Petroleum drilling process drilling scheme analysis system and method based on big data

Abstract

The invention relates to the technical field of petroleum drilling scheme planning, and discloses a petroleum drilling process drilling scheme analysis system and method based on big data, and the system comprises a petroleum drilling bit object processing module, a drilling bit working state processing module, and a petroleum drilling bit scheduling module. Intelligent analysis processing of drill bit types needed by petroleum drilling operation is carried out based on petroleum drilling stratum geological information and petroleum drilling operation daily production capacity information in combination with an intelligent recognition algorithm and petroleum drilling drill bit type information stored based on big data, and scientific analysis of matching of the drill bit types needed by petroleum drilling is achieved; the real-time lithologic characteristic information of the petroleum drilling bit end is combined with an intelligent search algorithm and the petroleum drilling bit working parameters stored based on big data to intelligently analyze the petroleum drilling bit operation state parameters, so that the petroleum drilling bit working parameters are accurately matched, and the intelligence and applicability of petroleum drilling are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of oil drilling plan planning, and specifically to an analysis system and method for oil drilling process drilling plans based on big data. Background Art

[0002] Oil drilling uses oil drilling equipment to drill through multiple sets of formations from the ground along the designed track to reach the predetermined target layer, forming a stable channel for oil and gas production or injection of required fluids, and during the drilling process and after the completion of drilling, core sampling, logging, well logging and testing work are completed to obtain various information required for exploration, development and drilling. It is a systematic project. In order to complete the drilling task of a well safely and quickly, careful drilling design must be done before construction. Drilling design usually includes two parts: drilling geological design and drilling engineering design. Drilling engineering design includes process technology design, construction period design and drilling cost budget, etc.; in the oil drilling process, it is impossible to scientifically select the bit type based on the geological characteristics and production plan of oil drilling, nor can it intelligently adjust the working parameters of the bit in oil drilling based on the lithology and bit type in oil drilling, reducing the safety and intelligence of oil drilling.

[0003] The Chinese patent application with the publication number CN117972922A discloses a method for optimizing drilling parameters. First, formation parameters, drilling parameters and bit structure parameters are obtained according to adjacent drilled well data; mathematical models of mechanical drilling rate, mechanical specific energy and bit life are established respectively and a multi-objective programming model is established, and at the same time, the objective function and constraint conditions of the multi-objective model are optimized; the Pareto optimal solution set under this model is calculated and screened; finally, the solutions screened are sorted according to the fuzzy decision-making theory; the above technical solutions cannot scientifically evaluate the bit type of oil drilling and dynamically correct the working parameters of the bit, reducing the working efficiency and intelligence of oil drilling. Summary of the Invention

[0004] (1) Technical Problems to be Solved

[0005] To solve the problems that in the above oil drilling process, it is impossible to scientifically select the bit type based on the geological characteristics and production plan of oil drilling, nor can it intelligently adjust the working parameters of the bit in oil drilling based on the lithology and bit type in oil drilling, reducing the safety and intelligence of oil drilling, and to achieve the purpose of scientifically analyzing the bit type information of oil drilling, intelligently analyzing the working parameters of oil drilling bits, intelligently and real-time scheduling oil drilling operations, and improving the safety and production efficiency of oil drilling.

[0006] (2) Technical Solutions

[0007] The present invention is realized through the following technical solutions: An analysis method for oil drilling process drilling plans based on big data, the method comprising the following steps:

[0008] S1. Collect the text data of the geological characteristics of the oil drilling formation and the data of the daily operation time of the oil drilling.

[0009] S2. Based on the data of the daily operation time of the oil drilling and the data of the daily production volume of the oil drilling operation at different times, perform the identification process of the daily production volume of the oil drilling operation to generate the data of the daily production volume of the oil drilling operation.

[0010] S3. Based on the text data of the geological characteristics of the oil drilling formation, the data of the daily production volume of the oil drilling operation, and the text data of the bit type characteristics of the oil drilling, perform the analysis process of the bit type required for the oil drilling operation to generate the text data of the target bit type characteristics of the oil drilling operation.

[0011] S4. Execute the oil drilling operation according to the text data of the target bit type characteristics of the oil drilling operation.

[0012] S5. Collect the text data of the real-time lithological characteristics at the bit end of the oil drilling.

[0013] S6. Based on the text data of the target bit type characteristics of the oil drilling operation, the text data of the real-time lithological characteristics at the bit end of the oil drilling, and the bit working data of the oil drilling operation, perform the analysis process of the bit running state parameters of the oil drilling operation to generate the target bit working data of the oil drilling operation.

[0014] S7. Construct the bit scheduling data of the oil drilling and execute the bit running state scheduling operation of the oil drilling.

[0015] Preferably, the operation steps of collecting the text data of the geological characteristics of the oil drilling formation and the data of the daily operation time of the oil drilling are as follows:

[0016] S11. Online collect the text information of the geological type characteristics of the oil drilling area through the oil drilling management platform and generate the text data L of the geological characteristics of the oil drilling formation. The text data of the geological characteristics of the oil drilling formation includes the text data of the sandstone geological characteristics, the text data of the carbonate rock geological characteristics, the text data of the shale geological characteristics, the text data of the salt rock geological characteristics, the text data of the volcanic rock geological characteristics, and the text data of the metamorphic rock geological characteristics.

[0017] Online collect the text information of the daily time point of the oil drilling operation through the oil drilling management platform and generate the data O of the daily operation time of the oil drilling. The unit of O consists of year, month, and day.

[0018] Preferably, the operation steps of performing the identification process of the daily production volume of the oil drilling operation based on the data of the daily operation time of the oil drilling and the data of the daily production volume of the oil drilling operation at different times to generate the data of the daily production volume of the oil drilling operation are as follows:

[0019] S21. Establish a data set A=(a1, …, a i , …, a α ) of the daily production volume data of oil drilling operations at different times, where i = 1, 2, 3, …, α; among them, a i represents the daily production volume data of oil drilling operations at different times corresponding to the i-th oil drilling operation day, α represents the maximum value of the number of oil drilling operation days, and the daily production volume data of oil drilling operations at different times represents the daily oil drilling depth parameters pre-compiled according to the production plan before the implementation of oil drilling. The unit of a i is meters;

[0020] S22. Use the Rabin-Karp search algorithm to perform character matching of the oil drilling operation time data O on the day with the daily production volume data a i of the oil drilling operations at different times in the data set A of the daily production volume data of oil drilling operations at different times, search for the daily production volume data a i corresponding to the oil drilling operation time data O on the day, and generate the daily production volume data a dangri of the oil drilling operation on the day through data identification, where the unit of a dangri is meters.

[0021] Preferably, based on the oil drilling formation geological feature text data, the daily production volume data of the oil drilling operation, and the oil drilling bit type feature text data, the operation steps for analyzing the bit type required for the oil drilling operation to generate the target oil drilling operation bit type feature text data are as follows:

[0022] S31. Establish a data set B=(b1, …, b j , …, b β ) of the oil drilling bit type feature text data, where j = 1, 2, 3, …, β; among them, b j represents the oil drilling bit type feature text data corresponding to the j-th oil drilling working condition parameter combination type, β represents the maximum value of the number of oil drilling working condition parameter combination types, and the oil drilling working condition parameter combination type represents an index data type formed by combining mainly the oil drilling formation geological information and the daily production volume parameter of the oil drilling operation for searching the oil drilling bit type information; the oil drilling bit type feature text data includes cemented carbide bit type feature text data, roller cone bit type feature text data, and diamond bit type feature text data;

[0023] S32. Combine the oil drilling formation geological feature text data L and the daily production volume data a dangriWith the petroleum drilling bit type feature text data b in the petroleum drilling bit type feature text data set B j Perform keyword matching on the formation geological features and production volume during petroleum drilling operations, and search for the petroleum drilling formation geological feature text data L and the production volume data a on the day of the petroleum drilling operation dangri The corresponding petroleum drilling bit type feature text data b j And generate the target petroleum drilling operation bit type feature text data b through data identification mubiao Execute the generation of the target petroleum drilling operation bit type feature text data b mubiao The specific operation steps are as follows:

[0024] S321. Initialization stage: Initialize the number N of the honey badger population for bit type search and the position of the honey badger individual for bit type search. The position of the honey badger individual for bit type search in the search space of the petroleum drilling bit type feature text data set B. The position update formula for the honey badger individual for bit type search is where r1 is a random number within (0, 1), and M x is the position of the x-th honey badger individual for bit type search in the search space of the petroleum drilling bit type feature text data set B; and σ are the upper and lower bounds in the search space of the petroleum drilling bit type feature text data set B;

[0025] S322. Define intensity, intensity and the concentration force of the prey and the distance between the honey badger individuals for bit type search, J x represents the odor intensity of the prey; J x The calculation formula is as follows: τ x = M prey - M x where represents the square of the distance difference between the position of the x-th honey badger individual for bit type search in the search space of the petroleum drilling bit type feature text data set B and the position of the (x + 1)-th honey badger individual for bit type search in the search space of the petroleum drilling bit type feature text data set B; τ x represents the distance between the prey and the x-th honey badger individual for bit type search in the search space of the petroleum drilling bit type feature text data set B. r2 is a random number within (0, 1), and M x+1 represents the position of the (x + 1)-th honey badger individual for bit type search in the search space of the petroleum drilling bit type feature text data set B, and M preyindicating to search for the petroleum drilling bit type feature text data b that matches the petroleum drilling formation geological feature text data L and the daily production data a of the petroleum drilling operation in the search space of the petroleum drilling bit type feature text data set B dangri that matches j Position of the prey;

[0026] S323. Update the density factor. The density factor l controls the random change of the density of honey badger individuals searching for the bit type to ensure a smooth transition from exploration to exploitation. The update formula for the density factor l is where T is the maximum number of iterations, and t represents the number of iterations, is a constant greater than or equal to 1, and exp is a random function;

[0027] S324. Jump out of the local optimum. S324, S325, and S326 are used to jump out of the local optimum area and scan the empty search space of the petroleum drilling bit type feature text data set B for the honey badger individuals searching for the bit type to search for the petroleum drilling bit type feature text data b that matches the petroleum drilling formation geological feature text data L and the daily production data a of the petroleum drilling operation dangri that matches j ;

[0028] S325. In the excavation stage, the honey badger individuals searching for the bit type perform actions similar to the shape of a cardioid. The cardioid motion is simulated by the following formula

[0029]

[0030] where M new represents the new position of the honey badger individual x searching for the bit type in the search space of the petroleum drilling bit type feature text data set B. υ represents the ability of the honey badger individual searching for the bit type to obtain food. r3, r4, and r5 represent three different random numbers between (0, 1); is a parameter for changing the search direction of the honey badger individual searching for the bit type in the search space of the petroleum drilling bit type feature text data set B. In the excavation stage, the honey badger individual searching for the bit type depends on the odor intensity of the prey, the distance to the prey, and the density factor l. During the excavation activity, the honey badger individual searching for the bit type will be disturbed, making it unable to find the optimal prey position;

[0031] S326. In the honey collection stage, the situation where the honey badger individuals searching for the bit type follow the honey guide badger to the honeycomb can be simulated by the formula where M' newTo search for a new position of the honey badger individual i in the search space of the petroleum drilling bit type feature text data set B during the honey collection stage, r7 is a random number between (0, 1);

[0032] S327. When the maximum number of iterations is reached, output the petroleum drilling bit type feature text data b dangri matching the petroleum drilling formation geological feature text data L and the daily production data a of the petroleum drilling operation j , and generate the target petroleum drilling operation bit type feature text data b through data identification mubiao .

[0033] Preferably, the operating steps for performing the petroleum drilling operation according to the target petroleum drilling operation bit type feature text data are as follows:

[0034] S41. The petroleum drilling management platform controls the petroleum drilling equipment to install the corresponding drilling bit to perform the petroleum drilling operation according to the target petroleum drilling operation bit type feature text data b mubiao .

[0035] Preferably, the operating steps for collecting the real-time lithological feature text data of the petroleum drilling bit end are as follows:

[0036] S51. During the process of performing the petroleum drilling operation, synchronously use a lithology measuring device to online measure the lithological feature information of the end of the petroleum drilling bit, and generate the real-time lithological feature text data U of the petroleum drilling bit end. The real-time lithological feature text data of the petroleum drilling bit end includes the compositional feature text data, structural feature text data, color feature text data, particle size feature text data, mineral composition feature text data, and cementation type feature text data of the underground rock at the end of the drill bit during the petroleum drilling operation; the lithology measuring device includes a resistivity meter, a density-neutron logging tool, a bit mechanical sensor, a debris collection device, and a microscopic imaging system.

[0037] Preferably, the operating steps for analyzing and processing the bit running state parameters of the petroleum drilling operation according to the target petroleum drilling operation bit type feature text data, the real-time lithological feature text data of the petroleum drilling bit end, and the petroleum drilling operation bit working data to generate the target petroleum drilling operation bit working data are as follows:

[0038] S61. Establish a petroleum drilling operation bit working data set C = (c1,…,c k ,…,c χ ), k = 1, 2, 3,…,χ; where c kDenote the working data of the oil drilling bit corresponding to the k-th type of oil drilling bit processing parameter combination. Let χ represent the maximum value of the number of types of oil drilling bit processing parameter combinations. The oil drilling bit processing parameter combination type represents an index data type formed by data combination mainly from the oil drilling bit type information and the real-time lithology information at the bit end of the oil drilling operation, which is used to search for the working parameters of the oil drilling bit. The working data of the oil drilling bit includes the real-time rotational speed working parameter, real-time weight on bit working parameter, real-time torque working parameter, real-time pump pressure working parameter, real-time displacement working parameter, real-time drilling speed working parameter, and real-time vibration working parameter of the oil drilling bit.

[0039] S62. Use the KMP search algorithm to process the target oil drilling bit type feature text data b mubiao and the real-time lithology feature text data U at the bit end of the oil drilling bit and the working data c of the oil drilling bit in the working data set C of the oil drilling bit k to perform keyword matching for the bit type and bit-end lithology features of the oil drilling bit during the oil drilling operation, and search for the target oil drilling bit type feature text data b mubiao and the working data c of the oil drilling bit corresponding to the real-time lithology feature text data U at the bit end of the oil drilling bit k , and generate the target working data c of the oil drilling bit through data identification mubiao . The target working data of the oil drilling bit represents the real-time optimal operating state parameters of the bit selected during the oil drilling operation.

[0040] Preferably, the operation steps of constructing the oil drilling bit scheduling data and performing the operation of scheduling the operating state of the oil drilling bit are as follows:

[0041] S71. Construct the oil drilling bit scheduling data f by data identification for the target working data c of the oil drilling bit mubiao .

[0042] S72. The oil drilling management platform controls the oil drilling equipment to perform the operation of scheduling the operating state of the oil drilling bit according to the working parameters corresponding to the oil drilling bit scheduling data f.

[0043] The drilling plan analysis system for the oil drilling process based on big data is used to implement the drilling plan analysis method for the oil drilling process based on big data. The system includes an oil drilling bit object processing module, a drilling bit working state processing module, and an oil drilling bit scheduling module.

[0044] The oil drilling bit object processing module includes an oil drilling formation geological feature information collection unit, an oil drilling daily operation time collection unit, a storage unit for the daily production volume of oil drilling operations at different times, an identification unit for the daily production volume of oil drilling operations on the current day, a storage unit for oil drilling bit type information, a target oil drilling bit type analysis unit, and an oil drilling operation execution unit;

[0045] The oil drilling formation geological feature information collection unit collects text data on the geological features of the oil drilling formation through the oil drilling management platform; the oil drilling daily operation time collection unit collects data on the daily operation time of the oil drilling through the oil drilling management platform; the storage unit for the daily production volume of oil drilling operations at different times is used to store data on the daily production volume of oil drilling operations at different times; the identification unit for the daily production volume of oil drilling operations on the current day performs identification processing on the daily production volume of the oil drilling operation based on the data on the daily operation time of the oil drilling and the data on the daily production volume of oil drilling operations at different times, and generates data on the daily production volume of the oil drilling operation; the storage unit for oil drilling bit type information is used to store text data on the type characteristics of the oil drilling bit; the target oil drilling bit type analysis unit performs analysis processing on the type of bit required for the oil drilling operation based on the text data on the geological features of the oil drilling formation, the data on the daily production volume of the oil drilling operation on the current day, and the text data on the type characteristics of the oil drilling bit, and generates text data on the type characteristics of the target oil drilling operation bit; the oil drilling operation execution unit controls the oil drilling equipment to install the corresponding drilling bit to perform the oil drilling operation according to the text data on the type characteristics of the target oil drilling operation bit through the oil drilling management platform;

[0046] The drilling bit working state processing module includes an oil drilling bit end real-time lithology information collection unit, a storage unit for the working parameters of the oil drilling bit, and a target oil drilling bit working parameter analysis unit;

[0047] The oil drilling bit end real-time lithology information collection unit collects text data on the real-time lithology characteristics of the oil drilling bit end through lithology measurement equipment; the storage unit for the working parameters of the oil drilling bit is used to store the working data of the oil drilling operation bit; the target oil drilling bit working parameter analysis unit performs analysis processing on the operating state parameters of the drilling bit for the oil drilling operation based on the text data on the type characteristics of the target oil drilling operation bit, the text data on the real-time lithology characteristics of the oil drilling bit end, and the working data of the oil drilling operation bit, and generates the working data of the target oil drilling operation bit;

[0048] The oil drilling bit scheduling module includes an oil drilling bit scheduling information construction unit and an oil drilling bit scheduling operation execution unit;

[0049] The oil drilling bit scheduling information construction unit is used to construct oil drilling bit scheduling data; the oil drilling bit scheduling operation execution unit, and the oil drilling management platform controls the oil drilling equipment to perform the oil drilling operation bit running state scheduling operation on the bit according to the working parameters corresponding to the oil drilling bit scheduling data.

[0050] (III) Beneficial effects

[0051] The present invention provides an oil drilling process drilling plan analysis system and method based on big data. It has the following beneficial effects:

[0052] 1. Accurately and reliably obtain the oil drilling formation geological information and the oil drilling daily operation time information through the oil drilling management platform, providing real data support for scientifically planning the oil drilling plan; accurately analyze the daily production volume of the oil drilling operation based on the oil drilling daily operation time information, combined with the intelligent search algorithm and the scientifically stored daily production volume parameters of the oil drilling operation at different times, to achieve dynamic and accurate monitoring of the planned daily production volume of the oil drilling operation; based on the oil drilling formation geological information and the oil drilling daily production volume information, combined with the intelligent recognition algorithm and the oil drilling bit type information stored in the big data, conduct intelligent analysis and processing of the bit type required for the oil drilling operation, to achieve scientific analysis of the matching of the bit type required for the oil drilling, and improve the safety of the oil drilling operation; the oil drilling management platform independently and precisely controls the oil drilling equipment to install the corresponding drilling bit to perform the oil drilling operation according to the target oil drilling operation bit type information, improving the reliability of the oil drilling operation.

[0053] 2. Dynamically collect the real-time lithological characteristic information at the end of the oil drilling bit through the lithology measurement equipment, providing real data support for scientifically analyzing the working parameters of the oil drilling bit; based on the target oil drilling operation bit type characteristic information, the real-time lithological characteristic information at the end of the oil drilling bit, combined with the intelligent search algorithm and the oil drilling operation bit working parameters stored in the big data, conduct intelligent analysis of the oil drilling operation bit running state parameters, to achieve precise matching of the oil drilling operation bit working parameters, and improve the intelligence and applicability of the oil drilling operation.

[0054] 3. Scientifically construct the oil drilling bit working parameter scheduling information based on data analysis, to achieve efficient and safe collection of the oil drilling bit working scheduling parameters. At the same time, the oil drilling management platform controls the oil drilling equipment to perform the oil drilling operation bit running state scheduling operation on the bit according to the oil drilling bit scheduling information, to achieve intelligent and accurate management of the oil drilling bit state, and improve the efficiency and quality of the oil drilling operation. Brief description of the drawings

[0055] Figure 1Schematic diagram of the modules of the drilling plan analysis system for oil drilling process based on big data provided by the present invention;

[0056] Figure 2 Flow chart of the drilling plan analysis method for oil drilling process based on big data provided by the present invention. Specific embodiments

[0057] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0058] Embodiments of the drilling plan analysis system and method for oil drilling process based on big data are as follows:

[0059] Embodiment 1:

[0060] Please refer to Figure 1 - Figure 2 , a drilling plan analysis method for oil drilling process based on big data, the method includes the following steps:

[0061] S1. Collect text data on the geological characteristics of the oil drilling formation and data on the daily operation time of the oil drilling;

[0062] S2. Identify and process the daily production volume of the oil drilling operation based on the daily operation time data of the oil drilling and the daily production volume data of the oil drilling operation at different times, and generate the daily production volume data of the oil drilling operation;

[0063] S3. Analyze and process the type of drill bit required for the oil drilling operation based on the text data on the geological characteristics of the oil drilling formation, the daily production volume data of the oil drilling operation, and the text data on the type characteristics of the oil drilling bit, and generate the text data on the type characteristics of the target oil drilling operation drill bit;

[0064] S4. Perform the oil drilling operation according to the text data on the type characteristics of the target oil drilling operation drill bit;

[0065] S5. Collect text data on the real-time lithological characteristics at the drill bit end of the oil drilling;

[0066] S6. Analyze and process the operating state parameters of the drill bit for the oil drilling operation based on the text data on the type characteristics of the target oil drilling operation drill bit, the text data on the real-time lithological characteristics at the drill bit end of the oil drilling, and the drill bit working data of the oil drilling operation, and generate the drill bit working data of the target oil drilling operation;

[0067] S7. Build the oil drilling bit scheduling data and execute the bit running status scheduling operation for the oil drilling operation.

[0068] Further, please refer to Figure 1 - Figure 2 , and the operating steps for collecting the text data of the geological characteristics of the oil drilling formation and the daily operation time data of the oil drilling are as follows:

[0069] S11. Online collect the text information of the geological type characteristics of the oil drilling area through the oil drilling management platform, and generate the text data L of the geological characteristics of the oil drilling formation. The text data of the geological characteristics of the oil drilling formation includes the text data of sandstone geological characteristics, carbonate rock geological characteristics, shale geological characteristics, salt rock geological characteristics, volcanic rock geological characteristics, and metamorphic rock geological characteristics;

[0070] Online collect the text information of the daily time points of the oil drilling operation through the oil drilling management platform, and generate the daily operation time data O of the oil drilling, where the unit of O consists of year, month, and day.

[0071] The operating steps for performing the identification process of the daily production volume of the oil drilling operation based on the daily operation time data of the oil drilling and the daily production volume data of the oil drilling operation at different times to generate the daily production volume data of the oil drilling operation are as follows:

[0072] S21. Establish a set A of daily production volume data of the oil drilling operation at different times A = (a1,…,a i ,…,a α ), i = 1, 2, 3,…,α; where a i represents the daily production volume data of the oil drilling operation at different times corresponding to the i-th oil drilling operation day, α represents the maximum value of the number of oil drilling operation days, the daily production volume data of the oil drilling operation at different times represents the daily oil drilling depth parameters pre-compiled according to the production plan before the oil drilling implementation, and the unit of a i is meters;

[0073] S22. Use the Rabin-Karp search algorithm to perform character matching of the oil drilling operation time between the daily operation time data O of the oil drilling and the daily production volume data a i of the oil drilling operation at different times in the set A of daily production volume data of the oil drilling operation at different times, search out the daily production volume data a i of the oil drilling operation at different times corresponding to the daily operation time data O of the oil drilling, and generate the daily production volume data a dangri of the oil drilling operation through data identification, where the unit of a dangri is meters.

[0074] Based on the text data of the geological characteristics of the oil drilling formation, the daily production data of the oil drilling operation, and the text data of the oil drilling bit type characteristics, the operation steps for analyzing the bit type required for the oil drilling operation and generating the target text data of the oil drilling bit type characteristics are as follows:

[0075] S31. Establish a set B of text data on oil drilling bit type characteristics, B = (b1, …, b j , …, b β ), where j = 1, 2, 3, …, β; b j represents the text data of the oil drilling bit type characteristics corresponding to the j-th type of oil drilling working condition parameter combination, and β represents the maximum value of the number of oil drilling working condition parameter combination types. The oil drilling working condition parameter combination type refers to an index data type formed by combining the geological information of the oil drilling formation and the daily production parameters of the oil drilling operation for searching for the information of the oil drilling bit type; the text data of the oil drilling bit type characteristics includes the text data of the cemented carbide bit type characteristics, the text data of the roller bit type characteristics, and the text data of the diamond bit type characteristics;

[0076] S32. Perform keyword matching on the formation geological characteristics and production volume of the oil drilling operation between the text data L of the oil drilling formation geological characteristics and the daily production data a dangri and the text data b j of the oil drilling bit type characteristics in the set B of the text data of the oil drilling bit type characteristics, search for the text data b dangri of the oil drilling bit type characteristics corresponding to the text data L of the oil drilling formation geological characteristics and the daily production data a j , and generate the target text data b mubiao of the oil drilling bit type characteristics through data identification. The specific operation steps for generating the target text data b mubiao of the oil drilling bit type characteristics are as follows:

[0077] S321. In the initialization stage, initialize the number N of the honey badger population for bit type search and the positions of the honey badger individuals for bit type search. The positions of the honey badger individuals for bit type search are in the search space of the set B of the text data of the oil drilling bit type characteristics. The formula for updating the positions of the honey badger individuals for bit type search is where r1 is a random number within (0, 1), and M x is the position of the x-th honey badger individual for bit type search in the search space of the set B of the text data of the oil drilling bit type characteristics; and σ are the upper and lower bounds in the search space of the set B of the text data of the oil drilling bit type characteristics;

[0078] S322. Define the intensity, which is related to the concentration of the prey and the distance between the drill bit type and the search for honey badger individuals, J x represents the odor intensity of the prey; J x The calculation formula is as follows: τ x = M prey - M x where represents the square of the distance difference between the position of the x-th drill bit type searching for honey badger individuals in the search space of the petroleum drilling bit type feature text data set B and the position of the (x + 1)-th drill bit type searching for honey badger individuals in the search space of the petroleum drilling bit type feature text data set B; τ x represents the distance between the prey and the x drill bit type searching for honey badger individuals in the search space of the petroleum drilling bit type feature text data set B, r2 is a random number within (0, 1), M x+1 represents the position of the (x + 1)-th drill bit type searching for honey badger individuals in the search space of the petroleum drilling bit type feature text data set B, M prey represents searching for the petroleum drilling bit type feature text data b that matches the petroleum drilling formation geological feature text data L and the daily production data a of the petroleum drilling operation on the day in the search space of the petroleum drilling bit type feature text data set B dangri in the search space of the petroleum drilling bit type feature text data set B j the position of the prey;

[0079] S323. Update the density factor. The density factor l controls the random change of the density of the drill bit type searching for honey badger individuals to ensure a smooth transition from exploration to exploitation. The update formula for the density factor l is where T is the maximum number of iterations, t represents the number of iterations, is a constant greater than or equal to 1, and exp is a random function;

[0080] S324. Jump out of the local optimum. S324, S325, and S326 are used to jump out of the local optimum area and search the empty search space of the petroleum drilling bit type feature text data set B of the drill bit type searching for honey badger individuals to search for the petroleum drilling bit type feature text data b that matches the petroleum drilling formation geological feature text data L and the daily production data a of the petroleum drilling operation on the day dangri in the search space of the petroleum drilling bit type feature text data set B j ;

[0081] S325. In the excavation stage, the drill bit type searching for honey badger individuals performs an action similar to a cardioid shape. The cardioid motion is simulated by the following formula as

[0082]

[0083] where M newIt represents the new position of the honey badger individual x searching for the drill bit type in the search space of the oil drilling drill bit type feature text data set B. υ represents the ability of the honey badger individual searching for the drill bit type to obtain food. r3, r4, and r5 represent three different random numbers between (0, 1). To change the search direction parameter of the honey badger individual searching for the drill bit type in the search space of the oil drilling drill bit type feature text data set B, during the excavation stage, the honey badger individual searching for the drill bit type depends on the odor intensity of the prey, the distance between the prey, and the density factor e. During the excavation activity, the honey badger individual searching for the drill bit type will be interfered with, making it impossible to find the optimal prey position.

[0084] S326. During the honey collection stage, the situation where the honey badger individual searching for the drill bit type follows the honey guide badger to the honeycomb can be simulated by the formula where M' new is the new position of the honey badger individual i searching for the drill bit type in the search space of the oil drilling drill bit type feature text data set B during the honey collection stage, and r7 is a random number between (0, 1).

[0085] S327. When the maximum number of iterations is reached, output the oil drilling drill bit type feature text data b dangri that matches the oil drilling formation geological feature text data L and the daily production data a of the oil drilling operation j , and generate the target oil drilling operation drill bit type feature text data b through data identification mubiao .

[0086] The operating steps for performing the oil drilling operation according to the target oil drilling operation drill bit type feature text data are as follows:

[0087] S41. The oil drilling management platform controls the oil drilling equipment to install the corresponding drilling bit to perform the oil drilling operation according to the target oil drilling operation drill bit type feature text data b mubiao .

[0088] Through the cooperation of the oil drilling formation geological feature information collection unit and the oil drilling daily operation time collection unit, the oil drilling management platform accurately and reliably obtains the oil drilling formation geological information and the oil drilling daily operation time information, providing real data support for the scientific planning of the oil drilling plan; the oil drilling operation daily production volume identification unit accurately analyzes the oil drilling operation daily production volume based on the oil drilling daily operation time information combined with the intelligent search algorithm and the scientifically stored oil drilling operation daily production volume parameters at different times, realizing the dynamic and accurate monitoring of the planned daily production volume of the oil drilling operation; the target oil drilling bit type analysis unit intelligently analyzes and processes the bit type required for the oil drilling operation based on the oil drilling formation geological information and the oil drilling operation daily production volume information combined with the intelligent identification algorithm and the oil drilling bit type information stored in the big data, realizing the scientific analysis of the matching of the bit type required for the oil drilling and improving the safety of the oil drilling operation; the oil drilling operation execution unit, the oil drilling management platform autonomously and precisely controls the oil drilling equipment to install the corresponding drilling bit to perform the oil drilling operation according to the target oil drilling operation bit type information, improving the reliability of the oil drilling operation.

[0089] Further, please refer to Figure 1 - Figure 2 , the operation steps for collecting the real-time lithological feature text data at the bit end of the oil drilling bit are as follows:

[0090] S51. During the execution of the oil drilling operation, synchronously use the lithology measurement equipment to online measure the lithological feature information at the end of the oil drilling bit, and generate the real-time lithological feature text data U at the bit end of the oil drilling bit. The real-time lithological feature text data at the bit end of the oil drilling bit includes the composition feature text data, structural feature text data, color feature text data, particle size feature text data, mineral composition feature text data, and cementation type feature text data of the underground rock at the bit end during the oil drilling operation; the lithology measurement equipment includes a resistivity meter, a density-neutron logging tool, a bit mechanical sensor, a debris collection device, and a microscopic imaging system.

[0091] Based on the target oil drilling operation bit type feature text data, the real-time lithological feature text data at the bit end of the oil drilling bit, and the oil drilling operation bit working data, the operation steps for analyzing and processing the bit running state parameters of the oil drilling operation and generating the target oil drilling operation bit working data are as follows:

[0092] S61. Establish a set C of oil drilling operation bit working data = (c1,…,c k ,…,c χ ), k = 1, 2, 3,…, χ; where c kDenote the working data of the oil drilling bit corresponding to the k-th type of oil drilling bit processing parameter combination. Let χ denote the maximum value of the number of types of oil drilling bit processing parameter combinations. The oil drilling bit processing parameter combination type refers to an index data type formed by data combination mainly from the oil drilling bit type information and the real-time lithology information at the bit end of the oil drilling operation, which is used to search for the working parameters of the oil drilling bit. The working data of the oil drilling bit includes the real-time rotational speed working parameter, real-time weight on bit working parameter, real-time torque working parameter, real-time pump pressure working parameter, real-time displacement working parameter, real-time drilling speed working parameter, and real-time vibration working parameter of the oil drilling bit operation.

[0093] S62. Use the KMP search algorithm to process the target oil drilling bit type feature text data b mubiao and the real-time lithology feature text data U at the bit end of the oil drilling bit and the working data c of the oil drilling bit in the set C of the working data of the oil drilling bit k to perform keyword matching for the bit type and the lithology feature at the bit end of the oil drilling bit, and search for the working data c of the oil drilling bit corresponding to the target oil drilling bit type feature text data b mubiao and the real-time lithology feature text data U at the bit end of the oil drilling bit k , and generate the target working data c of the oil drilling bit through data identification mubiao . The target working data of the oil drilling bit represents the real-time optimal operating state parameters of the bit selected during the oil drilling operation.

[0094] Through the real-time lithology information acquisition unit at the bit end of the oil drilling bit, use the lithology measurement equipment to dynamically collect the real-time lithology feature information at the bit end of the oil drilling bit, providing real data support for the scientific analysis of the working parameters of the oil drilling bit. The target oil drilling bit working parameter analysis unit, based on the target oil drilling bit type feature information and the real-time lithology feature information at the bit end of the oil drilling bit, combines intelligent search algorithms with the working parameters of the oil drilling bit stored in big data to perform intelligent analysis of the operating state parameters of the oil drilling bit, realizing precise matching of the working parameters of the oil drilling bit and improving the intelligence and applicability of the oil drilling operation.

[0095] Further, please refer to Figure 1 - Figure 2 , and the operating steps for constructing the oil drilling bit scheduling data and performing the operating state scheduling operation of the oil drilling bit are as follows:

[0096] S71. Construct the oil drilling bit scheduling data f through data identification of the target working data c of the oil drilling bit mubiao .

[0097] S72. The oil drilling management platform controls the oil drilling equipment to perform the drilling bit operation state scheduling operation of the oil drilling operation on the drilling bit according to the working parameters corresponding to the oil drilling bit scheduling data f.

[0098] Through the mutual cooperation of the oil drilling bit scheduling information construction unit and the oil drilling bit scheduling operation execution unit, the oil drilling bit working parameter scheduling information is scientifically constructed based on data analysis, realizing the efficient and safe acquisition of the oil drilling bit working scheduling parameters. At the same time, the oil drilling management platform controls the oil drilling equipment to perform the drilling bit operation state scheduling operation of the oil drilling operation on the drilling bit, realizing the intelligent and accurate management of the oil drilling bit state, and improving the efficiency and quality of the oil drilling operation.

[0099] Embodiment 2:

[0100] Please refer to Figure 1 - Figure 2 , a drilling plan analysis system for the oil drilling process based on big data, which is used to implement the drilling plan analysis method for the oil drilling process based on big data. The system includes an oil drilling bit object processing module, a drilling bit working state processing module, and an oil drilling bit scheduling module;

[0101] The oil drilling bit object processing module includes an oil drilling formation geological feature information acquisition unit, an oil drilling daily operation time acquisition unit, a storage unit for the daily production volume of the oil drilling operation at different times, an oil drilling operation daily production volume identification unit, an oil drilling bit type information storage unit, a target oil drilling bit type analysis unit, and an oil drilling operation execution unit;

[0102] The oil drilling formation geological feature information collection unit collects the text data of the oil drilling formation geological features through the oil drilling management platform; the oil drilling daily operation time collection unit collects the oil drilling daily operation time data through the oil drilling management platform; the oil drilling daily production storage unit for different times stores the oil drilling daily production data for different times; the oil drilling operation daily production identification unit performs the identification process of the oil drilling operation daily production based on the oil drilling daily operation time data and the oil drilling daily production data for different times, and generates the oil drilling operation daily production data; the oil drilling bit type information storage unit stores the text data of the oil drilling bit type features; the target oil drilling bit type analysis unit performs the analysis process of the bit type required for the oil drilling operation based on the oil drilling formation geological feature text data, the oil drilling operation daily production data and the oil drilling bit type feature text data, and generates the target oil drilling operation bit type feature text data; the oil drilling operation execution unit, and the oil drilling management platform controls the oil drilling equipment to install the corresponding drilling bit to perform the oil drilling operation according to the target oil drilling operation bit type feature text data;

[0103] The drilling bit working state processing module includes the oil drilling bit end real-time lithology information collection unit, the oil drilling bit working parameter storage unit, and the target oil drilling bit working parameter analysis unit;

[0104] The oil drilling bit end real-time lithology information collection unit collects the text data of the real-time lithology features of the oil drilling bit end through the lithology measurement equipment; the oil drilling bit working parameter storage unit stores the working data of the oil drilling operation bit; the target oil drilling bit working parameter analysis unit performs the analysis process of the bit running state parameters of the oil drilling operation based on the target oil drilling operation bit type feature text data, the oil drilling bit end real-time lithology feature text data and the oil drilling operation bit working data, and generates the target oil drilling operation bit working data;

[0105] The oil drilling bit scheduling module includes the oil drilling bit scheduling information construction unit and the oil drilling bit scheduling operation execution unit;

[0106] The oil drilling bit scheduling information construction unit is used to construct the oil drilling bit scheduling data; the oil drilling bit scheduling operation execution unit, and the oil drilling management platform controls the oil drilling equipment to perform the bit running state scheduling operation of the oil drilling operation bit according to the working parameters corresponding to the oil drilling bit scheduling data.

[0107] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for analyzing drilling plans during the oil drilling process based on big data, characterized in that, The method includes the following steps: S1. Collect the text data of the geological characteristics of the oil drilling formation and the data of the daily operation time of the oil drilling; S2. Based on the data of the daily operation time of the oil drilling and the data of the daily production volume of the oil drilling operation at different times, perform identification processing on the daily production volume of the oil drilling operation to generate the data of the daily production volume of the oil drilling operation; S3. Based on the text data of the geological characteristics of the oil drilling formation, the data of the daily production volume of the oil drilling operation, and the text data of the bit type characteristics of the oil drilling, perform analysis processing on the bit type required for the oil drilling operation to generate the text data of the target bit type characteristics of the oil drilling operation; S4. Perform the oil drilling operation according to the text data of the target bit type characteristics of the oil drilling operation; S5. Collect the text data of the real-time lithology characteristics at the bit end of the oil drilling; S6. Based on the text data of the target bit type characteristics of the oil drilling operation, the text data of the real-time lithology characteristics at the bit end of the oil drilling, and the working data of the oil drilling operation bit, perform analysis processing on the operating state parameters of the oil drilling operation bit to generate the working data of the target oil drilling operation bit; S7. Construct the oil drilling bit scheduling data and perform the scheduling operation of the operating state of the oil drilling operation bit.

2. The method for analyzing a drilling plan during the oil drilling process based on big data according to claim 1, wherein: The S1 includes the following steps: S11. Online collect the text information of the geological type characteristics of the oil drilling area through the oil drilling management platform and generate the text data L of the geological characteristics of the oil drilling formation; Online collect the text information of the daily time points of the oil drilling operation through the oil drilling management platform and generate the data O of the daily operation time of the oil drilling, where the unit of O consists of year, month, and day.

3. The method for analyzing a drilling plan during an oil drilling process based on big data according to claim 2, wherein: The S2 includes the following steps: S21. Establish a data set A of the daily production of oil drilling operations at different times, A = (a1, …, a i , …, a α ), where i = 1, 2, 3, …, α; among them, a i represents the daily production data of oil drilling operations at different times corresponding to the i-th oil drilling operation day, α represents the maximum value of the number of oil drilling operation days, and the unit of a i is meters; S22. Use the Rabin-Karp search algorithm to perform character matching on the time of the oil drilling operation between the O and the a in the A, and search for the a corresponding to the O i and generate the daily production data a of the oil drilling operation through data identification i , where the unit of a dangri is meters. dangri ​ 4. The method for analyzing a drilling plan during an oil drilling process based on big data according to claim 3, wherein: The S3 includes the following steps: S31. Establish the text data set B of the type characteristics of oil drilling bits, B = (b1, …, b j , …, b β ), where j = 1, 2, 3, …, β; where b j represents the text data of the type characteristics of the oil drilling bit corresponding to the j-th type of oil drilling working condition parameter combination, and β represents the maximum value of the number of types of oil drilling working condition parameter combinations; S32. Combine the L and the a dangri with the b in the B j Perform keyword matching on the formation geological characteristics and production volume during oil drilling operations to search for the corresponding b of the L and the a dangri and generate the target oil drilling operation bit type characteristic text data b through data identification j . The specific operation steps for generating the target oil drilling operation bit type characteristic text data b are as follows: mubiao Execute the generation of the target oil drilling operation bit type characteristic text data b mubiao ​ S321. In the initialization stage, initialize the population number N of the honey badger individuals searching for the bit type and the positions of the honey badger individuals searching for the bit type. The positions of the honey badger individuals searching for the bit type are in the search space of B; S322. Define the intensity, which is related to the concentration force of the prey and the distance between the honey badger individuals searching for the bit type; S323. Update the density factor l. The density factor l controls the random change of the density of the honey badger individuals searching for the bit type to ensure a smooth transition from exploration to exploitation; S324. Jump out of the local optimum. S324, S325, and S326 are used to jump out of the local optimum region and search the empty search space of the honey badger individuals of the drill bit type to scan and search for the b that matches the L and the a dangri matched j ; S325. In the excavation stage, the honey badger individuals searching for the bit type perform actions similar to the shape of a cardioid. In the excavation stage, the honey badger individuals searching for the bit type rely on the odor intensity of the prey, the distance between the prey, and the density factor l. During the excavation activity, the honey badger individuals searching for the bit type will be disturbed, making it impossible for them to find the optimal prey position; S326. In the honey collection stage, the honey badger individuals searching for the bit type follow the honey guide badger to the honeycomb; S327. When the maximum number of iterations is satisfied, output the b that matches the L and the a dangri and generate the target oil drilling operation bit type feature text data b through data identification j . mubiao .

5. The method for analyzing a drilling plan during an oil drilling process based on big data according to claim 4, wherein: The S4 includes the following steps: S41. The oil drilling management platform controls the oil drilling equipment to install the corresponding drilling bit to perform oil drilling operations according to the said b mubiao Control the oil drilling equipment to install the corresponding drilling bit to perform oil drilling operations.

6. The method for analyzing a drilling plan during an oil drilling process based on big data according to claim 5, wherein: The S5 includes the following steps: S51. During the process of performing the oil drilling operation, synchronously use the lithology measurement equipment to online measure the lithology characteristic information at the bit end of the oil drilling and generate the text data U of the real-time lithology characteristics at the bit end of the oil drilling; 7. The method for analyzing a drilling plan during an oil drilling process based on big data according to claim 6, wherein: The S6 includes the following steps: S61. Establish a set C of working data of the drill bit for oil drilling operations, C = (c1,…,c k ,…,c χ ), where k = 1, 2, 3,…, χ; where c k represents the working data of the drill bit for oil drilling operations corresponding to the k-th type of parameter combination of the oil drilling bit, and χ represents the maximum value of the number of types of parameter combinations of the oil drilling bit; S62. Use the KMP search algorithm to match the b mubiao and the U with the c in the C k for keyword matching of the bit type and bit-end lithology characteristics for oil drilling operations, and search for the b mubiao and the corresponding c of the U k , and generate target oil drilling operation bit working data c mubiao .

8. The method for analyzing the drilling plan during the oil drilling process based on big data according to claim 7, wherein: The S7 includes the following steps: S71. Take the c mubiao Construct the oil drilling bit scheduling data f through data identification; S72. The oil drilling management platform controls the oil drilling equipment to perform oil drilling operations on the drill bit and carry out drill bit operation state scheduling operations according to the working parameters corresponding to the f.

9. A drilling plan analysis system for oil drilling process based on big data, which is used to implement the method for analyzing drilling plan in the oil drilling process based on big data according to any one of claims 1-8, characterized in that: The system includes an oil drilling bit object processing module, a drilling bit working state processing module, and an oil drilling bit scheduling module.

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