Method for identifying fluid category based on energy storage index and apparent formation water resistivity variance

Abstract

The present application relates to the technical field of complex oil and gas reservoir logging evaluation in oil exploration, and is a method for identifying fluid categories based on a storage index and apparent formation water resistivity variance, comprising: calculating gravel, sandy and argillaceous lithological component factors by using the results of conglomerate core analysis; calculating n formation rock porosities and n apparent formation water resistivities; obtaining the variance of the n apparent formation water resistivities; calculating a sorting coefficient and a heterogeneity factor; calculating a fracture porosity and a fracture factor; constructing a storage index and apparent formation water resistivity variance and storage index fluid identification chart, and determining an oil layer standard. The present application is a method for identifying conglomerate reservoir fluid categories based on an electrical imaging storage index and apparent formation water resistivity variance, overcomes the limitations of the porosity and oil saturation crossplot method based on the Archie formula and the difference spectrum method based on nuclear magnetic resonance, provides a new method for identifying conglomerate reservoir fluid categories, and the chart application coincidence rate is 100%.

Description

Technical Field

[0001] This invention relates to the field of well logging evaluation technology for complex oil and gas reservoirs in petroleum exploration. It is a method for identifying fluid types based on energy storage index and apparent formation water resistivity variance, specifically a method for identifying fluid types in conglomerate reservoirs based on energy storage index and apparent formation water resistivity variance using electrical imaging. Background Technology

[0002] Currently, the main methods for identifying fluid types in conglomerate tight oil include the cross-plot method based on porosity and saturation content from conventional well logging curves, and the difference spectrum method and shift spectrum method based on nuclear magnetic resonance.

[0003] (1) The porosity-saturation cross-plot method based on Archie's formula is established according to the classic Archie formula:

[0004]

[0005] In the formula: S w R represents the water saturation of the reservoir, dimensionless; t The resistivity of pure oil and gas-bearing rocks, R w φ is the resistivity of formation water, in ohm-meters; φ is the effective porosity of the reservoir; a and b are proportionality coefficients related to lithology; m is the cementation index; and n is the saturation index, all dimensionless.

[0006] The classic Archie formula is suitable for pure sandstone with intergranular porosity and good permeability, exhibiting a relatively uniform pore size distribution and good pore-throat matching. For conventional sandstone reservoirs, the reservoir oil saturation can be accurately calculated, reflecting the oil saturation of a single sand layer. This method can only identify oil-bearing and non-oil-bearing layers, but not fluid types. However, for heterogeneous conglomerate reservoirs, multiple layers are often tested together, using a reservoir testing approach. Therefore, accurately identifying reservoir fluid types using porosity-saturation cross-plots is more difficult.

[0007] (2) Spectral shifting and difference methods based on nuclear magnetic resonance

[0008] Shift spectrum is a diffusion coefficient weighting method. By setting two echo intervals TE of different lengths, two sets of echo trains are measured at a sufficiently long waiting time TW. Since the diffusion coefficients D of water and gas or oil are different, their positions on the T2 distribution change, thereby identifying oil, gas and water in the reservoir.

[0009] The difference spectrum is mainly based on the large difference in the longitudinal relaxation time T1 between water and oil / gas. The longitudinal recovery rate of water is much faster than that of light hydrocarbons. By selecting two different waiting times, the observed echo trains will contain different signals. Since the signal of water can be fully polarized at a short waiting time, while the signal of hydrocarbons cannot be fully polarized at a short waiting time, the amplitude of the echo trains observed at the two different waiting times will differ. Dual TW logging uses this difference to identify whether the reservoir contains hydrocarbons.

[0010] Based on the principles of the two methods, the shift spectrum method is greatly affected by the viscosity of the reservoir fluid, the formation temperature, and the pore structure. Furthermore, it requires adjusting the appropriate echo interval to highlight the differences in the shift spectrum of the fluid, making it difficult to identify the fluid category in practical applications.

[0011] Existing nuclear magnetic resonance logging instruments have a very small range of long and short waiting times, especially for short TW. P-type nuclear magnetic resonance instruments can only select 1s and 2s. This means that in many cases, the water in large-diameter pores does not fully recover in a short waiting time, and the water layer still has a differential spectrum signal, which is basically the same as the oil layer. Therefore, it is impossible to use the differential spectrum method to identify oil and water. Summary of the Invention

[0012] This invention provides a method for identifying fluid types based on energy storage index and apparent formation water resistivity variance, overcoming the shortcomings of the prior art. It can effectively solve the inapplicability of porosity and saturation cross-plot method based on Archie formula and difference spectrum method based on nuclear magnetic resonance to the identification of fluid types in conglomerate tight oil.

[0013] The technical solution of this invention is achieved through the following measures: a method for identifying fluid categories based on energy storage index and apparent formation water resistivity variance, comprising the following steps:

[0014] Step 1: Use the gravel, sand, and clay content from conglomerate core analysis to calibrate electrical imaging, determine the electrical conductivity cutoff values ​​for gravel, sand, and clay, and use the cutoff values ​​to obtain the relative contents of gravel (VG), sand (VS), and clay (VSH) at each depth point in the electrical imaging measurement section. Calculate the lithological component factor (Lith) using the relative contents of gravel and clay.

[0015] Lith=VG / VSH (2)

[0016] In the formula, VG is the relative content of gravel and VSH is the relative content of clay.

[0017] The physical meaning reflected by the lithological component factor (Lith) is that the physical properties of conglomerate reservoirs are greatly affected by the relative content of each component. A high relative content of gravel results in well-developed intergranular pores and gravel surface fractures, leading to good reservoir physical properties. Conversely, a low relative content of gravel results in poor reservoir physical properties.

[0018] Step 2: Perform shallow resistivity calibration on the above electrical imaging data to obtain n imaging resistivity data points. Use Archie's formula and the imaging resistivity to calculate the porosity (Por) of n strata rocks. n ) and n apparent formation water resistivity (Rwn);

[0019] Step 3: Calculate the average porosity (Por) at each depth point;

[0020]

[0021] In the formula, Por n The average porosity (Por) is the physical meaning of the reservoir porosity, which is calculated by Archie's formula. The larger the porosity, the better the reservoir properties, and vice versa.

[0022] Step 4: Analyze the apparent formation water resistivity (Rw1, ..., Rw...) n Histogram statistics were performed to obtain the apparent formation water resistivity variance (w). This variance can characterize the distribution of formation fluids. A small variance indicates that the reservoir is mainly composed of water, while a large variance indicates that the reservoir is mainly composed of oil and gas.

[0023] Step 5: Perform histogram statistics on the obtained n imaging resistivity data to obtain the resistivity distribution quantiles p15, p50, and p75, and calculate the sorting coefficient (Sort) and the heterogeneity factor (Het).

[0024] Sort=((P75-P15)) / P50 (4)

[0025] Het = 1 / Sort (5)

[0026] In the formula, Sort is the sorting coefficient, Het is the heterogeneity factor, and p15, p50, and p75 are the quantiles of the resistivity distribution. The sorting coefficient (Sort) reflects the degree of sorting of the conglomerate, and the heterogeneity factor (Het) reflects the heterogeneity of the reservoir. The larger the sorting coefficient, the smaller the heterogeneity factor, the stronger the heterogeneity of the reservoir, and the worse the reservoir quality. Conversely, the smaller the sorting coefficient, the larger the heterogeneity factor, the weaker the heterogeneity of the reservoir, and the better the reservoir quality.

[0027] Step Six: Identify cracks on the electro-imaging image and calculate crack porosity. The crack factor (Fra) is calculated using crack porosity, as shown in the following formula:

[0028]

[0029] In the formula, Fra is the crack porosity, and Fra is the crack factor.

[0030] Step 7: Construct the energy storage index using the lithological composition factor (Lith), rock porosity (Por), heterogeneity factor (Het), and fracture factor (Fra) obtained from electrical imaging processing. The model is as follows:

[0031] R = Lith × Por × Het × Fra (7)

[0032] In the formula, R is the energy storage index, Lith is the lithological component factor, Por is the rock porosity, Het is the heterogeneity factor, and Fra is the fracture factor.

[0033] Step 8: Construct a conglomerate reservoir fluid identification chart based on energy storage index and apparent formation water resistivity variance, and determine the oil layer identification criteria.

[0034] This invention is a method for identifying fluid types in conglomerate reservoirs based on energy storage index and apparent formation water resistivity variance using electrical imaging. It overcomes the limitations of cross-plot method based on Archie formula porosity and oil saturation and difference spectrum method based on nuclear magnetic resonance, providing a new method for identifying fluid types in conglomerate reservoirs, and the map application has a 100% accuracy rate. Attached Figure Description

[0035] Appendix Figure 1 This is a flowchart of the present invention.

[0036] Appendix Figure 2 A fluid identification chart based on apparent formation water resistivity variance and energy storage index. Detailed Implementation

[0037] The present invention is not limited to the following embodiments, and specific implementation methods can be determined according to the technical solutions and actual conditions of the present invention.

[0038] The example selected is a well (mh005), in which coring was performed in the conglomerate section and electrical imaging (FMI) logging data was collected.

[0039] The present invention will be further described below with reference to embodiments:

[0040] Example: As attached Figure 1 As shown, the method for identifying fluid categories based on energy storage index and apparent formation water resistivity variance includes the following steps:

[0041] Step 101: Using the gravel, sand, and clay content from the conglomerate core analysis, calibrate the electrical imaging to determine the electrical conductivity cutoff values ​​for gravel, sand, and clay as 90 (1 / ohm.m) and 200 (1 / ohm.m), respectively. Using these cutoff values, obtain the relative contents of gravel (VG), sand (VS), and clay (VSH) at each depth point in the electrical imaging measurement section. Further calculate the lithological component factor (Lith) using the formula Lith = VG / VSH.

[0042] Step 102: Perform shallow resistivity calibration on the electrical imaging data to obtain 192 imaging resistivity data points. Use Archie's formula and the imaging resistivity to calculate the porosity of 192 formation rocks (Por) inversely. n ) and 192 apparent formation water resistivity (Rwn);

[0043] Step 103: Using the formula Calculate the average porosity (Por) at each depth point; calculate the apparent formation water resistivity (Rw1, ..., Rw) for n depth points. n Perform histogram statistics to obtain the variance (w);

[0044] Step 104: Statistically analyze 192 resistivity curves using histograms to obtain the resistivity distribution quantiles p15, p50, and p75. Calculate the sorting coefficient (Sort) and the heterogeneity factor (Het) using the formulas Sort=((P75-P15)) / P50 and Het=1 / Sort.

[0045] Step 105: Identify open-type cracks on the electro-imaging image and obtain crack porosity. Using formula Further calculate the crack factor (Fra);

[0046] Step 106: Construct an energy storage index using the lithological component factor (Lith), rock porosity (Por), heterogeneity factor (Het), and fracture factor (Fra) obtained from electrical imaging processing. Further construct an oil reservoir identification chart based on the apparent formation water resistivity variance-energy storage index to determine the oil reservoir standard. The energy storage index calculation formula is as follows:

[0047] R = Lith × Por × Het × Fra

[0048] Step 107: Construct a fluid identification chart based on apparent formation water resistivity variance and energy storage index, such as... Figure 2 As shown, this chart can effectively distinguish between oil layers, oil-bearing layers, and dry layers. The criteria for identification are: oil layer (R > 11.5 and W > 5), oil-bearing layer (R < and W > 5), and water layer (W < 5).

[0049] The above technical features constitute the embodiments of the present invention, which have strong adaptability and implementation effect. Unnecessary technical features can be added or removed according to actual needs to meet the needs of different situations.

Claims

1. A method for identifying fluid categories based on energy storage index and apparent formation water resistivity variance, characterized in that... Includes the following steps: Step 1: Use the gravel, sand and mud content from conglomerate core analysis to calibrate electrical imaging, determine the electrical conductivity cutoff values ​​for gravel, sand and mud, and use the cutoff values ​​to obtain the relative content of gravel, sand and mud at each depth point in the electrical imaging measurement section. Calculate the lithological component factor using the relative content of gravel and mud. Step 2: Perform shallow resistivity calibration on the above electrical imaging data to obtain n imaging resistivity data. Use Archie's formula and imaging resistivity to back-calculate n formation rock porosity and n apparent formation water resistivity. Step 3: Calculate the average porosity at each depth point; Step 4: Perform histogram statistics on the n apparent formation water resistivity values ​​to obtain the variance of the apparent formation water resistivity; Step 5: Perform histogram statistics on the obtained n imaging resistivity data to obtain the resistivity distribution quantiles p15, p50, and p75, and calculate the sorting coefficient and heterogeneity factor. Step Six: Identify cracks on the electro-imaging image and calculate crack porosity. Calculate the crack factor using the crack porosity, as shown in the following formula: In the formula, Fra is the crack porosity, and Fra is the crack factor. Step 7: Construct the energy storage index using the lithological component factor, rock porosity, heterogeneity factor, and fracture factor obtained from electrical imaging processing. The model is as follows: R = Lith × Por × Het × Fra (7) In the formula, R is the energy storage index, Lith is the lithological component factor, Por is the rock porosity, Het is the heterogeneity factor, and Fra is the fracture factor. Step 8: Construct a fluid identification chart for conglomerate reservoirs based on energy storage index and apparent formation water resistivity variance, and determine the criteria for oil layer identification; In step one, the lithological component factor is calculated using the relative contents of gravel and clay. Lith=VG / VSH (2) In the formula, VG is the relative content of gravel and VSH is the relative content of clay. In step three, the average porosity at each depth point is calculated as follows; In the formula, Por is the average porosity, and porn is the porosity of n individual porosities; In step five, the sorting coefficient and heterogeneity factor are calculated as follows: Sort=((P75-P15)) / P50 (4) Het = 1 / Sort (5) In the formula, Sort is the sorting coefficient, Het is the heterogeneity factor, and p15, p50, and p75 are the quantiles of the resistivity distribution.

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