Production prediction method and device based on time variation of reservoir parameters, equipment and medium

Abstract

This invention provides a method, apparatus, equipment, and medium for production prediction based on time-varying reservoir parameters. The method includes the following steps: S1, establishing a variable decline exponential production decline model for tight gas reservoir wells; S2, collecting historical gas production data of tight gas reservoir wells, and confirming the second undetermined characteristic parameter β and establishing a first linear model based on linear fitting of lnq and t / (lnt)β, wherein the slope of the first linear model is -α and the intercept is lnq. i Value; and S3, confirming the theoretical initial output q based on the first linear model. i The invention uses a first undetermined characteristic parameter α to predict gas well production using the variable decline exponent production decline model. This invention better reflects the production decline law of tight gas wells, is simple to operate, and has higher prediction accuracy than traditional decline models with a constant decline exponent. It can be widely used for production decline prediction in tight gas reservoirs.

Description

Technical Field

[0001] This invention relates to the field of oil and gas field exploration and development technology, and more specifically, to a production prediction method based on time-varying reservoir parameters, a production prediction device based on time-varying reservoir parameters, and equipment and computer-readable storage medium for implementing the production prediction method based on time-varying reservoir parameters. Background Technology

[0002] Currently, the Arps production decline model is widely used in the industry for gas well production prediction and recoverable reserve assessment. Different values ​​of the decline exponent *n* correspond to different decline types and production decline curves, and the production decline curve can only follow a fixed change pattern controlled by a single value of *n*. However, tight gas reservoirs exhibit significant time-varying reservoir parameters (matrix permeability, fracture conductivity, relative permeability curves, etc., change with production time), leading to a non-constant production decline exponent. This breaks the assumptions of the traditional Arps model, resulting in low accuracy in predicting production decline for tight gas reservoirs using the traditional Arps decline model with a constant decline exponent, making it difficult to meet field production needs. Therefore, it is necessary to propose a decline method under the influence of time-varying reservoir parameters to improve the accuracy of tight gas reservoir production prediction. Summary of the Invention

[0003] The purpose of this invention is to address at least one of the aforementioned shortcomings of the prior art. For example, one objective of this invention is to provide a production prediction method based on time-varying reservoir parameters to improve the accuracy of production decline prediction in tight gas reservoirs.

[0004] To achieve the above objectives, the present invention provides a production prediction method based on time-varying reservoir parameters.

[0005] The production prediction method based on time-varying reservoir parameters includes the following steps:

[0006] S1. Establish a variable decline index production decline model for tight gas reservoir wells, wherein the variable decline index production decline model includes:

[0007]

[0008] Where q(t) is the output at time t; t is time; q i The theoretical initial production rate is determined by fitting actual production data from gas wells; α is the first undetermined characteristic parameter; β is the second undetermined characteristic parameter.

[0009] S2. Collect historical gas production data of tight gas reservoir wells, and confirm the second undetermined characteristic parameter β by linear fitting based on lnq and t / (lnt)β, and establish the first linear model, in which the slope is -α and the intercept is lnq. i value.

[0010] S3. Confirm the theoretical initial output q based on the first linear model. i The gas well production is predicted using the variable declining exponential production decline model, along with the first undetermined characteristic parameter α.

[0011] In an exemplary embodiment of the production prediction method based on time-varying reservoir parameters of the present invention, step S1 may include:

[0012] S11. Establish a production decline rate model based on production time, wherein the production decline rate model includes:

[0013] D (t) =α(lnt) -β

[0014] Wherein, the D (t) Let t be the rate of decline in output.

[0015] S12. Establish a time-varying gas well decline coefficient model based on the production decline rate model, wherein the gas well decline coefficient model includes:

[0016]

[0017] Wherein, n (t) is the gas well decline coefficient at time t.

[0018] S13. Substitute the gas well decline coefficient model into the Arps decline model to obtain the variable decline index production decline model.

[0019] In an exemplary embodiment of the production prediction method based on time-varying reservoir parameters of the present invention, step S2 may further include: formatting the historical gas production data of the tight gas reservoir well in the same time format as days.

[0020] In an exemplary embodiment of the production prediction method based on time-varying reservoir parameters of the present invention, step S2 may further include: preprocessing the historical gas production data of the tight gas reservoir well.

[0021] In an exemplary embodiment of the production prediction method based on time-varying reservoir parameters of the present invention, the preprocessing includes:

[0022] Outliers and missing values ​​in the historical gas production data of the tight gas reservoir wells were removed, and the data was smoothed using a quadratic moving average method.

[0023] In an exemplary embodiment of the production prediction method based on time-varying reservoir parameters of the present invention, the variable decline exponential production decline model may further include:

[0024] In an exemplary embodiment of the production prediction method based on time-varying reservoir parameters of the present invention, the prediction accuracy of the variable decline exponential production decline model can be evaluated using an absolute error model, wherein the absolute error model may include:

[0025]

[0026] Where n is the total number of days selected, q is the actual daily production of the gas well, and q(t) is the daily production predicted by the variable decline exponential production decline model.

[0027] In another aspect, the present invention provides a production prediction device based on time-varying reservoir parameters. The production prediction device based on time-varying reservoir parameters includes a variable decreasing exponential production decreasing model module, a first linear model module, and a gas well production prediction model connected in sequence.

[0028] The variable decline index production decline model module is configured to establish a variable decline index production decline model for tight gas reservoir wells, wherein the variable decline index production decline model includes:

[0029]

[0030] Where q(t) is the output at time t; t is time; q i The theoretical initial production rate is determined by fitting actual production data from gas wells; α is the first undetermined characteristic parameter; β is the second undetermined characteristic parameter.

[0031] The first linear model module is configured to collect historical gas production data from tight gas reservoir wells, and to determine the second undetermined characteristic parameter β and establish the first linear model based on linear fitting of lnq and t / (lnt)β. The slope of the first linear model is -α, and the intercept is lnq. i value.

[0032] The gas well production prediction module is configured to confirm the theoretical initial production q based on the first linear model. i The gas well production is predicted using the variable declining exponential production decline model, along with the first undetermined characteristic parameter α.

[0033] In another aspect, the present invention provides a computer device, the computer device comprising:

[0034] Processor; memory storing a computer program that, when executed by the processor, implements the production prediction method based on time-varying reservoir parameters as described above.

[0035] In another aspect, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the production prediction method based on time-varying reservoir parameters as described above.

[0036] Compared with the prior art, the beneficial effects of the present invention include at least one of the following:

[0037] (1) This invention improves the accuracy of production decline prediction for tight gas reservoirs, adapts to different types of oil and gas reservoirs, and considers complex geological conditions, thereby improving decision support and long-term production forecasting. Classical models may not be accurate enough for unconventional oil and gas reservoirs (such as shale gas and tight gas). The extended model better fits actual production data by introducing more parameters and influencing factors. The extended Arps model adapts to the characteristics of unconventional oil and gas reservoirs. Through these improvements, the actual situation of oil and gas reservoirs can be reflected more accurately, providing more reliable decision support for oil and gas field development and management, optimizing production operations, improving economic efficiency, and helping to formulate reasonable development plans and investment strategies.

[0038] (2) This invention is based on the actual characteristic that the decline index is not constant during the production decline process of tight gas reservoirs. Based on the traditional Arps exponential decline model, it establishes a variable decline index production decline prediction model, which better conforms to the production decline law of tight gas wells. The invention theoretically elucidates a specific method for predicting production decline under the time-varying influence of reservoir parameters in tight gas reservoirs. This method is simple to operate and has higher prediction accuracy than the traditional decline model with a constant decline index. It can be widely used for production decline prediction in tight gas reservoirs. Attached Figure Description

[0039] The above and other objects and / or features of the present invention will become clearer from the following description taken in conjunction with the accompanying drawings, in which:

[0040] Figure 1 This invention illustrates an embodiment of the time-varying reservoir parameter-based production prediction method with different decline indices D. i A schematic diagram of the exponential decline curve for the given value.

[0041] Figure 2 The figure shows the fitting results of determining the parameters to be determined for the production expression of a tight gas reservoir X1 well, according to an embodiment of the production prediction method based on time-varying reservoir parameters of the present invention.

[0042] Figure 3 The figure shows a comparison between the production prediction results of a tight gas reservoir X1 well and a conventional model, based on an embodiment of the production prediction method of the present invention which is based on time-varying reservoir parameters. Detailed Implementation

[0043] In the following sections, the production prediction method, apparatus, device, and medium based on time-varying reservoir parameters of the present invention will be described in detail with reference to exemplary embodiments.

[0044] It should be noted that the terms “first,” “second,” “third,” and “S1,” “S2,” “S3,” etc. used in this invention are for ease of description and distinction only, and should not be construed as indicating or implying relative importance or used to describe a specific order or sequence.

[0045] To improve the accuracy of model applications in existing technologies, this invention establishes a variable decline exponential production decline model, thereby proposing a production decline prediction method based on tight gas reservoirs that considers the time-varying influence of reservoir parameters, with the aim of improving the accuracy of tight gas reservoir production decline prediction.

[0046] In a first exemplary embodiment of the production prediction method based on time-varying reservoir parameters of the present invention, the production prediction method based on time-varying reservoir parameters includes the following steps:

[0047] S1. Based on the traditional Arps decline model, considering the phenomenon that the production decline index of gas wells changes over time due to the influence of "time-varying reservoir parameters" during the production process of tight gas reservoirs, the production decline rate is first defined as a function of production time:

[0048] D (t) =α(lnt) -β Equation (1)

[0049] Where α and β are undetermined characteristic parameters.

[0050] From the expression for the decline rate, we can derive the expression for how the gas well decline coefficient n changes with time:

[0051]

[0052] By modifying the expression for the exponentially decreasing function using D(t) and substituting it into the traditional Arps exponentially decreasing model, we obtain:

[0053]

[0054] Where, q i The theoretical initial production rate needs to be determined by data fitting based on the actual production data of the gas well.

[0055] Further simplification of the above equation yields the variable-decreasing exponential product decline model:

[0056]

[0057] To determine the undetermined feature parameters in the model, linearizing the logarithm of both sides of the above equation yields:

[0058]

[0059] Furthermore, it can be seen from equation (5) that lnq(t) and t / (lnt) β There is a linear relationship between them.

[0060] S2. Data collection and preparation steps: Collect historical gas production data of tight gas reservoir wells and process the time format of the decreasing segment into days.

[0061] S3. Data preprocessing steps: Remove outliers and missing values ​​in gas well production, and use the quadratic moving average method to smooth the data, which can further reduce the impact of fluctuations on model fitting.

[0062] S4. Based on the actual gas production data of the gas well processed in step S3, plot the lnq-t / (lnt)β characteristic curve. By adjusting the value of β, use the least squares method to linearly fit lnq and t / (lnt)β. Stop adjusting when the fitting effect is best, thus determining the value of β. A linear expression can also be obtained:

[0063] y = -αx + lnq i Equation (6)

[0064] The slope is -α, and the intercept is lnq. i value.

[0065] S5. Based on the slope -α and lnq in the linear expression obtained in step S4 i Determine the parameter α to be determined and the theoretical initial output q. i Substituting this into equation (4) in step S1, we can obtain the specific gas well production prediction formula, which allows us to predict the decline in gas well production.

[0066] S6. Based on the obtained gas well production prediction formula, calculate the predicted gas production value at different times, and compare it with the actual production data. Evaluate the prediction accuracy of the model through absolute error. The formula for calculating absolute error is:

[0067]

[0068] Where n is the total number of days selected, q is the actual daily production of the gas well, and q(t) is the daily production predicted by the model.

[0069] In another aspect, the present invention provides a second exemplary embodiment of a production prediction device based on time-varying reservoir parameters. The production prediction device based on time-varying reservoir parameters includes a variable-decreasing exponential production decline model module, a first linear model module, and a gas well production prediction model connected in sequence.

[0070] The variable decline index production decline model module is configured to establish a variable decline index production decline model for tight gas reservoir wells. The variable decline index production decline model includes:

[0071] q(t)=q i e -αt / (lnt)β

[0072] Where q(t) is the output at time t; t is time; q i The theoretical initial production rate is determined by fitting actual production data from gas wells; α is the first undetermined characteristic parameter; β is the second undetermined characteristic parameter.

[0073] The first linear model module is configured to collect historical gas production data from tight gas reservoir wells, and to determine the second undetermined characteristic parameter β and establish the first linear model based on linear fitting of lnq and t / (lnt)β. The slope of the first linear model is -α, and the intercept is lnq. i value.

[0074] The gas well production prediction module is configured to confirm the theoretical initial production q based on the first linear model. i The first undetermined characteristic parameter α is used to predict gas well production using a variable declining exponential production decline model.

[0075] In another aspect, the present invention provides a third exemplary embodiment of a computer device. The computer device includes a processor and a memory. The memory stores a computer program. The computer program is executed by the processor, causing the processor to perform the computer program of the time-varying reservoir parameter-based production prediction method according to the present invention.

[0076] In another aspect, the present invention provides a fourth exemplary embodiment of a computer-readable storage medium storing a computer program. The computer-readable storage medium stores a computer program that, when executed by a processor, causes the processor to perform a production prediction method based on time-varying reservoir parameters according to the present invention. The computer-readable recording medium is any data storage device capable of storing data readable by a computer system. Examples of computer-readable recording media include: read-only memory, random access memory, read-only optical disk, magnetic tape, floppy disk, optical data storage device, and carrier waves (such as data transmission via the Internet through wired or wireless transmission paths).

[0077] To better understand the exemplary embodiments of the present invention described above, further descriptions are provided below in conjunction with specific embodiments and accompanying drawings, but the examples given are not intended to limit the present invention.

[0078] Example 1

[0079] In this embodiment, the production prediction method based on time-varying reservoir parameters can be implemented through the following steps:

[0080] S1: Based on the traditional Arps decline model, considering the phenomenon that the production decline index of gas wells changes over time due to the influence of "time-varying reservoir parameters" during the production process of tight gas reservoirs, the production decline rate is first defined as a function of production time:

[0081] D (t) =α(lnt) -β Equation (1)

[0082] Where α and β are undetermined characteristic parameters.

[0083] From the expression for the decline rate, we can derive the expression for how the gas well decline coefficient n changes with time:

[0084]

[0085] By modifying the expression for the exponentially decreasing function using D(t) and substituting it into the traditional Arps exponentially decreasing model, we obtain:

[0086]

[0087] Where, q i The theoretical initial production rate needs to be determined by data fitting based on the actual production data of the gas well.

[0088] Further simplification of the above equation yields the variable-decreasing exponential product decline model:

[0089]

[0090] To determine the undetermined feature parameters in the model, linearizing the logarithm of both sides of the above equation yields:

[0091]

[0092] Furthermore, it can be seen from formula (5) that lnq(t) and t / (lnt) are related. β There is a linear relationship between them.

[0093] S2: Data collection and preparation steps: Collect historical gas production data of well X1 in a tight gas reservoir and process the time format of the decreasing segment into days.

[0094] S3: Data preprocessing step, removing outliers and missing values ​​of gas production from well X1, and using the quadratic moving average method to smooth the data, which can further reduce the impact of fluctuations on model fitting.

[0095] S4: Based on the actual gas production data of well X1 processed in step S3, a lnq-t / (lnt)β characteristic curve is plotted. By adjusting the value of β, the least squares method is used to linearly fit lnq and t / (lnt)β. It is found that the fitting effect is best when β = 1.7. The linear expression can also be obtained:

[0096] y = -0.0529x - 0.0146 Equation (6)

[0097] The slope is -α, and the intercept is lnq. i value.

[0098] S5: Based on the slope -α and lnq in the linear expression obtained in step S4 i = -0.0146, thus we can obtain the parameters to be determined: α = 0.0529, β = 1.7, and the theoretical initial yield q. i =0.9855×10 4 m 3 Substituting / d into formula (4) in step S1, we can obtain the specific production prediction formula for well X1:

[0099]

[0100] S6: Based on the obtained production prediction formula for well X1, calculate the predicted gas production value at different times. Simultaneously, use the Arps model and Duong model to predict the production decline of well X1, and compare the results with actual production data. Evaluate the prediction accuracy of the model through absolute error. The formula for calculating the absolute error is:

[0101]

[0102] Where n is the total number of days selected, q is the actual daily production of the gas well, and q(t) is the daily production predicted by the model.

[0103] In this embodiment, different decreasing exponents D i The exponential decline curve for the given value is as follows: Figure 1 As shown, the fitting results for determining the parameters to be determined by the production expression of well X1 in tight gas reservoir are as follows. Figure 2 As shown, the comparison between the prediction results of the tight gas reservoir X1 well and the traditional model is as follows: Figure 3 As shown, compared with actual gas well data, the Duong model error is 22.35%; the Arps model error is 10.76%; and the production decline model error of this invention is 7.82%. It can be seen that the production decline model of this invention has significantly improved the prediction accuracy of production decline prediction in tight gas reservoirs compared with traditional models.

[0104] Although the present invention has been described above in conjunction with exemplary embodiments and accompanying drawings, those skilled in the art should understand that various modifications can be made to the above embodiments without departing from the spirit and scope of the claims.

Claims

1. A production prediction method based on time-varying reservoir parameters, characterized in that, The method includes the following steps: S1. Establish a variable decline index production decline model for tight gas reservoir wells, wherein the variable decline index production decline model includes: Where q(t) is the output at time t; t is time; q i The theoretical initial production rate is determined by fitting actual production data from gas wells; α is the first undetermined characteristic parameter; β is the second undetermined characteristic parameter. S2. Collect historical gas production data of tight gas reservoir wells, and confirm the second undetermined characteristic parameter β by linear fitting based on lnq and t / (lnt)β, and establish the first linear model, in which the slope is -α and the intercept is lnq. i Value; and S3. Confirm the theoretical initial output q based on the first linear model. i The gas well production is predicted using the variable declining exponential production decline model, along with the first undetermined characteristic parameter α.

2. The production prediction method based on time-varying reservoir parameters according to claim 1, characterized in that, Step S1 includes: S11. Establish a production decline rate model based on production time, wherein the production decline rate model includes: D (t) =α(lnt) -β Wherein, the D (t) The rate of decline in output at time t; S12. Establish a time-varying gas well decline coefficient model based on the production decline rate model, wherein the gas well decline coefficient model includes: Wherein, n (t) The gas well decline coefficient at time t; S13. Substitute the gas well decline coefficient model into the Arps decline model to obtain the variable decline index production decline model.

3. The production prediction method based on time-varying reservoir parameters according to claim 1, characterized in that, Step S2 further includes: formatting the historical gas production data of the tight gas reservoir wells in the same time format as days.

4. The production prediction method based on time-varying reservoir parameters according to claim 1, characterized in that, Step S2 further includes: preprocessing the historical gas production data of the tight gas reservoir well.

5. The production prediction method based on time-varying reservoir parameters according to claim 4, characterized in that, The preprocessing includes: Outliers and missing values ​​in the historical gas production data of the tight gas reservoir wells were removed, and the data was smoothed using a quadratic moving average method.

6. The production prediction method based on time-varying reservoir parameters according to claim 1, characterized in that, The variable declining exponential output model also includes: q(t) = 0.9855e -0.0529t / (lnt)1.7 .

7. The production prediction method based on time-varying reservoir parameters according to claim 1, characterized in that, The prediction accuracy of the variable declining exponential yield decline model is evaluated using an absolute error model, which includes: Where n is the total number of days selected, q is the actual daily production of the gas well, and q(t) is the daily production predicted by the variable decline exponential production decline model.

8. A production prediction device based on time-varying reservoir parameters, characterized in that, The time-varying reservoir parameter-based production prediction device includes a variable-decreasing exponential production decline model module, a first linear model module, and a gas well production prediction model connected in sequence. A variable-decline-index production decline model module is configured to establish a variable-decline-index production decline model for tight gas reservoir wells, wherein the variable-decline-index production decline model includes: Where q(t) is the output at time t; t is time; q i The theoretical initial production rate is determined by fitting actual production data from gas wells; α is the first undetermined characteristic parameter; β is the second undetermined characteristic parameter. The first linear model module is configured to collect historical gas production data from tight gas reservoir wells, and to determine the second undetermined characteristic parameter β and establish the first linear model based on linear fitting of lnq and t / (lnt)β. The slope of the first linear model is -α, and the intercept is lnq. i Value; and The gas well production prediction module is configured to confirm the theoretical initial production q based on the first linear model. i The gas well production is predicted using the variable declining exponential production decline model, along with the first undetermined characteristic parameter α.

9. A computer device, characterized in that, The computer device includes: At least one processor; and A memory storing program instructions configured to be executed by the at least one processor, the program instructions including instructions for executing the production prediction method based on time-varying reservoir parameters according to any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the production prediction method based on time-varying reservoir parameters as described in any one of claims 1 to 7.

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