CN116882639B - Petroleum drilling and production equipment management method and system based on big data analysis - Google Patents
Petroleum drilling and production equipment management method and system based on big data analysis Download PDFInfo
- Publication number
- CN116882639B CN116882639B CN202311152369.XA CN202311152369A CN116882639B CN 116882639 B CN116882639 B CN 116882639B CN 202311152369 A CN202311152369 A CN 202311152369A CN 116882639 B CN116882639 B CN 116882639B
- Authority
- CN
- China
- Prior art keywords
- petroleum
- drilling
- production
- equipment
- yield
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 239000003208 petroleum Substances 0.000 title claims abstract description 315
- 238000005553 drilling Methods 0.000 title claims abstract description 279
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 236
- 238000007726 management method Methods 0.000 title claims abstract description 23
- 238000007405 data analysis Methods 0.000 title claims abstract description 22
- 238000005065 mining Methods 0.000 claims abstract description 9
- 238000012423 maintenance Methods 0.000 claims description 54
- 238000011161 development Methods 0.000 claims description 48
- 238000000034 method Methods 0.000 claims description 30
- 238000004364 calculation method Methods 0.000 claims description 14
- 230000009467 reduction Effects 0.000 claims description 14
- 230000003247 decreasing effect Effects 0.000 claims description 12
- 238000004458 analytical method Methods 0.000 claims description 11
- 230000008569 process Effects 0.000 claims description 11
- 238000012417 linear regression Methods 0.000 claims description 10
- 238000012937 correction Methods 0.000 claims description 9
- 230000002159 abnormal effect Effects 0.000 claims description 7
- 230000007423 decrease Effects 0.000 claims description 6
- 239000003129 oil well Substances 0.000 claims description 4
- 230000008439 repair process Effects 0.000 claims description 3
- 230000018109 developmental process Effects 0.000 description 37
- 230000008859 change Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 230000032683 aging Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008094 contradictory effect Effects 0.000 description 1
- 230000010485 coping Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Animal Husbandry (AREA)
- Life Sciences & Earth Sciences (AREA)
- Agronomy & Crop Science (AREA)
- Educational Administration (AREA)
- Marine Sciences & Fisheries (AREA)
- Mining & Mineral Resources (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Testing Or Calibration Of Command Recording Devices (AREA)
Abstract
The invention discloses a petroleum drilling and production equipment management method and system based on big data analysis, which relate to the technical field of petroleum drilling and production and comprise the following steps: acquiring historical output data of the petroleum oil field from a database, and predicting output data of the petroleum oil field based on the historical output data of the petroleum oil field; determining the petroleum drilling and production amount of each petroleum drilling and production device based on the output data of the oil field, and recording the petroleum drilling and production amount as a set drilling and production amount; acquiring historical operation data of the petroleum drilling and production equipment from a database, and calculating estimated production quantity of the petroleum drilling and production equipment based on the historical operation data of the petroleum drilling and production equipment; judging whether the estimated production amount of the petroleum drilling and production equipment is larger than the set drilling and production amount. The invention has the advantages that: the device can be adjusted and managed in advance when petroleum is drilled and mined, so that the overall exploitation efficiency of the petroleum drilling and mining device meets the exploitation requirement of the petroleum field, the maximized drilling and mining yield of the petroleum field is further ensured, and the maximized exploitation efficiency requirement of the petroleum field is realized.
Description
Technical Field
The invention relates to the technical field of petroleum drilling and production, in particular to a petroleum drilling and production equipment management method and system based on big data analysis.
Background
In the fault problem of the petroleum drilling and production special equipment, four faults of damage type, ageing loosening type faults, disregulation type faults and performance failure caused by blockage leakage mainly exist, in the process of petroleum oil field exploitation, the four faults can be generally divided into four exploitation stages, namely an oil reservoir production stage, a high-yield stable production stage, a yield decline stage and a low-yield stage, the yield changes of the oil fields in the four stages are different, the drilling and production requirements of the petroleum drilling and production equipment are different, in the prior art, a petroleum drilling and production equipment management scheme which effectively combines oil field exploitation data and petroleum drilling and production equipment operation data is lacking, whether the exploitation performance of the petroleum drilling and production equipment in the current oil field can meet the exploitation yield requirement of the petroleum field is difficult to quantitatively judge, and the maximized petroleum oil field exploitation efficiency is difficult to realize.
Disclosure of Invention
In order to solve the technical problems, the technical scheme solves the problems that in the prior art, a set of petroleum drilling and production equipment management scheme which is used for effectively combining oil field development data and petroleum drilling and production equipment operation data is lacking, whether the exploitation performance of petroleum drilling and production equipment in the current oil field can meet the exploitation yield requirement of the petroleum oil field is difficult to quantitatively judge, and the maximized exploitation efficiency of the petroleum oil field is difficult to realize.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a petroleum drilling and production equipment management method based on big data analysis comprises the following steps:
acquiring historical output data of the petroleum oil field from a database, and predicting output data of the petroleum oil field based on the historical output data of the petroleum oil field;
determining the petroleum drilling and production amount of each petroleum drilling and production device based on the output data of the oil field, and recording the petroleum drilling and production amount as a set drilling and production amount;
acquiring historical operation data of the petroleum drilling and production equipment from a database, and calculating estimated production quantity of the petroleum drilling and production equipment based on the historical operation data of the petroleum drilling and production equipment;
judging whether the estimated production amount of the petroleum drilling and production equipment is larger than the set drilling and production amount, if so, outputting a drilling and production control signal to the petroleum drilling and production equipment according to the set drilling and production amount, and if not, outputting an equipment performance shortage signal.
Preferably, the obtaining the historical output data of the petroleum oil field from the database, and predicting the output data of the petroleum oil field based on the historical output data of the petroleum oil field specifically includes:
calculating a linear regression equation of historical production data of the petroleum oil field with respect to time in a set exploitation period to obtain linear correlation of petroleum production;
judging the current development stage of the petroleum field based on the set petroleum yield analysis logic, wherein the development stage comprises an oil reservoir production stage, a high-yield stable production stage, a yield decreasing stage and a low-yield stage;
calculating and predicting the yield data of the petroleum oil field in the next exploitation period through a petroleum yield formula by combining the historical yield, the linear relativity of the petroleum yield and the current development stage of the petroleum oil field;
the petroleum yield formula specifically comprises the following steps:
in the method, in the process of the invention,for production data of the petroleum field in the next production cycle, +.>For a set duration of the production cycle>For the linear regression equation of historical production data of petroleum field in previous production period with respect to time, +.>For the phase correction value, if the petroleum development phase is in the high-yield stable-yield phase or the low-yield phase, the phase correction value is 0, and if the petroleum development phase is in the oil reservoir production phase or the yield decreasing phase, the phase correction value is +.>-/>。
Preferably, the petroleum yield analysis logic is specifically:
if the linear correlation degree of the petroleum yield is larger than the first linear preset value in the set exploitation period, judging that the development stage is an oil reservoir production stage;
if the linear correlation degree of the petroleum output is smaller than the second linear preset value and the historical output of the petroleum field is larger than the first output preset value in the set exploitation period, judging that the development stage is a high-yield stable-yield stage;
if the linear correlation degree of the petroleum yield is smaller than the third linear preset value in the set exploitation period, judging that the development stage is a yield decreasing stage;
and if the linear correlation degree of the petroleum yield is smaller than the second linear preset value and the historical yield of the petroleum field is smaller than the second yield preset value in the set exploitation period, judging that the development stage is a low-yield stage.
Preferably, the acquiring the historical operation data of the petroleum drilling and production equipment from the database, and calculating the estimated production amount of the petroleum drilling and production equipment based on the historical operation data of the petroleum drilling and production equipment specifically includes:
acquiring a plurality of fault information existing in the petroleum drilling equipment based on the historical operation data of the petroleum drilling equipment;
determining maintenance time consumption corresponding to each fault based on historical maintenance data of the petroleum drilling equipment;
calculating a yield reduction value when each fault occurs by multiplying the drilling efficiency of the petroleum drilling equipment by the maintenance time consumption corresponding to each fault based on the drilling efficiency of the petroleum drilling equipment;
calculating the probability value of each fault of the petroleum drilling equipment in the following exploitation period;
calculating the estimated exploitation quantity of the petroleum drilling and exploitation equipment through a drilling and exploitation estimated formula based on the probability value of each fault occurrence of the petroleum drilling and exploitation equipment, the drilling and exploitation efficiency of the petroleum drilling and exploitation equipment and the yield reduction value of each fault occurrence;
wherein, the drilling and production estimation formula is:
in the method, in the process of the invention,estimated production for oil drilling equipment, +.>Drilling and production efficiency for petroleum drilling and production equipment, < >>For a set duration of the production cycle>For the total number of faults present->Probability value of occurrence of ith fault for petroleum drilling equipment,/for petroleum production equipment>Is the yield decrease value at the occurrence of the ith failure.
Preferably, the determining maintenance time consumption corresponding to each fault based on the historical maintenance data of the petroleum drilling equipment specifically includes:
acquiring a plurality of historical maintenance time-consuming data when the petroleum drilling equipment of the current model fails;
based on the Grabbs criterion, eliminating abnormal data which do not accord with normal distribution in a plurality of historical maintenance time-consuming data, and obtaining historical maintenance time-consuming standard data;
the average value of all the historical maintenance time consumption standard data is calculated and used as maintenance time consumption corresponding to the fault;
wherein, the expression of the glabros criterion is:
in the method, in the process of the invention,time consuming maintenance for the jth history, +.>Mean value of all historical maintenance time-consuming data, +.>Standard deviation for all historical repair time data, +.>The critical value is obtained through a difference Grabbs table;
if the expression of the Grabbs criterion is satisfied, thenFor abnormal data which does not meet normal distribution, if the expression of the Grabbs criterion is not met, +.>Is normal data.
Preferably, the calculating the probability value of each fault occurring in the petroleum drilling equipment in the following mining period specifically includes:
acquiring the total operation time of the petroleum drilling and production equipment, and recording the total operation time as the equipment operation time;
and calling the frequency of faults of a plurality of oil drilling equipment with the same model from the database under the operation time of the equipment, and calculating an average value, wherein the average value is used as a probability value of the faults of the oil drilling equipment in the next exploitation period.
Further, a petroleum drilling and production equipment management system based on big data analysis is provided, which is used for implementing the petroleum drilling and production equipment management method based on big data analysis, and includes:
the oil field output prediction module is used for acquiring historical output data of the oil field from the database, predicting the output data of the oil field based on the historical output data of the oil field and determining the oil drilling and production amount of each oil drilling and production device based on the output data of the oil field, and recording the oil drilling and production amount as a set drilling and production amount;
the drilling output estimating module is used for acquiring historical operation data of the petroleum drilling equipment from the database and calculating estimated exploitation quantity of the petroleum drilling equipment based on the historical operation data of the petroleum drilling equipment;
the performance judging module is electrically connected with the oil field yield predicting module and the drilling yield predicting module, and is used for judging whether the predicted yield of the petroleum drilling and production equipment is larger than the set drilling and production amount, if so, outputting a drilling and production control signal to the petroleum drilling and production equipment according to the set drilling and production amount, and if not, outputting an equipment performance shortage signal.
Optionally, the oilfield yield prediction module includes:
a linear calculation unit for calculating a linear regression equation of historical production data of the petroleum field with respect to time in a set production period to obtain a linear correlation of the petroleum production;
the phase judging unit is used for judging the current development phase of the petroleum oil field based on the set petroleum yield analysis logic;
and the yield prediction calculation unit is used for combining the historical yield of the petroleum oil field, the linear relativity of the petroleum yield and the current development stage of the petroleum oil field, and calculating and predicting the yield data of the petroleum oil field in the next exploitation period through a petroleum yield formula.
Optionally, the drill yield estimation module includes:
the fault determining unit is used for acquiring a plurality of fault information of the petroleum drilling equipment based on the historical operation data of the petroleum drilling equipment;
the fault influence determining unit is used for determining maintenance time consumption corresponding to each fault and drilling efficiency based on the petroleum drilling equipment based on the historical maintenance data of the petroleum drilling equipment, and calculating a yield reduction value when each fault occurs by multiplying the drilling efficiency of the petroleum drilling equipment by the maintenance time consumption corresponding to each fault;
a fault probability calculation unit for calculating a probability value of each fault occurring in the oil drilling equipment in a subsequent mining period;
the drilling and production estimating unit is used for calculating the estimated production amount of the petroleum drilling and production equipment through a drilling and production estimating formula based on the probability value of each fault of the petroleum drilling and production equipment, the drilling and production efficiency of the petroleum drilling and production equipment and the yield reduction value of each fault.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a management scheme of petroleum drilling and production equipment based on big data analysis, which predicts the yield data of a petroleum field based on the development stage of the petroleum field, and comprehensively predicts and judges whether the production performance of the petroleum drilling and production equipment in the current petroleum field can meet the production yield requirement of the petroleum field based on the operation data of the petroleum drilling and production equipment.
Drawings
FIG. 1 is a flow chart of a petroleum drilling and production equipment management method based on big data analysis;
FIG. 2 is a flow chart of a method of predicting production data for an oilfield in accordance with the present invention;
FIG. 3 is a schematic representation of the production variation during the development phase of a petroleum field;
FIG. 4 is a flow chart of a method of calculating an estimated production of an oil drilling apparatus according to the present invention;
FIG. 5 is a flow chart of a method of determining maintenance time consumption corresponding to each fault in the present invention;
FIG. 6 is a flow chart of a method of calculating a probability value of a failure of an oil drilling apparatus according to the present invention;
fig. 7 is a block diagram of an oil drilling and production equipment management system based on big data analysis.
Detailed Description
The following description is presented to enable one of ordinary skill in the art to make and use the invention. The preferred embodiments in the following description are by way of example only and other obvious variations will occur to those skilled in the art.
Referring to fig. 1, a petroleum drilling and production equipment management method based on big data analysis includes:
acquiring historical output data of the petroleum oil field from a database, and predicting output data of the petroleum oil field based on the historical output data of the petroleum oil field;
determining the petroleum drilling and production amount of each petroleum drilling and production device based on the output data of the oil field, and recording the petroleum drilling and production amount as a set drilling and production amount;
acquiring historical operation data of the petroleum drilling and production equipment from a database, and calculating estimated production quantity of the petroleum drilling and production equipment based on the historical operation data of the petroleum drilling and production equipment;
judging whether the estimated exploitation quantity of the petroleum drilling and exploitation equipment is larger than the set drilling and exploitation quantity, if so, outputting a drilling and exploitation control signal to the petroleum drilling and exploitation equipment according to the set drilling and exploitation quantity, otherwise, outputting an equipment performance deficiency signal, and performing advanced adjustment and management of the petroleum drilling and exploitation equipment by a worker based on the exploitation requirement of the petroleum oil field, so that the overall exploitation efficiency of the petroleum drilling and exploitation equipment meets the exploitation requirement of the petroleum oil field.
The method is characterized in that the method predicts the output data of the petroleum field based on the development stage of the petroleum field, comprehensively predicts and judges whether the exploitation performance of petroleum drilling and exploitation equipment in the current petroleum field can meet the exploitation output requirement of the petroleum field based on the operation data of the petroleum drilling and exploitation equipment, and can effectively conduct the advanced adjustment and management of the equipment during petroleum drilling and exploitation in such a way that the overall exploitation efficiency of the petroleum drilling and exploitation equipment meets the exploitation requirement of the petroleum field.
Referring to fig. 2, the obtaining the historical output data of the petroleum oil field from the database and predicting the output data of the petroleum oil field based on the historical output data of the petroleum oil field specifically includes:
calculating a linear regression equation of historical production data of the petroleum oil field with respect to time in a set exploitation period to obtain linear correlation of petroleum production;
based on the set petroleum yield analysis logic, judging the current development stage of the petroleum field, wherein the development stage comprises the following steps with reference to the diagram shown in fig. 3:
the oil reservoir production stage starts from the beginning of production of the development wells, and compared with a certain time when the production of a plurality of development wells is finished, the oil reservoir production stage is mainly characterized in that the oil wells are gradually produced and the production is rapidly increased;
the high-yield stable-yield stage starts from a certain time when the production of most development wells is finished and the yield reaches a high value, and is compared with a certain time when the yield changes from high to low and changes sharply. The stage is characterized by small production well number change, vigorous oil well and oil field productivity and small yield change, and is a gold stage of oil field development and production;
the yield decreasing stage starts at a certain time when the yield change appears obvious turning when the oil field is decreased from stable yield, and ends at a certain time when the yield is decreased very low after long-term decrease and the yield decrease is obviously slowed down. The phase is characterized in that the yield is continuously reduced, the yield is reduced for a long time and is high, and various contradictory interweaving, frequent adjustment and control and rapid rising of the oil extraction cost are realized in the oil field development;
a low-yield stage, which is mainly characterized in that the yield is extremely low, the number of production wells is continuously reduced due to flooding or exhaustion, and the yield is gradually reduced due to low yield;
calculating and predicting the yield data of the petroleum oil field in the next exploitation period through a petroleum yield formula by combining the historical yield, the linear relativity of the petroleum yield and the current development stage of the petroleum oil field;
the petroleum yield formula specifically comprises the following steps:
in the method, in the process of the invention,for production data of the petroleum field in the next production cycle, +.>For a set duration of the production cycle>For the linear regression equation of historical production data of petroleum field in previous production period with respect to time, +.>For the phase correction value, if the petroleum development phase is in the high-yield stable-yield phase or the low-yield phase, the phase correction value is 0, and if the petroleum development phase is in the oil reservoir production phase or the yield decreasing phase, the phase correction value is +.>-/>。
The petroleum yield analysis logic specifically comprises:
if the linear correlation degree of the petroleum yield is larger than the first linear preset value in the set exploitation period, judging that the development stage is an oil reservoir production stage;
if the linear correlation degree of the petroleum output is smaller than the second linear preset value and the historical output of the petroleum field is larger than the first output preset value in the set exploitation period, judging that the development stage is a high-yield stable-yield stage;
if the linear correlation degree of the petroleum yield is smaller than the third linear preset value in the set exploitation period, judging that the development stage is a yield decreasing stage;
and if the linear correlation degree of the petroleum yield is smaller than the second linear preset value and the historical yield of the petroleum field is smaller than the second yield preset value in the set exploitation period, judging that the development stage is a low-yield stage.
And identifying and judging the exploitation stage of the current oil field based on the historical output data of the oil field and the characteristics of each exploitation stage of the oil field, so as to provide a theoretical basis for the follow-up calculation of the output of the oil field.
Referring to fig. 4, the step of obtaining the historical operation data of the oil drilling and production equipment from the database, and calculating the estimated production amount of the oil drilling and production equipment based on the historical operation data of the oil drilling and production equipment specifically includes:
acquiring a plurality of fault information existing in the petroleum drilling equipment based on the historical operation data of the petroleum drilling equipment;
determining maintenance time consumption corresponding to each fault based on historical maintenance data of the petroleum drilling equipment;
calculating a yield reduction value when each fault occurs by multiplying the drilling efficiency of the petroleum drilling equipment by the maintenance time consumption corresponding to each fault based on the drilling efficiency of the petroleum drilling equipment;
calculating the probability value of each fault of the petroleum drilling equipment in the following exploitation period;
calculating the estimated exploitation quantity of the petroleum drilling and exploitation equipment through a drilling and exploitation estimated formula based on the probability value of each fault occurrence of the petroleum drilling and exploitation equipment, the drilling and exploitation efficiency of the petroleum drilling and exploitation equipment and the yield reduction value of each fault occurrence;
wherein, the drilling and production estimation formula is:
in the method, in the process of the invention,estimated production for oil drilling equipment, +.>Drilling and production efficiency for petroleum drilling and production equipment, < >>For a set duration of the production cycle>For the total number of faults present->Probability value of occurrence of ith fault for petroleum drilling equipment,/for petroleum production equipment>Is the yield decrease value at the occurrence of the ith failure.
Referring to fig. 5, the determining maintenance time consumption corresponding to each fault based on the historical maintenance data of the petroleum drilling equipment specifically includes:
acquiring a plurality of historical maintenance time-consuming data when the petroleum drilling equipment of the current model fails;
based on the Grabbs criterion, eliminating abnormal data which do not accord with normal distribution in a plurality of historical maintenance time-consuming data, and obtaining historical maintenance time-consuming standard data;
the average value of all the historical maintenance time consumption standard data is calculated and used as maintenance time consumption corresponding to the fault;
wherein, the expression of the glabros criterion is:
in the method, in the process of the invention,time consuming maintenance for the jth history, +.>Mean value of all historical maintenance time-consuming data, +.>Standard deviation for all historical repair time data, +.>The critical value is obtained through a difference Grabbs table;
if the expression of the Grabbs criterion is satisfied, thenFor abnormal data which does not meet normal distribution, if the expression of the Grabbs criterion is not met, +.>Is normal data.
It can be understood that, for the same fault, the maintenance time consumption is the same under normal conditions, however, in some extreme cases, the maintenance time consumption is increased, so that in the scheme, abnormal data which do not conform to normal distribution in the historical maintenance time consumption data are removed by adopting the glabros criterion, and the average value of the historical maintenance time consumption standard data is used as the maintenance time consumption corresponding to the fault, so that the coping standard time consumption for the fault can be effectively reflected.
Referring to fig. 6, the calculation of probability values of each fault occurring in the oil-drilling apparatus during the following production cycle specifically includes:
acquiring the total operation time of the petroleum drilling and production equipment, and recording the total operation time as the equipment operation time;
and calling the frequency of faults of a plurality of oil drilling equipment with the same model from the database under the operation time of the equipment, and calculating an average value, wherein the average value is used as a probability value of the faults of the oil drilling equipment in the next exploitation period.
It can be understood that the drilling and production equipment with the same model has similar operation performance, so that the frequency average value of faults of a plurality of petroleum drilling and production equipment with the same model is used as the probability value of faults of the petroleum drilling and production equipment under the same working time length in the embodiment, and the operation performance of the petroleum drilling and production equipment can be effectively reflected.
Furthermore, based on the same inventive concept as the petroleum drilling and production equipment management method based on big data analysis, the present disclosure proposes a petroleum drilling and production equipment management system based on big data analysis, including:
the oil field output prediction module is used for acquiring historical output data of the oil field from the database, predicting the output data of the oil field based on the historical output data of the oil field and determining the oil drilling and production amount of each oil drilling and production device based on the output data of the oil field, and recording the oil drilling and production amount as a set drilling and production amount;
the drilling output estimating module is used for acquiring historical operation data of the petroleum drilling equipment from the database and calculating estimated exploitation quantity of the petroleum drilling equipment based on the historical operation data of the petroleum drilling equipment;
the performance judging module is electrically connected with the oil field yield predicting module and the drilling yield predicting module, and is used for judging whether the predicted yield of the petroleum drilling and production equipment is larger than the set drilling and production amount, if so, outputting a drilling and production control signal to the petroleum drilling and production equipment according to the set drilling and production amount, and if not, outputting an equipment performance shortage signal.
The oilfield yield prediction module includes:
a linear calculation unit for calculating a linear regression equation of historical production data of the petroleum field with respect to time in a set production period to obtain a linear correlation of the petroleum production;
the phase judging unit is used for judging the current development phase of the petroleum oil field based on the set petroleum yield analysis logic;
and the yield prediction calculation unit is used for combining the historical yield of the petroleum oil field, the linear relativity of the petroleum yield and the current development stage of the petroleum oil field, and calculating and predicting the yield data of the petroleum oil field in the next exploitation period through a petroleum yield formula.
The drill yield estimation module comprises:
the fault determining unit is used for acquiring a plurality of fault information of the petroleum drilling equipment based on the historical operation data of the petroleum drilling equipment;
the fault influence determining unit is used for determining maintenance time consumption corresponding to each fault and drilling efficiency based on the petroleum drilling equipment based on the historical maintenance data of the petroleum drilling equipment, and calculating a yield reduction value when each fault occurs by multiplying the drilling efficiency of the petroleum drilling equipment by the maintenance time consumption corresponding to each fault;
a fault probability calculation unit for calculating a probability value of each fault occurring in the oil drilling equipment in a subsequent mining period;
the drilling and production estimating unit is used for calculating the estimated production amount of the petroleum drilling and production equipment through a drilling and production estimating formula based on the probability value of each fault of the petroleum drilling and production equipment, the drilling and production efficiency of the petroleum drilling and production equipment and the yield reduction value of each fault.
The petroleum drilling and production equipment system based on big data analysis comprises the following using processes:
step one: the oil field output prediction module acquires historical output data of the oil field from the database, and the linear calculation unit calculates a linear regression equation of the historical output data of the oil field with respect to time in a set exploitation period to acquire linear correlation of the oil output;
step two: the stage judging unit is used for judging the current development stage of the petroleum oil field based on the set petroleum yield analysis logic, and the petroleum yield analysis logic is as follows: if the linear correlation degree of the petroleum yield is larger than the first linear preset value in the set exploitation period, judging that the development stage is an oil reservoir production stage; if the linear correlation degree of the petroleum output is smaller than the second linear preset value and the historical output of the petroleum field is larger than the first output preset value in the set exploitation period, judging that the development stage is a high-yield stable-yield stage; if the linear correlation degree of the petroleum yield is smaller than the third linear preset value in the set exploitation period, judging that the development stage is a yield decreasing stage; if the linear correlation of the petroleum yield is smaller than the second linear preset value and the historical yield of the petroleum oil field is smaller than the second yield preset value in the set exploitation period, judging that the development stage is a low-yield stage;
step three: the yield prediction calculation unit is used for calculating and predicting yield data of the petroleum oil field in the next exploitation period through a petroleum yield formula by combining the historical yield of the petroleum oil field, the linear relativity of the petroleum yield and the current development stage of the petroleum oil field;
step four: the drilling output estimation module is used for acquiring historical operation data of the petroleum drilling equipment from the database, and acquiring a plurality of fault information of the petroleum drilling equipment by the fault determination unit;
step five: the fault influence determining unit determines maintenance time consumption corresponding to each fault and drilling efficiency based on the petroleum drilling equipment based on the historical maintenance data of the petroleum drilling equipment, and calculates a yield reduction value when each fault occurs by multiplying the drilling efficiency of the petroleum drilling equipment by the maintenance time consumption corresponding to each fault;
step six: the fault probability calculation unit calculates the probability value of each fault occurring in the petroleum drilling equipment in the following mining period;
step seven: the drilling and production estimating unit calculates the estimated production amount of the petroleum drilling and production equipment through a drilling and production estimating formula based on the probability value of each fault of the petroleum drilling and production equipment, the drilling and production efficiency of the petroleum drilling and production equipment and the yield reduction value of each fault;
step eight: the performance judging module judges whether the estimated exploitation amount of the petroleum drilling and production equipment is larger than the set drilling and production amount, if so, outputs a drilling and production control signal to the petroleum drilling and production equipment according to the set drilling and production amount, and if not, outputs an equipment performance shortage signal.
In summary, the invention has the advantages that: the device can be adjusted and managed in advance when petroleum is drilled and mined, so that the overall exploitation efficiency of the petroleum drilling and mining device meets the exploitation requirement of the petroleum field, the maximized drilling and mining yield of the petroleum field is further ensured, and the maximized exploitation efficiency requirement of the petroleum field is realized.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made therein without departing from the spirit and scope of the invention, which is defined by the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (7)
1. A petroleum drilling and production equipment management method based on big data analysis is characterized by comprising the following steps:
acquiring historical output data of the petroleum oil field from a database, and predicting output data of the petroleum oil field based on the historical output data of the petroleum oil field;
determining the petroleum drilling and production amount of each petroleum drilling and production device based on the output data of the oil field, and recording the petroleum drilling and production amount as a set drilling and production amount;
acquiring historical operation data of the petroleum drilling and production equipment from a database, and calculating estimated production quantity of the petroleum drilling and production equipment based on the historical operation data of the petroleum drilling and production equipment;
judging whether the estimated production amount of the petroleum drilling and production equipment is larger than the set drilling and production amount, if so, outputting a drilling and production control signal to the petroleum drilling and production equipment according to the set drilling and production amount, and if not, outputting an equipment performance shortage signal;
the method for obtaining the historical output data of the petroleum oil field from the database and predicting the output data of the petroleum oil field based on the historical output data of the petroleum oil field specifically comprises the following steps:
calculating a linear regression equation of historical production data of the petroleum oil field with respect to time in a set exploitation period to obtain linear correlation of petroleum production;
judging the current development stage of the petroleum field based on the set petroleum yield analysis logic, wherein the development stage comprises an oil reservoir production stage, a high-yield stable production stage, a yield decreasing stage and a low-yield stage;
calculating and predicting the yield data of the petroleum oil field in the next exploitation period through a petroleum yield formula by combining the historical yield, the linear relativity of the petroleum yield and the current development stage of the petroleum oil field;
the petroleum yield formula specifically comprises the following steps:
in the method, in the process of the invention,for production data of the petroleum field in the next production cycle, +.>For a set duration of the production cycle>For the linear regression equation of historical production data of petroleum field in previous production period with respect to time, +.>For the phase correction value, if the petroleum development phase is in the high-yield stable-yield phase or the low-yield phase, the phase correction value is 0, and if the petroleum development phase is in the oil reservoir production phase or the yield decreasing phase, the phase correction value is +.>-/>；
The method for obtaining the historical operation data of the petroleum drilling and production equipment from the database, and calculating the estimated exploitation amount of the petroleum drilling and production equipment based on the historical operation data of the petroleum drilling and production equipment specifically comprises the following steps:
acquiring a plurality of fault information existing in the petroleum drilling equipment based on the historical operation data of the petroleum drilling equipment;
determining maintenance time consumption corresponding to each fault based on historical maintenance data of the petroleum drilling equipment;
calculating a yield reduction value when each fault occurs by multiplying the drilling efficiency of the petroleum drilling equipment by the maintenance time consumption corresponding to each fault based on the drilling efficiency of the petroleum drilling equipment;
calculating the probability value of each fault of the petroleum drilling equipment in the following exploitation period;
calculating the estimated exploitation quantity of the petroleum drilling and exploitation equipment through a drilling and exploitation estimated formula based on the probability value of each fault occurrence of the petroleum drilling and exploitation equipment, the drilling and exploitation efficiency of the petroleum drilling and exploitation equipment and the yield reduction value of each fault occurrence;
wherein, the drilling and production estimation formula is:
in the method, in the process of the invention,estimated production for oil drilling equipment, +.>Drilling and production efficiency for petroleum drilling and production equipment, < >>For a set duration of the production cycle>For the total number of faults present->Probability value of occurrence of ith fault for petroleum drilling equipment,/for petroleum production equipment>Is the yield decrease value at the occurrence of the ith failure.
2. The oil drilling and production equipment management method based on big data analysis according to claim 1, wherein the oil production analysis logic specifically comprises:
if the linear correlation degree of the petroleum yield is larger than the first linear preset value in the set exploitation period, judging that the development stage is an oil reservoir production stage;
if the linear correlation degree of the petroleum output is smaller than the second linear preset value and the historical output of the petroleum field is larger than the first output preset value in the set exploitation period, judging that the development stage is a high-yield stable-yield stage;
if the linear correlation degree of the petroleum yield is smaller than the third linear preset value in the set exploitation period, judging that the development stage is a yield decreasing stage;
and if the linear correlation degree of the petroleum yield is smaller than the second linear preset value and the historical yield of the petroleum field is smaller than the second yield preset value in the set exploitation period, judging that the development stage is a low-yield stage.
3. The method for managing oil drilling equipment based on big data analysis according to claim 2, wherein determining maintenance time consumption corresponding to each fault based on historical maintenance data of the oil drilling equipment specifically comprises:
acquiring a plurality of historical maintenance time-consuming data when the petroleum drilling equipment of the current model fails;
based on the Grabbs criterion, eliminating abnormal data which do not accord with normal distribution in a plurality of historical maintenance time-consuming data, and obtaining historical maintenance time-consuming standard data;
the average value of all the historical maintenance time consumption standard data is calculated and used as maintenance time consumption corresponding to the fault;
wherein, the expression of the glabros criterion is:
in the method, in the process of the invention,time consuming maintenance for the jth history, +.>Mean value of all historical maintenance time-consuming data, +.>Standard deviation for all historical repair time data, +.>The critical value is obtained through a difference Grabbs table;
if the expression of the Grabbs criterion is satisfied, thenFor abnormal data which does not meet normal distribution, if the expression of the Grabbs criterion is not met, +.>Is normal data.
4. A method for managing oil-well equipment based on big data analysis according to claim 3, wherein said calculating the probability value of each fault occurring in the oil-well equipment during the following production cycle specifically comprises:
acquiring the total operation time of the petroleum drilling and production equipment, and recording the total operation time as the equipment operation time;
and calling the frequency of faults of a plurality of oil drilling equipment with the same model from the database under the operation time of the equipment, and calculating an average value, wherein the average value is used as a probability value of the faults of the oil drilling equipment in the next exploitation period.
5. A big data analysis based oil drilling and production equipment management system, which is used for realizing the big data analysis based oil drilling and production equipment management method according to any one of claims 1-4, comprising:
the oil field output prediction module is used for acquiring historical output data of the oil field from the database, predicting the output data of the oil field based on the historical output data of the oil field and determining the oil drilling and production amount of each oil drilling and production device based on the output data of the oil field, and recording the oil drilling and production amount as a set drilling and production amount;
the drilling output estimating module is used for acquiring historical operation data of the petroleum drilling equipment from the database and calculating estimated exploitation quantity of the petroleum drilling equipment based on the historical operation data of the petroleum drilling equipment;
the performance judging module is electrically connected with the oil field yield predicting module and the drilling yield predicting module, and is used for judging whether the predicted yield of the petroleum drilling and production equipment is larger than the set drilling and production amount, if so, outputting a drilling and production control signal to the petroleum drilling and production equipment according to the set drilling and production amount, and if not, outputting an equipment performance shortage signal.
6. The oil drilling equipment management system based on big data analysis of claim 5, wherein the oilfield production prediction module comprises:
a linear calculation unit for calculating a linear regression equation of historical production data of the petroleum field with respect to time in a set production period to obtain a linear correlation of the petroleum production;
the phase judging unit is used for judging the current development phase of the petroleum oil field based on the set petroleum yield analysis logic;
and the yield prediction calculation unit is used for combining the historical yield of the petroleum oil field, the linear relativity of the petroleum yield and the current development stage of the petroleum oil field, and calculating and predicting the yield data of the petroleum oil field in the next exploitation period through a petroleum yield formula.
7. The oil drilling and production equipment management system based on big data analysis of claim 6, wherein the drill yield estimation module comprises:
the fault determining unit is used for acquiring a plurality of fault information of the petroleum drilling equipment based on the historical operation data of the petroleum drilling equipment;
the fault influence determining unit is used for determining maintenance time consumption corresponding to each fault and drilling efficiency based on the petroleum drilling equipment based on the historical maintenance data of the petroleum drilling equipment, and calculating a yield reduction value when each fault occurs by multiplying the drilling efficiency of the petroleum drilling equipment by the maintenance time consumption corresponding to each fault;
a fault probability calculation unit for calculating a probability value of each fault occurring in the oil drilling equipment in a subsequent mining period;
the drilling and production estimating unit is used for calculating the estimated production amount of the petroleum drilling and production equipment through a drilling and production estimating formula based on the probability value of each fault of the petroleum drilling and production equipment, the drilling and production efficiency of the petroleum drilling and production equipment and the yield reduction value of each fault.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311152369.XA CN116882639B (en) | 2023-09-08 | 2023-09-08 | Petroleum drilling and production equipment management method and system based on big data analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311152369.XA CN116882639B (en) | 2023-09-08 | 2023-09-08 | Petroleum drilling and production equipment management method and system based on big data analysis |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116882639A CN116882639A (en) | 2023-10-13 |
| CN116882639B true CN116882639B (en) | 2023-12-08 |
Family
ID=88272201
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311152369.XA Active CN116882639B (en) | 2023-09-08 | 2023-09-08 | Petroleum drilling and production equipment management method and system based on big data analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116882639B (en) |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH09234652A (en) * | 1996-03-01 | 1997-09-09 | Tokyo Gas Co Ltd | Method for making plant facility repairing plan |
| CN104343492A (en) * | 2013-08-02 | 2015-02-11 | 上海杰之能信息科技有限公司 | Monitoring method and system for lubricating oil of aircraft and engine of aircraft |
| CN104680244A (en) * | 2014-12-24 | 2015-06-03 | 中国石油天然气股份有限公司 | Oil and gas field production capacity prediction method and device |
| KR20160087669A (en) * | 2015-01-14 | 2016-07-22 | 주식회사 에스위너스 | System for Construction of Error information Database and Failure Prediction in Port Equipment |
| CN110674989A (en) * | 2019-09-23 | 2020-01-10 | 煤炭科学技术研究院有限公司 | Prediction method, system and equipment for coal bed gas extraction amount and readable storage medium |
| WO2020101128A1 (en) * | 2018-11-15 | 2020-05-22 | 한국지질자원연구원 | Method for predicting shale gas production by using deep learning |
| CN111598287A (en) * | 2019-02-21 | 2020-08-28 | 中国石油天然气股份有限公司 | Recoverable reserve prediction method and device |
| WO2020192675A1 (en) * | 2019-03-27 | 2020-10-01 | 中国石油大学(华东) | Productivity prediction method for fractured horizontal well in tight oil reservoir |
| CN112819247A (en) * | 2021-02-25 | 2021-05-18 | 中国石油大学(北京) | Prediction analysis system and prediction method for oilfield development index |
| CN114254779A (en) * | 2021-12-23 | 2022-03-29 | 九号智能科技(上海)有限公司 | Production equipment health management system based on predictive maintenance |
| WO2022267879A1 (en) * | 2021-06-25 | 2022-12-29 | 上海三一重机股份有限公司 | Engineering machinery fault early warning method and apparatus, and engineering machinery |
| CN115906458A (en) * | 2022-11-15 | 2023-04-04 | 广东电网有限责任公司 | Natural gas exploitation operation control method and device, computer equipment and storage medium |
| CN116663734A (en) * | 2023-06-02 | 2023-08-29 | 东营海欣仓储有限公司 | Petroleum reserve determining method, petroleum reserve determining device, petroleum reserve determining equipment and readable storage medium |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7258175B2 (en) * | 2004-03-17 | 2007-08-21 | Schlumberger Technology Corporation | Method and apparatus and program storage device adapted for automatic drill bit selection based on earth properties and wellbore geometry |
| US10508520B2 (en) * | 2011-10-26 | 2019-12-17 | QRI Group, LLC | Systems and methods for increasing recovery efficiency of petroleum reservoirs |
| US20180293336A1 (en) * | 2017-04-06 | 2018-10-11 | Qingfeng TAO | Forecasting ultimate recovery of oil and oil production for a multiply-fractured horizontal well |
| CN112288118A (en) * | 2019-07-25 | 2021-01-29 | 中国石油天然气股份有限公司 | Method and device for predicting oil and gas yield of shale oil in-situ development |
| US11481413B2 (en) * | 2020-04-07 | 2022-10-25 | Saudi Arabian Oil Company | Systems and methods for evaluating petroleum data for automated processes |
| US20210350477A1 (en) * | 2020-05-11 | 2021-11-11 | Saudi Arabian Oil Company | Systems and methods for evaluating oil field simulation scenarios |
-
2023
- 2023-09-08 CN CN202311152369.XA patent/CN116882639B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH09234652A (en) * | 1996-03-01 | 1997-09-09 | Tokyo Gas Co Ltd | Method for making plant facility repairing plan |
| CN104343492A (en) * | 2013-08-02 | 2015-02-11 | 上海杰之能信息科技有限公司 | Monitoring method and system for lubricating oil of aircraft and engine of aircraft |
| CN104680244A (en) * | 2014-12-24 | 2015-06-03 | 中国石油天然气股份有限公司 | Oil and gas field production capacity prediction method and device |
| KR20160087669A (en) * | 2015-01-14 | 2016-07-22 | 주식회사 에스위너스 | System for Construction of Error information Database and Failure Prediction in Port Equipment |
| WO2020101128A1 (en) * | 2018-11-15 | 2020-05-22 | 한국지질자원연구원 | Method for predicting shale gas production by using deep learning |
| CN111598287A (en) * | 2019-02-21 | 2020-08-28 | 中国石油天然气股份有限公司 | Recoverable reserve prediction method and device |
| WO2020192675A1 (en) * | 2019-03-27 | 2020-10-01 | 中国石油大学(华东) | Productivity prediction method for fractured horizontal well in tight oil reservoir |
| CN110674989A (en) * | 2019-09-23 | 2020-01-10 | 煤炭科学技术研究院有限公司 | Prediction method, system and equipment for coal bed gas extraction amount and readable storage medium |
| CN112819247A (en) * | 2021-02-25 | 2021-05-18 | 中国石油大学(北京) | Prediction analysis system and prediction method for oilfield development index |
| WO2022267879A1 (en) * | 2021-06-25 | 2022-12-29 | 上海三一重机股份有限公司 | Engineering machinery fault early warning method and apparatus, and engineering machinery |
| CN114254779A (en) * | 2021-12-23 | 2022-03-29 | 九号智能科技(上海)有限公司 | Production equipment health management system based on predictive maintenance |
| CN115906458A (en) * | 2022-11-15 | 2023-04-04 | 广东电网有限责任公司 | Natural gas exploitation operation control method and device, computer equipment and storage medium |
| CN116663734A (en) * | 2023-06-02 | 2023-08-29 | 东营海欣仓储有限公司 | Petroleum reserve determining method, petroleum reserve determining device, petroleum reserve determining equipment and readable storage medium |
Non-Patent Citations (4)
| Title |
|---|
| Minimum gas injection of gas drilling while temperature and pressure coupling;Pengfei Ju等;《2020 10th Chinese Geosynthetics Conference & International Symposium on Civil Engineering and Geosynthetics》;第198卷;第2005页 * |
| 低渗-致密储集层地层含气量估算方法及其应用;陈金龙;黄志龙;董劲;胡森清;鲁法伟;***;;录井工程(第04期);第8-12, 145页 * |
| 基于灰色理论的新疆某油田石油产量预测分析;刘栋梁;;新疆石油科技(第04期);第9-10页 * |
| 油田产量预测***的研制;李宇峰;陈祥光;;微计算机信息(第02期);第181，191-192页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116882639A (en) | 2023-10-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11106190B2 (en) | System and method for predicting remaining lifetime of a component of equipment | |
| CN107977749B (en) | Prediction analysis method for oil field development index | |
| CN107944599B (en) | Method for predicting yield of oil-gas horizontal well | |
| CN104346511B (en) | A kind of deposit dynamic monitoring method and device | |
| CN110847968A (en) | Periodic pressure-bearing dividing method based on maximum working resistance curve of support cycle | |
| CN111767003A (en) | Mining equipment sensor data self-adaptive acquisition method based on different working conditions | |
| CN104993974A (en) | Detection method for identifying equipment working state, and upper computer | |
| CN113011089A (en) | Method and device for optimizing efficiency of mechanical production well system based on deep learning | |
| CN105484724A (en) | Drilling downhole anomaly monitoring method | |
| WO2016034945A2 (en) | Stuck pipe prediction | |
| US20160265323A1 (en) | Automatic pumping system commissioning | |
| CN116882639B (en) | Petroleum drilling and production equipment management method and system based on big data analysis | |
| CN115796610A (en) | Comprehensive monitoring method and system for operation of branch pipe forming system and storage medium | |
| CN110017161B (en) | Intelligent liquid supply method for fully mechanized coal mining face, storage medium, electronic equipment and system | |
| CN106321071B (en) | Production parameter optimization method for oil pumping unit | |
| CN112101625B (en) | Shale gas well production dynamic prediction method and system | |
| CN111614520B (en) | IDC flow data prediction method and device based on machine learning algorithm | |
| CN108386182B (en) | Oil well production abnormity early warning method based on SPC control chart and weighted decision tree | |
| CN117109805A (en) | Abnormal pressure detection and correction method for pressure sensing equipment | |
| WO2021096569A1 (en) | Value balancing for oil or gas drilling and recovery equipment using machine learning models | |
| CN113486538B (en) | Unconventional oil and gas well yield prediction and fracturing effect evaluation method | |
| CN110796282A (en) | Industry fluctuation period monitoring method based on correlation coefficient | |
| CN107784415B (en) | Lifting process technical index evaluation method and device | |
| CN116227691B (en) | Yield prediction method | |
| CN116432975B (en) | Exploitation management method, device, equipment and medium of shale gas multi-station platform |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |