WO2020192675A1

WO2020192675A1 - Productivity prediction method for fractured horizontal well in tight oil reservoir

Info

Publication number: WO2020192675A1
Application number: PCT/CN2020/081024
Authority: WO
Inventors: 苏玉亮; 唐梅荣; 王文东; 杜现飞; 范理尧; 马兵
Original assignee: 中国石油大学(华东); 中国石油天然气股份有限公司长庆油田分公司油气工艺研究院
Priority date: 2019-03-27
Filing date: 2020-03-25
Publication date: 2020-10-01
Also published as: CN109882163A

Abstract

Disclosed is a productivity prediction method for a fractured horizontal well in a tight oil reservoir. The method comprises: firstly, subdividing, based on a fuzzy set, fractured horizontal wells in a tight oil reservoir into different categories; secondly, determining a relationship between geological parameters, fracturing construction parameters and average daily yield peaks of different categories of horizontal wells; then determining main control parameters affecting the yields of the fractured horizontal wells in the tight oil reservoir; and finally, establishing, according to the average daily yield peaks of the fractured horizontal wells in the tight oil reservoir and corresponding main control parameter data, an average daily yield peak prediction model by means of a regression analysis method, so as to predict the productivity of a horizontal well to be fractured in the tight oil reservoir. In the method, geological factor parameters and fracturing construction parameters are combined to make a prediction result be closer to an actual situation, which can be effectively used to evaluate a fracturing effect, thereby further improving and optimizing a fracturing construction plan.

Description

A productivity prediction method for fractured horizontal wells in tight oil reservoirs

Technical field

The invention relates to the field of oil and gas field development engineering, and in particular to a productivity prediction method for fracturing horizontal wells in tight oil reservoirs.

Background technique

Tight oil reservoirs are oil reservoirs such as sandstone and limestone with overburden matrix permeability less than or equal to 0.1mD. Because tight oil reservoirs have the characteristics of tight reservoirs, complex pore-throat structures, poor physical properties, and low reservoir pressure coefficients, horizontal well fracturing technology is needed to transform them during development. The productivity prediction of fractured horizontal wells in tight oil reservoirs not only affects the optimization design of horizontal well fracturing and the evaluation of fracturing effect, but also the economic benefit evaluation of fracturing construction.

In the prior art, an analytical method is usually used to predict the productivity of a fractured horizontal well in a tight oil reservoir. This method is to establish a corresponding mathematical model for the reservoir by establishing multiple assumptions and physical models to describe oil and gas in terms of production or pressure. The flow in the reservoir is solved to obtain an analytical expression of production, and the production is predicted through the analytical expression of production.

The inventor found at least the following problems in the prior art:

In actual production, there are many factors that affect the productivity of fractured horizontal wells in tight oil reservoirs, including the geological parameters of each fractured well, fracturing construction modification, etc. However, the analytical method cannot comprehensively consider geological parameters and fracturing construction parameters (such as sand Analytical method is not enough to understand the main control factors of tight oil reservoir fracturing horizontal well productivity. The use of analytical method to predict the productivity of tight oil reservoirs is greater than the actual tight oil reservoir’s productivity. The gap cannot be effectively used to evaluate the fracturing effect and affect the further improvement and optimization of the fracturing construction plan.

Summary of the invention

In view of the above-mentioned problems, the present invention is proposed to provide a productivity prediction method for fracturing horizontal wells in tight oil reservoirs that overcomes or at least partially solves the above-mentioned problems, including:

Obtain daily production data, geological parameter data and fracturing construction parameter data of fractured horizontal wells in tight oil reservoirs;

According to the daily production data, calculate the peak average daily production of the fractured horizontal well;

Determine the classification of the fractured horizontal well based on the fuzzy set according to the peak average daily production;

Calculate the average value of the peak average daily production, the average value of the geological parameter data, and the average value of the fracturing construction parameter data of the corresponding fractured horizontal wells in each category;

According to the average value of the peak average daily production, the average value of the geological parameter data, and the average value of the fracturing construction parameter data, determine the main control parameters that affect the production of the fractured horizontal well, and the main control The parameter includes at least one of the geological parameter data and/or the fracturing construction parameter data;

According to the peak average daily production rate of the fractured horizontal well and the main control parameters, establish a peak average daily production prediction model based on regression analysis;

According to the peak average daily production prediction model, the production capacity of the horizontal well to be fractured is predicted.

In a possible design, the geological parameters include the average thickness, porosity, permeability and water saturation of the oil layer corresponding to the fractured horizontal well;

The fracturing construction parameters include the fracturing length of the fractured horizontal well, the number of fracturing sections, the average sand volume of a single section, the average liquid volume of a single section, the total displacement and the number of fracturing clusters.

In a possible design, the calculation of the peak average daily production of the fractured horizontal well based on the daily production data includes:

According to the daily production data, calculate the monthly average daily production of each month after the fractured horizontal well is put into production;

Determine the consecutive peak production month of the fractured horizontal well according to the monthly average daily production;

According to the consecutive peak production months, calculate the peak average daily production of the fractured horizontal well.

In a possible design, the determining the classification of the fractured horizontal well based on the fuzzy set theory according to the peak average daily production includes:

Determine the interval [b, a] according to the peak average daily output, where b represents the minimum value of the peak average daily output, and a represents the maximum value of the peak average daily output;

Divide the interval [b, a] into several equal divisions, and expand the set values to the left and right sides of the divided interval respectively, to obtain several two-by-two overlapping fuzzy sets U;

The several fuzzy sets U correspondingly divide the tight oil reservoir fractured horizontal wells into several types.

In a possible design, the fuzzy set U is:

Wherein, n is the number of equal divisions in the interval [a, b], j is the serial number between equal divisions, j=1, 2, 3...; e is a constant.

In a possible design, according to the average value of the peak average daily production, the average value of the geological parameter data and the average value of the fracturing construction parameter data, it is determined to affect the fractured horizontal well Main control parameters of output, including:

Normalizing the average value of the geological parameter data and the average value of the fracturing construction parameter data respectively;

Draw the relationship curve of yx in a rectangular coordinate system, wherein the y is the average value of the peak average daily production, and the x is the average value of the geological parameter data after normalization or the pressure Average value of construction parameter data;

Respectively calculating the slope of the largest straight line segment in the y-x relationship curve;

According to the magnitude of the slope, the main control parameters that affect the production of the fractured horizontal well in the tight oil reservoir are determined.

In a possible design, the main control parameters include: the average thickness of the oil layer, the length of the horizontal well, the number of fracturing sections of the horizontal well, the number of fracturing clusters of the horizontal well, the average sand of the single section Volume, the single-stage average liquid volume, and the total displacement.

The beneficial effects brought about by the technical solutions provided by the embodiments of the present invention include at least:

The productivity prediction method provided by the embodiment of the present invention firstly subdivides the fractured horizontal wells in tight oil reservoirs into different categories based on fuzzy sets, and secondly determines the geological parameters, fracturing construction parameters and peak average daily production of different types of horizontal wells. The main control parameters that affect the production of fractured horizontal wells in tight oil reservoirs are determined. Finally, according to the peak average daily production of fractured horizontal wells in tight oil reservoirs and the corresponding main control parameter data, the regression analysis method is used to establish the peak value. The average daily production prediction model is used to predict the production capacity of horizontal wells to be fractured in tight oil reservoirs. The productivity prediction method is based on fuzzy sets, which integrates geological factor parameters and fracturing construction parameters, so that the prediction results are closer to the actual situation, and can be effectively used to evaluate the fracturing effect, and further improve and optimize the fracturing construction plan.

Description of the drawings

In order to more clearly describe the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings used in the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained from these drawings without creative work.

Fig. 1 is a flowchart of a method for predicting productivity of a fractured horizontal well in a tight oil reservoir according to an embodiment of the present invention;

Figure 2a is a diagram showing the relationship between production month and monthly average daily production of Well W1 in a block of Changqing Oilfield in the embodiment of the present invention;

Figure 2b is a diagram showing the relationship between production month and monthly average daily production of Well W2 in a certain block of Changqing Oilfield in the embodiment of the present invention;

Figure 3 is a frequency distribution diagram of peak average daily production of 55 fractured horizontal wells in a tight oil reservoir in a block of Changqing Oilfield in an embodiment of the present invention;

Figure 4a is a classification diagram showing the classification of 55 tight oil reservoir fracturing horizontal wells in a block of Changqing Oilfield into 3 categories in an embodiment of the present invention;

Figure 4b is a classification diagram showing the classification of 55 tight oil reservoir fractured horizontal wells in a block of Changqing Oilfield into 4 categories in the embodiment of the present invention;

Figure 4c is a classification diagram showing the classification of 55 tight oil reservoir fracturing horizontal wells in a block of Changqing Oilfield into 5 categories in an embodiment of the present invention;

Figure 5 is a diagram showing the relationship between the length of the horizontal well and the peak average daily production when 55 fracturing horizontal wells in a block of Changqing Oilfield are divided into 32 types in the embodiment of the present invention;

Figure 6 is a diagram showing the relationship between the length of the horizontal well after the standardized treatment in Figure 5 and the peak average daily production;

Fig. 7 is a schematic diagram of obtaining the slope on the basis of Fig. 6;

Fig. 8 is a schematic diagram of the influence degree of a single parameter of a fracturing horizontal well of 55 tight oil reservoirs in a block of Changqing Oilfield on the peak average daily production in the embodiment of the present invention;

Fig. 9 is a comparison diagram between the predicted peak average daily output and the actual peak average daily output using the embodiment of the present invention.

detailed description

Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

It should be noted that the daily output in the early stage of production is an important indicator of oil well productivity and can be directly used to evaluate oil well productivity. Considering that the number of wells in tight reservoirs with volumetric fracturing horizontal wells with a long commissioning time is small, in order to increase the number of samples analyzed and to ensure the reliability of fracturing effect evaluation and analysis, the average of consecutive peak production months in the early stage of horizontal well commissioning can be selected Daily production value-the peak average daily production is used as an evaluation indicator to predict the productivity of a fractured horizontal well in a tight reservoir by predicting the peak average daily production.

The embodiment of the present invention provides a productivity prediction method for fracturing horizontal wells in tight oil reservoirs, as shown in FIG. 1, including the following steps:

Step 101: Obtain daily production data, geological parameter data, and fracturing construction parameter data of a fractured horizontal well in a tight oil reservoir;

Step 102: Calculate the peak average daily production of fractured horizontal wells according to the daily production data;

Step 103: Determine the classification of the fractured horizontal well based on the fuzzy set according to the peak average daily production;

Step 104: Calculate the average value of peak average daily production, the average value of geological parameter data, and the average value of fracturing construction parameter data of the corresponding fractured horizontal wells in each category;

Step 105: Determine the main control parameters that affect the production of fractured horizontal wells according to the average value of peak average daily production, the average value of geological parameter data, and the average value of fracturing construction parameter data;

Step 106: Use regression analysis to establish a peak average daily production prediction model based on the peak average daily production and main control parameters of the fractured horizontal well;

Step 107: Predict the productivity of the horizontal well to be fractured according to the peak average daily production prediction model.

In addition, the method of using analytical methods to predict production is complicated and difficult to implement. Compared with this, the prediction method provided by the present invention is simple, easy to implement, has strong generalizability, and can effectively guide oil well production allocation.

For step 101, “acquiring daily production data, geological parameter data, and fracturing construction parameter data of fractured horizontal wells in tight oil reservoirs” refers to obtaining daily production data and geological data of multiple horizontal wells in a certain block. Parameter data and fracturing construction parameter data.

The above data can be obtained from oilfield production materials. More specifically, the geological parameters may include the average thickness, porosity (average porosity of the reservoir), permeability (average permeability of the reservoir), and water saturation (average water saturation of the reservoir) of the reservoir corresponding to a fractured horizontal well. ; The fracturing construction parameters can include the fracturing length of the fractured horizontal well, the number of fractured sections of the horizontal well, the average sand volume of a single section (total sand volume/number of sections), the average liquid volume of a single section (total liquid volume/number of sections), and the total Displacement (volume of fracturing fluid injected into the formation per minute) and the number of fracturing clusters in horizontal wells.

For step 102, calculating the peak average daily production of fractured horizontal wells according to the daily production data, specifically, may include the following steps:

Step 1021: Calculate the monthly average daily production of each month after the fractured horizontal well is put into production according to the daily production data;

According to the daily production data, the monthly average daily production of each month after the fractured horizontal well is put into production can be calculated, and the monthly average daily production and production month can be plotted in a rectangular coordinate system to obtain the monthly average daily production. Relationship diagram, where the horizontal axis can represent the production month, and the vertical axis can represent the monthly average daily output. Taking Wells W1 and W2 in a block of Changqing Oilfield in the Ordos Basin as an example, the relationship between production month and monthly average daily production is shown in Figure 2a and Figure 2b, where the abscissa represents the production month , That is, the first few months after fracturing is put into production; the ordinate represents the monthly average daily production of the corresponding month.

Step 1022, according to the monthly average daily production, determine the consecutive peak production months before the fractured horizontal wells are put into production;

Step 1023: Calculate the peak average daily production of the fractured horizontal wells according to consecutive peak production months.

In application, according to the monthly average daily production, the months with large production in the early stage of the fractured horizontal well can be determined as the consecutive peak production months, and then the peak average daily production can be calculated based on the average daily production value corresponding to the consecutive peak production months.

For example, the peak average daily output can be calculated according to the following principles:

(1) In the early stage of horizontal well production, for horizontal wells where the monthly average daily production at the peak changes more slowly, the average monthly average daily production for 3-5 consecutive months at the beginning of decline can be selected as the peak average daily production. As shown in Figure 2a, the determined peak average daily production of fractured horizontal well W1 is 3.3 (t/d);

(2) In the initial stage of horizontal well production, for horizontal wells with large monthly average daily production changes at the peak, in order to avoid errors and ensure the rationality of the selection of peak average daily production, select the monthly average daily production to fluctuate stably after the peak The average monthly average daily production for 3-5 consecutive months in the range is regarded as the peak average daily production. As shown in Figure 2b, the peak average daily production of the determined fractured horizontal well W2 is 2.3 (t/d).

For step 103, according to the peak average daily production, the classification of the fractured horizontal well is determined based on the fuzzy set.

Fuzzy set theory is a method of describing fuzzy phenomena. This method regards the object to be investigated and the fuzzy concept reflecting it as a certain fuzzy set, establishes an appropriate membership function, and analyzes the fuzzy object through related operations and transformations of the fuzzy set. Fuzzy set theory is based on fuzzy mathematics and studies related imprecise phenomena. For horizontal production wells in tight oil reservoirs, high-yield wells and low-yield wells are fuzzy concepts, and how they belong is a key issue in evaluating production effects. The introduction of fuzzy set theory can solve this problem. In addition, the fuzzy classification of horizontal wells indirectly increases the number of samples to be analyzed, which can increase credibility and make the classified data show a better trend.

Specifically, it may include the following steps:

Step 1031: Determine the interval [b, a] according to the peak average daily output, where b represents the minimum value of the peak average daily output, and a represents the maximum value of the peak average daily output;

Step 1032: Divide the interval [b, a] into equal parts, and expand the set values to the left and right sides of the equally divided interval to obtain several fuzzy sets U that overlap each other;

In step 1033, a number of fuzzy sets U correspondingly divide the fractured horizontal wells into several types.

Among them, the "set value" can be set according to actual needs. For different equally divided intervals, the "set value" can be the same value or different values.

In a possible implementation, the fuzzy set

Among them, n is the number of equal divisions in the interval [a, b], j is the serial number between equal divisions, j can be 1, 2, 3...; e is a constant.

It is understandable that, in order to ensure that there are enough horizontal well samples in each equipartition area to make the calculation results more accurate, e can be set to different values within different peak average daily production ranges to ensure that horizontal wells are included. The smaller number of iso-zones has a larger expansion range, that is, try to ensure that each iso-zone is a horizontal well classification.

Based on this, before dividing the fuzzy set U, the frequency distribution of the peak average daily output can be analyzed first, and then the value of e is determined based on the frequency distribution of the peak average daily output, so that the constant e is different in different peak average daily output ranges Value in order to ensure a larger range of expansion in equal zones with fewer horizontal wells.

As an example, taking 55 fractured horizontal wells in a tight oil reservoir in a block of the Changqing Oilfield in the Ordos Basin as an example, the frequency distribution of the peak average daily production can be obtained as shown in Figure 3, and the peak average daily production lies in the interval [2, 9] has the largest number of wells and the largest well frequency; the wells with the peak average daily production in the interval (9,15) are the second; the peak average daily production in the interval (15,22) has the least number of wells, and the well frequency is the smallest. Frequency is the percentage of the number of wells with peak average daily production in a certain interval to the total number of wells (55). Figure 3 shows that the higher the peak average daily production, the fewer wells. In order to ensure a higher average daily production The fuzzy set U contains more horizontal well samples. The value of the definable constant e is shown in Table 1:

Table 1 Value of the constant e

After division, a number of fuzzy sets U correspond to the peak average daily production range that divides fractured horizontal wells into several types. All horizontal wells with peak average daily production within this range are classified as one type of horizontal well. If no corresponding horizontal well exists, this range is not selected for horizontal well classification.

In practical applications, n can be defined as different values, that is, horizontal wells can be divided into n types. As shown in Fig. 4a, Fig. 4b and Fig. 4c, the above-mentioned 55 wells are divided into

categories

3, 4 and 5 respectively. Taking Figure 4c as an example, 55 wells are classified into 5 categories according to their peak average daily production. After fuzzy classification, the total number of wells is 88. Among them, there are 23 low-yield wells, 21 poor wells, and 21 general wells. There are 17 good wells and 6 high-yield wells. This division significantly expands the number of samples in each fuzzy set U, making the analysis results more reliable.

For step 104, the average value of the peak average daily production of the fractured horizontal wells in each category, the average value of the geological parameter data, and the average value of the fracturing construction parameter data are calculated respectively.

In the above-mentioned capacity prediction method, each fuzzy set U corresponds to a category. For each category, according to the peak average daily production, geological parameter data, and fracturing construction parameter data of fractured horizontal wells contained in the corresponding fuzzy set U, the corresponding fractured horizontal wells in each category can be obtained The average value of peak average daily production, the average value of geological parameter data and the average value of fracturing construction parameter data.

When the fractured horizontal wells are divided into 5 types, 5 groups of corresponding peak average daily production average values, average values of geological parameter data, and average values of fracturing construction parameter data can be obtained.

For step 105, according to the average value of peak average daily production, the average value of geological parameter data and the average value of fracturing construction parameter data, the main control parameters that affect the production of fractured horizontal wells are determined.

When the number of horizontal well categories is increased, the relationship between the average value of peak average daily production in each category analyzed, the average value of geological parameter data, and the average value of fracturing construction parameter data will become apparent. For example, when the above-mentioned 55 horizontal wells are divided into 32 types, the relationship between the fracturing length of the horizontal well and the peak average daily production can be obtained as shown in Figure 5. Similarly, the relationship between other parameters and peak average daily output can also be determined separately. According to the relationship diagram between each parameter and the peak average daily output, the slope of the “maximum straight line segment” in each diagram can be determined, and the slope of the straight line can indicate the impact of each parameter on the output, so that the impact can be determined according to the slope Main control parameters for the production of fractured horizontal wells.

Specifically, it may include the following steps:

Step 1051: Normalize the average value of the geological parameter data and the average value of the fracturing construction parameter data respectively.

Normalization is a way of standardization, that is, a dimensional expression is transformed into a non-dimensional expression and becomes a scalar, so that the absolute value of the physical system value becomes a relative value relationship. In the embodiment of the present invention, in order to facilitate the comparison between the slopes of the straight lines to determine the main control parameters, it is necessary to standardize the analysis parameters.

For example, according to

Standardize the parameters.

Among them, N is a specific analysis parameter, such as the length of a fractured horizontal well; N _min is the minimum value of a specific analysis parameter, such as the minimum length of a fractured horizontal well; N _max is the maximum value of a specific analysis parameter Value, such as the maximum length of a fractured horizontal well, N _nor is a standardized parameter, such as the standardized length of a fractured horizontal well.

Step 1052: Draw a yx relationship curve in a rectangular coordinate system, where y is the average value of the peak average daily output, and x is the average value of the normalized geological parameter data or the average value of the fracturing construction parameter data .

Taking the above-mentioned relationship between the length of a horizontal well and the peak average daily production as an example, the relationship between the fracturing length of the horizontal well and the peak average daily production after standardized treatment is shown in Figure 6.

Step 1053: Calculate the slope of the largest straight line segment in the y-x relationship curve respectively.

The principle of obtaining the slope is as follows: in the yx relationship curve, select a straight line that is approximately the largest straight line segment, and use linear regression to find the value of its slope. The so-called "maximum straight line segment" needs to meet: ①The greatest possible reflection of the changing trend of the data; ②The maximum straight line segment has enough data. As shown in Figure 7, Figure 7 is a schematic diagram of using the linear regression method to obtain the slope. The relationship between the standardized horizontal well fracturing length x and the peak average daily production y is: y=22.682x+0.8198, goodness of fit R ² Is 0.9595.

Step 1054: Determine the main control parameters that affect the production of the fractured horizontal well according to the magnitude of the slope.

It is understandable that the slope of the relationship between the analysis parameter and the peak average daily output can reflect the magnitude of the influence of the parameter on the output, and the greater the slope obtained, the greater the influence of the parameter on the peak average daily output. Obtain the slopes in turn to get a schematic diagram of the influence of the parameters on the peak average daily production, as shown in Figure 8, which shows the influence of the parameters of the above 55 tight reservoir fractured horizontal wells on the peak average daily production. , And then can determine the main control parameters that affect the production of fracturing horizontal wells in tight oil reservoirs according to the schematic diagram of the influence degree. In a possible implementation, the main control parameters that can be determined include: the average thickness of the reservoir, the length of the horizontal well (fracturing Length), the number of fracturing sections of horizontal wells, the number of fracturing clusters of horizontal wells, the average sand volume of a single stage, the average liquid volume of a single stage and the total displacement.

For step 106, according to the peak average daily production and main control parameter data of the fractured horizontal well, a regression analysis method is used to establish a peak average daily production prediction model.

Regression analysis method is based on mastering a large amount of observation data, using mathematical statistics to establish the regression relationship function expression between the dependent variable and the independent variable. In the embodiment of the present invention, based on the determined main control parameters and the peak average daily production of fractured horizontal wells, regression analysis can be used to establish the regression relationship function expression between the peak average daily production and the main control parameters, namely For the peak average daily output forecast model.

Taking the above 55 wells as an example, based on the determined main control parameters and the peak average daily production of fractured horizontal wells, the peak average daily production prediction model can be obtained as:

Among them: y is the peak average daily production (t/d); x ₁ is the average thickness of the reservoir (m); x ₂ is the length of the horizontal well (m); x ₃ is the number of horizontal well fracturing sections; x ₄ is the horizontal well fracturing Number of clusters; x ₅ is the average sand volume of a single stage (m ³ ); x ₆ is the average liquid volume of a single stage (m ³ ); x ₇ is the total displacement (m ³ /min).

Furthermore, using the peak average daily production prediction model to predict other horizontal wells in the block, a comparison chart of the predicted peak average daily production and the actual peak average daily production for wells W3-W12 in a block of Changqing Oilfield is obtained. As shown in Figure 9, and the evaluation relative error of the calculated predicted value compared with the actual value is 7.6%, indicating that the peak average daily output predicted by the model is relatively reliable, and the model can be used to quickly predict the peak average daily output.

The daily output in the early stage of production is an important indicator of the productivity of an oil well and can be directly used to evaluate the productivity of an oil well.

Furthermore, through the correlation analysis of the relationship between the peak average daily production and the four-year cumulative production of 25 tight oil horizontal wells in the Changqing Oilfield of the Ordos Basin, the results show that the peak average daily production has a good linear relationship with the four-year cumulative production. , The goodness of fit is close to 0.9, which shows that the productivity of horizontal wells to be fractured can be predicted based on the peak average daily production (prediction model).

The foregoing description is only for the convenience of those skilled in the art to understand the technical solutions of the present invention, and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc., made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process can be rearranged without departing from the scope of protection of the present disclosure. The accompanying method claims present elements of the various steps in an exemplary order and are not intended to be limited to the specific order or hierarchy described.

In the above detailed description, various features are combined together in a single embodiment to simplify the present disclosure. This method of disclosure should not be interpreted as reflecting the intention that the implementation of the claimed subject matter needs to clearly state more features in each claim. On the contrary, as reflected in the appended claims, the present invention is in a state with fewer features than the disclosed single embodiment. Therefore, the appended claims are hereby clearly incorporated into the detailed description, with each claim standing alone as a separate preferred embodiment of the present invention.

Those skilled in the art should also understand that the various illustrative logical blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments herein can all be implemented as electronic hardware, computer software, or a combination thereof. In order to clearly illustrate the interchangeability between hardware and software, various illustrative components, blocks, modules, circuits, and steps are described above generally around their functions. As for whether this function is implemented as hardware or as software, it depends on the specific application and the design constraints imposed on the entire system. Skilled technicians can implement the described functions in a flexible manner for each specific application, but this implementation decision should not be interpreted as a departure from the protection scope of the present disclosure.

The foregoing description includes examples of one or more embodiments. Of course, it is impossible to describe all possible combinations of components or methods in order to describe the above-mentioned embodiments, but those of ordinary skill in the art should realize that the various embodiments can be further combined and arranged. Therefore, the embodiments described herein are intended to cover all such changes, modifications and variations that fall within the protection scope of the appended claims. In addition, with regard to the term "comprising" used in the specification or claims, the coverage of this word is similar to the term "including", just as "including," is explained as an adaptor in the claims. In addition, any term "or" used in the description of the claims is intended to mean a "non-exclusive or".

Claims

A productivity prediction method for fractured horizontal wells in tight oil reservoirs is characterized in that it includes the following steps:

Obtain daily production data, geological parameter data and fracturing construction parameter data of fractured horizontal wells in tight oil reservoirs;

According to the daily production data, calculate the peak average daily production of the fractured horizontal well, and determine the classification of the fractured horizontal well based on the fuzzy set according to the peak average daily production;

Calculate the average value of the peak average daily production, the average value of the geological parameter data, and the average value of the fracturing construction parameter data of the corresponding fractured horizontal wells in each category;

According to the average value of the peak average daily production, the average value of the geological parameter data, and the average value of the fracturing construction parameter data, determine the main control parameters that affect the production of the fractured horizontal well, and the main control The parameter includes at least one of the geological parameter data and/or the fracturing construction parameter data;

According to the peak average daily production rate of the fractured horizontal well and the main control parameters, establish a peak average daily production prediction model based on regression analysis;

According to the peak average daily production prediction model, the production capacity of the horizontal well to be fractured is predicted.
The productivity prediction method according to claim 1, wherein the geological parameters include the average thickness, porosity, permeability and water saturation of the oil layer corresponding to the fractured horizontal well; the fracturing operation parameters It includes the fracturing length, the number of fractured sections, the average sand volume of a single section, the average liquid volume of a single section, the total displacement and the number of fracturing clusters of the fractured horizontal well.
The productivity prediction method according to claim 1, wherein the calculation of the peak average daily production of the fractured horizontal well according to the daily production data comprises:

According to the daily production data, calculate the monthly average daily production of each month after the fractured horizontal well is put into production; according to the monthly average daily production, determine the continuous peak production month of the fractured horizontal well; For the consecutive peak production months, calculate the peak average daily production of the fractured horizontal well.
The productivity prediction method according to claim 1, wherein the determining the classification of the fractured horizontal well based on the fuzzy set theory according to the peak average daily production comprises:

According to the peak average daily output, determine the interval [b, a], divide the interval [b, a] into several equal parts, and expand the set values to the left and right sides of the equalized interval to obtain several two Two overlapping fuzzy sets U. Several of the fuzzy sets U correspondingly divide the tight reservoir fractured horizontal wells into several categories, where b represents the minimum value of the peak average daily production, and a represents the peak average The maximum daily output.
The production capacity prediction method according to claim 4, wherein the fuzzy set U is:

Wherein, n is the number of equal divisions in the interval [a, b], j is the serial number between equal divisions, j=1, 2, 3...; e is a constant.
The production capacity prediction method according to claim 2, wherein said determining the impact according to the average value of the peak average daily production, the average value of the geological parameter data and the average value of the fracturing construction parameter data The main control parameters of the production of the fractured horizontal well include:

The average value of the geological parameter data and the average value of the fracturing construction parameter data are respectively normalized; the relationship curve of yx is drawn in a rectangular coordinate system, where y is the peak average daily The average value of the production, where x is the average value of the normalized geological parameter data or the average value of the fracturing construction parameter data; respectively calculate the slope of the maximum straight line segment in the yx relationship curve ; According to the size of the slope, determine the main control parameters that affect the production of the tight reservoir fractured horizontal well.