.
3. The horizontal well mechanical drilling rate while drilling prediction method based on online learning of claim 1 is characterized in that abnormal points are removed and smoothed in step S2 according to hook load, wellhead torque, wellhead rotation speed, inlet flow and mechanical drilling rate time series data.
4. The horizontal well mechanical drilling speed while drilling prediction method based on online learning is characterized in that the calculation formula of the bit weight of the drill bit is as follows:
in the formula: f 0 Hook load, N; mu.s i The friction coefficient between the unit body of the i section and the well wall is zero dimension; alpha (alpha) ("alpha") i 、
The well bevel angle, dog-leg angle and rad at two ends of the unit body respectively; l is i Is the length of the ith section of unit body, m; delta alpha i Rads are the well angle increments at the two ends of the unit body of the i section; q. q.s m The floating weight N/m of the unit body at the i section in the drilling fluid is obtained; q Pi 、Q P(i-1) Shearing forces N are applied to the upper end and the lower end of the i-th section of unit body on the P plane; q Ri 、Q R(i-1) Shearing force is applied to the upper end and the lower end of the R plane of the unit body at the ith section, and N is applied to the upper end and the lower end of the R plane of the unit body at the ith section; n is a radical of hydrogen i The radial supporting force N borne by the i-th section of unit body.
5. The horizontal well mechanical drilling speed while drilling prediction method based on online learning is characterized in that the calculation formula of the bit torque is as follows:
if the screw drill is not adopted at the current moment, determining the drill bit rotating speed according to the following formula:
RPM＝RPM 0
in the formula: RPM is the rotating speed of the drill bit, r/min; RPM 0 The rotation speed of the wellhead is r/min;
if the screw drill is adopted at the current moment, determining the rotation speed of the drill bit according to the following formula:
in the formula: RPM is the rotating speed of the drill bit, r/min; RPM 0 The rotating speed of the wellhead is r/min; q is inlet flow, L/s; and q is the flow per revolution of the screw drill, L/r.
6. The horizontal well mechanical drilling speed while drilling prediction method based on online learning is characterized in that the calculation formula of the bit rotation speed is as follows:
if the screw drill is not used at the current time, determining the bit torque according to the following formula:
in the formula: t is 0 Is the torque of the well head, N.m; mu.s t The coefficient of circumferential friction resistance is zero dimension; r is i Is the outer diameter of the drill stem, mm; n is a radical of i The radial supporting force N borne by the i section unit body;
if a screw drill is used at the present time, determining the bit torque according to the following formula:
in the formula: t is 0 Is the torque of the well head, N.m; mu.s t The coefficient of circumferential friction resistance is zero dimension; r is i Is the outer diameter of the drill stem, mm; q is the flow per revolution of the screw drill, L/r; delta P is the pressure drop of the screw drill, MPa; n is a radical of i The radial supporting force N borne by the i-th section of unit body.
7. The horizontal well mechanical drilling speed while drilling prediction method based on online learning is characterized by comprising the following steps of: and (3) assuming that k-1 groups of samples exist at the current moment, performing offline training on the samples by adopting a neural network to obtain the optimal weight and deviation, and obtaining an offline neural network model.
8. The horizontal well mechanical drilling speed while drilling prediction method based on online learning is characterized by comprising the following steps of:
(1) assuming that the k-1 group training sample learning is finished, taking the weight and the deviation of the k-1 group online neural network as the kth group training data to learn the initial weight and the deviation of the training network online;
(2) calculating the actual output of the online neural network under the conditions of the initial weight and the deviation;
(3) calculating the output error of each neuron by using the actual output and the expected output of the online neural network, and further calculating the accumulated error amount;
(4) and (3) updating the weight and the deviation according to an online gradient descent method, iterating to obtain a new weight and a new deviation, taking the obtained new weight and the obtained new deviation as the initial weight and the deviation of the k +1 group of training data, and repeating the steps (1) to (4) to complete the online learning of the k +1 group of training data.
9. The horizontal well mechanical drilling speed while drilling prediction method based on online learning is characterized in that the calculation formula of the stratum drillability extreme value is as follows:
in the formula: RPM is the bit rotation speed, r/min; WOB is the weight on bit, kN; ROP is the mechanical drilling speed, m/h; d is the drill diameter, mm.
10. The horizontal well mechanical drilling rate while drilling prediction method based on online learning of claim 1 or 9 is characterized in that the specific steps of judging whether the stratum changes in the step S5 are as follows:
obtaining a drillability extreme value time sequence from the current stratum initial drilling moment to the current moment according to the stratum drillability extreme value, and taking values of the time sequence according to the depth at equal intervals to obtain a drillability sequence according to the depth;
the depth axis is taken as a time axis, and a time sequence singular spectrum analysis method is utilized to predict a stratum drillability extreme value of a stratum from the current depth h to the depth h +1(m), and the stratum drillability extreme value is a sequence TS 1 ；
Calculating the practical value of the stratum drillability extreme value of the stratum with the depth of h to h +1(m) according to the real drilling data to be the sequence TS 2 ；
Calculating sequences TS separately 1 And a sequence TS 2 If the difference between the two means is greater than 1, the formation change is determined.
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