A method for predicting the production capacity of argillaceous shale oil

Abstract

The present invention relates to the technical field of shale oil and gas exploitation, and particularly to a method for predicting the productivity of argillaceous shale oil, which includes: obtaining the geological engineering parameters and corresponding productivity of the wells that have been constructed, as well as the geological engineering parameters of the single well to be predicted; establishing a database of productivity influencing factors, and normalizing the geological engineering parameters of the wells that have been constructed; determining the main control factors of the wells that have been constructed, and constructing a database of correlation coefficients between the main control factors and productivity; determining the weight coefficients of each main control factor, and constructing a formula for the comprehensive geological engineering index; constructing an index-productivity regression curve; determining the design range of each engineering parameter of the single well to be predicted according to the geological engineering parameters and corresponding productivity of each well that has been constructed; calculating the range of the comprehensive geological engineering index of the single well to be predicted, and predicting the productivity interval of the single well to be predicted; determining the engineering parameters of the single well to be predicted. The present invention can accurately evaluate the influence of various geological engineering parameters on productivity, and improve the accuracy of production prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of shale oil and gas exploitation, and particularly to a method for predicting the productivity of argillaceous shale oil. Background Art

[0002] At present, the methods for predicting shale oil productivity mainly include reservoir engineering method, analytical solution, and numerical simulation method. The principles of these three methods for productivity prediction are mainly based on reservoir and seepage simulation technologies. On the one hand, rich reservoir, stimulation, and fluid data are required, which takes a lot of time and the accuracy of the prediction results cannot be guaranteed. On the other hand, high theoretical basis is required for application personnel, and it is difficult to achieve large-scale batch and simple and convenient application.

[0003] Chinese Patent Publication No. CN114519260A discloses a method, system, medium, device, and terminal for predicting shale oil productivity. The method includes the following steps: determining an evaluation index system according to the evaluation purpose to establish a database of production influencing factors; dividing the factors influencing production into four level values according to the existing development plan database; constructing an average effect database of production influencing factors according to the database of production influencing factors; constructing a range database of production influencing factors according to the average effect database of production influencing factors; constructing a comparison matrix between different factors according to the range database of production influencing factors; calculating the weights of influencing factors and performing consistency test; constructing a multiple regression fitting production formula. This method only reflects the geological conditions affecting productivity through the sweet spot drilling rate, and does not screen specific geological factors, so it is impossible to accurately evaluate the contribution of various geological parameters to productivity, resulting in low prediction accuracy of production.

[0004] Chinese Patent Authorization Publication No. CN113988475A discloses a method for predicting shale oil productivity based on the equivalent area value method of nuclear magnetic logging T2 spectrum. The method includes the following steps: within the same block, according to the geological conditions and research target requirements, selecting the nuclear magnetic logging data of nine consecutive and complete layers of nine wells in the study area, and extracting the nuclear magnetic logging T2 spectrum section corresponding to the oil testing and production section; quantifying the nuclear magnetic logging T2 spectrum values; respectively obtaining multiple groups of quantified transverse nuclear magnetic logging T2 spectrum areas corresponding to the oil testing and production section, and performing equivalence on the multiple groups of quantified transverse nuclear magnetic logging T2 spectrum areas to form an equivalent area value; establishing a shale oil productivity prediction model based on the equivalent area value of nuclear magnetic logging T2 spectrum: intersecting the equivalent area value of nuclear magnetic logging T2 spectrum with the daily oil production of the oil testing and production section and carrying out correlation analysis, so as to obtain a correlation formula representing the relationship between the nuclear magnetic equivalent area value and the daily oil production as the prediction model; finally, using the established shale oil productivity prediction model to carry out prediction. This method effectively improves the success rate of shale oil layer identification, but only predicts productivity through geological conditions and does not consider engineering factors such as liquid addition amount and sand addition amount; while the final production of shale oil development is affected by both geological and engineering factors.

[0005] Chinese Patent Grant Publication No. CN113971498A, a method and device for predicting shale oil production capacity considering bedding development. The method includes the following steps: obtaining prediction parameters for predicting the production capacity of shale oil horizontal wells; determining the lateral fracture density of the shale reservoir, the permeability and porosity of the fracture network, and the vertical bedding crossflow coefficient to construct a corresponding seepage model for the fractured horizontal well in the reservoir; obtaining the production capacity curve of the shale oil fractured horizontal well representing vertical bedding flow by solving the seepage equations corresponding to each region of the shale, where the regions include the external reservoir, the internal reservoir matrix, the internal reservoir fractures, and the hydraulic fracture; determining the production capacity of the shale oil according to the prediction parameters and the production capacity curve. This method constructs a new semi-analytical model to describe the vertical and horizontal heterogeneity of the reservoir reconstruction volume after the volume fracturing of shale oil horizontal wells. The reservoir reconstruction volume is divided into several small layers to characterize the vertical bedding heterogeneity of the shale formation; the permeability, porosity, and density fractal of the induced fractures are introduced to describe the lateral heterogeneity of each layer, and the vertical crossflow considering fractal is used to describe the flow rate change caused by the vertical bedding heterogeneity. However, for argillaceous shale reservoirs, the matrix permeability is extremely low, and it is difficult to obtain the reservoir permeability through conventional laboratory experimental methods. At the same time, the permeability after on-site reconstruction cannot be accurately obtained. Therefore, this method is not applicable and it is difficult to predict the production capacity of argillaceous shale oil.

[0006] Chinese Patent Grant Publication No. CN115146849A, a method for predicting shale oil production capacity by optimizing CNN with particle swarm optimization. The method includes the following steps: constructing a mathematical model for shale oil production capacity to characterize the relationship between characteristic parameters and target parameters; where the characteristic parameters include oil layer thickness, fracture half-length, matrix permeability, production pressure difference, oil saturation, horizontal well length, fracture conductivity, crude oil viscosity, injection volume, and cluster number, and the target parameter includes production; based on the mathematical model for shale oil production capacity, obtaining multiple sets of corresponding characteristic parameters and target parameters as model training data; establishing a convolutional neural network model for predicting shale oil production capacity, and using the particle swarm algorithm to obtain the optimal weight and optimal bias values of the convolutional neural network model; training the convolutional neural network model based on the model training data to obtain a shale oil production capacity prediction model. This method requires obtaining factors such as fracture half-length and matrix permeability. However, the interpretations of microseismic, wide-area electromagnetic, high-frequency pressure, etc., which are commonly used in the industry to monitor fracture half-length, vary greatly, so it is difficult to accurately obtain the fracture half-length in actual mines. Moreover, for laminated shale reservoirs, the matrix permeability is extremely low, and it is difficult to obtain the reservoir permeability through conventional laboratory experimental methods. Therefore, this method is not applicable and it is difficult to predict the production capacity of argillaceous shale oil. Summary of the Invention

[0007] To this end, the present invention provides a method for predicting the productivity of argillaceous shale oil, which is used to overcome the problem in the prior art that the influence contribution of various geological parameters on productivity cannot be accurately evaluated, resulting in low prediction accuracy of the output of argillaceous shale oil.

[0008] To achieve the above object, the present invention provides a method for predicting the productivity of argillaceous shale oil, including:

[0009] Step S1, obtaining a number of geological engineering parameters and corresponding productivities of several wells where argillaceous shale oil has been constructed, as well as various geological engineering parameters of a single well where argillaceous shale oil is to be predicted; the geological engineering parameters include geological parameters and engineering parameters;

[0010] Step S2, establishing a productivity influence factor database according to the target evaluation system, and normalizing various geological engineering parameters of the wells where argillaceous shale oil has been constructed;

[0011] Step S3, determining the main control factors of the wells where argillaceous shale oil has been constructed based on the normalization results, and calculating the correlation between each main control factor and productivity to construct a database of correlation coefficients between the main control factors and productivity; the main control factors include at least one geological parameter and at least one engineering parameter;

[0012] Step S4, determining the weight coefficients of each main control factor according to the database of correlation coefficients between the main control factors and productivity, constructing a geological engineering comprehensive index formula, and obtaining the geological engineering comprehensive index of each well where argillaceous shale oil has been constructed;

[0013] Step S5, constructing an index-productivity regression curve according to the geological engineering comprehensive index and the corresponding productivity of each well where argillaceous shale oil has been constructed;

[0014] Step S6, determining a design reference database of engineering parameters based on the geological parameters of the single well where argillaceous shale oil is to be predicted, and determining the design ranges of various engineering parameters of the single well to be predicted according to the geological engineering parameters and corresponding productivities of each well where argillaceous shale oil has been constructed;

[0015] Step S7, calculating the range of the geological engineering comprehensive index of the single well to be predicted according to the geological engineering comprehensive index formula, and performing regression in the index-productivity regression curve based on the range of the geological engineering comprehensive index of the single well to be predicted to predict the productivity interval of the single well where argillaceous shale oil is to be predicted;

[0016] Step S8, determining the geological engineering comprehensive index of the single well to be predicted corresponding to the optimal productivity based on the predicted productivity interval, and determining the engineering parameters of the single well to be predicted based on the geological engineering comprehensive index of the single well to be predicted corresponding to the optimal productivity and the design ranges of the engineering parameters;

[0017] Among them, the geological parameters include nuclear magnetic total porosity, total organic carbon content, free hydrocarbon content, acoustic wave, and density; the engineering parameters include liquid addition intensity, sand addition intensity, fracture pressure, and slick water ratio.

[0018] Furthermore, in step S2, each geological engineering parameter is normalized using the following formula:

[0019] X z =(X * -X min ) / (X max -X min ),

[0020] Among them, X z is the normalized result of each geological engineering parameter, and its value range is between 0 and 1; X * is the actual value of the normalized parameter; X max is the actual maximum value of the normalized parameter; X min is the actual minimum value of the normalized parameter.

[0021] Furthermore, step S3 includes:

[0022] Step S31, calculating the correlation coefficient and positive and negative correlation of each geo-engineering parameter based on the normalized result by a preset correlation coefficient calculation method;

[0023] Step S32, determining the main controlling factors of the operated muddy shale oil wells and the correlation between each main controlling factor and the production capacity based on the correlation calculation results;

[0024] Step S33: constructing a database of correlation coefficients between main control factors and production capacity based on the correlation coefficients and positive and negative correlations between each main control factor and production capacity.

[0025] Furthermore, the step S4 includes:

[0026] Step S41, determining the weight coefficient of each main control factor to the production capacity based on the main control factor and production capacity correlation coefficient database;

[0027] Step S42: constructing a geological engineering comprehensive index formula based on the main controlling factors and their weight coefficients on production capacity;

[0028] Step S43: Based on the geological engineering comprehensive index formula, obtain the geological engineering comprehensive index of each of the constructed muddy shale oil wells.

[0029] Furthermore, in step S42, the geological engineering comprehensive index formula is:

[0030]

[0031] Among them, M1, M2, …, M i , …, M n are the weight coefficients of the main control factors with positive correlation with production capacity among the main control factors, where i = 1, 2, …, n; n is the number of main control factors with positive correlation with production capacity among the main control factors; X1, X2, …, X i , …, X n are the main control factors with positive correlation with production capacity among the main control factors; M n+1 , M n+2 , …, M n+j , …, M n+m are the weight coefficients of the main control factors with negative correlation with production capacity among the main control factors, where j = 1, 2, …, m; m is the number of main control factors with negative correlation with production capacity among the main control factors; X n+1 , X n+2 , …, X n+j , …, X n+m are the main control factors with negative correlation with production capacity among the main control factors; n + m is the number of main control factors.

[0032] Furthermore, the step S41 includes:

[0033] Step S411, based on the main control factor and production capacity correlation coefficient database, process the correlation coefficients between each main control factor and production capacity through a preset method to determine the weight coefficients of each main control factor on production capacity, where the weight coefficients are calculated based on the correlation coefficients of each main control factor.

[0034] Furthermore, in the step S411, the preset method is implemented through the following formula:

[0035]

[0036] where, M Z is the calculation result of the weight coefficient of each main control factor on production capacity, and the value range is between 0 and 1; Y is the sum of the absolute values of the correlation coefficients between each main control factor and production capacity; Y * is the actual value of the correlation coefficient between each main control factor and production capacity.

[0037] Furthermore, the step S31 includes:

[0038] Step S311, based on the production capacity influencing factor database, determine the preset standard;

[0039] Step S312, based on the preset standard, filter out the geological engineering parameters that do not meet the preset standard in the normalization results of each geological engineering parameter to calculate the correlation coefficients of each geological engineering parameter.

[0040] Further, the step S6 includes:

[0041] Step S61, based on the geological parameters of the single well to be predicted for shale oil and the design reference database for determining engineering parameters;

[0042] Step S62, calculating the matching degree between the geological parameters of the single well to be predicted for shale oil and the geological parameters of each constructed well for shale oil;

[0043] Wherein, the matching degree MP is determined by the following formula:

[0044] MP = sqrt(∑ q p＝1 (YB p - EB p ) 2 ) ; p = 1, 2,..., q; sqrt() is a preset square root determination function; YB p is the p-th geological parameter of each constructed well for shale oil, EB p is the p-th geological parameter of the single well to be predicted for shale oil; q is the number of geological parameters;

[0045] Step S63, determining the candidate wells based on the constructed wells for shale oil with each matching degree greater than the preset matching degree threshold and the production capacity greater than the preset production capacity threshold, and determining the design range of each engineering parameter of the single well to be predicted according to the engineering parameters of the candidate wells; wherein, the minimum value of each engineering parameter of the candidate wells is determined as the minimum value of the design range of the corresponding engineering parameter of the single well to be predicted, and the maximum value of each engineering parameter of the candidate wells is determined as the maximum value of the design range of the corresponding engineering parameter of the single well to be predicted.

[0046] Further, the step S8 includes:

[0047] Step S81, based on the predicted production capacity interval, determining the geological and engineering comprehensive index of the single well to be predicted corresponding to the optimal production capacity; wherein, the maximum production capacity in the production capacity interval is determined as the optimal production capacity;

[0048] Step S82, determining the engineering parameters of the single well to be predicted based on the geological and engineering comprehensive index of the single well to be predicted corresponding to the optimal production capacity and the design range of the engineering parameters.

[0049] Compared with the prior art, the beneficial effects of the present invention are as follows. By establishing a database of production capacity influencing factors and normalizing various geological engineering parameters, the present invention can accurately analyze the influence contribution of various geological engineering parameters on production, and thereby determine the main control factors, filtering out factors with little influence on production and reducing the amount of data processing. By calculating the correlation between each main control factor and production capacity, a database of correlation coefficients between the main control factors and production capacity is constructed, and the weight coefficients of each main control factor are determined. By constructing a comprehensive geological engineering index formula, the influence of each main control factor on production can be further determined, and quantitative characterization of each main control factor can be achieved through the comprehensive geological engineering index formula, realizing the integration of geological parameters and engineering parameters. By constructing an index-production capacity regression curve, the relationship between the comprehensive geological engineering index and production can be accurately reflected. By determining the design range of each engineering parameter of the well to be predicted and calculating the range of the comprehensive geological engineering index of the well to be predicted, the production capacity interval of the well to be predicted can be predicted, and thus the engineering parameters corresponding to the optimal production capacity can be determined, not only accurately predicting the production capacity of the well to be predicted, but also guiding the optimization of engineering parameters and realizing the optimization of the production capacity of the shale oil well to be predicted.

[0050] Furthermore, by normalizing various geological engineering parameters, the present invention can reduce the influence of dimension units on the data calculation results to accurately evaluate the influence of various geological engineering parameters on production capacity.

[0051] Furthermore, by using a preset correlation coefficient calculation method to calculate the correlation between each main control factor and production capacity, the present invention can accurately reflect the correlation degree between each main control factor and production.

[0052] Furthermore, by determining the weight coefficients of each main control factor on production capacity and constructing a comprehensive geological engineering index formula, the present invention can fully reflect the difference in the contribution of each main control factor to the production result, and integrate the positive and negative correlation relationships of the contribution to production capacity, improving the accuracy of production prediction.

[0053] Furthermore, by processing the correlation coefficients between each main control factor and production capacity in a preset manner to determine the weight coefficients of each main control factor on production capacity, the present invention can clarify the comprehensive influence of each main control factor on production capacity.

[0054] Furthermore, by determining the design reference database of engineering parameters, the present invention can avoid the situation where the design ranges of various engineering parameters cannot be determined according to the matching degree of geological parameters in subsequent processing, and achieve accurate prediction of production. By the matching degree between the geological parameters of the well to be predicted and the geological parameters of each constructed well of the argillaceous shale oil, the constructed well with geological parameters relatively close to those of the well to be predicted can be determined. By taking the constructed well with higher production as the candidate well, the engineering parameters of the candidate well can be used as a reference to determine the design ranges of the engineering parameters of the well to be predicted, and process optimization of the construction parameters can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a flowchart of the method for predicting the production capacity of argillaceous shale oil of the present invention;

[0056] Figure 2 is a step-by-step schematic diagram of step S3 of the present invention;

[0057] Figure 3 is a step-by-step schematic diagram of step S4 of the present invention;

[0058] Figure 4 is a step-by-step schematic diagram of step S6 of the present invention;

[0059] Figure 5 is a step-by-step schematic diagram of step S8 of the present invention;

[0060] Figure 6 is an exponent-production capacity regression curve graph of an embodiment of the present invention;

[0061] Figure 7 is a comparison graph of the actual production capacity and predicted production capacity of three new wells to be predicted in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0062] In order to make the objectives and advantages of the present invention clearer, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0063] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0064] It should be noted that in the description of the present invention, the terms indicating directions or positional relationships such as "upper", "lower", "left", "right", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings. This is only for convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention.

[0065] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and defined, the terms "installation", "connection", and "coupling" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the communication inside two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0066] Please refer to Figure 1 as shown, which is a flowchart of the method for predicting the productivity of argillaceous shale oil of the present invention.

[0067] The embodiment of the present invention provides a method for predicting the productivity of argillaceous shale oil, including:

[0068] Step S1, obtaining a number of geological engineering parameters and corresponding productivities of several constructed wells of argillaceous shale oil, as well as various geological engineering parameters of a single well of argillaceous shale oil to be predicted; the geological engineering parameters include geological parameters and engineering parameters;

[0069] Among them, the geological parameters include total nuclear magnetic porosity, total organic carbon content, free hydrocarbon content, acoustic wave, density, and the engineering parameters include liquid addition intensity, sand addition intensity, fracture pressure, and slickwater ratio.

[0070] In implementation, the geological parameters further include transverse fracture density, matrix permeability, vertical bedding crossflow coefficient, fracture conductivity, crude oil viscosity, etc., and the engineering parameters further include liquid addition volume, sand addition volume, etc.

[0071] Step S2, establishing a productivity influencing factor database according to the target evaluation system, and normalizing various geological engineering parameters of the constructed wells of argillaceous shale oil;

[0072] In the actual application scenario, the actual implementers can design the target evaluation system based on the actual situation according to the requirements of production prediction accuracy and various geological engineering parameters in historical data.

[0073] Specifically, in the step S2, various geological engineering parameters are normalized through the following formula:

[0074] X z =(X * -X min ) / (X max -X min ),

[0075] where X z is the normalization result of various geological engineering parameters, and the value range is between 0 and 1; X* is the actual value of the parameter to be normalized; X max is the actual maximum value of the parameter to be normalized; X min is the actual minimum value of the parameter to be normalized.

[0076] In implementation, the present invention can reduce the influence of dimensional units on the data calculation results by normalizing various geological engineering parameters, so as to accurately evaluate the influence of various geological engineering parameters on production capacity.

[0077] Step S3, determine the main control factors of the wells that have been constructed for argillaceous shale oil based on the normalization results, and calculate the correlation between each main control factor and production capacity to construct a database of correlation coefficients between the main control factors and production capacity; the main control factors include at least one geological parameter and at least one engineering parameter;

[0078] Please refer to Figure 2 shown, which is a step-by-step schematic diagram of step S3 of the present invention.

[0079] Specifically, the step S3 includes:

[0080] Step S31, calculate the correlation coefficients and positive and negative correlations of various geological engineering parameters based on the normalization results through a preset correlation coefficient calculation method;

[0081] Specifically, the step S31 includes:

[0082] Step S311, determine the preset criteria based on the production capacity influence factor database;

[0083] In implementation, the preset criteria include the criteria corresponding to each geological engineering parameter after normalization.

[0084] It can be understood that the preset criteria can be determined according to the geological engineering parameters of several wells that have been constructed with relatively high historical production capacity in the production capacity influence factor database. For example, normalize each geological engineering parameter of several wells with relatively high production capacity, and calculate the average value corresponding to each normalized geological engineering parameter, and use it as the preset criteria.

[0085] Step S312, based on the preset criteria, filter out the geological engineering parameters in the normalization results of each geological engineering parameter that do not meet the preset criteria, so as to calculate the correlation coefficients of various geological engineering parameters.

[0086] Step S32, determine the main control factors of the wells that have been constructed for argillaceous shale oil and the correlation between each main control factor and production capacity according to the correlation calculation results;

[0087] In implementation, the preset correlation coefficient calculation method is to jointly analyze through three correlation coefficient analysis methods, namely the Spearman correlation coefficient, the Pearson correlation coefficient, and the Kendall correlation coefficient, to calculate the correlation between each main control factor and production capacity. In a specific embodiment, first, for the results calculated by each single correlation coefficient calculation method, take the geological engineering parameters corresponding to the correlation coefficients ranked in the top 6 to 9 in terms of correlation, and then take the union of the geological engineering parameters corresponding to various correlation coefficients. Each parameter in this union is recorded as the main control factor.

[0088] It should be noted that those skilled in the art know that any method capable of performing correlation analysis in the prior art falls within the protection scope of the present invention and will not be elaborated here.

[0089] Step S33: Construct a database of the correlation coefficients between the main control factors and production capacity according to the correlation coefficients between each main control factor and production capacity and their positive and negative correlations.

[0090] In implementation, by calculating the correlation between each main control factor and production capacity through the preset correlation coefficient calculation method, the present invention can accurately reflect the degree of correlation between each main control factor and production.

[0091] Step S4: Determine the weight coefficients of each main control factor according to the database of the correlation coefficients between the main control factors and production capacity, construct a formula for the comprehensive geological engineering index, and obtain the comprehensive geological engineering index of each constructed well of the argillaceous shale oil.

[0092] Please refer to Figure 3 as shown, which is a step-by-step schematic diagram of step S4 of the present invention.

[0093] Specifically, the step S4 includes:

[0094] Step S41: Determine the weight coefficients of each main control factor on production capacity based on the database of the correlation coefficients between the main control factors and production capacity.

[0095] Specifically, the step S41 includes:

[0096] Step S411: Based on the database of the correlation coefficients between the main control factors and production capacity, process the correlation coefficients between each main control factor and production capacity through a preset method to determine the weight coefficients of each main control factor on production capacity, where the weight coefficients are calculated based on the correlation coefficients of each main control factor.

[0097] Specifically, in the step S411, the preset method is implemented through the following formula:

[0098]

[0099] where M ZThe calculation results of the weight coefficients of each main control factor on production capacity, with the value range between 0 and 1; Y is the sum of the absolute values of the correlation coefficients between each main control factor and production capacity; Y * is the actual value of the correlation coefficient between each main control factor and production capacity.

[0100] In implementation, the present invention processes the correlation coefficients between each main control factor and production capacity through a preset method to determine the weight coefficients of each main control factor on production capacity, and can clarify the comprehensive influence of each main control factor on production capacity.

[0101] Step S42, construct a comprehensive geological engineering index formula according to each main control factor and the weight coefficients of each main control factor on production capacity;

[0102] Specifically, in the step S42, the comprehensive geological engineering index formula is:

[0103]

[0104] Among them, M1, M2, …, M i , …, M n are the weight coefficients of the main control factors with positive correlation with production capacity among each main control factor, i = 1, 2, …, n; n is the number of main control factors with positive correlation with production capacity among each main control factor; X1, X2, …, X i , …, X n are the main control factors with positive correlation with production capacity among each main control factor; M n+1 , M n+2 , …, M n+j , …, M n+m are the weight coefficients of the main control factors with negative correlation with production capacity among each main control factor, j = 1, 2, …, m; m is the number of main control factors with negative correlation with production capacity among each main control factor; X n+1 , X n+2 , …, X n+j , …, X n+m are the main control factors with negative correlation with production capacity among each main control factor; n + m is the number of main control factors.

[0105] Step S43, based on the comprehensive geological engineering index formula, obtain the comprehensive geological engineering index of each constructed well of the argillaceous shale oil.

[0106] In implementation, the present invention constructs a comprehensive geological engineering index formula by determining the weight coefficients of each main control factor on production capacity, can fully reflect the differences in the contributions of each main control factor to the production result, and integrates the positive and negative correlation relationships of the contributions to production capacity, and can improve the accuracy of production prediction.

[0107] Step S5, construct an index-production capacity regression curve based on the comprehensive geological engineering index and the corresponding production capacity of each constructed well of the argillaceous shale oil;

[0108] Step S6, determine a design reference database for engineering parameters based on the geological parameters of the single well to be predicted for argillaceous shale oil, and determine the design ranges of the engineering parameters of the single well to be predicted according to the geological engineering parameters and the corresponding production capacities of each constructed well of the argillaceous shale oil;

[0109] Please refer to Figure 4 as shown, which is a step-by-step schematic diagram of step S6 of the present invention.

[0110] Specifically, the step S6 includes:

[0111] Step S61, based on the geological parameters of the single well to be predicted for argillaceous shale oil and determine the design reference database for engineering parameters;

[0112] Step S62, calculate the matching degree between the geological parameters of the single well to be predicted for argillaceous shale oil and the geological parameters of each constructed well of the argillaceous shale oil;

[0113] Among them, the matching degree MP is determined by the following formula:

[0114] MP = sqrt(∑ q p＝1 (YB p -EB p ) 2 ); p = 1, 2,..., q; sqrt() is a preset square root determination function; YB p is the p-th geological parameter of each constructed well of the argillaceous shale oil, EB p is the p-th geological parameter of the single well to be predicted for argillaceous shale oil; q is the number of geological parameters;

[0115] Step S63, determine the candidate wells according to the constructed wells of the argillaceous shale oil with each matching degree greater than the preset matching degree threshold and the production capacity greater than the preset production capacity threshold, and determine the design ranges of the engineering parameters of the single well to be predicted according to the engineering parameters of the candidate wells; among them, determine the minimum value of the engineering parameters of the candidate wells as the minimum value of the design range of the corresponding engineering parameters of the single well to be predicted, and determine the maximum value of the engineering parameters of the candidate wells as the maximum value of the design range of the corresponding engineering parameters of the single well to be predicted.

[0116] In actual application scenarios, the actual implementer can set the preset matching degree threshold according to the actual situation. The larger the preset matching degree threshold is, the higher the matching degree requirement for the geological parameters of the shale oil well to be predicted and the geological parameters of each shale oil well that has been constructed. Preferably, the value range of the preset matching degree threshold can be set to 0.8 - 0.95. The actual implementer can set the preset production capacity threshold according to the actual situation. The larger the preset production capacity threshold is, the higher the production capacity requirement. Preferably, the value range of the preset production capacity threshold can be set to 16 t / day - 20 t / day.

[0117] In implementation, by determining the design reference database of engineering parameters, the present invention can avoid the situation where the design ranges of various engineering parameters cannot be determined according to the geological parameter matching degree in subsequent processing, and achieve accurate prediction of production. Through the matching degree between the geological parameters of the well to be predicted and the geological parameters of each shale oil well that has been constructed, the constructed well with geological parameters relatively close to those of the well to be predicted can be determined. By taking the constructed well with higher production as the candidate well, the engineering parameters of the candidate well can be used as a reference to determine the design ranges of the engineering parameters of the well to be predicted, and the process optimization of construction parameters can be realized.

[0118] Step S7: Calculate the range of the geological engineering comprehensive index of the well to be predicted according to the geological engineering comprehensive index formula, and perform regression in the index-production capacity regression curve based on the range of the geological engineering comprehensive index of the well to be predicted to predict the production capacity interval of the shale oil well to be predicted;

[0119] Step S8: Determine the geological engineering comprehensive index of the well to be predicted corresponding to the optimal production capacity based on the predicted production capacity interval, and determine the engineering parameters of the well to be predicted based on the geological engineering comprehensive index of the well to be predicted corresponding to the optimal production capacity and the design ranges of the engineering parameters;

[0120] Please refer to Figure 5 as shown, which is the step-by-step schematic diagram of step S8 of the present invention.

[0121] Specifically, step S8 includes:

[0122] Step S81: Determine the geological engineering comprehensive index of the well to be predicted corresponding to the optimal production capacity based on the predicted production capacity interval; wherein, the maximum production capacity in the production capacity interval is determined as the optimal production capacity;

[0123] Step S82: Determine the engineering parameters of the well to be predicted based on the geological engineering comprehensive index of the well to be predicted corresponding to the optimal production capacity and the design ranges of the engineering parameters.

[0124] By establishing a database of production capacity influencing factors and normalizing various geological engineering parameters, the present invention can accurately analyze the influence contribution of various geological engineering parameters on production, and thereby determine the main control factors, filtering out factors with little influence on production and reducing the data processing volume. By calculating the correlation between each main control factor and production capacity, a database of correlation coefficients between the main control factors and production capacity is constructed, and the weight coefficients of each main control factor are determined, and a comprehensive geological engineering index formula is constructed, which can further determine the influence of each main control factor on production, and through the comprehensive geological engineering index formula, quantitative characterization of each main control factor can be realized, achieving the integration of geological parameters and engineering parameters. By constructing an index-production capacity regression curve, the relationship between the comprehensive geological engineering index and production can be accurately reflected. By determining the design range of each engineering parameter of the well to be predicted and calculating the range of the comprehensive geological engineering index of the well to be predicted, the production capacity interval of the well to be predicted can be predicted, thereby determining the engineering parameters corresponding to the optimal production capacity, not only accurately predicting the production capacity of the well to be predicted, but also guiding the optimization of engineering parameters, realizing the optimization of the production capacity of the well to be predicted for shale oil with mudstone.

[0125] In a specific embodiment, the statistics of the main control factors of 8 constructed wells are shown in Table 1 as follows

[0126] Table 1 Statistics of the main control factors of the constructed wells

[0127]

[0128] The normalization results of each main control factor are shown in Table 2 as follows

[0129] Table 2 Statistics of the normalization results of the main control factors of the constructed wells

[0130] Hash sign A B C D E F G H Acoustic wave 0.9110 0.9657 0.1399 0.9647 0.1165 0.0000 1.0000 0.9019 Density 0.0000 0.5000 0.6250 0.5000 1.0000 0.5000 0.1250 0.6250 Nuclear magnetic total porosity 1.0000 0.2903 0.4839 0.2742 0.2097 0.1452 0.0000 0.1613 Total organic carbon content 0.0000 1.0000 0.5417 1.0000 0.7000 0.3083 0.3417 0.1083 Free hydrocarbon content 0.1784 0.9129 1.0000 0.9170 0.8880 0.5851 0.0000 0.2490 Fracture pressure 0.9285 0.4488 1.0000 0.0000 0.8808 0.9173 0.9383 0.6858 Liquid addition intensity 0.3010 0.2788 0.0000 1.0000 0.4321 0.3820 0.6378 0.7315 Sand addition intensity 0.0034 0.0000 0.1379 0.0103 0.0138 0.1172 0.1276 0.0069 Slickwater ratio 0.0000 0.8333 1.0000 0.2319 0.4928 0.1304 0.9565 0.5072

[0131] Calculate the correlation between each main control factor and production capacity according to the preset correlation coefficient calculation method, and the calculation results are as follows: nuclear magnetic total porosity = 0.6311, total organic carbon content = 0.5444, free hydrocarbon content = 0.3588, liquid addition intensity = 0.3600, acoustic wave = 0.2142, sand addition intensity = 0.0723, fracture pressure = -0.1900, density = -0.3400, slickwater ratio = -0.3800; among them, positive numbers represent the degree of positive correlation, and negative numbers represent the degree of negative correlation.

[0132] Please refer to Figure 6 as shown, which is the index-production capacity regression curve diagram of the embodiment of the present invention; calculate the weight coefficients of each main control factor on production capacity, and the results are as follows

[0133] Nuclear magnetic total porosity = 20.9%, total organic carbon content = 18.0%, free hydrocarbon content = 11.9%, liquid addition intensity = 9.6%, acoustic wave = 7.1%, sand addition intensity = 2.4%, fracture pressure = 6.3%, density = 11.3%, slickwater ratio = 12.5%.

[0134] Then the calculation formula for the comprehensive geological engineering index is as follows:

[0135]

[0136] Please refer to Figure 7 as shown, which is a comparison chart of the actual production capacity and predicted production capacity of 3 new wells to be predicted in the embodiment of the present invention. The statistical results of the main control factors of the 3 new wells to be predicted are shown in Table 3 as follows,

[0137] Table 3 Statistical table of main control factors of new wells

[0138] Well number Well I Well J Well K Nuclear magnetic total porosity, % 14.50 14.80 14.94 Total organic carbon content, % 2.38 2.41 2.00 Free hydrocarbon content, mg / g 9.86 9.13 8.40 <![CDATA[Liquid addition intensity, m 3 / m]]> 48.62 37.17 44.19 Acoustic wave, μs / ft 108.55 103.47 102.88 <![CDATA[Density, g / cm 3 > 2.48 2.50 2.50 Fracture pressure, MPa 60.42 60.70 60.40 <![CDATA[Sand addition strength, m 3 / m]]> 2.46 2.96 2.68 Slickwater ratio, % 35.80 43.90 43.27

[0139] Calculate the comprehensive geological engineering index R of the 3 new wells to be predicted. The calculation result is R I = 0.68, R J = 0.46, R K = 0.39; Substitute R I , R J , R K into the index-production capacity regression curve respectively, and the production capacities (thousand meters of oil and gas equivalent per day) of the 3 new wells to be predicted are obtained as follows: Well I predicted = 18.97 t / day, Well J predicted = 15.80 t / day, Well K predicted = 14.43 t / day;

[0140] The actual production capacities (thousand meters of oil and gas equivalent per day) of the 3 new wells to be predicted are respectively: Well I actual = 17.64 t / day, Well J actual = 14.54 t / day, Well K actual = 15.30 t / day. The comparison between the actual production capacity and predicted production capacity of the 3 new wells to be predicted is as Figure 6 shown. Then the calculated prediction errors are 7.55%, 8.64% and 5.65% respectively, and the average prediction error is only 7.28%. Therefore, the prediction result is accurate and the error is small, and the production capacity of new wells can be accurately predicted.

[0141] So far, the technical solution of the present invention has been described in combination with the preferred embodiments shown in the drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A method for predicting the production capacity of argillaceous shale oil, characterized in that Including: Step S1: Obtain a number of geological engineering parameters and corresponding production capacities of several wells that have been drilled for shale oil, as well as various geological engineering parameters of the single well to be predicted for shale oil; the geological engineering parameters include geological parameters and engineering parameters. Step S2: Establish a database of production capacity influencing factors according to the target evaluation system, and perform normalization processing on various geological engineering parameters of the wells that have been drilled for shale oil. Step S3: Determine the main control factors of the wells that have been drilled for shale oil based on the normalization results, and calculate the correlation between each main control factor and the production capacity to construct a database of correlation coefficients between the main control factors and the production capacity; the main control factors include at least one geological parameter and at least one engineering parameter. Step S4: Determine the weight coefficients of each main control factor according to the database of correlation coefficients between the main control factors and the production capacity, construct a formula for the comprehensive geological engineering index, and obtain the comprehensive geological engineering index of each well that has been drilled for shale oil. Step S5: Construct an index-production capacity regression curve based on the comprehensive geological engineering index and the corresponding production capacity of each well that has been drilled for shale oil. Step S6: Determine a design reference database for engineering parameters based on the geological parameters of the single well to be predicted for shale oil, and determine the design ranges of various engineering parameters of the single well to be predicted according to the geological engineering parameters and corresponding production capacities of each well that has been drilled for shale oil. Step S7: Calculate the range of the comprehensive geological engineering index of the single well to be predicted according to the formula for the comprehensive geological engineering index, and perform regression in the index-production capacity regression curve based on the range of the comprehensive geological engineering index of the single well to be predicted to predict the production capacity interval of the single well to be predicted for shale oil. Step S8: Determine the comprehensive geological engineering index of the single well to be predicted corresponding to the optimal production capacity based on the predicted production capacity interval, and determine the engineering parameters of the single well to be predicted based on the comprehensive geological engineering index of the single well to be predicted corresponding to the optimal production capacity and the design ranges of the engineering parameters. Wherein, the geological parameters include nuclear magnetic total porosity, total organic carbon content, free hydrocarbon content, acoustic wave, and density, and the engineering parameters include liquid addition intensity, sand addition intensity, fracture pressure, and slickwater ratio. The step S6 includes: Step S61: Determine a design reference database for engineering parameters based on the geological parameters of the single well to be predicted for shale oil. Step S62: Calculate the matching degree between the geological parameters of the single well to be predicted for shale oil and the geological parameters of each well that has been drilled for shale oil. Step S63: Determine candidate wells according to the wells that have been drilled for shale oil with each matching degree greater than the preset matching degree threshold and the production capacity greater than the preset production capacity threshold, and determine the design ranges of various engineering parameters of the single well to be predicted according to the engineering parameters of the candidate wells; wherein, the minimum value of each engineering parameter of the candidate wells is determined as the minimum value of the design range of the corresponding engineering parameter of the single well to be predicted, and the maximum value of each engineering parameter of the candidate wells is determined as the maximum value of the design range of the corresponding engineering parameter of the single well to be predicted.

2. The shale oil production capacity prediction method according to claim 1, wherein In the step S2, the normalization processing of various geological engineering parameters is carried out through the following formula: X z = (X * - X min ) / (X max - X min ), Among them, X z is the normalized result of each geological engineering parameter, and its value range is between 0 and 1; X * is the actual value of the parameter to be normalized; X max is the actual maximum value of the parameter to be normalized; X min is the actual minimum value of the parameter to be normalized.

3. The shale oil production capacity prediction method according to claim 2, wherein The step S3 includes: Step S31: Calculate the correlation coefficients and positive / negative correlations of each geological engineering parameter based on the normalization results through a preset correlation coefficient calculation method. Step S32: Determine the main control factors of the wells where shale oil has been constructed and the correlations between each main control factor and productivity according to the correlation calculation results. Step S33: Construct a database of correlation coefficients between main control factors and productivity based on the correlation coefficients and positive / negative correlations between each main control factor and productivity.

4. The method for predicting the productivity of argillaceous shale oil according to claim 3, wherein The said Step S4 includes: Step S41: Determine the weight coefficients of each main control factor on productivity based on the database of correlation coefficients between main control factors and productivity. Step S42: Construct a geological engineering comprehensive index formula according to each main control factor and the weight coefficients of each main control factor on productivity. Step S43: Obtain the geological engineering comprehensive index of each well where shale oil has been constructed based on the geological engineering comprehensive index formula.

5. The shale oil production capacity prediction method according to claim 4, wherein In the said Step S42, the geological engineering comprehensive index formula is: Among them, M1, M2, …, M i , …, M n are the weight coefficients of the main control factors that have a positive correlation with production capacity among the main control factors, where i = 1, 2, …, n; n is the number of main control factors that have a positive correlation with production capacity among the main control factors; X1, X2, …, X i , …, X n are the main control factors that have a positive correlation with production capacity among the main control factors; M n+1 , M n+2 , …, M n+j , …, M n+m are the weight coefficients of the main control factors that have a negative correlation with production capacity among the main control factors, where j = 1, 2, …, m; m is the number of main control factors that have a negative correlation with production capacity among the main control factors; X n+1 , X n+2 , …, X n+j , …, X n+m are the main control factors that have a negative correlation with production capacity among the main control factors; n + m is the number of main control factors.

6. The method for predicting the productivity of argillaceous shale oil according to claim 5, wherein The said Step S41 includes: Step S411: Based on the database of correlation coefficients between main control factors and productivity, process the correlation coefficients between each main control factor and productivity through a preset method to determine the weight coefficients of each main control factor on productivity, where the weight coefficients are calculated based on the correlation coefficients of each main control factor.

7. The shale oil production capacity prediction method according to claim 6, wherein In the said Step S411, the preset method is implemented through the following formula: Among them, M Z is the calculation result of the weight coefficient of each main control factor on production capacity, and its value range is between 0 and 1; Y is the sum of the absolute values of the correlation coefficients between each main control factor and production capacity; Y * is the actual value of the correlation coefficient between each main control factor and production capacity.

8. The method for predicting the productivity of argillaceous shale oil according to claim 7, wherein, The said Step S31 includes: Step S311: Determine a preset standard based on the productivity influence factor database. Step S312: Based on the preset standard, filter out the geological engineering parameters that do not meet the preset standard in the normalization results of each geological engineering parameter to calculate the correlation coefficients of each geological engineering parameter.

9. The shale oil production capacity prediction method according to claim 1, wherein In the said Step S62, the matching degree MP is determined through the following formula: MP = sqrt(∑ q p＝1 (YB p - EB p ) 2 ); p = 1, 2, …, q; sqrt() is a preset square root determination function; YB p is the p-th geological parameter of each well drilled in shale oil, and EB p is the p-th geological parameter of the single well to be predicted in shale oil; q is the number of geological parameters.

10. The method for predicting the productivity of argillaceous shale oil according to claim 1, wherein The said Step S8 includes: Step S81: Determine the geological engineering comprehensive index of the well to be predicted corresponding to the optimal productivity based on the predicted productivity interval; where the maximum productivity in the productivity interval is determined as the optimal productivity. Step S82: Determine the engineering parameters of the well to be predicted based on the geological engineering comprehensive index of the well to be predicted corresponding to the optimal productivity and the design range of the engineering parameters.

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