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

Abstract

The present application relates to a kind of oil production prediction technical field, it is a kind of shale oil horizontal well EUR prediction method, device, equipment and storage medium, including using flow period oil production prediction model group to obtain flow period oil production prediction value;Using input pumping period oil production prediction model to obtain pumping period oil production prediction value;Using full cycle oil production prediction model to obtain full cycle oil production prediction value;Flow period oil production prediction value and pumping period oil production prediction value are summed up, and with full cycle oil production prediction value do mean value.The present application constructs quantitative characterization model, oil production decline model, fracturing fracture oil production prediction model, pumping period oil production prediction model and full cycle oil production prediction model for different development stages, respectively corresponding oil production prediction value of flow period, pumping period, full cycle is predicted and combined, estimates shale oil horizontal well EUR prediction value, so that prediction is more accurate, realizes production development effect classification evaluation, guides future favorable area's production decision and scheme prediction.

Description

Technical Field

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

[0002] Existing shale reservoir development techniques primarily utilize horizontal wells coupled with multi-stage fracturing. Development experience demonstrates that predicting the productivity of fractured horizontal wells is crucial. To accurately characterize the oil-water flow characteristics during fracturing, shut-in, and production, and more accurately predict productivity, it is necessary to account for the retention, imbibition, and backflow of fracturing fluids in shale reservoirs during horizontal well productivity prediction. However, the varying flow patterns in media of varying scales make oil production prediction challenging. Therefore, developing a productivity prediction method for fractured horizontal wells is crucial for their subsequent development.

[0003] Current oil production prediction methods are mainly divided into the decline curve method, analytical / semi-analytical methods, numerical simulation methods, and neural network prediction methods. Although the decline curve method can quickly and accurately determine the decline pattern of the target reservoir, the Arps formula used in it is applicable under relatively stringent conditions and is mainly targeted at the late stage of oil field development. It is difficult to fully consider the production process of shale oil horizontal wells throughout their entire life cycle. Analytical / semi-analytical methods are widely used to calculate the production capacity of single-phase fluids in fractured horizontal wells based on a large number of assumptions, but they have certain limitations when predicting the multiphase flow of pressure-simmer-production in shale reservoirs. Numerical simulation methods are based on the use of Pebi grids combined with local encryption technology. Although they can provide a detailed description of the reservoir and predict the distribution of remaining oil, they are computationally time-consuming. 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 existing technologies 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 through the following measures: a shale oil horizontal well EUR prediction method, comprising:

[0006] Obtain existing production-related data during the pumping period and input it into a flow period oil production prediction model group, and take the average of all oil production output values ​​as the flow period oil production prediction value, wherein the flow 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 fracture oil production prediction model constructed by fitting production dynamic data;

[0007] Obtain pressure data during the flowing period and the pumping period and input them into the oil production prediction model during the pumping period to output the predicted value of 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 flow period ends; Q m is the oil production during the self-flowing 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 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 to 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(stew 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 entire 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 aforementioned flow-period oil production prediction model group includes: a quantitative characterization model constructed by combining peak oil production, peak time, and the ARPS decline model; an oil production decline model constructed by combining initial oil production and the ARPS decline model; and a fracture oil production prediction model constructed by fitting production dynamic data. The specific details are 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 crack 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 the evaluation of fracturing effects and production prediction of typical wells based on Topaze software, the determination of key development parameters, and the evaluation of fracturing effects, 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 through 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 a flow period oil production prediction model group, and takes the average of all oil production output values ​​as the flow period oil production prediction value, wherein the flow 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 fracture oil production prediction model constructed by fitting production dynamic data;

[0028] The second prediction unit obtains the pressure data during the flowing period and the pumping period and inputs the data into the oil production prediction model during the pumping period, and outputs the predicted value of the oil production during 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 flow period ends; Q m is the oil production during the self-flowing 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 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 full-cycle production data and inputs it into the full-cycle oil production prediction model to output 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(stew time 0.0748 +Oil pressure / sinking 0.0883 )

[0033] The prediction value determination unit sums the predicted oil production value during the flowing period and the predicted oil production value during the pumping period, and takes the average of the summation result and the predicted oil production value during the entire 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 uses Topaze software to evaluate the fracturing effect and production forecast of typical wells, determine key development parameters, and evaluate the fracturing effect. The key development parameters include formation permeability, fracture half-length, SRV area, well-controlled area, well-controlled reserves, production forecast, EUR determination, 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 according to 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 the interactive unit.

[0039] The present invention aims at different development stages (flowing period, pumping period, full cycle), and constructs a quantitative characterization model, an oil decline model, a fracturing fracture oil production prediction model, a pumping period oil production prediction model, 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. Based on the constructed models, the predicted oil production values ​​corresponding to the flowing period, pumping period, and full cycle are predicted, and combined with the predicted oil production values ​​corresponding to the flowing period, pumping period, and full cycle, the predicted EUR (total oil production value) of shale oil horizontal wells is estimated, so that the prediction is more accurate, the classified evaluation of production and development effects is realized, and the production decision-making and plan prediction of future favorable areas are guided. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Attachment Figure 1 This is a flow chart of a prediction method of the present invention.

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

[0042] Attachment 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] Attachment Figure 4 Schematic diagram of the device structure of the present invention.

[0044] Attachment Figure 5 Schematic diagram of the system structure of the present invention.

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

[0046] Attachment 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 to the following embodiments, and specific implementation methods can be determined based on the technical solutions of the present invention and actual conditions.

[0048] Explanation of the name: Horizontal well EUR is the total oil production of a horizontal well throughout 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 helps to formulate a reasonable development plan.

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

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

[0051] Step S110: Obtain existing production-related data during the pumping period and input it into a flow period oil production prediction model group, and take the average of all oil production output values ​​as the flow period oil production prediction value, wherein the flow period oil production prediction model group includes: a quantitative characterization model constructed by combining peak oil production, peak time, and an ARPS decline model, an oil production decline model constructed by combining initial oil production and an ARPS decline model, and a fracture oil production prediction model constructed by fitting 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 flow period ends; Q m is the oil production during the self-flowing 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 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(stew time 0.0748 +Oil pressure / sinking 0.0883 )

[0057] In step S140, 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 summed result and the predicted oil production value during the entire period is taken as the EUR predicted value of the shale oil horizontal well.

[0058] The present invention discloses a EUR prediction method for shale oil horizontal wells. Based on reservoir geological knowledge and production and development characteristic analysis, a quantitative characterization model, an oil decline model, a fracturing fracture oil production prediction model, a pumping period oil production prediction model, and a full-cycle oil production prediction model are constructed for different development stages (flowing period, pumping period, and full cycle) based on the full-cycle production changes of shale oil with clear physical significance characteristic parameters. The constructed models predict the oil production prediction values ​​corresponding to the flowing period, pumping period, and full cycle, respectively. Combined with the oil production prediction values ​​corresponding to the flowing period, pumping period, and full cycle, the EUR (total oil production value) prediction value of the shale oil horizontal well is estimated, making the prediction more accurate, realizing classified evaluation of production and development effects, and guiding future production decisions and plan predictions in favorable areas.

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

[0060] Step S210: Obtain existing production-related data during the pumping period and input it into a flow period oil production prediction model group, and take the average of all oil production output values ​​as the flow period oil production prediction value, wherein the flow period oil production prediction model group includes: a quantitative characterization model constructed by combining peak oil production, peak time, and an ARPS decline model, an oil production decline model constructed by combining initial oil production and an ARPS decline model, and a fracture oil production prediction model constructed by fitting production dynamic data;

[0061] The above-mentioned oil production prediction model group during the 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 and then decrease from the oil-seeking moment. The quantitative characterization model of the above formula is obtained by fitting the relevant characteristics based on 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 decrease 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 (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 an increasing period.

[0076] (3) When t = t m When D=0, the output change curve reaches the 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 basically 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] The above description shows 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 flowing period can be obtained through the oil production decline model.

[0080] (3) Fracturing 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 over-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 fracture oil production prediction model.

[0090] Step S220, 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;

[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 flow period ends; Q m is the oil production during the self-flowing 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 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 pseudo-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 the shale oil horizontal well during the pumping period (see the attached figure), the oil production rate is 200 s. The average oil production rate is 2.37 ... Figure 4 The model building process is as follows:

[0094] (1) Using a specific size nozzle 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 before 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. 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 flow period ends; Q m is the oil production during the self-flowing 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 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(stew 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 their main controlling factors and weights of 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 prediction is obtained by regression.

[0110] Step S240, summing the predicted oil production value during the flowing period and the predicted oil production value during the pumping period, and taking the average of the summation result and the predicted oil production value during the entire period as the EUR predicted value of 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, EUR determination, 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's full life cycle output forecast and actual output

[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. In the lower sweet spot, there are three wells with cumulative production forecasts exceeding 30,000 tons, accounting for 27%; three wells with cumulative production forecasts ranging from 20,000 tons to 30,000 tons, accounting for 27%; and three wells with cumulative production forecasts ranging from 10,000 tons to 20,000 tons, accounting for 36%. More than 80% of the wells have cumulative production exceeding 12,000 tons, and the development effect is generally better than that of the upper sweet spot wells.

[0124] 2. Some wells in the lower sweet spot have good production, exceeding 30,000 tons, and some wells even have cumulative production exceeding 40,000 tons. Among the 10 upper sweet spot wells surveyed, only two have production exceeding 30,000 tons, and the rest are all below 12,000 tons.

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

[0126] Example 4: As shown in the 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 a flow period oil production prediction model group, and takes the average of all oil production output values ​​as the flow period oil production prediction value, wherein the flow 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 fracture oil production prediction model constructed by fitting production dynamic data;

[0128] The second prediction unit obtains the pressure data during the flowing period and the pumping period and inputs the data into the oil production prediction model during the pumping period, and outputs the predicted value of the oil production during 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 flow period ends; Q m is the oil production during the self-flowing 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 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 full-cycle production data and inputs it into the full-cycle oil production prediction model to output 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(stew time 0.0748 +Oil pressure / sinking 0.0883 )

[0133] The prediction value determination unit sums the predicted oil production value during the flowing period and the predicted oil production value during the pumping period, and takes the average of the summation result and the predicted oil production value during the entire period as the EUR predicted 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. Key development parameters include formation permeability, fracture half-length, SRV area, well-controlled area, well-controlled reserves, production forecast, EUR determination, and recovery factor.

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

[0136] 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 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 the interactive unit.

[0138] Example 6: The embodiment of the present invention discloses a storage medium, on which a computer program that can be read by a computer is stored. 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, 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.

[0140] Example 7: An 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 the 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 so on. Memory may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memories, removable hard drives, magnetic disks, or optical disks.

[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 application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the 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 embodiment of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal translation 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 produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. 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 that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The 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 removed 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 it into a flow period oil production prediction model group, and take the average of all oil production output values ​​as the flow period oil production prediction value, wherein the flow 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 fracture oil production prediction model constructed by fitting production dynamic data; Obtain pressure data during the flowing period and the pumping period and input them into the oil production prediction model during the pumping period to output the predicted value of 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; p m is the wellhead oil pressure after the flow period ends; Q m is the oil production during the self-flowing 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 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 to 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(stew 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 entire 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 flow 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 fracture oil production prediction model constructed by fitting production dynamic data, specifically 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, Q c 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 crack oil production prediction model: Q y3 =q×(1-w%) Where q is the fluid production of the fracture; p i is the formation pressure; p wf is the bottom hole pressure; t is the production time; A is the total surface area of ​​the fracture; Q y3 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 according to 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 a flow period oil production prediction model group, and takes the average of all oil production output values ​​as the flow period oil production prediction value, wherein the flow 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 fracture oil production prediction model constructed by fitting production dynamic data; The second prediction unit obtains the pressure data during the flowing period and the pumping period and inputs the data into the oil production prediction model during the pumping period, and outputs 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; Q n is the current oil production; p m is the wellhead oil pressure after the flow period ends; Q m is the oil production during the self-flowing 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 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 full-cycle production data and inputs it into the full-cycle oil production prediction model to 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(stew time 0.0748 +Oil pressure / sinking 0.0883 ) The prediction value determination unit sums the predicted oil production value during the flowing period and the predicted oil production value during the pumping period, and takes the average of the summation result and the predicted oil production value during the entire 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 uses Topaze software to evaluate the fracturing effect and production forecast of typical wells, determine key development parameters, and evaluate the fracturing effect. The key development parameters include formation permeability, fracture half-length, SRV area, well-controlled area, well-controlled reserves, production forecast, EUR determination, 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 according to claim 4 or 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 the 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: The system 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 according to any one of claims 1 to 3.

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