A method for identifying cave-type reservoirs and its application

Abstract

The application discloses a kind of identification method of cave reservoir and its application, it is related to oil and gas exploration technical field.The identification method of cave reservoir disclosed in the application is based on drilling data, well logging data and well logging interpretation result data, uses drilling time, gas content C1 and loss amount Comprehensive identification cave reservoir method.The application can automatically identify cave reservoir, can meet the requirement of effective identification of reservoir in the case without conventional well logging and imaging logging data, reservoir description and reserves estimation.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration technology, and in particular to a method for identifying cave-like reservoirs and its application. Background Technology

[0002] Ultra-deep fault-controlled fracture-vuggy reservoirs are a new type of reservoir formed during intraplate small-to-medium scale strike-slip faulting, resulting from brittle strata fracturing and volume adjustment. They are primarily composed of fractures, pores, and caves, with cave reservoirs being the core and most valuable component. During oil and gas exploration and development, cave reservoirs often exhibit excellent storage properties and high permeability, leading to the accumulation of oil and gas. Compared to other reservoir types, cave reservoirs are relatively easier to develop and often yield better results, thus holding significant value in improving oil and gas exploration success rates and oil production.

[0003] Currently, the identification methods for basic reservoir space types mainly rely on conventional logging and imaging logging calculations. In conventional logging curves, cavernous reservoirs exhibit significantly reduced resistivity, significantly increased sonic transit time and neutron porosity, while imaging logging images show dark, high-conductivity patches or spots. However, when drilling encounters large-scale, high-quality reservoirs, it is often accompanied by severe mud loss, resulting in high well control risks and making it difficult to obtain complete logging and other data, which poses significant challenges to reservoir description and characterization.

[0004] Chinese patent CN112727440A discloses a reservoir identification method for fractured-vuggy oil and gas reservoirs based on drilling time data. Specifically, it includes the following steps: selecting wells in the fractured-vuggy oil and gas reservoirs that simultaneously possess drilling time data, logging data, and interpretation results as standard wells; determining the reservoir classification standard for the fractured-vuggy oil and gas reservoir based on drilling time data, according to the logging interpretation results of the standard wells and in conjunction with the drilling time data of the standard wells; collecting the original drilling time data of the target wells in the fractured-vuggy oil and gas reservoirs, and correcting the original drilling time data to obtain corrected drilling time data of the target wells to eliminate the influence of drilling operations; and determining the reservoir classification result of the target wells based on the corrected drilling time data and in conjunction with the reservoir classification standard. This patent identifies reservoir types based on drilling time data, but the identification logic is relatively complex.

[0005] Chinese patent CN113759424A discloses a method and system for analyzing karst reservoir filling based on spectral decomposition and machine learning, aiming to solve the problems of existing oil exploration technologies being unable to predict reservoirs with rapid lateral changes and unable to identify the development characteristics of carbonate cave-type reservoirs in complex basins over large areas. Specifically, it includes: acquiring standardized logging curve data; obtaining high-precision three-dimensional seismic amplitude data volumes through hybrid phase wavelet deconvolution and diffusion filtering; determining the optimal sample number parameter that reflects the overall geological situation, thereby determining the time-depth conversion relationship and reservoir-sensitive characteristic parameters; calculating the structural characteristics and spatial distribution patterns of fractured-vuggy reservoirs through wave impedance interpretation threshold values; and obtaining the three-dimensional spatial morphological characteristics of paleokarst cave fillings through cross-intersection analysis using interpretation threshold values. This patent does not disclose the concept of judging cave-type reservoirs based on drilling time, gas content (C1), and leakage in the method of identifying cave-type reservoirs of this invention, nor does it address the technical issues of this application.

[0006] In view of this, and to address the shortcomings of existing technologies, this invention provides a method for identifying cavernous reservoirs and its application. In the absence of conventional logging and imaging logging data, the cavernous reservoir identification method disclosed in this invention can accurately characterize the distribution features of cavernous reservoirs and the spatial distribution of oil and gas reservoirs. Summary of the Invention

[0007] The purpose of this invention is to provide a method for identifying cave-like reservoirs and its application, solving the problem of identifying and characterizing cave-like reservoirs when conventional logging data and imaging logging data are unavailable.

[0008] To achieve the above-mentioned objectives, the technical solution of this invention is as follows:

[0009] On the one hand, the present invention provides a method for identifying cave-like reservoirs. The method is based on drilling data, logging data and logging interpretation results, and uses drilling time, gas content C1 and leakage to comprehensively identify cave-like reservoirs.

[0010] Preferably, the drilling data includes drilling time, gas content C1, and leakage.

[0011] Preferably, the logging data includes natural gamma, wellbore, depth and shallow lateral logging, sonic transit time, density and neutron porosity.

[0012] Preferably, the well logging interpretation results data include mud content, oil saturation, gas saturation, porosity interpretation results, and reservoir type.

[0013] Preferably, the step of comprehensively identifying cavernous reservoirs using drilling time, gas content C1, and leakage is as follows:

[0014] S1. Calculate the difference between lnC1 of the current measuring point and lnC1 of the previous measuring point obtained by real-time drilling detection, i.e., calculate ΔlnC1, and monitor drilling data, drilling pattern and leakage data at the same time.

[0015] S2. When ΔlnC1>2, and the drilling morphology is bell-shaped, box-shaped, or completely vented, accompanied by leakage, it is automatically identified as a cavernous reservoir.

[0016] More preferably, in step S1, lnC1 represents the natural logarithm of the gas content of the fluid forming the fluid.

[0017] Preferably, the specific steps of the identification method are as follows:

[0018] Step 1: Acquisition of C1 logging data;

[0019] Step 2: Calculate lnC1;

[0020] Step 3: Calculate ΔlnC1, which is the difference between lnC1 of the current measuring point and lnC1 of the previous measuring point;

[0021] Step 4: Acquiring well logging data;

[0022] Step 5: Acquiring drilling time data;

[0023] Step 6: Obtaining missing data;

[0024] Step 7: Load data and draw a single-well bar chart;

[0025] Step 8: Using drilling data, if the reservoir is vented, it is directly identified as a cavernous reservoir; otherwise, proceed to step 9.

[0026] Step 9: If ΔlnC1>2, and the drilling morphology is bell-shaped or box-shaped, accompanied by leakage, it is automatically identified as a cavern-type reservoir.

[0027] Furthermore, this invention provides the application of the above-mentioned identification method in the oil and gas exploration process.

[0028] The beneficial effects of this invention are as follows:

[0029] This invention can automatically identify cave-like reservoirs, and can meet the requirements for effective reservoir identification in reservoir description and reserve estimation even without conventional logging and imaging logging data. Attached Figure Description

[0030] Figure 1 This is a flowchart of the identification method of the present invention.

[0031] Figure 2 This is a discrimination template diagram for the identification method of the present invention.

[0032] Figure 3 The drilling time patterns are bell-shaped and box-shaped.

[0033] Figure 4 Typical single-well columnar diagrams for bell-shaped and box-shaped drilling patterns. Detailed Implementation

[0034] The following non-limiting embodiments are intended to enable those skilled in the art to gain a more comprehensive understanding of the present invention, but do not limit the invention in any way. The following content is merely an exemplary description of the scope of protection claimed by the present invention, and those skilled in the art can make various changes and modifications to the invention based on the disclosed content, which should also fall within the scope of protection claimed in this application.

[0035] The present invention will be further described below by way of specific embodiments. Unless otherwise specified, all chemical reagents used in the embodiments of the present invention were obtained through conventional commercial means. Unless otherwise specified, all contents mentioned below are mass contents. Unless otherwise specified, it is understood that the process was carried out at room temperature.

[0036] Example 1

[0037] The specific steps for identifying cave-like reservoirs are as follows:

[0038] Step 1: Acquiring C1 logging data

[0039] Find the gas logging data table in the well completion data package. The gas logging data table includes drilling time and gas logging data. The gas logging data includes methane data, which is called C1 data. The C1 data is recorded in the format of one data point per meter.

[0040] Step 2: Calculate lnC1;

[0041] Step 3: Calculate ΔlnC1, which is the difference between lnC1 of the current measuring point and lnC1 of the previous measuring point;

[0042] Step 4: Acquiring well logging data.

[0043] Well logging data is collected from basic data, including natural gamma ray, well diameter, depth and shallow lateral logging, sonic transit time, density, neutron porosity, etc.; well logging interpretation results obtained after processing well logging data include clay content, oil saturation, gas saturation, porosity interpretation results, reservoir type, etc.

[0044] Step 5: Acquiring drilling time data.

[0045] Find the drilling time data from the well completion data package. The drilling time data is recorded in the format of one data point per meter.

[0046] Step 6: Obtaining missing data.

[0047] The leakage data was found in the drilling record of venting and leakage. The document records the specific details of each venting or leakage, including leakage location, leakage time, leakage rate and leakage amount.

[0048] Step 7: Load data and draw a single-well bar chart;

[0049] Step 8: Using drilling data, if the reservoir is vented, it is directly identified as a cavernous reservoir, and the venting or drilling time of 0 is recorded in the drilling gas logging data table. At the same time, the missing data is recorded in the venting and missing data document during drilling. If the reservoir is not vented, proceed to step 9.

[0050] Step 9: If ΔlnC1>2, the drilling pattern is bell-shaped (e.g., ...). Figure 3 a) Box-shaped (e.g.) Figure 3 If (b) is present and accompanied by leakage, it is automatically identified as a cavernous reservoir.

[0051] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for identifying cavernous reservoirs, characterized in that, The identification method is based on drilling data, logging data, and logging interpretation results, and uses drilling time, gas content C1, and leakage to comprehensively identify cavernous reservoirs.

2. The method for identifying cave-like reservoirs according to claim 1, characterized in that, The drilling data includes drilling time, gas content (C1), and leakage.

3. The method for identifying cave-like reservoirs according to claim 1, characterized in that, The logging data includes natural gamma, wellbore, depth and shallow lateral logging, sonic transit time, density and neutron porosity.

4. The method for identifying cave-like reservoirs according to claim 1, characterized in that, The well logging interpretation results include mud content, oil saturation, gas saturation, porosity interpretation results, and reservoir type.

5. The method for identifying cave-like reservoirs according to claim 1, characterized in that, The steps for comprehensively identifying cavernous reservoirs using drilling time, gas content (C1), and leakage are as follows: S1. Calculate the difference between lnC1 of the current measuring point and lnC1 of the previous measuring point obtained by real-time drilling detection, i.e., calculate ΔlnC1, and monitor drilling data, drilling pattern and leakage data at the same time. S2. When ΔlnC1>2, and the drilling morphology is bell-shaped, box-shaped, or completely vented, accompanied by leakage, it is automatically identified as a cavernous reservoir.

6. The method for identifying cave-like reservoirs according to claim 5, characterized in that, In step S1, lnC1 represents the natural logarithm of the gas content of the fluid.

7. The method for identifying cave-like reservoirs according to claim 1, characterized in that, The specific steps of the method for identifying cave-like reservoirs are as follows: Step 1: Acquisition of C1 logging data; Step 2: Calculate lnC1; Step 3: Calculate ΔlnC1, which is the difference between lnC1 of the current measuring point and lnC1 of the previous measuring point; Step 4: Acquiring well logging data; Step 5: Acquiring drilling time data; Step 6: Obtaining missing data; Step 7: Load data and draw a single-well bar chart; Step 8: Using drilling data, if the reservoir is vented, it is directly identified as a cavernous reservoir; otherwise, proceed to step 9. Step 9: If ΔlnC1>2, and the drilling morphology is bell-shaped or box-shaped, accompanied by leakage, it is automatically identified as a cavern-type reservoir.

8. The application of the method for identifying cave-like reservoirs according to any one of claims 1-7 in the oil and gas exploration process.

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