Method for controlling and checking oil field development dynamic data quality

Abstract

The invention relates to the technical field of oil exploration and development, and discloses a dynamic data quality control and check method for oilfield development, which comprises the following steps of: 1, controlling overall data by taking a block as a'entry point ', and checking whether the wellhead yield meets the quality standard by taking the block as the entry point and taking the soft skin well history as the standard; 2, controlling data in the table by taking the restriction table as an entry point, and dividing the oilfield development database into a dynamic database and a static database; a data quality description method of'points, lines, surfaces and bodies' is researched by taking a quality standard as an entry point, and a flow of'physical control, logic control and application control 'rolling circulation and overall control database quality and checking and ten data logic quality control method standards are established according to mine field practical experience summarization. The data accuracy of the database is greatly improved, and the requirements of oilfield development and application are met.

Description

Technical Field

[0001] The invention relates to the technical field of petroleum exploration and development, and in particular to a method for quality control and verification of dynamic data for oilfield development. Background Art

[0002] After years of development, oil fields have formed huge data assets, which are the basis for the formulation and implementation of oil field development plans, development plans, development adjustment plans, etc., and are also an important support for decision-making in scientific research and production. It can be said that the accuracy of basic data is directly related to the oil field's mining effect and benefits. At the same time, these data assets are the technical support and basic guarantee for the realization of digital oil fields. Taking an oil field in eastern my country as an example, the dynamic and static data formed by more than 60 years of development have formed nearly 10 master databases, and the annual scientific research and production work extracts more than 2T of application data.

[0003] In the application, it is found that there are database quality problems caused by various reasons in the current oilfield development database. The traditional physical verification method is to randomly select the physical data of 1 to 3 wells and check them with the data in the database. Generally speaking, the composition of a developed oilfield includes three-level management of "oil production plant, block, and single well". The data at the plant level is easy to control. The conventional method is to take the plant as the entry point, and the sum of the single wells is the oil production plant. The disadvantage of this method is that it hides the error values ​​of unit wells in different blocks horizontally and the error values ​​at different times vertically. Some inconsistent data are often encountered in database applications. For example, the total well history data is equal to the total of the whole plant and the sum of the blocks, but the total single well data in the block is not equal to the block data. Therefore, a series of oilfield development indicators such as "oil production index, water content, injection-production ratio" calculated by the data in the well history do not match the block data, which cannot fully meet the needs of scientific research and production, and restricts the transformation of data into information. At the same time, this method cannot meet the representative requirements of database quality for the giant and massive development database of oil fields.

[0004] The results of the two tables with the same field are different; some of the statistical results violate the development rules, such as the oil recovery index or apparent water absorption index is less than zero or varies greatly between adjacent years, and some are even infinite; the statistical thickness, permeability, interlayer, etc. do not conform to the actual situation of the oil field, etc. The results of the on-site simulation of 5684 records show that it takes more than one year for 100 skilled data entry personnel to complete the database quality verification task by using conventional physical methods to verify it. There is no successful model and experience to learn from. A method suitable for static and dynamic data quality verification in oil field development is needed. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a method for quality control and verification of dynamic data for oilfield development, which solves the problems of being unable to control the quality of data in the oilfield development database that does not conform to development rules and actual oilfield production, being unable to meet oilfield development needs, and having low work efficiency and oilfield development level.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for quality control and verification of dynamic data of oilfield development, comprising the following steps:

[0007] Step 1: Control the overall data with the block as the entry point, and use the block as the entry point and the soft skin well history as the standard to check whether the wellhead production meets the quality standards;

[0008] Step 2: Control the data in the table by taking the constraint table as the starting point. The oilfield development database is divided into two types: dynamic database and static database. A horizontal study is conducted on the tables of the oilfield development database. Various functional dependencies exist between the data. On the basis of clarifying the functional dependencies of related indicators, the constraint table can be clarified to control the data in the table.

[0009] Step 3: Use quality standards as the entry point to calibrate database quality. According to development requirements, formulate data quality standards for data tables, develop a three-dimensional description method for database data quality, and develop database quality acceptance standards.

[0010] Step 4: Develop the entry point for data quality verification, establish the time and space division standard of different regions that can reflect the production time vertically and the plane area horizontally, and adopt the optimal combination of different regions and time periods to cover the wells in different periods and different regions of the entire oil field as much as possible for sampling verification, so as to evaluate and control the data quality in the database;

[0011] Step 5: Through practical analysis and theoretical research, ten logic control methods are summarized for different problems;

[0012] Step six, after completing the data quality control and verification of the development database, the data quality of the database is tested again through annual planning, potential research, development effect evaluation and indicator prediction, and recoverable reserves evaluation in actual scientific research and production applications, to further test and improve the data quality and verification results of the development database.

[0013] Preferably, in step 2, the oilfield development data is divided into three types: one is basic data, such as effective thickness, permeability, saturation and other data obtained from the field and laboratory; one is measured and calculated data, including oil production, liquid production, water content development indicators; and one is split data, such as operating costs and other financial related indicators.

[0014] Preferably, in step 2, the logic control methods are respectively logic and physical double proofreading method, loop nesting method, data classification verification method, check and use combination method, practical table mutual verification method, one well is not greater than two method, discontinuous change method, internal and external specification method, curve method and theoretical experience method.

[0015] Preferably, in step 2, functional dependency is also called data function, which means that there is a certain mathematical calculation relationship or mathematical logical relationship between data in the database. The corresponding relationship between data in the database is called functional dependency.

[0016] Preferably, in step 2, the constraint table is also called a database quality control node. In the same database, when there are more than two data tables, the data of the fields between these tables will have a certain functional dependency, or have a certain functional dependency after being divided into categories. The fields in some tables control and constrain the same fields in other tables. This table is called a constraint table, and the table constrained by the constraint table is called a constrained table.

[0017] Preferably, in step 2, the degree to which the information data in the database conforms to the entity data is called database construction accuracy, which is the most important indicator in database quality control. After practice and theoretical research, point-data point, line-record set, line-field set, surface-record set, field set, solid-surface, offset value, and database quality description method are designed. The development database quality acceptance standard is formulated in accordance with development needs and information construction quality requirements, and a set of database data quality standards are included but not limited to the following contents as the basis for database data quality calibration and acceptance: ① Quality calibration of total data items; ② Quality calibration standards of records; ③ Field quality calibration standards; ④ Quality calibration standards of numerical deviations.

[0018] The present invention provides a method for quality control and verification of dynamic data of oilfield development. It has the following beneficial effects:

[0019] 1. The present invention controls data quality from different angles by taking "block unit, constraint table, and quality standard" as three key entry points, controlling the overall data according to the block unit, controlling the data between data tables with the constraint table, and evaluating and accepting the data with the quality standard.

[0020] 2. The present invention uses quality standards as the entry point to study the data quality description method of "points, lines, surfaces, and volumes". Based on the practical experience of the mine, it establishes a rolling cycle of "physical control, logical control, and application control", an overall control process of database quality and verification, and 10 methods and standards for data logic quality control. Through the application of this method, the data accuracy of the database can be greatly improved to meet the needs of oilfield development and application. DETAILED DESCRIPTION

[0021] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0022] Embodiment 1:

[0023] The embodiment of the present invention provides a method for oilfield development dynamic data quality control and verification, comprising the following steps:

[0024] Step 1: Determine the "three principles" and "nine standards" for data quality verification in oilfield development databases, and verify the development database based on them;

[0025] Step 2: Use the block as the “entry point” to control the overall data. Use the block as the entry point and the soft-skinned well history as the standard to check whether the wellhead production meets the quality standards;

[0026] Step 3: Control the data in the table by taking the constraint table as the starting point. The oilfield development database is divided into two types: dynamic database and static database. A horizontal study is conducted on the tables of the oilfield development database. Various functional dependencies exist between the data. On the basis of clarifying the functional dependencies of related indicators, the constraint table can be clarified to control the data in the table.

[0027] Step 4: Use quality standards as the entry point to calibrate database quality. According to development requirements, formulate data quality standards for data tables as the standards for "verification, rectification, and acceptance". Develop a three-dimensional description method for database data quality and develop database quality acceptance standards.

[0028] Step 5: Develop the entry point for data quality verification, establish the time-space division standard of different regions that can reflect production time vertically and plane area horizontally, adopt the optimal combination of regions and time periods, try to cover the wells in different periods and regions of the entire oil field for sampling verification, and realize the evaluation and control of data quality in the database;

[0029] Step 6: Through practical analysis and theoretical research, ten logic control methods are summarized for different problems;

[0030] Step seven, based on the application of "physical control, logical control" and other methods to complete the development database data quality control and verification, the data quality of the database is tested again through annual planning, potential research, development effect evaluation and indicator prediction, and recoverable reserves evaluation actual scientific research and production applications to further test and improve the development database data quality and verification results.

[0031] In step one, the three major principles are the principle of respecting history, namely, "the principle of respecting first-hand information", the principle of seeking truth from facts, namely, "the principle of improving the reliability and accuracy of data to promote application", and the principle of standardization, namely, "the principle of highlighting the timeliness of scientific quality control".

[0032] In step one, the nine criteria are:

[0033] (1) All thickness, porosity, permeability, saturation, connectivity data and comparison well numbers of a single well shall be based on the small layer data table and the interpretation results of the electrical logging curve;

[0034] (2) All types of wells in the single well basic information database are based on the monthly geological report;

[0035] (3) The well history data of a single well shall be based on the manual well history book and the selected value book;

[0036] (4) The block unit verification data shall be based on the geological monthly report data;

[0037] (5) The number of wells and production of other displacement methods such as chemical flooding shall be based on the data of the monthly geological report;

[0038] (6) Perforation shall be subject to the perforation notice;

[0039] (7) The drilling data shall be subject to the well completion report;

[0040] (8) The data of the measure well shall be based on the construction summary report;

[0041] (9)Pressure data shall be subject to actual measurement.

[0042] In step three, oilfield development data are divided into three types: one is basic data, such as effective thickness, permeability, saturation and other data obtained from the field and laboratory; one is measured and calculated data, such as development indicators such as oil production, liquid production, water content, etc.; and one is split data, such as financial related indicators such as operating costs.

[0043] In step three, the logic control methods are logic and physical double proofreading method, loop nesting method, data classification verification method, combination of check and use method, practical table mutual verification method, one well is not greater than two method, discontinuous change method, internal and external norms method, curve method and theoretical experience method.

[0044] The logical and physical double proofreading method is to use computer technology to find data that does not conform to the actual development of the oil field, and compare and check these data with the data source. The loop nesting method is to use different methods to perform statistical operations on the same field in the same database, and the results should be the same. The data classification verification method is to classify a field in the database according to a certain feature. Data of the same category should be similar. The combined method of checking and using is to combine the results of data processing with the practical experience of professional application personnel to check the reliability of the data. The mutual verification method of using tables is to cross-check the statistical results of related data tables and correct the data items with different results. The one well is not more than two method is the problem of well conversion in the actual situation of oil field development. The number of times the well appears in the same time period should not be If the number exceeds two times, the sum of the production time of the same well number shall be less than or equal to the calendar days of the month. The discontinuation method is to compare the sample library with the development database point by point, check the data items that do not match, and give a database quality report. The vertical and horizontal verification in the table is logically controlled to check the quality of the well number for complete data, duplicate records, incomplete records, empty data, etc. The internal and external standard method is to use the relevant database to verify a certain data in a certain database to check for illogical data points. The curve method is to make the data into a certain form of curve graph, observe whether there are abnormal points, and focus on checking the abnormal points. The theoretical experience method is to compare the empirical theory formed according to the actual oil field development with the data, and further check the data points that do not conform to the empirical theory.

[0045] The principle of respecting history means that manual well history data is not allowed to be changed; small layer data tables and electrical logging curve results are not allowed to be changed; related reports generated by perforation notices, drilling completions, and measure well construction are not allowed to be changed; test reports are not allowed to be changed; selection books and related research manuals that have been actually tested in the mine are not allowed to be changed. The principle of seeking truth from facts means that unfounded changes to the data in the well history are not allowed; if the well belongs to an incorrect block, the data of the relevant historical blocks will be recalculated, but the corresponding fields in the database will not be changed, and they will be recorded in the remarks, and a special field will be added to the table for researchers and users to use; if the data of the reported block or large development zone is indeed incorrect, after confirmation, the data of the corresponding block will be recalculated on the premise of ensuring the correctness of the single well, and the new data will be put into the remarks field for researchers and users to use; if it is indeed an obvious error, the data recorded in the corresponding record of the well history library will be changed to the approved data after confirmation, and an explanation will be given in the remarks. The principle of standardization means unifying the logical inspection procedures; unifying the database filling standards by comparing the database logical structure and filling regulations; unifying the verification standards; establishing block standards and well number standard libraries; and establishing a complete quality inspection record table.

[0046] In step three, functional dependency, also known as data function, refers to a certain mathematical calculation relationship or mathematical logical relationship between data in the database. The corresponding relationship between data in this database is called functional dependency.

[0047] For example, the difference between the original formation pressure YSDCYL in the single well static information library DAA01 in the oilfield development database and the current static pressure MQJY in the oilfield block dynamic information library DBA04 must be consistent with the total pressure difference ZYC in the monitoring information library DBB01, that is:

[0048] DAA01.YSDCYL-DBA04.MQJY=DBB01.ZYC.

[0049] In step three, the constraint table is also called the database quality control node. In the same database, when there are more than two data tables, the data of the fields between these tables will have certain functional dependencies, or have certain functional dependencies after being divided into categories. The fields in some tables control and constrain the same fields in other tables. This table is called the "constraint table", and the table constrained by the constraint table is called the "constrained" table.

[0050] In step three, the degree to which the information data in the database conforms to the entity data is called database construction accuracy, which is the most important indicator in database quality control. After practice and theoretical research, point-data point, line-record set, line-field set, surface-record set, field set, solid-surface, offset value, database quality description method are designed. The development of database quality acceptance standards is based on development needs and information construction quality requirements. A set of database data quality standards are formulated including but not limited to the following contents as the basis for database data quality calibration and acceptance: ① Quality calibration of total data items; ② Quality calibration standards of records; ③ Field quality calibration standards; ④ Quality calibration standards of numerical deviations.

[0051] In step 7, a technical method for physical verification of data is developed. The main verification contents are two aspects: (1) the summary of development data is checked against each record in the standard database; (2) the summary of financial data is checked against the financial statements within the factory. Based on actual experience, the following more practical methods for physical verification of development and financial data are summarized, including:

[0052] (1) Physical proofreading method

[0053] Use computer technology to find data that does not conform to the actual oil field development, and compare and check these data with the data source.

[0054] (2) Nested loop method

[0055] The results of statistical operations on the same field in the same database using different methods should be the same.

[0056] (3) Classification verification method

[0057] Classify a field in the database according to certain characteristics, and data in the same category should be similar.

[0058] (4) Combined search and application

[0059] Combine the results of data processing with the practical experience of professional application personnel to check the reliability of the data.

[0060] (5) Practical table mutual verification method

[0061] The associated data tables are used to cross-check the statistical results and to correct the data items with different results.

[0062] Embodiment 2:

[0063] 1. Main audit items: oil and gas type, oil products sold, reservoir type, commissioning date, cycle number, gas injection volume, cumulative gas injection volume, condensate oil production, liquefied gas production, and other by-product gas production

[0064] The main audit criteria are shown in Table 1.

[0065] Table 1 Main standards for physical review of development data of an oil field in the east

[0066]

[0067] The correctness and rationality of development data are ensured through physical verification of development data.

[0068] 2. Development data logic review

[0069] Main audit items: Wellhead liquid production, Wellhead oil production, Verified oil production, Wellhead gas production, Verified gas production

[0070] Main audit criteria:

[0071] (1) If SCSJ = 0, then JKCYL1 = JKCYL = HSCYL = JKCQL = HSCQL = 0

[0072] (2)If: SCSJ>0, then: JKCYL1>JKCYL≥HSCYL>0.

[0073] (3) If: SCSJ<0, the data is wrong

[0074] Among them, JKCYL1-wellhead liquid production; JKCYL-wellhead oil production; HSCYL-verified oil production; JKCQL-wellhead gas production; HSCQL-verified gas production; SCSJ-production time.

[0075] The rationality of development data in the benefit category calculation process is ensured through logical verification of development data.

[0076] 3. Physical audit of financial data

[0077] Main items for review: direct cost statement, central station, block, operation area, plant-level cost statement; condensate oil, natural gas, sales revenue of other by-products, sales tax, after-tax income.

[0078] The main review criteria are shown in Table 2.

[0079] Table 2 Main Criteria for Physical Review of Financial Data of a Certain Oilfield in the East

[0080]

[0081]

[0082] Ensure the rationality and correctness of financial data through physical verification of financial data

[0083] 4. Logical Review of Financial Data

[0084] Main items for review: period expenses, exploration expenses, direct material costs, direct fuel costs, direct power costs, direct labor costs, injection costs of displacement agents, other direct costs, plant and mine management fees, downhole operation costs, maintenance downhole operation costs, logging and testing costs, maintenance and repair costs, transportation costs, heavy oil thermal recovery costs, light hydrocarbon recovery costs, natural gas purification costs.

[0085] Main review criteria:

[0086] (1) ABS[FQJ / (SPL + DLY) - FBQJ] ≤ 0.01

[0087] (2) ABS[FKT / (SPL + DLY) - FBKT] ≤ 0.01

[0088] (3) If: SCSJ = 0, then: FQJ = FKT = 0, C = FJX + FZC < 200000 (annual, per well)

[0089] (4) If: SCSJ > 0, then: C, CSC, CYY, FQJ, FKT, FZC, FZRL, FZD, FZR, FQZ, FQT, FCK > 0, FJX, FWJX, FCJ, FWX, FYS ≥ 0, FCY = FQH = FTJ = 0, 0 < FZC ≤ 200000 (annual, per well)

[0090] (5) If: SCSJ < 0, the data is incorrect

[0091] ABS-absolute value function; FBQJ-standard value of period cost; FQJ-period cost; FBKT-standard value of exploration cost; FKT-exploration cost; SPL-original commodity volume; DLY-natural gas equivalent oil; C-operating cost CSC-production cost CYY-operating cost FZC-direct material cost FZRL-direct fuel cost FZD-direct power cost; FZR-direct personnel cost; FQZ-oil displacement material injection cost; FQT-other direct costs; FCK-factory and mine management fee; FJX-downhole operation fee; FWJX-maintenance downhole operation fee; FCJ-well logging and testing fee; FWX-maintenance and repair fee; FYS-transportation fee; FCY-heavy oil thermal recovery fee; FQH-light hydrocarbon recovery fee; FTJ-natural gas purification fee.

[0092] Logical verification of financial data ensures the rationality and accuracy of financial data in the benefit category calculation process.

[0093] 5. Audit of the degree of single well

[0094] Main items to be reviewed: direct cost table, natural station cost table, central station cost table, block cost table, operating area cost table, plant-level cost table, and oil and gas processing cost table.

[0095] The main audit criteria are shown in Table 3.

[0096] Table 3 Main standards for reviewing the degree of single well in an oil field in the east

[0097]

[0098] The accuracy of costs and expenses per well is ensured by verifying the data per well.

[0099] Applying the above method, the basic data of single well benefit evaluation in oilfield development is verified, which greatly improves the accuracy of dynamic data and financial data in oilfield development, and further improves the evaluation accuracy and feasibility of the application of evaluation results.

[0100] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for quality control and verification of dynamic data of oilfield development, characterized in that: The following steps are involved: Step 1: Control the overall data with the block as the entry point, and use the block as the entry point and the soft skin well history as the standard to check whether the wellhead production meets the quality standards; Step 2: Control the data in the table by taking the constraint table as the starting point. The oilfield development database is divided into two types: dynamic database and static database. A horizontal study is conducted on the tables of the oilfield development database. Various functional dependencies exist between the data. On the basis of clarifying the functional dependencies of related indicators, the constraint table can be clarified to control the data in the table. Step 3: Use quality standards as the entry point to calibrate database quality. According to development requirements, formulate data quality standards for data tables, develop a three-dimensional description method for database data quality, and develop database quality acceptance standards. Step 4: Develop the entry point for data quality verification, establish the time-space division standard that can reflect the production time vertically and the different areas of the plane horizontally, and adopt the optimal combination of different areas and time periods to cover the wells in different periods and different areas of the entire oil field as much as possible for sampling and verification, so as to evaluate and control the data quality in the database; Step 5: Through practical analysis and theoretical research, ten logic control methods are summarized for different problems; Step six, after completing the data quality control and verification of the development database, the data quality of the database is tested again through annual planning, potential research, development effect evaluation and indicator prediction, and recoverable reserves evaluation in actual scientific research and production applications, to further test and improve the data quality and verification results of the development database.

2. A method for quality control and verification of oilfield development dynamic data according to claim 1, characterized in that: In step 2, the oilfield development data is divided into three types: one is basic data, including effective thickness, permeability, and saturation data obtained from the field and laboratory; one is measured and calculated data, including oil production, liquid production, and water content development indicators; and one is split data, including financial related indicators of operating costs.

3. The method for quality control and verification of oilfield development dynamic data according to claim 1, characterized in that: In step two, the logic control methods are respectively logic and physical double proofreading method, loop nesting method, data classification verification method, check and use combination method, practical table mutual verification method, one well is not greater than two method, discontinuous change method, internal and external standard method, curve method and theoretical experience method.

4. The method for quality control and verification of oilfield development dynamic data according to claim 1, characterized in that: In step 2, functional dependency, also known as data function, refers to a certain mathematical calculation relationship or mathematical logical relationship between data in the database. The corresponding relationship between data in this database is called functional dependency.

5. The method for quality control and verification of oilfield development dynamic data according to claim 1, characterized in that: In step 2, the constraint table is a database quality control node. In the same database, when there are more than two data tables, the data of the fields between these tables will have a certain functional dependency, or have a certain functional dependency after being divided into categories. The fields in some tables control and constrain the same fields in other tables. This table is defined as a constraint table, and the table constrained by the constraint table is called a constrained table.

6. The method for quality control and verification of oilfield development dynamic data according to claim 1, characterized in that: In step 2, the degree to which the information data in the database conforms to the entity data is called database construction accuracy, which is the most important indicator in database quality control. After practice and theoretical research, point-data point, line-record set, line-field set, surface-record set, field set, solid-surface, offset value, and database quality description method are designed. The development database quality acceptance standard is formulated in accordance with development needs and information construction quality requirements. A set of database data quality standards includes but is not limited to the following contents as the basis for database data quality calibration and acceptance: ① Quality calibration of total data items; ② Quality calibration standards of records; ③ Field quality calibration standards; ④ Quality calibration standards of numerical deviations.

Images