CN109902360A - Optimize the method, apparatus and machinery equipment of engineering parameter in situ of drilling well operation - Google Patents
Optimize the method, apparatus and machinery equipment of engineering parameter in situ of drilling well operation Download PDFInfo
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Abstract
The disclosure discloses a kind of method for optimizing engineering parameter in situ of drilling well operation, comprising: obtains geologic data, tool data, mud data and the project data range set to carry out the operation of new well of new well operations；It is the particle random initializtion position of preset quantity in the optimizing space obtained according to constructed by project data range；According to position of each particle in optimizing space, using geologic data, tool data, mud data and corresponding project data value as the input of constructed big data model, prediction obtains efficiency of breaking rock corresponding to particle position；According to the efficiency of breaking rock predicted, corresponding search direction of the particle in optimizing space is adjusted, with the position where the optimal efficiency of breaking rock of determination；According to identified position obtain optimal efficiency of breaking rock corresponding to project data.To ensure that the high efficiency and real-time that obtain project data corresponding to optimal efficiency of breaking rock.
Description
The present application claims priority of chinese patent application CN2018109336177 entitled "a GPU parallel fast optimization method for big data intelligent drilling field work", filed on 8/16/2018, which is hereby incorporated by reference in its entirety.
Technical Field
The disclosure relates to the field of oil exploitation, in particular to a method, a device and a machine device for optimizing engineering parameters in drilling field operation.
Background
In the drilling operation, the drilling operation conditions such as geological structures, tools, mud and the like directly influence the rock breaking efficiency, and the drilling cost and the drilling time are directly influenced by the rock breaking efficiency.
Specifically, in the drilling process, different drilling operation conditions correspond to different rock breaking efficiencies, such as geological structure changes, for example, selected engineering parameters. In order to ensure the rock breaking efficiency of the drilling operation and realize efficient rock breaking, the operator is required to optimize adjustable engineering parameters in the drilling operation so as to ensure high rock breaking efficiency.
In the prior art, engineering parameters are generally optimized by experienced engineers according to work experience so as to maximize rock breaking efficiency, on one hand, the optimization of the engineering parameters is based on the experienced engineers, and the requirements and the dependence on the engineers are high; on the other hand, during drilling, drilling conditions change in real time, (e.g., drilling depth changes), thereby requiring real-time optimization of engineering parameters based on the changes in drilling conditions. The real-time requirement of optimization cannot be met by an engineer optimizing the engineering parameters according to the working experience.
As can be seen from the above, the prior art of engineering parameter optimization based on the working experience of engineers is inefficient and is not suitable for engineering parameter optimization in a drilling process that varies in real time.
Therefore, a method for performing engineering parameter optimization in a drilling operation site in an efficient and real-time manner is needed.
Disclosure of Invention
In order to solve the problems in the related art, the present disclosure provides a method, an apparatus and a machine for optimizing engineering parameters in a drilling field operation.
In a first aspect, a method of optimizing engineering parameters in a drilling site operation, comprising:
acquiring geological data, tool data and slurry data of new well operation and an engineering data range set for performing the new well operation;
in an optimization space constructed according to the engineering data range, randomly initializing positions for a preset number of particles, wherein each point in the optimization space corresponds to an engineering data value in the engineering data range;
according to the position of each particle in the optimizing space, the geological data, the tool data, the mud data and the corresponding engineering data value are used as the input of a constructed big data model, and the rock breaking efficiency corresponding to the position of the particle is obtained through prediction;
according to the predicted rock breaking efficiency, adjusting the optimizing direction of the corresponding particles in the optimizing space to determine the position of the optimal rock breaking efficiency;
and acquiring engineering data corresponding to the optimal rock breaking efficiency according to the determined position.
In a second aspect, an apparatus for optimizing engineering parameters in a drilling site operation, the apparatus comprising:
the data acquisition module is used for acquiring geological data, tool data and mud data of new well operation and an engineering data range set for the new well operation;
the initialization module is used for randomly initializing positions for a preset number of particles in an optimization space constructed according to the engineering data range, wherein each point in the optimization space corresponds to an engineering data value in the engineering data range;
the prediction module is used for taking the geological data, the tool data, the mud data and the corresponding engineering data values as the input of the constructed big data model according to the position of each particle in the optimizing space, and predicting the rock breaking efficiency corresponding to the position of the particle;
the adjusting module is used for adjusting the optimizing direction of the corresponding particles in the optimizing space according to the predicted rock breaking efficiency so as to determine the position of the optimal rock breaking efficiency;
and the engineering data acquisition module is used for acquiring engineering data corresponding to the optimal rock breaking efficiency according to the determined position.
In one embodiment, the apparatus further comprises:
the system comprises a drilling operation data acquisition module, a data processing module and a data processing module, wherein the drilling operation data acquisition module is used for acquiring a plurality of drilling operation data acquired in the drilling operation and actual rock breaking efficiency corresponding to the drilling operation data, and the drilling operation data comprises geological data, tool data, mud data and engineering data in the drilling operation process;
the preprocessing module is used for preprocessing the drilling operation data and the corresponding actual rock breaking efficiency;
and the iterative training module is used for performing iterative training by taking the corresponding actual rock breaking efficiency as a target through the preprocessed drilling operation data to construct and obtain the big data model.
In one embodiment, the pre-processing module comprises:
the normalization unit is used for performing normalization processing on geological data, tool data, mud data and engineering data in each drilling operation data;
the correlation coefficient calculation unit is used for calculating and obtaining correlation coefficients of two different types of parameters according to each drilling operation data after normalization processing and the corresponding actual rock breaking efficiency;
and the data removing unit is used for removing data with low correlation with the rock breaking efficiency from the drilling operation data according to the calculated correlation coefficient, processing pairwise data with high correlation from the drilling operation data, and obtaining the drilling operation data for constructing the big data model.
In one embodiment, the iterative training module comprises:
the prediction unit is used for predicting the rock breaking efficiency in the pre-constructed model according to the preprocessed drilling operation data to obtain the corresponding predicted rock breaking efficiency;
the weight parameter adjusting unit is used for adjusting the weight parameters of the pre-constructed model according to the predicted rock breaking efficiency and the corresponding actual rock breaking efficiency so as to minimize the distance between the corresponding predicted rock breaking efficiency and the corresponding actual rock breaking efficiency;
and the big data model construction unit is used for taking the pre-constructed model after the weight parameters are adjusted as the big data model.
In one embodiment, the apparatus further comprises:
and the GPU distribution module is used for distributing the corresponding GPUs for the preset number of particles according to the number of the configured GPUs, and predicting the rock breaking efficiency of each particle and/or adjusting the optimizing direction in parallel through the configured GPUs.
In one embodiment, the initialization module includes:
the optimizing space construction unit is used for constructing and obtaining the optimizing space according to the range of top pressure, the range of displacement and the range of top drive rotating speed in the engineering data range, and the coordinate of each point in the optimizing space indicates the corresponding top pressure, displacement and top drive rotating speed;
and the initialized position determining unit is used for randomly arranging a preset number of particles in the optimizing space and obtaining the position determined by initialization of each particle.
In one embodiment, the adjustment module includes:
the updating unit is used for updating the position of the individual optimal rock breaking efficiency of the particles and updating the position of the group optimal rock breaking efficiency according to the predicted rock breaking efficiency;
the optimizing direction adjusting unit is used for adjusting the optimizing direction of the particles in the optimizing space according to the individual history optimal position and the group history optimal position;
a moving unit, configured to adjust a position of the particle in the optimization space according to the adjusted optimization direction until an iteration end condition is reached;
and the position determining unit is used for determining the position of the group optimal rock breaking efficiency as the position of the optimal rock breaking efficiency when the iteration ending condition is reached.
In one embodiment, the update unit includes:
the acquisition unit is used for acquiring the individual optimal rock breaking efficiency and the group optimal rock breaking efficiency stored in each particle;
an individual optimal rock breaking efficiency adjusting unit, configured to adjust a location of the individual optimal rock breaking efficiency of the particle to a location of the predicted rock breaking efficiency if the predicted rock breaking efficiency is greater than the individual optimal rock breaking efficiency of the particle;
and the group optimal rock breaking efficiency adjusting unit is used for adjusting the position of the group optimal rock breaking efficiency to the position of the maximum individual optimal rock breaking efficiency if the maximum individual optimal rock breaking efficiency in the individual optimal rock breaking efficiencies of each particle after updating is greater than the group optimal rock breaking efficiency.
In a third aspect, a machine device, the device comprising:
a processor; and
a memory having computer readable instructions stored thereon which, when executed by the processor, implement a method as described above.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:
and optimizing by utilizing a preset number of particles in an optimization space constructed according to the engineering data range, determining the position of the optimal rock breaking efficiency, and further obtaining the engineering data corresponding to the optimal rock breaking efficiency. Therefore, the engineering data corresponding to the optimal rock breaking efficiency can be obtained in real time on the drilling operation site. The method solves the problem that engineering parameter optimization in engineering data depends on engineers in the prior art, can optimize the engineering parameters according to real-time drilling conditions, and has high efficiency and real-time performance.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a block diagram illustrating a server in accordance with an exemplary embodiment;
FIG. 2 is a flow chart illustrating a method of optimizing engineering parameters in a drilling site operation according to an exemplary embodiment;
FIG. 3 is a flowchart of steps preceding step S150 of the corresponding embodiment of FIG. 2;
FIG. 4 is a flow diagram of step S230 of the corresponding embodiment of FIG. 3 in one embodiment;
FIG. 5 is a flow diagram of step S250 of the corresponding embodiment of FIG. 3 in one embodiment;
FIG. 6 is a flow diagram of step S130 of the corresponding embodiment of FIG. 2 in one embodiment;
FIG. 7 is a flow diagram of step S170 of the corresponding embodiment of FIG. 2 in one embodiment;
FIG. 8 is a flowchart of step S171 of the corresponding embodiment of FIG. 7 in one embodiment;
FIG. 9 is a block diagram illustrating an apparatus for optimizing engineering parameters in a drilling site operation in accordance with an exemplary embodiment;
FIG. 10 is a block diagram illustrating a machine device in accordance with an example embodiment.
While specific embodiments of the invention have been shown by way of example in the drawings and will be described in detail hereinafter, such drawings and description are not intended to limit the scope of the inventive concepts in any way, but rather to explain the inventive concepts to those skilled in the art by reference to the particular embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
FIG. 1 is a block diagram illustrating a server in accordance with an exemplary embodiment. The server 200 may execute the technical solution of the present disclosure as an execution subject of the present disclosure.
It should be noted that the server 200 is only an example adapted to the present invention, and should not be considered as providing any limitation to the scope of the present invention. The server 200 is also not to be construed as necessarily dependent upon or having one or more components of the exemplary server 200 shown in fig. 2.
The hardware structure of the server 200 may be greatly different due to different configurations or performances, as shown in fig. 2, the server 200 includes: a power supply 210, an interface 230, at least one memory 250, and at least one Central Processing Unit (CPU) 270.
The power supply 210 is used to provide operating voltage for each hardware device on the server 200.
The interface 230 includes at least one wired or wireless network interface 231, at least one serial-to-parallel conversion interface 233, at least one input/output interface 235, and at least one USB interface 237, etc. for communicating with external devices.
The storage 250 is used as a carrier for resource storage, and may be a read-only memory, a random access memory, a magnetic disk or an optical disk, etc., and the resources stored thereon include an operating system 251, an application 253 or data 255, etc., and the storage manner may be a transient storage or a permanent storage. The operating system 251 is used for managing and controlling each hardware device and the application 253 on the server 200 to implement the computation and processing of the mass data 255 by the central processing unit 270, and may be windows server, Mac OS XTM, unix, linux, FreeBSDTM, FreeRTOS, and the like. The application 253 is a computer program that performs at least one specific task on the operating system 251, and may include at least one module (not shown in fig. 2), each of which may contain a series of computer-readable instructions for the server 200. Data 255 may be drilling operation data collected during a drilling operation, or the like.
The central processor 270 may include one or more processors and is arranged to communicate with the memory 250 via a bus for computing and processing the mass data 255 in the memory 250.
As described in detail above, a server 200 to which the present invention is applicable will implement a method of optimizing engineering parameters in a drilling site operation by a central processor 270 reading a series of computer readable instructions stored in memory 250.
Furthermore, the present invention can be implemented by hardware circuitry or by a combination of hardware circuitry and software instructions, and thus, implementation of the present invention is not limited to any specific hardware circuitry, software, or combination of both.
FIG. 2 is a flow chart illustrating a method of optimizing engineering parameters in a drilling site operation according to an exemplary embodiment. The method of optimizing engineering parameters in a drilling site operation, which may be performed by the server 200, may include the following steps.
Step S110, geological data, tool data, mud data of the new well operation and a project data range set for performing the new well operation are acquired.
Drilling, among other things, refers to the process of drilling a desired hole in a subterranean formation with a tool, such as drilling for the purpose of producing hydrocarbons.
The new well is for a well that is currently about to be drilled, the new well being obtained by drilling at the corresponding geographical location. Correspondingly, the ranges of geological data, tool data, mud data, and engineering data mentioned are also for new wells. In other words, different drilling sites have corresponding geological data, tool data, mud data, and engineering data ranges. Of course, for wells that have already been drilled, the engineering data is determined, i.e. the engineering parameter values used during the drilling process.
For either drilling operation, the drilling operation is performed directly by the tool on the geological structure to be drilled. In other words, the tools used for drilling, the parameters of the tools and the address structure to be worked on will all affect the efficiency of the drilling operation, such as the rock breaking efficiency mentioned below.
In order to ensure the high efficiency of well drilling, the well logging is carried out on the position to be drilled before the well drilling is carried out, and the geological data of the corresponding geological structure is obtained. That is, geological data is data that is used to describe the geological structure in which the drilling operation is to be performed. Geophysical data such as temperature, pressure, porosity, permeability, lithology, and mineral composition.
In a particular embodiment, the geological data includes neutron porosity, gamma density, gamma energy spectrum, resistivity, and the like. And these geological data may be obtained by a logging tool. Wherein, the neutron porosity is the formation porosity measured by a neutron logging instrument which is used for measuring in a neutron graduated well. Gamma density is the formation porosity measured using various effects occurring with gamma rays and geological media. Gamma spectroscopy measures the energy spectrum of gamma rays that are naturally present in the formation, i.e., by means of a tool, such as a natural gamma spectroscopy instrument, from which the formation and various geological problems associated with the distribution of radioactive elements in the formation can be detailed.
Tool data refers to data describing the tools used to perform new well operations. Such as casing, open hole size, number of ports, port area, bit size, etc. Tool data is selected by an existing tool prior to a new well operation.
The casing drilling is to apply torque and bit pressure to the drill bit by using a casing instead of a drill rod so as to realize the rotation and drilling of the drill bit. The casing referred to in the tool data means that the drilling is performed by means of casing drilling. In order to keep the stratum of some special stratum sections in the original state during the drilling process, some special stratum sections are not cased in the drilling process, and the open hole refers to a mode of drilling without casing.
In the drilling process, in order to ensure the service life of the drill bit, the drill bit is generally provided with nozzles, namely drill bit nozzles, also called drill bit water holes, which are pore channels formed in proper positions of the drill bit body and communicated with a flow channel in the inner cavity of the drill bit body, so that a passage for drilling fluid to enter the bottom of a well from the inside of a drill rod is formed. The number of water holes refers to the number of water holes to be used in the new well operation. The port area refers to the cross-sectional area of the port. Bit size refers to a bit parameter such as bit diameter.
Drilling mud, also called drilling fluid and mud, is inevitably used in the drilling process to assist in drilling. Mud data is data that describes the parameters of the mud used for a new well operation. Mud data such as mud density, funnel viscosity, solids content, dynamic shear force, etc. And the mud data may be obtained by laboratory measurements of the mud to be used.
The engineering data is made up of engineering parameters that are selectable and adjustable during the drilling process. In a particular embodiment, the engineering parameters include weight-on-bit, displacement, and top drive rotational speed. These engineering data may be adjusted during real-time drilling. The engineering data range is the range of selection and adjustment set by each engineering parameter.
Step S130, in the optimization space constructed according to the engineering data range, for the random initialization positions of the particles in the preset number, each point in the optimization space corresponds to an engineering data value in the engineering data range.
The constructed optimization space is constructed according to the parameter ranges set for the engineering parameters in the engineering data range. And defining the value space of each engineering parameter in the engineering data through the constructed optimization space.
In one embodiment, as shown in fig. 6, step S130 includes:
and S131, constructing an optimization space according to the range of the top pressure, the range of the displacement and the range of the top drive rotating speed in the engineering data range, wherein the coordinate of each point in the optimization space indicates the corresponding top pressure, displacement and top drive rotating speed.
Step S133, randomly arranging a preset number of particles in the optimization space, and obtaining the position determined by initialization of each particle.
That is, the values of the weight on bit, the displacement and the top drive rotation speed are respectively used as the coordinate values of each point in the optimization space, so that each point in the optimization space corresponds to the corresponding weight on bit value, displacement value and top drive rotation speed value in the engineering data range.
The random initialization position of the particles is that the particles with preset number are randomly distributed in the optimization space, and therefore the coordinates of the particles are the initialization positions of the particles.
The number of the particles can be set according to actual calculation accuracy, namely, the accuracy of the engineering data corresponding to the optimal rock breaking efficiency is obtained, and certainly, under the condition that the hardware capability of the equipment is met, the more the number of the particles is, the higher the calculation accuracy is. In a specific embodiment, the number of the particles may be preset, so that optimization is performed in an optimization space according to the set number of the particles, and engineering data corresponding to the optimal rock breaking efficiency is obtained.
And S150, according to the position of each particle in the optimizing space, taking geological data, tool data, mud data and corresponding engineering data values as the input of a constructed big data model, and predicting the rock breaking efficiency corresponding to the position of the particle.
As described above, for each point in the optimization space there is engineering data associated with it, such as weight on bit, displacement, and top drive speed. Correspondingly, for a particle existing at a point in the optimization space, the corresponding engineering data can be determined according to the position of the particle.
The breaking efficiency during drilling is directly related to the value of each parameter in the geological data, tool data, mud data and engineering data. Therefore, according to the technical scheme, the rock breaking efficiency is predicted according to geological data, tool data, mud data and engineering data.
The prediction is carried out by adopting a machine learning mode through the constructed big data model. Before step S150, a big data model is constructed by drilling operation data collected in the completed drilling operation and corresponding actual rock breaking efficiency, which will be described in detail below. The rock breaking efficiency can be predicted according to geological data, tool data, mud data and engineering data in operation through the constructed big data model.
The big data model is not specifically limited herein, for example, a model constructed based on Boosting and a neural network.
And S170, adjusting the optimizing direction of the corresponding particles in the optimizing space according to the predicted rock breaking efficiency so as to determine the position of the optimal rock breaking efficiency.
In the technical scheme of the disclosure, a particle swarm algorithm is adopted to search and determine the position of the optimal rock breaking efficiency in the optimizing space, namely, the position of the optimal rock breaking efficiency is determined through the movement of a preset number of particles in the optimizing space.
In the particle swarm optimization, in order to search and determine the position where the optimal rock breaking efficiency is located in the optimizing space, the particles continuously move in the optimizing space, namely the moving speed and the position of the particles in the optimizing space are continuously adjusted.
In one embodiment, as shown in fig. 7, step S170 includes:
and S171, updating the position of the individual optimal rock breaking efficiency of the particles and updating the position of the group optimal rock breaking efficiency according to the predicted rock breaking efficiency.
The position of the individual optimal rock breaking efficiency of the particles is the position corresponding to the maximum rock breaking efficiency in the positions where the particles pass.
The position of the group optimal rock breaking efficiency is the position corresponding to the maximum rock breaking efficiency in the positions where each particle passes in the particle swarm composed of the preset number of particles.
As before, in step S150, the rock breaking efficiency of each particle at the random initialization position is obtained through big data model prediction. Therefore, the position of the individual optimal rock breaking efficiency of the particles and the position of the group optimal rock breaking efficiency of the particles are adjusted according to the predicted rock breaking efficiency corresponding to the position of each particle.
In one embodiment, as shown in fig. 8, step S171 includes:
and S310, acquiring the individual optimal rock breaking efficiency and the group optimal rock breaking efficiency stored in each particle.
For each particle in the optimizing space, the particle stores the individual optimal rock breaking efficiency and the group optimal rock breaking efficiency. Therefore, when the particles pass through one position, the stored individual optimal rock breaking efficiency and the group optimal rock breaking efficiency are correspondingly updated according to the rock breaking efficiency corresponding to the position.
And S320, if the predicted rock breaking efficiency is greater than the individual optimal rock breaking efficiency of the particles, adjusting the position of the individual optimal rock breaking efficiency of the particles to the position of the predicted rock breaking efficiency.
Step S330, if the maximum individual optimal rock breaking efficiency in the updated individual optimal rock breaking efficiencies of each particle is larger than the group optimal rock breaking efficiency, adjusting the position of the group optimal rock breaking efficiency to the position of the maximum individual optimal rock breaking efficiency.
After the position is randomly initialized for the particle, because the position of the individual optimal rock breaking efficiency stored in the particle and the position of the group optimal rock breaking efficiency are null, the current position of the particle is directly used as the position of the individual optimal rock breaking efficiency, and the position of the particle with the highest rock breaking efficiency in the particle swarm is used as the position of the group optimal rock breaking efficiency.
And in the subsequent updating, after the rock breaking efficiency is obtained according to the prediction of the position of the particle, the position of the individual optimal rock breaking efficiency of the particle and the position of the group optimal rock breaking efficiency are continuously updated.
And step S172, adjusting the optimizing direction of the particles in the optimizing space according to the individual history optimal position and the group history optimal position.
The optimizing direction refers to the moving direction of the particles in the optimizing space. Of course, the target of the particles in the optimization space is the location where the optimum breaking efficiency is located. And in each movement of the particles, determining the optimizing direction based on the current position of the particles and the updated position of the optimal rock breaking efficiency of the group. In other words, the optimization shortest path is constructed by the particles according to the current position and the updated position of the optimal rock breaking efficiency, so that the direction in which the current position of the particles points to the updated position of the optimal rock breaking efficiency is the optimization direction.
And step S173, adjusting the position of the particles in the optimizing space according to the adjusted optimizing direction until an iteration end condition is reached.
Wherein the optimizing direction of the particles, i.e. the velocity of the renewed particles, is adjusted. In the particle swarm optimization, the velocity update formula of the particles is as follows:
the velocity component of the d-th dimension of the particle i in the optimizing space is calculated for the k-th iteration; omega is inertia weight and is not negative; c. C1，c2Is an acceleration constant; r is1，r2Is two random numbers with the value range of [0, 1%]；pbesti,dD-dimension component of the position of the individual optimal rock breaking efficiency stored in the particle i; gbestdAnd d-dimension component of the position where the optimal rock breaking efficiency of the group stored for the particle is located.
The position update formula of the particle is:
is the d-th dimension component of the position vector of the k-th iteration particle i.
Therefore, the speed and the position of the particles in the optimizing space are updated according to the formula, and after the position is updated, the processes of calculating the rock breaking efficiency corresponding to the updated position, adjusting the optimizing direction and updating the positions of the particles are repeatedly executed until a preset iteration ending condition is reached. Each update of the velocity and position of the particle is considered as an iteration.
And step S174, when the iteration ending condition is reached, determining the position of the group optimal rock breaking efficiency as the position of the optimal rock breaking efficiency.
And when the iteration ending condition is reached, determining the position of the stored group optimal rock breaking efficiency as the position of the optimal rock breaking efficiency in the optimizing space. If the iteration ending condition is, for example, a preset maximum iteration number, when the iteration number reaches the maximum iteration number, the iteration ending condition is considered to be reached; for another example, if the iteration end condition is that the group history optimal position remains unchanged in N consecutive iterations, then in N consecutive iterations, the group history optimal position of the particle group is the same point in the optimization space, and the iteration end condition is considered to be reached. Of course, the above is only an exemplary example of the iteration ending condition, and in a specific embodiment, the iteration ending condition may also be other set conditions.
And S190, obtaining engineering data corresponding to the optimal rock breaking efficiency according to the determined position.
As described above, each point in the optimization space corresponds to an engineering data value within the engineering data range. Therefore, the engineering data corresponding to the optimal rock breaking efficiency can be obtained according to the determined position. Therefore, on the new well operation site, the new well operation is performed according to the obtained engineering data, for example, the drilling pressure is applied according to the corresponding bit pressure, and the drilling operation is performed according to the corresponding displacement and the corresponding top drive rotating speed.
In the drilling operation, in order to ensure the rock breaking efficiency, the optimization of engineering parameters in engineering data in the drilling process needs to be carried out, and in the prior art, the engineering parameters are generally optimized by experienced engineers according to the working experience so as to maximize the rock breaking efficiency; on the other hand, during the drilling process, the drilling conditions change in real time (for example, when the drilling depths are different, the drilling geological data are also different, and the engineering data corresponding to the corresponding optimal rock breaking efficiency may also be different), and the optimization of the engineering parameters based on the working experience of the engineer is relatively low in efficiency, and cannot be applied to the optimization of the engineering parameters during the drilling process which changes in real time. By the technical scheme, the dependence of engineering parameter optimization on engineers can be solved, the engineering parameter optimization can be carried out according to real-time drilling conditions, and the method has high efficiency and real-time performance.
In an embodiment, as shown in fig. 3, before step S150, the method further includes:
step S210, a plurality of drilling operation data collected in the drilling operation and actual rock breaking efficiency corresponding to the drilling operation data are obtained, and the drilling operation data comprise geological data, tool data, mud data and engineering data in the drilling operation process.
And step S230, preprocessing the drilling operation data and the corresponding actual rock breaking efficiency.
In one embodiment, as shown in fig. 4, step S230 includes:
step S231, normalization processing is performed on the geological data, tool data, mud data, and engineering data in each drilling operation data.
Wherein the normalization process is performed by calculating the average value and standard deviation of each parameter according to each parameter value in the collected drilling operation data, thereby obtaining the average value and standard deviation of each parameter according to a formula
A transformation of the respective parameter values is performed, wherein,the average value of the parameter X is represented, and σ represents the standard deviation of the parameter X, so that the parameter X is transformed into X' by the above transformation, thereby realizing normalization of the parameter X.
Such as neutron porosity in geological data, resistivity, etc. It is worth mentioning that the normalization process is performed by normalizing each parameter of the geological data, the tool data, the mud data and the engineering data in the drilling operation data according to the above formula.
In one embodiment, the collected drilling operation data is cleaned and mapped prior to step S231. The cleaning is to remove abnormal data or error data in the drilling operation data and/or to fill up missing data, so that the constructed big data model is constructed based on complete and real data to ensure the effectiveness of the big data model.
Wherein the mapping is performed to map qualitative (textual) parameters in the drilling operation data to quantitative (numerical) parameters. For example, the above-mentioned casing mode is mapped to 0, and the naked eye mode is mapped to 1, so that the quantification of text parameters is realized through the mapping, and the text parameters are used for constructing a big data model.
And step S232, calculating to obtain correlation coefficients of two different types of parameters according to each drilling operation data after normalization processing and the corresponding actual rock breaking efficiency.
Wherein the calculation of the correlation coefficient is carried out, namely, the correlation coefficient between two different parameters in the parameters of geological data, mud data, tool data, engineering data and rock breaking efficiency in the drilling operation data is calculated.
The correlation coefficient is calculated by the formula:
where Cov (X, Y) is the covariance of the parameter X and the parameter Y, σxIs the variance, σ, of the parameter XyIs the variance of the parameter Y, and thus the correlation coefficient of the parameter X and the parameter Y is calculated according to the formula. The correlation coefficient calculated is in the range of [0,1 ]]0 means no correlation and 1 means a complete linear correlation.
Thus, the magnitude of the correlation between the two parameters is determined from the calculated correlation coefficient.
And S231, removing data with low correlation with the rock breaking efficiency from the drilling operation data and processing pairwise data with high correlation from the drilling operation data according to the calculated correlation coefficient to obtain the drilling operation data for constructing a large data model.
Specifically, corresponding threshold values are set for high correlation and low correlation, respectively, and for example, the threshold value set for high correlation is a: namely, when the correlation coefficient exceeds the threshold A, the correlation between the two parameters is considered to be high; for another example, the threshold set for low correlation is B: i.e. the correlation coefficient is lower than the threshold B, the correlation between the two parameters is considered to be low.
Further, data having low correlation with the rock breaking efficiency among the drilling operation data is removed. And for each pair of data with high correlation in the drilling operation data, one data in each pair of data can be represented by a relational expression of the other data, so that one data in each pair of data can be removed, and iterative training is carried out according to the other data.
And S250, performing iterative training by taking the corresponding actual rock breaking efficiency as a target through the preprocessed drilling operation data, and constructing to obtain a big data model.
And performing iterative training, namely constructing a loss function of the model through the preprocessed drilling operation data, and constructing and obtaining a big data model by solving the corresponding loss function and adjusting the parameters of the model. The loss function is a function of the difference between a predicted value predicted by the characterization model and an actual value corresponding to the drilling operation data.
In one embodiment, as shown in fig. 5, step S250 includes:
and S251, predicting the rock breaking efficiency in the pre-constructed model according to the preprocessed drilling operation data to obtain the corresponding predicted rock breaking efficiency.
Wherein the pre-constructed model can be a model constructed by Boosting, neural networks, and the like. And predicting the rock breaking efficiency according to each piece of well drilling operation data after processing through the pre-constructed model to obtain the corresponding predicted rock breaking efficiency, namely the predicted value mentioned above. Correspondingly, the actual rock breaking efficiency corresponding to the drilling operation data is an actual value.
Step S252, the weight parameters of the pre-constructed model are adjusted according to the predicted rock breaking efficiency and the corresponding actual rock breaking efficiency so as to minimize the distance between the corresponding predicted rock breaking efficiency and the corresponding actual rock breaking efficiency.
And step S253, taking the pre-constructed model after the weight parameters are adjusted as a big data model.
The distance between the predicted rock breaking efficiency and the actual rock breaking efficiency may be represented by a euclidean distance or the like, and is not particularly limited herein.
Therefore, a big data model for predicting the rock breaking efficiency is constructed and obtained through the collected drilling operation data and the corresponding actual rock breaking efficiency.
In an embodiment, before step S130, the method further includes:
and distributing corresponding GPUs for the preset number of particles according to the number of the configured GPUs, and performing prediction and/or adjustment of optimizing directions of the rock breaking efficiency of the particles in parallel through the configured GPUs.
Therefore, for the particles in the optimizing space, the distributed GPU is used for setting threads to predict the rock breaking efficiency corresponding to the particles and adjust the optimizing direction, so that the parallel operation of multiple GPUs is realized, the running time is shortened, the speed of obtaining engineering data corresponding to the optimal rock breaking efficiency on a drilling operation site is further improved, and the real-time requirement of the drilling operation site is met. The allocation performed therein is, for example, based on the computation capability of the GPU, and is not particularly limited herein.
The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed method for optimizing engineering parameters in a drilling site operation performed by the above-described server 200. For details not disclosed in the disclosed embodiments of the apparatus, reference is made to the disclosed embodiments of the method for optimizing engineering parameters in a drilling site operation.
FIG. 9 is a block diagram illustrating an apparatus for optimizing engineering parameters during a drill site operation that may be deployed in the server 200 of FIG. 2, according to an exemplary embodiment, in which all or a portion of the steps of any of the above-described methods for optimizing engineering parameters during a drill site operation are performed. As shown in fig. 9, the apparatus includes, but is not limited to: a data acquisition module 110, an initialization module 130, a prediction module 150, an adjustment module 170, and an engineering data acquisition module 190. Wherein,
the data acquisition module 110 is configured to acquire geological data, tool data, mud data, and engineering data ranges set for performing operations on a new well.
And an initialization module 130, connected to the data acquisition module 110, for randomly initializing the positions for a predetermined number of particles in the optimization space constructed according to the engineering data range, where each point in the optimization space corresponds to an engineering data value in the engineering data range.
And the prediction module 150 is connected with the initialization module 130 and is used for taking geological data, tool data, mud data and corresponding engineering data values as the input of the constructed big data model according to the position of each particle in the optimization space, and predicting the rock breaking efficiency corresponding to the position of the particle.
And the adjusting module 170 is connected with the predicting module 150 and is used for adjusting the optimizing direction of the corresponding particles in the optimizing space according to the predicted rock breaking efficiency so as to determine the position of the optimal rock breaking efficiency.
And the engineering data acquisition module 190 is connected with the adjustment module 170 and is used for acquiring engineering data corresponding to the optimal rock breaking efficiency according to the determined position.
The implementation processes of the functions and the functions of the modules in the device are specifically detailed in the implementation processes of the corresponding steps in the method for optimizing the engineering parameters in the drilling field operation, and are not repeated herein.
It is understood that these modules may be implemented in hardware, software, or a combination of both. When implemented in hardware, these modules may be implemented as one or more hardware modules, such as one or more application specific integrated circuits. When implemented in software, the modules may be implemented as one or more computer programs executing on one or more processors, such as programs stored in memory 250 for execution by central processor 270 of FIG. 2.
In one embodiment, the apparatus for optimizing engineering parameters in a drilling site operation further comprises:
the drilling operation data acquisition module is used for acquiring a plurality of drilling operation data acquired in the drilling operation and actual rock breaking efficiency corresponding to the drilling operation data, and the drilling operation data comprises geological data, tool data, mud data and engineering data in the drilling operation process.
And the preprocessing module is used for preprocessing the drilling operation data and the corresponding actual rock breaking efficiency.
And the iterative training module is used for performing iterative training by taking the corresponding actual rock breaking efficiency as a target through the preprocessed drilling operation data to construct and obtain a big data model.
In one embodiment, the pre-processing module comprises:
and the normalization unit is used for performing normalization processing on geological data, tool data, mud data and engineering data in each drilling operation data.
And the correlation coefficient calculating unit is used for calculating and obtaining correlation coefficients of two different types of parameters according to each drilling operation data after normalization processing and the corresponding actual rock breaking efficiency.
And the data removing unit is used for removing data with low correlation with the rock breaking efficiency in the drilling operation data and processing pairwise data with high correlation in the drilling operation data according to the calculated correlation coefficient to obtain the drilling operation data for constructing a large data model.
In one embodiment, the iterative training module comprises:
and the prediction unit is used for predicting the rock breaking efficiency in the pre-constructed model according to the preprocessed drilling operation data to obtain the corresponding predicted rock breaking efficiency.
And the weight parameter adjusting unit is used for adjusting the weight parameters of the pre-constructed model according to the predicted rock breaking efficiency and the corresponding actual rock breaking efficiency so as to minimize the distance between the corresponding predicted rock breaking efficiency and the corresponding actual rock breaking efficiency.
And the big data model building unit is used for taking the pre-built model after the weight parameters are adjusted as the big data model.
In one embodiment, the apparatus for optimizing engineering parameters in a drilling site operation further comprises:
and the GPU distribution module is used for distributing the corresponding GPUs for the particles with the preset number according to the number of the configured GPUs and performing prediction and/or adjustment of the rock breaking efficiency and/or the optimizing direction of each particle in parallel through the configured GPUs.
In one embodiment, the initialization module includes:
and the optimization space construction unit is used for constructing an optimization space according to the range of the top pressure, the range of the displacement and the range of the top drive rotating speed in the engineering data range, and the coordinates of each point in the optimization space indicate the corresponding top pressure, displacement and top drive rotating speed.
And the initialized position determining unit is used for randomly arranging a preset number of particles in the optimizing space and obtaining the initialized and determined position of each particle.
In one embodiment, the adjustment module includes:
and the updating unit is used for updating the position of the individual optimal rock breaking efficiency of the particles and updating the position of the group optimal rock breaking efficiency according to the predicted rock breaking efficiency.
And the optimizing direction adjusting unit is used for adjusting the optimizing direction of the particles in the optimizing space according to the individual history optimal position and the group history optimal position.
And the moving unit is used for adjusting the position of the particles in the optimizing space according to the adjusted optimizing direction until an iteration end condition is reached.
And the position determining unit is used for determining the position of the group optimal rock breaking efficiency as the position of the optimal rock breaking efficiency when the iteration ending condition is reached.
In one embodiment, the update unit includes:
and the acquisition unit is used for acquiring the individual optimal rock breaking efficiency and the group optimal rock breaking efficiency stored in each particle.
And the individual optimal rock breaking efficiency adjusting unit is used for adjusting the position of the individual optimal rock breaking efficiency of the particles to the position of the predicted rock breaking efficiency if the predicted rock breaking efficiency is greater than the individual optimal rock breaking efficiency of the particles.
And the group optimal rock breaking efficiency adjusting unit is used for adjusting the position of the group optimal rock breaking efficiency to the position of the maximum individual optimal rock breaking efficiency if the maximum individual optimal rock breaking efficiency in the updated individual optimal rock breaking efficiency of each particle is greater than the group optimal rock breaking efficiency.
The implementation processes of the functions and the functions of the modules in the device are specifically detailed in the implementation processes of the corresponding steps in the method for optimizing the engineering parameters in the drilling field operation, and are not repeated herein.
Optionally, the present disclosure also provides a machine apparatus that may be used to perform all or part of the steps of any of the above method embodiments of the method of optimizing engineering parameters in a drilling site operation. As shown in fig. 10, the machine apparatus includes:
a processor 1001; and
memory 1002, the memory 1002 having stored thereon computer readable instructions which, when executed by the processor 1001, implement the method of any of the above method implementations.
Wherein the executable instructions, when executed by the processor 1001, implement the method in any of the above embodiments. Such as computer readable instructions, which when executed by the processor 1001, read stored in the memory via the communication line/bus 1003 connected to the memory.
The specific manner in which the processor performs the operations in this embodiment has been described in detail in relation to this embodiment of the method of optimizing engineering parameters in a drilling site operation and will not be elaborated upon here.
In an exemplary embodiment, a computer-readable storage medium is also provided, on which a computer program is stored which, when being executed by a processor, carries out the method in any of the above method embodiments. Such as memory 250 containing a computer program that is executable by central processor 270 of server 200 to implement the methods described above.
The specific manner in which the processor performs the operations in this embodiment has been described in detail in relation to the embodiment of the method of optimizing engineering parameters in a drilling site operation and will not be described in detail herein.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (10)
1. A method of optimizing engineering parameters in a drilling site operation, comprising:
acquiring geological data, tool data and slurry data of new well operation and an engineering data range set for performing the new well operation;
in an optimization space constructed according to the engineering data range, randomly initializing positions for a preset number of particles, wherein each point in the optimization space corresponds to an engineering data value in the engineering data range;
according to the position of each particle in the optimizing space, the geological data, the tool data, the mud data and the corresponding engineering data value are used as the input of a constructed big data model, and the rock breaking efficiency corresponding to the position of the particle is obtained through prediction;
according to the predicted rock breaking efficiency, adjusting the optimizing direction of the corresponding particles in the optimizing space to determine the position of the optimal rock breaking efficiency;
and acquiring engineering data corresponding to the optimal rock breaking efficiency according to the determined position.
2. The method according to claim 1, wherein before the geological data, tool data, mud data and corresponding engineering data values are used as input parameters of the constructed big data model according to the position of each particle in the optimization space, and the rock breaking efficiency corresponding to the position of the particle is predicted, the method further comprises:
acquiring a plurality of drilling operation data acquired in drilling operation and actual rock breaking efficiency corresponding to the drilling operation data, wherein the drilling operation data comprises geological data, tool data, mud data and engineering data in the drilling operation process;
preprocessing the drilling operation data and the corresponding actual rock breaking efficiency;
and performing iterative training by taking the corresponding actual rock breaking efficiency as a target through the preprocessed drilling operation data to construct and obtain the big data model.
3. The method of claim 2, wherein the pre-processing the drilling operation data and the corresponding actual rock breaking efficiency comprises:
performing normalization processing on geological data, tool data, mud data and engineering data in each drilling operation data;
calculating to obtain correlation coefficients of two different types of parameters according to each drilling operation data after normalization processing and the corresponding actual rock breaking efficiency;
and removing data with low correlation with the rock breaking efficiency from the drilling operation data according to the calculated correlation coefficient, and processing pairwise data with high correlation from the drilling operation data to obtain the drilling operation data for constructing the big data model.
4. The method of claim 2, wherein the drilling operation data after preprocessing is subjected to iterative training with the corresponding actual rock breaking efficiency as a target, and the building of the big data model comprises:
predicting rock breaking efficiency in a pre-constructed model according to the preprocessed drilling operation data to obtain corresponding predicted rock breaking efficiency;
adjusting the weight parameters of the pre-constructed model according to the predicted rock breaking efficiency and the corresponding actual rock breaking efficiency so as to minimize the distance between the corresponding predicted rock breaking efficiency and the corresponding actual rock breaking efficiency;
and taking the pre-constructed model after the weight parameters are adjusted as the big data model.
5. The method of claim 1, wherein the randomly initializing positions for a predetermined number of particles in a search space constructed from the engineering data range, each point in the search space corresponding to a value of engineering data in the engineering data range, further comprises:
and distributing corresponding GPUs for the preset number of particles according to the number of the configured GPUs, and performing prediction and/or adjustment of optimizing directions of the rock breaking efficiency of the particles in parallel through the configured GPUs.
6. The method of claim 1, wherein the randomly initializing positions for a predetermined number of particles in the optimization space constructed from the engineering data range, each point in the optimization space corresponding to an engineering data value in the engineering data range comprises:
constructing and obtaining the optimization space according to the range of top pressure, the range of displacement and the range of top drive rotating speed in the engineering data range, wherein the coordinates of each point in the optimization space indicate the corresponding top pressure, displacement and top drive rotating speed;
randomly arranging a preset number of particles in the optimizing space, and obtaining the position determined by initialization of each particle.
7. The method of claim 1, wherein the adjusting the optimizing direction of the corresponding particle in the optimizing space according to the predicted rock breaking efficiency to determine the position of the optimal rock breaking efficiency comprises:
updating the position of the individual optimal rock breaking efficiency of the particles and the position of the group optimal rock breaking efficiency according to the predicted rock breaking efficiency;
adjusting the optimizing direction of the particles in the optimizing space according to the individual historical optimal position and the group historical optimal position;
adjusting the position of the particle in the optimizing space according to the adjusted optimizing direction until an iteration end condition is reached;
and when the iteration ending condition is reached, determining the position of the group optimal rock breaking efficiency as the position of the optimal rock breaking efficiency.
8. The method of claim 7, wherein updating the location of the individual optimal rock-breaking efficiency of the particles and updating the location of the population optimal rock-breaking efficiency according to the predicted rock-breaking efficiency comprises:
obtaining the individual optimal rock breaking efficiency and the group optimal rock breaking efficiency stored in each particle;
if the predicted rock breaking efficiency is greater than the individual optimal rock breaking efficiency of the particles, adjusting the position of the individual optimal rock breaking efficiency of the particles to the position of the predicted rock breaking efficiency;
if the maximum individual optimal rock breaking efficiency in the individual optimal rock breaking efficiencies of each particle after updating is larger than the group optimal rock breaking efficiency, adjusting the position of the group optimal rock breaking efficiency to the position of the maximum individual optimal rock breaking efficiency.
9. An apparatus for optimizing engineering parameters in a drilling site operation, the apparatus comprising:
the data acquisition module is used for acquiring geological data, tool data and mud data of new well operation and an engineering data range set for the new well operation;
the initialization module is used for randomly initializing positions for a preset number of particles in an optimization space constructed according to the engineering data range, wherein each point in the optimization space corresponds to an engineering data value in the engineering data range;
the prediction module is used for taking the geological data, the tool data, the mud data and the corresponding engineering data values as the input of the constructed big data model according to the position of each particle in the optimizing space, and predicting the rock breaking efficiency corresponding to the position of the particle;
the adjusting module is used for adjusting the optimizing direction of the corresponding particles in the optimizing space according to the predicted rock breaking efficiency so as to determine the position of the optimal rock breaking efficiency;
and the engineering data acquisition module is used for acquiring engineering data corresponding to the optimal rock breaking efficiency according to the determined position.
10. A machine device, characterized in that the device comprises:
a processor; and
a memory having computer readable instructions stored thereon which, when executed by the processor, implement the method of any of claims 1 to 8.
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