A method for identifying fluid properties in carbonate reservoirs using well logging data

Abstract

The present invention discloses a method for identifying the fluid properties of carbonate reservoirs using logging data, which relates to the technical field of oil and gas field exploration and development, and includes obtaining reservoir fluid property data; obtaining and screening logging data that are sensitive to different fluid properties; constructing reservoir fluid coefficients and gas content coefficients based on the screened logging data; calculating reservoir fluid coefficients and gas content coefficients for all well sections, and establishing a typical fluid identification chart based on the reservoir fluid property data, and completing fluid property identification. The fluid property identification chart using logging data established by the present invention is better than single logging or logging data in identifying the fluid properties of deep carbonate reservoirs, and focuses on using logging response characteristics that highlight the gas-bearing properties of the formation, which can distinguish dry layers, poor gas layers and gas layers from the physical property level, and can also distinguish gas layers, gas-water layers and water layers from the gas-bearing level, laying a foundation for the effective evaluation of carbonate gas reservoirs.

Description

Technical Field

[0001] The invention relates to the technical field of oil and gas field exploration and development, and more particularly to a method for identifying the properties of carbonate reservoir fluids by using well logging data. Background Art

[0002] At present, the microscopic pore throat structure of carbonate rocks determines the physical properties and fluid electrical response characteristics of complex structural reservoirs. Reservoir fluid properties are key parameters that affect oil and gas field exploration and development plans. For my country's carbonate reservoirs, special logging methods such as nuclear magnetic logging and array acoustic logging are relatively expensive, and fluid identification currently relies mainly on logging data. In complex structural reservoirs and low-porosity and low-permeability reservoirs, affected by factors such as pore structure characteristics and drilling fluid invasion, the resistivity curves and three-porosity curves used to identify fluid properties show similar logging response characteristics. There is ambiguity and multiple solutions in the understanding of fluid properties, and the interpretation effect is not ideal.

[0003] In conventional reservoirs, especially for the identification of gas-water two-phase fluids, the deep and shallow resistivity curves have good response characteristics to different fluids. In principle, the difference between gas layers, poor gas layers, and dry layers mainly stems from the physical property differences of reservoirs with the same fluid properties; changes in the relative content of gas-water two-phase fluids in reservoirs are often reflected in changes in deep resistivity and changes in intrusion conditions. For the identification of gas-water two-phase fluid properties in carbonate rocks, it is a valuable idea to use deep and shallow resistivities to construct new derived parameters to further clarify the boundaries between different fluids. The commonly used deep-shallow resistivity ratio and deep resistivity cross-plot uses the difference in deep and shallow resistivities and the deep resistivity to reflect the seepage capacity and fluid properties of the reservoir, and can be used to identify different fluids. However, this method is not effective for fluid identification in carbonate reservoirs, so the deep-shallow resistivity ratio should be optimized. The present invention proposes a reservoir fluid coefficient.

[0004] In many gas reservoirs where there is no heavy hydrocarbon display in gas logging, gas logging data cannot be well utilized, and many gas logging derived parameters such as oil-gas ratio, humidity ratio, etc. cannot be constructed. Fluid identification methods based on these gas logging derived parameters such as triangulation method, Pixler ratio method, etc. cannot be used. In the case of only two types of gas logging data, total hydrocarbons and gas logging methane, the ratio or difference ratio of total hydrocarbons and gas logging methane is also difficult to identify fluid properties well. For gas-water two-phase fluid identification, it is very important to pay attention to the impact of gas on reservoir properties. From the perspective of the degree of impact, the gas layer is the most obvious, followed by the poor gas layer, and then the dry layer, gas-water layer and water layer. This can be clearly reflected in gas logging. In addition to gas logging, the three-porosity curve can reflect the excavation effect caused by gas in the formation. However, in the actual interpretation process, the three-porosity curve is difficult to identify fluid properties alone, and the excavation effect coefficient calculated using the three-porosity curve is also difficult to identify fluid properties. Therefore, the gas logging derived parameters and the excavation effect coefficient should be optimized, and the present invention proposes a gas content coefficient.

[0005] Therefore, how to innovatively construct derivative parameters suitable for identifying the properties of carbonate gas-water two-phase fluids based on conventional logging data and form a typical fluid identification chart is an urgent problem that technicians in this field need to solve. Summary of the invention

[0006] In view of this, the present invention provides a method for identifying the fluid properties of carbonate reservoirs using logging data. On the basis of conventional logging data, the present invention innovatively constructs derivative parameters suitable for identifying the properties of carbonate gas-water two-phase fluids, forms a typical fluid identification chart, improves the accuracy of predicting the fluid properties of carbonate reservoirs, and solves the problems existing in the background technology.

[0007] In order to achieve the above object, the present invention adopts the following technical solution:

[0008] A method for identifying the properties of carbonate reservoir fluid using well logging data, comprising:

[0009] Obtain reservoir fluid property data;

[0010] Obtain and screen the logging data that are sensitive to different fluid properties; wherein, typical fluid logging response characteristics are collected and classified according to different fluid properties, and the logging responses are intersected in pairs to obtain a number of intersection diagrams, thereby screening the logging responses that are sensitive to fluid properties;

[0011] According to the selected logging data, the reservoir fluid coefficient and gas content coefficient are constructed;

[0012] The reservoir fluid coefficient and gas content coefficient are calculated for all well sections, and based on the reservoir fluid property data, a typical fluid identification chart is established in combination with the reservoir fluid coefficient and gas content coefficient to complete the fluid property identification.

[0013] Optionally, the reservoir fluid property data includes perforation sections and oil testing results, and the reservoir fluid properties are classified according to industry evaluation standards.

[0014] Optionally, the logging data include natural gamma curve, deep resistivity curve, shallow resistivity curve, sonic time difference curve, compensated density curve, compensated neutron curve, gas logging methane curve, and total hydrocarbon curve.

[0015] Optionally, the reservoir fluid coefficient construction step is:

[0016] Based on the logging data, calculate the normalized value of deep resistivity and the normalized value of shallow resistivity;

[0017] The deep-shallow resistivity difference coefficient is calculated according to the difference between the deep resistivity normalized value and the shallow resistivity normalized value;

[0018] The reservoir fluid coefficient is then obtained by multiplying the normalized value of the deep resistivity and the deep and shallow resistivity difference coefficient.

[0019] Optionally, the formula for calculating the normalized value of deep resistivity is:

[0020]

[0021] Where RDX is the normalized value of deep resistivity, decimal; RD is the deep resistivity, Ω·m; R max is the maximum deep resistivity of the region, Ω·m, R min is the regional minimum deep resistivity, Ω·m.

[0022] Optionally, the formula for calculating the normalized value of shallow resistivity is:

[0023]

[0024] Where RSX is the normalized value of shallow resistivity, decimal; RS is the shallow resistivity, Ω·m; R max is the maximum deep resistivity of the region, Ω·m; R min is the regional minimum deep resistivity, Ω·m.

[0025] Optionally, the formula for calculating the deep and shallow resistivity difference coefficient is:

[0026]

[0027] Wherein, RB is the difference coefficient of deep and shallow resistivity, a decimal; RDX is the normalized value of deep resistivity, a decimal; RSX is the normalized value of shallow resistivity, a decimal; a is the difference constant, a decimal.

[0028] Optionally, the gas content coefficient construction steps are:

[0029] Based on the logging data, calculate the normalized value of gas logging methane and the mining effect coefficient;

[0030] The gas content coefficient is obtained by multiplying the normalized value of gas-measured methane and the mining effect coefficient.

[0031] Optionally, the formula for calculating the normalized value of gas-measured methane is:

[0032]

[0033] In the formula, YS is the normalized value of methane measured by gas, in decimal; C1 is the methane value measured by gas, in percentage; C 1min 110% of the lower limit of methane measured in gas, percentage; C 1max It is 90% of the upper limit of methane measured in gas, percentage.

[0034] Optionally, the formula for calculating the mining effect coefficient is:

[0035]

[0036] Where DIFF is the mining effect coefficient, a decimal; CNL is the compensated neutron logging value, a percentage; CNL min To compensate for the minimum neutron logging value, percentage; CNL max is the maximum neutron logging value, percentage; DEN is the compensated density logging value, g / cm 3 ;DEN min is the minimum logging value of compensated density, g / cm 3 ;DEN max is the maximum logging value of compensated density, g / cm 3 ; AC is the compensated acoustic time difference logging value, μm / ft; AC min To compensate for the minimum logging value of acoustic time difference, μm / ft; AC max To compensate for the maximum logging value of acoustic time difference, μm / ft.

[0037] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a method for identifying the fluid properties of carbonate reservoirs using logging data. This method can comprehensively incorporate a variety of logging response characteristics for discussion, and more comprehensively consider the impact of fluid properties on formation properties. In view of the problem that the currently commonly used derivative parameters and fluid identification plates are not well applicable to carbonate fluid identification, by studying the impact of carbonate fluids on reservoir properties, more suitable fluid identification derivative parameters and typical plates are constructed to improve the accuracy of reservoir fluid property prediction. The logging data required for this method is relatively easy to obtain, and the typical fluid identification plates established are more accurate, and can be used as a feasible and effective tool for reservoir development. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] 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 only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0039] Figure 1 A flow chart of a method for identifying fluid properties of carbonate reservoirs using well logging data provided by the present invention;

[0040] Figure 2 A compensation density-natural gamma cross-plot provided by an embodiment of the present invention;

[0041] Figure 3 A shallow-deep resistivity cross-plot provided by an embodiment of the present invention;

[0042] Figure 4 A compensation acoustic wave-compensation neutron intersection diagram provided by an embodiment of the present invention;

[0043] Figure 5 A gas detection methane-total hydrocarbon cross-plot provided by an embodiment of the present invention;

[0044] Figure 6 A typical fluid identification plate provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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. 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.

[0046] The embodiment of the present invention discloses a method for identifying the fluid properties of carbonate reservoirs using well logging data, such as Figure 1 As shown, including:

[0047] Obtain reservoir fluid property data;

[0048] Obtain and screen the logging data that are sensitive to different fluid properties; wherein, typical fluid logging response characteristics are collected and classified according to different fluid properties, and the logging responses are intersected in pairs to obtain a number of intersection diagrams, thereby screening the logging responses that are sensitive to fluid properties;

[0049] According to the selected logging data, the reservoir fluid coefficient and gas content coefficient are constructed;

[0050] The reservoir fluid coefficient and gas content coefficient are calculated for all well sections, and based on the reservoir fluid property data, a typical fluid identification chart is established in combination with the reservoir fluid coefficient and gas content coefficient to complete the fluid property identification.

[0051] In a specific embodiment, reservoir fluid property data is obtained by conducting oil testing experiments on carbonate rocks; the reservoir fluid property data includes perforation sections and oil testing results, and reservoir fluid properties are divided according to the industry evaluation standard "Method for Evaluation of Oil and Gas Exploration Benefits".

[0052] In a specific embodiment, the logging data include natural gamma curve, deep resistivity curve, shallow resistivity curve, acoustic time difference curve, compensated density curve, compensated neutron curve, gas logging methane curve, and total hydrocarbon curve.

[0053] In a specific embodiment, the reservoir fluid coefficient construction step is:

[0054] The formula used Calculate the normalized value of deep resistivity: where RDX is the normalized value of deep resistivity, a decimal; RD is the deep resistivity, Ω·m; R max is the maximum deep resistivity of the region, Ω·m, R min is the regional minimum deep resistivity, Ω·m.

[0055] Using the formula Calculate the normalized value of shallow resistivity: where RSX is the normalized value of shallow resistivity, a decimal; RS is the shallow resistivity, Ω·m; R max is the maximum deep resistivity of the region, Ω·m; R min is the regional minimum deep resistivity, Ω·m.

[0056] Using the formula Calculate the deep and shallow resistivity difference coefficient, where RB is the deep and shallow resistivity difference coefficient, a decimal; RDX is the normalized value of the deep resistivity, a decimal; RSX is the normalized value of the shallow resistivity, a decimal; and a is the difference constant, a decimal.

[0057] The reservoir fluid coefficient is calculated using the formula RXB=RDX×RB, where RXB is the reservoir fluid coefficient, a decimal; RDX is the normalized value of the deep resistivity, a decimal; and RB is the deep and shallow resistivity difference coefficient, a decimal.

[0058] In a specific embodiment, the steps of constructing the air content coefficient are:

[0059] The formula used Calculate the normalized value of methane measured by gas, where YS is the normalized value of methane measured by gas, in decimal; C1 is the methane value measured by gas, in percentage; C 1min 110% of the lower limit of methane measured in gas, percentage; C 1max It is 90% of the upper limit of methane measured in gas, percentage.

[0060] Using the formula Calculate the mining effect coefficient, where DIFF is the mining effect coefficient, a decimal; CNL is the compensated neutron logging value, a percentage; CNL min To compensate for the minimum neutron logging value, percentage; CNL max is the maximum neutron logging value, percentage; DEN is the compensated density logging value, g / cm 3 ;DEN min is the minimum logging value of compensated density, g / cm 3 ;DEN max is the maximum logging value of compensated density, g / cm 3 ; AC is the compensated acoustic time difference logging value, μm / ft; AC min To compensate for the minimum logging value of acoustic time difference, μm / ft; AC max To compensate for the maximum logging value of acoustic time difference, μm / ft.

[0061] The gas content coefficient is calculated using the formula GC=DIFF×YS, where GC is the gas content coefficient, a decimal; DIFF is the mining effect coefficient, a decimal; and YS is the normalized value of methane measured by gas, a decimal.

[0062] The reservoir fluid coefficient and gas content coefficient are calculated for all well sections. The typical responses of the reservoir fluid coefficient and gas content coefficient are selected according to the perforation well sections and oil testing results, and a typical fluid identification plate is established to complete the fluid identification.

[0063] In a specific embodiment, 125 sets of logging data and oil testing data of the Dengying Formation carbonate strata in a certain area in the central Sichuan Basin are taken as an example to illustrate the specific technical scheme of the invention;

[0064] Step 1: Conduct oil testing on carbonate reservoirs to obtain reservoir fluid property data, including perforation sections, oil testing results, etc.

[0065] Table 1 Some well test data

[0066]

[0067]

[0068] Among them, the reservoir fluid properties are divided according to the industry evaluation standard "Method for Evaluation of Oil and Gas Exploration Benefits". The Sinian Dengying Formation, which is generally located below minus 4,000 meters above sea level, belongs to the ultra-deep carbonate reservoir. Therefore, the medium and strong stable daily production capacity, that is, the daily gas production of oil test is higher than 120,000 cubic meters, is classified as a gas layer, and the low stable daily production capacity, that is, the non-dry layer with a daily gas production of less than 120,000 cubic meters is classified as a poor gas layer. In addition, the layer that only produces water is classified as a water layer, the layer that produces both gas and water is classified as a gas-water layer, and the layer with no production is classified as a dry layer.

[0069] Step 2: Obtain logging data, including natural gamma curve, deep resistivity curve, shallow resistivity curve, sonic time difference curve, compensated density curve, compensated neutron curve, gas logging methane curve, total hydrocarbon curve, etc.

[0070] According to different fluid properties, typical fluid logging response characteristics are collected and classified, and the logging responses are intersected in pairs to obtain compensated density-natural gamma crossplot, shallow-deep resistivity crossplot, compensated acoustic wave-compensated neutron crossplot, gas logging methane-total hydrocarbon crossplot, such as Figure 2 , Figure 3 , Figure 4 , Figure 5 As shown, the logging response that is sensitive to fluid properties is screened.

[0071] Step 3: The steps for constructing the reservoir fluid coefficient are:

[0072] The formula used Calculate the normalized value of deep resistivity: where RDX is the normalized value of deep resistivity, a decimal; RD is the deep resistivity, Ω·m; R max is the maximum deep resistivity of the region, Ω·m, R min is the regional minimum deep resistivity, Ω·m.

[0073] Using the formula Calculate the normalized value of shallow resistivity: where RSX is the normalized value of shallow resistivity, a decimal; RS is the shallow resistivity, Ω·m; R max is the maximum deep resistivity of the region, Ω·m; R minis the regional minimum deep resistivity, Ω·m.

[0074] Using the formula Calculate the deep and shallow resistivity difference coefficient, where RB is the deep and shallow resistivity difference coefficient, a decimal; RDX is the normalized value of deep resistivity, a decimal; RSX is the normalized value of shallow resistivity, a decimal; a is the difference constant, a decimal. The difference constant is used to control the upper limit of the difference, focusing on distinguishing the gas layer from the poor gas layer and the dry layer in terms of permeability. Therefore, the difference constant can be obtained by calculating the normalized difference of deep and shallow resistivity and taking the absolute value of the layer segment between the gas layer and the poor gas layer according to the oil test results. In this study area, the normalized difference of deep and shallow resistivity is calculated for multiple layers with oil test conclusions of 100,000 to 150,000 cubic meters of daily gas production, and the absolute value is taken as 0.06.

[0075] The reservoir fluid coefficient is calculated using the formula RDB=RDX×RB, where RXB is the reservoir fluid coefficient, a decimal; RDX is the normalized value of deep resistivity, a decimal; and RB is the deep and shallow resistivity difference coefficient, a decimal.

[0076] The steps to construct the gas content coefficient are:

[0077] The formula used Calculate the normalized value of methane measured by gas, where YS is the normalized value of methane measured by gas, in decimal; C1 is the methane value measured by gas, in percentage; C 1min 110% of the lower limit of methane measured in gas, percentage; C 1max It is 90% of the upper limit of methane measured in gas, percentage.

[0078] Using the formula Calculate the mining effect coefficient, where DIFF is the mining effect coefficient, a decimal; CNL is the compensated neutron logging value, a percentage; CNL min To compensate for the minimum neutron logging value, percentage; CNL max is the maximum neutron logging value, percentage; DEN is the compensated density logging value, g / cm 3 ;DEN min is the minimum logging value of compensated density, g / cm 3 ;DEN max is the maximum logging value of compensated density, g / cm 3 ; AC is the compensated acoustic time difference logging value, μm / ft; AC min To compensate for the minimum logging value of acoustic time difference, μm / ft; AC max To compensate for the maximum logging value of acoustic time difference, μm / ft.

[0079] The gas content coefficient is calculated using the formula GC=DIFF×YS, where GC is the gas content coefficient, a decimal; DIFF is the mining effect coefficient, a decimal; and YS is the normalized value of methane measured by gas, a decimal.

[0080] Table 2 Some reservoir fluid coefficients and gas content coefficients and their basic parameters

[0081]

[0082] Step 4: Calculate the reservoir fluid coefficient and gas content coefficient for all well sections. Select typical responses of reservoir fluid coefficient and gas content coefficient based on the perforation well section and oil test results, and establish a typical fluid identification chart. The typical fluid identification chart is as follows: Figure 6 shown.

[0083] Figure 6 The fluid identification chart is divided into five areas, with the reservoir fluid coefficient as the X-axis and the gas coefficient as the Y-axis. The identification criteria are shown in Table 3.

[0084] Table 3 Identification criteria for carbonate fluids in the Dengying Formation in the study area

[0085] Fluid Type Reservoir fluid coefficient (X axis) Gas content coefficient (Y axis) Dry layer 0-0.4 0-0.05 Poor air layer 0-0.4 0.05-0.2 Air layer 0.4-0.6 0.2-1 Gas and water in the same layer 0.6-0.8 0-0.05 Water layer 0.8-1 0-0.05

[0086] According to the typical fluid identification plate, each fluid property has 25 data points, a total of 125 data points, of which 117 data points meet the fluid identification standards, and the identification accuracy rate reaches 93.6%.

[0087] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0088] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for identifying the properties of carbonate reservoir fluids using well logging data, characterized in that: include: Obtain reservoir fluid property data; Obtain and screen the logging data that are sensitive to different fluid properties; wherein, typical fluid logging response characteristics are collected and classified according to different fluid properties, and the logging responses are intersected in pairs to obtain a number of intersection diagrams, thereby screening the logging responses that are sensitive to fluid properties; According to the selected logging data, the reservoir fluid coefficient and gas content coefficient are constructed; Calculate the reservoir fluid coefficient and gas content coefficient for all well sections, and establish a typical fluid identification chart based on the reservoir fluid property data and the reservoir fluid coefficient and gas content coefficient, and complete the fluid property identification; The steps of constructing the reservoir fluid coefficient are: Based on the logging data, calculate the normalized value of deep resistivity and the normalized value of shallow resistivity; The deep-shallow resistivity difference coefficient is calculated according to the difference between the deep resistivity normalized value and the shallow resistivity normalized value; Then, the reservoir fluid coefficient is obtained by multiplying the normalized value of deep resistivity and the difference coefficient of deep and shallow resistivity; The formula for calculating the normalized value of deep resistivity is: Where RDX is the normalized value of deep resistivity, decimal; RD is the deep resistivity, Ω·m; R max is the maximum deep resistivity of the region, Ω·m, R min is the regional minimum deep resistivity, Ω·m; The formula for calculating the normalized value of shallow resistivity is: Where RSX is the normalized value of shallow resistivity, decimal; RS is the shallow resistivity, Ω·m; R max is the maximum deep resistivity of the region, Ω·m; R min is the regional minimum deep resistivity, Ω·m; The formula for calculating the deep and shallow resistivity difference coefficient is: Where, RB is the deep and shallow resistivity difference coefficient, decimal; RDX is the normalized value of deep resistivity, decimal; RSX is the normalized value of shallow resistivity, decimal; a is the difference constant, decimal; The gas content coefficient construction steps are: Based on the logging data, calculate the normalized value of gas logging methane and the mining effect coefficient; The gas content coefficient is obtained by multiplying the normalized value of methane measured by gas measurement and the mining effect coefficient; The formula for calculating the normalized value of gas-measured methane is: In the formula, YS is the normalized value of methane measured by gas, in decimal; C1 is the methane value measured by gas, in percentage; C 1min 110% of the lower limit of methane measured in gas, percentage; C 1max 90% of the upper limit of methane measured in gas, percentage; The formula for calculating the mining effect coefficient is: Where DIFF is the mining effect coefficient, a decimal; CNL is the compensated neutron logging value, a percentage; CNL min To compensate for the minimum neutron logging value, percentage; CNL max is the maximum neutron logging value, percentage; DEN is the compensated density logging value, g / cm 3 ;DEN min is the minimum logging value of compensated density, g / cm 3 ;DEN max is the maximum logging value of compensated density, g / cm 3 ; AC is the compensated acoustic time difference logging value, μm / ft; AC min To compensate for the minimum logging value of acoustic time difference, μm / ft; AC max To compensate for the maximum logging value of acoustic time difference, μm / ft.

2. The method for identifying the fluid properties of carbonate reservoirs using well logging data according to claim 1, characterized in that: The reservoir fluid property data includes perforation sections and oil testing results, and the reservoir fluid properties are classified according to industry evaluation standards.

3. The method for identifying the properties of carbonate reservoir fluids using well logging data according to claim 1, characterized in that: The logging data include natural gamma curve, deep resistivity curve, shallow resistivity curve, acoustic time difference curve, compensated density curve, compensated neutron curve, gas logging methane curve, and total hydrocarbon curve.

Images