KR20200043668A - method and system of prediction of drilling time using probabilistic analysis method - Google Patents

Abstract

The present invention provides a drilling period prediction method using a probabilistic analysis method so that the accuracy of drilling period prediction is improved. The method includes: a first step in which a work process by drilling section including drilling rig movement and installation, drilling, circulating, casing, cementing, tripping, and testing is individually separated, input to an input unit, and stored in a database unit such that a drilling hole database is established in accordance with drilling work and a drilling hole section and a database by mining area is established; a second step in which a function distribution method is extracted from data stored in the database unit and a distribution factor with respect to drilling work time of probability distribution by individual drilling section is calculated and stored in the database unit such that a generation range of randomly generated random numbers is set; a third step in which individual probability distribution with respect to drilling work time by individual drilling section depending on the drilling depth is calculated by a calculation unit and stored in the database unit as the random number is generated through a random number generation unit and a Monte Carlo simulation is conducted within the range of the distribution factor stored in the database unit; and a fourth step in which the calculation unit calculates a total predicted drilling work period by adding the drilling work time by individual drilling section and the total predicted drilling work period is stored in the database unit, the total predicted drilling work period required for a drilling work process being predicted through the stored total predicted drilling work period.

Description

Method and system of prediction of drilling time using probabilistic analysis method

The present invention relates to a method and system for predicting a drilling period using a probabilistic analysis method, and more particularly, to a method and system for predicting a drilling period using a stochastic analysis method in which accuracy is improved when predicting a drilling period.

In general, in the resource development project, it is essential to directly check for the existence of oil or gas through drilling, and the economic feasibility of the project depends largely on whether the drilling work is efficiently performed according to the plan.

Here, as the drilling operation progresses, the drilling period gradually increases and the drilling depth increases, so that the risk of problems during the drilling operation and the drilling cost increase. Therefore, it is important to perform drilling by predicting the depth of drilling hole excavation over time through efficient planning and execution of drilling.

In recent years, probabilistic drilling period prediction technology considering uncertainty of drilling has been researched and developed around major oil companies operating large scale mines. Here, when drilling, there is uncertainty due to the state of the reservoir, drilling equipment, weather, etc., and a drilling plan should be established in consideration of this.

On the other hand, although a program for establishing a drilling data database has been developed in Korea, research and development is insufficient due to relatively inexpensive operating experience, and technical accumulation related to the establishment of a drilling plan is continuously required.

In particular, it is difficult to accurately estimate the drilling time or cost because the uncertainty increases when establishing a drilling plan due to uncertain factors that may occur during drilling, such as loss of drilling completion, abnormal pressure layer discovery, and instability of the drilling hole. There was a problem.

Accordingly, human and material costs due to the establishment of a drilling plan different from the actual drilling process are increased, and thus there is a problem that economic efficiency is deteriorated.

Korean Patent Publication No. 10-2014-0055871

In order to solve the above problems, the present invention provides a method and system for predicting a drilling period using a probabilistic analysis method in which accuracy is improved when predicting a drilling period.

In order to solve the above problems, the present invention includes drilling and rig movement and installation, drilling, circulating, casing, cementing, tripping, testing, so as to build a borehole database and a wide area database according to the drilling operation and borehole section A first step in which work processes for each section are individually classified and input to an input unit and stored in a database unit; Based on the data stored in the database unit, the function distribution method is extracted, and distribution factors for the drilling time of the probability distribution for each drilling section are calculated and stored so that the randomly generated random number generation range is set and stored in the database unit Step 2; As the random number is generated through the random number generation unit within the range of the distribution factor stored in the database unit and the Monte Carlo simulation is performed, the individual probability distribution for the drilling time for each drilling section according to the drilling depth is calculated and calculated by the calculation unit. A third step stored in the database unit; And a fourth step in which the drilling time for each drilling section is summed by the calculation unit to calculate the total estimated drilling time required for the drilling process.

On the other hand, according to the present invention, the drilling rig movement and installation, drilling, circulating, casing, cementing, tripping, and testing processes for each drilling section including testing are individually input, a probability distribution method is extracted, and random numbers generated at random An input unit for calculating a distribution factor for a drilling time of a probability distribution for each drilling section so that a generation range of the is set; A database unit that stores the work process for each drilling section and the probability distribution method and the distribution factors input through the input unit, and stores the drilling work time for each drilling section for the drilling depth and the entire estimated drilling work period; A random number generator randomly generating random numbers to perform a Monte Carlo simulation within the range of the distribution factors stored in the database unit; The Monte Carlo simulation of the drilling time within the range of the above distribution factors is performed to calculate the individual probability distribution for the drilling time for each drilling section according to the drilling depth, and the total estimated drilling time is summed by adding the drilling time for each drilling section. A calculation unit for calculating a work period; And a borehole database according to a drilling operation and a borehole section and a database for each mining section are selectively output, including a drilling depth according to the elapse of drilling time, a drilling time for each drilling section, and a display unit for selectively outputting the entire estimated drilling period It provides a drilling period prediction system using a stochastic analysis method.

Through the above solution, the present invention provides the following effects.

First, it is possible to flexibly judge the results when making decisions, as the total probability distribution for each drilling section is accurately calculated by summing up the individual probability distribution of each drilling section calculated through the Monte Carlo simulation. Compared to the technique, the result can be produced with the concept of a range rather than a single value.

Second, it is optimized for predicting the drilling period by extracting and calculating the function distribution method and distribution factors for at least one of uniform distribution, triangular distribution, putt distribution, normal distribution, and log normal distribution in response to drilling technology data. Since the input value is derived, the prediction precision can be significantly improved.

Third, the drilling period is reflected in the individual probability distribution calculated individually for each work process per drilling section, because the time required by uncertainty due to the reservoir status, drilling loss, abnormal pressure layer discovery, borehole instability, drilling equipment, weather, etc. Prediction precision can be significantly improved during prediction.

1 is a flowchart illustrating a method for predicting a drilling period using a stochastic analysis method according to an embodiment of the present invention.
2 is a block diagram showing a drilling period prediction system using a stochastic analysis method according to an embodiment of the present invention.
3 is a conceptual diagram showing a drilling process in a drilling period prediction method using a stochastic analysis method according to an embodiment of the present invention.
4 is a conceptual diagram showing a probability distribution function and a cumulative probability distribution function in a drilling period prediction method using a stochastic analysis method according to an embodiment of the present invention.
5 is an exemplary view showing a database for each borehole in a method for predicting a drilling period using a stochastic analysis method according to an embodiment of the present invention.
6 is an exemplary view showing a database for each segment in a method for predicting a drilling period using a stochastic analysis method according to an embodiment of the present invention.
7 is a graph showing drilling time according to drilling depth in a drilling period prediction method using a stochastic analysis method according to an embodiment of the present invention.
8 is a graph showing drilling time for each drilling operation section in a drilling period prediction method using a stochastic analysis method according to an embodiment of the present invention.
9 is an exemplary view showing a drilling operation time for each drilling operation section in a drilling period prediction method using a stochastic analysis method according to an embodiment of the present invention.
10 is an exemplary view showing a drilling operation time for each drilling operation in a drilling period prediction method using a stochastic analysis method according to an embodiment of the present invention.
11 is a graph showing the probability of the entire estimated drilling operation period in the drilling period prediction method using the probabilistic analysis method according to an embodiment of the present invention.

Hereinafter, a method and system for predicting a drilling period using a stochastic analysis method according to a preferred embodiment of the present invention will be described in detail with reference to the accompanying drawings.

1 is a flowchart illustrating a drilling period prediction method using a stochastic analysis method according to an embodiment of the present invention, and FIG. 2 is a block diagram showing a drilling period prediction system using a stochastic analysis method according to an embodiment of the present invention It is. And, Figure 3 is a conceptual diagram showing a drilling process in the drilling period prediction method using a stochastic analysis method according to an embodiment of the present invention, Figure 4 is a drilling using a stochastic analysis method according to an embodiment of the present invention This is a conceptual diagram showing the probability distribution function and the cumulative probability distribution function in the period prediction method. And, Figure 5 is an exemplary view showing a database for each borehole in the drilling period prediction method using a stochastic analysis method according to an embodiment of the present invention, Figure 6 is using the stochastic analysis method according to an embodiment of the present invention It is an example showing a database for each mining area in a method for predicting a drilling period. And, Figure 7 is a graph showing the drilling time according to the drilling depth in the drilling period prediction method using a stochastic analysis method according to an embodiment of the present invention, Figure 8 is a stochastic analysis according to an embodiment of the present invention It is a graph showing the drilling time for each drilling work section in the method of predicting the drilling period using the method. And, Figure 9 is an exemplary view showing a drilling operation time for each drilling operation section in the drilling period prediction method using a stochastic analysis method according to an embodiment of the present invention, Figure 10 is a stochastic according to an embodiment of the present invention This is an exemplary diagram showing the drilling time for each drilling task in the method of predicting the drilling period using the analysis method. And, Figure 11 is a graph showing the probability for the entire estimated drilling operation period in the drilling period prediction method using a stochastic analysis method according to an embodiment of the present invention.

1 to 11, the drilling period prediction system 100 using the probabilistic analysis method according to an embodiment of the present invention includes an input unit 10, a database unit 20, a random number generator 30, It is preferable to include the calculation unit 40 and the display unit 50.

In addition, in the method of predicting a drilling period using the probabilistic analysis method according to an embodiment of the present invention, a work process for each drilling section is classified and input and stored (s10), probability distribution method extraction, and distribution factor calculation and storage (s20) , As the Monte Carlo simulation is performed through random number generation, it includes the steps of deriving the drilling work time for each drilling section for the drilling depth (s30), and adding the drilling work time for each drilling section to calculate the total estimated drilling work period (s40). Preferably.

First, drilling and moving, installation, drilling, circulating, casing, cementing, tripping, so as to build a borehole database and a wide area database according to the drilling operation and borehole section (Tripping), the operation process for each drilling section including testing (Testing) is individually classified and input to the input unit 10 and stored in the database unit 20 (s10).

Here, the work process for each drilling section may be divided into a drilling work section (Phase), an individual drilling work (Activity), and a drilling hole section (Hole Section), and the drilling work section preferably includes a plurality of individual drilling work. . In addition, the drilling operation refers to an operation of directly excavating a geological layer against the promising structure derived from the exploration and confirming the existence of oil / natural gas. In addition, the mining means an area where oil / natural gas, minerals, and the like are mined, and the borehole means a hole that is excavated for mining or geological investigation of oil / natural gas, exploration of deposits, and the like. For example, at least one of the boreholes may be included in one photosphere.

At this time, when establishing a drilling plan for drilling, there are uncertainties and risks due to reservoir conditions, loss of drilling water, abnormal pressure layer discovery, instability of the borehole, drilling equipment, weather, etc. Moreover, the cost required for the drilling exploration stage is about 70 ~ 80%, and in the development and production stage, the production well, which is the channel through which the oil and natural gas can be produced in the reservoir according to the drilling plan, is provided. It is important.

In addition, the drilling process for each drilling section is automatically classified based on the existing drilling hole and mining drilling technology data stored on the basis of the Daily Drilling Report (DDR) and input to the input section 10 It may be, or may be manually input to the input unit 10 by a drilling plan expert.

Referring to FIG. 3, it is preferable that the operation process for each drilling section includes drilling rig movement and installation, drilling, circulating, casing, cementing, tripping, and testing. Moreover, the work process for each drilling section may further include a stuck pipe, a problem occurring in a borehole, and an emergency work process such as a delay in drilling due to weather.

At this time, the drilling process is as follows. First, in order to form a borehole, a drilling rig may be installed to be moved to a drilling target point. Then, the ground may be drilled from the surface of the drilling target point to the bottom by the drilling rig to form a borehole. Subsequently, mud or the like generated as the borehole is drilled is discharged through a pump or the like, and the inner borehole can be cemented and cased to withstand various forces such as collapse, rupture, and tensile breakage.

In addition, the above-described work process including the drilling and casing can be repeated so that the oil / natural gas reaches the final depth of the ground where it is or is expected to be located. And, as the borehole is formed up to the final depth, work processes such as drilling, cementing, casing, tripping, and testing may be finished.

Accordingly, the drilling rig movement and installation, drilling, circulating, casing, cementing, tripping, and testing of each drilling section, including testing, are individually classified and input to the input section 10 and input to the database section 20 Since it is stored, the prediction precision can be improved when the drilling period is predicted.

On the other hand, based on the data stored in the database unit 20, at least one of the function distribution method among the uniform distribution, triangular distribution, PERT distribution, normal distribution, and log normal distribution is extracted or input, and randomly generated The distribution factor for the drilling operation time of the probability distribution for each drilling section is automatically calculated so that the generation range of the random number is set and stored in the database unit 20 (s20).

Here, referring to FIG. 4, the function distribution method is based on data stored in the database unit 20, at least one of a plurality of probability distribution functions (PDF) previously stored in the database unit 20. It is preferably extracted from the input unit (10).

At this time, the function distribution method includes an even distribution, a triangular distribution, a PERT distribution, a normal distribution, and a log normal distribution, but is not limited thereto. In addition, the fact that the function distribution method is extracted means that the shape of the probability distribution function graph generated as the random number is generated is determined based on the data stored in the database unit 20, and the actual probability distribution function graph is generated. It is desirable to understand that the probability distribution function is determined instead of being.

In addition, the function distribution method is extracted based on the data stored in the database unit 20, and the data refers to the overall depth of the existing borehole depth and the mining hole stored based on the Daily Drilling Report (DDR). It is desirable to understand the drilling technology data in a comprehensive sense.

In addition, the data stored in the database unit 20 includes Bit / BHA information such as bit type, bit size, and nozzle size, and Weight of Blend (WOB), Rate of Penetration (ROP), Revolutions Per Minute (RPM), and Bits. Drilling-related degrees such as hydraulic power, torque, mud-related information such as density, temperature, viscosity, and other information such as health, safety and environmental (HSE), equipment inventory, and weather may be included.

Here, referring to FIG. 5, it is preferable that the database unit 20 stores ROPs capable of representing drilling speeds according to depths by dividing them into borehole sections, and data on time required for main drilling operations. In addition, referring to FIG. 6, it is preferable that the database unit 20 stores data for a time taken during the main drilling operation of each borehole based on the data shown in FIG. 5 described above.

In addition, the function distribution method may be extracted based on the provision amount of data stored in the database unit 20, and according to the degree of the data provision amount, an even distribution, triangular distribution, putt distribution, normal distribution, and log normalization At least one function distribution method of the distribution may be extracted.

Accordingly, the function distribution method is extracted or input to at least one of a plurality of probability distribution functions pre-stored in the database unit 20 based on data stored in the database unit 20 and input from the input unit 10 It may be stored in the database unit 20.

Of course, in some cases, the function distribution method may be manually input to the input unit 10 and stored in the database unit 20 as the function distribution method is determined by a drilling plan expert.

Meanwhile, at least one of a minimum value, a median value, a maximum value, an average value, and a standard deviation is automatically calculated by the input unit so that a generation range of a random number and a distribution density of random numbers are generated to calculate individual drilling time. It is preferably stored in the database.

Here, at least one of a minimum value, a median value, a maximum value, an average value, and a standard deviation is automatically calculated so that a generation range of a random number is set corresponding to the function distribution method extracted based on the data stored in the database unit 20. It may be stored in the database unit.

At this time, the function distribution method is calculated based on the data including the environmental data and the drilling technique data stored in the database unit 20, and is calculated as a numerical value optimized by the calculation unit 40 to the input unit 10 Can be entered in

Accordingly, when performing Monte Carlo simulation through random number generation, since the probability distribution function graph is generated corresponding to the function distribution method and the stored values of the distribution factors, prediction precision can be improved when predicting a drilling period.

Of course, in some cases, the distribution factor may be manually input to the input unit 10 and stored in the database unit 20 by the drilling plan expert.

Meanwhile, referring to FIG. 4, the probability distribution function may be calculated by Equation 1 and Equation 2 below.

In Equation 1 and Equation 2, a is a minimum value of the distribution factor, b is a maximum value of the distribution factor, x is a drilling operation time, and f (x) is preferably a probability distribution function. At this time, the probability distribution function calculated by Equation 1 and Equation 2 is shown in the form of an even distribution.

At this time, the uniform distribution is the simplest form of probability distribution among continuous probability distributions, and has the same probability for all parts in the section between the minimum and maximum values of the distribution factor, and the probability is 0 in the remaining sections.

In addition, when determining the distribution type of the uniform distribution, the distribution factors are defined as the minimum value and the maximum value, and the uniform distribution has a limited sample data, or if the reliability of the data is low or an appropriate distribution is not known, to determine the approximate distribution. Can be extracted and used.

Meanwhile, referring to FIG. 4, the probability distribution function may be calculated by Equation 3 and Equation 4 below.

In Equations 3 and 4, a is the minimum value of the distribution factor, b is the maximum value of the distribution factor, c is the median value of the distribution factor, x is the drilling time, and f (x) is the probability. It is preferable to mean the distribution function. At this time, the probability distribution function calculated by Equation 3 and Equation 4 is shown in the form of a triangular distribution.

At this time, the triangular distribution has different probabilities within the interval between the minimum, median, and maximum values of the distribution factor. Specifically, the probability increases from the minimum value to the median value, and the probability decreases from the median value to the maximum value. Form.

In addition, the triangular distribution generally has only limited sample data and can be extracted and used when it is difficult to determine the probability distribution that can explain the population. In addition, a uniform distribution or a triangular distribution requires minimum and maximum values, and a triangular distribution requires more intermediate values. Here, since the triangular distribution has a smaller standard deviation than the even distribution, a less conservative prediction result can be derived for the uncertainty of drilling.

Meanwhile, referring to FIG. 4, the probability distribution function may be calculated by Equation 5 below.

In Equation 5, a is the minimum value of the distribution factor, b is the maximum value of the distribution factor, c is the median value of the distribution factor, x is the drilling time, and f (x) means the probability distribution function. This is preferred. At this time, the probability distribution function calculated by Equation 5 is shown in the form of a PERT distribution.

At this time, the PERT distribution is determined as the minimum, median, and maximum values in the same way as the triangular distribution, but unlike the triangular distribution, it appears in the form of a smooth curve. In addition, while the mean value of the triangular distribution is defined as the arithmetic mean of the minimum value, the median value, and the maximum value, the average of the PUT distribution is defined as a weighted average obtained by multiplying the median by a weight of 4 times. Therefore, the probability has a biased form closer to the median than the triangular distribution, and a conservative prediction result can be derived compared to the result using the triangular distribution.

Meanwhile, referring to FIG. 4, the probability distribution function may be calculated by Equation 6 below.

In Equation 6, it is preferable that μ is an average value of the distribution factor, σ is a standard deviation according to a normal distribution curve, x is a drilling operation time, and f (x) means a probability distribution function. At this time, the probability distribution function calculated by Equation 6 is shown in the form of a normal distribution.

In this case, the normal distribution is a probability distribution of a bell shape whose shape is determined by an average value and a standard deviation, also called a Gauss distribution. In general, as the number of data increases, the normal distribution is followed by the Central Limit Theorem. Due to these characteristics, the normal distribution can be extracted and used to verify the results of different probability distributions.

Also, unlike the normal distribution or the triangular distribution, the minimum value is not set, so when performing stochastic prediction according to the normal distribution, abnormal values may be generated, so it may not be suitable when the standard deviation is larger than the average value. .

Meanwhile, referring to FIG. 4, the probability distribution function may be calculated by Equation 7 below.

In Equation 7, it is preferable that μ is an average value of the distribution factor, σ is a standard deviation according to a normal distribution curve, x is a drilling time, and f (x) means a probability distribution function. At this time, the probability distribution function calculated by Equation 7 is shown in the form of a lognormal distribution.

In this case, the log normal distribution means a probability distribution indicating a normal distribution when a log is taken for a value of data, and appears as a normal distribution when arbitrary variables are distributed in a logarithmic form. These lognormal distributions can be extracted and used when changes occur from natural phenomena to logarithmic scales, and are also widely used in fields such as economics and reliability analysis. In addition, the lognormal distribution can be shaped by the mean value and the standard deviation as well as the normal distribution.

Accordingly, it is optimized for the prediction of the drilling period by extracting and calculating at least one function distribution method and distribution factors related to the uniform distribution, triangular distribution, putt distribution, normal distribution, and log normal distribution in response to drilling technology data. The predicted precision can be significantly improved since the input value is derived.

Meanwhile, a plurality of random random numbers are generated through a random number generation unit within a range of the distribution factors stored in the database unit 20 at a preset number of times, and according to the drilling depth of each drilling section according to the drilling depth as the Monte Carlo simulation is performed. The individual probability distribution for is calculated by the operation unit 40 and stored in the database unit 20 (s30).

Here, it is preferable to understand that the individual probability distribution for each drilling section for each drilling section is calculated by calculating the target drilling depth and the respective probability distribution function for the estimated drilling work time for each classified drilling section. . In addition, it is preferable to understand the drilling work time for each drilling section as a concept encompassing the above-described drilling work section and individual drilling work.

In addition, it is preferable to understand that the Monte Carlo simulation is performed as the random number is randomly generated as a plurality of random numbers are generated at a predetermined number of times based on the probability distribution method and the distribution factor, which are extracted and stored.

In addition, the individual probability distribution may be individually calculated for each drilling section, such as moving and installing a drilling rig, drilling, circulating, casing, cementing, tripping, testing, and the like. In addition, the individual probability distribution is based on the data stored in the database unit 20, such as pipe clogging, problems occurring in boreholes, emergency work processes such as drilling delay due to weather, etc. Individual probability distributions according to the occurrence may be further included.

Accordingly, the individual probability distribution calculated individually for each work process for each drilling section reflects the time factor required by uncertainty due to the state of the reservoir, loss of the number of drilling holes, abnormal pressure layer discovery, instability of the borehole, drilling equipment, and weather. Prediction precision can be significantly improved during period prediction.

On the other hand, it is preferable that the individual probability distribution is integrated by the calculating unit 40 to calculate the individual cumulative probability distribution so that the time required for each drilling section for the drilling depth according to the elapse of the drilling operation time is estimated through the Monte Carlo simulation. . At this time, it is preferable to understand that the individual cumulative distribution function (CDF) is calculated by calculating the integral from the probability distribution function of the individual probability distribution.

In addition, the probability distribution function and the individual cumulative distribution function for each drilling section may be selectively output through the display unit 50 as they are stored in the database unit 20. Accordingly, the probability value of the time required for each drilling section with respect to the drilling depth can be grasped from the individual cumulative probability distribution calculated from the individual probability distribution.

On the other hand, the drilling operation time for each drilling section is summed by the calculation unit 40 to calculate the total estimated drilling operation time required for the drilling process is stored in the database unit 20 (s40). In this case, it is preferable to understand that the entire estimated drilling operation period is a drilling period required in the entire drilling process for each drilling section including moving and installing a drilling rig, drilling, circulating, casing, cementing, tripping, and testing.

Here, when the individual probability distribution is integrated by the operation unit 40 to calculate the individual cumulative probability distribution, the individual cumulative probability distribution may be summed by the operation unit 40 to calculate the total cumulative probability distribution.

Of course, in some cases, the respective probability distributions for each drilling operation time for each drilling section according to the drilling depth are sequentially added according to the drilling operation procedure to calculate a probability distribution function for the entire probability distribution. The total probability distribution may be calculated by integrating the probability distribution function.

Accordingly, the individual probability distributions of the work processes for each drilling section calculated through the Monte Carlo simulation are summed up, so that the entire estimated drilling work time required for the drilling work process is accurately predicted. The result can be produced with the concept of a range rather than a single value compared to a theoretical technique. In addition, since it is possible to establish an optimal drilling plan based on the entire estimated drilling operation period, economical efficiency can be significantly improved by preventing unnecessary waste of cost.

Meanwhile, the drilling period prediction system 100 using the probabilistic analysis method according to an embodiment of the present invention includes an input unit 10, a database unit 20, a random number generation unit 30, a calculation unit 40, a display unit ( 50).

In detail, it is preferable that the input section 10 individually inputs a work process for each drilling section, which includes drilling rig movement and installation, drilling, circulating, casing, cementing, tripping, and testing.

In addition, the probability distribution method is extracted to the input unit 10, and it is preferable that the distribution factor for the drilling operation time of the probability distribution for each drilling section is calculated so that a randomly set number of randomly generated random number generation ranges are set. Do.

In addition, it is preferable that the database unit 20 stores the work process for each drilling section and the probability distribution method and the distribution factors input through the input unit 10.

In addition, the database unit 20 stores the drilling time for each drilling section for the drilling depth and the entire estimated drilling work period, and the individual probability distribution function, the individual cumulative probability distribution function, and the overall probability distribution calculated through the Monte Carlo simulation. The function and the overall cumulative probability distribution function can be stored.

In addition, the random number generation unit 30 is preferably provided to generate a plurality of random numbers at random so that Monte Carlo simulation is performed within the range of the distribution factors stored in the database unit 20.

In addition, the calculation unit 40 performs a Monte Carlo simulation of the drilling work time within the range of the distribution factor to calculate an individual probability distribution for the drilling work time for each drilling section according to the drilling depth, and drilling for each drilling section It is preferable that the working hours are summed to calculate the total estimated drilling operation period.

On the other hand, the display unit 50 may be selectively output a borehole database and a database for each hole according to the drilling operation and borehole section. In addition, the display unit 50 may selectively output the drilling depth according to the passage of the drilling operation time, the drilling operation time for each individual drilling section, and the entire estimated drilling operation period.

5 and 6, various data stored in the database unit 20 may be selectively output and illustrated through the display unit 50.

Here, referring to FIG. 5, a database for each borehole organized based on a classification of drilling operations may be output and illustrated as a table. In detail, a general information column for a borehole is shown at the upper left of FIG. 5, and a list of drilling work time divided for each drilling work section and each drilling work may be shown at the bottom of the general information field.

And, referring to FIG. 6, a database for each borehole may be output and shown as a table by collecting a database for each borehole for utilization of the constructed database. In detail, in FIG. 6, the drilling work time of each borehole is listed, and the drilling work time may be divided and illustrated for each borehole section. In addition, the distribution factor required in stochastic analysis may be calculated and illustrated in the upper right of FIG. 6.

In addition, referring to FIG. 7, a graph for a drilling depth according to a drilling time calculated through the entire probability distribution function or the entire cumulative probability distribution may be selectively displayed on the display unit 50.

In addition, referring to FIG. 8, a graph of a time required for each drilling operation section calculated through a probability distribution function according to the individual probability distribution may be selectively output and illustrated on the display unit 50.

Moreover, referring to FIGS. 9 and 10, the display section 50 selectively outputs a table of drilling time intervals and time required for each operation, respectively, calculated through the probability distribution function according to the individual probability distribution, and Can be shown.

In addition, referring to FIG. 11, a graph of the total probability distribution function and the total cumulative probability distribution function calculated by summing the individual probability distributions may be selectively output and illustrated on the display unit 50. At this time, when interpreting the graph shown in FIG. 11, for example, it may be interpreted as meaning that a probability that the entire estimated drilling operation period will take 400 hours may occur with a probability of 20%.

Therefore, the overall probability distribution for the entire estimated drilling operation period is derived through the summation of the individual probability distributions calculated and classified for each drilling section, so that the entire estimated drilling operation period required for the drilling operation process is accurately predicted. Since drilling plans can be established, economics and predictive precision can be significantly improved.

At this time, the terms "include", "compose" or "prepare" as described above means that the corresponding component can be intrinsic, unless specifically stated otherwise, excluding other components. It should not be interpreted as being able to further include other components. All terms, including technical or scientific terms, have the same meaning as generally understood by a person skilled in the art to which the present invention belongs, unless otherwise defined. Commonly used terms, such as predefined terms, should be interpreted as being consistent with the contextual meaning of the related art, and are not to be interpreted as ideal or excessively formal meanings unless explicitly defined in the present invention.

As described above, the present invention is not limited to each of the above-described embodiments, and it is possible to be modified by a person skilled in the art to which the present invention pertains without departing from the scope of the claims of the present invention. The implementation of such modifications is within the scope of the present invention.

100: drilling period prediction system using probabilistic analysis method
10: input unit 20: database unit
30: random number generation unit 40: operation unit
50: display unit

Claims (13)

Drilling rig movement and drilling process, including drilling rig movement and installation, drilling, circulating, casing, cementing, tripping, and testing, are separately categorized and input to the input section so that a borehole database and a wide area database are built according to the drilling and borehole sections. The first step is stored in the database unit;
Based on the data stored in the database unit, the function distribution method is extracted, and distribution factors for the drilling time of the probability distribution for each drilling section are calculated and stored so that the randomly generated random number generation range is set and stored in the database unit Step 2;
As the random number is generated through the random number generation unit within the range of the distribution factor stored in the database unit and the Monte Carlo simulation is performed, the individual probability distribution for the drilling time for each drilling section according to the drilling depth is calculated and calculated by the calculation unit. A third step stored in the database unit; And
Drilling period prediction method using a probabilistic analysis method comprising a fourth step of calculating the total estimated drilling time required for the drilling process by summing the drilling time for each drilling section by the calculation unit. According to claim 1,
In the second step, the function distribution method is extracted from the input unit as at least one of a uniform distribution, a triangular distribution, a putt distribution, a normal distribution, and a log normal distribution based on data stored in the database unit,
The distribution factor is characterized in that at least one of a minimum value, a median value, a maximum value, an average value, and a standard deviation is calculated at the input unit and stored in the database unit so that a generation range of a random number generated for calculating an individual drilling operation time is set. Drilling period prediction method using stochastic analysis method. According to claim 2,
In the second step, the probability distribution function is

Calculated by, wherein a is the minimum value of the distribution factor, b is the maximum value of the distribution factor, and x is a drilling period prediction method using a probabilistic analysis method, characterized in that the drilling time. According to claim 2,
In the second step, the probability distribution function is

Calculated by, wherein a is the minimum value of the distribution factor, b is the maximum value of the distribution factor, c is the median value of the distribution factor, and x is a drilling using a probabilistic analysis method characterized in that it means the drilling time How to predict duration. According to claim 2,
In the second step, the probability distribution function is

Calculated by, wherein a is the minimum value of the distribution factor, b is the maximum value of the distribution factor, c is the median value of the distribution factor, and x is a drilling using a probabilistic analysis method characterized in that it means the drilling time How to predict duration. According to claim 2,
In the second step, the probability distribution function is

Calculated by, wherein μ is the mean value of the distribution factor, σ is the standard deviation according to the normal distribution curve, and x is a drilling period prediction method using a probabilistic analysis method characterized in that it refers to a drilling time. According to claim 2,
In the second step, the probability distribution function is

Calculated by, wherein μ is the mean value of the distribution factor, σ is the standard deviation according to the normal distribution curve, and x is a drilling period prediction method using a probabilistic analysis method characterized in that it refers to a drilling time. According to claim 1,
In the third step,
Using the Monte Carlo simulation, the individual probability distribution is integrated by the calculating unit so that the individual cumulative probability distribution is calculated so that the drilling depth and the time required for each drilling section are estimated according to the passage of the drilling work time. Method for predicting drilling period. The method of claim 8,
In the fourth step, the drilling period prediction method using a stochastic analysis method characterized in that the individual cumulative probability distribution is summed by the calculation unit to calculate the total cumulative probability distribution. According to claim 1,
In the fourth step,
Each of the individual probability distributions for the drilling time for each drilling section according to the drilling depth is sequentially summed according to the drilling operation procedure to calculate the total probability distribution for the entire estimated drilling operation period.
A method of predicting a drilling period using a probabilistic analysis method characterized in that the total probability distribution is integrated to calculate the total cumulative probability distribution so that the entire estimated drilling operation period required for the drilling operation process is calculated. Drilling rig movement and installation, drilling, circulating, casing, cementing, tripping, and testing process for each drilling section including input are individually input, probability distribution method is extracted, and randomly generated random number generation range is set. An input unit for calculating a distribution factor for a drilling time of a probability distribution for each drilling section;
A database unit that stores the work process for each drilling section and the probability distribution method and the distribution factors input through the input unit, and stores the drilling work time for each drilling section for the drilling depth and the entire estimated drilling work period;
A random number generator randomly generating random numbers to perform a Monte Carlo simulation within the range of the distribution factors stored in the database unit;
The Monte Carlo simulation of the drilling time within the range of the above distribution factors is performed to calculate the individual probability distribution for the drilling time for each drilling section according to the drilling depth, and the total estimated drilling time is summed by adding the drilling time for each drilling section. A calculation unit for calculating a work period; And
A borehole database according to the drilling operation and a borehole section and a database for each mining section are selectively output, and a display unit including a drilling depth according to the elapse of the drilling time, a drilling time for each drilling section, and a total estimated drilling duration is selectively output. Drilling period prediction system using probabilistic analysis method. The method of claim 10,
The calculating unit calculates the individual cumulative probability distribution by integrating the individual probability distribution so that the drilling depth is estimated according to the lapse of drilling time through the Monte Carlo simulation, and sums the individual cumulative probability distribution to calculate the total cumulative probability distribution. Drilling period prediction system using probabilistic analysis method. The method of claim 10,
In the database unit, the probability distribution method is pre-stored as a plurality of probability distribution functions, and the probability distribution method is extracted from any one of the probability distribution functions in the input unit, and the distribution factor is generated to calculate an individual drilling operation time. A drilling period prediction system using a probabilistic analysis method characterized in that at least one of a minimum value, a median value, a maximum value, and an average value is input to set a generation range of random numbers.

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