Shale oil horizontal well EUR prediction method, device and equipment and storage medium

Abstract

The invention relates to the technical field of oil production prediction, in particular to a shale oil horizontal well EUR prediction method, device and equipment and a storage medium. The method comprises the steps that a flowing period oil production prediction model set is used for obtaining a flowing period oil production prediction value; an oil pumping period oil production prediction value is obtained by inputting the oil pumping period oil production prediction model; obtaining a full-period oil production prediction value by using the full-period oil production prediction model; and summing the oil production predicted value in the self-spraying period and the oil production predicted value in the oil pumping period, and averaging the sum and the oil production predicted value in the whole period. According to the method, a quantitative characterization model, an oil quantity decline model, a fracturing crack oil production prediction model, an oil pumping period oil production prediction model and a full-period oil production prediction model are constructed for different development stages, and the shale oil horizontal well EUR prediction value is estimated by predicting and combining oil production prediction values corresponding to a flowing period, an oil pumping period and a full period. Therefore, prediction is more accurate, classified evaluation of production and development effects is realized, and commissioning decision and scheme prediction of future favorable areas are guided.

Description

Technical Field

[0001] The present invention relates to the technical field of oil production prediction, and is a shale oil horizontal well EUR prediction method, device, equipment and storage medium. Background Art

[0002] The existing development technology for shale oil reservoirs mostly uses horizontal wells plus multi-stage fracturing operations. Development practice shows that the capacity prediction of fractured horizontal wells is the key. In order to correctly characterize the flow characteristics of fracturing-well-production oil and water and more accurately predict the capacity, it is necessary to consider the retention, imbibition and backflow process of shale oil reservoir fracturing fluid in the process of horizontal well capacity prediction. However, the seepage laws in media of different scales are different, and it is difficult to predict oil production. Therefore, it is very important to establish a capacity prediction method for fractured horizontal wells for the subsequent development of horizontal wells.

[0003] The current oil production prediction methods are mainly divided into the decline curve method, analytical / semi-analytical method, numerical simulation method and neural network prediction method. Although the decline curve method can quickly and accurately determine the decline law of the target reservoir, the Arps formula used in it is applicable under relatively harsh conditions. It is mainly aimed at the late stage of oil field development and it is difficult to fully consider the production process of shale oil horizontal wells throughout their life cycle. The analytical / semi-analytical method is widely used to calculate the single-phase fluid production capacity of fractured horizontal wells based on many assumptions, but it has certain limitations in predicting the multiphase flow of shale reservoirs. The numerical simulation method is based on the use of Pebi grid combined with local encryption technology. Although it can provide a detailed description of the reservoir and predict the distribution of remaining oil, it is time-consuming to calculate. Summary of the invention

[0004] The present invention provides a shale oil horizontal well EUR prediction method, device, equipment and storage medium, which overcomes the shortcomings of the above-mentioned prior art and can effectively solve the problem that the existing oil production prediction method cannot combine the entire life cycle of the shale oil horizontal well to predict the oil production, resulting in inaccurate prediction.

[0005] One of the technical solutions of the present invention is achieved by the following measures: A shale oil horizontal well EUR prediction method, comprising:

[0006] Obtain existing production-related data during the pumping period and input them into the oil production prediction model group during the self-flowing period, and take the average of all oil production output values ​​as the oil production prediction value during the self-flowing period, wherein the oil production prediction model group during the self-flowing period includes: a quantitative characterization model constructed by combining peak oil production, peak time and ARPS decline model, an oil production decline model constructed by combining initial oil production and ARPS decline model, and a fracturing fracture oil production prediction model constructed after fitting with production dynamic data;

[0007] Obtain the pressure data during the self-flowing period and the pumping period and input them into the oil production prediction model during the pumping period, and output the predicted value of the oil production during the pumping period;

[0008]

[0009] Among them, C t is the rock compression coefficient; p ini is the reservoir pressure; p n is the wellhead oil pressure at the initial moment; V0 is the reservoir void volume; Q n is the current oil production; p m is the wellhead oil pressure after the self-flowing period ends; Q m is the oil production during the self-spraying period; p p represents the dynamic liquid surface pressure at the bubble point pressure; p end is the wellhead pressure after the pumping period ends; f w1 is the average moisture content during the self-spraying period, f w2 is the average water content during the pumping period; R s is the oil-gas ratio;

[0010] Obtain full-cycle production data and input it into the full-cycle oil production prediction model, and output the full-cycle oil production prediction value;

[0011] Q 全周期 =ln(mobile porosity 0.0861 +Oil Saturation 0.0412 +Reservoir thickness 0.0527 +Horizontal well section 0.0746 +Drilling rate 0 . 0861 + Type I oil layer drilling rate 0.0995 +Drilling rate of Class II oil layers 0.0786 +Viscosity 0.0492 )·ln(segment spacing 0.0565 +Number of cracks 0. 0561 +Fracturing fluid volume 0.0839 +Support dosage 0.0724 )·ln(well-stemming time 0.0748 +Oil pressure / sinking 0.0883 )

[0012] The predicted oil production value during the flowing period is summed with the predicted oil production value during the pumping period, and the average of the summation result and the predicted oil production value during the whole period is taken as the EUR predicted value of the shale oil horizontal well.

[0013] The following are further optimizations and / or improvements to the above technical solutions:

[0014] The above-mentioned self-flowing period oil production prediction model group includes: a quantitative characterization model constructed by combining peak oil production, peak time and ARPS decline model, an oil production decline model constructed by combining initial oil production and ARPS decline model, and a fracturing fracture oil production prediction model constructed after fitting with production dynamic data, as follows:

[0015] Quantitative characterization model:

[0016]

[0017] Among them, Q max is the peak oil production; t is the production time; t m is the peak moment; t0 is the oil-seeking moment; D av is the average decline rate;

[0018] Oil production decline model:

[0019]

[0020] Among them, Q0 is the output at the initial stage of output decline; t is the production time; D0 is the initial decline rate; n is the decline index;

[0021] Fracturing fracture oil production prediction model:

[0022]

[0023] Q = q × (1-w%)

[0024] Where q is the fluid production of the fracture; p i is the formation pressure; p wf is the bottom hole flowing pressure; t is the production time; A is the total surface area of ​​the fracture; Q is the oil production of the fracture; w% is the water content.

[0025] The above also includes fracturing effect evaluation and production prediction of typical wells based on Topaze software, determination of key development parameters, and evaluation of fracturing effect, where key development parameters include formation permeability, fracture half-length, SRV area, well-controlled area, well-controlled reserves, production prediction, determination of EUR, and recovery factor.

[0026] The second technical solution of the present invention is achieved by the following measures: a shale oil horizontal well EUR prediction device, comprising:

[0027] The first prediction unit obtains existing production-related data during the pumping period and inputs the data into the oil production prediction model group during the flowing period, and takes the average of all oil production output values ​​as the oil production prediction value during the flowing period, wherein the oil production prediction model group during the flowing period includes: a quantitative characterization model constructed by combining the peak oil production, the peak time and the ARPS decline model, an oil production decline model constructed by combining the initial oil production and the ARPS decline model, and a fracturing fracture oil production prediction model constructed after fitting with production dynamic data;

[0028] The second prediction unit obtains the pressure data of the self-flowing period and the pumping period and inputs the pressure data into the oil production prediction model of the pumping period, and outputs the predicted value of the oil production of the pumping period;

[0029]

[0030] Among them, C t is the rock compression coefficient; p ini is the reservoir pressure; p n is the wellhead oil pressure at the initial moment; V0 is the reservoir void volume; Q n is the current oil production; p m is the wellhead oil pressure after the self-flowing period ends; Q m is the oil production during the self-spraying period; p p represents the dynamic liquid surface pressure at the bubble point pressure; p end is the wellhead pressure after the pumping period ends; f w1 is the average moisture content during the self-spraying period, f w2 is the average water content during the pumping period; R s is the oil-gas ratio;

[0031] The third prediction unit obtains the full-cycle production data and inputs it into the full-cycle oil production prediction model, and outputs the full-cycle oil production prediction value;

[0032] Q 全周期 =ln(mobile porosity 0.0861 +Oil Saturation 0.0412 +Reservoir thickness 0.0527 +Horizontal well section 0.0746 +Drilling rate 0. 0861 + Type I oil layer drilling rate 0.0995 +Drilling rate of Class II oil layers 0.0786 +Viscosity 0.0492 )·ln(segment spacing 0.0565 +Number of cracks 0 . 0561 +Fracturing fluid volume 0.0839 +Support dosage 0.0724 )·ln(well-stemming time 0.0748 +Oil pressure / sinking 0.0883 )

[0033] The prediction value determination unit sums the predicted value of oil production during the self-flowing period and the predicted value of oil production during the pumping period, and takes the average of the summation result and the predicted value of oil production during the whole period as the EUR predicted value of the shale oil horizontal well.

[0034] The following are further optimizations and / or improvements to the above technical solutions:

[0035] The above also includes an evaluation unit, which evaluates the fracturing effect and predicts the production of typical wells based on Topaze software, determines key development parameters, and evaluates the fracturing effect, where the key development parameters include formation permeability, fracture half-length, SRV area, well-controlled area, well-controlled reserves, production prediction, determination of EUR, and recovery factor.

[0036] The third technical solution of the present invention is achieved by the following measures: a shale oil horizontal well EUR prediction system, comprising:

[0037] A shale oil horizontal well EUR prediction device, wherein the shale oil horizontal well EUR prediction device is the shale oil horizontal well EUR prediction device as described in claims 4 to 5;

[0038] An interactive device is used for an operator to communicate with a shale oil horizontal well EUR prediction device, so that the operator provides relevant data to the shale oil horizontal well EUR prediction device through an interactive unit.

[0039] The present invention aims at different development stages (spontaneous flowing period, pumping period, full cycle), and constructs a quantitative characterization model, an oil decline model, a fracturing fracture oil production prediction model, an oil production prediction model for the pumping period, and a full cycle oil production prediction model based on several characteristic parameters with clear physical significance for the full life cycle production changes of shale oil. The oil production prediction values ​​corresponding to the spontaneous flowing period, the pumping period, and the full cycle are predicted based on the constructed models, and the EUR (total oil production value) prediction value of the shale oil horizontal well is estimated in combination with the oil production prediction values ​​corresponding to the spontaneous flowing period, the pumping period, and the full cycle, so as to make the prediction more accurate, realize the classified evaluation of production and development effects, and guide the production decision and plan prediction of future favorable areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Attached Figure 1 The figure is a flow chart of a prediction method of the present invention.

[0041] Attached Figure 2 This is another flow chart of the prediction method of the present invention.

[0042] Attached Figure 3 This is a schematic diagram of a shale oil horizontal wellbore during the pumping period in Example 2 of the present invention.

[0043] Attached Figure 4 It is a schematic diagram of the device structure of the present invention.

[0044] Attached Figure 5 It is a schematic diagram of the system structure of the present invention.

[0045] Attached Figure 6 It is the JXXX_H self-spraying period prediction curve diagram of the present invention.

[0046] Attached Figure 7 It is the JHW00X self-spraying period prediction curve diagram of the present invention. DETAILED DESCRIPTION

[0047] The present invention is not limited by the following embodiments, and specific implementation methods can be determined based on the technical solution of the present invention and actual conditions.

[0048] Explanation of the name: Horizontal well EUR is the total oil production of the horizontal well during its entire life cycle. By predicting the horizontal well EUR, we can understand the production trend of the horizontal well and the future production potential of the oil and gas well, which is helpful to formulate a reasonable development plan.

[0049] The present invention will be further described below in conjunction with embodiments and drawings:

[0050] Embodiment 1: As attached Figure 1 As shown, the embodiment of the present invention discloses a shale oil horizontal well EUR prediction method, comprising:

[0051] Step S110, obtaining existing production-related data during the pumping period and inputting them into the oil production prediction model group during the self-flowing period, and taking the average of all oil production output values ​​as the oil production prediction value during the self-flowing period, wherein the oil production prediction model group during the self-flowing period includes: a quantitative characterization model constructed by combining the peak oil production, peak time and ARPS decline model, an oil production decline model constructed by combining the initial oil production and the ARPS decline model, and a fracturing fracture oil production prediction model constructed after fitting with production dynamic data;

[0052] Step S120, obtaining pressure data during the self-flowing period and the pumping period and inputting the pressure data into the pumping period oil production prediction model, and outputting the predicted value of the pumping period oil production;

[0053]

[0054] Among them, C t is the rock compression coefficient; p ini is the reservoir pressure; p n is the wellhead oil pressure at the initial moment; V0 is the reservoir void volume; Q n is the current oil production; p m is the wellhead oil pressure after the self-flowing period ends; Q m is the oil production during the self-spraying period; p p represents the dynamic liquid surface pressure at the bubble point pressure; p end is the wellhead pressure after the pumping period ends; fw1 is the average moisture content during the self-spraying period, f w2 is the average water content during the pumping period; R s is the oil-gas ratio;

[0055] Step S130, obtaining full-cycle production data and inputting it into a full-cycle oil production prediction model, and outputting a full-cycle oil production prediction value;

[0056] Q 全周期 =ln(mobile porosity 0.0861 +Oil Saturation 0.0412 +Reservoir thickness 0.0527 +Horizontal well section 0.0746 +Drilling rate 0 . 0861 + Type I oil layer drilling rate 0.0995 +Drilling rate of Class II oil layers 0.0786 +Viscosity 0.0492 )·ln(segment spacing 0.0565 +Number of cracks 0. 0561 +Fracturing fluid volume 0.0839 +Support dosage 0.0724 )·ln(well-stemming time 0.0748 +Oil pressure / sinking 0.0883 )

[0057] Step S140, summing the predicted value of oil production during the self-flowing period and the predicted value of oil production during the pumping period, and taking the average of the summation result and the predicted value of oil production during the whole period as the predicted value of EUR of the shale oil horizontal well.

[0058] The present invention discloses a EUR prediction method for shale oil horizontal wells. On the basis of reservoir geological knowledge and production and development characteristic analysis, for different development stages (spontaneous blowing period, pumping period, full cycle), a quantitative characterization model, an oil decline model, a fracturing fracture oil production prediction model, an oil production prediction model for the pumping period, and a full cycle oil production prediction model are constructed based on a number of characteristic parameters with clear physical significance and production changes in the full life cycle of shale oil. The constructed models are used to predict the oil production prediction values ​​corresponding to the spontaneous blowing period, the pumping period, and the full cycle, respectively. Combined with the oil production prediction values ​​corresponding to the spontaneous blowing period, the pumping period, and the full cycle, the EUR (total oil production value) prediction value of the shale oil horizontal well is estimated, so that the prediction is more accurate, the classification evaluation of the production and development effect is realized, and the production decision-making and plan prediction of the future favorable areas are guided.

[0059] Embodiment 2: As attached Figure 2 As shown, the embodiment of the present invention discloses a shale oil horizontal well EUR prediction method, comprising:

[0060] Step S210, obtaining existing production-related data during the pumping period and inputting them into the oil production prediction model group during the self-flowing period, and taking the average of all oil production output values ​​as the oil production prediction value during the self-flowing period, wherein the oil production prediction model group during the self-flowing period includes: a quantitative characterization model constructed by combining the peak oil production, peak time and ARPS decline model, an oil production decline model constructed by combining the initial oil production and the ARPS decline model, and a fracturing fracture oil production prediction model constructed after fitting with production dynamic data;

[0061] The above-mentioned oil production prediction model group during the self-flowing period is as follows:

[0062] (1) Quantitative characterization model:

[0063]

[0064] Among them, Q max is the peak oil production; t is the production time; t m is the peak moment; t0 is the oil-seeking moment; D av is the average decline rate;

[0065] During the production process, the variation curve between daily oil production and production time is shown in the attached figure. Figure 3 As shown, the curve characteristics increase from the oil-seeking moment and then decrease. The quantitative characterization model of the above formula is obtained by fitting the relevant characteristics in combination with the change relationship.

[0066] (2) Oil production decline model:

[0067]

[0068] Among them, Q0 is the output at the initial stage of output decline; t is the production time; D0 is the initial decline rate; and n is the decline index.

[0069] In the above, we take n=0 and get the exponential decreasing formula:

[0070]

[0071] Among them, the relationship between the instantaneous decline rate and the average decline rate is:

[0072]

[0073] According to the above formula, we can get:

[0074] (1) When t = t0, that is, when the well is soaked (when oil is produced), the oil production Q0 is 0, and the instantaneous decline rate tends to infinity, which is a singular point.

[0075] (2) When t m When >t>t0, the instantaneous decline rate is negative, indicating that the output is in the rising period.

[0076] (3) When t = t m When D = 0, the output change curve reaches an extreme value, that is, Q0 = Q 0max , at this time, the horizontal well production reaches its maximum.

[0077] (4) When t>t m When , the instantaneous decline rate is positive and close to a constant value, indicating that the daily oil production decreases at a constant rate.

[0078] (5) When t→∞, that is, when time tends to infinity, the oil production Q0 tends to 0, which satisfies the dynamic change law of shale oil horizontal well production.

[0079] Through the above description, we can get the relationship between the instantaneous decline rate and the average decline rate over time, which proves that the corresponding oil production prediction value during the self-flowing period can be obtained through the oil production decline model.

[0080] (3) Fracturing fracture oil production prediction model:

[0081]

[0082] Q = q × (1-w%)

[0083] Where q is the fluid production of the fracture; p i is the formation pressure; p wf is the bottom hole flowing pressure; t is the production time; A is the total surface area of ​​the fracture; Q is the oil production of the fracture; w% is the water content.

[0084] The above model is obtained by fitting key parameters, including:

[0085] The fitting function is as follows:

[0086] y=ax+b

[0087] The excess parameters in the fitting function are as follows:

[0088]

[0089] The excess parameters are brought into the fitting function for fitting to obtain the final fracturing fracture oil production prediction model.

[0090] Step S220, obtaining the pressure data of the self-flowing period and the pumping period and inputting them into the oil production prediction model of the pumping period, and outputting the predicted value of the oil production of the pumping period;

[0091]

[0092] Among them, C t is the rock compression coefficient; p ini is the reservoir pressure; p nis the wellhead oil pressure at the initial moment; V0 is the reservoir void volume; Q n is the current oil production; p m is the wellhead oil pressure after the self-flowing period ends; Q m is the oil production during the self-spraying period; p p represents the dynamic liquid surface pressure at the bubble point pressure; p end is the wellhead pressure after the pumping period ends; f w1 is the average moisture content during the self-spraying period, f w2 is the average water content during the pumping period; R s is the oil-gas ratio;

[0093] The above-mentioned prediction model of oil production during the pumping period is obtained based on material balance, that is, under the quasi-steady-state flow condition, the reservoir pressure drop value is positively correlated with the liquid production. Combined with the relationship between oil pressure and liquid production at the beginning and end of the self-flowing period, the cumulative liquid production under the equivalent oil pressure at the end of the pumping period is extrapolated. Combined with the wellbore schematic diagram of shale oil horizontal wells during the pumping period (as shown in the attached figure), the oil production prediction model of the above-mentioned material balance is obtained based on the quasi-steady-state flow condition, that is, under the quasi-steady-state flow condition, the reservoir pressure drop value is positively correlated with the liquid production. Combined with the relationship between oil pressure and liquid production at the beginning and end of the self-flowing period, the cumulative liquid production under the equivalent oil pressure at the end of the pumping period is extrapolated. Figure 4 The model building process is as follows:

[0094] (1) Use nozzles of specific sizes to stabilize production:

[0095] P n =p n +ρgH

[0096] (2) At the end of the self-spraying period:

[0097] P m =p m +ρgH

[0098] (3) Pumping out, reservoir pressure drops to below the bubble point pressure:

[0099] p p =-ρgL P p =p p +ρgH

[0100]

[0101]

[0102] (4) At the end of the pumping period, the reservoir is in a quasi-steady-state flow. Based on the equivalent wellhead "oil pressure" value at the pumping depth and the correction of the effect of crude oil degassing, the average reservoir pressure and the cumulative liquid and oil production can be calculated:

[0103] P end =ρg(Hh)

[0104] (5) Cumulative oil production during the pumping period: The cumulative oil production during the pumping period can be calculated by combining the cumulative production during the self-flowing period and the cumulative production during the entire period:

[0105]

[0106] Among them, C t is the rock compression coefficient; p ini is the reservoir pressure; p n is the wellhead oil pressure at the initial moment; V0 is the reservoir void volume; Q n is the current oil production; p m is the wellhead oil pressure after the self-flowing period ends; Q m is the oil production during the self-spraying period; p p represents the dynamic liquid surface pressure at the bubble point pressure; p end is the wellhead pressure after the pumping period ends; f w1 is the average moisture content during the self-spraying period, f w2 is the average water content during the pumping period; R s is the oil-gas ratio.

[0107] Step S230, obtaining full-cycle production data and inputting it into a full-cycle oil production prediction model, and outputting a full-cycle oil production prediction value;

[0108] Q 全周期 =ln(mobile porosity 0.0861 +Oil Saturation 0.0412 +Reservoir thickness 0.0527 +Horizontal well section 0.0746 +Drilling rate 0. 0861 + Type I oil layer drilling rate 0.0995 +Drilling rate of Class II oil layers 0.0786 +Viscosity 0.0492 )·ln(segment spacing 0.0565 +Number of cracks 0 . 0561 +Fracturing fluid volume 0.0839 +Support dosage 0.0724 )·ln(well-stemming time 0.0748 +Oil pressure / sinking 0.0883 )

[0109] The above-mentioned full-cycle oil production prediction model is based on statistics of oil wells that have been put into production and have relatively stable pumping periods. The improved hierarchical analysis method is used to calculate the main controlling factors and weights of their production capacity. Based on the main controlling factors and weights of production capacity, the target well's self-flowing period, pumping period and full-cycle production capacity forecast are obtained by regression.

[0110] Step S240, summing the predicted value of oil production during the self-flowing period and the predicted value of oil production during the pumping period, and taking the average of the summation result and the predicted value of oil production during the whole period as the predicted value of EUR for the shale oil horizontal well;

[0111] Step S250, based on Topaze software, fracturing effect evaluation and production prediction are performed on typical wells, key development parameters are determined, and fracturing effect is evaluated, where key development parameters include formation permeability, fracture half-length, SRV area, well-controlled area, well-controlled reserves, production prediction, determination of EUR, and recovery factor.

[0112] Example 3: The production of typical production wells JXXX_H and JHW00X was predicted and analyzed using different stage production prediction methods.

[0113] JXXX_H self-spraying period prediction curve is as attached Figure 6 As shown, the quantitative characterization model after JXXX_H fitting is as follows:

[0114]

[0115] Combined with the attached figures, the comparison between the full life cycle production forecast and actual production of JXXX_H is shown in the following table.

[0116] Table 1 Comparison of JXXX_H life cycle production forecast and actual production

[0117]

[0118] JHW00X self-spraying period prediction curve is as attached Figure 7 As shown, the quantitative characterization model after JHW00X fitting is as follows:

[0119]

[0120] Table 2 Comparison of the predicted and actual production of JHW00X well throughout its life cycle

[0121]

[0122] The prediction results show that:

[0123] 1. There are 3 wells with cumulative production forecasts of more than 30,000 tons in the lower sweet spot, accounting for 27%; 3 wells with cumulative production forecasts of 20,000 to 30,000 tons, accounting for 27%; 3 wells with cumulative production forecasts of 10,000 to 20,000 tons, accounting for 36%; more than 80% of the wells have cumulative productions of more than 12,000 tons, and the development effect is generally better than that of the upper sweet spot wells;

[0124] 2. The production of some wells in the lower sweet spot is good, exceeding 30,000 tons, and some wells even have a cumulative production of more than 40,000 tons; among the 10 upper sweet spot wells, only two have a production of more than 30,000 tons, and the rest are less than 12,000 tons;

[0125] 3. The material balance method established during the pumping period predicts the cumulative oil production relatively accurately (error <10%). The cumulative oil production is affected by the given water content and the fracturing fluid backflow rate, and there is a certain error. In the later stage, the influence of the elastic compression coefficient on the change of production can be considered to further improve the prediction accuracy;

[0126] Embodiment 4: As attached Figure 4 As shown, the embodiment of the present invention discloses a shale oil horizontal well EUR prediction device, comprising:

[0127] The first prediction unit obtains existing production-related data during the pumping period and inputs the data into the oil production prediction model group during the flowing period, and takes the average of all oil production output values ​​as the oil production prediction value during the flowing period, wherein the oil production prediction model group during the flowing period includes: a quantitative characterization model constructed by combining the peak oil production, the peak time and the ARPS decline model, an oil production decline model constructed by combining the initial oil production and the ARPS decline model, and a fracturing fracture oil production prediction model constructed after fitting with production dynamic data;

[0128] The second prediction unit obtains the pressure data of the self-flowing period and the pumping period and inputs the pressure data into the oil production prediction model of the pumping period, and outputs the predicted value of the oil production of the pumping period;

[0129]

[0130] Among them, C t is the rock compression coefficient; p ini is the reservoir pressure; p n is the wellhead oil pressure at the initial moment; V0 is the reservoir void volume; Q n is the current oil production; p m is the wellhead oil pressure after the self-flowing period ends; Q m is the oil production during the self-spraying period; p p represents the dynamic liquid surface pressure at the bubble point pressure; p end is the wellhead pressure after the pumping period ends; f w1 is the average moisture content during the self-spraying period, f w2 is the average water content during the pumping period; R s is the oil-gas ratio;

[0131] The third prediction unit obtains the full-cycle production data and inputs it into the full-cycle oil production prediction model, and outputs the full-cycle oil production prediction value;

[0132] Q 全周期 =ln(mobile porosity 0.0861 +Oil Saturation 0.0412 +Reservoir thickness 0.0527 +Horizontal well section 0.0746 +Drilling rate 0 . 0861 + Type I oil layer drilling rate 0.0995+Drilling rate of Class II oil layers 0.0786 +Viscosity 0.0492 )·ln(segment spacing 0.0565 +Number of cracks 0 . 0561 +Fracturing fluid volume 0.0839 +Support dosage 0.0724 )·ln(well-stemming time 0.0748 +Oil pressure / sinking 0.0883 )

[0133] The prediction value determination unit sums the predicted value of oil production during the self-flowing period and the predicted value of oil production during the pumping period, and takes the average of the summation result and the predicted value of oil production during the whole period as the EUR prediction value of the shale oil horizontal well;

[0134] The evaluation unit evaluates the fracturing effect and production forecast of typical wells based on Topaze software, determines key development parameters, and evaluates the fracturing effect. The key development parameters include formation permeability, fracture half-length, SRV area, well-controlled area, well-controlled reserves, production forecast, determination of EUR, and recovery factor.

[0135] Embodiment 5: As attached Figure 5 As shown, the embodiment of the present invention discloses a shale oil horizontal well EUR prediction system, including:

[0136] A EUR prediction device for a shale oil horizontal well, wherein the EUR prediction device for a shale oil horizontal well is the EUR prediction device for a shale oil horizontal well described in the above embodiment;

[0137] An interactive device is used for an operator to communicate with a shale oil horizontal well EUR prediction device, so that the operator provides relevant data to the shale oil horizontal well EUR prediction device through an interactive unit.

[0138] Embodiment 6: The embodiment of the present invention discloses a storage medium, on which is stored a computer program that can be read by a computer, and the computer program is configured to execute a shale oil horizontal well EUR prediction method when running.

[0139] The above storage medium may include, but is not limited to: a USB flash drive, a read-only memory, a mobile hard disk, a magnetic disk or an optical disk, and other media that can store computer programs.

[0140] Embodiment 7: The embodiment of the present invention discloses an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the computer program is loaded and executed by the processor to implement a shale oil horizontal well EUR prediction method.

[0141] The processor may be a central processing unit (CPU), a general purpose processor, a digital signal processor (DSP), an ASIC, an FPGA or other programmable logic device, a transistor logic device, a hardware component or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. It may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like. The memory may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory, a mobile hard disk, a magnetic disk or an optical disk.

[0142] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0143] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0144] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0145] The above technical features constitute the best embodiment of the present invention, which has strong adaptability and best implementation effect. Non-essential technical features can be added or reduced according to actual needs to meet the requirements of different situations.

Claims

1. A shale oil horizontal well EUR prediction method, characterized in that: include: Obtain existing production-related data during the pumping period and input them into the oil production prediction model group during the self-flowing period, and take the average of all oil production output values ​​as the oil production prediction value during the self-flowing period, wherein the oil production prediction model group during the self-flowing period includes: a quantitative characterization model constructed by combining peak oil production, peak time and ARPS decline model, an oil production decline model constructed by combining initial oil production and ARPS decline model, and a fracturing fracture oil production prediction model constructed after fitting with production dynamic data; Obtain the pressure data during the self-flowing period and the pumping period and input them into the oil production prediction model during the pumping period, and output the predicted value of the oil production during the pumping period; Among them, C t is the rock compression coefficient; p ini is the reservoir pressure; p n is the wellhead oil pressure at the initial moment; V0 is the reservoir void volume; Q n is the current oil production; p m is the wellhead oil pressure after the self-flowing period ends; Q m is the oil production during the self-spraying period; p p represents the dynamic liquid surface pressure at the bubble point pressure; p end is the wellhead pressure after the pumping period ends; f w1 is the average moisture content during the self-spraying period, f w2 is the average water content during the pumping period; R s is the oil-gas ratio; Obtain full-cycle production data and input it into the full-cycle oil production prediction model, and output the full-cycle oil production prediction value; Q 全周期 =ln(mobile porosity 0.0861 +Oil Saturation 0.0412 +Reservoir thickness 0.0527 +Horizontal well section 0.0746 +Drilling rate 0 . 0861 + Type I oil layer drilling rate 0.0995 +Drilling rate of Class II oil layers 0.0786 +Viscosity 0.0492 )·ln(segment spacing 0.0565 +Number of cracks 0 . 0561 +Fracturing fluid volume 0.0839 +Support dosage 0.0724 )·ln(well-stemming time 0.0748 +Oil pressure / sinking 0.0883 ) The predicted oil production value during the flowing period is summed with the predicted oil production value during the pumping period, and the average of the summation result and the predicted oil production value during the whole period is taken as the EUR predicted value of the shale oil horizontal well.

2. The EUR prediction method for shale oil horizontal wells according to claim 1, characterized in that: The self-flowing period oil production prediction model group includes: a quantitative characterization model constructed by combining peak oil production, peak time and ARPS decline model, an oil production decline model constructed by combining initial oil production and ARPS decline model, and a fracturing fracture oil production prediction model constructed after fitting with production dynamic data, which are as follows: Quantitative characterization model: Among them, Q max is the peak oil production; t is the production time; t m is the peak moment; t0 is the oil-seeking moment; D av is the average decline rate; Oil production decline model: Among them, Q0 is the output at the initial stage of output decline; t is the production time; D0 is the initial decline rate; n is the decline index; Fracturing fracture oil production prediction model: Q = q × (1-w%) Where q is the fluid production of the fracture; p i is the formation pressure; p wf is the bottom hole flowing pressure; t is the production time; A is the total surface area of ​​the fracture; Q is the oil production of the fracture; w% is the water content.

3. The EUR prediction method for shale oil horizontal wells according to claim 1 or 2, characterized in that: It also includes fracturing effect evaluation and production prediction of typical wells based on Topaze software, determination of key development parameters, and evaluation of fracturing effect. Key development parameters include formation permeability, fracture half-length, SRV area, well-controlled area, well-controlled reserves, production prediction, determination of EUR, and recovery factor.

4. A shale oil horizontal well EUR prediction device using the method as described in any one of claims 1 to 3, characterized in that: include: The first prediction unit obtains existing production-related data during the pumping period and inputs the data into the oil production prediction model group during the flowing period, and takes the average of all oil production output values ​​as the oil production prediction value during the flowing period, wherein the oil production prediction model group during the flowing period includes: a quantitative characterization model constructed by combining the peak oil production, the peak time and the ARPS decline model, an oil production decline model constructed by combining the initial oil production and the ARPS decline model, and a fracturing fracture oil production prediction model constructed after fitting with production dynamic data; The second prediction unit obtains the pressure data of the self-flowing period and the pumping period and inputs the pressure data into the oil production prediction model of the pumping period, and outputs the predicted value of the oil production of the pumping period; Among them, C t is the rock compression coefficient; p ini is the reservoir pressure; p n is the wellhead oil pressure at the initial moment; V0 is the reservoir void volume; Q n is the current oil production; p m is the wellhead oil pressure after the self-flowing period ends; Q m is the oil production during the self-spraying period; p p represents the dynamic liquid surface pressure at the bubble point pressure; p end is the wellhead pressure after the pumping period ends; f w1 is the average moisture content during the self-spraying period, f w2 is the average water content during the pumping period; R s is the oil-gas ratio; The third prediction unit obtains the full-cycle production data and inputs it into the full-cycle oil production prediction model, and outputs the full-cycle oil production prediction value; Q 全周期 =ln(mobile porosity 0.0861 +Oil Saturation 0.0412 +Reservoir thickness 0.0527 +Horizontal well section 0.0746 +Drilling rate 0 . 0861 + Type I oil layer drilling rate 0.0995 +Drilling rate of Class II oil layers 0.0786 +Viscosity 0.0492 )·ln(segment spacing 0.0565 +Number of cracks 0 . 0561 +Fracturing fluid volume 0.0839 +Support dosage 0.0724 )·ln(well-stemming time 0.0748 +Oil pressure / sinking 0.0883 ) The prediction value determination unit sums the predicted value of oil production during the self-flowing period and the predicted value of oil production during the pumping period, and takes the average of the summation result and the predicted value of oil production during the whole period as the EUR predicted value of the shale oil horizontal well.

5. The EUR prediction device for shale oil horizontal wells according to claim 4, characterized in that: It also includes an evaluation unit, which evaluates the fracturing effect and predicts the production of typical wells based on Topaze software, determines key development parameters, and evaluates the fracturing effect. The key development parameters include formation permeability, fracture half-length, SRV area, well-controlled area, well-controlled reserves, production prediction, determination of EUR, and recovery factor.

6. A shale oil horizontal well EUR prediction system, characterized in that: include: A shale oil horizontal well EUR prediction device, wherein the shale oil horizontal well EUR prediction device is the shale oil horizontal well EUR prediction device as described in claims 4 to 5; An interactive device is used for an operator to communicate with a shale oil horizontal well EUR prediction device, so that the operator provides relevant data to the shale oil horizontal well EUR prediction device through an interactive unit.

7. A storage medium, characterized in that: The storage medium stores a computer program that can be read by a computer, and the computer program is configured to execute the EUR prediction method for shale oil horizontal wells according to any one of claims 1 to 3 when running.

8. An electronic device, characterized in that: It comprises a processor and a memory, wherein the memory stores a computer program, and the computer program is loaded and executed by the processor to implement the EUR prediction method for shale oil horizontal wells as described in any one of claims 1 to 3.

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