RU2756340C1 - System, machine-readable medium and method for processing quality data of formation fluid samples - Google Patents

Abstract

FIELD: data processing.

SUBSTANCE: invention relates to the field of data processing, namely the quality data of samples of reservoir oils. A method for processing data on the quality of samples of reservoir oils, performed using at least one processor, includes the following steps: a project is formed to assess the quality of samples, including at least one control sample and at least one target sample and their identifiers; scenarios of criteria are formed, and each scenario includes one or more acts, where: each act is assigned a correspondence to a planning stage or a stage of work, acts are linked in a sequence, after execution, for each act, its coefficient k is formed, the act may be associated with a mandatory transition to the next act or be a terminal act that completes the processing of the scenario, or the act may indicate the possibility of completing the scenario with the formation of the coefficient k, or the act may indicate a possible completion without the formation of the coefficient k with a proposal for corrective measures; give each criterion scenario a rating r, a criterion weight G; specify the sources of data receipt are specified; set handlers for acts where preliminary processing of data coming from specified sources is required; form a work plan for the selection and quality assessment of samples, including a sequence of criteria scenarios and related entities; procedures for the selection and assessment of the quality of samples are sequentially performed, each procedure is associated with the corresponding generated scenario, and the data obtained during the execution of the procedure is transmitted, with the indication of the sample identifier, to the scenario corresponding to the current procedure, according to the generated work plan, according to the formed work plan, moreover, when the script is executed, the sequential execution of acts occurs, and if during the execution of the act there are critical deviations, then the execution of the script ends and the quality indicator of the sample is reset to zero; in the absence of critical deviations, the quality of the samples is determined on the basis of the generated coefficients k obtained during the execution of the acts, the ratings r of the performed criteria scenarios, the weights of the criteria scenarios G; based on the certain quality of the samples, a decision is made to transport the samples to the research laboratory for further analysis; analyze and profiling the performance of criteria scenarios and, in the presence of discrepancies, form recommendations for eliminating deficiencies and modifying sampling procedures.

EFFECT: improving the efficiency of detecting deviations and managing the quality of samples and the reasons for such deviations, increasing the efficiency of sample processing.

9 cl, 2 dwg, 10 tbl

Description

FIELD OF TECHNOLOGY

The technical solution relates to the field of data processing, namely, data on the quality of samples of reservoir oils.

LEVEL OF TECHNOLOGY

At the moment, there are a number of problems that arise when taking downhole samples of reservoir oils, namely:

- lack of uniform regulatory and methodological approaches to assessing the quality of samples of reservoir fluids;

- the lack of a single information space for all participants and the inability to monitor processes in real time, leads to a partial loss of information about the sample and the process, and sometimes to the impossibility of re-sampling due to (for example, seasonal) inaccessibility for sampling.

- the lack of transparency and the accumulation of structured data in ongoing processes does not allow to track where the largest number of defects and errors occur for the purpose of adjusting the process and making recommendations.

Currently, there are various systems that partly solve the stated problems, but do not have an integrated approach.

The claimed technical solution is aimed at solving these problems.

ESSENCE

The technical result is an increase in the efficiency of detecting deviations and managing the quality of samples and the reasons for such deviations, increasing the efficiency of processing data on samples (including in real time).

A method for processing quality data of reservoir oil samples performed using at least one processor includes the following steps:

a project is formed to assess the quality of samples, including at least one control sample and at least one target sample and their identifiers

form criteria scenarios including at least:

○ "pressure in the sample chamber" for the control sample,

○ "pressure in the sample chamber" for the target sample,

○ "sample recovery" for the control and target samples,

○ "PV-isotherm of fluid in the chamber" for the control sample,

○ "PV-isotherm of fluid in the chamber" for the target sample,

○ "historical data, or data on an analogue object about the gas content of the fluid, the gas content of the sample and the gas factor" for the control sample,

○ "sample water cut" for the control sample,

○ "historical data, or data on an analogue object on the density of the degassed fluid, the density of oil extracted from the sampler, and the density of degassed oil of the well product" for the control sample,

moreover, each scenario includes one or more acts, where:

○ each act is assigned a correspondence to a planning stage or a work stage,

○ acts are linked in sequence,

○ after execution, for each act, its coefficient k is formed,

○ the act may be associated with a mandatory transition to the next act or be a terminal act that completes the processing of the scenario, or the act may indicate the possibility of completing the scenario with the formation of the coefficient k, or the act may indicate a possible completion without the formation of the coefficient k with a proposal for corrective measures;

give each criterion scenario a rating r, a criterion weight G;

specify the sources of data receipt;

set handlers for acts where preliminary processing of data coming from specified sources is required;

form a work plan for the selection and quality assessment of samples, including a sequence of criteria scenarios and related entities;

procedures for the selection and assessment of the quality of samples are sequentially performed, each procedure is associated with the corresponding generated scenario, and the data obtained during the execution of the procedure with the indication of the sample identifier is transferred to the scenario corresponding to the current procedure, according to the generated work plan, and during the execution of the scenario, the sequential execution of acts occurs , and if there are critical deviations during the execution of the act, then the execution of the script ends and the quality indicator of the sample is reset to zero (becomes equal to zero, is rejected);

in the absence of critical deviations, the quality of the samples is determined on the basis of the formed coefficients k obtained during the execution of acts, the ratings of the fulfilled scenarios of the criteria, the weights of the scenarios of the criteria G;

based on the certain quality of the samples, a decision is made to transport the samples to the research laboratory for further analysis;

analyze and profiling the performance of criteria scenarios and, in the presence of discrepancies, form recommendations for eliminating deficiencies and modifying sampling procedures.

In some implementations, an act may contain one or more actions.

In some implementations, the formation of the coefficient k during the execution of the act is carried out dynamically or statically based on data coming from external sources.

In some implementations, the data source is the equipment used in the collection and / or processing of sample data.

In some implementations, the entities associated with the sampling and quality assessment work plan are the actual executors of the operations.

In some implementations, MPC predictive model control is used to analyze and profile the execution of scenarios.

In some embodiments, samples with the highest sample quality q are taken to transport samples to a research laboratory for further analysis.

In some implementations, scenarios are implemented using state machines or graphs.

In some embodiments, a computer system for processing reservoir oil sample quality data includes at least one processor, random access memory, and computer readable instructions executed by said processors using random access memory, the computer readable instructions comprising the steps of a method for processing reservoir oil sample quality data.

In some embodiments, a computer system for processing the quality of reservoir oil samples includes at least one processor, random access memory and computer-readable instructions executed by said processors using random-access memory, and the computer-readable instructions contain steps of a method for processing quality data of reservoir oil samples, the system is configured to obtaining data from external sources and processing them in real time.

In some embodiments, the computer-readable medium comprises instructions for performing a method for processing quality data of reservoir oil samples.

In some implementations, scripts and / or acts are executed in real time.

DETAILED DESCRIPTION

A pipeline is a sequence of states through which script execution passes.

Rating r - is a dimensionless value and reflects the significance of a single criterion.

In some implementations, the rating r is established by an expert survey.

In some implementations, an expert (system user) is provided with an interface that allows the display and processing of rating input for each criterion.

In some embodiments, the base rate of the criterion weight g is determined based on the formula:

где

where

i = 1 ... n is the number of quality criteria for samples;

j = 1 ... m is the total number of samples;

r _ij - base rate of the criterion rating;

Criterion weight G - is the dimensional (in percentage) value of the criterion rating:

In some implementations, scenarios can be a connected, directed (directed) graph, in which vertices represent acts, and edges represent transitions between acts (sequence of execution of acts).

In some implementations, the scenario is a state machine in which the transition between acts occurs based on the interpretation of input data.

In some implementations, when an act is executed, data associated with this act is obtained from an external source, and the coefficient k is generated.

In some implementations, the coefficient k can be set statically (for example, using tables), where the specified value (detected, obtained, isolated, detected, found) corresponds to the value of the coefficient k for a given act.

In some implementations of the coefficient k can be set dynamically, by performing various mathematical or other data processing operations used in the execution of this act.

In some implementations, when analyzing and profiling the execution of scripts, a database of previously executed scripts can be used to search for similar / similar sequences of script execution.

For each scenario of the criteria for the execution of each act, the input data of the act and the generated coefficient k represent (describe) the characteristics of the execution of this scenario. The sequence of such characteristics can describe the process of data processing of control and target depth samples and can be used to identify the similarity of performance or determine the critical points of such performance and make recommendations.

In some implementations, machine learning algorithms are used to assess the similarity of pipelines, for example, clustering algorithms (k-means, k-means, fuzzy c-means, Gustafson-Kessel, EM-algorithm Expectation-Maximization, CLOPE, selection of connected components, minimum spanning tree , layer-by-layer clustering).

In some implementations, partial isomorphism extraction is used to assess the similarity of pipelines.

In some implementations, a knowledge base is used to generate recommendations, containing fragments of pipelines with a description of probable problems and recommendations.

In some implementations, graphs are defined using an adjacency matrix and / or an incidence matrix and / or an adjacency list and / or a list of arcs and / or Berge's description.

In some implementations, a plan for sampling and assessing the quality of samples is formed, including a sequence of criteria scenarios and related entities.

In some implementations, the work plan is generated by the user (using a graphical, textual or other interface) or obtained from an external source (server, database).

In some implementations, the user selects one of the preset work plan options and modifies it to suit his needs.

In some implementations, a work plan can be generated, formed on the basis of a questionnaire (answers to questions), taking into account the known dependencies between scenarios and acts, for example, using data on the desired minimum quality of samples, available equipment at the performer, and available processes.

In some implementations, the mismatch of the claimed equipment (for example, pumps, sensors) and actually connected equipment is detected. In some implementations, equipment models are used for detection (virtual, for example, based on MPC or neural networks or implemented as virtual devices or using a mathematical model) and determine the discrepancies (based on the difference between the simulated processes and the actual ones) or based on the difference between the provided data. and alleged. In some implementations, hardware directories are additionally used.

In some embodiments, the sample identifier is set / formed as a unique value consisting of numbers and / or letters.

In some embodiments, the sample identifier is applied in the form of a barcode, qr-code, RFID-tag and can be read by appropriate equipment when executing scripts / acts.

In some implementations, the probe identifier is used explicitly or implicitly when executing scripts / acts.

In some implementations, identifiers can be applied to the equipment, which are read during the execution of scenarios / acts or are used to form a work plan.

In some implementations, processing occurs in real time using a hard real-time operating system or a soft real-time operating system.

Some options for implementing the criteria scenarios will be described below.

In some implementations, the sampling chamber pressure criterion scenario for the control and target samples includes at least the following acts:

○ At the stage of work planning (on sampling and assessing the quality of samples):

• the type of sampler used,

• the technology used to determine the pressure in the chamber,

• the principle of generating information about pressure,

• the principle of generating information about the volume,

• the principle of generating information about temperature;

○ At the stage of work execution (sampling and quality assessment of samples):

• results of end-to-end measurement,

• results of spot measurements at ambient temperature,

• coefficient k of the sample recovery act,

• results of spot measurement at sampling temperature after sample recovery;

In some implementations, the sampling chamber pressure criterion scenario for the control and target samples includes at least the following acts:

○ At the stage of work planning (on sampling and assessing the quality of samples):

• the type of sampler used,

• the technology used to determine the pressure in the chamber,

• the principle of generating information about pressure,

• the principle of generating information about the volume,

• the principle of generating information about temperature;

○ At the stage of work execution (sampling and quality assessment of samples):

• results of end-to-end measurement,

• results of spot measurements at ambient temperature,

• coefficient k of the sample recovery act,

• the results of spot measurements at the sampling temperature after sample recovery,

In some implementations, the "Pressure and temperature curve in the chamber during sample recovery" criterion scenario for the control and target samples "includes at least the following acts:

○ At the stage of work planning (for sampling and assessing the quality of samples):

• the type of sampler used,

• the presence of an internal mixing element in the sampler chamber,

• possibility of camera fixation to prevent camera movement during its explosive depressurization,

• the principle of generating information about pressure,

• the principle of generating information about the volume,

• the principle of generating information about temperature,

• the number of temperature sensors of the installation for each sample to be recovered;

○ At the stage of work execution (sampling and quality assessment of samples):

• curve of the dependence of the temperature of the chamber on the recovery time,

• curve of the dependence of the pressure in the chamber on the recovery time;

In some embodiments, the scenario of the criterion "PV-isotherm of fluid in the chamber" for the control and target samples includes at least the following acts:

○ At the stage of work planning (on sampling and assessing the quality of samples):

• the type of sampler used,

• the type of pump used,

• the presence of an internal mixing element in the sampler chamber,

• the principle of generating information about pressure,

• the principle of generating information about the volume,

• the principle of generating information about temperature;

○ At the stage of work execution (sampling and quality assessment of samples):

• results of spot measurements at ambient temperature,

• coefficient k of the sample recovery act,

• results of spot measurement at sampling temperature after sample recovery;

In some implementations, the scenario of the criterion "historical data, or data on an analogue object on the gas content of the fluid, the gas content of the sample and the gas factor" for the control sample includes at least the following acts:

○ At the stage of work planning (on sampling and assessing the quality of samples):

• availability of external data (including at least: historical data on the gas content of the fluid; data on the analogue object on the gas content of the fluid; the results of the well test with the measurement of the gas ratio) on the gas content of the reservoir oil;

• the principle of generating information on the volume of released gas;

• technology for measuring the volume of liquid phases;

• technology for measuring the volume of released gas;

○ At the stage of work execution (sampling and quality assessment of samples):

• discrepancy between gas content and external data on gas content;

In some implementations, the “sample water cut” criterion scenario for the control sample includes at least the following acts:

○ At the stage of work planning (for sampling and assessing the quality of samples):

• technology for measuring the volume of liquid phases;

○ At the stage of work execution (sampling and quality assessment of samples):

• measurement results when the camera is discharged,

• the presence of a graphic image of a measuring cylinder;

In some implementations, the criterion scenario "Historical data, or data on an analogue object on the density of the degassed fluid, the density of oil extracted from the sampler, and the density of degassed oil of the well product" for the control sample includes at least the following acts:

○ At the stage of work planning (for sampling and assessing the quality of samples):

• availability of external data (including: historical data on the density of the degassed fluid; data on the analogue object on the density of the degassed fluid, the results of the well test with measuring the density of the degassed fluid) on the density of degassed oil of the well product,

• the principle of generating information about the measured density,

• technology for measuring density;

○ At the stage of work execution (sampling and quality assessment of samples):

• discrepancy between the measured density and the density of degassed oil from well production;

After the execution (upon completion) of the act, the following logic can be implemented in the script: the act can be a terminal act (it has a sign of completion of the script execution / completion), completing the processing of the script, or the act can indicate the possibility of completing the script with the formation of the coefficient k, or the act can indicate a possible completion without the formation of a coefficient k with a proposal for corrective actions.

Some acts may be linked to other acts within the scenario.

Some acts may have a sign of the end of the script execution (terminal act).

In some implementations, there can be more than one transition from one act to other acts (the act is associated with several other acts) and the transition is carried out depending on the current data obtained during the execution of the act.

Below are examples of scenarios given for illustrative purposes in tabular form.

In some implementations, scripts can be specified in a programming language such as Lua, JavaScript, Python, and others.

In some implementations, the handlers associated with acts (handlers for acts) can perform data normalization, mathematical operations, and logical operations. In some implementations, handlers may be user-defined or obtained from external sources.

In some implementations, a handler can be used for the "Presence of a graphical image of a graduated cylinder" act to check that the loaded image matches the required one using computer vision (CV) algorithms, for example, OpenCV.

In some implementations, the data source can be equipment used when receiving and / or processing samples (pumps, sensors, pressure gauges, etc.), for example, connected to the Internet or storing data available by downloading the corresponding log or other files. (storing information about equipment and / or operations performed by it).

In some embodiments, the data sources can be the computer equipment of the executor of the work data, for example, a smartphone, laptop or other computerized device capable of sending data about equipment, operations, and processing results. In some embodiments, such equipment is configured to display prompts (for example, using an interface), corrective actions based on the processing of the provided data.

As an illustrative example, consider a possible sequence of acts of the scenario "pressure in the sample chamber" and their actual interpretation.

If a through measurement is made:

1. Analysis of the results showed the absence of uncontrolled and inconsistent changes in the sampler chamber. The act ends, the scenario ends, the act coefficient k is maximum.

2. Analysis of the results showed the presence of uncontrolled and inconsistent changes in the sample chamber (suppose there was a leak). The act ends, the scenario ends, a critical deviation is recorded, the quality of the sample is found to be unsatisfactory and its disposal is proposed.

If the end-to-end measurement has not been implemented, objective information on the thermobaric conditions in the sampler chamber is irretrievably lost, therefore the act (according to all subsequent acts) is assigned a reduction factor of 0.5. In this case, the mandatory procedure is as follows:

1. Measured at ambient temperature;

2. If there is a sample recovery unit, the sample is restored to the sampling conditions;

3. The measurement is made at the sampling temperature.

In STO RMNTK 153-39.2-002-2003 “Oil. Sampling of formation fluids "provides for an assessment of the permissible pressure drop in the sampler chamber when the temperature changes from the sampling temperature to the temperature of the day surface, which is 0.2 ÷ 2.5 atm / ° C. Let's pretend that:

1. If the pressure drop is less than 0.2 atm / ° С - the data are most reliable, the maximum coefficient is assigned to the act;

2. When the value of the pressure drop is from 0.2 to 2.5 atm / ° C, the probability of the conditionality of the sample is inversely proportional to the value of the pressure drop; the coefficient is calculated by the formula:

where:

P '- the difference between the pressure in the sampler chamber at ambient temperature P _to , and the pressure at the sampling point P _o , referred to the difference between the temperature of the sampling point T _o and the ambient temperature T _to , atm / ° C.

3. When the pressure drop is more than 2.5 atm / ° C, the act is assigned a minimum value of 0.1.

4. When the pressure in the chamber is equal to atmospheric, the sampler has a direct hydrodynamic connection with the atmospheric one, which is a critical deviation; the analysis ends, the act and the script are completed.

Below is one of the illustrative options for the implementation of criteria rating, criterion criticality and criterion weight, given in a tabular form.

In some embodiments, during the passage of each act, data is entered into automatically generated / generated forms and their subsequent receipt according to the script and the formation of recommendations for the possibility of examining samples.

- Determine the scenario coefficient K for each scenario as follows:

- численный коэффициент акта критерия

;

- the numerical coefficient of the criterion act

;

○ S = 1… s - the total number of actions performed within the criterion scenario.

- Determine (generate a report) q for a given sample as follows:

- Determine the Q of a set of samples by the combined coherence feature (e.g. taken by a single user) as follows:

○ Where m is the total number of samples combined by an identification feature.

In some embodiments, after determining the quality of the samples (set of samples) based on the sample quality index q, a decision is made to transport the samples to the laboratory, and the sample is recognized as suitable for research if the sample quality index q is more than zero.

In some embodiments, samples with the highest sample quality q are selected for laboratory testing.

In some embodiments, on the basis of a certain quality of samples, recommendations for sampling for further transportation to the laboratory are formed.

In some embodiments, the analysis and profiling of the criteria scenarios are performed, and in the presence of discrepancies, recommendations are made to eliminate the shortcomings and modify the sampling procedures. As a non-limiting illustrative example, the following guidelines are provided:

○ to increase the quality indicator of the sample according to the scenario "Pressure in the sampler chamber" for the control sample according to the act "Technology for determining the pressure in the chamber" when working with a jack pump, a recommendation can be formed to use a sampler with an autonomous pressure sensor in the receiving chamber;

○ to increase the quality indicator of the sample according to the scenario "Sample recovery" for the target sample "according to the" Curve of heating temperature versus recovery time "act at an unstabilized temperature, a recommendation may be generated to continue heating the sample until the temperature is completely stabilized;

○ to increase the quality indicator of the sample according to the scenario "PV-isotherm of fluid in the chamber" for the control sample "according to the act" Principle of generating information on pressure "in the case of performing work with manual data recording, a recommendation can be formed to contact the manufacturer of the pressure gauge to discuss the possibility of upgrading equipment and setting up automatic data collection;

○ to increase the sample quality indicator according to the scenario "Historical data, or data on an analogue object on the gas content of the fluid, the gas content of the sample and the gas factor" for the control sample "according to the act" Technology for measuring the volume of liquid phases "if the contractor does not have a graduated cylinder a recommendation was formed to contact the supplier of laboratory glassware for the purchase of a graduated cylinder.

In some embodiments, an indicator of the quality of a set of samples is determined by the sign of connectivity. Signs of connectivity can be signs:

○ project (samples taken within one project);

○ sampler (samples taken by one sampler model);

○ work performer;

○ the period of work.

In some implementations, if the attribute "Work performer" is used as a sign of connectivity, then the quality indicator of a set of samples with this sign can be used to form a rating of the work performer for a specified period.

In some implementations, the quality of the samples of the contractor is analyzed using technical analysis (for example, a trend line) to determine the trend of changes in the quality of samples over time and make subsequent decisions. In some implementations, such an analysis takes into account changes in equipment and execution of scenarios (acts) over a period of time with the identification / determination of bottlenecks that influenced the quality indicators of samples.

In some embodiments, the system may include one or more computerized processing devices performing a method for processing quality data of reservoir oil samples, one or more computerized executor devices used to enter data (as a data source) according to an executable scenario.

In some implementations, for profiling and recommendations, model predictive control (MPC) is used, which is an improvement of the classical negative feedback control, which takes into account the prediction of the behavior of the control object for various types of input actions. Feedback is used to correct inaccuracies associated with external interference and inaccuracy of the mathematical model of the control object. The regulator relies on an empirical process model to predict future behavior based on previous values of the state variables. The plant model is usually chosen to be linear.

In some embodiments, a visual display of the execution of scenarios / acts and an indicator of the quality of the sample (s) is generated (Fig. 1).

FIG. 2 shows an example of a general-purpose computer system used to implement the described method - a personal computer or server 20 containing a central processor 21, system memory 22 and a system bus 23, which contains various system components, including memory associated with the central processor 21. System bus 23 is implemented as any bus structure known in the art, which in turn contains a bus memory or bus memory controller, a peripheral bus and a local bus that is capable of interfacing with any other bus architecture. System memory contains read-only memory (ROM) 24, random access memory (RAM) 25. The main input / output system (BIOS) 26 contains basic procedures that transfer information between the elements of the personal computer 20, for example, at the time of loading the operating room. systems using ROM 24.

The personal computer 20, in turn, contains a hard disk 27 for reading and writing data, a magnetic disk drive 28 for reading and writing to removable magnetic disks 29 and an optical drive 30 for reading and writing to removable optical disks 31, such as CD-ROM, DVD -ROM and other optical media. The hard disk 27, the magnetic disk drive 28, and the optical drive 30 are connected to the system bus 23 via the hard disk interface 32, the magnetic disk interface 33, and the optical drive interface 34, respectively. Drives and corresponding computer storage media are non-volatile storage media for computer instructions, data structures, program modules and other data of a personal computer 20.

The present description discloses an implementation of a system that uses a hard disk 27, but it should be understood that it is possible to use other types of computer storage media that are capable of storing data in a computer readable form (solid state drives, flash memory cards, digital disks, random access memory (RAM), etc.) that are connected to the system bus 23.

Computer 20 has a file system 36, where the recorded operating system 35 is stored, as well as additional software applications 37, other program modules 38 and program data 39. The user has the ability to enter commands and information into the personal computer 20 through input devices (keyboard 40, manipulator " mouse "42). Other input devices can be used (not shown): microphone, joystick, game console, scanner, etc. Similar input devices, as usual, are connected to computer system 20 via USB interface 46, which in turn is connected to the system bus, but can be connected in another way, for example, using a parallel port, game port. A monitor 47 or other type of display device is also connected to the system bus 23 via an interface such as a video adapter 48. In addition to the monitor 47, the personal computer may be equipped with other peripheral output devices (not shown).

The personal computer 20 is capable of operating in a networked environment using a network connection with other or more remote computers 49. The remote computer (or computers) 49 are the same personal computers or servers that have most or all of the elements mentioned earlier in the description of the entity. the personal computer 20 shown in FIG. 2. In a computer network, there may also be other devices, such as routers, network stations, peer-to-peer devices, or other network nodes.

Network connections can form a local area network (LAN) 50 and a wide area network (WAN). Such networks are used in corporate computer networks, internal networks of companies and, as a rule, have access to the Internet. In LAN or WAN networks, a personal computer 20 is connected to a local network 50 through a network adapter or network interface 51. When using networks, a personal computer 20 may use a router 54 or other means of providing connectivity to a wide area network such as the Internet. Router 54, which is an internal or external device, is connected to the system bus 23 via a USB port 46. It should be noted that the network connections are only exemplary and are not required to reflect the exact configuration of the network, i. E. in fact, there are other ways of establishing a connection by technical means of communication of one computer with another.

Claims (32)

1. A method for processing quality data of samples of reservoir oils, performed using at least one processor, including the following steps: - a project is being formed to assess the quality of samples, including at least one control sample and at least one target sample and their identifiers; - form criteria scenarios including at least: "Pressure in the sampling chamber" for the control sample, "Pressure in the sample chamber" for the target sample, "Sample recovery" for the control and target samples, "PV-isotherm of fluid in the chamber" for the control sample, "PV-isotherm of fluid in the chamber" for the target sample, "Historical data or data on an analogue object on the gas content of the fluid, the gas content of the sample and the gas factor" for the control sample, "Sample water cut" for the control sample, "Historical data or data on an analogue object on the density of the degassed fluid, the density of oil extracted from the sampler, and the density of degassed oil of the well product" for the control sample, moreover, each scenario includes one or more acts, where: each act is assigned a correspondence to a planning stage or a stage of work, acts are linked in sequence, after execution, for each act, its coefficient k is formed, the act may be associated with a mandatory transition to the next act or be a terminal act that completes the processing of the scenario, or the act may indicate the possibility of completing the scenario with the formation of the coefficient k, or the act may indicate a possible completion without the formation of the coefficient k with a proposal for corrective measures; - assign each criterion scenario a rating r, a criterion weight G; - specify the sources of data receipt; - specify handlers for acts where preliminary processing of data coming from specified sources is required; - form a work plan for the selection and assessment of the quality of samples, including a sequence of criteria scenarios and related entities; - procedures for the selection and assessment of the quality of samples are sequentially performed, each procedure is associated with the corresponding generated scenario, and the data obtained during the execution of the procedure is transmitted, with the indication of the sample identifier, to the scenario corresponding to the current procedure, according to the generated work plan, and when the scenario is executed, a sequential execution of acts, and if there are critical deviations during the execution of an act, then the execution of the script ends and the quality indicator of the sample is reset to zero; - in the absence of critical deviations, the quality of the samples is determined on the basis of the generated coefficients k obtained during the execution of acts, the ratings r of the performed criteria scenarios, the weights of the G criteria scenarios; - on the basis of a certain quality of samples, a decision is made to transport samples to a research laboratory for further analysis; - analyze and profiling the fulfillment of criteria scenarios and, in the presence of discrepancies, form recommendations for eliminating deficiencies and modifying sampling procedures. 2. The method according to claim 1, in which the formation of the coefficient k during the execution of the act is carried out dynamically or statically based on data coming from external sources. 3. The method according to claim 1, wherein the data source is equipment used in the collection and / or processing of sample data. 4. The method according to claim 1, in which the entities associated with the work plan for sampling and assessing the quality of samples are the actual performers of operations. 5. The method of claim 1, wherein the scripts are implemented using state machines or graphs. 6. The method of claim 1, wherein predictive MPC control is used to analyze and profile the execution of scenarios. 7. The method according to claim 1, wherein for transporting the samples to the research laboratory for further analysis, samples with the highest sample quality q are taken. 8. A system for computer processing and description of a core image, including at least one processor, random access memory and computer-readable instructions executed by said processors using random-access memory, the computer-readable instructions comprising steps of the method for processing quality data of reservoir oil samples according to claim 1. 9. A computer-readable medium containing instructions for a method for processing quality data of samples of reservoir oils according to claim 1.

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