Method for identifying low-resistance hydrocarbon reservoir of complex fault block oil and gas field

Abstract

The invention relates to an identification method for a low-resistance hydrocarbon reservoir of a complex fault block oil and gas field, and belongs to the technical field of oil exploitation. The method comprises the following steps that logging is conducted through core scales, a reservoir parameter interpretation model of the shale content, the porosity, the permeability and the saturation is established, and reservoir parameter calculation is completed through the interpretation model; the reservoir physical property lower limit of the research block is determined through a test method and an empirical statistical method; on the basis of four-relation research, an interpretation chart and an interpretation standard are respectively established; on the basis of the Indonesia formula, a single-well water layer resistivity curve is calculated, a water layer resistivity comparison method is provided through numerical comparison of the water layer resistivity curve and a logging resistivity curve, and effective identification of a low-resistivity hydrocarbon reservoir in a research area is carried out in combination with stratigraphic comparison and adjacent well data. According to the method, secondary correction is carried out on the low-resistance hydrocarbon reservoir, the recognition precision is improved, the measure efficiency is improved, and powerful support is provided for reservoir classification evaluation and fine development requirements.

Description

Technical Field

[0001] The invention relates to a method for identifying low-resistance oil and gas layers in a complex fault block oil and gas field, belonging to the technical field of oil production. Background Art

[0002] At present, the remaining oil in the later stage of complex fault block oil and gas field development is highly dispersed, the oil-water relationship is complex, and the degree of reservoir understanding is low. The original logging interpretation standard of the block cannot meet the needs of reservoir classification evaluation and fine development. The conventional oil, gas and water identification method is not accurate enough to identify low-resistance oil and gas layers. It is necessary to re-establish the oil, gas and water identification chart, apply the water layer resistivity comparison method combined with production dynamic data, accurately identify the low-resistance potential layer and tap the remaining oil between wells. Summary of the invention

[0003] In order to solve the above-mentioned problems existing in the prior art, the present invention discloses a method for identifying low-resistance oil and gas layers in complex fault block oil and gas fields, which performs secondary correction on the low-resistance oil and gas layers to improve the identification accuracy, accurately determine the remaining oil-rich areas, improve the efficiency of measures, and provide strong support for reservoir classification evaluation and fine development needs.

[0004] The technical solution adopted by the present invention is: a method for identifying low-resistance oil and gas layers in complex fault-block oil and gas fields, and the specific steps are as follows:

[0005] Step 1: Make full use of various core analysis data, and based on the characteristics of the study area mainly consisting of a single porosity logging series, establish a reservoir parameter interpretation model of mud content, porosity, permeability, and saturation through core calibration logging, and use the interpretation model to complete the calculation of reservoir parameters to provide data support for the lower limit study of reservoir physical properties;

[0006] Step 2: The lower limit of reservoir physical properties in the research area is determined by testing and empirical statistical methods, providing a basis for reservoir division;

[0007] Step 3: Based on the data of well logging, core analysis, mud logging, gas logging and production testing, the relationship between the lithology, physical property, electrical property and oil content of the study area was studied. On the basis of the relationship between the four properties, interpretation charts and interpretation standards were established respectively;

[0008] Step 4: Based on the Indonesian formula, the water layer resistivity is used as the scale to calculate the water layer resistivity curve of a single well. By comparing the water layer resistivity curve with the logging resistivity curve, a water layer resistivity comparison method is proposed. Combined with formation comparison and adjacent well data, the low-resistivity oil and gas layers in the study area are effectively identified.

[0009] The invention discloses a method for identifying low-resistivity oil and gas layers in complex fault-block oil and gas fields. The beneficial effect is that the well logging secondary interpretation of the oil, gas and water layers in the study area is carried out through interpretation plates and standards and new low-resistivity identification technologies, and the single-well low-resistivity oil and gas layers are re-identified. Measures are proposed for 10 wells, and the efficiency of the measures is as high as 80%. The daily oil production capacity is increased by 48t, the cumulative oil production in the stage is 1.2×104 tons, and the stage benefits are 7 million yuan. In the next 3-5 years, it is expected to be promoted and applied 30 wells each year, creating benefits of 21 million yuan, greatly improving the effect of oil field development. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0011] Figure 1 This is a comparison chart between the shale content model and the shale content analyzed by core analysis;

[0012] Figure 2 Compute effective porosity comparison chart for porosity model and well logging model;

[0013] Figure 3 is the irreducible water saturation model;

[0014] Figure 4 It is a comparison chart of permeability and core permeability;

[0015] Figure 5 Comparison chart of porosity and permeability parameters calculated by logging for Hainan Well 13 and core analysis;

[0016] Figure 6 Identify oil and gas layers by comparing water layer resistivity (Hainan 1 Well);

[0017] Figure 7 This is a comparison chart of the Hainan 13-5, Hainan 1, and Hainan 11-5 strata;

[0018] Figure 8 Identify oil and gas layers using the water layer resistivity comparison method (Hainan 3-5C well);

[0019] Fig. 9 This is a comparison chart of Hainan 3-3 to Hainan 3-5C strata;

[0020] Fig.10 This is the cross-plot of the production test method;

[0021] Fig.11This is a schematic diagram of the logging interpretation chart of the oil and gas layers in the d3Ⅰ layer of Hainan Oilfield.

[0022] Specific implementation methods

[0023] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present invention and its application or use. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] In order to further understand the content of the present invention, the present invention is further described below in conjunction with specific implementation methods.

[0025] Example 1: This example takes Hainan Oilfield as the research area;

[0026] like Figure 1-11 As shown in FIG. 1 , a method for identifying low-resistivity oil and gas layers in complex fault-block oil and gas fields is described. The specific steps are as follows:

[0027] Step 1: Make full use of various core analysis data, and based on the characteristics of the study area mainly consisting of a single porosity logging series, establish a reservoir parameter interpretation model of mud content, porosity, permeability, and saturation through core calibration logging, and use the interpretation model to complete the calculation of reservoir parameters to provide data support for the lower limit study of reservoir physical properties;

[0028] The reservoir parameter interpretation model of mud content, porosity, permeability and saturation is established, and the reservoir parameter calculation is completed using the interpretation model as follows:

[0029] 1.1 Mud content model

[0030] The formation shale index S is calculated using the natural gamma curve GR, and then S is calibrated using the core shale content analysis data to obtain the final formation shale content V. sh .

[0031] S=(GR-GR min ) / (GR max -GR min )

[0032] V sh =5.05068e 3.6405·S

[0033] Where V SHrepresents the mud content, GR represents natural gamma, GR max Represents the natural gamma of mudstone, GR min Represents the natural gamma of sandstone.

[0034] Compare the shale content calculated by the model with the shale content of the core analysis, e.g. Figure 1 As shown in the figure, the two have good consistency, with an average relative error of 8%, and the mud content model meets the requirements of reservoir evaluation.

[0035] 1.2 Porosity model

[0036] The porosity model is established using the acoustic transit time. The acoustic transit time logging data is affected by the different degrees of compaction at burial depth, and needs to be corrected for compaction first. After correction, the porosity of the core analysis is used for calibration to establish the total porosity model.

[0037] PORT=56.223ln(AC)-292.43

[0038] After shale correction, the reservoir effective porosity PORR is obtained:

[0039]

[0040] In the above formula, PORT is the total porosity, PORR is the effective porosity, V sh is the calculated mud content, TSH is the AC value of pure mudstone, TMA is the AC value of rock skeleton, and TF is the AC value of fluid.

[0041] Figure 2 The core analysis porosity after shale correction was compared with the effective porosity calculated by the logging model. The two showed good consistency, with an average relative error of 8%.

[0042] 1.3 Irreducible water saturation model

[0043] Because there is no core analysis data available for irreducible water saturation, the irreducible water saturation data provided by the nuclear magnetic logging of Hainan Well 17-29 is used as a calibration, and the irreducible water saturation of the formation is calculated using the mud content, such as Figure 3 shown.

[0044] SIRR=-0.0197Vsh 2 +2.1865Vsh+21.664

[0045] Where SIRR is the irreducible water saturation of the formation (%), and Vsh is the calculated shale content (%).

[0046] 1.4 Permeability model

[0047] Permeability is another important parameter for evaluating reservoir properties and production capacity. Based on the understanding of Timmur's classical theoretical formula, the following permeability model was established using porosity and irreducible water saturation at the core analysis data scale:

[0048]

[0049] Where PERM is permeability (mD), PORR is effective porosity (%), and SIRR is irreducible water saturation (%).

[0050] The permeability calculated by the model is compared with the permeability of the core analysis. Figure 4 As shown, it can be seen that the two have good consistency, the error is within one order of magnitude, and meets the accuracy requirements of reservoir evaluation.

[0051] 1.5 Water saturation model

[0052] The reservoir lithology of Hainan Oilfield is siltstone and muddy siltstone. The lithology is fine and the reservoir contains a certain amount of mud. In addition, the formation water salinity is low. Therefore, this study chose the Indonesian formula to calculate the water saturation of the formation. This formula is suitable for low-mineralization formations and can better realize the correction of mud-added conductivity.

[0053] Indonesian equation:

[0054] In the formula, R o =a*Rw*PORR -m C=0.5(2-Vsh)

[0055] Where Sw represents the formation saturation, V sh represents mud content, n represents saturation index, Rt represents deep lateral resistivity (Ω.m), Rsh represents mudstone resistivity (Ω.m), Rw represents formation water resistivity (Ω.m), a represents lithology additional conductivity correction coefficient, PORR represents effective porosity (%), and m represents porosity index.

[0056] The formation water resistivity (Rw) is mainly obtained based on water analysis data. In actual calculations, the wellbore is surrounded by a mixture of formation fluid and mud filtrate. The mineralization varies with depth, temperature, mud filtrate resistivity, etc., resulting in different resistivity values ​​for the formation mixed fluid. Rw can be adjusted accordingly.

[0057] Table 1 Formation water resistivity of non-main strata in Hainan Oilfield

[0058]

[0059]

[0060] 1.6 Model Validation

[0061] Using the calculation model of reservoir mud content, porosity, permeability and saturation determined above, a single well processing and interpretation program was compiled on the CifLog logging interpretation platform. The single well reservoir parameters were obtained using the processing and interpretation program, and the obtained reservoir parameters were compared with the core analysis data. The comparison results are shown in the figure. Figure 5 The second blue column on the right is the core porosity, and the third yellow column is the core permeability. Figure 5 It can be seen that the porosity and permeability of the core analysis are well consistent with those calculated by the model, indicating that the model established by logging is reasonable and meets the accuracy requirements of reservoir evaluation and reserve calculation.

[0062] Step 2: The reservoir lower limit of the research block is determined by testing method and empirical statistical method, which provides a basis for reservoir division;

[0063] This study used production testing method and empirical statistical method to comprehensively determine the lower limit of porosity and permeability of non-main reservoir layers in Hainan Oilfield and determine the identification standard of low-resistivity oil and gas layers.

[0064] (1) Production test method

[0065] This method uses production or test layer data to produce porosity and permeability cross-plots, that is, Fig.10 As shown in the cross-plot, the upper limits of porosity and permeability corresponding to the dry layer with no liquid production capacity confirmed by production or oil testing are the lower limits of porosity and permeability in the study area. The cross-plot shows that the lower limits of the physical properties of the non-main layer in Hainan Oilfield are porosity 16% and permeability 5mD.

[0066] (2) Empirical Statistics

[0067] This is a mathematical statistics method, which considers the porosity determined by the cumulative loss of 5% of reservoir capacity as the lower limit of porosity. The permeability is determined by multiplying the average permeability of the core by 5%. According to this method, the lower limit of the porosity of the non-main layer reservoir in the South Oilfield is determined to be 16%, and the lower limit of the permeability is 7mD.

[0068] Based on the lower limits of porosity and permeability obtained by the above two methods, the lower limit of porosity of the non-main reservoir of Hainan Oilfield is determined to be 16% and the lower limit of permeability is 5mD by taking the minimum value as the comprehensive method.

[0069] Step 3: Based on the data of well logging, core analysis, mud logging, gas logging and production testing, the relationship between lithology, physical property, electrical property and oil content of the study area was studied. On the basis of the relationship between the four properties, interpretation charts and interpretation standards were established, such as Fig.11 As shown in Table 2;

[0070] Table 2: Well logging interpretation standards for oil and gas layers in d3Ⅰ layer of Hainan Oilfield

[0071]

[0072] Step 4: Based on the Indonesian formula, the water layer resistivity is used as the scale to calculate the water layer resistivity curve of a single well. By comparing the water layer resistivity curve with the deep lateral resistivity value, a water layer resistivity comparison method is proposed. Combined with stratigraphic comparison and adjacent well data, the low-resistivity oil and gas layers in the study area are effectively identified.

[0073] The water layer resistivity comparison method is based on the Indonesian saturation model. It uses the standard water layer for calibration, calculates a single well water layer resistivity curve Ro, and compares Ro with the logging resistivity curve Rt. When Rt is greater than Ro, it indicates that the reservoir contains oil and gas. When Rt is greater than Ro, the larger the intersection area, the greater the resistivity increase coefficient, which means that the reservoir contains oil and gas saturation. In general, the resistivity increase coefficient of the oil and gas layer in the study area is more than 1.5 times, such as: the resistivity increase coefficient of the 21st layer of Hainan Well 1 is 1.6 (see Figure 6 ), when the shale content is high and the bound water saturation is high, the resistivity increase coefficient can be less than 1.5, such as: Hainan 1 well 1 layer, the shale content is 23%, the resistivity increase coefficient is 1.3 (see Figure 6 ); When Rt is less than or equal to Ro, it indicates that the reservoir is a water layer or a dry layer, such as: Hainan Well 1 23 water layer (see Figure 6 ).

[0074] The formula for calculating the resistivity of the water layer is as follows:

[0075]

[0076] Where: R O To calculate the resistivity of the water layer; V sh is the mud content, d is the mud content index; porr is the porosity; Rw is the formation water resistivity; Rsh is the mudstone resistivity; SW o is the water saturation of the water layer; a is the additional conductivity correction coefficient of the lithology, m is the porosity index, and n is the saturation index.

[0077] In the actual interpretation process, the water layer resistivity calculated by the water layer resistivity comparison method is compared with the logging resistivity curve, and a comprehensive analysis is carried out in combination with formation comparison, which can better identify the oil and gas layers in the study area, especially the low resistivity oil and gas layers.

[0078] Figure 6This is an example of well logging interpretation in Hainan 1 Well. Although the resistivity of Layer 21, Supplement 1 and Layer 22 in the figure is relatively low, the logging Rt is significantly higher than the water layer resistivity Ro. These are low-resistivity oil and gas layers caused by high mud bound water content. The resistivity increase coefficients are 1.6, 1.3 and 1.5 respectively, with obvious gas-bearing characteristics. Figure 7 The Hainan 1 well was compared with the neighboring wells Hainan 13-5 and Hainan 11-5. Hainan 11-5 is located at a lower position, and the 12th and 13th layers were confirmed to be gas layers after production. The 9th, 10th and 11th layers of the Hainan 13-5 well are located in the A area of ​​the interpretation chart of the d2Ⅲ oil layer group and can be well identified. The vertical depth of the Hainan 1 well is between Hainan 3-5 and Hainan 11-5. In this study, the 21st layer, the supplementary 1st layer and the 22nd layer are interpreted as gas layers.

[0079] Figure 8 This is an example of well logging interpretation of Hainan 3-5C Well. The resistivity of 29 and 30 in the figure is relatively low, but the logging Rt is significantly higher than the calculated water layer resistivity Ro. It is a low-resistivity oil and gas layer caused by high mud bound water content. The resistivity increase coefficients are 1.6 and 2.2 respectively, and the oil and gas characteristics are obvious. Fig. 9 Hainan 3-5C was compared with the adjacent well Hainan 3-3. Through the comparison, it can be seen that the corresponding layers of Hainan 3-3 well are 33b and 33d, which have been put into production and the production results are oil layers. The positions of 29 and 30 in this well are higher than the corresponding layers of the adjacent wells. According to the comprehensive analysis results of the water layer comparison method and the formation comparison, 29 is interpreted as a poor oil layer and 30 can be interpreted as an oil layer. The interpretation results are consistent with the results of the combined production of 29 and 30 layers, which are oil layers.

[0080] Based on the above example analysis, it can be seen that the low-resistivity oil and gas layer identification technology in the study area formed by the water layer resistivity comparison method and the formation comparison multi-well analysis method can effectively identify the low-resistivity oil and gas layers in the study area.

[0081] According to Example 1, 10 wells were proposed, with an efficiency of 80%, an additional daily oil production capacity of 48t, an accumulated increase of 1.2×104 tons of oil, and a phased benefit of 7 million yuan. In the next 3-5 years, it is expected to be promoted and applied 30 wells per year, creating a benefit of 21 million yuan, greatly improving the effect of oilfield development.

[0082] Theoretical calculation formula: E = (1-30%) × F × Q × (P-TC) - I

[0083] E—net present value of results, yuan;

[0084] F—dividing coefficient;

[0085] Q—increased oil and gas production, tons, thousand cubic meters;

[0086] P—price of crude oil or natural gas; Yuan / ton, Yuan / thousand cubic meters (excluding tax);

[0087] T—taxes (urban maintenance and construction tax, education surcharge, resource tax), yuan / ton, yuan / thousand cubic meters;

[0088] C—production cost (operating cost + depreciation), RMB / ton; RMB / thousand cubic meters;

[0089] I—Investment, yuan.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying low-resistance oil and gas layers in a complex fault block oil and gas field, comprising the following steps: Step 1: Make full use of various core analysis data, and based on the characteristics of the study area mainly using single porosity logging series, establish a reservoir parameter interpretation model of mud content, porosity, permeability, and saturation through core calibration logging, and use the interpretation model to complete the reservoir parameter calculation; Step 2: Determine the lower limit of reservoir properties in the study area through testing and empirical statistical methods; Step 3: Based on the data of well logging, core analysis, mud logging, gas logging and production testing, the relationship between the lithology, physical property, electrical property and oil content of the study area was studied. On the basis of the relationship between the four properties, interpretation charts and interpretation standards were established respectively; Step 4: Based on the Indonesian formula, the water layer resistivity is used as the scale to calculate the single well water layer resistivity curve. The water layer resistivity curve Ro is compared with the logging resistivity curve Rt, and the low resistivity oil and gas layers in the study area are effectively identified in combination with formation comparison and adjacent well data.

2. The method for identifying low-resistance oil and gas layers in complex fault-block oil and gas fields according to claim 1, characterized in that: In step 1, a reservoir parameter interpretation model of mud content, porosity, permeability, and saturation was established, and the reservoir parameter calculation was completed using the interpretation model; the details are as follows: 1.

1. Mud content model The formation shale index S is calculated using the natural gamma curve GR, and then S is calibrated using the core shale content analysis data to obtain the final formation shale content V. sh ; S=(GR-GR min ) / (GR max -GR min ) In sh =5.05068e 3.6405·S Where V SH represents the mud content, GR represents natural gamma, GR max Represents the natural gamma of mudstone, GR min represents the natural gamma of sandstone; 1.2 Porosity model The porosity model is established using the acoustic time difference. The acoustic time difference logging data is affected by the different degrees of compaction at the burial depth, so it is necessary to first perform a compaction correction. After correction, the porosity of the core analysis is used for calibration to establish the total porosity model: PORT=56.223ln(AC)-292.43 After shale correction, the reservoir effective porosity PORR is obtained: In the above formula, PORT is the total porosity, PORR is the effective porosity, V sh is the calculated mud content, TSH is the AC value at pure mudstone, TMA is the AC value of rock skeleton, and TF is the AC value of fluid; 1.3 Irreducible water saturation model Calculate the irreducible water saturation of the formation using the mud content: SIRR=-0.0197Vsh 2 +2.1865Vsh+21.664 Where, SIRR is the irreducible water saturation of the formation (%), Vsh is the calculated shale content (%); 1.4 Permeability Model Based on the understanding of Timmur's classical theoretical formula, the following permeability model was established using porosity and irreducible water saturation at the core analysis data scale: Where PERM is permeability (mD), PORR is effective porosity (%), and SIRR is irreducible water saturation (%); 1.5 Water Saturation Model The Indonesian formula is used to calculate the water saturation of the formation. This formula is suitable for low-mineralization formations and can realize the correction of additional conductivity of mud. Indonesian equation: In the formula, R o =a*Rw*PORR -m C=0.5(2-Vsh) Where Sw represents the formation saturation, V sh represents mud content, n represents saturation index, Rt represents deep lateral resistivity (Ω.m), Rsh represents mudstone resistivity (Ω.m), Rw represents formation water resistivity (Ω.m), a represents lithology additional conductivity correction coefficient, PORR represents effective porosity (%), and m represents porosity index.

3. The method for identifying low-resistance oil and gas layers in complex fault-block oil and gas fields according to claim 1, characterized in that: In step 2, the production test method and the empirical statistical method are used to comprehensively determine the lower limits of the porosity and permeability of the non-main reservoirs of the oil field with the minimum value, and determine the identification standard of the low-resistance oil and gas layer.

4. The method for identifying low-resistance oil and gas layers in complex fault-block oil and gas fields according to claim 1, characterized in that: The calculation formula for the water layer resistivity Ro in step 4 is as follows: Where: R O To calculate the resistivity of the water layer; V sh is the mud content, d is the mud content index; porr is the porosity; Rw is the formation water resistivity; Rsh is the mudstone resistivity; SW o is the water saturation of the water layer; a is the additional conductivity correction coefficient of the lithology, m is the porosity index, and n is the saturation index.

5. The method for identifying low-resistance oil and gas layers in complex fault-block oil and gas fields according to claim 1, characterized in that: In step 4, the water layer resistivity curve Ro is compared with the well logging resistivity curve Rt, specifically: When Rt is greater than Ro, it indicates that the reservoir contains oil and gas. The larger the intersection area when Rt is greater than Ro, the greater the resistivity increase coefficient, which means that the oil and gas saturation of the reservoir is greater. When Rt is less than or equal to Ro, it indicates that the reservoir is a water layer or a dry layer.

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