Quantitative evaluation method for carbonate reservoir

Abstract

The invention discloses a carbonate reservoir quantitative evaluation method, which comprises the following steps: taking well logging, logging and production data of each acid fracturing construction well in a target area as sub-factors, taking the daily gas production rate after acid fracturing as a parent factor, and determining a main control factor influencing the parent factor from the sub-factors; determining a correlation coefficient of the main control factor and the parent factor, and calculating a single well reservoir quantification coefficient of each acid fracturing construction well by using the correlation coefficient; classifying the carbonate reservoirs in the target area according to the single-well reservoir quantification coefficient; quantitatively evaluating the carbonate reservoir of the new well in the target area by using the classification result of the carbonate reservoir; the problem that an existing carbonate rock evaluation and interpretation method cannot specifically guide engineering transformation is effectively solved; and the evaluation capability of the carbonate reservoir is improved, and support is provided for accurately guiding engineering design and exploration and development of oil and gas fields.

Description

Technical Field

[0001] The present invention relates to the field of oil and gas development, specifically a quantitative evaluation calculation method for carbonate rock reservoirs. Background Art

[0002] The statements in this section only provide background information related to the present disclosure and do not constitute prior art.

[0003] The marine carbonate rock reservoirs in the Sichuan Basin have currently become the key targets for exploration and development of the Daqing Oilfield in Sichuan and Chongqing. Since the transfer of blocks in 2018, it has entered the stage of accelerating exploration and development. With the Qixia and Maokou formations as the main target layers, multi-layer three-dimensional exploration has been carried out, achieving good results. The carbonate rock reservoirs in the Qixia and Maokou formations are highly heterogeneous. Affected by geological karstification, there are large differences in the development of natural fractures and dissolution pores in different regions spatially, and the lithology also varies greatly, with interbeds of dolomite and limestone developed, and the reservoir types are extremely complex. In recent years, through the exploration and research work on carbonate rock reservoirs, seismic attribute evaluation methods, three-dimensional seismic coherence interpretation techniques, and seismic-logging joint interpretation and analysis methods have been summarized, and a set of carbonate rock reservoir interpretation methods has been established, with relatively accurate prediction of the reservoirs, which has become an important technical support for the well location deployment and engineering transformation of exploration wells, evaluation wells, and development wells.

[0004] Through the practice of several exploration wells, it is found that there are significant differences in the productivity and stable production capacity after acid fracturing. With the continuous deepening of the exploration and development work in the Sichuan-Chongqing exploration area of the Daqing Oilfield, in order to optimize the productivity after acid fracturing construction, the previously formed carbonate rock evaluation and interpretation methods cannot specifically guide engineering transformation. Often, some wells are interpreted as gas layers, but the gas testing production fails to meet the expected target. There are also some wells with initial production, but the production drops rapidly after a period of production, unable to achieve effective stable production capacity and unable to meet the targeted engineering transformation requirements of carbonate rocks. Therefore, in view of the strong heterogeneity of carbonate rocks, how to establish a new quantitative evaluation calculation method for carbonate rock reservoirs to further improve the evaluation ability of carbonate rock reservoirs and accurately guide engineering reservoir transformation is the main problem faced in the current exploration and development of carbonate rocks.

[0005] It should be noted that the information disclosed in the above background art section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute prior art. Summary of the Invention

[0006] In view of this, the present disclosure provides a quantitative evaluation method for carbonate rock reservoirs to solve the problem that the current carbonate rock evaluation and interpretation methods can no longer specifically guide engineering transformation in view of the strong heterogeneity of carbonate rocks.

[0007] To achieve the above-mentioned invention purpose, the quantitative evaluation method for carbonate rock reservoirs includes:

[0008] Taking the logging, mud logging and production data of each acid fracturing well in the target area as sub-factors and the daily gas production after acid fracturing as the mother factor, determine the main control factors affecting the mother factor from the sub-factors;

[0009] Determine the correlation coefficient between the main control factor and the mother factor, and calculate the single-well reservoir quantification coefficient of each acid fracturing well by using the correlation coefficient;

[0010] Classify the carbonate rock reservoirs in the target area according to the single-well reservoir quantification coefficient;

[0011] Use the classification result of the carbonate rock reservoir to quantitatively evaluate the carbonate rock reservoir type of the new wells in the target area.

[0012] In the present disclosure and possible embodiments, the method for determining the main control factors affecting the mother factor from the sub-factors includes:

[0013] Calculate the correlation degree between the sub-factors and the mother factor, and determine the main control factor according to the correlation degree.

[0014] In the present disclosure and possible embodiments, the calculation method of the correlation degree includes:

[0015] Taking the daily gas production data after acid fracturing as the mother sequence of the correlation analysis, denoted as {X0(i)}, (i = 1, 2..., n), where n is the sequence length;

[0016] Taking the logging, mud logging and production data as the sub-sequences of the correlation analysis, denoted as {X j (i)}, (j = 1, 2,..., m; i = 1, 2,..., n), where m is the number of influencing factors;

[0017] Adopt the mean method to perform dimensionless processing on the mother sequence and the sub-sequences, and the mean method processing formula is:

[0018]

[0019] In the formula: i = 1, 2,..., n; j = 0, 1,..., m;

[0020] Calculate the absolute value △ oj (i) of the difference between each sub-factor and the mother factor according to the following formula:

[0021] △ oj (i) = |Y0(i) - Y j (i)|;

[0022] In the formula: i = 1, 2,..., n; j = 0, 1,..., m;

[0023] Find the maximum value △ from the absolute value of the difference max and the minimum value △ min , and calculate the correlation coefficient between the mother sequence and the child sequence according to the following formula:

[0024]

[0025] Calculate the correlation degree γ according to the following formula oj :

[0026]

[0027] In the formula: i = 1, 2,..., n; j = 0, 1,..., m.

[0028] In the present disclosure and possible embodiments, the factor with the correlation degree greater than 0.7 is used as the main control factor.

[0029] In the present disclosure and possible embodiments, the method for determining the correlation coefficient between the main control factor and the mother factor includes:

[0030] Take the daily gas production data after acid fracturing as the mother sequence of correlation analysis, denoted as {X0(i)}, (i = 1, 2..., n), where n is the sequence length;

[0031] Take the main control factor data as the child sequence of correlation analysis, denoted as {X j (i)}, (j = 1, 2,..., m; i = 1, 2,..., n), where m is the number of influencing factors;

[0032] Calculate the correlation coefficient C according to the following formula j :

[0033]

[0034] In the formula: i = 1, 2,..., n; j = 1,..., k.

[0035] In the present disclosure and possible embodiments, calculate the single-well reservoir quantification coefficient Bi of each acid fracturing well according to the following formula:

[0036] B i = C1·Y1 + C2·Y2 + C3·Y3 + ··· + C k ·Y k ;

[0037] In the formula: i = 1, 2,..., n; Y k is the dimensionless processing result of the main control factor sub-sequence.

[0038] In the present disclosure and possible embodiments, the method for classifying the carbonate reservoir types in the target area includes:

[0039] Establishing a relationship curve between the single-well reservoir quantification coefficient and the gas test production in the target area;

[0040] Classifying the carbonate reservoirs in the target area according to the relationship curve within the set gas test production and single-well stimulation coefficient range;

[0041] The classified carbonate reservoir types are high-quality reservoirs, good reservoirs, average reservoirs, and poor reservoirs.

[0042] In the present disclosure and possible embodiments, for the set high-quality reservoir, the gas test production Q (×10 4 m 3 / d) ≥ 120, and the single-well stimulation coefficient range ≥ 2;

[0043] For the set good reservoir, the gas test production Q (×10 4 m 3 / d) < 150, ≥ 50; and the single-well stimulation coefficient range ≥ 2;

[0044] For the set average reservoir, the gas test production Q (×10 4 m 3 / d) < 50, ≥ 20; and the single-well stimulation coefficient range < 1.5, ≥ 1;

[0045] For the set poor reservoir, the gas test production Q (×10 4 m 3 / d) < 10, and the single-well stimulation coefficient range < 1.

[0046] In the present disclosure and possible embodiments, the method for quantitatively evaluating the carbonate reservoir types of new wells in the target area includes:

[0047] Calculating the reservoir quantification coefficient of the new well using the correlation coefficient;

[0048] Quantitatively evaluating the carbonate reservoir type of the new well according to the reservoir quantification coefficient of the new well in combination with the carbonate reservoir classification result.

[0049] In the present disclosure and possible embodiments, before calculating the reservoir quantification coefficient of the new well, the dimensionless processing of each main control factor of the new well is performed using the mean method.

[0050] The present disclosure has the following beneficial effects:

[0051] The quantitative evaluation method for carbonate rock reservoirs of the present disclosure uses the logging, mud logging, and production data of each acid fracturing well in the target area as sub-factors, and the daily gas production after acid fracturing as the mother factor. The main control factors affecting the mother factor are determined from the sub-factors, and the correlation coefficient between the main control factor and the mother factor is determined. Then, the single-well reservoir quantitative coefficient of each acid-fractured well in the target area is calculated using the correlation coefficient, and the carbonate rock reservoirs in the target area are classified based on the single-well reservoir quantitative coefficient. Finally, using the classification results of the carbonate rock reservoirs, the carbonate rock reservoirs of new wells in the target area are quantitatively evaluated. The method of the present invention is a new method for calculating the quantitative evaluation of carbonate rock reservoirs, thus effectively solving the problem that the current carbonate rock evaluation and interpretation methods can no longer specifically guide engineering transformation, improving the evaluation ability of carbonate rock reservoirs, providing necessary technical guidance for well and layer selection for engineering, fracturing design, fracturing model analysis, and on-site construction, etc., and further reducing investment risks and playing a role in improving quality and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Through the description of the embodiments of the present disclosure with reference to the following drawings, the above and other objects, features, and advantages of the present disclosure will become clearer. In the drawings:

[0053] Figure 1 is a schematic flow chart of the quantitative evaluation method for carbonate rock reservoirs of the embodiments of the present disclosure;

[0054] Figure 2 is a relationship curve between the single-well reservoir quantitative coefficient and the gas testing production in the target area of the embodiments of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] The following is a description of the present disclosure based on embodiments. However, it should be noted that the present disclosure is not limited to these embodiments. In the following detailed description of the present disclosure, some specific details are described in detail. However, for the parts not described in detail, those skilled in the art can also fully understand the present disclosure.

[0056] In addition, those of ordinary skill in the art should understand that the provided drawings are only for illustrating the purpose, features, and advantages of the present disclosure, and the drawings are not actually drawn to scale. At the same time, unless the context clearly requires, the words "including", "comprising", and other similar words throughout the specification and claims should be interpreted as having the meaning of inclusion rather than exclusion or exhaustion; that is, the meaning of "including but not limited to".

[0057] To make the purpose, technical solutions, and advantages of the present invention clearer, the following further describes the present invention in detail with reference to the drawings and by way of examples.

[0058] Referring to Figure 1 as shown, Figure 1The flow of the quantitative evaluation method for carbonate rock reservoirs is shown, specifically including the following steps:

[0059] S1: Determine the main controlling factors affecting the productivity of carbonate rock reservoirs in the target area;

[0060] S2: Determine the single-well reservoir quantification coefficient in the target area;

[0061] S3: Classify the carbonate rock reservoirs according to the single-well reservoir quantification coefficients of the wells that have been drilled in the target area;

[0062] S4: Conduct a quantitative evaluation of the reservoirs of the new wells in the target area.

[0063] Furthermore, taking the carbonate rocks in the Sichuan-Chongqing exploration area of the Daqing Oilfield as an example, the above steps are described in detail.

[0064] In this implementation, the main controlling factors affecting the productivity of carbonate rock reservoirs in the target area are as follows:

[0065] (1) Statistically analyze the logging, mud logging, production and other data of the acid fracturing wells in the target area, and establish a data table; there are 22 wells for gas testing of carbonate rocks in the Sichuan-Chongqing exploration area of the Daqing Oilfield. Statistically analyze the logging, mud logging, production and other data of the acid fracturing wells in the carbonate rock target area of the Sichuan-Chongqing exploration area of the Daqing Oilfield, and establish a data table, as shown in Table 1:

[0066] Table 1 Data table of logging, etc. in the target area (partial wells)

[0067]

[0068]

[0069] (2) Pretreat the original data, and the specific method is as follows:

[0070] ① Determine the analysis sequence:

[0071] Due to the large number of samples, select the post-acid fracturing daily gas production data that can best evaluate the productivity of carbonate rock reservoirs as the mother sequence for correlation analysis, denoted as {X0(i)}, (i = 1, 2..., n), where n is the sequence length. Select logging, mud logging, production and other parameter data as the sub-sequences (including drilling fluid loss, maximum total hydrocarbon in gas logging, well diameter, dolomite content, reservoir thickness, effective thickness, acoustic travel time, deep lateral resistivity, shallow lateral resistivity, natural gamma, porosity, pressure coefficient, compensated neutron, litho-density, water saturation, acid injection volume, total injection fluid volume, pump shut-off pressure, construction displacement, etc.), denoted as {X j (i)}, (j = 1, 2,..., m; i = 1, 2,..., n), where m is the number of influencing factors.

[0072] In this implementation, the daily gas production data after acid fracturing is selected as the mother sequence for correlation analysis, denoted as {X0(i)}, (i = 1, 2..., n), where n is the sequence length.

[0073] Select the parameter data of logging, mud logging, production, etc. as the sub-sequences, denoted as {X j (i)}, (j = 1, 2,..., m; i = 1, 2,..., n), where m is the number of influencing factors.

[0074] ② Non-dimensionalization processing:

[0075] Since the physical meanings and units of the factors in the original reference sequence are different, and the numerical values of the data vary greatly, a correct evaluation result cannot be obtained by direct comparison. Therefore, for the convenience of analysis, the mean method is used to perform non-dimensionalization processing on the mother sequence and the sub-sequences before calculation.

[0076] In this example, the non-dimensionalization processing is carried out according to the mean method:

[0077]

[0078] In the formula: i = 1, 2,..., n; j = 0, 1,..., m.

[0079] (3) Calculation of correlation coefficients:

[0080] In this example, the absolute value △ of the difference between each sub-factor and the mother factor at each reference point is calculated according to the following formula oj (i):

[0081] △ oj (i) = |Y0(i) - Y j (i)| (2)

[0082] In the formula: i = 1, 2,..., n; j = 0, 1,..., m.

[0083] Find the maximum value △ from the absolute values of the differences max and the minimum value △ min .

[0084] Then the calculation formula for the correlation coefficient between the mother sequence and the sub-sequence at each point is as follows:

[0085]

[0086] (4) Calculation of correlation degree:

[0087] In this example, the correlation degree γ between each sub-factor and the mother factor is calculated according to the following formula oj :

[0088]

[0089] Where: i = 1, 2,..., n; j = 0, 1,..., m.

[0090] (5) Determination of main control factors:

[0091] Correlation degree γ oj The closer the value is to 1, the closer the relationship between the sub-factor and the mother factor. The factors with a correlation degree greater than 0.7 are selected as the main control factors affecting the productivity of carbonate rocks.

[0092] In this example, 6 factors with a correlation degree greater than 0.7, namely porosity, drilling fluid loss, dolomite content, reservoir thickness, acoustic time difference, and maximum value of total hydrocarbon in gas logging, are selected as the main control factors affecting the productivity of carbonate rocks.

[0093] Furthermore, in this implementation, the determination of the quantitative coefficient of the single-well reservoir in the target area is as follows:

[0094] (1) Determination of the correlation coefficients of each main control factor:

[0095] ① Determination of the analysis sequence:

[0096] Similarly, the daily gas production data after acid fracturing, which can best evaluate the productivity of carbonate rock reservoirs, is selected as the mother sequence for correlation analysis, also denoted as {X0(i)}, (i = 1, 2..., n), where n is the sequence length. The main control factors determined by screening are selected as the sub-sequences, {X j (i)}, (j = 1, 2,..., k; i = 1, 2,..., n), where k is the number of main control factors affecting productivity.

[0097] In this implementation, the daily gas production data after acid fracturing is selected as the mother sequence for correlation analysis, also denoted as {X0(i)}, (i = 1, 2..., n), where n is the sequence length. The 6 main control factors determined by screening are selected as the sub-sequences, {X j (i)}, (j = 1, 2,..., k; i = 1, 2,..., n), where k is the number of main control factors affecting productivity.

[0098] ② Calculation of the correlation coefficients of the main control factors:

[0099] In this implementation, the correlation coefficients C between each main control factor and the mother factor are calculated according to the following formula j , that is

[0100]

[0101] Where: i = 1, 2,..., n; j = 1,..., k.

[0102] ③ Calculation of the quantitative coefficient of the single-well reservoir:

[0103] In this implementation, the single-well reservoir quantization coefficient B is calculated according to the following formula i , that is

[0104] B i = C1·Y1 + C2·Y2 + C3·Y3 + ··· + C k ·Y k (6)

[0105] In the formula: i = 1, 2,..., n; Y k is the dimensionless processing of the main control factor subsequence, which can be calculated by formula (1).

[0106] Furthermore, in this implementation, the carbonate rock reservoir is classified according to the single-well reservoir quantization coefficient of the wells that have been constructed in the entire target area, specifically as follows

[0107] (1) Calculate the single-well reservoir quantization coefficient B of the previous wells in the target area i , and establish a relationship curve between the single-well reservoir quantization coefficient in the target area and the gas test production, as Figure 2 shown

[0108] (2) According to the relationship curve, determine the reservoir quantization coefficient intervals of the four types of reservoirs, namely high-quality reservoirs, better reservoirs, average reservoirs, and poor reservoirs in the target area according to the size of the gas test production, providing a basis for the quantitative evaluation of new wells; the specific coefficient intervals are shown in Table 2

[0109] Table 2 Interval of single-well transformation coefficient for different reservoirs

[0110]

[0111] Furthermore, in this implementation, the quantitative evaluation of new wells is as follows

[0112] (1) New well data processing

[0113] Establish a data table of new wells and previous wells in the target area, and calculate the dimensionless processing results Y 新 (j) of each main control factor of the new well by formula (1);

[0114] (2) Calculate the single-well reservoir quantization coefficient of the new well, that is

[0115] B 新井 = C1·Y1 + C2·Y2 + C3·Y3 + ··· + C k ·Y k (7)

[0116] (3) New well reservoir evaluation

[0117] According to the magnitude of the new well reservoir quantification coefficient, refer to Table 2 to determine the evaluated reservoir type, quantitatively reflect the quality of the reservoir, and provide necessary technical guidance for well and layer selection for engineering, fracturing design, fracturing model analysis, and on-site construction, etc.

[0118] The above-described embodiments are only for expressing the implementation manners of the present disclosure. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present disclosure. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present disclosure, several deformations, equivalent substitutions, improvements, etc. can be made, and these all belong to the protection scope of the present disclosure. Therefore, the protection scope of the patent of the present disclosure shall be subject to the appended claims.

Claims

1. A method for quantitatively evaluating carbonate rock reservoirs, characterized in that Including: Taking the logging, mud logging and production data of each acid fracturing well in the target area as sub-factors, and the daily gas production after acid fracturing as the mother factor, and determining the main control factors affecting the mother factor from the sub-factors; Determining the correlation coefficient between the main control factor and the mother factor, and calculating the single-well reservoir quantification coefficient of each acid fracturing well by using the correlation coefficient; Classifying the carbonate reservoirs in the target area according to the single-well reservoir quantification coefficient; Using the classification result of the carbonate reservoir to quantitatively evaluate the carbonate reservoir of the new wells in the target area.

2. The carbonate reservoir quantitative evaluation method according to claim 1, wherein The method for determining the main control factors affecting the mother factor from the sub-factors includes: Calculating the correlation degree between the sub-factors and the mother factor, and determining the main control factors according to the correlation degree.

3. The carbonate reservoir quantitative evaluation method according to claim 2, wherein The calculation method of the correlation degree includes: Taking the daily gas production data after acid fracturing as the mother sequence of correlation analysis, denoted as {X0(i)}, (i = 1, 2..., n), where n is the sequence length; Taking logging, mud logging and production data as subsequences for correlation analysis, denoted as {X j (i)}, (j = 1, 2, ..., m; i = 1, 2, ..., n), where m is the number of influencing factors; Using the mean method to perform dimensionless processing on the mother sequence and the sub-sequences, and the processing formula of the mean method is: In the formula: i = 1, 2,..., n; j = 0, 1,..., m; Calculate the absolute value △ of the difference between each sub-factor and the parent factor according to the following formula oj (i): △ oj (i) = |Y0(i) - Y j (i)|; In the formula: i = 1, 2,..., n; j = 0, 1,..., m; Find the maximum value △ from the absolute values of the differences max and the minimum value △ min , and calculate the correlation coefficient between the mother sequence and the child sequence according to the following formula: Calculating the correlation degree γoj according to the following formula: In the formula: i = 1, 2,..., n; j = 0, 1,..., m.

4. The carbonate reservoir quantitative evaluation method according to claim 3, characterized in that: Taking the factors with the correlation degree greater than 0.7 as the main control factors.

5. The method for quantitatively evaluating carbonate rock reservoirs according to any one of claims 1-4, characterized in that The method for determining the correlation coefficient between the main control factor and the mother factor includes: Taking the daily gas production data after acid fracturing as the mother sequence of correlation analysis, denoted as {X0(i)}, (i = 1, 2..., n), where n is the sequence length; Taking the master factor data as the subsequence for correlation analysis, denoted as {X j (i)}, (j = 1, 2, ..., m; i = 1, 2, ..., n), where m is the number of influencing factors; Calculate the correlation coefficient C according to the following formula j :[[]]END]] In the formula: i = 1, 2,..., n; j = 1,..., k.

6. The carbonate reservoir quantitative evaluation method according to claim 5, wherein Calculate the single-well reservoir quantification coefficient B of each acid fracturing well according to the following formula i : B i = C1·Y1 + C2·Y2 + C3·Y3 + ··· + C k ·Y k ; where: i = 1, 2,..., n; Y k is the dimensionless processing result of the main control factor subsequence.

7. The method for quantitative evaluation of carbonate rock reservoirs according to any one of claims 1-4 or claim 6, characterized in that The method for classifying the carbonate reservoir types in the target area includes: Establishing the relationship curve between the single-well reservoir quantification coefficient in the target area and the gas test production; According to the relationship curve, classifying the carbonate reservoirs in the target area according to the set gas test production and the range of single-well stimulation coefficient; The classified carbonate reservoir types are high-quality reservoirs, better reservoirs, average reservoirs and poor reservoirs.

8. The carbonate reservoir quantitative evaluation method according to claim 7, characterized in that: The set gas test production Q (×10 4 m 3 / d) of the high-quality reservoir is ≥120, and the range of the single-well transformation coefficient is ≥2; The set better reservoir gas testing production Q (×10 4 m 3 / d) < 150, ≥ 50; the single well reconstruction coefficient range ≥ 2; The set gas testing production Q (×10 4 m 3 / d) of the general reservoir is < 50 and ≥ 20; the range of the single - well transformation coefficient is < 1.5 and ≥ 1; The set gas testing production Q (×10 4 m 3 / d) of the differential reservoir is less than 10, and the range of the single-well transformation coefficient is less than 1.

9. The carbonate reservoir quantitative evaluation method according to any one of claims 1-4, 6 or 8, characterized in that The method for quantitatively evaluating the carbonate reservoir of the new wells in the target area includes: Calculating the reservoir quantification coefficient of the new wells by using the correlation coefficient; According to the reservoir quantification coefficient of the new wells, combining with the classification result of the carbonate reservoir, quantitatively evaluating the carbonate reservoir type of the new wells.

10. The carbonate reservoir quantitative evaluation method according to claim 9, characterized in that: Before calculating the reservoir quantification coefficient of the new wells, using the mean method to perform dimensionless processing on each main control factor of the new wells.

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