Quantitative prediction method for porosity of fine-grained mixed carbonate rock

Abstract

The invention relates to a method for quantitatively predicting the porosity of fine-grained mixed carbonate rocks, which comprises the following steps: firstly, measuring core samples of the fine-grained mixed carbonate rocks within a specific depth range to obtain a data set of the core samples; then, sequentially carrying out correlation judgment and constructing a porosity prediction model under a specific depth; then, a diagenesis numerical simulation method is adopted to calculate a compaction coefficient, and then porosity prediction models under different depths are obtained; and finally, predicting the to-be-measured fine-grained mixed carbonate rock by adopting the porosity prediction models at different depths to obtain a porosity prediction value. The porosity quantitative prediction method provided by the invention can rapidly and accurately predict the porosity of the fine-grained mixed carbonate reservoir under the specific mineral component content on the basis of actually measured porosity data.

Description

Technical Field

[0001] The invention relates to the field of oil and gas exploration and development, and in particular to a method for quantitatively predicting the porosity of fine-grained mixed carbonate rocks. Background Art

[0002] In petroleum and natural gas geology, reservoir porosity refers to the ratio of the volume of pore space between rock skeletons in oil and gas reservoir rocks to the total volume of the rock, and is one of the key parameters for oil and gas reservoir evaluation. On the one hand, reservoir porosity determines the oil and gas storage capacity, and on the other hand, it is also one of the important indicators for measuring the effectiveness of the reservoir; the higher the reservoir porosity, the stronger the ability of fluid to pass through. At present, although the quantitative prediction method of porosity of clastic reservoirs has been relatively complete, the quantitative prediction method for porosity of carbonate reservoirs is still in the research stage.

[0003] Carbonate reservoirs account for a considerable proportion in the world's oil and gas landscape. Globally, carbonate reservoirs account for 20% of sedimentary rocks and more than 50% of proven oil and gas reserves. In my country, with the continuous deepening of oil and gas exploration in marine carbonate reservoirs in recent years, a number of large and medium-sized oil and gas fields have been discovered in Bohai Bay, Tarim, Ordos, and Sichuan Basins, demonstrating the huge potential of carbonate reservoir exploration. Due to the long sedimentary age of carbonate reservoirs, they have experienced multiple tectonic movements, suffered strong weathering, erosion and leaching, and have strong stratum heterogeneity, resulting in the quantitative evaluation of carbonate reservoirs becoming one of the main technical difficulties in the study of carbonate salt reservoirs.

[0004] For example, CN108345962A discloses a quantitative prediction method for the diagenetic simulation porosity of carbonate reservoirs. This method is based on the study of carbonate reservoir sedimentary phases, diagenetic materials, sedimentary cycles, diagenesis and formation thickness, and establishes a "geological parameter-diagenesis-porosity" prediction model, thereby making a lateral prediction of the porosity size of carbonate reservoirs, determining the spatial distribution of pores in carbonate reservoirs, and providing a basis for reservoir evaluation.

[0005] Fine-grained mixed carbonate rock is a special type of carbonate rock that is widely developed in saline lake environments. Its characteristics include: (1) The rock and mineral components are complex, mainly carbonate minerals, and are also rich in felsic (such as terrigenous debris), clay minerals, and gypsum minerals; (2) The carbonate minerals are in the size of mud crystals and powder crystals; (3) The reservoir space is mainly intercrystalline pores rather than dissolution pores.

[0006] Since the reservoir space of fine-grained mixed carbonate rocks is mainly intercrystalline pores, and the development degree of intercrystalline pores is controlled by various mineral crystals, providing a method for quantitatively predicting the porosity of fine-grained mixed carbonate rocks based on mineral composition has extremely high application value for the evaluation of unconventional oil and gas fields. Summary of the invention

[0007] In view of the above problems, the purpose of the present invention is to provide a method for quantitatively predicting the porosity of fine-grained mixed carbonate rocks. Compared with the prior art, the method for quantitatively predicting the porosity of fine-grained mixed carbonate reservoirs can quickly and accurately predict the porosity of fine-grained mixed carbonate reservoirs at a specific mineral component content based on measured porosity data.

[0008] In order to achieve the purpose of the invention, the present invention adopts the following technical solutions:

[0009] The present invention provides a method for quantitatively predicting the porosity of fine-grained mixed carbonate rocks, and the method for quantitatively predicting the porosity comprises the following steps:

[0010] (1) In the target stratum of the study area, a core sample of fine-grained mixed carbonate rock within a specific depth range is measured to obtain a data set of the core sample; the data set includes the types and contents of mineral components contained in the core sample and the porosity of the core sample;

[0011] (2) performing correlation judgment on the data set obtained in step (1), and then constructing a porosity prediction model at a specific depth based on the mineral content and porosity of the core sample at a specific depth;

[0012] (3) Calculating the compaction coefficient using a diagenetic numerical simulation method, and obtaining a porosity prediction model at different depths based on the compaction coefficient;

[0013] (4) The porosity prediction model at different depths obtained in step (3) is used to predict the fine-grained mixed carbonate rock to be tested, thereby obtaining a porosity prediction value.

[0014] In the present invention, firstly, fine-grained mixed carbonate core samples in the target stratum of the study area and in a specific depth range are measured to obtain a data set of the core samples; then, by performing a correlation analysis, if the correlation requirement is met, the reservoir can be predicted using the porosity quantitative prediction method provided by the present invention, and then a porosity prediction model at a specific depth is constructed according to the mineral component content and the porosity; then, the compaction coefficient is calculated according to the diagenetic numerical simulation, and then the porosity prediction model at different depths is obtained; finally, the porosity prediction model at different depths is used to predict the fine-grained mixed carbonate rock to be measured, and a porosity prediction value is obtained.

[0015] Preferably, the determination method in step (1) comprises:

[0016] Perform X-ray diffraction whole-rock mineral analysis on the core sample to obtain the mineral component types contained in the core sample, which are recorded as a1, a2, a3, ..., an in sequence; and obtain the mineral component contents corresponding to the mineral component types, which are recorded as Ca1 , C a2 , C a3 , …, C an ;

[0017] The porosity of the core sample is measured to obtain the porosity of the core sample, which is recorded as φ.

[0018] Preferably, the data of core samples with carbonate mineral content lower than 30% in the data set obtained in step (1) are excluded.

[0019] In the present invention, by eliminating the data of core samples with carbonate mineral content lower than 30%, for example, it can be 28%, 26%, 24%, 22% or 20%, but not limited to the listed values, other unlisted values ​​within the numerical range are also applicable, thereby ensuring that the detected lithology is carbonate rock.

[0020] Preferably, the method for correlation judgment in step (2) includes: performing Pearson correlation test on the content of each mineral component and porosity. If the content of at least one mineral component in the core sample is correlated with the porosity, and the absolute value of the correlation coefficient is in the range of [0.5, 1], the correlation requirement is met.

[0021] Preferably, the construction method in step (2) comprises: taking the mineral content as the independent variable and the porosity as the dependent variable, and adopting the Pearson correlation stepwise regression method to obtain a porosity prediction model at a specific depth.

[0022] Preferably, the expression of the porosity prediction model at a specific depth in step (2) is Φ A =f(C a1 , C a2 , C a3 , …, C an ).

[0023] Preferably, the calculation process of the compaction coefficient in step (3) includes: according to the quantitative relationship between porosity and burial depth: Φ = φ a exp(-mH), using the porosity and depth of core samples at different depths in the target layer to fit, to obtain the values ​​of compaction coefficient and initial porosity of deposition;

[0024] Among them, Φ represents the porosity of the core sample; H represents the depth of the core sample with a porosity of Φ; m represents the compaction coefficient; φ a represents the initial porosity of the deposition.

[0025] Preferably, the value of the compaction coefficient obtained in step (3) is substituted into Φ B =Φ0exp[m(h0-h)], the porosity prediction model at different depths is obtained;

[0026] Among them, Φ B represents the porosity to be measured; Φ0 represents the porosity of the core sample in the same target layer as the fine-grained mixed carbonate rock to be measured; m represents the compaction coefficient; h0 represents the depth corresponding to the core sample with a porosity of Φ0; and h represents the depth to be measured.

[0027] Preferably, the prediction method in step (4) comprises: substituting the depth h of the fine-grained mixed carbonate rock to be tested, the porosity Φ0 and the depth h0 of the core sample in the same target layer as the fine-grained mixed carbonate rock to be tested into the porosity prediction model at different depths obtained in step (3), to obtain a porosity prediction value.

[0028] As a preferred technical solution of the present invention, the porosity quantitative prediction method comprises the following steps:

[0029] (1) In the target horizon of the study area, X-ray diffraction whole-rock mineral analysis and porosity measurement are performed on core samples of fine-grained mixed carbonate rocks within a specific depth range to obtain a data set of the core samples; the data set includes the types of mineral components contained in the core samples and their contents and the porosity of the core samples; the types of mineral components are sequentially recorded as a1, a2, a3, ..., an; the contents of mineral components are sequentially recorded as C a1 , C a2 , C a3 , …, C an ; The porosity of the core sample is recorded as φ; the data of the core samples with carbonate mineral content less than 30% in the data set are eliminated;

[0030] (2) performing a Pearson correlation test on the content of each mineral component and porosity in the data set obtained in step (1); if the content of at least one mineral component in the core sample is correlated with the porosity, and the absolute value of the correlation coefficient is in the range of [0.5, 1], the correlation requirement is met;

[0031] Then, according to the mineral content and porosity of the core sample at a specific depth, the porosity prediction model at a specific depth was obtained by using the Pearson correlation stepwise regression method, with the mineral content as the independent variable and the porosity as the dependent variable. The expression is Φ A =f(C a1 , C a2 , C a3 , …, C an );

[0032] (3) The compaction coefficient is calculated by using a diagenetic numerical simulation method. The calculation process of the compaction coefficient includes: according to the quantitative relationship between porosity and burial depth: Φ = φ aexp(-mH), the porosity and depth of core samples at different depths in the target layer are used for fitting to obtain the values ​​of compaction coefficient and initial porosity of deposition; where Φ represents the porosity of the core sample; H represents the depth of the core sample with porosity Φ; m represents the compaction coefficient; φ a represents the initial porosity of the deposition;

[0033] Substitute the obtained value of the compaction coefficient into Φ B =Φ0exp[m(h0-h)], the porosity prediction model at different depths is obtained; where Φ B represents the porosity to be measured; Φ0 represents the porosity of the core sample in the same target layer as the fine-grained mixed carbonate rock to be measured; m represents the compaction coefficient; h0 represents the depth corresponding to the core sample with a porosity of Φ0; h represents the depth to be measured;

[0034] (4) Substituting the depth h of the fine-grained mixed carbonate rock to be tested, the porosity Φ0 and the depth h0 of the core sample in the same target layer as the fine-grained mixed carbonate rock to be tested into the porosity prediction model at different depths obtained in step (3) to obtain a porosity prediction value.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The porosity quantitative prediction method provided by the present invention can quickly and accurately predict the porosity of fine-grained mixed carbonate reservoirs in the current oil field target layer under the condition of specific mineral component content based on the existing measured porosity data; further using the measured data and logging data normalization processing, it can realize the porosity prediction of fine-grained mixed carbonate reservoirs without core sections; further combined with the compaction coefficient, it can realize the porosity prediction of deep fine-grained mixed carbonate reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a correlation diagram between the porosity and dolomite content of the core sample described in Example 1 of the present invention;

[0038] Figure 2 is a correlation diagram between the porosity and clay content of the core sample described in Example 1 of the present invention;

[0039] Figure 3 is a correlation coefficient diagram of the porosity and main mineral component content of the core sample described in Example 1 of the present invention;

[0040] Figure 4 It is a relationship diagram between the predicted porosity value and the measured porosity value described in Example 1 of the present invention. DETAILED DESCRIPTION

[0041] The technical solution of the present invention is further described below by specific implementation methods. It should be understood by those skilled in the art that the embodiments are only to help understand the present invention and should not be regarded as specific limitations of the present invention.

[0042] Example 1

[0043] This embodiment provides a method for quantitatively predicting the porosity of fine-grained mixed carbonate rocks. Taking the Neogene strata in the Y area of ​​the Qaidam Basin as an example, the Qaidam Basin was a saline lake basin under an arid climate in the Neogene. In addition to the terrigenous clastic sedimentary system developed at the edge of the lake basin, fine-grained mixed rock deposits developed on a large scale inside the lake basin, including multiple sets of mixed carbonate rocks dominated by carbonates. The Neogene strata in the Y area are a set of fine-grained mixed carbonate rock strata, and their reservoir space is mainly nano-micron dolomite intercrystalline pores. This geological condition is suitable for the use of the quantitative porosity prediction method of the present invention;

[0044] The porosity quantitative prediction method comprises the following steps:

[0045] (1) In the target stratum of the study area, the core samples of fine-grained mixed carbonate rocks within a specific depth range were measured using the Ultra Pore-400 porosity meter and the porosity of the core samples was recorded as φ;

[0046] The whole-rock mineral composition types of the core samples detected by X'Pert Pro X-ray diffractometer are recorded as a1, a2, a3, ..., an in sequence; and the mineral composition contents corresponding to the above mineral composition types are recorded as C a1 , C a2 , C a3 , …, C an ;

[0047] A data set of core samples was obtained based on the above data, and the data of core samples with carbonate mineral content less than 30% were excluded;

[0048] (2) Pearson correlation test is performed on the content of each mineral component and porosity in the data set obtained in step (1), and the test results show that: the correlation diagram between the porosity and dolomite content of the core sample of the Neogene fine-grained mixed carbonate rock in the Y area of ​​the Qaidam Basin is as follows: Figure 1 The correlation diagram between the porosity and clay content of the core sample is shown in Figure 2 The correlation coefficient between the porosity of the core sample and the content of the main mineral components is shown in Figure 3 As shown above Figure 1-3 The test results show that the correlation coefficient of dolomite is 0.79, and the correlation coefficient of clay is -0.63, which meets the correlation requirements;

[0049] Then, according to the mineral content and porosity of the core sample at a specific depth, the porosity prediction model Φ at a specific depth in the target layer is obtained by using the Pearson correlation stepwise regression method with the mineral content as the independent variable and the porosity as the dependent variable. A =f(C a1 , C a2 , C a3 , …, C an );

[0050] Taking the core sample of the Neogene fine-grained mixed carbonate rock in the Y area of ​​the Qaidam Basin as an example, its porosity is denoted as Φ0. The expression of Φ0 obtained by the above method is recorded as expression (1), which is: Φ0 = 0.104 × C dol -0.048×C clay +3.174, where Φ0 represents the porosity of the core sample at depth h0, C dol Indicates dolomite content, C clay Indicates clay content;

[0051] From the above formula, it can be seen that the porosity of fine-grained mixed carbonate rocks in the Y area is mainly related to the dolomite and clay contents, and is positively correlated with the dolomite content and negatively correlated with the clay content;

[0052] (3) The compaction coefficient is calculated using the method of diagenetic numerical simulation. The calculation process includes: According to the quantitative relationship between porosity and burial depth: Φ = φ a exp(-mH), the porosity and depth of core samples at different depths in the target layer are used for fitting to obtain the values ​​of compaction coefficient and initial porosity of deposition; where Φ represents the porosity of the core sample; H represents the depth of the core sample with porosity Φ; m represents the compaction coefficient; φ a represents the initial porosity of the deposition;

[0053] The above fitting results in m = 0.00012, which is substituted into Φ B =Φ0exp[m(h0-h)], the porosity prediction model at different depths is obtained, which is expressed as expression (2), as follows: Φ B =Φ0exp[0.00012(h0-h)]; where Φ B represents the porosity to be measured; Φ0 represents the porosity of the core sample in the same target layer as the fine-grained mixed carbonate rock to be measured; m represents the compaction coefficient; h0 represents the depth corresponding to the core sample with a porosity of Φ0; h represents the depth to be measured;

[0054] In expression (2), the porosity Φ0 of the core sample in the same target layer as the fine-grained mixed carbonate rock to be tested can be calculated by expression (1), and the final porosity prediction model is expressed as expression (3), which is: ΦB =(0.104×C dol -0.048×C clay +3.174)×exp[0.00012(h0-h)];

[0055] (4) Substitute the depth h of the fine-grained mixed carbonate rock to be tested, the dolomite content, clay content and depth h0 of the core sample in the same target layer as the fine-grained mixed carbonate rock to be tested into expression (3) to obtain the predicted porosity value;

[0056] The porosity of the fine-grained mixed carbonate rock to be tested is measured using the Ultra Pore-400 porosity tester to obtain the measured porosity value. The relationship between the porosity prediction value and the measured porosity value of the same fine-grained mixed carbonate rock to be tested is shown in the figure below. Figure 4 As shown, from Figure 4 It can be seen that the predicted values ​​of the porosity prediction method provided by the present invention are highly consistent with the measured values, indicating that the porosity quantitative prediction method provided by the present invention can more accurately predict the porosity of fine-grained mixed carbonate reservoirs with specific mineral component contents.

[0057] In summary, the porosity quantitative prediction method provided by the present invention can, on the basis of the existing measured porosity data, quickly and accurately predict the porosity of the fine-grained mixed carbonate reservoir in the current oil field target layer under the condition of specific mineral component content; further utilizing the normalization processing of the measured data and the logging data, the porosity prediction of the fine-grained mixed carbonate reservoir without the core section can be realized; further combining with the compaction coefficient, the porosity prediction of the deep fine-grained mixed carbonate reservoir can be realized.

[0058] The applicant declares that the above is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention shall fall within the protection scope and disclosure scope of the present invention.

Claims

1. A method for quantitatively predicting the porosity of fine-grained mixed carbonate rocks, characterized in that: The porosity quantitative prediction method comprises the following steps: (1) In the target stratum of the study area, a core sample of fine-grained mixed carbonate rock within a specific depth range is measured to obtain a data set of the core sample; the data set includes the types and contents of mineral components contained in the core sample and the porosity of the core sample; (2) performing correlation judgment on the data set obtained in step (1), and then constructing a porosity prediction model at a specific depth based on the mineral content and porosity of the core sample at a specific depth; (3) Calculating the compaction coefficient using a diagenetic numerical simulation method, and obtaining a porosity prediction model at different depths based on the compaction coefficient; (4) The porosity prediction model at different depths obtained in step (3) is used to predict the fine-grained mixed carbonate rock to be tested, thereby obtaining a porosity prediction value.

2. The porosity quantitative prediction method according to claim 1, characterized in that: The determination method in step (1) comprises: Perform X-ray diffraction whole-rock mineral analysis on the core sample to obtain the mineral component types contained in the core sample, which are recorded as a1, a2, a3, ..., an in sequence; and obtain the mineral component contents corresponding to the mineral component types, which are recorded as C a1 , C a2 , C a3 , …, C an ; The porosity of the core sample is measured and the porosity of the core sample is obtained, which is recorded as 3. The method for quantitative prediction of porosity according to claim 1 or 2, characterized in that: The data of the core samples with carbonate mineral content less than 30% in the data set obtained in step (1) are eliminated.

4. The porosity quantitative prediction method according to any one of claims 1 to 3, characterized in that: The method for correlation judgment in step (2) includes: performing Pearson correlation test on the content of each mineral component and porosity. If the content of at least one mineral component in the core sample is correlated with the porosity, and the absolute value of the correlation coefficient is in the range of [0.5, 1], then the correlation requirement is met.

5. The porosity quantitative prediction method according to any one of claims 1 to 4, characterized in that: The construction method of step (2) includes: taking the mineral component content as the independent variable and the porosity as the dependent variable, and using the Pearson correlation stepwise regression method to obtain a porosity prediction model at a specific depth.

6. The porosity quantitative prediction method according to claim 5, characterized in that: The expression of the porosity prediction model at a specific depth in step (2) is Φ A =f(C a1 , C a2 , C a3 , …, C an ).

7. The porosity quantitative prediction method according to any one of claims 1 to 6, characterized in that: The calculation process of the compaction coefficient in step (3) includes: according to the quantitative relationship between porosity and burial depth: The porosity and depth of core samples at different depths in the target layer are used for fitting to obtain the values ​​of compaction coefficient and initial porosity of deposition; Wherein, Φ represents the porosity of the core sample; H represents the depth of the core sample with a porosity of Φ; m represents the compaction coefficient; represents the initial porosity of the deposition.

8. The porosity quantitative prediction method according to claim 7, characterized in that: Substitute the value of the compaction coefficient obtained in step (3) into Φ B =Φ0exp[m(h0-h)], the porosity prediction model at different depths is obtained; Among them, Φ B represents the porosity to be measured; Φ0 represents the porosity of the core sample in the same target layer as the fine-grained mixed carbonate rock to be measured; m represents the compaction coefficient; h0 represents the depth corresponding to the core sample with a porosity of Φ0; and h represents the depth to be measured.

9. The porosity quantitative prediction method according to claim 8, characterized in that: The prediction method in step (4) includes: substituting the depth h of the fine-grained mixed carbonate rock to be tested, the porosity Φ0 and the depth h0 of the core sample in the same target layer as the fine-grained mixed carbonate rock to be tested into the porosity prediction model at different depths obtained in step (3) to obtain a porosity prediction value.

10. The porosity quantitative prediction method according to any one of claims 1 to 9, characterized in that: The porosity quantitative prediction method comprises the following steps: (1) In the target horizon of the study area, X-ray diffraction whole-rock mineral analysis and porosity measurement are performed on core samples of fine-grained mixed carbonate rocks within a specific depth range to obtain a data set of the core samples; the data set includes the types of mineral components contained in the core samples and their contents and the porosity of the core samples; the types of mineral components are sequentially recorded as a1, a2, a3, ..., an; the contents of mineral components are sequentially recorded as C a1 , C a2 , C a3 , …, C an The porosity of the core sample is recorded as Eliminate the data of core samples with carbonate mineral content less than 30% in the data set; (2) performing a Pearson correlation test on the content of each mineral component and porosity in the data set obtained in step (1); if the content of at least one mineral component in the core sample is correlated with the porosity, and the absolute value of the correlation coefficient is in the range of [0.5, 1], the correlation requirement is met; Then, according to the mineral content and porosity of the core sample at a specific depth, the porosity prediction model at a specific depth was obtained by using the Pearson correlation stepwise regression method, with the mineral content as the independent variable and the porosity as the dependent variable. The expression is Φ A =f(C a1 , C a2 , C a3 , …, C an ); (3) The compaction coefficient is calculated by using a diagenetic numerical simulation method. The calculation process of the compaction coefficient includes: according to the quantitative relationship between porosity and burial depth: The porosity and depth of core samples at different depths in the target layer are used for fitting to obtain the values ​​of compaction coefficient and initial porosity of deposition; where Φ represents the porosity of the core sample; H represents the depth of the core sample with a porosity of Φ; and m represents the compaction coefficient; represents the initial porosity of the deposition; Substitute the obtained value of the compaction coefficient into Φ B =Φ0exp[m(h0-h)], the porosity prediction model at different depths is obtained; where Φ B represents the porosity to be measured; Φ0 represents the porosity of the core sample in the same target layer as the fine-grained mixed carbonate rock to be measured; m represents the compaction coefficient; h0 represents the depth corresponding to the core sample with a porosity of Φ0; h represents the depth to be measured; (4) Substituting the depth h of the fine-grained mixed carbonate rock to be tested, the porosity Φ0 and the depth h0 of the core sample in the same target layer as the fine-grained mixed carbonate rock to be tested into the porosity prediction model at different depths obtained in step (3) to obtain a porosity prediction value.

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