Dual-model tight gas sandstone reservoir productivity prediction method based on logging technology

Abstract

The invention discloses a dual-model tight gas sandstone reservoir productivity prediction method based on a logging technology. The method comprises the following steps: S1, determining gas logging parameters related to the gas content of a reservoir; s2, determining a shaft information productivity prediction model of the tight gas reservoir; the step S2 specifically comprises the following steps: S21, data selection; s22, introducing new parameters; s23, establishing a multi-layer gas testing capacity weight distribution model; a productivity distribution model adopted by the multi-layer gas testing well is single-layer AOF decomposition = AOF * S layer / sigma S total; s24, establishing a double-model tight gas sandstone reservoir productivity prediction model; wherein the productivity prediction model established according to the single-layer gas testing well data is AOF1 = 0.0861 * (TG product / VTG / TTG * JTG) 0.6262, and R2 = 0.7543; a productivity prediction model established based on the data of the multi-layer gas testing well is AOF2 = 0.2096 * (TG product / VTG / TTG * JTG) 0.8473, and R2 = 0.7542. The straight and inclined shaft productivity prediction of the tight gas sandstone reservoir can be realized, the prediction precision is high, and the cost is low; the method is suitable for the oil and gas exploitation industry and is used for evaluating the productivity of the unconventional oil and gas reservoir.

Description

Technical Field

[0001] This invention belongs to the field of unconventional tight oil and gas exploration, specifically a dual-model method for predicting the productivity of tight gas sandstone reservoirs based on well logging technology. Background Technology

[0002] Production capacity assessment is a technique for comprehensively evaluating the oil-producing capacity of reservoirs, and it is of paramount importance to the exploration and development of oil and gas fields. Production capacity assessment can not only improve the efficiency of exploration and development, but also provide crucial scientific basis for the deployment and planning of development strategies.

[0003] Tight gas is a low-abundance, low-porosity, and low-permeability natural gas resource stored in tight underground rocks. Tight gas in the Ordos Basin is characterized by deeper burial, poorer reservoir conditions, and irregular distribution. For medium- and high-permeability reservoirs, oil-water flow generally follows Darcy's law, making productivity assessment relatively simple. However, the low-velocity flow in low-permeability reservoirs does not conform to Darcy's law. This is because, in addition to viscous resistance, it is also subject to adsorption resistance between the fluid and the rock, or attraction resistance from a water film. Only by overcoming this resistance can the liquid flow. Therefore, productivity assessment of such low-porosity, low-permeability, or ultra-low-permeability layers exhibiting initiation pressure phenomena becomes extremely difficult.

[0004] Currently, the most reliable data for reservoir productivity evaluation comes from oil testing and production testing data, but this method has high upfront costs. Summary of the Invention

[0005] The present invention aims to provide a dual-model method for predicting the productivity of tight gas sandstone reservoirs based on well logging technology, in order to solve the problem of high upfront costs caused by using test oil and production data to evaluate reservoir productivity in existing technologies.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A dual-model method for predicting the productivity of tight gas sandstone reservoirs based on well logging technology, comprising the following steps:

[0008] S1. Determine the gas measurement parameters related to reservoir gas content; gas measurement parameters include total hydrocarbon value (TG), hydrocarbon slope (Gr), moisture ratio (WH), and equilibrium ratio (BH);

[0009] S2. Determine the tight gas reservoir wellbore information production prediction model; this step includes...

[0010] S21. Data selection: Based on the test gas section, data is organized and analyzed according to the principles of relatively single-layer and independent multi-layer classification.

[0011] S22. Introduction of New Parameters: Statistical methods are used to process the total hydrocarbon (TG) value from gas logging, introducing integral and standard deviation processing methods to form new parameters for production capacity prediction and evaluation. By introducing these new parameters, reservoir energy and heterogeneity are optimized, thereby ensuring that the total hydrocarbon value parameters accurately reflect formation energy information.

[0012] S23. Establish a multi-layered gas testing capacity weight allocation model;

[0013] The production capacity allocation model used in multi-layer test wells is a single-layer AOF. 分解 =AOF×S 层 / ∑S 总 In the formula, AOF is the unobstructed flow rate, S 层 S is the integral area of ​​the total hydrocarbon value of a single layer. 总 The sum of the integral areas of the total hydrocarbon values ​​of the test layer;

[0014] S24. Establish a dual-model production capacity prediction model for tight gas sandstone reservoirs;

[0015] The production capacity prediction model established based on single-layer test well data is as follows: Predicted production capacity AOF1 = 0.0861 × (TG) 积 / V TG / T TG ×J TG ) 0.6262 R 2 =0.7543;

[0016] The production capacity prediction model established based on multi-layer gas testing well data is as follows: Predicted production capacity AOF2 = 0.2096 × (TG) 积 / V TG / T TG ×J TG ) 0.8473 R 2 =0.7542.

[0017] As a limitation of the present invention: the total hydrocarbon value parameter in step S22 includes the total hydrocarbon integral area (TG). 积 Total hydrocarbon coefficient of variation V TG Total hydrocarbon mutation coefficient T TG and total hydrocarbon range J TG Among them, TG 积 V refers to the integral area of ​​the total hydrocarbon value in the gas testing formation logging. TG T refers to the ratio of the standard deviation of total hydrocarbons to the average value of total hydrocarbons. TG J refers to the ratio of the maximum value of all hydrocarbons to the average value of all hydrocarbons. TG It refers to the ratio of the maximum value of total hydrocarbons to the minimum value of total hydrocarbons.

[0018] As another limitation of the present invention: step S2 further includes,

[0019] S25. Applicability correction of the dual-model tight gas sandstone reservoir productivity prediction model;

[0020] This correction is for data collected using a non-quantitative degasser, and introduces a correction coefficient K to modify the capacity prediction model obtained in step S24:

[0021] Predicted production capacity AOF1 = K × 0.0861 × (TG) 积 / V TG / T TG ×J TG ) 0.6262 R 2 =0.7543;

[0022] Predicted production capacity AOF2 = K × 0.2096 × (TG) 积 / V TG / T TG ×J TG ) 0.8473 R 2 =0.7542;

[0023] Specifically, when a quantitative degasser is used, K = 1; when a non-quantitative degasser is used, K = 3 when the total hydrocarbon value is less than 60%; and K = 6 when the total hydrocarbon value is greater than 60%.

[0024] By adopting the above technical solution, the beneficial effects achieved by the present invention compared with the prior art are as follows:

[0025] This invention includes steps S1, determining gas logging parameters related to reservoir gas content, and S2, determining a tight gas reservoir wellbore information production capacity prediction model. Step S2 includes S21, data selection; S22, introduction of new parameters; S23, establishing a multi-layer gas testing production capacity weight allocation model; and S24, establishing a dual-model tight gas sandstone reservoir production capacity prediction model. This invention uses well logging technology to form a dual-model tight gas sandstone production capacity prediction model, including a production capacity prediction model established using single-layer gas testing well data, where AOF1 = 0.0861 × (TG). 积 / V TG / T TG ×J TG ) 0.6262 And the production capacity prediction model AOF2 = 0.2096 × (TG) established based on multi-layer test well data. 积 / V TG / T TG ×J TG ) 0.8473 This invention enables high-precision and low-cost prediction of tight gas sandstone reservoir production capacity in both vertical and inclined wells; it also provides a basis for the efficient development of tight gas.

[0026] In summary, this invention can predict the production capacity of vertical and deviated wells in tight gas sandstone reservoirs with high accuracy and low cost. This invention is applicable to the oil and gas extraction industry and can be used to evaluate the production capacity of unconventional oil and gas reservoirs. Attached Figure Description

[0027] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0028] Figure 1 This is a flowchart of an embodiment of the present invention;

[0029] Figure 2 This is a flowchart of step S2 in an embodiment of the present invention;

[0030] Figure 3(a) shows the relationship between production capacity and total hydrocarbon value (TG) in an embodiment of the present invention.

[0031] Figure 3(b) shows the relationship between production capacity and hydrocarbon slope Gr in the embodiment of the present invention;

[0032] Figure 3(c) is a graph showing the relationship between production capacity and humidity ratio WH in an embodiment of the present invention;

[0033] Figure 3(d) is a graph showing the relationship between production capacity and balance ratio BH in an embodiment of the present invention;

[0034] Figure 4 This is a production capacity prediction chart established using single-layer test gas data in an embodiment of the present invention;

[0035] Figure 5 This is a production capacity prediction chart established using multi-layer test gas data in an embodiment of the present invention. Detailed Implementation

[0036] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the dual-model tight gas sandstone reservoir productivity prediction method based on logging technology described herein is a preferred embodiment and is only used for illustration and explanation of the present invention, and does not constitute a limitation thereof.

[0037] like Figure 1 , 2 As shown, this embodiment includes the following steps:

[0038] S1. Determine the gas measurement parameters related to reservoir gas content.

[0039] This step analyzes the correlation between logging parameters and production capacity in tight gas reservoirs. It involves analyzing data from over one hundred wells in the Southern Jiangsu block and summarizing the gas logging parameters according to reservoir production capacity classification. The reservoir production capacity classification is divided into three categories: Class I, Class II, and Class III. Gas logging parameters include total hydrocarbon value (TG), hydrocarbon slope (Gr), moisture ratio (WH), and equilibrium ratio (BH). Specifically, Gr = C2 / C3, WH = (C2+C3+C4+C5) / (C1+C2+C3+C4+C5)×100, and BH = (C1+C2) / (C2+C3+C4).

[0040] The distribution range of gas measurement parameters varies for different production capacities, as shown in Figure 3. Among them, the unobstructed flow rate of production layer I is greater than or equal to 100,000 m³ / h. 3 / d; Unobstructed flow rate of Class II producing layer 4-100,000 m³ / d 3 / d; the unobstructed flow rate of the third type of producing layer III is less than 40,000 m³. 3 / d.

[0041] As shown in Figure 3: Firstly, the distribution ranges of gas logging parameters TG, WH, Gr, and BH are not significantly different between Class I and Class II production layers. However, compared to Class III production layer, and between Class II and Class III production layer, the distribution ranges of gas logging parameters TG, WH, Gr, and BH show significant differences. Therefore, gas logging parameters are well correlated with reservoir gas content and production capacity. It should be noted that the TG, WH, Gr, and BH values ​​mentioned here are average values. Secondly, [the text abruptly ends here, likely due to an incomplete sentence or missing information]. Figures 3(a)-3(d) By comparison, it can be found that the curves in Figure 3(a) have fewer overlapping points and more obvious differences in data, thus showing a clear division of production capacity. However, the differences in data are not very obvious in the distribution maps of hydrocarbon slope Gr parameter, humidity ratio WH parameter, and balance ratio BH parameter.

[0042] S2. Determine the production capacity prediction model for tight gas reservoir wellbore information.

[0043] This step specifically includes the following:

[0044] S21. Data selection.

[0045] Based on the test gas layer, the data is organized and analyzed according to the principles of relatively single-layer and independent multi-layer classification.

[0046] S22. New parameters are introduced.

[0047] As learned in step S1, among the four gas logging parameters—total hydrocarbon value (TG), hydrocarbon slope (Gr), moisture ratio (WH), and equilibrium ratio (BH)—the total hydrocarbon value (TG) shows the most significant differences. Furthermore, the total hydrocarbon value (TG) is a continuously measured hydrocarbon parameter, and the amplitude and morphological changes of the total hydrocarbon curve are rich in information about reservoir oil, gas, water, formation pressure, and formation properties. The total hydrocarbon value (TG) can, to a certain extent, evaluate the abundance of oil and gas, and the morphological characteristics of the total hydrocarbons can qualitatively evaluate the energy level of the formation. A higher total hydrocarbon value indicates a higher abundance of oil and gas in the reservoir, and a greater likelihood of obtaining high-yield oil and gas flows.

[0048] Therefore, the gas measurement parameter determined in this step is the total hydrocarbon value (TG).

[0049] The total hydrocarbon value (TG) of gas-derived hydrocarbons is processed using statistical methods, including integral and standard deviation methods, to form new parameters for production capacity prediction and evaluation. By introducing these new parameters, the reservoir energy and heterogeneity are optimized, thereby enabling the total hydrocarbon value parameters to accurately reflect formation energy information.

[0050] The total hydrocarbon value parameter in this step includes the total hydrocarbon integral area (TG). 积 Total hydrocarbon coefficient of variation V TG Total hydrocarbon mutation coefficient T TG and total hydrocarbon range J TG .

[0051] Among them, TG 积 V refers to the integral area of ​​the total hydrocarbon value in the gas testing formation logging. TG T refers to the ratio of the standard deviation of total hydrocarbons to the average value of total hydrocarbons. TG J refers to the ratio of the maximum value of all hydrocarbons to the average value of all hydrocarbons. TG It refers to the ratio of the maximum value of total hydrocarbons to the minimum value of total hydrocarbons.

[0052] S23. Establish a multi-layered gas testing capacity weight allocation model.

[0053] The production capacity allocation model used in multi-layer test wells is a single-layer AOF. 分解 =AOF×S 层 / ∑S 总 In the formula, AOF is the unobstructed flow rate, S 层 S is the integral area of ​​the total hydrocarbon value of a single layer. 总 The sum of the integral areas of the total hydrocarbon values ​​of the test layer;

[0054] S24. Establish a dual-model production capacity prediction model for tight gas sandstone reservoirs.

[0055] See the production capacity prediction chart established using single-layer test gas data with probabilistic statistical methods. Figure 4 According to the capacity forecast chart, the linear relationship is y = 0.0861x. 0.6262 ,R 2 =0.7543, R2 This represents the correlation. Therefore, the production capacity prediction model based on single-layer test well data is: Predicted production capacity AOF1 = 0.0861 × (TG) 积 / V TG / T TG ×J TG ) 0.6262 R 2 =0.7543;

[0056] See the capacity forecasting chart built from multi-layer test gas data. Figure 5 According to the capacity forecast chart, the linear relationship is y = 0.2096x. 0.8473 ,R 2 =0.7542, R 2 This represents the correlation. Therefore, the production capacity prediction model established based on multi-layer test well data is: Predicted production capacity AOF2 = 0.2096 × (TG) 积 / V TG / T TG ×J TG ) 0.8473 R 2 =0.7542;

[0057] S25. Applicability correction of the dual-model tight gas sandstone reservoir productivity prediction model.

[0058] The prediction model data selected in step S24 is based on the regional gas measurement data collection based on the quantitative degasser. Therefore, the prediction model needs to be modified for data collected using the non-quantitative degasser (i.e., electric degasser).

[0059] Experiments and research have shown that when the total hydrocarbon ratio analyzed by the electric degasser and the quantitative degasser is between 3.0 and 6.0, and the total hydrocarbon value is less than 60%, the total hydrocarbon ratio analyzed by the electric degasser and the quantitative degasser is mainly concentrated in the 3-fold range. When the total hydrocarbon value is greater than 60%, the total hydrocarbon ratio analyzed by the electric degasser and the quantitative degasser is mainly concentrated in the 6-fold range.

[0060] In summary, a correction coefficient K is introduced to revise the capacity prediction model obtained in step S24:

[0061] Predicted production capacity AOF1 = K × 0.0861 × (TG) 积 / V TG / T TG ×J TG ) 0.6262 R 2 =0.7543;

[0062] Predicted production capacity AOF2 = K × 0.2096 × (TG) 积 / V TG / T TG×J TG ) 0.8473 R 2 =0.7542;

[0063] Specifically, when a quantitative degasser is used, K = 1; when a non-quantitative degasser is used, K = 3 when the total hydrocarbon value is less than 60%; and K = 6 when the total hydrocarbon value is greater than 60%.

[0064] The effect of using this embodiment is:

[0065] Based on this embodiment, a quantitative degasser was used to predict the production capacity of 14 wells, and the prediction results are shown in Table 1.

[0066] Table 1. Comparison of Predicted and Actual Capacity (Quantitative Degasser)

[0067]

[0068]

[0069] As shown in Table 1, based on the production capacity classification verification, 11 wells were found to be compliant, while 3 wells were not, resulting in a prediction compliance rate of 78.57%. The largest prediction errors were mainly concentrated in wells with production capacities exceeding 30 × 10⁻⁶. 4 m 3 / d well.

[0070] Based on this embodiment, a non-quantitative degasser was used to predict the production capacity of 16 wells. The prediction results are shown in Table 2.

[0071] Table 2. Comparison of Predicted and Actual Capacity (Non-quantitative Degasser)

[0072]

[0073] According to Table 2, based on the verification by production capacity, 12 wells were found to be compliant, while 4 wells were not, resulting in a prediction compliance rate of 75.00%.

[0074] Combining Tables 1 and 2, it can be seen that the accuracy of predicting production capacity using this embodiment is above 75.00%. Compared with traditional methods of obtaining production capacity data through gas testing and trial production, this method has a faster data acquisition speed, lower input costs, and higher accuracy. It can provide production capacity data before extraction, providing a basis for subsequent drilling operations and development plans, thus reducing oil and gas development costs.

[0075] It should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can still modify the technical solutions described in the above embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A dual-model method for predicting the productivity of tight gas sandstone reservoirs based on well logging technology, characterized in that, The method includes the following steps: S1. Determine the gas measurement parameters related to reservoir gas content; gas measurement parameters include total hydrocarbon value (TG), hydrocarbon slope (Gr), moisture ratio (WH), and equilibrium ratio (BH); S2. Determine the tight gas reservoir wellbore information production prediction model; this step includes... S21. Data selection: Based on the test gas section, data is organized and analyzed according to the principles of relatively single-layer and independent multi-layer classification. S22. Introduction of New Parameters: Statistical methods are used to process the total hydrocarbon (TG) value from gas logging, introducing integral and standard deviation processing methods to form new parameters for production capacity prediction and evaluation. By introducing these new parameters, reservoir energy and heterogeneity are optimized, thereby ensuring that the total hydrocarbon value parameters accurately reflect formation energy information. S23. Establish a multi-layered gas testing capacity weight allocation model; The production capacity allocation model used in multi-layer test wells is a single-layer AOF. 分解 =AOF×S 层 / ∑S 总 In the formula, AOF is the unobstructed flow rate, S 层 S is the integral area of ​​the total hydrocarbon value of a single layer. 总 The sum of the integral areas of the total hydrocarbon values ​​of the test layer; S24. Establish a dual-model production capacity prediction model for tight gas sandstone reservoirs; The production capacity prediction model established based on single-layer test well data is as follows: Predicted production capacity AOF1 = 0.0861 × (TG) 积 / V TG / T TG ×J TG ) 0.6262 R 2 =0.7543; The production capacity prediction model established based on multi-layer gas testing well data is as follows: Predicted production capacity AOF2 = 0.2096 × (TG) 积 / V TG / T TG ×J TG ) 0.8473 R 2 =0.7542.

2. The dual-model tight gas sandstone reservoir productivity prediction method based on logging technology according to claim 1, characterized in that, The total hydrocarbon value parameter in step S22 includes the total hydrocarbon integral area (TG). 积 Total hydrocarbon coefficient of variation V TG Total hydrocarbon mutation coefficient T TG and total hydrocarbon range J TG Among them, TG 积 V refers to the integral area of ​​the total hydrocarbon value in the gas testing formation logging. TG T refers to the ratio of the standard deviation of total hydrocarbons to the average value of total hydrocarbons. TG J refers to the ratio of the maximum value of all hydrocarbons to the average value of all hydrocarbons. TG It refers to the ratio of the maximum value of total hydrocarbons to the minimum value of total hydrocarbons.

3. The dual-model tight gas sandstone reservoir productivity prediction method based on logging technology according to claim 1 or 2, characterized in that, Step S2 also includes, S25. Applicability correction of the dual-model tight gas sandstone reservoir productivity prediction model; This correction is for data collected using a non-quantitative degasser, and introduces a correction coefficient K to modify the capacity prediction model obtained in step S24: Predicted production capacity AOF1 = K × 0.0861 × (TG) 积 / V TG / T TG ×J TG ) 0.6262 R 2 =0.7543; Predicted production capacity AOF2 = K × 0.2096 × (TG) 积 / V TG / T TG ×J TG ) 0.8473 R 2 =0.7542; Specifically, when a quantitative degasser is used, K = 1; when a non-quantitative degasser is used, K = 3 when the total hydrocarbon value is less than 60%; and K = 6 when the total hydrocarbon value is greater than 60%.

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