Method and device for predicting fractured lake basin shale oil single well productivity and curve model

Abstract

The invention relates to the technical field of oil-gas exploration and development, in particular to a method and device for predicting the single well productivity of fractured lake basin shale oil and a curve model. The method for predicting the fractured lake basin shale oil single well productivity comprises the steps that peak monthly yields and single well productivity of multiple fractured lake basin shale oil single wells are obtained, and a curve model used for predicting the fractured lake basin shale oil single well productivity is established based on the obtained peak monthly yields and single well productivity; on the basis of the curve model, productivity prediction is conducted on the broken lake basin shale oil single well with productivity to be predicted, and a prediction result is obtained.The productivity of the single well can be rapidly calculated, operation is convenient and easy, the problems that in the prior art, the number of parameters is large, and steps are complex are solved, and the method is suitable for popularization and application. And the productivity evaluation of the broken lake basin shale oil well can be completed more quickly and accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration and development, and in particular to a method, a prediction device and a curve model for predicting the production capacity of a single well of shale oil in a fault lake basin. Background Art

[0002] As an important unconventional oil and gas resource, the exploitation and utilization of shale oil is of great significance to the national energy security. Shale oil production capacity forecast is crucial to the oil and gas extraction process. Accurately predicting the production capacity of a single shale oil well can better arrange and optimize resource allocation, making mining activities more efficient and effective. If the production capacity forecast is too low, it may lead to resource waste, while if the forecast is too high, it may lead to insufficient resources. In addition, accurately predicting the production capacity of a single shale oil well can help companies formulate more reasonable production plans and improve production efficiency.

[0003] In the prior art. Patent document CN115330060A discloses a method for calculating the productivity of shale oil horizontal wells based on reservoir and engineering factor analysis, and the technical solution adopted is: first, determine the productivity data of developed horizontal wells of shale oil; select typical wells and establish a data set of productivity influencing factors; analyze the geological factors affecting productivity, determine its main controlling factors, and clarify the relationship between the main controlling geological factors and productivity; analyze the engineering factors affecting productivity, determine its main controlling factors, and clarify the changing relationship between the main controlling engineering factors and productivity; construct a horizontal well productivity evaluation index, and construct a shale oil horizontal well productivity evaluation index based on the correlation between the main geological controlling factors, the main engineering controlling factors and productivity; establish a horizontal well productivity calculation model. Predict the productivity of horizontal wells under different reservoir types and different fracturing parameters, and give the plane distribution characteristics of the cumulative oil production. Patent document CN115146849A discloses a method for predicting shale oil production capacity by particle swarm optimization CNN, by constructing a shale oil production capacity mathematical model, the shale oil production capacity mathematical model is used to characterize the relationship between characteristic parameters and target parameters; based on the shale oil production capacity mathematical model, multiple groups of corresponding characteristic parameters and target parameters are obtained as model training data; a convolutional neural network model for predicting shale oil production capacity is established, and the particle swarm algorithm is used to obtain the optimal weight and optimal deviation value of the convolutional neural network model; the convolutional neural network model is trained based on the model training data to obtain a shale oil production capacity prediction model. Patent document CN114519260A discloses a shale oil production capacity prediction method, which determines the evaluation index system according to the evaluation purpose and establishes a production influencing factor database; divides the factors affecting production into four level values ​​according to the existing development plan database; constructs an average effect database of production influencing factors according to the production influencing factor database; constructs a production influencing factor range database according to the average effect database of production influencing factors; constructs a comparison matrix between different factors according to the production influencing factor range database; calculates the weight of the influencing factor and performs consistency test; and constructs a multivariate regression fitting production formula. It can determine the weight of influencing factors through quantitative calculation, eliminate possible errors caused by human judgment, perform multivariate regression based on several influencing factors with larger weights and production capacity, and fit a more reasonable and accurate production capacity formula. Patent CN113988475A discloses a shale oil production capacity prediction method based on the equivalent area value method of nuclear magnetic logging T2 spectrum. First, the nuclear magnetic logging T2 spectrum segment of the oil test and production test section with the most likely oil production or high oil production is selected through nuclear magnetic logging data, and then the nuclear magnetic T2 spectrum equivalent area value of the quantified nuclear magnetic spectrum is used to quantitatively judge the daily oil production model. This model is used to quantitatively judge the production capacity of the acquired nuclear magnetic logging data.In addition, patent document CN111042811A discloses a shale oil production capacity evaluation method based on sensitive parameter superposition, which includes logging the shale wells to obtain several logging curves, and calculating the shale porosity, total organic carbon content and brittleness index; conducting a comprehensive quality evaluation of shale oil to obtain a comprehensive evaluation index curve; and obtaining a production capacity evaluation parameter curve by calculating the superposition area of ​​the brittleness index curve and the porosity curve, and the brittleness index curve and the total organic carbon content curve after reverse scaling. The shale oil production capacity can be evaluated by combining the production capacity evaluation parameter curve with the oil test data.

[0004] In summary, although the prior art discloses methods for evaluating shale oil production, there are few methods that have simple steps, few parameters, convenient calculations, accuracy and speed, and can improve the efficiency of shale oil well production capacity evaluation. It is urgent to develop a method for evaluating the production capacity of a single shale oil well that has simple steps, few parameters, convenient calculations, accuracy and speed. Summary of the invention

[0005] The purpose of the present invention is to provide a method for predicting the productivity of a single shale oil well, which has simple steps and few parameters, accurate prediction results, and can improve the efficiency of shale oil well productivity evaluation.

[0006] The present invention includes the following technical solutions:

[0007] A first aspect of the present invention provides a method for predicting the productivity of a single well of shale oil in a fault lake basin, comprising:

[0008] Obtain the peak monthly production and single-well productivity of shale oil wells in multiple fault lake basins;

[0009] Based on the obtained peak monthly production and single well productivity, a curve model for predicting the single well productivity of shale oil in fault lake basins is established;

[0010] Based on the curve model, the production capacity of a single shale oil well in the predicted fault lake basin is predicted to obtain a prediction result.

[0011] Furthermore, establishing a curve model for predicting the single-well production capacity of shale oil in the fault lake basin includes: using the obtained peak monthly production and single-well production capacity to establish a correlation chart, and obtaining a curve model for predicting the single-well production capacity of shale oil in the fault lake basin through linear fitting.

[0012] Furthermore, the curve model is: y=ax+b, wherein y is the peak monthly production, in tons; x is the single well production capacity, in tons; a and b are correlation coefficients.

[0013] Furthermore, the curve model is: y=384.85x+109.59, y is the peak monthly production, in tons; x is the single well production capacity, in ten thousand tons.

[0014] Furthermore, the single-well production capacities of the multiple fault lake basin shale oil wells are predicted based on an improved hyperbolic decline model.

[0015] Furthermore, the number of the plurality of fault lake basin shale oil single wells is greater than or equal to 30 。

[0016] Furthermore, the multiple fault lake basin shale oil wells are single wells with a production time of ≥6 months and monthly production has passed the peak period.

[0017] A second aspect of the present invention provides a fault lake basin shale oil single well productivity prediction device, comprising a data acquisition module, wherein the data acquisition module is used to obtain the peak monthly production and single well productivity of multiple fault lake basin shale oil single wells;

[0018] A model building module, the model building module is used to establish a curve model for predicting the single-well production capacity of shale oil in a fault lake basin based on the peak monthly production and single-well production capacity obtained by the data acquisition module;

[0019] The production capacity prediction module predicts the production capacity of a single shale oil well in the fault lake basin to be predicted based on the curve model established by the model establishment module to obtain a prediction result.

[0020] Furthermore, the model building module is configured to use the peak monthly production and single well production capacity obtained by the data acquisition module to establish a correlation chart, and obtain a curve model for predicting the single well production capacity of shale oil in the fault lake basin through linear fitting. The curve model is: y=ax+b, where y is the peak monthly production, in tons; x is the single well production capacity, in ten thousand tons; a and b are correlation coefficients.

[0021] The third aspect of the present invention provides a curve model for predicting the single-well production capacity of shale oil in a fault lake basin, wherein the curve model is: y=ax+b, y is the peak monthly production of shale oil in the fault lake basin, in tons; x is the single-well production capacity of shale oil in the fault lake basin, in ten thousand tons.

[0022] By adopting the above technical solution, the present invention has at least the following beneficial effects:

[0023] The method of the present invention is based on the production characteristics of a single shale oil well in a fault lake basin. By establishing a relationship between the peak monthly production of shale oil and the production capacity of a single well, the production capacity of a single well can be quickly calculated based on the peak monthly production. The method is easy to operate and avoids the problems of multiple parameters and complicated steps in the prior art methods. The production capacity evaluation of shale oil wells in fault lake basins can be completed more quickly and accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 Shown is a chart showing the correlation between peak monthly production and single well productivity;

[0026] Figure 2 Shown is the production curve of one single well among the 31 shale oil single wells in the area;

[0027] Figure 3 The figure shows the relationship between the monthly peak production of a single shale oil well and the length of the horizontal section of the single well;

[0028] Figure 4 The chart shows the relationship between the monthly peak production of a single shale oil well and the volume of fracturing fluid per 100 meters;

[0029] Figure 5 The figure shows the relationship between the monthly peak production of a single shale oil well and the effective thickness of the oil layer (residual organic carbon content ≥ 2.0%);

[0030] Figure 6 The figure shows the relationship between the monthly peak production of a single shale oil well and the effective thickness of the oil layer (deep resistivity ≥ 20 ohm.m);

[0031] Figure 7 Shown is the improved hyperbolic decline model adopted in this application. DETAILED DESCRIPTION

[0032] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the embodiments of the present invention are further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0033] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0034] Unless otherwise defined, all scientific and technical terms used in the present invention have the same meanings as commonly understood by one of ordinary skill in the art to which the present invention relates.

[0035] In the present invention, the term "rift lake basin" means that the rift lake basin is a kind of lake basin, which refers to a lake basin formed by crustal fracture and collapse, and is characterized by a relatively simple lake plane morphology, often in a narrow and long shape, a relatively straight shoreline, a steep bank slope, a large depth, and a certain regularity. The sediments in the rift lake basin are relatively thick, and the sedimentary sequence is relatively complete, which can reflect the history of the formation and evolution of the lake basin. This kind of lake basin is distributed all over the world, for example, Dianchi Lake and Erhai Lake in Yunnan, my country, Qionghai Lake in Sichuan, Hulun Lake in Inner Mongolia, and Qinghai Lake in Qinghai Province are all typical rift lake basins.

[0036] In the present invention, the term "single-well productivity of shale oil in a fault lake basin" refers to the total oil production of a single shale oil well in a fault lake basin during the entire life cycle of the single well from exploitation to final depletion.

[0037] In the present invention, the term "peak period" refers to the period when the production of a single well in a fault lake basin is the highest. Specifically, when the channel of a single well in a fault lake basin is just opened, due to the continuous outflow of impurities such as mud and sand, the monthly production of the single well gradually increases from low to high at the beginning. After the single well in the fault lake basin has been producing for several months (one month or more, generally less than 6 months), the production of shale oil reaches a balance, and then the monthly production of the single well in the fault lake basin will decline slightly (generally speaking, the time is greater than or equal to 6 months). When the monthly production of a single well in a fault lake basin declines, the monthly production values ​​of the single well in the fault lake basin recorded in the history are observed, and the month with the highest monthly production value is the peak period, and the corresponding value of the monthly production of the single well in the fault lake basin is the peak monthly production of the single well in the fault lake basin.

[0038] In the present invention, shale oil wells generally have the characteristics of large initial production fluctuations, rapid production increase, rapid decline after reaching a certain height, and then gradually tending to stability, showing an obvious hyperbolic decline law, indicating that the formation energy changes from strong to weak and then stabilizes. This is because the reservoir permeability is extremely low, and the formation fluid communicates flow channels after fracturing and seaming. After the production reaches a peak, the formation pressure is slowly released, and the fluid flow energy is then weakened. Therefore, the judgment basis of the peak monthly production of the present invention is: the peak monthly production is the instantaneous maximum production of the monthly production of the fault lake basin shale oil well after the exploitation, which rises rapidly, reaches the highest peak, and then enters the decline period.

[0039] In the present invention, the term "improved hyperbolic decline model" refers to: in the field of shale oil and gas, the most widely used model in North America, which is divided into two stages (see Figure 7), the first stage is the hyperbolic decline model, which uses the hyperbolic decline model (i.e. b>1) to predict production in the early stage of shale oil and gas single well production; the second stage is the exponential decline model. In the second stage, since the seepage is mainly affected by the matrix, the production is low and the decline is slow, the exponential decline model is used instead. The improved hyperbolic decline model is different from the conventional (exponential, hyperbolic and harmonic) model. The decline index is between 0 and 1. The hyperbolic decline index of the first stage of the improved hyperbolic decline model is greater than 1, so it is called super hyperbolic. After a period of time, it enters exponential decline.

[0040] The beneficial effects of the present invention are further illustrated below in conjunction with specific embodiments.

[0041] Example 1: Model selection for predicting single well productivity of shale oil in fault lake basin

[0042] Take 31 shale oil wells in the fault basin of Dagang Oilfield as an example:

[0043] Based on the collection and collation of production data of 31 shale oil single wells in the study area, it is found that the single wells in the area generally have large production fluctuations in the early stage of production, with rapid production increase, rapid decline after reaching a certain height, and then slow decline and gradually tend to be stable, showing an obvious hyperbolic decline law, indicating that after the formation energy is quickly released, it reaches a balanced state and then stabilizes (such as Figure 2 shown).

[0044] However, even for shale oil wells within the range of adjacent development wells, the peak production that can be achieved by each well is different. Taking into account geological, drilling, engineering and other factors, we try to find the reasons that affect the production capacity of a single well. Based on the monthly production data of shale oil wells, we establish the relationship between the horizontal section length of a single well (unit, m), the volume of fracturing fluid per hundred meters (unit, cubic meters), the effective thickness of the oil layer (residual organic carbon content ≥ 2.0%), the effective thickness of the oil layer (deep resistivity ≥ 20 ohm·m), and the decline model to predict the single well production capacity (unit, 10,000 tons) and the peak monthly production (as shown in Figure 2). Figure 1 , Figure 3 to Figure 6 ), and finally found that the correlation between the single well peak monthly production and the single well productivity is good.

[0045] The reasons are summarized as follows: geological, drilling, and engineering factors are complex and changeable. The influencing factors that can be mastered at present are too single, and the correlation between parameters is low, which cannot systematically reflect the advantages and disadvantages of shale oil single wells. The decline model based on production data has outstanding performance because production data is the final reflection after integrating various influencing factors, and has a natural inherent multi-parameter integration attribute. The peak monthly production just reflects the height that a single shale oil well can reach, and there must be some inherent connection with the single well production capacity. When using the decline model to predict the production capacity of a single well, it is only necessary to consider optimizing the decline parameters according to the production data, which makes the evaluation method simple and feasible.

[0046] Example 2 Prediction of single well production capacity of shale oil in fault lake basins Specific method for determining

[0047] With the large-scale development of continental shale oil in the second section of the Cangdong Kong in the Dagang Oilfield, in the work of predicting the productivity of shale oil wells, based on the production data of 31 wells (see Table 1 for specific data), it was found that the correlation between the peak monthly production of a single well and the single well productivity was good, with a correlation coefficient of 0.832. The regression formula y=384.85x+109.59 (y is the peak monthly production, tons; x is the single well productivity, 10,000 tons), as shown in Figure 1 As shown in the figure (n is the number of single wells, r is the correlation coefficient, n=31, r=0.8320), according to this formula, the production capacity of a single shale oil well can be quickly inferred based on the known peak monthly production of the well, thus avoiding the problems of multiple parameters and complex steps in other methods, and the production capacity evaluation of shale oil wells in fault lake basins can be completed more quickly.

[0048] Table 1 Basic data of shale oil single well

[0049]

[0050]

[0051]

[0052] It should be particularly pointed out that the various components or steps in the above-mentioned embodiments can be cross-linked, replaced, added, or deleted with each other. Therefore, the combinations formed by these reasonable arrangements and combinations should also fall within the scope of protection of the present invention, and the scope of protection of the present invention should not be limited to the embodiments.

[0053] The above are exemplary embodiments disclosed in the present invention. The order disclosed in the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. However, it should be noted that the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope (including claims) disclosed in the embodiments of the present invention is limited to these examples. Various changes and modifications may be made without departing from the scope defined in the claims. The functions, steps and / or actions of the method claims according to the disclosed embodiments described herein do not need to be performed in any particular order. In addition, although the elements disclosed in the embodiments of the present invention may be described or required in individual form, they may also be understood as multiple unless explicitly limited to the singular.

[0054] A person skilled in the art should understand that the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the disclosure of the embodiments of the present invention (including the claims) is limited to these examples; under the concept of the embodiments of the present invention, the technical features in the above embodiments or different embodiments can also be combined, and there are many other changes in different aspects of the embodiments of the present invention as described above, which are not provided in detail for the sake of simplicity. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present invention should be included in the protection scope of the embodiments of the present invention.

Claims

1. A method for predicting the productivity of a single well of shale oil in a fault lake basin. It is characterized in that include: Obtain the peak monthly production and single-well productivity of shale oil wells in multiple fault lake basins; Based on the obtained peak monthly production and single well productivity, a curve model for predicting the single well productivity of shale oil in fault lake basins is established; Based on the curve model, the production capacity of a single shale oil well in the predicted fault lake basin is predicted to obtain a prediction result.

2. The method according to claim 1, It is characterized in that Establishing a curve model for predicting the single-well productivity of shale oil in a fault lake basin includes: using the obtained peak monthly production and single-well productivity to establish a correlation chart, and obtaining a curve model for predicting the single-well productivity of shale oil in a fault lake basin through linear fitting.

3. The method according to claim 1, It is characterized in that The curve model is: y=ax+b, wherein y is the peak monthly production, in tons; x is the single well production capacity, in tons; a and b are correlation coefficients.

4. The method according to claim 1 or 2, It is characterized in that The curve model is: y=384.85x+109.59, y is the peak monthly production, in tons; x is the single well production capacity, in ten thousand tons.

5. The method according to claim 1, It is characterized in that The single-well production capacities of the multiple fault lake basin shale oil wells are predicted based on an improved hyperbolic decline model.

6. The method according to claim 1, It is characterized in that The number of the plurality of fault lake basin shale oil wells is greater than or equal to 30.

7. The method according to claim 1, It is characterized in that The multiple fault lake basin shale oil wells are single wells with a production time of ≥6 months and whose monthly production has passed the peak period.

8. A single well productivity prediction device for shale oil in fault lake basins. It is characterized in that include: A data acquisition module, wherein the data acquisition module is used to obtain the peak monthly production and single-well production capacity of multiple fault-depression lake basin shale oil single wells; A model building module, the model building module is used to establish a curve model for predicting the single-well production capacity of shale oil in a fault lake basin based on the peak monthly production and single-well production capacity obtained by the data acquisition module; The production capacity prediction module predicts the production capacity of a single shale oil well in the fault lake basin to be predicted based on the curve model established by the model establishment module to obtain a prediction result.

9. The prediction device according to claim 8, It is characterized in that The model building module is configured to use the peak monthly production and single well production capacity obtained by the data acquisition module to establish a correlation chart, and obtain a curve model for predicting the single well production capacity of shale oil in the fault lake basin through linear fitting. The curve model is: y=ax+b, where y is the peak monthly production, in tons; x is the single well production capacity, in ten thousand tons; a and b are correlation coefficients.

10. A curve model for predicting the productivity of single wells in shale oil reservoirs in fault lake basins. It is characterized in that The curve model is: y=ax+b, y is the peak monthly production of shale oil in the fault lake basin, in tons; x is the single well production capacity of shale oil in the fault lake basin, in ten thousand tons.

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