Method for rapidly predicting broken lake basin shale oil well EUR

Abstract

The invention relates to a method for quickly predicting the EUR of a broken lake basin shale oil well, which comprises the following steps of: establishing a chart of which the ordinate is yield qa under unit pressure drop and the abscissa is material balance time lgtm through data, determining a function relationship between the yield qa under the unit pressure drop and the material balance time lgtm according to the established chart, and fitting the function relationship; obtaining a function relationship between the shale oil well daily output q and the production time t; the relational expression between the shale oil well cumulative yield and the production time is obtained, when the oil well yield reaches the waste yield, the corresponding production time at the moment is obtained, and the oil well cumulative yield Q at the moment is the oil well EUR; the method for rapidly predicting the broken lake basin shale oil well EUR is easy to operate, rapid, effective and easy to popularize and use, and shale oil well EUR evaluation can be rapidly completed.

Description

Technical Field

[0001] The present invention belongs to the technical field of exploration and development of petroleum and natural gas geological resources, and in particular relates to a method for rapidly predicting the EUR of shale oil wells in fault lake basins. Background Art

[0002] Oil and natural gas are important national strategic resources and the lifeblood of the national economy. Estimating the ultimate recoverable reserves (EUR) of individual wells is a key research topic in shale oil development programs. The most widely used model for predicting EUR for shale oil wells is the modified hyperbolic decline model. This model uses a super-hyperbolic decline model (i.e., b > 1) for production prediction in the first phase of shale oil and gas production, and an exponential decline model in the second phase. The key parameters for predicting EUR for individual wells using the hyperbolic decline model include the initial point, initial and final decline rates, limiting production rate, and the decline exponent. The initial point and decline rate are determined based on current production conditions, while the limiting production rate is calculated based on local economic indicators. The decline exponent, b, has high uncertainty and requires long-term tracking and analysis over a long production cycle, significantly impacting the EUR of individual wells. Consequently, there is currently a lack of evaluation methods that can rapidly predict the EUR of shale oil wells, particularly those targeting shale oil wells in fault-depression lake basins. Summary of the Invention

[0003] The present invention solves the technical problem by adopting the following technical solutions:

[0004] The method for rapidly predicting the EUR of shale oil wells in fault lake basins includes the following steps:

[0005] Obtain the original production data of a single shale oil well and process the data;

[0006] The processed data is used to establish the vertical axis as the output q under unit pressure drop a , the horizontal axis is the material balance time lgt m According to the established chart, the output q under unit pressure drop is determined a Equilibrium time with material lgt m The functional relationship between them is found and the functional relationship is fitted;

[0007] in,

[0008] The output per unit pressure drop is:

[0009] Material balance time is:

[0010] q is the daily production of shale oil wells; p i is the initial formation pressure; p wf is the bottom hole flowing pressure; Q is the cumulative production of the oil well;

[0011] Get p i -p wf The functional relationship between the daily output q of shale oil wells and the production time t is obtained;

[0012] Obtain the relationship between the cumulative production of shale oil wells and production time. When the oil well production reaches the abandoned production, obtain the corresponding production time at this time. The cumulative production Q of the oil well at this time is the EUR of the oil well.

[0013] Furthermore, the output q under unit pressure drop is determined based on the established chart. a Equilibrium time with material lgt m The functional relationship between them is an exponential relationship, an exponential equation is established, and the functional relationship is fitted using the processed data to obtain the fitting parameters A and B in the exponential equation;

[0014] The exponential equation is:

[0015]

[0016] Where: A and B are fitting parameters and are dimensionless.

[0017] Furthermore, the functional relationship between the daily production q of shale oil wells and the production time t is expressed by a pan-exponential production decline model with m = 0.5, and the model is processed to obtain the initial theoretical daily production q of shale oil wells i and fitting parameter c;

[0018] Among them, the pan-exponential yield decline model is:

[0019]

[0020] Among them, q i is the initial theoretical daily production of shale oil well; c is the fitting parameter.

[0021] Furthermore, p i -p wf The functional relationship between θ and the production time t adopts a logarithmic relationship, and the processed data is used to fit the logarithmic relationship to obtain fitting parameters C and D in the logarithmic relationship;

[0022] Wherein, the logarithmic relationship is:

[0023] p i -p wf =C ln(t)+D (4)

[0024] In the formula: C, D are fitting parameters.

[0025] Furthermore, the pan-exponential production decline model is processed to obtain the initial theoretical daily production of shale oil wells q i And the method of fitting parameter c is:

[0026] Taking the natural logarithm of the pan-exponential yield decline model, we get the linear regression relationship:

[0027] ln q=α-βt 0.5 (5)

[0028] α=ln q i (6)

[0029]

[0030] Where: α, β are fitting parameters;

[0031] And use the processed data to fit lnq and t 0.5 The relationship is used to obtain the initial theoretical daily production of shale oil wells q i and fitting parameter c.

[0032] Furthermore, the relationship between the cumulative production of shale oil wells and production time is obtained as follows:

[0033]

[0034] Furthermore, the original production data includes original formation pressure, production time, oil pressure, casing pressure, bottom hole flowing pressure, and daily oil production.

[0035] Furthermore, the original production data needs to be obtained from shale oil wells in the lake basin that have been in production for more than half a year and have a clear decreasing trend.

[0036] Furthermore, when processing the acquired raw production data of a single shale oil well, it is necessary to remove the shut-in points in the production data.

[0037] The advantages and positive effects of the present invention are:

[0038] The present invention provides a method for quickly predicting the EUR of shale oil wells in fault lake basins. The method is simple to operate, fast and effective, easy to promote and use, and can quickly complete the EUR evaluation of shale oil wells. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The technical solutions of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. However, it should be understood that these drawings are designed for illustrative purposes only and are not intended to limit the scope of the present invention. Furthermore, unless otherwise specified, these drawings are intended only to conceptually illustrate the structures described herein and are not necessarily drawn to scale.

[0040] Figure 1 A schematic flow chart of a method for rapidly predicting EUR of shale oil wells in a fault lake basin provided by an embodiment of the present invention;

[0041] Figure 2 This is the production curve provided by Example 2 of the present invention;

[0042] Figure 3 The production curve provided in Example 2 of the present invention is a production diagram after removing the shut-in points;

[0043] Figure 4 To utilize Figure 3 The vertical axis of the data is the output q under unit pressure drop a , the horizontal axis is the material balance time lgt m 's picture;

[0044] Figure 5 for Figure 4 Fitting curve of the mid-exponential part;

[0045] Figure 6 The pressure drop p provided in Example 2 of the present invention is i -p wf The relationship between time t and fitting curve;

[0046] Figure 7 lnq and t provided in Example 2 of the present invention 0.5 Relationship fitting curve; DETAILED DESCRIPTION

[0047] First of all, it should be noted that the specific structure, characteristics and advantages of the present invention will be specifically described below in an exemplary manner. However, all descriptions are only used for illustration and should not be understood as limiting the present invention. In addition, any single technical feature described or implied in the embodiments mentioned herein, or any single technical feature displayed or implied in the drawings, can still be combined or deleted between these technical features (or their equivalents) to obtain more other embodiments of the present invention that may not be directly mentioned herein. In addition, in order to simplify the drawings, the same or similar technical features may be marked in only one place in the same drawing.

[0048] It should be noted that, unless there is any conflict, the embodiments and features in the embodiments of this application can be combined with each other.

[0049] Example 1

[0050] The method provided in this embodiment for rapidly predicting the EUR of a shale oil well in a fault lake basin includes the following steps:

[0051] Obtain the original production data of a single shale oil well and process the data;

[0052] The processed data is used to establish the vertical axis as the output q under unit pressure drop a , the horizontal axis is the material balance time lgt m According to the established chart, the output q under unit pressure drop is determined a Equilibrium time with material lgt m The functional relationship between them is found and the functional relationship is fitted;

[0053] in,

[0054] The output per unit pressure drop is:

[0055] Material balance time is:

[0056] q is the daily production of shale oil wells; p i is the initial formation pressure; p wf is the bottom hole flowing pressure; Q is the cumulative production of the oil well;

[0057] Get p i -p wf The functional relationship between the daily output q of shale oil wells and the production time t is obtained;

[0058] Obtain the relationship between the cumulative production of shale oil wells and production time. When the oil well production reaches the abandoned production, obtain the corresponding production time at this time. The cumulative production Q of the oil well at this time is the EUR of the oil well.

[0059] Specifically, the output q under unit pressure drop is determined according to the established chart. a Equilibrium time with material lgt m The functional relationship between them is an exponential relationship, an exponential equation is established, and the functional relationship is fitted using the processed data to obtain the fitting parameters A and B in the exponential equation;

[0060] The exponential equation is:

[0061]

[0062] Where: A and B are fitting parameters and are dimensionless.

[0063] In addition, the functional relationship between the daily production q of shale oil wells and the production time t is expressed by a pan-exponential production decline model with m = 0.5, and the model is processed to obtain the initial theoretical daily production q of shale oil wells i and fitting parameter c;

[0064] Among them, the pan-exponential yield decline model is:

[0065]

[0066] Among them, q i is the initial theoretical daily production of shale oil well; c is the fitting parameter.

[0067] Can be considered, p i -p wf The functional relationship between θ and the production time t adopts a logarithmic relationship, and the processed data is used to fit the logarithmic relationship to obtain fitting parameters C and D in the logarithmic relationship;

[0068] Wherein, the logarithmic relationship is:

[0069] p i -p wf =C ln(t)+D (4)

[0070] In the formula: C, D are fitting parameters.

[0071] The pan-exponential production decline model is processed to obtain the initial theoretical daily production of shale oil wells q i And the method of fitting parameter c is:

[0072] Taking the natural logarithm of the pan-exponential yield decline model, we get the linear regression relationship:

[0073] ln q=α-βt 0.5 (5)

[0074] α=ln q i (6)

[0075]

[0076] Where: α, β are fitting parameters;

[0077] And use the processed data to fit lnq and t 0.5 The relationship is used to obtain the initial theoretical daily production of shale oil wells q i and fitting parameter c.

[0078] From equations (1), (2), (3), (4), and (8), the relationship between the cumulative production of shale oil wells and production time is obtained as follows:

[0079]

[0080] When the oil well production reaches the abandoned production, the corresponding production time t is calculated by formula (3). The cumulative production Q of the oil well at this time is the EUR of the oil well.

[0081] Example 2

[0082] This embodiment provides a method for quickly predicting the EUR of shale oil wells in fault lake basins. The operation is simple and efficient, and the method is of great significance for the formulation of shale oil development plans in fault lake basins.

[0083] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0084] Take the EUR prediction analysis of Well G1 in Bohai Bay Basin as an example. Figure 1 As shown, the specific steps include:

[0085] The first step is to collect production performance data for Well G1. This data includes initial formation pressure, production time (number of days), oil pressure, casing pressure, bottomhole flowing pressure, and daily oil production. The production performance data must be collected for Well G1 for a period of more than six months and show a clear decreasing trend.

[0086] Step 2: Draw Figure 2 The production curve shown is the output q under unit pressure drop on the vertical axis. a , the horizontal axis is the material balance time lgt m , and remove the shut-in points in the production data. After removing the shut-in points, the production curve is as follows Figure 3 As shown;

[0087] The third step is to process the production data after removing the shut-in point according to formulas (1) and (2), and plot the vertical axis as the production q under unit pressure drop. a , the horizontal axis is lgt m The output-material balance time chart under unit pressure drop is as follows: Figure 4 As shown;

[0088] Step 4: Interception Figure 4 The exponential part of the data points is fitted, such as Figure 5 As shown, the parameters A=147.59 and B=2.283 are determined;

[0089] Step 5: Draw the pressure drop p i -p wf The relationship curve with time t, the fitting formula is as follows: Figure 6 As shown, the parameters C = 3.6717, D = 19.547 are determined;

[0090] Step 6: Use the production data without the shut-in point to plot lnq and t 0.5 Relationship, fitting formula, such as Figure 7 As shown, the parameters α = 3.9555, β = 0.0767 are determined, and according to formulas (6) and (7), q is obtained. i =52.22, c=13.04;

[0091] Step 7: Substitute the above parameter values ​​into formula (9). Given the abandoned production of well G1, q ab is 0.4t / d, which corresponds to production time t=4036d, and the initial formation pressure p i is 50.05MPa, and the EUR is 8032.543t.

[0092] The above embodiments describe the present invention in detail, but the contents described are only preferred embodiments of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for rapidly predicting EUR of shale oil wells in fault lake basins, characterized in that: The steps include: Obtain the original production data of a single shale oil well and process the data; The processed data is used to establish the vertical axis as the unit pressure drop output q a , the horizontal axis is the material equilibrium time lgt m According to the established chart, the output q under unit pressure drop is determined a Equilibrium time with material lgt m The functional relationship between them is obtained and the functional relationship is fitted; in, The output per unit pressure drop is: The material balance time is: q is the daily production of shale oil wells; p i is the initial formation pressure; p wf is the bottom hole flowing pressure; Q is the cumulative production of the oil well; Get p i -p wf The functional relationship between the daily production q of shale oil wells and the production time t is obtained; Obtain the relationship between the cumulative production of shale oil wells and production time. When the production of the oil well reaches the abandoned production, obtain the corresponding production time at this time. The cumulative production Q of the oil well at this time is the EUR of the oil well.

2. The method for rapidly predicting EUR of shale oil wells in fault lake basins according to claim 1, characterized in that: Determine the output q per unit pressure drop based on the established chart a Equilibrium time with material lgt m The functional relationship between them is an exponential relationship, an exponential equation is established, and the functional relationship is fitted using the processed data to obtain fitting parameters A and B in the exponential equation; The exponential equation is: Where: A and B are fitting parameters and are dimensionless.

3. The method for rapidly predicting EUR of shale oil wells in fault lake basins according to claim 1 is characterized by: The functional relationship between the daily production q of shale oil wells and the production time t is expressed by a pan-exponential production decline model with m = 0.5, and the model is processed to obtain the initial theoretical daily production q of shale oil wells i and fitting parameter c; Among them, the pan-exponential yield decline model is: Among them, q i is the initial theoretical daily production of shale oil well; c is the fitting parameter.

4. The method for rapidly predicting EUR of shale oil wells in fault lake basins according to claim 1 is characterized by: p i -p wf The functional relationship between the production time t and the production time t adopts a logarithmic relationship, and the logarithmic relationship is fitted using the processed data to obtain fitting parameters C and D in the logarithmic relationship; Wherein, the logarithmic relationship is: p i -p wf =Cln(t)+D (4) In the formula: C, D are fitting parameters.

5. The method for rapidly predicting EUR of shale oil wells in fault lake basins according to claim 3 is characterized by: The pan-exponential production decline model is processed to obtain the initial theoretical daily production of shale oil wells q i And the method of fitting parameter c is: Taking the natural logarithm of the pan-exponential yield decline model, we get the linear regression equation: lnq=α-βt 0.5 (5) α=lnq i (6) Where: α, β are fitting parameters; And use the processed data to fit lnq and t 0.5 The relationship is used to obtain the initial theoretical daily production of shale oil wells q i and the fitting parameter c.

6. The method for rapidly predicting EUR of shale oil wells in fault lake basins according to claim 1, characterized in that: The relationship between the cumulative production of shale oil wells and production time is obtained as follows:

7. The method for rapidly predicting EUR of shale oil wells in fault lake basins according to claim 1, characterized in that: The original production data include original formation pressure, production time, oil pressure, casing pressure, bottom hole flow pressure, and daily oil production.

8. The method for rapidly predicting EUR of shale oil wells in fault lake basins according to claim 1, characterized in that: The original production data needs to be obtained from shale oil wells in the lake basin that have been in production for more than half a year and have a clear decreasing trend.

9. The method for rapidly predicting EUR of shale oil wells in fault lake basins according to claim 1, characterized in that: When processing the original production data of a single shale oil well, it is necessary to remove the well shut-in points in the production data.

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