CN116305702A - Method and system for analyzing data fluctuation shale gas well yield experience decreasing curve - Google Patents
Method and system for analyzing data fluctuation shale gas well yield experience decreasing curve Download PDFInfo
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Abstract
The invention provides a method and a system for analyzing a data fluctuation shale gas well yield experience decreasing curve, wherein the method is characterized in that production dynamic data of production time, daily gas production and accumulated gas production are collected for a well to be evaluated, low-quality data of an early production fluctuation section and abnormal data caused by test errors or field processes are screened out, a relationship curve is drawn by utilizing accumulated gas production logarithm and power operation data of the production time through an accumulated gas production curve fitting step, and power parameters are adjusted to enable a linear relationship to be met; based on the coefficients of the calculation model according to the slope and intercept data of the linear straight line after fitting, a yield experience diminishing model for representing the yield diminishing characteristic of the fluctuation shale gas well and predicting future development changes is determined. By adopting the scheme, the application limitation of the shale gas well aiming at data fluctuation in the prior art can be effectively overcome, the influence of the data fluctuation is avoided, and the reliable and accurate analysis of the shale gas well yield experience decreasing curve is realized based on a concise calculation process.
Description
Technical Field
The invention relates to the technical field of shale gas development and evaluation, in particular to a method and a system for analyzing a data fluctuation shale gas well yield experience decreasing curve.
Background
Because of the complex seepage process of shale gas, the shale gas well is evaluated by a seepage mechanism and needs more parameters, and the calculation process is complex. Therefore, the production and energy characteristics of the shale gas well are evaluated by adopting an experience decrementing method which is simple and convenient in field, few in required data types and accurate in calculation result.
The conventional experience decrementing method for the shale gas well comprises an Arps decrementing method, a generalized Arps decrementing method, a power law index method, an expansion index method, a Duong method, an SEPD method and the like, corresponding application effects are obtained at different areas at home and abroad, and based on the corresponding application effects, the prediction effect of the Duong decrementing method on the shale gas field in the country is obtained by comparing the conventional decrementing analysis methods at home and abroad with the prediction effect of the conventional decrementing analysis method at home and abroad, so that the conclusion of the Duong decrementing method on the best fitting effect of the shale gas in the country is obtained;
however, the Duong decrementing method requires that the logarithm of the yield data and the logarithm of the time are firstly plotted, the fitting parameters are calculated by utilizing the slope and the intercept of the fitting straight line, then the daily gas production and the time function are plotted, and the final decrementing curve equation is obtained by utilizing the slope of the fitting straight line, so that the prediction accuracy is extremely sensitive to the data fluctuation (such as Joshi K, lee J. Comparison of various deterministic forecasting techniques in shale gas reservoirs [ C ]. Paper SPE 163870presented at the SPE Hydraulic Fracturing Technology Conference held in The Woodlands,Texas,USA,4-6February 2013); however, a large number of shale gas wells in China have frequent well closing phenomenon, the data fluctuation is severe, the method is difficult to be applied to solving by a Duong decreasing method, and the accuracy and the reliability of a calculation result cannot be ensured.
The information disclosed in the background section of the invention is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
In order to solve the problems, the invention provides a method for analyzing an empirical decreasing curve of the yield of a data fluctuation shale gas well, which utilizes the accumulation and time relation to solve the improved Duong decreasing analysis method of key parameters of the decreasing curve, and avoids the requirement of the Duong decreasing method on the continuity and stability of daily gas production data in an analysis production section. In one embodiment, the method comprises:
a production data preparation step, namely collecting production dynamic data aiming at a well to be evaluated, wherein the production dynamic data comprises production time, daily gas production and accumulated gas production;
a data arrangement step, namely eliminating data of an early production fluctuation section in the production dynamic data, and screening abnormal data caused by test errors or field processes according to a set principle to obtain the arranged production dynamic data;
a cumulative gas production curve fitting step, namely drawing a relation curve by utilizing power operation data of cumulative gas production logarithm and production time, and adjusting power parameters of the production time by taking a linear relation as a fitting target according to the curve relation;
and a decreasing model determining step, namely calculating coefficients of the model according to the slope and intercept data of the linear straight line after fitting, and determining a yield experience decreasing model for representing the yield decreasing characteristic of the fluctuation shale gas well and predicting future development change.
In particular, in one embodiment, in the production data preparation step, the production time employs a cumulative production time to control the effect of the data fluctuation feature as much as possible.
Preferably, in one embodiment, after the decremental model determining step, the method further comprises:
model optimization, namely calculating the daily production and cumulative production data of the well to be evaluated in a set period by using the constructed yield experience decreasing model, comparing the daily production and cumulative production data with actual production data in a corresponding period, and judging the fitting degree of the yield experience decreasing model;
if the fitting degree does not reach the set requirement, repeatedly starting the cumulative gas production curve fitting step, and further adjusting the power parameter until the fitting degree of the yield experience decremental model meets the set requirement.
Further, in one embodiment, in collecting production dynamics data, for a well to be evaluated for which the cumulative production time is not recorded, the cumulative production time is calculated by:
wherein, the angle mark i represents the i day, t represents the accumulated production time, and d; g represents cumulative yield, 10 4 m 3 The method comprises the steps of carrying out a first treatment on the surface of the q represents daily yield, 10 4 m 3 /d。
Further, in one embodiment, in the data sorting step, it includes: drawing a curve of daily gas production data changing along with time, identifying daily gas production data points with the degree of shifting the whole curve reaching the set requirement in the production process, and removing the daily gas production data points;
and (3) for the production data with the changed working system, only the corresponding data of the working system to be analyzed is reserved, and other production data are removed.
Specifically, in one embodiment, in the step of fitting the cumulative gas production curve, the power parameter C of the production time during the adjustment is kept in the range of-1 < C < 0.
Optionally, in one embodiment, the step of fitting the cumulative gas production curve further includes: before drawing the curve, the data are divided according to the production system, so that the data in the curve fitting process belong to the data under the same production system.
Specifically, in one embodiment, in the decreasing model determining step, an empirical decreasing model of yield is determined as shown in the following formula:
wherein m=1 to C
a＝BC
q 1 ＝BCe (A+B)
Wherein q (t) and G (t) correspond to daily and cumulative yields, a, m and q, respectively, at time t 1 And (3) for the fitting coefficient, A is the slope of the power operation data relation straight line of the accumulated gas logarithm after fitting and the production time, B is the intercept of the power operation data relation straight line of the accumulated gas logarithm after fitting and the production time, and C is the power parameter of the production time.
Based on other aspects of the method described in any one or more of the embodiments above, the present invention also provides a storage medium having stored thereon program code that can implement the method described in any one or more of the embodiments above.
Based on the application aspect of the method described in any one or more of the embodiments above, the present invention also provides a system for analyzing an empirical decline in production curve of a data fluctuating shale gas well, the system performing the method as described in any one or more of the embodiments above.
Compared with the closest prior art, the invention has the following beneficial effects:
the invention provides a method and a system for analyzing a data fluctuation shale gas well yield experience decreasing curve, wherein the method is used for collecting production dynamic data of production time, daily gas production and accumulated gas production aiming at a well to be evaluated, and screening out low-quality data of an early production fluctuation section and abnormal data caused by test errors or field processes; the adopted parameters belong to actual measurement data in development operation, the parameters are calculated without the help of an intermediate theory, the reliability of a data source is higher, the interference of low-quality data or abnormal data on the calculation accuracy is avoided, and reliable support is provided for the development of subsequent calculation on the basis of the data;
drawing a relation curve by utilizing the accumulated gas logarithm and the power operation data of the production time through the accumulated gas curve fitting step, and adjusting the power parameters to meet the linear relation; calculating coefficients of the model according to the slope and intercept data of the linear straight line after fitting; the improved yield experience decreasing analysis method for solving key parameters of decreasing curve by utilizing the relation between cumulative yield and time avoids the rigid requirement for continuity and stability of daily gas production data in analysis production section, overcomes the application limitation of the shale gas well aiming at data fluctuation in the prior art, ensures the prediction precision of the shale gas well causing data fluctuation due to frequent well closing and the like, and obtains the accurate decreasing rule curve of daily yield and cumulative yield.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention, without limitation to the invention. In the drawings:
FIG. 1 is a flow chart of a method for analyzing empirical decline in production curves of a data fluctuating shale gas well according to one embodiment of the present invention;
FIG. 2 is a flowchart detailing the method of analyzing the empirical decline in production curve of a data fluctuating shale gas well in accordance with another embodiment of the present invention;
FIG. 3 is a graph of production dynamics data illustrating a method of analyzing an empirical decline in production curve for a data fluctuating shale gas well provided by an embodiment of the present invention;
FIG. 4 is an exemplary graph of results of a conventional analysis method for a method of analyzing a data fluctuation shale gas well production experience decline curve provided by a further embodiment of the present invention;
FIG. 5 is a schematic diagram of cumulative production time versus daily gas production and cumulative gas production for a method for analyzing a data fluctuation shale gas well yield empirical decay curve provided by an embodiment of the present invention;
FIG. 6 is a graph LnG and t illustrating a method for analyzing empirical decline in production of a data fluctuating shale gas well in accordance with one embodiment of the present invention C Linearly fitting the plate;
FIG. 7 is a graph of predicted cumulative versus actual cumulative for a method of analyzing an empirical decline in production curve for a data fluctuating shale gas well provided by yet another embodiment of the present invention;
FIG. 8 is a schematic diagram of a system for analyzing empirical decline in production curves of a data fluctuating shale gas well in accordance with one embodiment of the present invention.
Detailed Description
The following will explain the embodiments of the present invention in detail with reference to the drawings and examples, so that the practitioner of the present invention can fully understand how to apply the technical means to solve the technical problems, achieve the implementation process of the technical effects, and implement the present invention according to the implementation process. It should be noted that, as long as no conflict is formed, each embodiment of the present invention and each feature of each embodiment may be combined with each other, and the formed technical solutions are all within the protection scope of the present invention.
Although a flowchart depicts operations as a sequential process, many of the operations can be performed in parallel, concurrently, or at the same time. The order of the operations may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figures. The processes may correspond to methods, functions, procedures, subroutines, and the like.
The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items. When an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may be present.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
Because of the complex seepage process of shale gas, the shale gas well is evaluated by a seepage mechanism and needs more parameters, and the calculation process is complex. Therefore, the production and energy characteristics of the shale gas well are evaluated by adopting an experience decrementing method which is simple and convenient in field, few in required data types and accurate in calculation result.
The currently common experience decrementing methods of shale gas wells comprise Arps decrementing and generalized Arps decrementing methods, power law exponent methods, expansion exponent methods, duong methods, SEPD methods and the like, which all obtain better application effects in different domestic and foreign areas, wangke et al (Wangke, li Haitao, li Liujie, et. 3 common shale gas well experience decrementing methods) -taking Sichuan basin Wifar blocks as an example [ J ]. Natural gas earth science, 2019,30 (7): 946-954.). Compared with the predictive effects of domestic and foreign common decrementing analysis methods in Fuling shale gas fields, the conclusion that the Duong decrementing method has the best fitting effect on shale gas in China is obtained because the Duong decrementing method is not summarized on the actual seepage law of a large number of gas wells like other experience decrementing methods, but is derived according to the actual seepage law of crack flow, in particular, the Duong decrementing method is based on the actual production data of a large number of shale gas, the ratio and the accumulated production time are found to be in a linear relation in a double logarithmic system, the fact that the crack flow is a main crack flow is a linear matrix in the state of the occurrence of the gas well, the crack flow is a main development state is difficult to occur in a steady state, and the crack flow is difficult to develop in a radial state, and the crack flow is a main, the crack flow is a main state is a good, the crack flow is a main, and a good is a good, a condition is a good in a state is a low.
However, the Duong decreasing method needs to map the logarithm Ln (q/Gp) of the yield data and the logarithm Lnt of the time first, calculate the fitting parameters a and m by using the slope and intercept of the fitting straight line, then map the daily gas production q and the time function t (a, m), and obtain the final decreasing curve equation by using the slope of the fitting straight line, so that the prediction accuracy is extremely sensitive to the data fluctuation (Joshi K, lee j. Compison of various deterministic forecasting techniques in shale gas reservoirs [ C ]. Paper SPE 163870presented at the SPE Hydraulic Fracturing Technology Conference held in The Woodlands,Texas,USA,4-6february 2013), while the frequent well closing phenomenon exists in a large number of shale gas wells in China, and the data fluctuation is severe, so that the method is difficult to be suitable for solving by the Duong decreasing method.
Aiming at the problems that the Duong decreasing method is extremely sensitive to data fluctuation and is difficult to be suitable for shale gas wells with severe data fluctuation in China, the invention provides a method for analyzing the yield experience decreasing curve of the shale gas wells with data fluctuation.
The detailed flow of the method of embodiments of the present invention is described in detail below based on the attached drawing figures, where the steps shown in the flowchart of the figures may be performed in a computer system containing, for example, a set of computer executable instructions. Although a logical order of steps is depicted in the flowchart, in some cases the steps shown or described may be performed in a different order than presented.
Example 1
Fig. 1 is a flow chart of a method for analyzing an empirical decreasing curve of the production of a data fluctuation shale gas well according to an embodiment of the present invention, and referring to fig. 1, the method comprises the following steps.
A production data preparation step, namely collecting production dynamic data aiming at a well to be evaluated, wherein the production dynamic data comprises production time, daily gas production and accumulated gas production;
a data arrangement step, namely eliminating data of an early production fluctuation section in the production dynamic data, and screening abnormal data caused by test errors or field processes according to a set principle to obtain the arranged production dynamic data;
a cumulative gas production curve fitting step, namely drawing a relation curve by utilizing power operation data of cumulative gas production logarithm and production time, and adjusting power parameters of the production time by taking a linear relation as a fitting target according to the curve relation;
and a decreasing model determining step, namely calculating coefficients of the model according to the slope and intercept data of the linear straight line after fitting, and determining a yield experience decreasing model for representing the yield decreasing characteristic of the fluctuation shale gas well and predicting future development change.
Based on the implementation logic in the embodiment, the key parameters of the decreasing model are acquired by utilizing the relation between cumulative production and time, so that improved yield experience decreasing analysis is realized, the influence of data fluctuation on calculation accuracy is avoided, and support is better provided for representing the decreasing characteristics of the fluctuation shale gas well yield and predicting future development changes in actual engineering.
In practical application, in a preferred embodiment, after the step of determining the decremental model, the method further comprises:
model optimization, namely calculating the daily production and cumulative production data of the well to be evaluated in a set period by using the constructed yield experience decreasing model, comparing the daily production and cumulative production data with actual production data in a corresponding period, and judging the fitting degree of the yield experience decreasing model;
if the fitting degree does not reach the set requirement, repeatedly starting the cumulative gas production curve fitting step, and further adjusting the power parameter until the fitting degree of the yield experience decremental model meets the set requirement.
FIG. 2 is a detailed flow chart of an implementation of a method for analyzing an empirical decline curve of the production of a data fluctuation shale gas well, which is provided by the embodiment of the invention, and further, as shown in FIG. 2, for the collected production time, the accumulated production time is adopted as much as possible due to the specificity of the data fluctuation gas well; thus, in one embodiment, in the production data preparation step, the production time employs a cumulative production time to control the effect of the data fluctuation feature as much as possible.
Specifically, in one embodiment, in the process of collecting production dynamics data, for a well to be evaluated for which the cumulative production time is not recorded, the cumulative production time is calculated by:
wherein, the angle mark i represents the i day, t represents the accumulated production time, and d; g represents cumulative yield, 10 4 m 3 The method comprises the steps of carrying out a first treatment on the surface of the q represents daily yield, 10 4 m 3 /d。
In order to ensure the effectiveness of the collected production dynamic data, researchers design to screen the production data before formal input calculation, remove early production fluctuation segments and data abnormal points caused by test errors, field processes and the like, and remove data points with overlarge integral curve of daily gas production non-reason deviation in the production process by drawing daily gas production change data, so that the invention has the following steps: in one embodiment, in the data sort step, it includes: drawing a curve of daily gas production data changing along with time, identifying daily gas production data points with the degree of shifting the whole curve reaching the set requirement in the production process, and removing the daily gas production data points;
in addition, the method further comprises the steps of: and (3) for the production data with the changed working system, only the corresponding data of the working system to be analyzed is reserved, and other production data are removed.
Further, through the cumulative gas production curve fitting step, a relation curve is drawn by utilizing the cumulative gas production logarithm and the power operation data of the production time, the power parameters of the production time are adjusted by taking the curve relation to meet the linear relation as a fitting target, and in actual application, the cumulative gas production logarithm LnG and the power t of the production time are utilized C Drawing a relation curve, and adjusting the value of the coefficient C to change the fitting relation into a linear relation; setting a value of C manually by a technical worker at the beginning, and then gradually adjusting;
specifically, in general, the larger the power parameter value and the 0 deviation, the larger the calculation result error is at a number (e.g., -0.03, -0.2, etc.) smaller than 0 but close to 0. Therefore, in a preferred embodiment, in the step of fitting the cumulative gas production curve, the power parameter C of the production time during the adjustment is kept in the range of-1 < C <0, and the coefficient C is prevented from being larger than zero or excessively large from zero difference during the adjustment.
Further, considering that when there are multiple production regimes in the fitting data, the final fitting image will have multiple straight lines corresponding to it, in one embodiment, the design further includes in the cumulative gas production curve fitting step: before drawing the curve, the data are divided according to the production system, so that the data in the curve fitting process belong to the data under the same production system, and the unification of the working system of the analysis production section before fitting is ensured.
After fitting to the cumulative gas production curve meets the linear relation, starting a decremental model determining step, and determining a yield experience decremental model for representing the yield decremental characteristic of the fluctuating shale gas well and predicting future development change according to the slope of the linear straight line after fitting and the coefficient of the intercept data calculation model;
specifically, in one time embodiment, the final obtained yield empirical reduction model can be represented by the following formula:
wherein each fitting coefficient in the decreasing model can be determined by:
m＝1-C
a＝BC
q 1 ＝BCe (A+B)
wherein q (t) and G (t) respectively correspond to daily output and cumulative output at the moment t, a, m and q1 are fitting coefficients, A is the slope of a power operation data relation straight line of the cumulative gas production logarithm after fitting and the production time, B is the intercept of the power operation data relation straight line of the cumulative gas production logarithm after fitting and the production time, and C is a power parameter of the production time.
Further, considering that the yield experience decreasing model determined by the logic is not matched with the real yield data in percentage, the invention calculates the daily yield and cumulative yield data of the well to be evaluated in a set period by using the constructed yield experience decreasing model through a model optimizing step, and compares the daily yield and cumulative yield data with the actual yield data in a corresponding period to judge the fitting degree of the yield experience decreasing model so as to verify the fitting reliability of a model curve relative to the actual yield data;
further, if there is a large error between the calculation result and the existing data, the coefficient C needs to be readjusted to obtain a fitting curve with a higher linear fitting degree (generally denoted by R2), so there are: if the fitting degree does not meet the set requirement, repeatedly starting the cumulative gas production curve fitting step, further adjusting power parameters until the fitting degree of the yield experience decreasing model meets the set requirement, applying the finally obtained yield decreasing model to the subsequent shale gas well development work, analyzing decreasing characteristics and predicting future development index changes.
Compared with the prior art, the method is suitable for analyzing the yield experience decreasing curve of the data fluctuation shale gas well, and the method utilizes the relation between cumulative yield and time to acquire key parameters of the decreasing curve only through one-time linear fitting, so that the Duong decreasing analysis method is improved, and the influence of data fluctuation on calculation accuracy is avoided. The method eliminates the application limitation of the Duong decreasing analysis method of the shale gas well, and well solves the problems of shale gas well yield data analysis and production index prediction of data fluctuation caused by frequent well closing and the like.
Implementation case:
the invention will be further described with reference to the accompanying drawings.
Production data of a production well A, which is a shale gas block of a Sichuan basin and causes severe fluctuation of data due to frequent well closing, is selected one year before the production well A, and is shown in a figure 3. Because of frequent well shut-in, the Duong method cannot be adopted for decreasing analysis on the A well, the relation between the daily gas production q and the time function t (a, m) is not a straight line, but is in an exponentially fast-increasing form, and linear fitting cannot be carried out, as shown in fig. 4. The method of the invention is adopted for carrying out the decremental analysis:
(1) The daily gas production, the accumulated gas production and the accumulated production time data are collected, the accumulated production time data can be directly used in the embodiment, and the relationship between the accumulated production time and the daily gas production and the accumulated gas production is shown in figure 5.
(2) And screening production data, wherein the A well is not subjected to production system change, and meanwhile, production fluctuation sections are all caused by well closing, so that no phenomenon of non-cause production data mutation exists, and data deletion is not performed.
(3) Utilizing accumulated gas log LnG and C power t of production time C When the relation curve is drawn and the adjustment coefficient c= -0.2, the data point fitting relation becomes a linear relation, as shown in fig. 6. At this point the straight line intercept a= 15.969 and the slope b= -8.066 are fitted.
(4) Calculating a fitting coefficient m=1.2; a= 1.6132; q 1 = 4364.33, whereby the a-well yield and cumulative yield experience decreasing model was obtained as:
(5) And (3) calculating the comparison of the cumulative yield and the actual cumulative yield again by using the model, and completely fitting the previous annual production data to obtain a decreasing model, namely a final A well decreasing model.
The predicted daily yield and cumulative yield of the well A for the next 3 years are calculated, compared with the actual production characteristics of the well A, the result shows that the daily yield after 3 years is predicted to be 14630 square per day, the cumulative yield is 1043 square per square, the actual cumulative yield of the well A is 1020 square per square, the fitting effect is good, and the result is proved to be reliable, as shown in figure 7.
For the foregoing method embodiments, for simplicity of explanation, the methodologies are shown as a series of acts, but one of ordinary skill in the art will appreciate that the present invention is not limited by the order of acts, as some steps may, in accordance with the present invention, occur in other orders or concurrently. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present invention.
It should be noted that in other embodiments of the present invention, the method may also be used to obtain a new yield empirical reduction curve analysis method by combining one or more of the above embodiments to achieve optimization of shale gas well development efforts.
It should be noted that, based on the method in any one or more of the foregoing embodiments of the present invention, the present invention further provides a storage medium, where a program code is stored on the storage medium, where the program code can implement the method in any one or more of the foregoing embodiments, where the program code, when executed by an operating system, can implement the method for analyzing the empirical decreasing curve of production of a data fluctuation shale gas well as described above.
Example two
The method is described in detail in the embodiments disclosed above, and may be implemented by various forms of apparatus or systems, and therefore, based on other aspects of the method described in any one or more of the embodiments, the present invention also provides a system for analyzing a data-carrying out the method for analyzing a data-carrying shale gas well yield empirical degradation curve described in any one or more of the embodiments. Specific examples are given below for details.
Specifically, a schematic structural diagram of a system for analyzing an empirical decline curve of production of a data fluctuation shale gas well provided in an embodiment of the invention is shown in fig. 8, and the system comprises:
a production data preparation module 81 configured to collect production dynamic data for the well to be evaluated, including production time, daily gas production, cumulative gas production;
the data sorting module 83 is configured to sort out the data of the early production fluctuation section in the production dynamic data, sort out abnormal data caused by test errors or field processes according to a set principle, and obtain sorted production dynamic data;
the cumulative gas production curve fitting module 85 is configured to draw a relation curve by using the cumulative gas production logarithm and the power operation data of the production time, and adjust the power parameter of the production time by taking the curve relation as a fitting target and meeting the linear relation;
a decremental model determination module 87 configured to determine a yield empirical decremental model for characterizing the fluctuating shale gas well yield decremental features and predicting future development variations based on the slope and intercept data of the fitted linear straight line calculation model.
In one embodiment, the production data preparation module is configured to collect the cumulative production time as a production time investment calculation to control as much as possible the impact of the data fluctuation signature.
Further, in a preferred embodiment, the system further comprises:
a curve optimization module 89 configured to calculate the daily production and cumulative production data of the well to be evaluated in the set period according to the established production experience decreasing model, compare the daily production and cumulative production data with the actual production data in the corresponding period, and judge the fitting degree of the production experience decreasing model;
if the fitting degree does not reach the set requirement, repeatedly starting the cumulative gas production curve fitting step, and further adjusting the power parameter until the fitting degree of the yield experience decremental model meets the set requirement.
Specifically, in one embodiment, in the process of collecting production dynamics data, for a well to be evaluated for which the cumulative production time is not recorded, the cumulative production time is calculated by:
wherein i represents the i-th day, t represents the accumulated production time, and d; g represents cumulative yield, 10 4 m 3 The method comprises the steps of carrying out a first treatment on the surface of the q represents daily yield, 10 4 m 3 /d。
Preferably, in one embodiment, the data sort module is specifically configured to perform the following operations:
drawing a curve of daily gas production data changing along with time, identifying daily gas production data points with the degree of shifting the whole curve reaching the set requirement in the production process, and removing the daily gas production data points;
and (3) for the production data with the changed working system, only the corresponding data of the working system to be analyzed is reserved, and other production data are removed.
Further, in one embodiment, the power parameter C of the production time is set to remain within a range of less than 0 and greater than-1 during the adjustment of the power parameter by the cumulative gas production curve fitting module.
In an alternative embodiment, the cumulative gas production curve fitting module is further configured to: before drawing the curve, the data are divided according to the production system, so that the data in the curve fitting process belong to the data under the same production system.
Preferably, in one embodiment, the decreasing model determining module specifically determines a decreasing model of the yield experience as shown in the following formula:
wherein m=1 to C
a＝BC
q 1 ＝BCe (A+B)
Wherein q (t) and G (t) correspond to daily and cumulative yields, a, m and q, respectively, at time t 1 And (3) for the fitting coefficient, A is the slope of the power operation data relation straight line of the accumulated gas logarithm after fitting and the production time, B is the intercept of the power operation data relation straight line of the accumulated gas logarithm after fitting and the production time, and C is the power parameter of the production time.
In the system for analyzing the data fluctuation shale gas well yield experience decreasing curve, each module or unit structure can independently operate or operate in a combined mode according to actual analysis requirements and calculation requirements so as to achieve corresponding technical effects.
It is to be understood that the disclosed embodiments are not limited to the specific structures, process steps, or materials disclosed herein, but are intended to extend to equivalents of these features as would be understood by one of ordinary skill in the relevant arts. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.
Reference in the specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Thus, the appearances of the phrase "one embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment.
Although the embodiments of the present invention are described above, the embodiments are only used for facilitating understanding of the present invention, and are not intended to limit the present invention. Any person skilled in the art can make any modification and variation in form and detail without departing from the spirit and scope of the present disclosure, but the scope of the present disclosure is still subject to the scope of the appended claims.
Claims (10)
1. A method of analyzing an empirical decay curve of data fluctuating shale gas well production, the method comprising:
a production data preparation step, namely collecting production dynamic data aiming at a well to be evaluated, wherein the production dynamic data comprises production time, daily gas production and accumulated gas production;
a data arrangement step, namely eliminating data of an early production fluctuation section in the production dynamic data, and screening abnormal data caused by test errors or field processes according to a set principle to obtain the arranged production dynamic data;
a cumulative gas production curve fitting step, namely drawing a relation curve by utilizing power operation data of cumulative gas production logarithm and production time, and adjusting power parameters of the production time by taking a linear relation as a fitting target according to the curve relation;
and a decreasing model determining step, namely calculating coefficients of the model according to the slope and intercept data of the linear straight line after fitting, and determining a yield experience decreasing model for representing the yield decreasing characteristic of the fluctuation shale gas well and predicting future development change.
2. The method of claim 1, wherein in the production data preparation step, the production time employs a cumulative production time to control the effect of the data fluctuation feature as much as possible.
3. The method of claim 1, further comprising, after the decrementing model determining step:
model optimization, namely calculating the daily production and cumulative production data of the well to be evaluated in a set period by using the constructed yield experience decreasing model, comparing the daily production and cumulative production data with actual production data in a corresponding period, and judging the fitting degree of the yield experience decreasing model;
if the fitting degree does not reach the set requirement, repeatedly starting the cumulative gas production curve fitting step, and further adjusting the power parameter until the fitting degree of the yield experience decremental model meets the set requirement.
4. The method of claim 1, wherein in collecting production dynamics data, for a well to be evaluated for which no cumulative production time is recorded, the cumulative production time is calculated by:
wherein i represents the i-th day, t represents the accumulated production time, and d; g represents cumulative yield, 10 4 m 3 The method comprises the steps of carrying out a first treatment on the surface of the q represents daily yield, 10 4 m 3 /d。
5. The method according to claim 1, characterized in that in the data sort step, it comprises:
drawing a curve of daily gas production data changing along with time, identifying daily gas production data points with the degree of shifting the whole curve reaching the set requirement in the production process, and removing the daily gas production data points;
and (3) for the production data with the changed working system, only the corresponding data of the working system to be analyzed is reserved, and other production data are removed.
6. The method according to claim 1, characterized in that in the cumulative gas production curve fitting step the power parameter C of the production time is kept in the range-1 < C <0 during the adjustment.
7. The method of claim 1 or 6, wherein the step of fitting the cumulative gas production curve further comprises: before drawing the curve, the production dynamic data are divided according to the production system, so that the data in the curve fitting process belong to the data under the same production system.
8. The method according to claim 1, wherein in the decreasing model determining step, an empirical decreasing model of yield is determined as shown in the following formula:
wherein m=1 to C
a＝BC
q 1 ＝BCe (A+B )
Wherein q (t) and G (t) correspond to daily and cumulative yields, a, m and q, respectively, at time t 1 And (3) for the fitting coefficient, A is the slope of the power operation data relation straight line of the accumulated gas logarithm after fitting and the production time, B is the intercept of the power operation data relation straight line of the accumulated gas logarithm after fitting and the production time, and C is the power parameter of the production time.
9. A storage medium having stored thereon program code for implementing the method of any of claims 1-8.
10. A system for analyzing an empirical decline in production of a data fluctuating shale gas well, wherein the system performs the method of any of claims 1-8.
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