Coal rock gas horizontal well EUR prediction method based on sensitive factor analysis and related device

Abstract

The invention discloses a coal rock gas horizontal well EUR prediction method based on sensitive factor analysis and a related device, and belongs to the technical field of oil and gas field development. According to the method, correlation between all production factors and the EUR of the coal-rock gas horizontal well is analyzed through historical data, and sensitive factors are obtained; constructing a coal rock gas horizontal well EUR prediction model based on the sensitive factors; and obtaining sensitive factor parameters of a to-be-predicted coal-rock gas horizontal well, inputting the sensitive factor parameters into the coal-rock gas horizontal well EUR prediction model, and predicting to obtain the coal-rock gas horizontal well EUR. According to the method, a big data analysis means is introduced, and a quantitative formula linearly fitted by sensitive composite factors is selected as a coal rock gas horizontal well EUR prediction formula; batch prediction of the single-well EUR of the coal-rock gas horizontal well is achieved, the prediction coincidence rate exceeds 93%, and the bottleneck that at present, the number of production wells of the coal-rock gas horizontal well is small, the production time is short, and EUR prediction is difficult is broken through.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas field development technology, and relates to a method and related device for predicting the EUR of horizontal wells in coal and rock gas based on sensitivity factor analysis. Background Technology

[0002] Coal-rock gas is a new type of unconventional natural gas sourced within a single reservoir. It is characterized by the coexistence of conventional and unconventional reservoirs, the symbiosis of free and adsorbed gas, and the complementary accumulation of endogenous and exogenous gas. It features high reservoir pressure and high gas content, requiring large-scale extraction through horizontal well volumetric fracturing. In recent years, deep coal-rock gas exploration and development has entered a new stage, attracting significant attention and rapid progress within the natural gas industry. This has propelled coal-rock gas into a crucial practical area for increasing natural gas reserves and production, playing a vital role in further ensuring energy security.

[0003] The No. 8 coal seam in the Ordos Basin is widely distributed, with preliminary estimates indicating resources exceeding 17 trillion cubic meters, representing enormous exploration and development potential. In the 1960s, the coal-bearing strata in the basin were not included in the exploration sequence as source rocks. In the 1990s, Changqing Oilfield opened three shallow-to-medium source gas test well groups in the Daning-Jixian area targeting the No. 5 and No. 8 coal seams, testing 40 wells with an average test gas production of approximately 1,000 cubic meters per day. From 2019 to 2021, risk assessment and test production were conducted on wells Y160 and M172, achieving test gas production of 1,670 cubic meters per day and 4,713 cubic meters per day respectively, demonstrating certain exploration potential for deep coal-bearing gas. Since 2022, Changqing Oilfield has accelerated the pace of coal-bearing gas exploration and evaluation, with peak gas production from horizontal wells ranging from 32,000 to 198,000 cubic meters per day, including four wells with cumulative gas production exceeding 10 million cubic meters, achieving significant breakthroughs in exploration and development. However, the coal and gas deposits in the Ordos Basin are buried at depths of 2,000 to 3,500 meters, and the geological characteristics vary significantly across regions. Furthermore, the production characteristics of gas wells differ considerably under different geological and technological conditions.

[0004] Ultimate recoverable reserves (EUR) assessment of gas wells is a crucial step in the large-scale development of coalbed methane. It serves as a vital basis for formulating development plans, scientifically deploying well production, and making comprehensive adjustments, requiring continuous evaluation. For coalbed methane, the primary method used is a combination of production instability analysis, empirical production decline analysis, and numerical simulation to predict single-well production capacity and EUR. While production instability analysis and numerical simulation have wide applicability, they are complex to operate, and their accuracy depends heavily on the model's input parameters. Empirical production decline analysis, on the other hand, is simple, fast, and convenient to apply, but its results are significantly affected by data fluctuations and flow stages. All of these methods are only suitable for wells with long production histories and cannot meet the needs of large-scale prediction of numerous newly built gas wells in the development of deep coalbed methane. Summary of the Invention

[0005] The purpose of this invention is to provide a method and related apparatus for predicting the ultimate recoverable reserves (EUR) of horizontal wells in coal and rock gas based on sensitivity factor analysis, so as to solve the technical problem that the existing methods for predicting the ultimate recoverable reserves of gas wells are difficult to meet the batch prediction needs of a large number of newly built gas wells for large-scale development of deep coal and rock gas.

[0006] To achieve the above objectives, the present invention employs the following technical solution:

[0007] In a first aspect, the present invention provides a method for predicting the EUR (Effective Urge Scale) of horizontal wells in coal and gas based on sensitivity factor analysis, comprising the following steps:

[0008] Sensitive factors were identified by analyzing the correlation between all production factors and the EUR of horizontal coal and gas wells using historical data.

[0009] Constructing a EUR prediction model for horizontal wells in coal and rock gas based on sensitive factors;

[0010] The sensitive factor parameters of the horizontal coal gas well to be predicted are obtained and input into the EUR prediction model of the horizontal coal gas well to predict the EUR of the horizontal coal gas well.

[0011] Furthermore, the step of analyzing the correlation between all production factors and the EUR of horizontal coal and gas wells through historical data to obtain sensitive factors specifically includes:

[0012] Select a number of historical coal and gas horizontal wells with stable production and long production time, calculate the EUR, and collect all production factor parameters of the historical coal and gas horizontal wells;

[0013] Based on big data analysis, the impact weight of each production factor on EUR is evaluated, and the production factors that are strongly correlated with EUR are identified as sensitive factors.

[0014] Furthermore, the production factors include horizontal section length, coal and rock length, density, sand content, total liquid volume, burial depth, coal and rock thickness, reservoir-cap combination, coal and rock structure, gas content, and average gas measurement value.

[0015] Furthermore, it also includes: based on the results and understanding of single-factor analysis, introducing the concept of geological engineering composite factors, analyzing the impact of the interaction between different production factors on the EUR of horizontal coal and gas wells, and evaluating the multi-factor sensitivity of the EUR of deep coal and gas wells.

[0016] Furthermore, the sensitive factors include: coal and rock length, coal and rock thickness, gas content, total liquid volume, and sand addition.

[0017] Furthermore, the EUR prediction model for the coal and rock gas horizontal well is a prediction model based on Linear_model.

[0018] Furthermore, the expression for the EUR prediction model for horizontal coal gas wells is as follows:

[0019] EUR=-43835+955.4*ln(LHQ)+30789.3*ln(S) / ln(W)+680.2*ln(WS)

[0020] In the formula, EUR represents the final recoverable reserves of the gas well; L represents the length of the coal and rock; H represents the thickness of the coal and rock; Q represents the gas content; W represents the total liquid volume; and S represents the amount of sand added.

[0021] Secondly, the present invention provides a coal gas horizontal well EUR prediction system based on sensitivity factor analysis, comprising:

[0022] The analysis module is used to analyze the correlation between all production factors and the EUR of horizontal coal and gas wells through historical data to identify sensitive factors;

[0023] The modeling module is used to construct EUR prediction models for horizontal wells in coal and gas based on sensitive factors;

[0024] The prediction module is used to obtain the sensitive factor parameters of the horizontal coal gas well to be predicted, input them into the EUR prediction model of the horizontal coal gas well, and predict the EUR of the horizontal coal gas well.

[0025] Thirdly, the present invention provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.

[0026] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described above.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] This invention discloses a method and related apparatus for predicting the EUR (Earning Equivalent to Variable Interest Scale) of horizontal coal gas wells based on sensitive factor analysis. It introduces big data analysis techniques and selects a quantitative formula derived from linear fitting of sensitive composite factors as the EUR prediction formula for horizontal coal gas wells. Technically, this method incorporates advanced artificial intelligence algorithms to achieve batch prediction of EUR for single horizontal coal gas wells, with a prediction accuracy exceeding 93%, breaking through the current bottleneck of limited EUR prediction due to the small number of producing horizontal coal gas wells and short production times. Economically, the application of this invention improves work efficiency and reduces experimental and labor costs, resulting in significant economic benefits. Socially, this invention is green and feasible. In summary, this invention has broad application prospects and significant practical value. Attached Figure Description

[0029] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a flowchart of the method of the present invention;

[0031] Figure 2 This is a schematic diagram of the system of the present invention;

[0032] Figure 3 a represents the material balance of the N1H well in this embodiment of the invention. Figure 3 b represents the NPI chart. Figure 3 c is the pressure history fitting curve;

[0033] Figure 4 This is a heatmap of various parameters of a horizontal coal-rock gas well according to an embodiment of the present invention.

[0034] Figure 5 This invention provides a correlation analysis of the EUR (Earning Equivalent Value) of a single horizontal well with geological static factors in an embodiment of the present invention. Figure 5 'a' represents the thickness of the coal and rock. Figure 5 b represents the gas content. Figure 5 c represents the average gas measurement;

[0035] Figure 6 This invention provides a correlation analysis of the EUR (Earnings Per Hour) of a single horizontal well with engineering factors in an embodiment of the present invention. Figure 6 'a' represents the length of coal and rock encountered during drilling. Figure 6 b represents the amount of sand added. Figure 6 c represents the total liquid volume;

[0036] Figure 7 This invention provides a correlation analysis of the initial daily production of horizontal wells at a depth of 100 meters with geological and engineering factors in an embodiment of the present invention. Figure 7 'a' represents the thickness of the coal and rock. Figure 7 b represents the sand strength;

[0037] Figure 8 This invention provides a correlation analysis of the EUR per 100 meters in horizontal wells with geological engineering factors in an embodiment of the present invention. Figure 8 'a' represents the thickness of the coal and rock. Figure 8 b represents the sand strength;

[0038] Figure 9 Correlation analysis of first-year daily production and composite factors of horizontal wells in this embodiment of the invention. Figure 9 'a' represents the length of coal and rock encountered during drilling, L, and the thickness of the coal and rock, H. Figure 9b represents the length of coal and rock encountered during drilling, L * the thickness of coal and rock, H * the amount of sand added, S. Figure 9 c. Length of coal and rock encountered during drilling L* Coal and rock thickness H* Gas ​​content Q* Sand addition S;

[0039] Figure 10 This invention provides a correlation analysis of the EUR and composite factors in a single horizontal well according to an embodiment of the present invention. Figure 10 'a' represents the length of coal and rock encountered during drilling, L, and the thickness of the coal and rock, H. Figure 10 b represents the length of coal and rock encountered during drilling, L * the thickness of coal and rock, H * the sand-addition intensity, Sq. Figure 10 c represents the length of coal and rock encountered during drilling, L * the thickness of coal and rock, H * the amount of sand added, S.

[0040] Figure 11 This is a comparison diagram of the EUR prediction methods for horizontal wells in coal and rock gas according to embodiments of the present invention;

[0041] Figure 12 This is a schematic diagram of the computer device structure of the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0043] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0044] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0045] In the description of the embodiments of the present invention, it should be noted that if terms such as "upper," "lower," "horizontal," or "inner" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of the invention is in use, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Furthermore, terms such as "first" and "second" are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0046] Furthermore, the use of the term "horizontal" does not imply that the component must be absolutely horizontal, but rather that it can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0047] In the description of the embodiments of the present invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention according to the specific circumstances.

[0048] Currently, the main method for predicting single-well production capacity and EUR (Earnings Per Hour) in coal and shale gas is a combination of production instability analysis, empirical production decline analysis, and numerical simulation. Production instability analysis and numerical simulation have wide applicability but are complex to operate, and their accuracy depends heavily on the model input parameters. Empirical production decline analysis, on the other hand, is simple, fast, and convenient to apply, but its prediction results are significantly affected by data fluctuations and flow stages. All of these methods are only suitable for gas wells with long production histories and cannot meet the needs of large-scale prediction for numerous newly built gas wells in deep coal and shale gas development (Table 1).

[0049] Table 1 Summary of Evaluation Methods for Coal Rock Gas Well Indicators

[0050]

[0051]

[0052] This invention is based on the EUR evaluation results of the unstable production analysis method for coal and shale gas already in production. It introduces big data analysis methods to deeply mine the multi-factor correlation rules and evaluate the influence weight of each factor on production capacity. It also selects machine learning intelligent algorithms to create quantitative evaluation formulas and intelligently and accurately predicts the EUR of horizontal coal and shale gas wells.

[0053] The present invention will now be described in further detail with reference to the accompanying drawings:

[0054] See Figure 1 This invention discloses a method for predicting the EUR (Effective Potential) of horizontal coal gas wells based on sensitivity factor analysis, comprising the following steps:

[0055] S1, by analyzing the correlation between all production factors and the EUR of horizontal coal and gas wells through historical data, sensitive factors are obtained;

[0056] Step 1: Select a number of stable coal and gas horizontal wells with a long production history and use the production instability analysis method to determine the EUR.

[0057] Typical case: See Figure 3 Well N1H was put into production on November 14, 2022, with a peak daily production of 57,000 cubic meters. Currently, its daily gas production is 27,000 cubic meters, with wellhead casing pressure of 2.0 / 5.3 MPa and a cumulative gas production of 10.605 million cubic meters. Using decline analysis, typical charts, and numerical models, the well-controlled area was determined to be 0.68 km². 2 Dynamic reserves: 5716×10 4 m 3 The EUR is 5030×10 4 m 3 .

[0058] Step 2: Collect the geological, drilling, and fracturing parameters of the above wells, such as coal seam thickness, gas content, horizontal section length, effective length, amount of sand added for stimulation, and total fluid volume (see Table 2 below);

[0059] Table 2. Geological and engineering parameters of gas wells in production and EUR prediction results using the unstable production analysis method.

[0060]

[0061]

[0062] Step 3: Based on big data analysis, deeply mine multi-factor association rules and evaluate the impact weight of each factor on cumulative gas production. Figure 4 Based on this, cross-plot analysis was used to evaluate the correlation between different geological and engineering factors and the EUR of a single horizontal well, prioritizing sensitive factors. The results showed that static geological factors such as coal thickness, gas content, and average gas measurement of the encountered coal section had a weak correlation with the EUR of a single horizontal well, while engineering parameters such as the length of the encountered coal section, total fluid volume, and sand addition had a better correlation with the EUR of a single horizontal well. Figure 5 , Figure 6 This reflects that increasing the well control volume is key to achieving high production during the development of horizontal wells in coal and rock reservoirs.

[0063] To further clarify the controlling effect of geological engineering factors on the productivity of coal-rock horizontal wells, the first-year daily production and single-well EUR of different horizontal wells were converted to 100-meter coal-rock sections for correlation analysis, referred to as first-year daily production per 100 meters and EUR per 100 meters, respectively. The results show that coal-rock thickness, sand addition intensity, and first-year daily production per 100 meters and EUR per 100 meters are generally positively correlated. Figure 7 , Figure 8 Therefore, given a certain coal and rock thickness, the sand addition intensity should be increased as much as possible to effectively increase the gas production of a single well.

[0064] Based on the results and understanding of single-factor analysis, the concept of a geological engineering composite factor is introduced to further analyze the importance of the interaction between different influencing factors on the energy return (EUR) of horizontal coal gas wells, and to finely evaluate the multi-factor sensitivity of deep coal gas EUR. Following the integrated geological engineering analysis concept, a composite factor is established mainly using key factors such as the length and thickness of the encountered coal seam, the amount of sand added, and the sand addition intensity. The correlation between the composite factor and the first-year daily production and single-well EUR of deep coal gas horizontal wells is comprehensively evaluated. The results show that the correlation between the geological composite factor and production capacity is significantly stronger than that of single factors. Furthermore, with the addition of process parameters such as sand addition amount and sand addition intensity, the correlation between the geological engineering composite factor and production capacity further improves. Figure 9 , Figure 10 ).

[0065] Based on the correlation studies of single and composite factors, it is believed that the length of the coal and rock encountered, the thickness of the coal and rock, the gas content, the sand addition intensity, and the sand addition amount are the main controlling factors affecting the high production of deep coal and rock gas horizontal wells.

[0066] S2, Constructing a EUR prediction model for horizontal coal gas wells based on sensitive factors;

[0067] Based on machine learning intelligent algorithms, different models were selected to evaluate and predict sensitive factors and EUR. RandomForestRegressor, PolynomialFeatures, and Linear_model were used to evaluate and predict EUR for all factors, sensitive factors (L, H, Q, W, S, Ro), and composite sensitive factors (LHQ, S / W, SW), respectively. The prediction results are shown below. Figure 11 Table 3.

[0068] Table 3. EUR prediction results for horizontal wells in coal gas under various schemes.

[0069]

[0070]

[0071] Conduct model evaluation and select the optimal quantitative evaluation formula.

[0072] Option 1 utilizes the RandomForestRegressor model to predict EUR using all parameters. While the model comprehensively considers factors, its accuracy is poor due to limited raw data (correlation coefficient 0.93). Option 2 uses the PolynomialFeatures model to perform polynomial fitting on sensitive factor parameters, achieving a high correlation (0.98) with the original EUR evaluation results. However, the polynomial model is complex, hindering rapid calculation. Option 3 mines the association rules among factors, selects the most sensitive composite factors, and uses the Linear_model for linear regression. This approach yields high accuracy, a simple formula, and ease of practical application and generalization (Table 4). Therefore, Option 3's quantitative evaluation formula is selected as the EUR prediction formula for horizontal coal and gas wells, as follows:

[0073] EUR=-43835+955.4*ln(LHQ)+30789.3*ln(S) / ln(W)+680.2*ln(WS)

[0074] In the formula, L: coal and rock length; H: coal and rock thickness; Q: gas content; W: total liquid volume; S: sand addition amount.

[0075] Table 4 Comparison of EUR prediction methods for horizontal coal gas wells

[0076]

[0077] S3: Obtain the sensitive factor parameters of the horizontal coal gas well to be predicted, input them into the EUR prediction model of the horizontal coal gas well, and predict the EUR of the horizontal coal gas well.

[0078] This invention, based on the EUR (Effective Urge) evaluation results of the unstable production analysis method for already operational coal gas wells, introduces big data analysis techniques and selects three machine learning models for fitting evaluation. Finally, a quantitative formula linearly fitted by sensitive composite factors is chosen as the EUR prediction formula for horizontal coal gas wells. Technically, this method incorporates advanced artificial intelligence algorithms to achieve batch prediction of EUR for single horizontal coal gas wells, with a prediction accuracy exceeding 93%, breaking through the current bottleneck of limited production capacity and short production time for horizontal coal gas wells, making EUR prediction difficult. Economically, the application of this invention improves work efficiency and reduces experimental and labor costs, resulting in significant economic benefits. Socially, this invention is green and feasible. In summary, this invention has broad application prospects and significant practical value.

[0079] See Figure 2This invention discloses a EUR prediction system for horizontal coal gas wells based on sensitivity factor analysis, comprising an analysis module, a modeling module, and a prediction module. The analysis module is used to analyze the correlation between all production factors and the EUR of horizontal coal gas wells through historical data to obtain sensitive factors. The modeling module is used to construct a EUR prediction model for horizontal coal gas wells based on the sensitive factors. The prediction module is used to obtain the sensitive factor parameters of the horizontal coal gas well to be predicted, input them into the EUR prediction model, and predict the EUR of the horizontal coal gas well.

[0080] Example:

[0081] This embodiment applies the results of the present invention. Based on the reservoir geology and process parameters of each block, the formula EUR=-43835+955.4*ln(LHQ)+30789.3*ln(S) / ln(W)+680.2ln(WS) is used to predict the EUR of horizontal wells in Benxi coal-gas in nine blocks of the Ordos Basin (Table 5). The predicted EUR for a single well is 44 million to 59 million cubic meters, with an average EUR of 54.68 million cubic meters, providing an important basis for the optimization and scientific formulation of coal-gas development plans.

[0082] Table 5 Static parameters and production indicators of the eastern development block of the Ordos Basin

[0083]

[0084]

[0085] In one embodiment of the invention, see [link to embodiment]. Figure 12A computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions from the computer storage medium to achieve a corresponding method flow or function. The processor described in this embodiment can be used in the operation of a coalbed methane horizontal well EUR prediction method based on sensitivity factor analysis.

[0086] This invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the EUR prediction method for horizontal coal gas wells based on sensitivity factor analysis in the above embodiments.

[0087] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0088] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0089] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0090] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for predicting the EUR (Earnings Regulator) of horizontal coal gas wells based on sensitivity factor analysis, characterized in that, Includes the following steps: Sensitive factors were identified by analyzing the correlation between all production factors and the EUR of horizontal coal and gas wells using historical data. Constructing a EUR prediction model for horizontal wells in coal and rock gas based on sensitive factors; The sensitive factor parameters of the horizontal coal gas well to be predicted are obtained and input into the EUR prediction model of the horizontal coal gas well to predict the EUR of the horizontal coal gas well.

2. The method for predicting EUR (Effective Urge Result) in horizontal coal gas wells based on sensitivity factor analysis according to claim 1, characterized in that, The step of analyzing the correlation between all production factors and the EUR of horizontal coal and gas wells through historical data to obtain sensitive factors specifically includes: Select a number of historical coal and gas horizontal wells with stable production and long production time, calculate the EUR, and collect all production factor parameters of the historical coal and gas horizontal wells; Based on big data analysis, the impact weight of each production factor on EUR is evaluated, and the production factors that are strongly correlated with EUR are identified as sensitive factors.

3. The method for predicting EUR (Effective Risk) in horizontal coal gas wells based on sensitivity factor analysis according to claim 2, characterized in that, The production factors include horizontal section length, coal and rock length, density, sand content, total liquid volume, burial depth, coal and rock thickness, reservoir-capsule combination, coal and rock structure, gas content, and average gas measurement value.

4. The method for predicting EUR (Effective Urge Result) in horizontal coal gas wells based on sensitivity factor analysis according to claim 2, characterized in that, Also includes: Based on the results and understanding of single-factor analysis, the concept of geological engineering composite factors is introduced to analyze the impact of the interaction between different production factors on the EUR of horizontal coal-rock gas wells and evaluate the multi-factor sensitivity of deep coal-rock gas EUR.

5. The method for predicting EUR (Effective Urge Result) in horizontal coal gas wells based on sensitivity factor analysis according to claim 2, characterized in that, The sensitive factors include: coal and rock length, coal and rock thickness, gas content, total liquid volume, and sand addition.

6. The method for predicting EUR (Effective Urge Result) in horizontal coal gas wells based on sensitivity factor analysis according to claim 1, characterized in that, The EUR prediction model for horizontal coal and rock gas wells is a prediction model based on Linear_model.

7. The method for predicting EUR (Effective Urge Result) in horizontal coal gas wells based on sensitivity factor analysis according to claim 1, characterized in that, The expression for the EUR prediction model for horizontal coal gas wells is as follows: EUR = -43835 + 955.4 * ln(LHQ) + 30789.3 * ln(S) / ln(W) + 680.2 * ln(WS) Where EUR represents the final recoverable reserves of the gas well; L represents the length of the coal and rock; H represents the thickness of the coal and rock; Q represents the gas content; W represents the total liquid volume; and S represents the amount of sand added.

8. A coal gas horizontal well EUR prediction system based on sensitivity factor analysis, characterized in that, include: The analysis module is used to analyze the correlation between all production factors and the EUR of horizontal coal and gas wells through historical data to identify sensitive factors; The modeling module is used to construct EUR prediction models for horizontal wells in coal and gas based on sensitive factors; The prediction module is used to obtain the sensitive factor parameters of the horizontal coal gas well to be predicted, input them into the EUR prediction model of the horizontal coal gas well, and predict the EUR of the horizontal coal gas well.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-7.

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