Method for determining quality of compact glutenite reservoir based on pore structure factors

Abstract

The invention relates to the technical field of oil-gas exploration logging interpretation and evaluation, in particular to a compact glutenite reservoir quality determination method based on pore structure factors. The method comprises the following steps: calculating the porosity POR through the density; the method comprises the following steps: performing correlation fitting analysis on permeability and pore structure parameters by using mercury injection data of a compact glutenite reservoir, screening out parameters with high correlation, and constructing a pore structure factor KX; performing rendezvous analysis on the pore structure factor KX and conventional logging data, screening out logging data with high correlation, and constructing a reservoir pore structure factor KX comprehensive classification evaluation model; calculating the shale content Vsh through a neutron-density intersection method; and establishing a reservoir classification evaluation standard by using the pore structure factor KX, the shale content Vsh, the porosity POR and the lithologic factor H, and determining the quality of the compact glutenite reservoir. The method has the advantage of high accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil and gas exploration well interpretation and evaluation, and particularly relates to a method for determining quality of tight glutenite reservoir based on pore structure factor. BACKGROUND

[0002] Compared with the traditional sandstone reservoir, the glutenite reservoir has the characteristics of various sedimentary types, fast lithology change, poor sorting, strong heterogeneity, low porosity and low permeability, and it is difficult to predict high-quality reservoirs. The reservoir classification and logging evaluation has geological significance for finding high-quality reservoirs and guiding oil and gas production.

[0003] The existing method generally qualitatively describes the reservoir physical property through core experiment, thin section observation and other means, and then quantitatively calculates the porosity, oil saturation and shale content according to the logging data, but fails to establish the connection between the reservoir pore structure and the logging scale. Or, the connection between the capillary pressure curve experimental parameters and the logging data is established by using the correlation fractal dimension method and the neural network algorithm to construct and train the model, and the pore structure is classified, but a large amount of experimental data is required for training, and the regional limitation is relatively strong. Whether it is diagenesis or the pore structure and size of the reservoir, it is essentially subject to or affects the microstructure of the reservoir. Since the rock thin section and the mercury injection experiment are very limited, it is difficult to divide and evaluate the quality of the reservoir and the "sweet spot" of the continuous formation. In combination with the core analysis and test data, the correlation between various logging data and the pore structure factor is explored, the pore structure factor representation method based on the conventional logging data is established, the continuity representation of the pore structure of the tight glutenite reservoir is realized, and it is helpful to find the high-quality glutenite reservoir section.

[0004] Therefore, it is necessary to develop a method for determining the quality of the tight glutenite reservoir based on the pore structure factor, which can solve the above technical problems. SUMMARY

[0005] The purpose of the present application is to overcome the shortcomings of the prior art and provide a method for determining the quality of the tight glutenite reservoir based on the pore structure factor with high accuracy.

[0006] In the present application, three pore structure sensitive parameters with good correlation with permeability, i.e. the sorting coefficient (SC), the average capillary radius (RC) and the drainage pressure (Pd), are selected by the mercury injection experiment, and the pore structure factor is constructed by using the mathematical method. In combination with the core analysis and test data, the correlation between various logging data and the pore structure factor is explored, the logging data with good correlation is selected, and the representation method of the pore structure factor KX based on the conventional logging data in the research area is established. Finally, in combination with the shale content, the porosity, the lithology and the like, the productivity characteristics are analyzed, the classification and evaluation standard of the tight glutenite reservoir is established, and the glutenite reservoir is divided into class I, class II and class III, wherein the quality of the class I reservoir is the best, the quality of the class II reservoir is medium, and the quality of the class III reservoir is poor.

[0007] Both diagenesis and the pore structure and size of reservoirs are essentially constrained by or influenced by the reservoir's microstructure. Due to the limited availability of core experiments such as thin section analysis and mercury intrusion porosimetry, it is difficult to classify reservoir quality and evaluate "sweet spots" in continuous formations. This invention can quantitatively characterize reservoir pore structure even without core experimental data, saving experimental costs.

[0008] This invention is achieved through the following technical solutions:

[0009] A method for determining the quality of tight sandstone and conglomerate reservoirs based on pore structure factors includes the following steps:

[0010] 1) Calculate porosity (POR) using density logging;

[0011] 2) Using mercury injection data from tight sandstone and conglomerate reservoirs, correlation fitting analysis was performed on permeability and pore structure parameters to screen out the correlation R... 2 Mercury porosimetry parameters >0.65;

[0012] 3) Combining the correlation R from step 2) 2 A pore structure factor KX is constructed using a parameter greater than 0.65;

[0013] 4) Perform cross-plot analysis between the pore structure factor KX and conventional logging data to screen out the correlation coefficients R. 2 Well logging data POR / CNL >0.8;

[0014] 5) Combining the correlation R from step 4) 2 A well logging characterization method for constructing reservoir porosity structure factor KX using well logging data >0.8;

[0015] 6) Calculate clay content V using the neutron-density cross-plot method sh ;

[0016] 7) Construct lithology factor H using resistivity and sonic transit time logging;

[0017] 8) Utilizing pore structure factor KX and clay content V sh A reservoir classification and evaluation standard was established using porosity (POR) and lithology factor (H) to determine the quality of tight sandstone and conglomerate reservoirs.

[0018] The formula for calculating porosity (POR) in step 1) is as follows:

[0019] POR = a·ρ b +b (1)

[0020] In the formula: ρ bThe density logging value for the target layer is g / cm3. a and b are obtained by fitting the density logging value with the measured porosity from the core experiment.

[0021] The pore structure parameters mentioned in step 2) include sorting coefficient, average capillary radius, displacement pressure, and maximum throat radius.

[0022] Preferably, the correlation R in step 2) 2 Parameters >0.65 include sorting coefficient SC, average capillary radius RC, and exhaust pressure Pd.

[0023] The sorting coefficient SC and the average capillary radius RC are directly proportional to the permeability.

[0024] The displacement pressure Pd is inversely proportional to the permeability.

[0025] In step 3), a basic correlation factor (SC*RC / Pd) is set. Based on this, the pore structure factor KX is constructed using mathematical methods. The formula for calculating the pore structure factor KX is as follows:

[0026] KX=2ln(10000·SC·RC / Pd) (2)

[0027] In the formula: SC is the sorting coefficient; RC is the average capillary radius, μm; Pd is the exhaust pressure, MPa.

[0028] In step 4), the pore structure factor KX corresponding to the core well is subjected to cross-plot analysis with data points from various conventional logging curves. Natural gamma, resistivity and density logging have poor correlations with the KX factor, while neutron porosity logging (CNL) and porosity calculated from density (POR) have good correlations with the pore structure factor KX. KX is negatively correlated with CNL and positively correlated with POR.

[0029] The selected logging data includes neutron porosity logging (CNL) and porosity (POR), and the correlation coefficient R between KX and (POR / CNL) is... 2 >0.8, thus establishing a comprehensive classification and evaluation model for reservoir porosity structure factor KX based on conventional logging curve parameters (POR / CNL).

[0030] The KX logging characterization model for reservoir porosity structure factor in step 5) is as follows:

[0031] KX=c·(POR / CNL)+d (3)

[0032] The coefficients c and d are determined by fitting (POR / CNL) with KX constructed from the experimental parameters in formula (2).

[0033] Step 6) Clay content V shClay content is one of the key factors affecting reservoir properties. Clay content is inversely proportional to reservoir properties; as clay content increases, reservoir porosity and permeability decrease significantly. Since density and neutron logging are relatively sensitive to clay and fluid characteristics and are unaffected by clay distribution and compaction, clay content is calculated using the neutron-density cross-plot method (see Formula 4).

[0034]

[0035] In the formula: ρ ma ρ f These are the rock skeleton density value and the formation fluid density value, respectively; φ Nma φ Nsh φ Nf These represent the neutron values ​​for the rock skeleton, mudstone, and fluid, respectively; ρ sh ρ represents the density of mudstone. b φ N These are the target layer density logging values, in g / cm³. 3 Neutron logging value, v / v. After substituting and simplifying, we get the final calculation formula:

[0036] V sh =e·ρ b +f·φ N +g (5)

[0037] In the formula: ρ b The target layer density logging value is in g / cm³. 3 φ N ν is the neutron logging value, v / v.

[0038] Step 7) By amplifying the difference between the resistivity and the sonic transit time ratio, the lithology factor H is extracted (see Formula 6). It is found that when the lithology factor H < 2, mudstone can be better distinguished. The formula for calculating the lithology factor H is as follows:

[0039] H = 10·RT / AC (6)

[0040] Where: RT is the formation resistivity, ohm mm; AC is the acoustic transit time, us / ft.

[0041] Step 8) Using the pore structure factor KX, combined with clay content, porosity, lithology, etc., analyze the productivity characteristics and establish reservoir classification and evaluation criteria. The classification criteria are shown in Table 1:

[0042] Table 1. Quality Classification Standards for Tight Sandstone and Conglomerate Reservoirs

[0043]

[0044] Preferably, the reservoir classification and evaluation criteria in step 8) are as follows:

[0045] Class I: Porosity (POR) ≥ 9%, clay content (V) sh ≤3%, pore structure factor KX≥20, lithology factor H≥4, lithology type is gravelly sandstone or sandy conglomerate;

[0046] Class II: Porosity 6% < POR < 9%, clay content 3% < V sh When the porosity factor is <8%, the pore structure factor is 16 < KX < 20, and the lithology factor is H ≥ 2, the lithology type is sandy conglomerate or fine-to-coarse sandstone.

[0047] Class III: Porosity (POR) ≤ 6%, clay content (V) sh ≥8%, pore structure factor KX<16, lithology factor 2<H<4, lithology type is argillaceous sandy conglomerate or fine-to-coarse sandstone.

[0048] The beneficial effects of this invention are:

[0049] This invention establishes a connection between conventional logging data and pore structure by analyzing the microscopic pore structure of rocks through mercury intrusion porosimetry (MIP) experiments. This enables continuous characterization of the pore structure of tight sandstone and conglomerate reservoirs, solving the problem of quantitatively characterizing reservoir pore structure even without core data, thus saving experimental costs. This invention has been applied in 31 key wells of the Triassic Baikouquan Formation on the Mabei slope of the Junggar Basin. The method for classifying and evaluating the quality of sandstone and conglomerate reservoirs based on pore structure factors shows good correspondence with oil testing results, solving the problem of comprehensive classification and evaluation of effective reservoirs based on conventional logging. This has played an important role in the discovery and exploration of deep oil and gas reserves. Attached Figure Description

[0050] Figure 1 This is a graph showing the relationship between density and porosity.

[0051] Figure 2 A fitting relationship diagram for optimal pore structure parameters.

[0052] Figure 3 This is a graph showing the fitting relationship between pore structure factor and well logging data.

[0053] Figure 4 This is a histogram showing the frequency distribution of lithological factors for different lithologies.

[0054] Figure 5 Example of a single-well map for classifying the quality of tight sandstone and conglomerate reservoirs based on pore structure factors. Detailed Implementation

[0055] The present invention will be further described below with reference to specific embodiments, and the advantages and features of the present invention will become clearer as a result. However, these embodiments are merely exemplary and do not constitute any limitation on the scope of the present invention. Those skilled in the art should understand that modifications or substitutions can be made to the details and form of the technical solutions of the present invention without departing from the spirit and scope of the present invention, but all such modifications and substitutions fall within the protection scope of the present invention.

[0056] The technical solution of this invention is illustrated using a deep reservoir in a well in the Junggar Basin as an example.

[0057] Example 1

[0058] A method for determining the quality of tight sandstone and conglomerate reservoirs based on pore structure factors includes the following steps:

[0059] (1) Calculate reservoir porosity using density logging according to (Formula 1). The relationship between density and porosity is shown in the diagram below. Figure 1 As shown, the coefficients a and b in Formula 1 are determined to be -52.614 and 141.13, respectively.

[0060] Formula (1) is as follows: POR = -52.614·ρ b +141.13.

[0061] (2) Using mercury injection data from tight sandstone and conglomerate reservoirs, a correlation fitting analysis was performed between permeability and pore structure parameters. The optimal fitting relationship diagram for pore structure parameters is shown in the figure below. Figure 2 As shown, the correlation coefficient R is preferred. 2 Parameters >0.65: sorting coefficient SC, average capillary radius RC, and displacement pressure Pd. A pore structure factor KX (Formula 2) was constructed using experimental parameters. Then, the pore structure factor was correlated with the corresponding well logging data, and the optimal correlation R was selected. 2 For logging data >0.8: Porosity (POR) and neutron porosity (CNL) calculated from density logging are used to calculate the pore structure factor KX using POR and CNL according to (Formula 3). The fitting relationship between the pore structure factor and logging data is shown in the figure below. Figure 3 As shown.

[0062] Formula (2) is as follows: KX = 2ln(10000·SC·RC / Pd);

[0063] Formula (3) is as follows: KX = 19.304·(POR / CNL) + 7.3859.

[0064] (3) Calculate the clay content by logging density and neutron porosity according to (Formula 4).

[0065] Formula (4) is as follows:

[0066] In the formula: ρ ma ρ f These are the density values ​​of the rock skeleton (taken as 2.67 g / cm³). 3 Formation fluid density (1.0 g / cm³) 3 );φ Nma φ Nsh φ Nf These represent the neutron values ​​for the rock framework (-0.03), mudstone (0.3), and fluid (1.0), respectively; ρ sh The density value of mudstone is 2.71 g / cm³. 3 );ρ b φ N These are the density logging value and the neutron logging value of the target layer, respectively. After substituting and simplifying, we obtain the final calculation formula (5): V sh =1.74·ρ b +2.82·φ N -4.56.

[0067] (4) Calculate the lithology factor using resistivity and sonic logging according to (Formula 6). The frequency distribution histograms of lithology factors for different lithologies are shown below. Figure 4 As shown. Formula (6) is as follows: H = 10·RT / AC.

[0068] (5) Classify the reservoir quality level according to the classification criteria in Table 1.

[0069] Figure 5Here is an example of the application of this method in a certain well: The first channel on the right shows the reservoir classification results, with red representing Class I reservoirs, green representing Class II reservoirs, purple representing Class III reservoirs, and white representing mudstone sections; the second channel on the right shows the oil testing results, and the third channel shows the lithology factor curve. Lithology factors less than 2 are filled in gray, representing mudstone, and lithology factors greater than 4 are filled in yellow, representing sandstone and conglomerate sections. The mudstone factor curve can distinguish mudstone well, and the gray-filled sections correspond well with the mudstone sections in the well logging. Based on the pore structure factor-based classification and evaluation criteria for sandstone and conglomerate reservoir quality, the T1b2 section has the best reservoir quality, with the highest Class I reservoir thickness reaching 23.66m. The thickness of Class II reservoirs is roughly equivalent to that of Class I reservoirs, while the thickness of Class III reservoirs is relatively low at 8.75m. The T1b3 section (3563m–3637.65m) is dominated by Class II reservoirs with a thickness of 21.59m, while the thickness of Class I reservoirs is only 3.73m, and the thickness of Class III reservoirs is 19.79m. The T1b1 section (3705.03m–3790.63m) has poorer reservoir quality, dominated by Class III reservoirs with a thickness of 58.56m. The T1b2 section produced 17.33t of oil per day during testing, concluding it is an oil-bearing reservoir; the T1b1 section produced only 3.16t of oil per day, also concluding it is an oil-bearing reservoir. Application results show that the thicker sections of Class I and II reservoirs correspond to relatively higher oil production. The Class III reservoir in section T1b1 has a high density and poor physical properties, and no significant increase in gas detection was observed, so no oil testing was conducted.

[0070] This invention has been applied to 31 key wells in the Triassic Baikouquan Formation of the Mabei Slope in the Junggar Basin. Table 2 shows the statistics of reservoir thickness and oil production in the Baikouquan Formation in the Mabei area. From the vertical well tests, wells with both Class I and Class II reservoirs (Wells X7, X9, and X12) produced more than 8 tons of fluid; vertical wells with only Class II reservoirs produced less than 8 tons (Well X7); horizontal wells with Class I and Class II reservoirs (Wells X3_H, X4_H, X5_H, X8_H, X10_H, and X11_H) generally produced more than 16 tons of fluid. Application examples demonstrate that the sandstone and conglomerate reservoir quality classification and evaluation method based on the pore structure factor of this invention has high accuracy in both the quality assessment and oil testing results. It solves the problem of comprehensive classification and evaluation of effective reservoirs based on conventional well logging, playing an important role in the discovery and exploration of deep oil and gas reserves.

[0071]

[0072] The above detailed description is a specific description of one of the feasible embodiments of the present invention. This embodiment is not intended to limit the patent scope of the present invention. All equivalent implementations or modifications that do not depart from the present invention should be included within the scope of the technical solution of the present invention.

Claims

1. A method for determining the quality of tight sandstone and conglomerate reservoirs based on pore structure factors, characterized in that, Includes the following steps: 1) Calculate porosity (POR) using density; 2) Using mercury injection data from tight sandstone and conglomerate reservoirs, a correlation fitting analysis was performed between permeability and pore structure parameters to select the correlation coefficient R. 2 Parameters >0.65; 3) Combine the correlation coefficient R from step 2). 2 A pore structure factor KX is constructed using a parameter greater than 0.65; 4) Perform cross-plot analysis between the pore structure factor KX and conventional logging data to screen out the correlation coefficients R. 2 Well logging data >0.8; 5) Combining the correlation R from step 4) 2 Well logging data with a value >0.8 were used to characterize the pore structure factor KX in step 3). 6) Calculate clay content V using the neutron-density cross-plot method sh ; 7) Construct lithology factor H using resistivity and sonic transit time logging; 8) Utilizing pore structure factor KX and clay content V sh A reservoir classification and evaluation standard was established using porosity (POR) and lithology factor (H) to determine the quality of tight sandstone and conglomerate reservoirs.

2. The method according to claim 1, characterized in that, The porosity (POR) formula in step 1) is as follows: POR = a·ρ b +b(1) In the formula: ρ b The target layer density logging value is in g / cm³. 3 a and b were obtained by fitting the density logging values ​​with the measured porosity from the core experiment.

3. The method according to claim 1, characterized in that, The correlation coefficient R mentioned in step 2) 2 Parameters >0.65 include sorting coefficient SC, average capillary radius RC, and exhaust pressure Pd. The sorting coefficient SC and the average capillary radius RC are directly proportional to the permeability. The displacement pressure Pd is inversely proportional to the permeability.

4. The method according to claim 2, characterized in that, The porosity factor KX constructed in step 3) is characterized by the following formula: KX=2ln(10000·SC·RC / Pd)(2) In the formula: SC is the sorting coefficient; RC is the average capillary radius, μm; Pd is the exhaust pressure, MPa.

5. The method according to claim 1, characterized in that, The correlation R mentioned in step 4) 2 Logging data >0.8 includes neutron porosity logging (CNL) and porosity logging (POR): The porosity factor KX is negatively correlated with neutron porosity logging (CNL) and positively correlated with porosity (POR).

6. The method according to claim 4, characterized in that, The reservoir porosity structure factor KX logging characterization model constructed in step 5) is as follows: KX=c·(POR / CNL)+d(3) In the formula, POR is the density porosity calculated by formula (1), in %; CNL is the neutron porosity logging value, %; the coefficients c and d are determined by fitting (POR / CNL) with KX calculated by formula (2).

7. The method according to claim 1, characterized in that, Step 6) Clay content V sh The calculation formula is as follows: V sh =e·ρ b +f·φ N +g(4) In the formula: ρ b The target layer density logging value is in g / cm³. 3 ;φ N The value is neutron logging value, v / v; the coefficients e, f, and g are determined by simplifying and substituting the corresponding parameters using the neutron-density cross plot method.

8. The method according to claim 1, characterized in that, The formula for calculating the lithological factor H in step 7) is as follows: H = 10·RT / AC(5) Where: RT is the formation resistivity, ohm mm; AC is the acoustic transit time, us / ft.

9. The method according to claim 1, characterized in that, The reservoir classification and evaluation criteria in step 8) are as follows: Class I: Porosity (POR) ≥ 9%, clay content (V) sh ≤3%, pore structure factor KX≥20, lithology factor H≥4, lithology type is gravelly sandstone or sandy conglomerate; Class II: Porosity 6% < POR < 9%, clay content 3% < V sh <8%, pore structure factor 16 < KX < 20, lithology factor H ≥ 2, lithology type is sandy conglomerate or fine-to-coarse sandstone; Class III: Porosity (POR) ≤ 6%, clay content (V) sh ≥8%, pore structure factor KX<16, lithology factor 2<H<4, lithology type is argillaceous sandy conglomerate or fine-to-coarse sandstone.

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