SEC Proven Reserves Evaluation Method for Thermal Recovery Heavy Oil Reservoirs

Abstract

The present invention provides a SEC confirmed reserve evaluation method for thermal recovery heavy oil reservoirs, comprising: step 1, applying a probability method to calculate static geological reserves; step 2, calculating the correlation coefficient of normal distribution, log-normal distribution and triangular distribution fitting of parameter samples; step 3, selecting the optimal interval number and distribution type with the largest correlation coefficient as the distribution type of the corresponding reserve parameter; step 4, using a volumetric method to calculate the probability distribution diagram of the static geological reserves of the thermal recovery heavy oil reservoir; step 5, using a time normalization method to prepare an average single well production decline curve of an evaluation unit; step 6, using a two-stage decline rate value selection method to select the decline rate of an undeveloped evaluation unit confirmed by the thermal recovery heavy oil reservoir. The SEC confirmed reserve evaluation method for thermal recovery heavy oil reservoirs enables the decline rate, a key parameter in the dynamic method evaluation, to comply with both production practice and the SEC evaluation criteria, thereby ensuring the rationality of the evaluation result of the remaining economically recoverable reserves.

Description

Technical Field

[0001] The invention relates to the technical field of oilfield development, and in particular to a SEC confirmed reserve evaluation method for thermal recovery heavy oil reservoirs. Background Art

[0002] SEC proven reserves are the remaining economically recoverable reserves calculated according to the standards of the U.S. Securities and Exchange Commission (SEC). They are an important basis for measuring the exploration, production and operating conditions of oilfield companies. According to the different development status, proven reserves are divided into proven developed reserves and proven undeveloped reserves.

[0003] Shengli Oilfield thermal recovery heavy oil reservoirs are mainly located in Shanjiasi, Le'an, Gudao, Gudong, Wangzhuang, Chenjiazhuang and other oil fields, with proven geological reserves of 470 million tons and 355 million tons of reserves in use. In 2018, the SEC-confirmed developed reserves of thermal recovery heavy oil reservoirs accounted for 13.8% of the total developed reserves confirmed by Shengli Oilfield Branch, so it is of great significance to do a good job in evaluating the proven reserves of thermal recovery heavy oil reservoirs.

[0004] At present, there is no evaluation method for SEC proved reserves in thermal recovery heavy oil reservoirs in China. The evaluation methods and value selection basis used are based on the standards of conventional water drive reservoirs. However, due to the shallow burial depth, loose cementation, and complex formation of edge and bottom water in thermal recovery heavy oil reservoirs, it is difficult to accurately determine the reserve parameters when using the volumetric method to evaluate static geological reserves; in addition, due to the diverse development methods of thermal recovery heavy oil reservoirs and different development rules at different development stages, the selection of initial production and decline rate is also quite different when dynamically evaluating recoverable reserves. Therefore, it is necessary to conduct in-depth research on the evaluation method of proved reserves of thermal recovery heavy oil reservoirs and establish a set of SEC proved reserves evaluation methods from static evaluation to dynamic evaluation suitable for thermal recovery heavy oil reservoirs.

[0005] To this end, we invented a new SEC confirmed reserves evaluation method for thermal recovery heavy oil reservoirs to solve the above technical problems. Summary of the invention

[0006] The purpose of the present invention is to provide a thermal recovery heavy oil reservoir SEC proven reserves evaluation method which is more in line with the thermal recovery heavy oil development characteristics and SEC proven reserves evaluation criteria.

[0007] The object of the present invention can be achieved by the following technical measures: a thermal recovery heavy oil reservoir SEC confirmed reserve evaluation method, the thermal recovery heavy oil reservoir SEC confirmed reserve evaluation method comprising: step 1, applying a probability method to calculate static geological reserves; step 2, according to different set sample intervals, respectively calculating the correlation coefficients of normal distribution, log-normal distribution and triangular distribution fitting of parameter samples; step 3, according to the size of the correlation coefficient, selecting the optimal interval number and distribution type with the largest correlation coefficient as the distribution type of the corresponding reserve parameter; step 4, using a volumetric method to calculate the probability distribution diagram of the static geological reserves of the thermal recovery heavy oil reservoir; step 5, using a time normalization method to prepare an average single well production decline curve of an evaluation unit; step 6, using a two-stage decline rate value selection method to select the decline rate of an undeveloped evaluation unit confirmed by the thermal recovery heavy oil reservoir.

[0008] The purpose of the present invention can also be achieved by the following technical measures:

[0009] In step 1, a method for prioritizing the probability distribution type of the original data is established. Different sample intervals are set for the reserve calculation parameters with more selected values, including effective thickness, porosity and oil saturation. The sample interval is between 5 and 8.

[0010] In step 2, the normal distribution cumulative distribution function is:

[0011] In the formula, erf is the error function, x is the random variable, μ is the mean, and σ is the standard deviation;

[0012] The lognormal cumulative distribution function is:

[0013] In the formula, erf is the error function, x is the random variable, μ is the mean, and σ is the standard deviation;

[0014] The probability density function of the triangular distribution is:

[0015] In the formula, x is a random variable, and a, b, and c are interval values.

[0016] In step 3, after sorting the correlation coefficients obtained in step 2, it is selected that the normal distribution fitting accuracy is highest when the sample interval is 7. Therefore, the normal distribution with a sample interval of 7 can be used as the probability distribution type of porosity.

[0017] In step 4, the volumetric formula is N = 100A O h oi / B oi ,

[0018] Where: N—geological reserves of crude oil, 10 4 m3 , A o —Oil-bearing area, km 2 , h—effective thickness, Φ—effective porosity, f, S oi —Original oil saturation, f, B oi —Volume coefficient of original crude oil;

[0019] The reserves corresponding to the probability of 50% in the reserve probability distribution diagram are used as the best assessment value of static geological reserves.

[0020] In step 5, when the dynamic method is used to evaluate the remaining economically recoverable reserves, the parameter decline rate has the greatest impact on the evaluation results. The following principles are used to determine the decline rate: (1) The decline rate of the single well production curve of the test well should be used; if the production is composed of the natural production of the old well, the production of the old well measures and the production of the new well, it will weaken and affect the change law of the periodic oil production, and the decline rate will not correspond to the decline law of the proven reserves; (2) When the number of cycle days is inconsistent, the unit decline rate cannot use the cycle-to-cycle decline rate; (3) When the periodic steam injection volume is unstable, the oil-steam ratio-date curve cannot be used to determine the decline rate.

[0021] In step 5, the time normalization method is used to produce the average single well production decline curve of the evaluation unit; the time normalization method is to bring the production of single wells put into production at different times to the same starting time, so as to reflect the decline law.

[0022] In step 6, the first large decline rate adopts the intra-cycle decline rate; the second decline rate is analogous to the basic decline rate of the same type of reservoir with a long development time.

[0023] In step 6, the first large decline rate adopts the intra-cycle decline rate, which proves that the undeveloped reserve unit has not yet shown the intra-cycle decline law because of its short production time. Therefore, the first decline rate adopts the intra-cycle decline rate. The decline rate formula is D i =1-Q1 / Q0, where: Q0-initial output, Q1-current output, Di-decline rate.

[0024] In step 6, the duration of the first large decline rate is determined as the time period from production start to the occurrence of the inter-cycle decline law. It can also be determined by combining the decline law of the new well and analogizing the intra-cycle and inter-cycle decline laws of adjacent blocks.

[0025] In step 6, the analogy method is used to determine the decline rate of the second stage, which is analogous to the basic decline rate of the same type of reservoirs with a long development time. The specific value of the basic decline rate depends on the specific conditions of the analogy block.

[0026] The SEC proven reserves evaluation method for thermal recovery heavy oil reservoirs of the present invention establishes a method for preferentially selecting the probability distribution type of original data when evaluating static geological reserves by probability method, thereby improving the calculation accuracy of static geological reserves; clarifies the value-taking principle of the proven reserves decline rate of thermal recovery heavy oil reservoirs when evaluating remaining economically recoverable reserves by dynamic method, thereby ensuring that the decline rate is within a reasonable value range and effectively preventing large fluctuations in evaluation results; and establishes a two-stage decline rate value-taking method for thermal recovery heavy oil reservoirs, and describes in detail the first stage decline rate, the length of the first stage decline time and the determination method of the basic decline rate, thereby making the decline rate, a key parameter in the dynamic method evaluation, conform to both production practice and the evaluation criteria of SEC, thereby ensuring the rationality of the evaluation result of the remaining economically recoverable reserves. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of a specific embodiment of the SEC confirmed reserve evaluation method for thermal recovery heavy oil reservoirs of the present invention;

[0028] Figure 2 It is a schematic diagram of the reserve calculation result of the probability method in a specific embodiment of the present invention;

[0029] Figure 3 It is a schematic diagram of the production change curve of different initial production levels of thermally recovered heavy oil in a specific embodiment of the present invention;

[0030] Figure 4 It is a schematic diagram of a production change curve of a thermal recovery heavy oil unit in a specific embodiment of the present invention;

[0031] Figure 5 A schematic diagram of a yield decline curve in a specific embodiment of the present invention;

[0032] Figure 6 It is a schematic diagram of the production curve of a confirmed unit of Shanjiasi Oilfield in a specific embodiment of the present invention. DETAILED DESCRIPTION

[0033] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0034] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations and / or combinations thereof.

[0035] like Figure 1 As shown, Figure 1 The present invention is a flow chart of the SEC confirmed reserves evaluation method for thermal recovery heavy oil reservoirs.

[0036] Step 101, when applying the probability method to calculate static geological reserves, in order to reduce the randomness and subjectivity of the selection of probability distribution types of raw data, a method for preferentially selecting the probability distribution types of raw data is established.

[0037] The probability method is a static geological reserve evaluation method recognized by both SPE and SEC. The probability method is also called the Monte Carlo method. The probability method reserve estimation is based on probability theory. It considers the reserve parameters as random variables that vary within a certain range. The estimation result is a reserve probability distribution curve, and the geological reserves are estimated according to the specified probability value.

[0038] In existing Monte Carlo simulation software, the probability distribution type of the original data must be input in advance before the Monte Carlo simulation operation can be performed. The choice of probability distribution type determines the calculation accuracy of the model. Therefore, in order to improve the calculation accuracy of the probability method, the probability distribution type of each parameter must be accurately established.

[0039] In order to reduce the randomness and subjectivity of the selection of probability distribution type of raw data when applying the probability method to calculate static geological reserves, the present invention establishes a method for preferentially selecting the probability distribution type of raw data.

[0040] For reserve calculation parameters with more selected values, such as effective thickness, porosity and oil saturation, different sample intervals are set, and the general sample interval is between 5 and 8.

[0041] For example, because of the core analysis values ​​and logging interpretation values, the parameter porosity has obtained more selected values, so the number of sample intervals with different porosities is set to 5, 6, and 7 respectively.

[0042] Step 102, according to the different sample intervals set, respectively calculate the correlation coefficients of the normal distribution, lognormal distribution and triangular distribution fitting of the parameter sample. The normal distribution cumulative distribution function is The lognormal cumulative distribution function is The probability density function of the triangular distribution is The CORREL function can be used in EXCEL to calculate the correlation coefficient values ​​between the sample probability and the normal distribution probability, lognormal distribution probability, and triangular distribution probability.

[0043] For example, when the parameter porosity selects different sample interval numbers of 5, 6, and 7, the correlation coefficients of normal, lognormal, and triangular distributions when the sample interval number is 5 can be fitted respectively; the correlation coefficients of normal, lognormal, and triangular distributions when the sample interval is 6; and the correlation coefficients of normal, lognormal, and triangular distributions when the sample interval is 7. In this way, 9 correlation coefficients are obtained.

[0044] Step 103, according to the size of the correlation coefficient, select the optimal interval number and distribution type with the largest correlation coefficient as the distribution type of the corresponding reserve parameter.

[0045] After sorting the 9 correlation coefficients obtained above, R 2 :0.999>0.989>0.973>0.971>0.954>0.906>0.899>0.881>0.862. The normal distribution fitting accuracy is the highest when the sample interval is 7. Therefore, the normal distribution with a sample interval of 7 can be used as the probability distribution type of porosity.

[0046] Step 104, after the probability distribution type of the reserve calculation parameter is determined by the above method, the probability distribution diagram of the static geological reserves of the thermal recovery heavy oil reservoir is calculated by the volumetric method; the formula of the volumetric method is N = 100A O h oi / B oi , (where: N—geological reserves of crude oil, 10 4 m 3 , A o —Oil-bearing area, km 2 , h—effective thickness, Φ—effective porosity, f, S oi —Original oil saturation, f, B oi —Original crude oil volume coefficient) uses the reserves corresponding to a probability of 50% in the reserve probability distribution diagram as the best assessment value of static geological reserves, such as Figure 2 As shown, Figure 2 It is a schematic diagram of the reserve calculation result of the probability method in a specific embodiment of the present invention;

[0047] Step 105, when the dynamic method is used to evaluate the remaining economically recoverable reserves, the parameter decline rate has the greatest impact on the evaluation results. The following principles are used to determine the decline rate: (1) The decline rate of the single well production curve of the test well should be used. If the production is composed of the natural production of the old well, the production of the old well measures and the production of the new well, it will weaken and affect the change law of the periodic oil production, and the selection of the decline rate will not correspond to the decline law of the confirmed reserves. (2) When the number of days in the cycle is inconsistent, the unit decline rate cannot use the cycle-to-cycle decline rate. (3) When the periodic steam injection volume is unstable, the oil-steam ratio-date curve cannot be used to determine the decline rate.

[0048] First, the time normalization method is used to produce the average single well production decline curve of the evaluation unit. The time normalization method is to bring the production of single wells put into production at different times to the same starting time, so as to reflect the decline law.

[0049] Step 106, in order to make the decline rate selection value better reflect the development characteristics of steam-throwing heavy oil reservoirs and ensure that it is within a reasonable range to prevent large fluctuations in the evaluation results, the decline rate selection value of the undeveloped evaluation unit of the thermal recovery heavy oil reservoir adopts a two-stage decline rate selection method. Generally, a large decline is given in the initial stage, and then the basic decline rate is predicted.

[0050] The first stage of the decline rate adopts the intra-cycle decline rate, which proves that the undeveloped reserve unit has not yet shown the cycle decline law because of the short production time. Therefore, the first stage of the decline rate adopts the intra-cycle decline rate. The decline rate formula is D i =1-Q1 / Q0 (where: Q0-initial output, Q1-current output, Di-decline rate).

[0051] The initial periodic decline rate of heavy oil reservoirs in Shengli oilfield is between 30% and 70%. There is a certain matching relationship between the periodic decline rate and the initial production. The higher the initial production, the greater the periodic decline rate.

[0052] According to statistics, when the initial production level of a single well in a thermal recovery heavy oil reservoir is 0-5t, the average comprehensive decline rate is 3%; when the initial production level of a single well is 5-10t, the average comprehensive decline rate is 8.2%; when the initial production level of a single well is 10-15t, the average comprehensive decline rate is 10.1%; when the initial production level of a single well is 15-25t, the average comprehensive decline rate is 15.6%. The comprehensive decline rate is a comprehensive reflection of the initial decline rate and the basic decline rate. The basic decline rates of the same type of heavy oil reservoirs are very close, so the difference mainly lies in the initial decline rate. The initial decline rate and the initial production also have the above matching relationship. Figure 3 As shown, Figure 3 It is a schematic diagram of the yield change curve of different initial production levels of heavy oil in medium thermal recovery of the present invention;

[0053] Determine the duration of the first large decline rate. Generally, it is the time period from the start of production to the appearance of the periodic decline law (usually 1 to 2 years). It can also be determined by combining the decline law of new wells and analogizing the decline law within and between cycles of adjacent blocks. Taking a heavy oil reservoir block in Chunfeng Oilfield as an example, it is confirmed that the first large decline rate of undeveloped reserves is 70% and lasts for 9 months. Figure 4 As shown, Figure 4 It is a schematic diagram of the first section of the large decline rate of a production curve of a thermal recovery heavy oil unit in the present invention;

[0054] The analogy method is used to determine the decline rate of the second stage. The basic decline rate of the same type of reservoir with a long development time is compared. The basic decline rate of thermal recovery heavy oil reservoirs in Shengli Oilfield is between 10% and 20%, and the specific value depends on the specific conditions of the analogy block. Figure 5 As shown, Figure 5 It is a schematic diagram of the base decline rate of a production curve of a certain thermal recovery heavy oil unit in the present invention;

[0055] In a specific embodiment of the present invention, the following steps are included:

[0056] Step 1) Apply the probability method to calculate the static geological reserves, firstly give priority to the data probability distribution type of the reserve calculation parameters.

[0057] Taking a proven reserve unit of thermal recovery of heavy oil in Le'an Oilfield as an example, the probability method is used to calculate static reserves. The porosity in the parameter has core analysis values ​​and logging interpretation values, and many selected values ​​are obtained, so the number of sample intervals with different porosities is set to 5, 6, and 7 respectively.

[0058] Step 2) According to the different sample intervals set, the correlation coefficients of the normal distribution, lognormal distribution and triangular distribution fitting of the parameter samples are calculated respectively.

[0059] In this example, the parameter porosity selects different sample interval numbers of 5, 6, and 7, and fits the correlation coefficients of normal, lognormal, and triangular distribution when the sample interval is 5; the correlation coefficients of normal, lognormal, and triangular distribution when the sample interval is 6; the correlation coefficients of normal, lognormal, and triangular distribution when the sample interval is 7. In this way, 9 correlation coefficients are obtained: 0.862, 0.954, 0.971, 0.881, 0.899, 0.906, 0.999, 0.973, 0.989

[0060] Step 3) According to the size of the correlation coefficient, the optimal number of intervals and distribution type with the largest correlation coefficient are selected as the distribution type of the corresponding reserve parameter.

[0061] After sorting the 9 correlation coefficients obtained above, R 2 :0.999>0.989>0.973>0.971>0.954>0.906>0.899>0.881>0.862 It is selected that the normal distribution fitting accuracy is the highest when the sample interval is 7. Therefore, in this example, the normal distribution with a sample interval of 7 can be used as the probability distribution type of porosity.

[0062] Step 4) After the probability distribution type of the reserve calculation parameters is determined by the above method, the probability distribution diagram of the static geological reserves of the thermal recovery heavy oil reservoir is calculated by the volumetric method, and the reserves corresponding to the probability of 50% in the reserve probability distribution diagram are used as the best evaluation value of the static geological reserves.

[0063] This example uses the volumetric method. After 10,000 iterations, the geological reserves with a probability of 90%, 50%, and 10% are 4.4689 million tons, 4.624 million tons, and 4.8659 million tons, respectively. Figure 6 ), which is slightly different from the geological reserves calculated by the determination method approved by the State Reserves Commission (4.6049 million tons), as shown in Table 1.

[0064] Table 1 Comparison of reserve calculation results

[0065]

[0066] Step 5) When evaluating the remaining economically recoverable reserves using the dynamic method, firstly, the time normalization method is used to produce the average single well production decline curve of the evaluation unit.

[0067] Take a heavy oil reservoir unit in Shanjiasi Oilfield as an example. This unit was put into production in 2016 with steam injection, geological reserves of 1.52 million tons and 14 oil wells. At the end of 2017, it was used as a proven reserve unit for SEC reserve assessment. First, the time normalization method was used to produce the average single well daily oil curve. Figure 6 shown.

[0068] Step 6) The decline rate of the confirmed undeveloped evaluation unit of the thermal recovery heavy oil reservoir is selected by a two-stage decline rate selection method. A large decline is given in the initial stage, and then predicted according to the basic decline rate. The first large decline rate adopts the decline rate within the cycle; the second decline rate is analogous to the basic decline rate of the same type of reservoir with a long development time.

[0069] In this example, according to the decline law of the production curve of this unit, the decline rate is selected as follows: the first decline rate is a large decline rate of 42%, and the decline time is 10 months; the second decline rate is the basic decline rate of 22% of the proven developed unit with a long development time of this oil field. The dynamic assessment of the remaining recoverable reserves is 85,700 tons. Figure 6 As shown, Figure 6 It is a schematic diagram of the production curve of a confirmed unit of Shanjiasi Oilfield in a specific embodiment of the present invention.

[0070] According to the domestic recoverable reserves calibration recovery rate calculation technology, the recoverable reserves are 310,400 tons, and the remaining recoverable reserves are 182,100 tons. At present, the ratio of the confirmed reserves of the Shengli Oilfield listed dynamic assessment to the domestic static calibrated recoverable reserves does not exceed 70%, so according to this ratio standard estimation, the result of this dynamic assessment is within a reasonable range, indicating that the value of the decline rate is reasonable.

[0071] Finally, it should be noted that the above is only 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 aforementioned embodiments, those skilled in the art can still modify the technical solutions recorded in the aforementioned embodiments or replace some of the technical features therein with equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0072] Except for the technical features described in the specification, all other technical features are known technologies to those skilled in the art.

Claims

1. A method for evaluating SEC-confirmed reserves of thermally recovered heavy oil reservoirs, characterized in that: The SEC proven reserves evaluation method for thermal recovery heavy oil reservoirs includes: Step 1, calculate the static geological reserves using the probability method; Step 2, according to different set sample intervals, respectively calculate the correlation coefficients of normal distribution, lognormal distribution and triangular distribution fitting of the parameter sample; Step 3, according to the size of the correlation coefficient, select the optimal interval number and distribution type with the largest correlation coefficient as the distribution type of the corresponding reserve parameter; Step 4, using a volumetric method to calculate a probability distribution diagram of static geological reserves of a thermal recovery heavy oil reservoir; Step 5: Use the time normalization method to create the average single well production decline curve of the evaluation unit Step 6, using a two-stage decline rate value selection method to select the decline rate of the confirmed undeveloped evaluation unit of the thermal recovery heavy oil reservoir; In step 2, the normal distribution cumulative distribution function is: In the formula, erf is the error function, x is the random variable, μ is the mean, and σ is the standard deviation; The lognormal cumulative distribution function is: In the formula, erf is the error function, x is the random variable, μ is the mean, and σ is the standard deviation; The probability density function of the triangular distribution is: In the formula, x is a random variable, and a, b, and c are interval values.

2. The SEC confirmed reserves evaluation method for thermal recovery heavy oil reservoirs according to claim 1, characterized in that: In step 1, a method for prioritizing the probability distribution type of the original data is established. Different sample intervals are set for the reserve calculation parameters with more selected values, including effective thickness, porosity and oil saturation. The sample interval is between 5 and 8.

3. The SEC confirmed reserves evaluation method for thermal recovery heavy oil reservoirs according to claim 1, characterized in that: In step 3, after sorting the correlation coefficients obtained in step 2, it is selected that the normal distribution fitting accuracy is highest when the sample interval is 7. Therefore, the normal distribution with a sample interval of 7 can be used as the probability distribution type of porosity.

4. The SEC confirmed reserves evaluation method for thermal recovery heavy oil reservoirs according to claim 1, characterized in that: In step 4, the volumetric formula is N = 100A O h oi / B oi , Where: N—geological reserves of crude oil, 10 4 m 3 , A o —Oil-bearing area, km 2 , h—effective thickness, Φ—effective porosity, f, S oi —Original oil saturation, f, B oi —Volume coefficient of original crude oil; The reserves corresponding to the probability of 50% in the reserve probability distribution diagram are used as the best assessment value of static geological reserves.

5. The SEC confirmed reserves evaluation method for thermal recovery heavy oil reservoirs according to claim 1, characterized in that: In step 5, when the dynamic method is used to evaluate the remaining economically recoverable reserves, the parameter decline rate has the greatest impact on the evaluation results. The following principles are used to determine the decline rate: (1) The decline rate of the single well production curve of the test well should be used; if the production is composed of the natural production of the old well, the production of the old well measures and the production of the new well, it will weaken and affect the change law of the periodic oil production, and the decline rate will not correspond to the decline law of the proven reserves; (2) When the number of cycle days is inconsistent, the unit decline rate cannot use the cycle-to-cycle decline rate; (3) When the periodic steam injection volume is unstable, the oil-steam ratio-date curve cannot be used to determine the decline rate.

6. The SEC confirmed reserves evaluation method for thermal recovery heavy oil reservoirs according to claim 1, characterized in that: In step 5, the time normalization method is used to produce the average single well production decline curve of the evaluation unit; the time normalization method is to bring the production of single wells put into production at different times to the same starting time, so as to reflect the decline law.

7. The SEC confirmed reserves evaluation method for thermal recovery heavy oil reservoirs according to claim 1, characterized in that: In step 6, the first large decline rate adopts the intra-cycle decline rate; the second decline rate is analogous to the basic decline rate of the same type of reservoir with a long development time.

8. The SEC confirmed reserves evaluation method for thermal recovery heavy oil reservoirs according to claim 7, characterized in that: In step 6, the first large decline rate adopts the intra-cycle decline rate, which proves that the undeveloped reserve unit has not yet shown the intra-cycle decline law because of its short production time. Therefore, the first decline rate adopts the intra-cycle decline rate. The decline rate formula is D i =1-Q1 / Q0, where: Q0-initial output, Q1-current output, Di-decline rate.

9. The SEC confirmed reserves evaluation method for thermal recovery heavy oil reservoirs according to claim 8, characterized in that: In step 6, the duration of the first large decline rate is determined as the time period from production start to the occurrence of the inter-cycle decline law. It can also be determined by combining the decline law of the new well and analogizing the intra-cycle and inter-cycle decline laws of adjacent blocks.

10. The SEC confirmed reserve evaluation method for thermal recovery heavy oil reservoirs according to claim 7, characterized in that: In step 6, the analogy method is used to determine the decline rate of the second stage, which is analogous to the basic decline rate of the same type of reservoirs with a long development time. The specific value of the basic decline rate depends on the specific conditions of the analogy block.

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