CN112796746B - Drilling method for petroleum geological exploration - Google Patents
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- 238000005553 drilling Methods 0.000 title claims abstract description 45
- 239000003208 petroleum Substances 0.000 title claims abstract description 44
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Abstract
The invention discloses a drilling method for petroleum geological exploration, which comprises the following steps: s1, acquiring petroleum geological exploration historical detection data based on geological cloud, preprocessing the acquired historical detection data, and constructing a historical detection data set; s2, constructing a geological type recognition model based on the convolutional neural network, and training the geological type recognition model by adopting a historical detection data set; s3, drilling after connecting a plurality of optical fiber distributed sensors on the drilling device, acquiring corresponding parameter data of a well exploration and a drill bit in real time through the optical fiber distributed sensors, and acquiring formation parameters based on the real-time acquired well exploration parameter data; and S4, inputting the stratum parameters acquired in real time into the trained geological type recognition model to obtain the geological type distribution of the stratum, and adjusting the angle of the drill bit in real time based on the geological type distribution of the stratum to finish drilling. The invention can effectively improve the drilling efficiency, shorten the period of petroleum geological exploration and reduce the exploration cost.
Description
Technical Field
The invention relates to the technical field of petroleum geological exploration, in particular to a drilling method for petroleum geological exploration.
Background
Petroleum is one of the most important energy sources in the world, and with the development of global socioeconomic and the improvement of technological level, the demand of the whole international society for petroleum is increasing. The petroleum exploitation amount of China cannot meet the requirement of the national social development, which brings great pressure to the innovation and development of the petroleum exploration technology of China, and the pressure of the development of petroleum resources is doubled. In order to ensure the sustainable and healthy development of national economy and social productivity and the sustainable exploitation of petroleum resources in China, the exploration technology of petroleum geology must be enhanced and innovated, and the exploitation quality and efficiency of petroleum resources in China are comprehensively improved. Innovations in oil exploration technologies have become a necessary trend throughout the oil industry.
However, the conventional petroleum geological exploration technology has great defects in the aspects of petroleum exploitation to the maximum extent and investment and expenditure, the quality of petroleum exploration and development is smaller and smaller along with the development of social economy, the defects of the conventional petroleum geological exploration technology are more and more obvious, and continuous innovation of the petroleum geological exploration technology becomes necessary for the development of the times. Especially in the aspect of drilling, since the petroleum exploration and development process is composed of a plurality of stages with different properties and different tasks, the purpose and task of drilling are different in the different stages, and the conventional petroleum geological exploration technology needs to drill the wells in the whole petroleum exploration and development process, including: the oil and gas well is characterized by comprising a reference well, a profile well, a parameter well, a construction well, a detection well, a data well, a production well, a water injection well, an inspection well, an observation well and an adjustment well, so that the exploration period is prolonged, and the cost of petroleum geological exploration is greatly increased.
With the development of information technologies such as big data, cloud computing, internet of things and the like, artificial intelligence and address exploration are fused, and the drilling method for petroleum geological exploration is particularly necessary.
Disclosure of Invention
The invention aims to provide a drilling method for petroleum geological exploration, which aims to solve the technical problems in the prior art, effectively improve the drilling efficiency, shorten the period of the petroleum geological exploration and reduce the exploration cost.
In order to achieve the purpose, the invention provides the following scheme: the invention provides a drilling method for petroleum geological exploration, which comprises the following steps:
s1, acquiring petroleum geological exploration historical detection data based on geological cloud, preprocessing the acquired historical detection data, and constructing a historical detection data set;
s2, constructing a geological type recognition model based on the convolutional neural network, and training the geological type recognition model by adopting a historical detection data set;
s3, drilling after connecting a plurality of optical fiber distributed sensors on the drilling device, acquiring corresponding parameter data of a well exploration and a drill bit in real time through the optical fiber distributed sensors, and acquiring formation parameters based on the well exploration parameter data acquired in real time;
and S4, inputting the stratum parameters acquired in real time into the trained geological type recognition model to obtain the geological type distribution of the stratum, and adjusting the angle of the drill bit in real time based on the geological type distribution of the stratum to finish drilling.
Preferably, in step S1, the acquired petroleum geological exploration historical detection data includes: detection parameter data and a geological type corresponding to the detection parameter data.
Preferably, the detection parameter data comprises: resistivity, permeability, rock density, fluid density, water content of the formation.
Preferably, in step S1, the data preprocessing method includes: and (4) outlier rejection and data normalization processing.
Preferably, in step S2, in the training process of the geological type recognition model, a random gradient descent algorithm is used to update parameters of the convolutional neural network.
Preferably, in step S3, the optical fiber distributed sensor includes: a resistivity sensor, a stress sensor, an acoustic wave sensor, a liquid density sensor, a dielectric constant sensor, a temperature sensor, an angle sensor and a laser sensor; wherein the content of the first and second substances,
the resistivity sensor is used for acquiring the resistivity of the stratum; the water layer and the oil layer can be accurately judged through the acquired resistivity;
the stress sensor is used for collecting the stress of the stratum;
the acoustic sensor is used for acquiring acoustic data in the exploratory well;
the liquid density sensor is used for collecting the liquid density in the stratum;
the dielectric constant sensor is used for collecting the water content of the stratum;
the temperature sensor is used for acquiring the borehole wall temperature of the exploratory well;
the angle sensor is used for acquiring the angle between the axis of the drill bit and the direction vertical to the ground;
the laser sensor is used for collecting the distance between the bottom of the well and the ground, namely the depth of the exploratory well.
Preferably, in step S3, the fixing method of the optical fiber distributed sensor includes:
firstly, the optical fiber distributed sensor and an optical fiber are welded, and the optical fiber is coiled on a cable car of the drilling device;
and secondly, hanging a stretching block at one end of the optical fiber extending into the exploratory well.
Preferably, in step S4, the method for adjusting the drill angle specifically includes:
the method comprises the steps of inputting stratum parameters acquired by real-time acquired well exploration data into a trained geological type recognition model to obtain geological types at different moments, obtaining geological type distribution of the stratum based on corresponding well exploration depths at different moments, obtaining the optimal drilling direction based on the geological type distribution of the stratum, and adjusting the angle of a drill bit in real time according to the angle between the acquired drill bit axis and the direction perpendicular to the ground.
The invention discloses the following technical effects:
(1) according to the invention, a geological type recognition model is constructed based on a convolutional neural network, historical detection data is obtained through geological cloud to train the geological type recognition model, so that organic fusion of big data and intelligent petroleum geological exploration is realized, and accurate prediction of geological exploration type can be realized through the trained geological type recognition model, thus the drilling efficiency is improved, and the exploration period is reduced;
(2) according to the invention, through the optical fiber distributed sensor, a plurality of logging data are acquired in real time while drilling, and the data are rapidly and effectively transmitted through the optical fiber without considering the problem of underground data storage in the process of acquiring a large amount of data; meanwhile, the data acquired by the optical fiber distributed sensor can be rapidly and accurately predicted through the geological type recognition model, the number of drilled wells is effectively reduced, the period of petroleum geological exploration is greatly shortened, and the exploration cost is reduced;
(3) according to the invention, the angle of the drill bit is adjusted in real time through the geological type distribution of the stratum, so that the oil extraction quality can be effectively ensured, and the drilling efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a flow chart of a drilling method for petroleum geological exploration according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious 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.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Referring to fig. 1, the present embodiment provides a drilling method for petroleum geological exploration, comprising the steps of:
s1, acquiring petroleum geological exploration historical detection data based on geological cloud, preprocessing the acquired historical detection data, and constructing a historical detection data set;
the geological cloud is a set of comprehensive geological information service system hosted and researched by the Chinese geological survey bureau, adopts a classic 4-layer cloud architecture, integrates four subsystems of geological survey, service management, data sharing and public service, provides an intelligent geological survey working platform in a cloud environment for geological survey technicians, and innovates a novel geological survey working mode; the method is used for providing one-stop comprehensive business management under a cloud environment and auxiliary decision support under big data support for geological survey managers, and realizing one-stop services of geological survey projects, personnel, finance, equipment and the like; the method provides a plurality of types of professional data sharing services such as basic geology, mineral geology, hydraulic engineering ring geology, marine geology and the like for various geological survey professionals; the method is oriented to the social public and provides various geological information product services.
The acquired petroleum geological exploration historical detection data comprises: detecting parameter data and a geological type corresponding to the detecting parameter data; the detection parameter data includes: resistivity, permeability, rock density, fluid density, water content of the formation.
The data preprocessing method comprises the following steps:
and removing outliers in the historical detection data by adopting a hierarchical clustering method based on average distance, and normalizing the remaining data after removal, so as to reduce interference caused by different variable dimensions.
S2, constructing a geological type recognition model based on the convolutional neural network, and training the geological type recognition model by adopting a historical detection data set;
the convolutional neural network comprises an input layer, a convolutional layer, an activation layer, an output layer and an auxiliary layer; the input layer is used for inputting detection parameter data in a historical data set;
the convolutional layer is used for carrying out feature extraction on input detection parameter data;
the auxiliary layer is used for reducing transition fitting of data, and is beneficial to improving the capability of generating training data by the network and reducing the training time; the auxiliary layer comprises a cutting layer and a batch standardization layer; the phase layer is used for cutting off the connection among partial neurons and reducing transition fitting, and the batch standardization layer is used for carrying out standardization operation on batch input data and reducing the dependence of neural network learning on a parameter initialization method, so that the input data are stably distributed, the convergence rate of the neural network is accelerated, and the training speed of the neural network is accelerated.
The output layer is used for outputting the geological type; and updating the numerical value of the parameter of the convolutional neural network by adopting a random gradient descent algorithm, and reducing the value of the loss function so that the predicted geological type and the actual geological type are gradually converged.
S3, drilling a well after connecting a plurality of optical fiber distributed sensors on the drilling device, acquiring corresponding parameter data of a exploratory well and a drill bit in real time through the optical fiber distributed sensors, and acquiring formation parameters based on the exploratory well parameter data acquired in real time;
the optical fiber distributed sensor includes: a resistivity sensor, a stress sensor, an acoustic wave sensor, a liquid density sensor, a dielectric constant sensor, a temperature sensor, an angle sensor and a laser sensor;
the resistivity sensor is used for acquiring the resistivity of the stratum; the water layer and the oil layer can be accurately judged through the acquired resistivity;
the stress sensor is used for collecting the stress of the stratum; based on the corresponding relation between the formation stress and the permeability, the permeability of the formation can be effectively calculated through the collected formation stress;
the acoustic sensor is used for acquiring acoustic data in the exploratory well; based on the relation between the sound wave propagation speed and the rock density, the rock density of the stratum can be effectively calculated through the collected sound wave data;
the liquid density sensor is used for collecting the liquid density in the stratum;
the dielectric constant sensor is used for collecting the water content of the stratum;
the temperature sensor is used for acquiring the borehole wall temperature of the exploratory well; in the drilling process, the temperature near the bottom of the well is very high, and the thermal stress generated by the temperature change of the wall of the well in the drilling process can cause the formation stress near the wall of the well to change, so that the temperature of the wall of the well is monitored in real time, the exploration well is cooled through the circulation of the drilling fluid under the condition that the temperature of the wall of the well exceeds a preset threshold value, and the accuracy and the effectiveness of the acquired exploration well parameters can be effectively guaranteed.
The angle sensor is used for acquiring the angle between the axis of the drill bit and the direction vertical to the ground, and the direction of the drill bit can be adjusted through the angle;
the laser sensor is used for collecting the distance between the well bottom and the ground, namely the exploratory well depth; by collecting the distance between the well bottom and the ground, the well depth of the exploratory well can be obtained, data support is provided for follow-up oil extraction, the distributed optical fiber sensor can be adjusted in real time according to the well depth, and the distributed optical fiber sensor can be kept consistent with the position of a drill bit.
The fixing method of the optical fiber distributed sensor comprises the following steps:
firstly, the optical fiber distributed sensor and an optical fiber are welded, and the optical fiber is coiled on a cable car of the drilling device; the optical fiber is wrapped in the optical cable, so that the optical fiber is prevented from being damaged by the severe environment in the exploratory well;
secondly, hanging a stretching block at one end of the optical fiber extending into the exploratory well, so that the optical fiber distributed sensor moves downwards along with the drill bit, and the position of the distributed optical fiber sensor can be kept consistent with that of the drill bit; meanwhile, the bending or knotting of the optical fiber can be effectively avoided, and the effective transmission of the data collected by the optical fiber distributed sensor is ensured.
Through optical fiber transmission, simultaneous transmission of data collected by a plurality of optical fiber distributed sensors can be realized, and the problem of underground data storage does not need to be considered.
And S4, inputting the stratum parameters acquired in real time into the trained geological type recognition model to obtain the geological type distribution of the stratum, and adjusting the angle of the drill bit in real time based on the geological type distribution of the stratum to finish drilling.
The method specifically comprises the following steps: the method comprises the steps of inputting stratum parameters acquired by real-time acquired exploratory well data into a trained geological type recognition model to obtain geological types at different moments, obtaining geological type distribution of the stratum based on exploratory well depths corresponding to the different moments, obtaining the optimal drilling direction based on the geological type distribution of the stratum, and adjusting the angle of a drill bit in real time according to the angle between the axis of the acquired drill bit and the direction perpendicular to the ground, so that the oil extraction quality can be effectively guaranteed, and the drilling efficiency is improved.
The invention has the following technical effects:
(1) according to the invention, a geological type recognition model is constructed based on a convolutional neural network, historical detection data is obtained through geological cloud to train the geological type recognition model, so that organic fusion of big data and intelligent petroleum geological exploration is realized, and accurate prediction of geological exploration type can be realized through the trained geological type recognition model, thus the drilling efficiency is improved, and the exploration period is reduced;
(2) according to the invention, through the optical fiber distributed sensor, a plurality of logging data are acquired in real time while drilling, and the data are rapidly and effectively transmitted through the optical fiber without considering the problem of underground data storage in the process of acquiring a large amount of data; meanwhile, the data acquired by the optical fiber distributed sensor can be rapidly and accurately predicted through the geological type recognition model, the number of drilled wells is effectively reduced, the period of petroleum geological exploration is greatly shortened, and the exploration cost is reduced;
(3) according to the invention, the angle of the drill bit is adjusted in real time through the geological type distribution of the stratum, so that the oil extraction quality can be effectively ensured, and the drilling efficiency is improved.
The above-described embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solutions of the present invention can be made by those skilled in the art without departing from the spirit of the present invention, and the technical solutions of the present invention are within the scope of the present invention defined by the claims.
Claims (4)
1. A drilling method for petroleum geological exploration, characterized in that it comprises the following steps:
s1, acquiring petroleum geological exploration historical detection data based on geological cloud, preprocessing the acquired historical detection data, and constructing a historical detection data set;
the pretreatment method comprises the following steps: outlier rejection and data normalization processing;
s2, constructing a geological type recognition model based on the convolutional neural network, and training the geological type recognition model by adopting a historical detection data set;
the convolutional neural network comprises an input layer, a convolutional layer, an activation layer, an output layer and an auxiliary layer; the convolutional layer is used for carrying out feature extraction on input detection parameter data; the auxiliary layer is used for reducing transition fitting of data; the auxiliary layer comprises a cutting layer and a batch standardization layer; the truncation layer is used for truncating the connection between partial neurons; the batch standardization layer is used for carrying out standardization operation on batch input data and reducing the dependence of neural network learning on a parameter initialization method; the output layer is used for outputting the geological type;
in the training process of the geological type recognition model, updating parameters of the convolutional neural network by adopting a random gradient descent algorithm;
s3, drilling after connecting a plurality of optical fiber distributed sensors on the drilling device, acquiring corresponding parameter data of a well exploration and a drill bit in real time through the optical fiber distributed sensors, and acquiring formation parameters based on the well exploration parameter data acquired in real time;
the optical fiber distributed sensor includes: a resistivity sensor, a stress sensor, an acoustic wave sensor, a liquid density sensor, a dielectric constant sensor, a temperature sensor, an angle sensor and a laser sensor; wherein the content of the first and second substances,
the resistivity sensor is used for acquiring the resistivity of the stratum; the water layer and the oil layer can be accurately judged through the acquired resistivity;
the stress sensor is used for collecting the stress of the stratum;
the acoustic sensor is used for acquiring acoustic data in the exploratory well;
the liquid density sensor is used for collecting the liquid density in the stratum;
the dielectric constant sensor is used for collecting the water content of the stratum;
the temperature sensor is used for acquiring the borehole wall temperature of the exploratory well;
the angle sensor is used for acquiring the angle between the axis of the drill bit and the direction vertical to the ground;
the laser sensor is used for collecting the distance between the well bottom and the ground, namely the exploratory well depth;
the fixing method of the optical fiber distributed sensor comprises the following steps:
firstly, the optical fiber distributed sensor and an optical fiber are welded, and the optical fiber is coiled on a cable car of the drilling device;
secondly, hanging a stretching block at one end of the optical fiber extending into the exploratory well;
and S4, inputting the stratum parameters acquired in real time into the trained geological type recognition model to obtain the geological type distribution of the stratum, and adjusting the angle of the drill bit in real time based on the geological type distribution of the stratum to finish drilling.
2. The drilling method for petroleum geological exploration, as claimed in claim 1, characterized in that in said step S1, said acquisition of historical exploration data of petroleum geological exploration comprises: detection parameter data and a geological type corresponding to the detection parameter data.
3. The drilling method for petroleum geological exploration, according to claim 2, characterized in that said detection parameter data comprise: resistivity, permeability, rock density, fluid density, water content of the formation.
4. The drilling method for petroleum geological exploration, according to claim 1, characterized in that in said step S4, the adjustment method of the drill angle comprises in particular:
the method comprises the steps of inputting stratum parameters acquired by real-time acquired well exploration data into a trained geological type recognition model to obtain geological types at different moments, obtaining geological type distribution of the stratum based on corresponding well exploration depths at different moments, obtaining the optimal drilling direction based on the geological type distribution of the stratum, and adjusting the angle of a drill bit in real time according to the angle between the acquired drill bit axis and the direction perpendicular to the ground.
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| CN114016911A (en) * | 2021-10-29 | 2022-02-08 | 中国石油天然气集团有限公司 | Push-type rotary steering system-based while-drilling forward probing method and device |
| CN114139458B (en) * | 2021-12-07 | 2024-06-18 | 西南石油大学 | Drilling parameter optimization method based on machine learning |
| CN114486330B (en) * | 2022-01-25 | 2024-05-10 | 吴凤萍 | Intelligent sampling system for geological exploration |
| CN116256814A (en) * | 2023-03-15 | 2023-06-13 | 东营市睿建石油装备有限公司 | Petroleum geological exploration monitoring method and device |
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